From 2e83f649d02a6e4dc37660fe819dd034b612bbc9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mateusz=20Hordy=C5=84ski?= Date: Fri, 12 Sep 2025 10:39:24 +0200 Subject: [PATCH 01/43] chore: bump versions --- packages/ragbits-agents/pyproject.toml | 4 ++-- packages/ragbits-chat/pyproject.toml | 4 ++-- packages/ragbits-cli/pyproject.toml | 4 ++-- packages/ragbits-core/pyproject.toml | 2 +- packages/ragbits-document-search/pyproject.toml | 4 ++-- packages/ragbits-evaluate/pyproject.toml | 4 ++-- packages/ragbits-guardrails/pyproject.toml | 4 ++-- packages/ragbits/pyproject.toml | 4 ++-- uv.lock | 14 +++++++------- 9 files changed, 22 insertions(+), 22 deletions(-) diff --git a/packages/ragbits-agents/pyproject.toml b/packages/ragbits-agents/pyproject.toml index 77a5137fb..c471c2af5 100644 --- a/packages/ragbits-agents/pyproject.toml +++ b/packages/ragbits-agents/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ragbits-agents" -version = "1.3.0" +version = "1.4.0.dev202509121038" description = "Building blocks for rapid development of GenAI applications" readme = "README.md" requires-python = ">=3.10" @@ -31,7 +31,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ] -dependencies = ["ragbits-core==1.3.0"] +dependencies = ["ragbits-core==1.4.0.dev202509121038"] [project.optional-dependencies] a2a = [ diff --git a/packages/ragbits-chat/pyproject.toml b/packages/ragbits-chat/pyproject.toml index 908e33ba4..21f6c1740 100644 --- a/packages/ragbits-chat/pyproject.toml +++ b/packages/ragbits-chat/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ragbits-chat" -version = "1.3.0" +version = "1.4.0.dev202509121038" description = "Building blocks for rapid development of GenAI applications" readme = "README.md" requires-python = ">=3.10" @@ -31,7 +31,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ] -dependencies = ["fastapi>=0.115.0,<1.0.0", "uvicorn>=0.31.0,<1.0.0", "httpx>=0.28.1,<1.0.0", "bcrypt>=4.2.0", "python-jose[cryptography]>=3.5.0", "ragbits-core==1.3.0"] +dependencies = ["fastapi>=0.115.0,<1.0.0", "uvicorn>=0.31.0,<1.0.0", "httpx>=0.28.1,<1.0.0", "bcrypt>=4.2.0", "python-jose[cryptography]>=3.5.0", "ragbits-core==1.4.0.dev202509121038"] [project.urls] "Homepage" = "https://github.com/deepsense-ai/ragbits" diff --git a/packages/ragbits-cli/pyproject.toml b/packages/ragbits-cli/pyproject.toml index 4ed82fd37..9d56e9174 100644 --- a/packages/ragbits-cli/pyproject.toml +++ b/packages/ragbits-cli/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ragbits-cli" -version = "1.3.0" +version = "1.4.0.dev202509121038" description = "A CLI application for ragbits - building blocks for rapid development of GenAI applications" readme = "README.md" requires-python = ">=3.10" @@ -31,7 +31,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ] -dependencies = ["typer>=0.12.5,<1.0.0", "ragbits-core==1.3.0"] +dependencies = ["typer>=0.12.5,<1.0.0", "ragbits-core==1.4.0.dev202509121038"] [project.urls] "Homepage" = "https://github.com/deepsense-ai/ragbits" diff --git a/packages/ragbits-core/pyproject.toml b/packages/ragbits-core/pyproject.toml index 5274a60f9..036a95b22 100644 --- a/packages/ragbits-core/pyproject.toml +++ b/packages/ragbits-core/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ragbits-core" -version = "1.3.0" +version = "1.4.0.dev202509121038" description = "Building blocks for rapid development of GenAI applications" readme = "README.md" requires-python = ">=3.10" diff --git a/packages/ragbits-document-search/pyproject.toml b/packages/ragbits-document-search/pyproject.toml index e4ee2ace8..f2e553f10 100644 --- a/packages/ragbits-document-search/pyproject.toml +++ b/packages/ragbits-document-search/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ragbits-document-search" -version = "1.3.0" +version = "1.4.0.dev202509121038" description = "Document Search module for Ragbits" readme = "README.md" requires-python = ">=3.10" @@ -29,7 +29,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ] -dependencies = ["docling>=2.15.1,<3.0.0", "opencv-python>=4.11.0.86,<5.0.0.0", "rerankers>=0.6.1,<1.0.0", "filetype>=1.2.0,<2.0.0", "python-pptx>=1.0.0,<2.0.0", "ragbits-core==1.3.0"] +dependencies = ["docling>=2.15.1,<3.0.0", "opencv-python>=4.11.0.86,<5.0.0.0", "rerankers>=0.6.1,<1.0.0", "filetype>=1.2.0,<2.0.0", "python-pptx>=1.0.0,<2.0.0", "ragbits-core==1.4.0.dev202509121038"] [project.urls] "Homepage" = "https://github.com/deepsense-ai/ragbits" diff --git a/packages/ragbits-evaluate/pyproject.toml b/packages/ragbits-evaluate/pyproject.toml index 35b32d3eb..e9b7f4eeb 100644 --- a/packages/ragbits-evaluate/pyproject.toml +++ b/packages/ragbits-evaluate/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ragbits-evaluate" -version = "1.3.0" +version = "1.4.0.dev202509121038" description = "Evaluation module for Ragbits components" readme = "README.md" requires-python = ">=3.10" @@ -32,7 +32,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ] -dependencies = ["hydra-core>=1.3.2,<2.0.0", "neptune[optuna]>=1.12.0,<2.0.0", "optuna>=4.0.0,<5.0.0", "distilabel>=1.5.0,<2.0.0", "datasets>=3.0.1,<4.0.0", "ragbits-core==1.3.0"] +dependencies = ["hydra-core>=1.3.2,<2.0.0", "neptune[optuna]>=1.12.0,<2.0.0", "optuna>=4.0.0,<5.0.0", "distilabel>=1.5.0,<2.0.0", "datasets>=3.0.1,<4.0.0", "ragbits-core==1.4.0.dev202509121038"] [project.urls] "Homepage" = "https://github.com/deepsense-ai/ragbits" diff --git a/packages/ragbits-guardrails/pyproject.toml b/packages/ragbits-guardrails/pyproject.toml index 78bfedddf..d7e50df0d 100644 --- a/packages/ragbits-guardrails/pyproject.toml +++ b/packages/ragbits-guardrails/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ragbits-guardrails" -version = "1.3.0" +version = "1.4.0.dev202509121038" description = "Guardrails module for Ragbits components" readme = "README.md" requires-python = ">=3.10" @@ -31,7 +31,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ] -dependencies = ["ragbits-core==1.3.0"] +dependencies = ["ragbits-core==1.4.0.dev202509121038"] [project.urls] "Homepage" = "https://github.com/deepsense-ai/ragbits" diff --git a/packages/ragbits/pyproject.toml b/packages/ragbits/pyproject.toml index 69089fd77..2234f6a55 100644 --- a/packages/ragbits/pyproject.toml +++ b/packages/ragbits/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ragbits" -version = "1.3.0" +version = "1.4.0.dev202509121038" description = "Building blocks for rapid development of GenAI applications" dynamic = ["readme"] requires-python = ">=3.10" @@ -31,7 +31,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ] -dependencies = ["ragbits-document-search==1.3.0", "ragbits-cli==1.3.0", "ragbits-evaluate==1.3.0", "ragbits-guardrails==1.3.0", "ragbits-chat==1.3.0", "ragbits-core==1.3.0"] +dependencies = ["ragbits-document-search==1.4.0.dev202509121038", "ragbits-cli==1.4.0.dev202509121038", "ragbits-evaluate==1.4.0.dev202509121038", "ragbits-guardrails==1.4.0.dev202509121038", "ragbits-chat==1.4.0.dev202509121038", "ragbits-core==1.4.0.dev202509121038"] [project.urls] "Homepage" = "https://github.com/deepsense-ai/ragbits" diff --git a/uv.lock b/uv.lock index 92f1817a5..1d42b00cc 100644 --- a/uv.lock +++ b/uv.lock @@ -4917,7 +4917,7 @@ wheels = [ [[package]] name = "ragbits-agents" -version = "1.3.0" +version = "1.4.0.dev202509121038" source = { editable = "packages/ragbits-agents" } dependencies = [ { name = "ragbits-core" }, @@ -4953,7 +4953,7 @@ provides-extras = ["a2a", "mcp", "openai", "cli"] [[package]] name = "ragbits-chat" -version = "1.3.0" +version = "1.4.0.dev202509121038" source = { editable = "packages/ragbits-chat" } dependencies = [ { name = "bcrypt" }, @@ -5001,7 +5001,7 @@ dev = [ [[package]] name = "ragbits-cli" -version = "1.3.0" +version = "1.4.0.dev202509121038" source = { editable = "packages/ragbits-cli" } dependencies = [ { name = "ragbits-core" }, @@ -5016,7 +5016,7 @@ requires-dist = [ [[package]] name = "ragbits-core" -version = "1.3.0" +version = "1.4.0.dev202509121038" source = { editable = "packages/ragbits-core" } dependencies = [ { name = "aiohttp" }, @@ -5154,7 +5154,7 @@ dev = [ [[package]] name = "ragbits-document-search" -version = "1.3.0" +version = "1.4.0.dev202509121038" source = { editable = "packages/ragbits-document-search" } dependencies = [ { name = "docling" }, @@ -5210,7 +5210,7 @@ dev = [ [[package]] name = "ragbits-evaluate" -version = "1.3.0" +version = "1.4.0.dev202509121038" source = { editable = "packages/ragbits-evaluate" } dependencies = [ { name = "datasets" }, @@ -5258,7 +5258,7 @@ dev = [ [[package]] name = "ragbits-guardrails" -version = "1.3.0" +version = "1.4.0.dev202509121038" source = { editable = "packages/ragbits-guardrails" } dependencies = [ { name = "ragbits-core" }, From ee680a44d66b3bd3145fc891ce7175601f24604d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mateusz=20Hordy=C5=84ski?= Date: Fri, 12 Sep 2025 10:44:49 +0200 Subject: [PATCH 02/43] chore: fix docs build --- .github/workflows/publish-docs.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/publish-docs.yaml b/.github/workflows/publish-docs.yaml index de307fb8e..0dda20d56 100644 --- a/.github/workflows/publish-docs.yaml +++ b/.github/workflows/publish-docs.yaml @@ -15,6 +15,9 @@ jobs: contents: write steps: - uses: actions/checkout@v4 + with: + ref: gh-pages + fetch-depth: 1 - name: Deploy docs shell: bash From ecf3508173d1c7dc51f63a0b2c7a6691dfbe4fac Mon Sep 17 00:00:00 2001 From: jakubduda-dsai Date: Tue, 16 Sep 2025 10:28:21 +0200 Subject: [PATCH 03/43] init: TODO agent --- examples/agents/todo_example.py | 296 ++++++++++++++++++ .../src/ragbits/agents/__init__.py | 4 + .../src/ragbits/agents/todo_agent.py | 149 +++++++++ 3 files changed, 449 insertions(+) create mode 100644 examples/agents/todo_example.py create mode 100644 packages/ragbits-agents/src/ragbits/agents/todo_agent.py diff --git a/examples/agents/todo_example.py b/examples/agents/todo_example.py new file mode 100644 index 000000000..209fad848 --- /dev/null +++ b/examples/agents/todo_example.py @@ -0,0 +1,296 @@ +#!/usr/bin/env python3 +"""Simplified Hiking Trip Planning Agent - Process Under Hood, Stream Final Summary.""" + +import asyncio +import json +import os + +from pydantic import BaseModel + +from ragbits.agents.todo_agent import TodoAgent +from ragbits.core.llms import LiteLLM +from ragbits.core.prompt.prompt import Prompt + + +class HikingPlanInput(BaseModel): + """Simplified input for hiking trip planning.""" + + location: str + duration_days: int + group_size: int + skill_level: str + season: str + preferences: str + + +class PlanningPrompt(Prompt[HikingPlanInput]): + """Unified prompt for all hiking planning tasks.""" + + system_prompt = """ + You are an expert hiking guide with 15+ years of experience. You create comprehensive, + actionable hiking trip plans by working through planning tasks systematically. + + Always prioritize safety while maximizing the adventure experience. + Provide specific recommendations with real names, locations, costs, and practical details. + """ + + user_prompt = """ + Plan a {{ duration_days }}-day hiking trip for {{ group_size }} {{ skill_level }} hikers + in {{ location }} during {{ season }}. + + Preferences: {{ preferences }} + + Complete this specific planning task with detailed, actionable recommendations. + """ + + +class TaskGenerationPrompt(Prompt[HikingPlanInput]): + """Prompt for AI to generate planning tasks.""" + + system_prompt = """ + You are an expert hiking trip planner. Create a JSON array of planning tasks. + + IMPORTANT: Return ONLY the JSON array, no other text or explanations. + + Create 3-5 specific planning tasks. Each task should be actionable and focused. + + Example response: + ["Research trail routes and difficulty levels", "Find accommodation near trailheads", + "Analyze weather and gear needs"] + """ + + user_prompt = """ + Create planning tasks for: + - {{ duration_days }}-day hiking trip in {{ location }} + - {{ group_size }} {{ skill_level }} hikers + - Season: {{ season }} + - Preferences: {{ preferences }} + + Return only the JSON array of task descriptions. + """ + + +class SummaryPrompt(Prompt[HikingPlanInput]): + """Prompt for creating final comprehensive summary.""" + + system_prompt = """ + You are an expert hiking guide creating a comprehensive trip plan summary. + Based on your planning analysis, create a complete, actionable hiking trip plan + that covers all essential aspects of the adventure. + + Structure your response clearly with specific recommendations and practical details. + """ + + user_prompt = """ + Create a comprehensive hiking trip plan for: + - {{ duration_days }}-day trip in {{ location }} + - {{ group_size }} {{ skill_level }} hikers + - Season: {{ season }} + - Preferences: {{ preferences }} + + Provide a complete plan including: + • Trail routes and difficulty assessments + • Accommodation recommendations near trailheads + • Detailed budget breakdown with costs + • Weather analysis and gear recommendations + • Daily schedule with sunrise/sunset times + • Post-hike activities and relaxation options + • Safety protocols and emergency contacts + + Make this a step-by-step plan someone could follow immediately. + """ + + +def clear_console() -> None: + """Clear the console screen.""" + os.system("clear" if os.name == "posix" else "cls") # noqa: S605 + + +def display_task_list(agent: TodoAgent, title: str = "Planning Progress") -> None: + """Display current task list with visual status indicators.""" + clear_console() + print("🏔️ AI Hiking Trip Planner") + print("=" * 50) + print(f"\n📋 {title}") + print("─" * 30) + + for task in agent.tasks: + if task.status.value == "pending": + status_icon = "⏳" + elif task.status.value == "in-progress": + status_icon = "🔄" + else: # done + status_icon = "✅" + + print(f" {status_icon} {task.description}") + + print(f"\n📊 Progress: {len(agent.done_tasks)}/{len(agent.tasks)} completed") + + +async def smart_hiking_planner() -> None: + """Smart hiking planner that processes tasks under the hood and streams final summary.""" + llm = LiteLLM(model_name="gpt-4o-mini") + + clear_console() + print("🏔️ Smart AI Hiking Trip Planner") + print("=" * 50) + + # Trip configuration + trip_input = HikingPlanInput( + location="Tatra Mountains - Poland", + duration_days=1, + group_size=2, + skill_level="intermediate", + season="late September/early October", + preferences="scenic views, no longer than 15km, no crowds, avoid Morskie Oko/Giewont/Kasprowy Wierch", + ) + + print("\n📋 Trip Configuration:") + print(f" 📍 Location: {trip_input.location}") + print(f" 📅 Duration: {trip_input.duration_days} day(s)") + print(f" 👥 Group: {trip_input.group_size} {trip_input.skill_level} hikers") + print(f" 🌤️ Season: {trip_input.season}") + print(f" ⭐ Preferences: {trip_input.preferences}") + + await asyncio.sleep(2) + + # AI creates planning tasks + print("\n🤖 AI analyzing trip requirements and generating tasks...") + + # Create task generation agent + task_generator = TodoAgent(llm=llm, prompt=TaskGenerationPrompt) + + # Generate tasks with AI + task_response = await task_generator.run(trip_input) + + # Parse AI-generated tasks with robust error handling + try: + # Extract response content - AgentResult has .content attribute + if hasattr(task_response, "content"): + response_content = task_response.content.strip() + else: + response_content = str(task_response).strip() + + # Multiple strategies to extract JSON + import re + + # Strategy 1: Try direct JSON parsing + try: + planning_tasks = json.loads(response_content) + except json.JSONDecodeError: + # Strategy 2: Extract JSON array from text + json_patterns = [ + r"\[(?:[^[\]]*(?:\[[^\]]*\])*)*\]", # Complex nested array pattern + r"\[.*?\]", # Simple array pattern + ] + + planning_tasks = None + for pattern in json_patterns: + json_match = re.search(pattern, response_content, re.DOTALL) + if json_match: + json_str = json_match.group(0) + try: + planning_tasks = json.loads(json_str) + break + except json.JSONDecodeError: + continue + + if planning_tasks is None: + raise ValueError("No valid JSON array found in response") from None + + # Validate it's a list of strings + if not isinstance(planning_tasks, list): + raise ValueError(f"Expected list, got {type(planning_tasks)}") + + if not all(isinstance(task, str) for task in planning_tasks): + raise ValueError("All tasks must be strings") + + if len(planning_tasks) == 0: + raise ValueError("No tasks generated") + + except Exception as e: + print(f"⚠️ AI task generation failed ({e}), using fallback tasks...") + planning_tasks = [ + "Research optimal trail routes and scenic viewpoints", + "Find accommodation close to trailheads", + "Analyze weather conditions and gear requirements", + "Plan daily schedule with sunrise/sunset times", + "Research post-hike activities and relaxation spots", + ] + + # Create planning agent + planning_agent = TodoAgent(llm=llm, prompt=PlanningPrompt, keep_history=True) + planning_agent.create_todo_list(planning_tasks) + + await asyncio.sleep(1) + display_task_list(planning_agent, "AI-Generated Planning Tasks") + await asyncio.sleep(2) + + print("\n🚀 Processing tasks under the hood...") + await asyncio.sleep(1) + + # Process all tasks under the hood (user sees progress, not details) + for task in planning_agent.tasks: + # Show current progress + planning_agent.mark_task(task.id, "in-progress") + display_task_list(planning_agent, f"Processing: {task.description[:40]}...") + + # Process task with AI (under the hood) + await planning_agent.run(trip_input) + + # Mark as completed + planning_agent.mark_task(task.id, "done") + await asyncio.sleep(1) + + # Show all tasks completed + display_task_list(planning_agent, "All Planning Tasks Completed!") + await asyncio.sleep(2) + + # Generate and stream comprehensive summary + print("\n🎯 Generating comprehensive trip plan...") + print("🤖 AI compiling all research into actionable plan...") + await asyncio.sleep(2) + + # Create summary agent with all gathered information + summary_agent = TodoAgent(llm=llm, prompt=SummaryPrompt) + + # Clear screen for final summary + clear_console() + print(f"{'='*60}") + print("🏔️ COMPLETE HIKING TRIP PLAN") + print(f"{'='*60}") + print() + + # Stream the comprehensive summary + streaming_result = summary_agent.run_streaming(trip_input) + + async for chunk in streaming_result: + if isinstance(chunk, str): + print(chunk, end="", flush=True) + + print(f"\n\n{'='*60}") + print("✅ Trip planning completed!") + print(f"📊 Agent processed {len(planning_agent.tasks)} planning tasks") + print("🎉 Ready for your adventure!") + + +async def main() -> None: + """Run the smart hiking planner.""" + print("🚀 Smart AI Hiking Trip Planner") + print("AI processes planning tasks under the hood, then streams comprehensive plan") + print("\nPress Ctrl+C to exit...") + await asyncio.sleep(3) + + try: + await smart_hiking_planner() + print("\n✨ Planning completed successfully!") + + except KeyboardInterrupt: + print("\n👋 Planning interrupted") + except Exception as e: + print(f"\n❌ Error: {e}") + print("Make sure OPENAI_API_KEY is set") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/packages/ragbits-agents/src/ragbits/agents/__init__.py b/packages/ragbits-agents/src/ragbits/agents/__init__.py index 7a7036895..d1775a117 100644 --- a/packages/ragbits-agents/src/ragbits/agents/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/__init__.py @@ -7,6 +7,7 @@ AgentRunContext, ToolCallResult, ) +from ragbits.agents.todo_agent import Task, TaskStatus, TodoAgent from ragbits.agents.types import QuestionAnswerAgent, QuestionAnswerPromptInput, QuestionAnswerPromptOutput __all__ = [ @@ -19,5 +20,8 @@ "QuestionAnswerAgent", "QuestionAnswerPromptInput", "QuestionAnswerPromptOutput", + "Task", + "TaskStatus", + "TodoAgent", "ToolCallResult", ] diff --git a/packages/ragbits-agents/src/ragbits/agents/todo_agent.py b/packages/ragbits-agents/src/ragbits/agents/todo_agent.py new file mode 100644 index 000000000..722b2a83c --- /dev/null +++ b/packages/ragbits-agents/src/ragbits/agents/todo_agent.py @@ -0,0 +1,149 @@ +"""Clean TodoAgent implementation - extends Agent directly.""" + +import uuid +from collections.abc import Callable +from enum import Enum +from typing import Generic, Literal + +from ragbits.agents._main import Agent, AgentOptions +from ragbits.agents.mcp.server import MCPServer +from ragbits.core.llms.base import LLM, LLMClientOptionsT +from ragbits.core.prompt.base import ChatFormat +from ragbits.core.prompt.prompt import Prompt, PromptInputT, PromptOutputT + + +class TaskStatus(str, Enum): + """Task status options.""" + + PENDING = "pending" + IN_PROGRESS = "in-progress" + DONE = "done" + + +class Task: + """Simple task representation with UUID.""" + + def __init__(self, description: str, task_id: str | None = None): + self.id = task_id or str(uuid.uuid4()) + self.description = description + self.status = TaskStatus.PENDING + + def __repr__(self) -> str: + return f"Task(id={self.id[:8]}..., description='{self.description}', status={self.status.value})" + + +class TodoAgent( + Agent[LLMClientOptionsT, PromptInputT, PromptOutputT], Generic[LLMClientOptionsT, PromptInputT, PromptOutputT] +): + """Agent with clean, simple todo functionality.""" + + def __init__( + self, + llm: LLM[LLMClientOptionsT], + prompt: str | type[Prompt[PromptInputT, PromptOutputT]] | Prompt[PromptInputT, PromptOutputT] | None = None, + *, + history: ChatFormat | None = None, + keep_history: bool = False, + tools: list[Callable] | None = None, + mcp_servers: list[MCPServer] | None = None, + default_options: AgentOptions[LLMClientOptionsT] | None = None, + ) -> None: + """Initialize TodoAgent - just like regular Agent but with todo capabilities.""" + super().__init__( + llm=llm, + prompt=prompt, + history=history, + keep_history=keep_history, + tools=tools, + mcp_servers=mcp_servers, + default_options=default_options, + ) + + # Simple todo storage - just a list of tasks + self.tasks: list[Task] = [] + + def create_todo_list(self, tasks: dict[int, str] | list[str]) -> list[Task]: + """ + Create todo list from tasks. + + Args: + tasks: Either dict {1: "task1", 2: "task2"} or list ["task1", "task2"] + + Returns: + List of created Task objects + """ + self.tasks.clear() + + task_descriptions = list(tasks.values()) if isinstance(tasks, dict) else tasks + for description in task_descriptions: + task = Task(description) + self.tasks.append(task) + + return self.tasks.copy() + + def add_task(self, description: str) -> Task: + """Add a single task.""" + task = Task(description) + self.tasks.append(task) + return task + + def mark_task(self, task_id: str | int, status: Literal["pending", "in-progress", "done"]) -> Task: + """ + Mark task status by ID. + + Args: + task_id: Task ID (string UUID) or index (int) + status: New status + + Returns: + Updated task + """ + task = self._find_task(task_id) + task.status = TaskStatus(status) + return task + + def get_task(self, task_id: str | int) -> Task: + """Get task by ID or index.""" + return self._find_task(task_id) + + def get_tasks_by_status(self, status: TaskStatus) -> list[Task]: + """Get all tasks with specific status.""" + return [task for task in self.tasks if task.status == status] + + @property + def pending_tasks(self) -> list[Task]: + """Get pending tasks.""" + return self.get_tasks_by_status(TaskStatus.PENDING) + + @property + def in_progress_tasks(self) -> list[Task]: + """Get in-progress tasks.""" + return self.get_tasks_by_status(TaskStatus.IN_PROGRESS) + + @property + def done_tasks(self) -> list[Task]: + """Get completed tasks.""" + return self.get_tasks_by_status(TaskStatus.DONE) + + def _find_task(self, task_id: str | int) -> Task: + """Find task by ID or index.""" + if isinstance(task_id, int): + # Use as index + if 0 <= task_id < len(self.tasks): + return self.tasks[task_id] + else: + raise ValueError(f"Task index {task_id} out of range") + + # Use as UUID string + for task in self.tasks: + if task.id == task_id or task.id.startswith(str(task_id)): + return task + + raise ValueError(f"Task {task_id} not found") + + def __repr__(self) -> str: + total = len(self.tasks) + pending = len(self.pending_tasks) + in_progress = len(self.in_progress_tasks) + done = len(self.done_tasks) + return f"TodoAgent(tasks={total}, pending={pending}, in_progress={in_progress}, done={done})" From 234ec2c90c917b32e1b1c22d809c2b1609c32f2b Mon Sep 17 00:00:00 2001 From: jakubduda-dsai Date: Tue, 16 Sep 2025 11:25:54 +0200 Subject: [PATCH 04/43] gather tasks results --- examples/agents/todo_example.py | 61 ++++++++++++++++++++++++--------- 1 file changed, 45 insertions(+), 16 deletions(-) diff --git a/examples/agents/todo_example.py b/examples/agents/todo_example.py index 209fad848..5d851d4e2 100644 --- a/examples/agents/todo_example.py +++ b/examples/agents/todo_example.py @@ -43,6 +43,17 @@ class PlanningPrompt(Prompt[HikingPlanInput]): Complete this specific planning task with detailed, actionable recommendations. """ +class SummaryInput(BaseModel): + """Input for summary generation with task results.""" + + location: str + duration_days: int + group_size: int + skill_level: str + season: str + preferences: str + task_results: str # Compiled task results + class TaskGenerationPrompt(Prompt[HikingPlanInput]): """Prompt for AI to generate planning tasks.""" @@ -70,7 +81,7 @@ class TaskGenerationPrompt(Prompt[HikingPlanInput]): """ -class SummaryPrompt(Prompt[HikingPlanInput]): +class SummaryPrompt(Prompt[SummaryInput]): """Prompt for creating final comprehensive summary.""" system_prompt = """ @@ -82,22 +93,18 @@ class SummaryPrompt(Prompt[HikingPlanInput]): """ user_prompt = """ - Create a comprehensive hiking trip plan for: + Create a comprehensive hiking trip plan based on the completed research: + + TRIP DETAILS: - {{ duration_days }}-day trip in {{ location }} - {{ group_size }} {{ skill_level }} hikers - Season: {{ season }} - Preferences: {{ preferences }} - Provide a complete plan including: - • Trail routes and difficulty assessments - • Accommodation recommendations near trailheads - • Detailed budget breakdown with costs - • Weather analysis and gear recommendations - • Daily schedule with sunrise/sunset times - • Post-hike activities and relaxation options - • Safety protocols and emergency contacts + COMPLETED RESEARCH: + {{ task_results }} - Make this a step-by-step plan someone could follow immediately. + Based on this research, provide a complete plan... """ @@ -229,14 +236,19 @@ async def smart_hiking_planner() -> None: print("\n🚀 Processing tasks under the hood...") await asyncio.sleep(1) - # Process all tasks under the hood (user sees progress, not details) + # Process all tasks and capture results + task_results = [] for task in planning_agent.tasks: # Show current progress planning_agent.mark_task(task.id, "in-progress") display_task_list(planning_agent, f"Processing: {task.description[:40]}...") - # Process task with AI (under the hood) - await planning_agent.run(trip_input) + # Process task with AI and CAPTURE results + task_result = await planning_agent.run(trip_input) + task_results.append({ + "task": task.description, + "result": task_result.content if hasattr(task_result, 'content') else str(task_result) + }) # Mark as completed planning_agent.mark_task(task.id, "done") @@ -246,12 +258,29 @@ async def smart_hiking_planner() -> None: display_task_list(planning_agent, "All Planning Tasks Completed!") await asyncio.sleep(2) + # Compile task results + compiled_results = "\n\n".join([ + f"TASK: {result['task']}\nRESULT: {result['result']}" + for result in task_results + ]) + + # Create summary input with all task results + summary_input = SummaryInput( + location=trip_input.location, + duration_days=trip_input.duration_days, + group_size=trip_input.group_size, + skill_level=trip_input.skill_level, + season=trip_input.season, + preferences=trip_input.preferences, + task_results=compiled_results + ) + # Generate and stream comprehensive summary print("\n🎯 Generating comprehensive trip plan...") print("🤖 AI compiling all research into actionable plan...") await asyncio.sleep(2) - # Create summary agent with all gathered information + # Summary agent now has access to all task research! summary_agent = TodoAgent(llm=llm, prompt=SummaryPrompt) # Clear screen for final summary @@ -262,7 +291,7 @@ async def smart_hiking_planner() -> None: print() # Stream the comprehensive summary - streaming_result = summary_agent.run_streaming(trip_input) + streaming_result = summary_agent.run_streaming(summary_input) async for chunk in streaming_result: if isinstance(chunk, str): From 661e4f75b61c673db3f750ce5ad1b06ba131e1eb Mon Sep 17 00:00:00 2001 From: akotyla <79326805+akotyla@users.noreply.github.com> Date: Tue, 16 Sep 2025 15:34:13 +0200 Subject: [PATCH 05/43] feat: support wrapping downstream agents as tools (#819) --- packages/ragbits-agents/CHANGELOG.md | 2 + .../src/ragbits/agents/_main.py | 46 +++++++++++- .../ragbits-agents/src/ragbits/agents/tool.py | 72 ++++++++++++++++++- 3 files changed, 117 insertions(+), 3 deletions(-) diff --git a/packages/ragbits-agents/CHANGELOG.md b/packages/ragbits-agents/CHANGELOG.md index 52949840d..3ebf57e6b 100644 --- a/packages/ragbits-agents/CHANGELOG.md +++ b/packages/ragbits-agents/CHANGELOG.md @@ -2,6 +2,8 @@ ## Unreleased +- Support wrapping downstream agents as tools (#818) + ## 1.3.0 (2025-09-11) ### Changed diff --git a/packages/ragbits-agents/src/ragbits/agents/_main.py b/packages/ragbits-agents/src/ragbits/agents/_main.py index d6e08f7a6..6e15b7027 100644 --- a/packages/ragbits-agents/src/ragbits/agents/_main.py +++ b/packages/ragbits-agents/src/ragbits/agents/_main.py @@ -219,6 +219,8 @@ class Agent( def __init__( self, llm: LLM[LLMClientOptionsT], + name: str | None = None, + description: str | None = None, prompt: str | type[Prompt[PromptInputT, PromptOutputT]] | Prompt[PromptInputT, PromptOutputT] | None = None, *, history: ChatFormat | None = None, @@ -232,6 +234,8 @@ def __init__( Args: llm: The LLM to run the agent. + name: Optional name of the agent. Used to identify the agent instance. + description: Optional description of the agent. prompt: The prompt for the agent. Can be: - str: A string prompt that will be used as system message when combined with string input, or as the user message when no input is provided during run(). @@ -248,7 +252,17 @@ def __init__( self.id = uuid.uuid4().hex[:8] self.llm = llm self.prompt = prompt - self.tools = [Tool.from_callable(tool) for tool in tools or []] + self.name = name + self.description = description + self.tools = [] + for tool in tools or []: + if isinstance(tool, tuple): + agent, kwargs = tool + self.tools.append(Tool.from_agent(agent, **kwargs)) + elif isinstance(tool, Agent): + self.tools.append(Tool.from_agent(tool)) + else: + self.tools.append(Tool.from_callable(tool)) self.mcp_servers = mcp_servers or [] self.history = history or [] self.keep_history = keep_history @@ -555,7 +569,10 @@ async def _get_all_tools(self) -> dict[str, Tool]: return tools_mapping async def _execute_tool( - self, tool_call: ToolCall, tools_mapping: dict[str, Tool], context: AgentRunContext | None = None + self, + tool_call: ToolCall, + tools_mapping: dict[str, Tool], + context: AgentRunContext | None = None, ) -> ToolCallResult: if tool_call.type != "function": raise AgentToolNotSupportedError(tool_call.type) @@ -577,10 +594,22 @@ async def _execute_tool( else tool.on_tool_call(**call_args) ) + if isinstance(tool_output, AgentResultStreaming): + async for _ in tool_output: + pass + + tool_output = { + "content": tool_output.content, + "metadata": tool_output.metadata, + "tool_calls": tool_output.tool_calls, + "usage": tool_output.usage, + } + outputs.result = { "tool_output": tool_output, "tool_call_id": tool_call.id, } + except Exception as e: outputs.result = { "error": str(e), @@ -758,3 +787,16 @@ def from_pydantic_ai(cls, pydantic_ai_agent: "PydanticAIAgent") -> Self: tools=[tool.function for _, tool in pydantic_ai_agent._function_tools.items()], mcp_servers=cast(list[MCPServer], mcp_servers), ) + + def to_tool(self, name: str | None = None, description: str | None = None) -> Tool: + """ + Convert the agent into a Tool instance. + + Args: + name: Optional override for the tool name. + description: Optional override for the tool description. + + Returns: + Tool instance representing the agent. + """ + return Tool.from_agent(self, name=name or self.name, description=description or self.description) diff --git a/packages/ragbits-agents/src/ragbits/agents/tool.py b/packages/ragbits-agents/src/ragbits/agents/tool.py index eebdf5b21..3da403782 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tool.py +++ b/packages/ragbits-agents/src/ragbits/agents/tool.py @@ -1,13 +1,19 @@ from collections.abc import Callable from contextlib import suppress from dataclasses import dataclass -from typing import Any, Literal +from typing import TYPE_CHECKING, Any, Literal +from pydantic import BaseModel from typing_extensions import Self +from ragbits.core.llms.base import LLMClientOptionsT +from ragbits.core.prompt.prompt import PromptInputT, PromptOutputT from ragbits.core.utils.decorators import requires_dependencies from ragbits.core.utils.function_schema import convert_function_to_function_schema, get_context_variable_name +if TYPE_CHECKING: + from ragbits.agents import Agent, AgentResultStreaming + with suppress(ImportError): from pydantic_ai import Tool as PydanticAITool @@ -96,5 +102,69 @@ def to_pydantic_ai(self) -> "PydanticAITool": description=self.description, ) + @classmethod + def from_agent( + cls, + agent: "Agent[LLMClientOptionsT, PromptInputT, PromptOutputT]", + name: str | None = None, + description: str | None = None, + ) -> "Tool": + """ + Wraps a downstream agent as a single tool. The tool parameters are inferred from + the downstream agent's prompt input. + + Args: + agent: The downstream agent to wrap as a tool. + name: Optional override for the tool name. + description: Optional override for the tool description. + + Returns: + Tool instance representing the agent. + """ + display_name = name or agent.name or "agent" + variable_name = display_name.replace(" ", "_").lower() + description = description or agent.description + + input_model_cls = getattr(agent.prompt, "input_type", None) + if input_model_cls and issubclass(input_model_cls, BaseModel): + fields = input_model_cls.model_fields + properties = {} + required = list(fields.keys()) + + for field_name in fields: + param_desc = None + for t in getattr(agent, "tools", []): + t_params = getattr(t, "parameters", {}).get("properties", {}) + if field_name in t_params: + param_desc = t_params[field_name].get("description") + break + + properties[field_name] = { + "type": "string", + "title": field_name.capitalize(), + "description": param_desc, + } + else: + properties = {"input": {"type": "string", "description": "Input for the downstream agent"}} + required = ["input"] + + parameters = {"type": "object", "properties": properties, "required": required} + + def _on_tool_call(**kwargs: dict) -> "AgentResultStreaming": + if input_model_cls and issubclass(input_model_cls, BaseModel): + model_input = input_model_cls(**kwargs) + else: + model_input = kwargs.get("input") + + return agent.run_streaming(model_input) + + return cls( + name=variable_name, + description=description, + parameters=parameters, + on_tool_call=_on_tool_call, + context_var_name=get_context_variable_name(agent.run), + ) + ToolChoice = Literal["auto", "none", "required"] | Callable From 61d673987939529842bd51d7af887dc8a48c468b Mon Sep 17 00:00:00 2001 From: dazy-ds <135028324+dazy-ds@users.noreply.github.com> Date: Wed, 17 Sep 2025 10:57:22 +0200 Subject: [PATCH 06/43] feat: class-based agents (#820) --- docs/how-to/agents/define_and_use_agents.md | 29 ++++- examples/agents/with_decorator.py | 88 +++++++++++++ packages/ragbits-agents/CHANGELOG.md | 1 + .../src/ragbits/agents/_main.py | 117 +++++++++++++++++- .../ragbits-agents/tests/unit/test_agent.py | 44 +++++++ 5 files changed, 273 insertions(+), 6 deletions(-) create mode 100644 examples/agents/with_decorator.py diff --git a/docs/how-to/agents/define_and_use_agents.md b/docs/how-to/agents/define_and_use_agents.md index da3ccb6d5..14f23157f 100644 --- a/docs/how-to/agents/define_and_use_agents.md +++ b/docs/how-to/agents/define_and_use_agents.md @@ -32,7 +32,7 @@ Use a structured prompt to instruct the LLM. For details on writing prompts with from pydantic import BaseModel from ragbits.core.prompt import Prompt ---8<-- "examples/agents/tool_use.py:51:70" +--8<-- "examples/agents/tool_use.py:51:72" ``` ### Run the agent @@ -49,6 +49,33 @@ The result is an [AgentResult][ragbits.agents.AgentResult], which includes the m You can find the complete code example in the Ragbits repository [here](https://github.com/deepsense-ai/ragbits/blob/main/examples/agents/tool_use.py). +### Alternative approach: inheritance with `prompt_config` + +In addition to explicitly attaching a Prompt instance, Ragbits also supports defining agents through a combination of inheritance and the `@Agent.prompt_config` decorator. + +This approach lets you bind input (and optionally output) models directly to your agent class. The agent then derives its prompt structure automatically, without requiring a prompt argument in the constructor. + +```python +from pydantic import BaseModel +from ragbits.agents import Agent + +--8<-- "examples/agents/with_decorator.py:51:71" +``` + +The decorator can also accept an output type, allowing you to strongly type both the inputs and outputs of the agent. If you do not explicitly define a `user_prompt`, Ragbits will default to `{{ input }}`. + +Once defined, the agent class can be used directly, just like any other subclass of Agent: + +```python +import asyncio +from ragbits.agents import Agent +from ragbits.core.llms import LiteLLM + +--8<-- "examples/agents/with_decorator.py:73:84" +``` + +You can find the complete code example in the Ragbits repository [here](https://github.com/deepsense-ai/ragbits/blob/main/examples/agents/with_decorator.py). + ## Tool choice To control what tool is used at first call you could use `tool_choice` parameter. There are the following options: - "auto": let model decide if tool call is needed diff --git a/examples/agents/with_decorator.py b/examples/agents/with_decorator.py new file mode 100644 index 000000000..c1ae47024 --- /dev/null +++ b/examples/agents/with_decorator.py @@ -0,0 +1,88 @@ +""" +Ragbits Agents Example: Syntax Sugar + +This example demonstrates how to use agent with tools by using new syntax sugar. +We provide a single method as a tool to the agent and expect it to call it when answering query. + +To run the script, execute the following command: + + ```bash + uv run examples/agents/tool_use.py + ``` +""" + +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "ragbits-core", +# "ragbits-agents", +# ] +# /// +import asyncio +import json + +from pydantic import BaseModel + +from ragbits.agents import Agent +from ragbits.agents._main import AgentOptions +from ragbits.core.llms import LiteLLM + + +def get_weather(location: str) -> str: + """ + Returns the current weather for a given location. + + Args: + location: The location to get the weather for. + + Returns: + The current weather for the given location. + """ + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + + +class WeatherAgentInput(BaseModel): + """ + Input format for the WeatherAgent + """ + + location: str + + +@Agent.prompt_config(WeatherAgentInput) +class WeatherAgent(Agent): + """ + Agent that returns weather for a given location. + """ + + system_prompt = """ + You are a helpful assistant that responds to user questions about weather. + """ + user_prompt = """ + Tell me the temperature in {{ location }}. + """ + + +async def main() -> None: + """ + Run the example. + """ + llm = LiteLLM(model_name="gpt-4o-2024-08-06", use_structured_output=True) + agent = WeatherAgent( + llm=llm, + tools=[get_weather], + default_options=AgentOptions(max_total_tokens=500, max_turns=5), + ) + response = await agent.run(WeatherAgentInput(location="Paris"), tool_choice=get_weather) + print(response) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/packages/ragbits-agents/CHANGELOG.md b/packages/ragbits-agents/CHANGELOG.md index 3ebf57e6b..2a722f747 100644 --- a/packages/ragbits-agents/CHANGELOG.md +++ b/packages/ragbits-agents/CHANGELOG.md @@ -3,6 +3,7 @@ ## Unreleased - Support wrapping downstream agents as tools (#818) +- Add syntax sugar allowing easier Agents definition (#820) ## 1.3.0 (2025-09-11) ### Changed diff --git a/packages/ragbits-agents/src/ragbits/agents/_main.py b/packages/ragbits-agents/src/ragbits/agents/_main.py index 6e15b7027..6b15a00e6 100644 --- a/packages/ragbits-agents/src/ragbits/agents/_main.py +++ b/packages/ragbits-agents/src/ragbits/agents/_main.py @@ -1,4 +1,5 @@ import asyncio +import types import uuid from collections.abc import AsyncGenerator, AsyncIterator, Callable from contextlib import suppress @@ -7,9 +8,12 @@ from datetime import timedelta from inspect import iscoroutinefunction from types import ModuleType, SimpleNamespace -from typing import ClassVar, Generic, TypeVar, cast, overload +from typing import Any, ClassVar, Generic, TypeVar, cast, overload -from pydantic import BaseModel, Field +from pydantic import ( + BaseModel, + Field, +) from typing_extensions import Self from ragbits import agents @@ -27,7 +31,7 @@ from ragbits.agents.mcp.utils import get_tools from ragbits.agents.tool import Tool, ToolCallResult, ToolChoice from ragbits.core.audit.traces import trace -from ragbits.core.llms.base import LLM, LLMClientOptionsT, LLMResponseWithMetadata, ToolCall, Usage +from ragbits.core.llms.base import LLM, LLMClientOptionsT, LLMOptions, LLMResponseWithMetadata, ToolCall, Usage from ragbits.core.options import Options from ragbits.core.prompt.base import BasePrompt, ChatFormat, SimplePrompt from ragbits.core.prompt.prompt import Prompt, PromptInputT, PromptOutputT @@ -42,6 +46,9 @@ from ragbits.core.llms import LiteLLM +_Input = TypeVar("_Input", bound=BaseModel) +_Output = TypeVar("_Output") + @dataclass class AgentResult(Generic[PromptOutputT]): @@ -215,6 +222,11 @@ class Agent( options_cls: type[AgentOptions] = AgentOptions default_module: ClassVar[ModuleType | None] = agents configuration_key: ClassVar[str] = "agent" + # Syntax sugar fields + user_prompt: ClassVar[str] = "{{ input }}" + system_prompt: ClassVar[str | None] = None + input_type: PromptInputT | None = None + prompt_cls: ClassVar[type[Prompt] | None] = None def __init__( self, @@ -267,6 +279,12 @@ def __init__( self.history = history or [] self.keep_history = keep_history + if getattr(self, "system_prompt", None) and not getattr(self, "input_type", None): + raise ValueError( + f"Agent {type(self).__name__} defines a system_prompt but has no input_type. ", + "Use Agent.prompt decorator to properly assign it.", + ) + @overload async def run( self: "Agent[LLMClientOptionsT, None, PromptOutputT]", @@ -518,14 +536,38 @@ def _get_llm_options( llm_options.max_tokens = min(actual_limits) - usage.total_tokens return llm_options - def _get_prompt_with_history(self, input: PromptInputT) -> SimplePrompt | Prompt[PromptInputT, PromptOutputT]: + def _prepare_synthesized_prompt( + self, input: PromptInputT + ) -> SimplePrompt | Prompt[PromptInputT, PromptOutputT] | None: curr_history = deepcopy(self.history) + if ( + hasattr(self, "prompt_cls") + and self.prompt_cls + and isinstance(self.prompt_cls, type) + and issubclass(self.prompt_cls, Prompt) + ): + prompt_cls = cast(type[Prompt[PromptInputT, PromptOutputT]], self.prompt_cls) + if self.keep_history: + self.prompt = cast(Prompt[PromptInputT, PromptOutputT], prompt_cls(input, curr_history)) + return self.prompt + else: + return cast(Prompt[PromptInputT, PromptOutputT], prompt_cls(input)) + if isinstance(self.prompt, type) and issubclass(self.prompt, Prompt): if self.keep_history: self.prompt = self.prompt(input, curr_history) return self.prompt else: - return self.prompt(input, curr_history) + return self.prompt(input) + + return None + + def _get_prompt_with_history(self, input: PromptInputT) -> SimplePrompt | Prompt[PromptInputT, PromptOutputT]: + curr_history = deepcopy(self.history) + new_prompt = self._prepare_synthesized_prompt(input) + + if new_prompt: + return new_prompt if isinstance(self.prompt, Prompt): self.prompt.add_user_message(input) @@ -800,3 +842,68 @@ def to_tool(self, name: str | None = None, description: str | None = None) -> To Tool instance representing the agent. """ return Tool.from_agent(self, name=name or self.name, description=description or self.description) + + @staticmethod + def _make_prompt_class_for_agent_subclass(agent_cls: type["Agent[Any, Any, Any]"]) -> type[Prompt] | None: + system = getattr(agent_cls, "system_prompt", None) + if not system: + raise ValueError(f"Agent {agent_cls.__name__} has no system_prompt") + user = getattr(agent_cls, "user_prompt", "{{ input }}") + + input_type: type[BaseModel] | None = getattr(agent_cls, "input_type", None) + if input_type is None: + raise ValueError(f"Agent {agent_cls.__name__} cannot build a Prompt without input_type") + + base_generic = Prompt + base = base_generic[input_type] + + attrs = { + "system_prompt": system, + "user_prompt": user, + } + + return types.new_class( + f"_{agent_cls.__name__}Prompt", + (base,), + exec_body=lambda ns: ns.update(attrs), + ) + + @overload + @staticmethod + def prompt_config( + input_model: type[_Input], + ) -> Callable[[type[Any]], type["Agent[LLMOptions, _Input, str]"]]: ... + @overload + @staticmethod + def prompt_config( + input_model: type[_Input], + output_model: type[_Output], + ) -> Callable[[type[Any]], type["Agent[LLMOptions, _Input, _Output]"]]: ... + @staticmethod + def prompt_config( + input_model: type[_Input], + output_model: type[_Output] | type[NotGiven] = NotGiven, + ) -> Callable[[type[Any]], type["Agent[LLMOptions, _Input, _Output]"]]: + """ + Decorator to bind both input and output types of an Agent subclass, with runtime checks. + + Raises: + TypeError: if the decorated class is not a subclass of Agent, + or if input_model is not a Pydantic BaseModel. + """ + if not isinstance(input_model, type) or not issubclass(input_model, BaseModel): + raise TypeError(f"input_model must be a subclass of pydantic.BaseModel, got {input_model}") + + if not isinstance(output_model, type): + raise TypeError(f"output_model must be a type, got {output_model}") + + def decorator(cls: type[Any]) -> type["Agent[LLMOptions, _Input, _Output]"]: + if not isinstance(cls, type) or not issubclass(cls, Agent): + raise TypeError(f"Can only decorate subclasses of Agent, got {cls}") + + cls.input_type = input_model + cls.prompt_cls = Agent._make_prompt_class_for_agent_subclass(cls) + + return cast(type["Agent[LLMOptions, _Input, _Output]"], cls) + + return decorator diff --git a/packages/ragbits-agents/tests/unit/test_agent.py b/packages/ragbits-agents/tests/unit/test_agent.py index 10fd2efbf..4444e4c53 100644 --- a/packages/ragbits-agents/tests/unit/test_agent.py +++ b/packages/ragbits-agents/tests/unit/test_agent.py @@ -769,3 +769,47 @@ async def test_tool_choice_history_preservation(llm_with_tool_call: MockLLM, met # Should include tool call in history tool_call_messages = [msg for msg in agent.history if msg.get("role") == "tool"] assert len(tool_call_messages) >= 1 + + +async def test_explicit_input_type_prompt_creation(): + class CustomInput(BaseModel): + foo: int + bar: str + + @Agent.prompt_config(CustomInput) + class AgentWithExplicitInput(Agent): + system_prompt = "System prompt" + user_prompt = "{{ foo }} {{ bar }}" + + prompt_cls = AgentWithExplicitInput.prompt_cls + assert prompt_cls is not None + assert issubclass(prompt_cls, Prompt) + assert prompt_cls.system_prompt == "System prompt" + assert prompt_cls.user_prompt == "{{ foo }} {{ bar }}" + + +async def test_default_user_prompt_is_input_placeholder(): + class CustomInputModel(BaseModel): + input: int + + @Agent.prompt_config(CustomInputModel) + class AgentExplicitPrompt(Agent): + system_prompt = "Explicit system" + + prompt_cls2 = AgentExplicitPrompt.prompt_cls + assert prompt_cls2 is not None + assert issubclass(prompt_cls2, Prompt) + assert prompt_cls2.user_prompt == "{{ input }}" + + +async def test_input_type_check_with_system_prompt(llm_with_tool_call: MockLLM): + class AgentExplicitPrompt(Agent): + system_prompt = "Explicit system" + + with pytest.raises(ValueError): + AgentExplicitPrompt( + llm=llm_with_tool_call, + prompt="You are a helpful assistant", + tools=[get_weather], + keep_history=True, + ) From 1115edbca5a2276dac7953d899e318d9f38d1792 Mon Sep 17 00:00:00 2001 From: jakubduda-dsai Date: Thu, 18 Sep 2025 11:11:14 +0200 Subject: [PATCH 07/43] save --- examples/agents/todo_example.py | 325 -------------- examples/agents/todo_tools_example.py | 119 ++++++ .../src/ragbits/agents/__init__.py | 7 +- .../src/ragbits/agents/todo_agent.py | 149 ------- .../src/ragbits/agents/tools/__init__.py | 6 +- .../src/ragbits/agents/tools/todo.py | 398 ++++++++++++++++++ 6 files changed, 524 insertions(+), 480 deletions(-) delete mode 100644 examples/agents/todo_example.py create mode 100644 examples/agents/todo_tools_example.py delete mode 100644 packages/ragbits-agents/src/ragbits/agents/todo_agent.py create mode 100644 packages/ragbits-agents/src/ragbits/agents/tools/todo.py diff --git a/examples/agents/todo_example.py b/examples/agents/todo_example.py deleted file mode 100644 index 5d851d4e2..000000000 --- a/examples/agents/todo_example.py +++ /dev/null @@ -1,325 +0,0 @@ -#!/usr/bin/env python3 -"""Simplified Hiking Trip Planning Agent - Process Under Hood, Stream Final Summary.""" - -import asyncio -import json -import os - -from pydantic import BaseModel - -from ragbits.agents.todo_agent import TodoAgent -from ragbits.core.llms import LiteLLM -from ragbits.core.prompt.prompt import Prompt - - -class HikingPlanInput(BaseModel): - """Simplified input for hiking trip planning.""" - - location: str - duration_days: int - group_size: int - skill_level: str - season: str - preferences: str - - -class PlanningPrompt(Prompt[HikingPlanInput]): - """Unified prompt for all hiking planning tasks.""" - - system_prompt = """ - You are an expert hiking guide with 15+ years of experience. You create comprehensive, - actionable hiking trip plans by working through planning tasks systematically. - - Always prioritize safety while maximizing the adventure experience. - Provide specific recommendations with real names, locations, costs, and practical details. - """ - - user_prompt = """ - Plan a {{ duration_days }}-day hiking trip for {{ group_size }} {{ skill_level }} hikers - in {{ location }} during {{ season }}. - - Preferences: {{ preferences }} - - Complete this specific planning task with detailed, actionable recommendations. - """ - -class SummaryInput(BaseModel): - """Input for summary generation with task results.""" - - location: str - duration_days: int - group_size: int - skill_level: str - season: str - preferences: str - task_results: str # Compiled task results - - -class TaskGenerationPrompt(Prompt[HikingPlanInput]): - """Prompt for AI to generate planning tasks.""" - - system_prompt = """ - You are an expert hiking trip planner. Create a JSON array of planning tasks. - - IMPORTANT: Return ONLY the JSON array, no other text or explanations. - - Create 3-5 specific planning tasks. Each task should be actionable and focused. - - Example response: - ["Research trail routes and difficulty levels", "Find accommodation near trailheads", - "Analyze weather and gear needs"] - """ - - user_prompt = """ - Create planning tasks for: - - {{ duration_days }}-day hiking trip in {{ location }} - - {{ group_size }} {{ skill_level }} hikers - - Season: {{ season }} - - Preferences: {{ preferences }} - - Return only the JSON array of task descriptions. - """ - - -class SummaryPrompt(Prompt[SummaryInput]): - """Prompt for creating final comprehensive summary.""" - - system_prompt = """ - You are an expert hiking guide creating a comprehensive trip plan summary. - Based on your planning analysis, create a complete, actionable hiking trip plan - that covers all essential aspects of the adventure. - - Structure your response clearly with specific recommendations and practical details. - """ - - user_prompt = """ - Create a comprehensive hiking trip plan based on the completed research: - - TRIP DETAILS: - - {{ duration_days }}-day trip in {{ location }} - - {{ group_size }} {{ skill_level }} hikers - - Season: {{ season }} - - Preferences: {{ preferences }} - - COMPLETED RESEARCH: - {{ task_results }} - - Based on this research, provide a complete plan... - """ - - -def clear_console() -> None: - """Clear the console screen.""" - os.system("clear" if os.name == "posix" else "cls") # noqa: S605 - - -def display_task_list(agent: TodoAgent, title: str = "Planning Progress") -> None: - """Display current task list with visual status indicators.""" - clear_console() - print("🏔️ AI Hiking Trip Planner") - print("=" * 50) - print(f"\n📋 {title}") - print("─" * 30) - - for task in agent.tasks: - if task.status.value == "pending": - status_icon = "⏳" - elif task.status.value == "in-progress": - status_icon = "🔄" - else: # done - status_icon = "✅" - - print(f" {status_icon} {task.description}") - - print(f"\n📊 Progress: {len(agent.done_tasks)}/{len(agent.tasks)} completed") - - -async def smart_hiking_planner() -> None: - """Smart hiking planner that processes tasks under the hood and streams final summary.""" - llm = LiteLLM(model_name="gpt-4o-mini") - - clear_console() - print("🏔️ Smart AI Hiking Trip Planner") - print("=" * 50) - - # Trip configuration - trip_input = HikingPlanInput( - location="Tatra Mountains - Poland", - duration_days=1, - group_size=2, - skill_level="intermediate", - season="late September/early October", - preferences="scenic views, no longer than 15km, no crowds, avoid Morskie Oko/Giewont/Kasprowy Wierch", - ) - - print("\n📋 Trip Configuration:") - print(f" 📍 Location: {trip_input.location}") - print(f" 📅 Duration: {trip_input.duration_days} day(s)") - print(f" 👥 Group: {trip_input.group_size} {trip_input.skill_level} hikers") - print(f" 🌤️ Season: {trip_input.season}") - print(f" ⭐ Preferences: {trip_input.preferences}") - - await asyncio.sleep(2) - - # AI creates planning tasks - print("\n🤖 AI analyzing trip requirements and generating tasks...") - - # Create task generation agent - task_generator = TodoAgent(llm=llm, prompt=TaskGenerationPrompt) - - # Generate tasks with AI - task_response = await task_generator.run(trip_input) - - # Parse AI-generated tasks with robust error handling - try: - # Extract response content - AgentResult has .content attribute - if hasattr(task_response, "content"): - response_content = task_response.content.strip() - else: - response_content = str(task_response).strip() - - # Multiple strategies to extract JSON - import re - - # Strategy 1: Try direct JSON parsing - try: - planning_tasks = json.loads(response_content) - except json.JSONDecodeError: - # Strategy 2: Extract JSON array from text - json_patterns = [ - r"\[(?:[^[\]]*(?:\[[^\]]*\])*)*\]", # Complex nested array pattern - r"\[.*?\]", # Simple array pattern - ] - - planning_tasks = None - for pattern in json_patterns: - json_match = re.search(pattern, response_content, re.DOTALL) - if json_match: - json_str = json_match.group(0) - try: - planning_tasks = json.loads(json_str) - break - except json.JSONDecodeError: - continue - - if planning_tasks is None: - raise ValueError("No valid JSON array found in response") from None - - # Validate it's a list of strings - if not isinstance(planning_tasks, list): - raise ValueError(f"Expected list, got {type(planning_tasks)}") - - if not all(isinstance(task, str) for task in planning_tasks): - raise ValueError("All tasks must be strings") - - if len(planning_tasks) == 0: - raise ValueError("No tasks generated") - - except Exception as e: - print(f"⚠️ AI task generation failed ({e}), using fallback tasks...") - planning_tasks = [ - "Research optimal trail routes and scenic viewpoints", - "Find accommodation close to trailheads", - "Analyze weather conditions and gear requirements", - "Plan daily schedule with sunrise/sunset times", - "Research post-hike activities and relaxation spots", - ] - - # Create planning agent - planning_agent = TodoAgent(llm=llm, prompt=PlanningPrompt, keep_history=True) - planning_agent.create_todo_list(planning_tasks) - - await asyncio.sleep(1) - display_task_list(planning_agent, "AI-Generated Planning Tasks") - await asyncio.sleep(2) - - print("\n🚀 Processing tasks under the hood...") - await asyncio.sleep(1) - - # Process all tasks and capture results - task_results = [] - for task in planning_agent.tasks: - # Show current progress - planning_agent.mark_task(task.id, "in-progress") - display_task_list(planning_agent, f"Processing: {task.description[:40]}...") - - # Process task with AI and CAPTURE results - task_result = await planning_agent.run(trip_input) - task_results.append({ - "task": task.description, - "result": task_result.content if hasattr(task_result, 'content') else str(task_result) - }) - - # Mark as completed - planning_agent.mark_task(task.id, "done") - await asyncio.sleep(1) - - # Show all tasks completed - display_task_list(planning_agent, "All Planning Tasks Completed!") - await asyncio.sleep(2) - - # Compile task results - compiled_results = "\n\n".join([ - f"TASK: {result['task']}\nRESULT: {result['result']}" - for result in task_results - ]) - - # Create summary input with all task results - summary_input = SummaryInput( - location=trip_input.location, - duration_days=trip_input.duration_days, - group_size=trip_input.group_size, - skill_level=trip_input.skill_level, - season=trip_input.season, - preferences=trip_input.preferences, - task_results=compiled_results - ) - - # Generate and stream comprehensive summary - print("\n🎯 Generating comprehensive trip plan...") - print("🤖 AI compiling all research into actionable plan...") - await asyncio.sleep(2) - - # Summary agent now has access to all task research! - summary_agent = TodoAgent(llm=llm, prompt=SummaryPrompt) - - # Clear screen for final summary - clear_console() - print(f"{'='*60}") - print("🏔️ COMPLETE HIKING TRIP PLAN") - print(f"{'='*60}") - print() - - # Stream the comprehensive summary - streaming_result = summary_agent.run_streaming(summary_input) - - async for chunk in streaming_result: - if isinstance(chunk, str): - print(chunk, end="", flush=True) - - print(f"\n\n{'='*60}") - print("✅ Trip planning completed!") - print(f"📊 Agent processed {len(planning_agent.tasks)} planning tasks") - print("🎉 Ready for your adventure!") - - -async def main() -> None: - """Run the smart hiking planner.""" - print("🚀 Smart AI Hiking Trip Planner") - print("AI processes planning tasks under the hood, then streams comprehensive plan") - print("\nPress Ctrl+C to exit...") - await asyncio.sleep(3) - - try: - await smart_hiking_planner() - print("\n✨ Planning completed successfully!") - - except KeyboardInterrupt: - print("\n👋 Planning interrupted") - except Exception as e: - print(f"\n❌ Error: {e}") - print("Make sure OPENAI_API_KEY is set") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/agents/todo_tools_example.py b/examples/agents/todo_tools_example.py new file mode 100644 index 000000000..c26bf904b --- /dev/null +++ b/examples/agents/todo_tools_example.py @@ -0,0 +1,119 @@ +"""Example demonstrating the new single tool-based todo functionality.""" + +import asyncio + +from ragbits.agents import Agent, AgentOptions, ToolCallResult, get_todo_instruction_tpl, todo_manager +from ragbits.core.llms import LiteLLM, ToolCall + + +async def main(): + """Demonstrate the new single tool-based todo approach with streaming and logging.""" + + # Create an agent with higher turn limit and todo capabilities + my_agent = Agent( + llm=LiteLLM("gpt-4o-mini"), + prompt=""" + You are an expert hiking guide. You can either answer questions or + create a comprehensive, detailed hiking trip plan. + + WORKFLOW: + 1. If query is complex you have access to todo_manager tool to create a todo list with specific tasks + 2. If query is simple question, you work without todo_manager tool, just answer the question + 3. If you use todo_manager tool, you must follow the todo workflow below + + For hiking plans include: + - Specific route names, distances, elevation gain + - Detailed gear recommendations with quantities + - Transportation details with times, costs, parking info + - Weather considerations and backup plans + - Safety information and emergency contacts + """ + get_todo_instruction_tpl(task_range=(3, 5), enforce_workflow=True), + tools=[todo_manager], + default_options=AgentOptions(max_turns=30) + ) + + print("=== Enhanced Todo Workflow Example ===\n") + print("🚀 Hiking trip planning with systematic workflow:\n") + + # Simpler query to reduce complexity + query = "Plan a 1-day hiking trip for 2 people in Tatra Mountains, Poland. Focus on scenic routes under 15km, avoiding crowds." + # query = "How long is hike to Giewont from Kuźnice?" + # query = "Is it difficult to finish Orla Perć? Would you recommend me to go there if I've never been in mountains before?" + + stream = my_agent.run_streaming(query) + + async for response in stream: + match response: + case str(): + if response.strip(): + print(response, end="", flush=True) + + case ToolCall(): + if response.name == "todo_manager": + action = response.arguments.get("action", "unknown") + print(f"\n🔧 [TOOL] {action.upper()}", end="", flush=True) + + if action == "create": + tasks = response.arguments.get("tasks", []) + tasks_count = len(tasks) + print(f" - Creating {tasks_count} tasks", flush=True) + for i, task in enumerate(tasks, 1): + print(f" {i}. {task}") + + case ToolCallResult(): + if response.name == "todo_manager": + result = response.result + action = result.get("action", "unknown") + + if action == "create": + total = result.get('total_count', 0) + print(f" ✅ {total} tasks created successfully", flush=True) + current_task = result.get("current_task") + if current_task: + print(f"📋 Next: {current_task['description']}") + elif action == "update": + task = result.get("task", {}) + task_desc = task.get("description", "Unknown task")[:50] + new_status = task.get("status", "unknown") + print(f" ✅ Status changed to '{new_status}'", flush=True) + print(f"📋 Task: {task_desc}{'...' if len(task.get('description', '')) > 50 else ''}") + elif action == "complete_with_summary": + progress = result.get("progress", "unknown") + all_done = result.get("all_completed", False) + completed_task = result.get("completed_task", {}) + task_desc = completed_task.get("description", "Unknown task") + + if all_done: + print(f" ✅ FINAL TASK COMPLETED! Progress: {progress}", flush=True) + print(f"🎉 All tasks finished! Preparing final summary...") + else: + print(f" ✅ Task completed. Progress: {progress}", flush=True) + next_task = result.get("next_task") + if next_task: + print(f"📋 Next: {next_task['description']}") + elif action == "get_final_summary": + total_completed = result.get("total_completed", 0) + print(f" ✅ Final summary ready ({total_completed} tasks)", flush=True) + print("📄 Complete results below:") + elif action == "get_current": + current_task = result.get("current_task") + all_completed = result.get("all_completed", False) + if all_completed: + print(f" ✅ All tasks completed!", flush=True) + elif current_task: + print(f" ✅ Current task identified", flush=True) + print(f"📋 Current: {current_task['description']}") + else: + print(f" ✅ No current task", flush=True) + elif action == "get": + progress = result.get("progress", "unknown") + print(f" ✅ Task overview retrieved (Progress: {progress})", flush=True) + else: + print(f" ✅ {action} completed", flush=True) + + print("\n\n" + "="*50) + print("🎉 Systematic hiking trip planning completed!") + + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/packages/ragbits-agents/src/ragbits/agents/__init__.py b/packages/ragbits-agents/src/ragbits/agents/__init__.py index d1775a117..0551ab543 100644 --- a/packages/ragbits-agents/src/ragbits/agents/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/__init__.py @@ -7,7 +7,7 @@ AgentRunContext, ToolCallResult, ) -from ragbits.agents.todo_agent import Task, TaskStatus, TodoAgent +from ragbits.agents.tools import get_todo_instruction_tpl, todo_manager from ragbits.agents.types import QuestionAnswerAgent, QuestionAnswerPromptInput, QuestionAnswerPromptOutput __all__ = [ @@ -20,8 +20,7 @@ "QuestionAnswerAgent", "QuestionAnswerPromptInput", "QuestionAnswerPromptOutput", - "Task", - "TaskStatus", - "TodoAgent", "ToolCallResult", + "get_todo_instruction_tpl", + "todo_manager", ] diff --git a/packages/ragbits-agents/src/ragbits/agents/todo_agent.py b/packages/ragbits-agents/src/ragbits/agents/todo_agent.py deleted file mode 100644 index 722b2a83c..000000000 --- a/packages/ragbits-agents/src/ragbits/agents/todo_agent.py +++ /dev/null @@ -1,149 +0,0 @@ -"""Clean TodoAgent implementation - extends Agent directly.""" - -import uuid -from collections.abc import Callable -from enum import Enum -from typing import Generic, Literal - -from ragbits.agents._main import Agent, AgentOptions -from ragbits.agents.mcp.server import MCPServer -from ragbits.core.llms.base import LLM, LLMClientOptionsT -from ragbits.core.prompt.base import ChatFormat -from ragbits.core.prompt.prompt import Prompt, PromptInputT, PromptOutputT - - -class TaskStatus(str, Enum): - """Task status options.""" - - PENDING = "pending" - IN_PROGRESS = "in-progress" - DONE = "done" - - -class Task: - """Simple task representation with UUID.""" - - def __init__(self, description: str, task_id: str | None = None): - self.id = task_id or str(uuid.uuid4()) - self.description = description - self.status = TaskStatus.PENDING - - def __repr__(self) -> str: - return f"Task(id={self.id[:8]}..., description='{self.description}', status={self.status.value})" - - -class TodoAgent( - Agent[LLMClientOptionsT, PromptInputT, PromptOutputT], Generic[LLMClientOptionsT, PromptInputT, PromptOutputT] -): - """Agent with clean, simple todo functionality.""" - - def __init__( - self, - llm: LLM[LLMClientOptionsT], - prompt: str | type[Prompt[PromptInputT, PromptOutputT]] | Prompt[PromptInputT, PromptOutputT] | None = None, - *, - history: ChatFormat | None = None, - keep_history: bool = False, - tools: list[Callable] | None = None, - mcp_servers: list[MCPServer] | None = None, - default_options: AgentOptions[LLMClientOptionsT] | None = None, - ) -> None: - """Initialize TodoAgent - just like regular Agent but with todo capabilities.""" - super().__init__( - llm=llm, - prompt=prompt, - history=history, - keep_history=keep_history, - tools=tools, - mcp_servers=mcp_servers, - default_options=default_options, - ) - - # Simple todo storage - just a list of tasks - self.tasks: list[Task] = [] - - def create_todo_list(self, tasks: dict[int, str] | list[str]) -> list[Task]: - """ - Create todo list from tasks. - - Args: - tasks: Either dict {1: "task1", 2: "task2"} or list ["task1", "task2"] - - Returns: - List of created Task objects - """ - self.tasks.clear() - - task_descriptions = list(tasks.values()) if isinstance(tasks, dict) else tasks - for description in task_descriptions: - task = Task(description) - self.tasks.append(task) - - return self.tasks.copy() - - def add_task(self, description: str) -> Task: - """Add a single task.""" - task = Task(description) - self.tasks.append(task) - return task - - def mark_task(self, task_id: str | int, status: Literal["pending", "in-progress", "done"]) -> Task: - """ - Mark task status by ID. - - Args: - task_id: Task ID (string UUID) or index (int) - status: New status - - Returns: - Updated task - """ - task = self._find_task(task_id) - task.status = TaskStatus(status) - return task - - def get_task(self, task_id: str | int) -> Task: - """Get task by ID or index.""" - return self._find_task(task_id) - - def get_tasks_by_status(self, status: TaskStatus) -> list[Task]: - """Get all tasks with specific status.""" - return [task for task in self.tasks if task.status == status] - - @property - def pending_tasks(self) -> list[Task]: - """Get pending tasks.""" - return self.get_tasks_by_status(TaskStatus.PENDING) - - @property - def in_progress_tasks(self) -> list[Task]: - """Get in-progress tasks.""" - return self.get_tasks_by_status(TaskStatus.IN_PROGRESS) - - @property - def done_tasks(self) -> list[Task]: - """Get completed tasks.""" - return self.get_tasks_by_status(TaskStatus.DONE) - - def _find_task(self, task_id: str | int) -> Task: - """Find task by ID or index.""" - if isinstance(task_id, int): - # Use as index - if 0 <= task_id < len(self.tasks): - return self.tasks[task_id] - else: - raise ValueError(f"Task index {task_id} out of range") - - # Use as UUID string - for task in self.tasks: - if task.id == task_id or task.id.startswith(str(task_id)): - return task - - raise ValueError(f"Task {task_id} not found") - - def __repr__(self) -> str: - total = len(self.tasks) - pending = len(self.pending_tasks) - in_progress = len(self.in_progress_tasks) - done = len(self.done_tasks) - return f"TodoAgent(tasks={total}, pending={pending}, in_progress={in_progress}, done={done})" diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py index 9bb922491..1e5b1154f 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py @@ -1,3 +1,5 @@ -from ragbits.agents.tools.openai import get_code_interpreter_tool, get_image_generation_tool, get_web_search_tool +"""Agent tools for extending functionality.""" -__all__ = ["get_code_interpreter_tool", "get_image_generation_tool", "get_web_search_tool"] +from .todo import get_todo_instruction_tpl, todo_manager + +__all__ = ["todo_manager", "get_todo_instruction_tpl"] \ No newline at end of file diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py new file mode 100644 index 000000000..d15cfc191 --- /dev/null +++ b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py @@ -0,0 +1,398 @@ +"""Todo list management tool for agents with workflow enforcement.""" + +import uuid +import threading +from dataclasses import dataclass, field +from datetime import datetime +from enum import Enum +from typing import Any, Literal + + +class TaskStatus(str, Enum): + """Task status options.""" + + PENDING = "pending" + IN_PROGRESS = "in_progress" + COMPLETED = "completed" + CANCELLED = "cancelled" + + +@dataclass +class Task: + """Structured task representation.""" + + id: str + description: str + status: TaskStatus = TaskStatus.PENDING + order: int = 0 + created_at: datetime = field(default_factory=datetime.now) + started_at: datetime | None = None + completed_at: datetime | None = None + summary: str | None = None + + def to_dict(self) -> dict[str, Any]: + """Convert to dictionary for JSON serialization.""" + return { + "id": self.id, + "description": self.description, + "status": self.status.value, + "order": self.order, + "created_at": self.created_at.isoformat(), + "started_at": self.started_at.isoformat() if self.started_at else None, + "completed_at": self.completed_at.isoformat() if self.completed_at else None, + "summary": self.summary + } + + +@dataclass +class TodoSession: + """Todo session with workflow enforcement.""" + + session_id: str + tasks: dict[str, Task] = field(default_factory=dict) + current_task_index: int = 0 + created_at: datetime = field(default_factory=datetime.now) + + def get_current_task(self) -> Task | None: + """Get the current task that should be worked on.""" + ordered_tasks = self.get_ordered_tasks() + if self.current_task_index < len(ordered_tasks): + return ordered_tasks[self.current_task_index] + return None + + def get_ordered_tasks(self) -> list[Task]: + """Get tasks in execution order.""" + return sorted(self.tasks.values(), key=lambda t: t.order) + + def can_start_task(self, task_id: str) -> tuple[bool, str]: + """Check if a task can be started (workflow enforcement).""" + task = self.tasks.get(task_id) + if not task: + return False, f"Task {task_id} not found" + + current_task = self.get_current_task() + if current_task and current_task.id != task_id: + return False, f"Must complete current task '{current_task.description}' first" + + if task.status != TaskStatus.PENDING: + return False, f"Task is already {task.status.value}" + + return True, "OK" + + def advance_to_next_task(self): + """Move to next pending task.""" + self.current_task_index += 1 + + +# Thread-safe storage +_storage_lock = threading.Lock() +_SESSIONS: dict[str, TodoSession] = {} + +def _get_or_create_session(session_id: str | None = None) -> str: + """Get existing session or auto-find active session.""" + if session_id: + if session_id in _SESSIONS: + return session_id + else: + raise ValueError(f"Session {session_id} not found") + + # No session_id provided - try to find active session + if len(_SESSIONS) == 1: + # Only one session exists, use it + return list(_SESSIONS.keys())[0] + elif len(_SESSIONS) == 0: + raise ValueError("No active sessions. Create a todo list first.") + else: + raise ValueError(f"Multiple sessions exist ({len(_SESSIONS)}). Please specify session_id.") + + +def todo_manager( + action: Literal["create", "update", "get", "get_current", "complete_with_summary", "get_final_summary"], + session_id: str | None = None, + tasks: list[str] | None = None, + task_id: str | None = None, + status: Literal["pending", "in_progress", "completed", "cancelled"] | None = None, + summary: str | None = None, +) -> dict[str, Any]: + """ + Enhanced todo manager with workflow enforcement. + + Args: + action: Action to perform - "create", "update", "get", "get_current", "complete_with_summary", "get_final_summary" + session_id: Session identifier (auto-generated for create if None) + tasks: List of task descriptions (required for "create" action) + task_id: Task identifier (required for "update" and "complete_with_summary") + status: New status (required for "update" action) + summary: Task completion summary (required for "complete_with_summary") + + Returns: + Dictionary with action results + + Examples: + # Create a structured todo list + todo_manager(action="create", tasks=["Task 1", "Task 2", "Task 3"]) + + # Get the current task that should be worked on + todo_manager(action="get_current", session_id="session123") + + # Start working on current task + todo_manager(action="update", session_id="session123", task_id="task456", status="in_progress") + + # Complete task with summary (enforces workflow) + todo_manager(action="complete_with_summary", session_id="session123", task_id="task456", summary="Completed analysis of requirements") + + # Get full progress + todo_manager(action="get", session_id="session123") + """ + with _storage_lock: + if action == "create": + if not tasks or len(tasks) == 0: + raise ValueError("'tasks' parameter is required and must not be empty") + if len(tasks) > 10: + raise ValueError("Maximum 10 tasks allowed per session") + + session_id = session_id or str(uuid.uuid4()) + + # Create new session + session = TodoSession(session_id=session_id) + + # Create tasks with validation + for i, description in enumerate(tasks): + if not description or not description.strip(): + raise ValueError(f"Task {i+1} description cannot be empty") + + task = Task( + id=str(uuid.uuid4()), + description=description.strip(), + order=i + ) + session.tasks[task.id] = task + + _SESSIONS[session_id] = session + + return { + "action": "create", + "session_id": session_id, + "tasks": [task.to_dict() for task in session.get_ordered_tasks()], + "total_count": len(session.tasks), + "current_task": session.get_current_task().to_dict() if session.get_current_task() else None, + "message": f"Created {len(tasks)} tasks. Use 'get_current' to see what task to work on first." + } + + elif action == "get_current": + session_id = _get_or_create_session(session_id) + + session = _SESSIONS[session_id] + current_task = session.get_current_task() + + if not current_task: + completed_count = sum(1 for t in session.tasks.values() if t.status == TaskStatus.COMPLETED) + completed_tasks = [t for t in session.get_ordered_tasks() if t.status == TaskStatus.COMPLETED] + + # Aggregate all task summaries for final output + final_summary = "" + if completed_tasks: + final_summary = "\n\n".join([ + f"**{i+1}. {task.description}**: {task.summary}" + for i, task in enumerate(completed_tasks) + if task.summary + ]) + + return { + "action": "get_current", + "session_id": session_id, + "current_task": None, + "message": f"All tasks completed! ({completed_count}/{len(session.tasks)})", + "all_completed": True, + "final_summary": final_summary, + "completed_tasks": [task.to_dict() for task in completed_tasks] + } + + return { + "action": "get_current", + "session_id": session_id, + "current_task": current_task.to_dict(), + "progress": f"{session.current_task_index + 1}/{len(session.tasks)}", + "message": f"Current task: '{current_task.description}'. Mark as 'in_progress' to start working." + } + + elif action == "update": + if not all([task_id, status]): + raise ValueError("task_id and status are required for update") + + session_id = _get_or_create_session(session_id) + + session = _SESSIONS[session_id] + task = session.tasks.get(task_id) + if not task: + raise ValueError(f"Task {task_id} not found") + + # Workflow enforcement + if status == "in_progress": + can_start, reason = session.can_start_task(task_id) + if not can_start: + current = session.get_current_task() + raise ValueError(f"Workflow violation: {reason}. Use 'get_current' to see the correct task.") + task.started_at = datetime.now() + + elif status == "completed": + if task.status != TaskStatus.IN_PROGRESS: + raise ValueError("Task must be 'in_progress' before completing. Use 'complete_with_summary' instead for proper completion.") + task.completed_at = datetime.now() + session.advance_to_next_task() + + task.status = TaskStatus(status) + + next_task = session.get_current_task() + return { + "action": "update", + "task": task.to_dict(), + "next_task": next_task.to_dict() if next_task else None, + "message": f"Task '{task.description}' updated to {status}." + } + + elif action == "complete_with_summary": + if not all([task_id, summary]): + raise ValueError("task_id and summary are required for complete_with_summary") + + session_id = _get_or_create_session(session_id) + + session = _SESSIONS[session_id] + task = session.tasks.get(task_id) + if not task: + raise ValueError(f"Task {task_id} not found") + + if task.status != TaskStatus.IN_PROGRESS: + raise ValueError("Task must be 'in_progress' to complete with summary. Start the task first.") + + # Complete task with summary + task.status = TaskStatus.COMPLETED + task.completed_at = datetime.now() + task.summary = summary.strip() + session.advance_to_next_task() + + next_task = session.get_current_task() + completed_count = sum(1 for t in session.tasks.values() if t.status == TaskStatus.COMPLETED) + + return { + "action": "complete_with_summary", + "completed_task": task.to_dict(), + "next_task": next_task.to_dict() if next_task else None, + "progress": f"{completed_count}/{len(session.tasks)}", + "message": f"✅ Task completed: '{task.description}'. Summary: {summary[:100]}..." if len(summary) > 100 else f"✅ Task completed: '{task.description}'. Summary: {summary}", + "all_completed": next_task is None + } + + elif action == "get": + session_id = _get_or_create_session(session_id) + + if not session_id: + return { + "action": "get", + "session_id": session_id, + "tasks": [], + "summary": {"total": 0, "pending": 0, "in_progress": 0, "completed": 0, "cancelled": 0}, + "message": "No active session found." + } + + session = _SESSIONS[session_id] + tasks_list = session.get_ordered_tasks() + + summary = { + "total": len(tasks_list), + "pending": sum(1 for t in tasks_list if t.status == TaskStatus.PENDING), + "in_progress": sum(1 for t in tasks_list if t.status == TaskStatus.IN_PROGRESS), + "completed": sum(1 for t in tasks_list if t.status == TaskStatus.COMPLETED), + "cancelled": sum(1 for t in tasks_list if t.status == TaskStatus.CANCELLED), + } + + current_task = session.get_current_task() + return { + "action": "get", + "session_id": session_id, + "tasks": [task.to_dict() for task in tasks_list], + "summary": summary, + "current_task": current_task.to_dict() if current_task else None, + "progress": f"{summary['completed']}/{summary['total']}", + "message": f"Progress: {summary['completed']}/{summary['total']} tasks completed." + } + + elif action == "get_final_summary": + session_id = _get_or_create_session(session_id) + + session = _SESSIONS[session_id] + completed_tasks = [t for t in session.get_ordered_tasks() if t.status == TaskStatus.COMPLETED] + + if not completed_tasks: + return { + "action": "get_final_summary", + "session_id": session_id, + "final_summary": "", + "message": "No completed tasks found." + } + + # Create comprehensive final summary + final_content = [] + for i, task in enumerate(completed_tasks): + if task.summary: + final_content.append(f"**{i+1}. {task.description}**:\n{task.summary}") + else: + final_content.append(f"**{i+1}. {task.description}**: Completed") + + final_summary = "\n\n".join(final_content) + + return { + "action": "get_final_summary", + "session_id": session_id, + "final_summary": final_summary, + "completed_tasks": [task.to_dict() for task in completed_tasks], + "total_completed": len(completed_tasks), + "message": f"Final summary with {len(completed_tasks)} completed tasks." + } + + else: + raise ValueError(f"Unknown action: {action}. Use 'create', 'update', 'get', 'get_current', 'complete_with_summary', 'get_final_summary'") + + +def get_todo_instruction_tpl( + task_range: tuple[int, int] = (3, 6), + enforce_workflow: bool = True +) -> str: + """Generate system prompt instructions for structured todo workflow.""" + min_tasks, max_tasks = task_range + + if enforce_workflow: + return f""" + + ## Todo Workflow + + 1. Work through each task one by one + 2. For each task: get_current → update to in_progress → do the work → complete_with_summary + 3. When all tasks are done, use get_final_summary to present the complete results + + CRITICAL: When completing tasks with summaries, provide DETAILED, COMPREHENSIVE information. + + Available actions: + - `todo_manager(action="create", tasks=[...])`: Create {min_tasks}-{max_tasks} tasks + - `todo_manager(action="get_current")`: Get current task (session auto-detected) + - `todo_manager(action="update", task_id="...", status="in_progress")`: Start task + - `todo_manager(action="complete_with_summary", task_id="...", summary="...")`: Complete with summary + - `todo_manager(action="get_final_summary")`: Get comprehensive final summary of all completed work + + MANDATORY WORKFLOW: + 1. Create todo list first + 2. For each task: get_current → update(in_progress) → [work] → complete_with_summary + 3. System enforces sequential order + 4. When all tasks done, use get_final_summary to present the complete results + + IMPORTANT: Task summaries should be DETAILED and COMPREHENSIVE (3-5 sentences minimum). + Include specific information, recommendations, and actionable details. + + NOTE: Session ID is auto-detected after creation. Just use the actions as shown. + """ + else: + return f""" + + ## Todo Management + Create {min_tasks}-{max_tasks} tasks for complex requests and track progress systematically. + """ From bd9cb55d54007621335e15927a35b3d0b65173c1 Mon Sep 17 00:00:00 2001 From: jakubduda-dsai Date: Thu, 18 Sep 2025 11:18:34 +0200 Subject: [PATCH 08/43] todo_manager --- examples/agents/todo_tools_example.py | 2 +- .../src/ragbits/agents/tools/todo.py | 444 +++++------------- 2 files changed, 124 insertions(+), 322 deletions(-) diff --git a/examples/agents/todo_tools_example.py b/examples/agents/todo_tools_example.py index c26bf904b..7fd140430 100644 --- a/examples/agents/todo_tools_example.py +++ b/examples/agents/todo_tools_example.py @@ -27,7 +27,7 @@ async def main(): - Transportation details with times, costs, parking info - Weather considerations and backup plans - Safety information and emergency contacts - """ + get_todo_instruction_tpl(task_range=(3, 5), enforce_workflow=True), + """ + get_todo_instruction_tpl(task_range=(3, 5)), tools=[todo_manager], default_options=AgentOptions(max_turns=30) ) diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py index d15cfc191..5a4441e86 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py @@ -1,7 +1,6 @@ -"""Todo list management tool for agents with workflow enforcement.""" +"""Todo list management tool for agents.""" import uuid -import threading from dataclasses import dataclass, field from datetime import datetime from enum import Enum @@ -10,389 +9,192 @@ class TaskStatus(str, Enum): """Task status options.""" - PENDING = "pending" IN_PROGRESS = "in_progress" COMPLETED = "completed" - CANCELLED = "cancelled" @dataclass class Task: - """Structured task representation.""" - + """Simple task representation.""" id: str description: str status: TaskStatus = TaskStatus.PENDING order: int = 0 - created_at: datetime = field(default_factory=datetime.now) - started_at: datetime | None = None - completed_at: datetime | None = None summary: str | None = None - def to_dict(self) -> dict[str, Any]: - """Convert to dictionary for JSON serialization.""" - return { - "id": self.id, - "description": self.description, - "status": self.status.value, - "order": self.order, - "created_at": self.created_at.isoformat(), - "started_at": self.started_at.isoformat() if self.started_at else None, - "completed_at": self.completed_at.isoformat() if self.completed_at else None, - "summary": self.summary - } - @dataclass -class TodoSession: - """Todo session with workflow enforcement.""" - - session_id: str - tasks: dict[str, Task] = field(default_factory=dict) - current_task_index: int = 0 - created_at: datetime = field(default_factory=datetime.now) +class TodoList: + """Simple todo list for one agent run.""" + tasks: list[Task] = field(default_factory=list) + current_index: int = 0 def get_current_task(self) -> Task | None: - """Get the current task that should be worked on.""" - ordered_tasks = self.get_ordered_tasks() - if self.current_task_index < len(ordered_tasks): - return ordered_tasks[self.current_task_index] + """Get current task to work on.""" + if self.current_index < len(self.tasks): + return self.tasks[self.current_index] return None - def get_ordered_tasks(self) -> list[Task]: - """Get tasks in execution order.""" - return sorted(self.tasks.values(), key=lambda t: t.order) - - def can_start_task(self, task_id: str) -> tuple[bool, str]: - """Check if a task can be started (workflow enforcement).""" - task = self.tasks.get(task_id) - if not task: - return False, f"Task {task_id} not found" - - current_task = self.get_current_task() - if current_task and current_task.id != task_id: - return False, f"Must complete current task '{current_task.description}' first" + def advance_to_next(self): + """Move to next task.""" + self.current_index += 1 - if task.status != TaskStatus.PENDING: - return False, f"Task is already {task.status.value}" - - return True, "OK" - - def advance_to_next_task(self): - """Move to next pending task.""" - self.current_task_index += 1 - - -# Thread-safe storage -_storage_lock = threading.Lock() -_SESSIONS: dict[str, TodoSession] = {} - -def _get_or_create_session(session_id: str | None = None) -> str: - """Get existing session or auto-find active session.""" - if session_id: - if session_id in _SESSIONS: - return session_id - else: - raise ValueError(f"Session {session_id} not found") - - # No session_id provided - try to find active session - if len(_SESSIONS) == 1: - # Only one session exists, use it - return list(_SESSIONS.keys())[0] - elif len(_SESSIONS) == 0: - raise ValueError("No active sessions. Create a todo list first.") - else: - raise ValueError(f"Multiple sessions exist ({len(_SESSIONS)}). Please specify session_id.") +# Storage - just one todo list per agent run +_current_todo: TodoList | None = None def todo_manager( - action: Literal["create", "update", "get", "get_current", "complete_with_summary", "get_final_summary"], - session_id: str | None = None, + action: Literal["create", "get_current", "start_task", "complete_task", "get_final_summary"], tasks: list[str] | None = None, task_id: str | None = None, - status: Literal["pending", "in_progress", "completed", "cancelled"] | None = None, summary: str | None = None, ) -> dict[str, Any]: """ - Enhanced todo manager with workflow enforcement. - - Args: - action: Action to perform - "create", "update", "get", "get_current", "complete_with_summary", "get_final_summary" - session_id: Session identifier (auto-generated for create if None) - tasks: List of task descriptions (required for "create" action) - task_id: Task identifier (required for "update" and "complete_with_summary") - status: New status (required for "update" action) - summary: Task completion summary (required for "complete_with_summary") - - Returns: - Dictionary with action results - - Examples: - # Create a structured todo list - todo_manager(action="create", tasks=["Task 1", "Task 2", "Task 3"]) - - # Get the current task that should be worked on - todo_manager(action="get_current", session_id="session123") - - # Start working on current task - todo_manager(action="update", session_id="session123", task_id="task456", status="in_progress") - - # Complete task with summary (enforces workflow) - todo_manager(action="complete_with_summary", session_id="session123", task_id="task456", summary="Completed analysis of requirements") - - # Get full progress - todo_manager(action="get", session_id="session123") + Simplified todo manager for agent runs. + + Actions: + - create: Create todo list with tasks + - get_current: Get current task to work on + - start_task: Mark current task as in progress + - complete_task: Complete current task with summary + - get_final_summary: Get all completed work """ - with _storage_lock: - if action == "create": - if not tasks or len(tasks) == 0: - raise ValueError("'tasks' parameter is required and must not be empty") - if len(tasks) > 10: - raise ValueError("Maximum 10 tasks allowed per session") - - session_id = session_id or str(uuid.uuid4()) + global _current_todo - # Create new session - session = TodoSession(session_id=session_id) + if action == "create": + if not tasks: + raise ValueError("Tasks required for create action") - # Create tasks with validation - for i, description in enumerate(tasks): - if not description or not description.strip(): - raise ValueError(f"Task {i+1} description cannot be empty") + _current_todo = TodoList() + for i, desc in enumerate(tasks): + task = Task( + id=str(uuid.uuid4()), + description=desc.strip(), + order=i + ) + _current_todo.tasks.append(task) - task = Task( - id=str(uuid.uuid4()), - description=description.strip(), - order=i - ) - session.tasks[task.id] = task - - _SESSIONS[session_id] = session - - return { - "action": "create", - "session_id": session_id, - "tasks": [task.to_dict() for task in session.get_ordered_tasks()], - "total_count": len(session.tasks), - "current_task": session.get_current_task().to_dict() if session.get_current_task() else None, - "message": f"Created {len(tasks)} tasks. Use 'get_current' to see what task to work on first." - } + return { + "action": "create", + "tasks": [{"id": t.id, "description": t.description, "order": t.order} for t in _current_todo.tasks], + "total_count": len(_current_todo.tasks), + "message": f"Created {len(tasks)} tasks" + } - elif action == "get_current": - session_id = _get_or_create_session(session_id) - - session = _SESSIONS[session_id] - current_task = session.get_current_task() - - if not current_task: - completed_count = sum(1 for t in session.tasks.values() if t.status == TaskStatus.COMPLETED) - completed_tasks = [t for t in session.get_ordered_tasks() if t.status == TaskStatus.COMPLETED] - - # Aggregate all task summaries for final output - final_summary = "" - if completed_tasks: - final_summary = "\n\n".join([ - f"**{i+1}. {task.description}**: {task.summary}" - for i, task in enumerate(completed_tasks) - if task.summary - ]) - - return { - "action": "get_current", - "session_id": session_id, - "current_task": None, - "message": f"All tasks completed! ({completed_count}/{len(session.tasks)})", - "all_completed": True, - "final_summary": final_summary, - "completed_tasks": [task.to_dict() for task in completed_tasks] - } + if not _current_todo: + raise ValueError("No todo list exists. Create one first.") + if action == "get_current": + current = _current_todo.get_current_task() + if not current: return { "action": "get_current", - "session_id": session_id, - "current_task": current_task.to_dict(), - "progress": f"{session.current_task_index + 1}/{len(session.tasks)}", - "message": f"Current task: '{current_task.description}'. Mark as 'in_progress' to start working." + "current_task": None, + "all_completed": True, + "message": "All tasks completed!" } - elif action == "update": - if not all([task_id, status]): - raise ValueError("task_id and status are required for update") - - session_id = _get_or_create_session(session_id) - - session = _SESSIONS[session_id] - task = session.tasks.get(task_id) - if not task: - raise ValueError(f"Task {task_id} not found") - - # Workflow enforcement - if status == "in_progress": - can_start, reason = session.can_start_task(task_id) - if not can_start: - current = session.get_current_task() - raise ValueError(f"Workflow violation: {reason}. Use 'get_current' to see the correct task.") - task.started_at = datetime.now() - - elif status == "completed": - if task.status != TaskStatus.IN_PROGRESS: - raise ValueError("Task must be 'in_progress' before completing. Use 'complete_with_summary' instead for proper completion.") - task.completed_at = datetime.now() - session.advance_to_next_task() - - task.status = TaskStatus(status) + return { + "action": "get_current", + "current_task": {"id": current.id, "description": current.description, "status": current.status.value}, + "progress": f"{_current_todo.current_index + 1}/{len(_current_todo.tasks)}", + "message": f"Current task: {current.description}" + } - next_task = session.get_current_task() - return { - "action": "update", - "task": task.to_dict(), - "next_task": next_task.to_dict() if next_task else None, - "message": f"Task '{task.description}' updated to {status}." - } + elif action == "start_task": + current = _current_todo.get_current_task() + if not current: + raise ValueError("No current task to start") - elif action == "complete_with_summary": - if not all([task_id, summary]): - raise ValueError("task_id and summary are required for complete_with_summary") + current.status = TaskStatus.IN_PROGRESS + return { + "action": "start_task", + "task": {"id": current.id, "description": current.description, "status": current.status.value}, + "message": f"Started task: {current.description}" + } - session_id = _get_or_create_session(session_id) + elif action == "complete_task": + if not summary: + raise ValueError("Summary required for complete_task") - session = _SESSIONS[session_id] - task = session.tasks.get(task_id) - if not task: - raise ValueError(f"Task {task_id} not found") + current = _current_todo.get_current_task() + if not current: + raise ValueError("No current task to complete") - if task.status != TaskStatus.IN_PROGRESS: - raise ValueError("Task must be 'in_progress' to complete with summary. Start the task first.") + if current.status != TaskStatus.IN_PROGRESS: + raise ValueError("Task must be started before completing") - # Complete task with summary - task.status = TaskStatus.COMPLETED - task.completed_at = datetime.now() - task.summary = summary.strip() - session.advance_to_next_task() + current.status = TaskStatus.COMPLETED + current.summary = summary.strip() + _current_todo.advance_to_next() - next_task = session.get_current_task() - completed_count = sum(1 for t in session.tasks.values() if t.status == TaskStatus.COMPLETED) + next_task = _current_todo.get_current_task() + completed_count = sum(1 for t in _current_todo.tasks if t.status == TaskStatus.COMPLETED) - return { - "action": "complete_with_summary", - "completed_task": task.to_dict(), - "next_task": next_task.to_dict() if next_task else None, - "progress": f"{completed_count}/{len(session.tasks)}", - "message": f"✅ Task completed: '{task.description}'. Summary: {summary[:100]}..." if len(summary) > 100 else f"✅ Task completed: '{task.description}'. Summary: {summary}", - "all_completed": next_task is None - } + return { + "action": "complete_task", + "completed_task": {"id": current.id, "description": current.description, "summary": current.summary}, + "next_task": {"id": next_task.id, "description": next_task.description} if next_task else None, + "progress": f"{completed_count}/{len(_current_todo.tasks)}", + "all_completed": next_task is None, + "message": f"Completed: {current.description}" + } - elif action == "get": - session_id = _get_or_create_session(session_id) - - if not session_id: - return { - "action": "get", - "session_id": session_id, - "tasks": [], - "summary": {"total": 0, "pending": 0, "in_progress": 0, "completed": 0, "cancelled": 0}, - "message": "No active session found." - } - - session = _SESSIONS[session_id] - tasks_list = session.get_ordered_tasks() - - summary = { - "total": len(tasks_list), - "pending": sum(1 for t in tasks_list if t.status == TaskStatus.PENDING), - "in_progress": sum(1 for t in tasks_list if t.status == TaskStatus.IN_PROGRESS), - "completed": sum(1 for t in tasks_list if t.status == TaskStatus.COMPLETED), - "cancelled": sum(1 for t in tasks_list if t.status == TaskStatus.CANCELLED), - } + elif action == "get_final_summary": + completed_tasks = [t for t in _current_todo.tasks if t.status == TaskStatus.COMPLETED] - current_task = session.get_current_task() + if not completed_tasks: return { - "action": "get", - "session_id": session_id, - "tasks": [task.to_dict() for task in tasks_list], - "summary": summary, - "current_task": current_task.to_dict() if current_task else None, - "progress": f"{summary['completed']}/{summary['total']}", - "message": f"Progress: {summary['completed']}/{summary['total']} tasks completed." + "action": "get_final_summary", + "final_summary": "", + "message": "No completed tasks found." } - elif action == "get_final_summary": - session_id = _get_or_create_session(session_id) - - session = _SESSIONS[session_id] - completed_tasks = [t for t in session.get_ordered_tasks() if t.status == TaskStatus.COMPLETED] + # Create comprehensive final summary + final_content = [] + for i, task in enumerate(completed_tasks): + if task.summary: + final_content.append(f"**{i+1}. {task.description}**:\n{task.summary}") + else: + final_content.append(f"**{i+1}. {task.description}**: Completed") - if not completed_tasks: - return { - "action": "get_final_summary", - "session_id": session_id, - "final_summary": "", - "message": "No completed tasks found." - } + final_summary = "\n\n".join(final_content) - # Create comprehensive final summary - final_content = [] - for i, task in enumerate(completed_tasks): - if task.summary: - final_content.append(f"**{i+1}. {task.description}**:\n{task.summary}") - else: - final_content.append(f"**{i+1}. {task.description}**: Completed") + # Clean up after getting final summary + _current_todo = None - final_summary = "\n\n".join(final_content) - - return { - "action": "get_final_summary", - "session_id": session_id, - "final_summary": final_summary, - "completed_tasks": [task.to_dict() for task in completed_tasks], - "total_completed": len(completed_tasks), - "message": f"Final summary with {len(completed_tasks)} completed tasks." - } + return { + "action": "get_final_summary", + "final_summary": final_summary, + "total_completed": len(completed_tasks), + "message": f"Final summary with {len(completed_tasks)} completed tasks." + } - else: - raise ValueError(f"Unknown action: {action}. Use 'create', 'update', 'get', 'get_current', 'complete_with_summary', 'get_final_summary'") + else: + raise ValueError(f"Unknown action: {action}") -def get_todo_instruction_tpl( - task_range: tuple[int, int] = (3, 6), - enforce_workflow: bool = True -) -> str: - """Generate system prompt instructions for structured todo workflow.""" +def get_todo_instruction_tpl(task_range: tuple[int, int] = (3, 5)) -> str: + """Generate system prompt instructions for todo workflow.""" min_tasks, max_tasks = task_range - if enforce_workflow: - return f""" + return f""" ## Todo Workflow - 1. Work through each task one by one - 2. For each task: get_current → update to in_progress → do the work → complete_with_summary - 3. When all tasks are done, use get_final_summary to present the complete results - - CRITICAL: When completing tasks with summaries, provide DETAILED, COMPREHENSIVE information. - Available actions: - `todo_manager(action="create", tasks=[...])`: Create {min_tasks}-{max_tasks} tasks - - `todo_manager(action="get_current")`: Get current task (session auto-detected) - - `todo_manager(action="update", task_id="...", status="in_progress")`: Start task - - `todo_manager(action="complete_with_summary", task_id="...", summary="...")`: Complete with summary - - `todo_manager(action="get_final_summary")`: Get comprehensive final summary of all completed work - - MANDATORY WORKFLOW: - 1. Create todo list first - 2. For each task: get_current → update(in_progress) → [work] → complete_with_summary - 3. System enforces sequential order - 4. When all tasks done, use get_final_summary to present the complete results - - IMPORTANT: Task summaries should be DETAILED and COMPREHENSIVE (3-5 sentences minimum). - Include specific information, recommendations, and actionable details. + - `todo_manager(action="get_current")`: Get current task + - `todo_manager(action="start_task")`: Start current task + - `todo_manager(action="complete_task", summary="...")`: Complete with detailed summary + - `todo_manager(action="get_final_summary")`: Get comprehensive final results - NOTE: Session ID is auto-detected after creation. Just use the actions as shown. - """ - else: - return f""" + WORKFLOW: + 1. Create todo list + 2. For each task: get_current → start_task → [do work] → complete_task + 3. When done: get_final_summary - ## Todo Management - Create {min_tasks}-{max_tasks} tasks for complex requests and track progress systematically. + IMPORTANT: Task summaries should be DETAILED and COMPREHENSIVE (3-5 sentences). + Include specific information, recommendations, and actionable details. """ From fd87b7f7c04e25a5e1f227c76c41ba8fb8b5e7ab Mon Sep 17 00:00:00 2001 From: jakubduda-dsai Date: Thu, 18 Sep 2025 11:19:44 +0200 Subject: [PATCH 09/43] remove task_id --- packages/ragbits-agents/src/ragbits/agents/tools/todo.py | 1 - 1 file changed, 1 deletion(-) diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py index 5a4441e86..a5f3db6b0 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py @@ -47,7 +47,6 @@ def advance_to_next(self): def todo_manager( action: Literal["create", "get_current", "start_task", "complete_task", "get_final_summary"], tasks: list[str] | None = None, - task_id: str | None = None, summary: str | None = None, ) -> dict[str, Any]: """ From 63faf9096b0841f202087dbe6ced3353ede138b7 Mon Sep 17 00:00:00 2001 From: jakubduda-dsai Date: Thu, 18 Sep 2025 11:21:15 +0200 Subject: [PATCH 10/43] clear prints --- examples/agents/todo_tools_example.py | 52 --------------------------- 1 file changed, 52 deletions(-) diff --git a/examples/agents/todo_tools_example.py b/examples/agents/todo_tools_example.py index 7fd140430..ffe542a5b 100644 --- a/examples/agents/todo_tools_example.py +++ b/examples/agents/todo_tools_example.py @@ -51,7 +51,6 @@ async def main(): case ToolCall(): if response.name == "todo_manager": action = response.arguments.get("action", "unknown") - print(f"\n🔧 [TOOL] {action.upper()}", end="", flush=True) if action == "create": tasks = response.arguments.get("tasks", []) @@ -60,57 +59,6 @@ async def main(): for i, task in enumerate(tasks, 1): print(f" {i}. {task}") - case ToolCallResult(): - if response.name == "todo_manager": - result = response.result - action = result.get("action", "unknown") - - if action == "create": - total = result.get('total_count', 0) - print(f" ✅ {total} tasks created successfully", flush=True) - current_task = result.get("current_task") - if current_task: - print(f"📋 Next: {current_task['description']}") - elif action == "update": - task = result.get("task", {}) - task_desc = task.get("description", "Unknown task")[:50] - new_status = task.get("status", "unknown") - print(f" ✅ Status changed to '{new_status}'", flush=True) - print(f"📋 Task: {task_desc}{'...' if len(task.get('description', '')) > 50 else ''}") - elif action == "complete_with_summary": - progress = result.get("progress", "unknown") - all_done = result.get("all_completed", False) - completed_task = result.get("completed_task", {}) - task_desc = completed_task.get("description", "Unknown task") - - if all_done: - print(f" ✅ FINAL TASK COMPLETED! Progress: {progress}", flush=True) - print(f"🎉 All tasks finished! Preparing final summary...") - else: - print(f" ✅ Task completed. Progress: {progress}", flush=True) - next_task = result.get("next_task") - if next_task: - print(f"📋 Next: {next_task['description']}") - elif action == "get_final_summary": - total_completed = result.get("total_completed", 0) - print(f" ✅ Final summary ready ({total_completed} tasks)", flush=True) - print("📄 Complete results below:") - elif action == "get_current": - current_task = result.get("current_task") - all_completed = result.get("all_completed", False) - if all_completed: - print(f" ✅ All tasks completed!", flush=True) - elif current_task: - print(f" ✅ Current task identified", flush=True) - print(f"📋 Current: {current_task['description']}") - else: - print(f" ✅ No current task", flush=True) - elif action == "get": - progress = result.get("progress", "unknown") - print(f" ✅ Task overview retrieved (Progress: {progress})", flush=True) - else: - print(f" ✅ {action} completed", flush=True) - print("\n\n" + "="*50) print("🎉 Systematic hiking trip planning completed!") From 2f7146a6a4a27ae52d4d8411fbf0c3cf11e264a0 Mon Sep 17 00:00:00 2001 From: jakubduda-dsai Date: Thu, 18 Sep 2025 11:22:15 +0200 Subject: [PATCH 11/43] clear prints --- examples/agents/todo_tools_example.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/examples/agents/todo_tools_example.py b/examples/agents/todo_tools_example.py index ffe542a5b..9bcaabd6c 100644 --- a/examples/agents/todo_tools_example.py +++ b/examples/agents/todo_tools_example.py @@ -32,10 +32,6 @@ async def main(): default_options=AgentOptions(max_turns=30) ) - print("=== Enhanced Todo Workflow Example ===\n") - print("🚀 Hiking trip planning with systematic workflow:\n") - - # Simpler query to reduce complexity query = "Plan a 1-day hiking trip for 2 people in Tatra Mountains, Poland. Focus on scenic routes under 15km, avoiding crowds." # query = "How long is hike to Giewont from Kuźnice?" # query = "Is it difficult to finish Orla Perć? Would you recommend me to go there if I've never been in mountains before?" @@ -53,6 +49,9 @@ async def main(): action = response.arguments.get("action", "unknown") if action == "create": + print("=== Enhanced Todo Workflow Example ===\n") + print("🚀 Hiking trip planning with systematic workflow:\n") + tasks = response.arguments.get("tasks", []) tasks_count = len(tasks) print(f" - Creating {tasks_count} tasks", flush=True) From 2d29e5b06280b378af5ba04dfe93dba114cd48bd Mon Sep 17 00:00:00 2001 From: jakubduda-dsai Date: Fri, 19 Sep 2025 08:52:31 +0200 Subject: [PATCH 12/43] remove global container for todo list --- examples/agents/todo_tools_example.py | 13 +- .../src/ragbits/agents/__init__.py | 4 +- .../src/ragbits/agents/tools/__init__.py | 4 +- .../src/ragbits/agents/tools/todo.py | 126 ++++++++++-------- 4 files changed, 86 insertions(+), 61 deletions(-) diff --git a/examples/agents/todo_tools_example.py b/examples/agents/todo_tools_example.py index 9bcaabd6c..c811a12a2 100644 --- a/examples/agents/todo_tools_example.py +++ b/examples/agents/todo_tools_example.py @@ -1,13 +1,18 @@ -"""Example demonstrating the new single tool-based todo functionality.""" +"""Example demonstrating the new instance-based todo functionality.""" import asyncio -from ragbits.agents import Agent, AgentOptions, ToolCallResult, get_todo_instruction_tpl, todo_manager +from ragbits.agents import Agent, AgentOptions +from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl from ragbits.core.llms import LiteLLM, ToolCall async def main(): - """Demonstrate the new single tool-based todo approach with streaming and logging.""" + """Demonstrate the new instance-based todo approach with streaming and logging.""" + + # Create a dedicated TodoList instance for this agent + my_todo_list = TodoList() + my_todo_manager = create_todo_manager(my_todo_list) # Create an agent with higher turn limit and todo capabilities my_agent = Agent( @@ -28,7 +33,7 @@ async def main(): - Weather considerations and backup plans - Safety information and emergency contacts """ + get_todo_instruction_tpl(task_range=(3, 5)), - tools=[todo_manager], + tools=[my_todo_manager], # Use the instance-specific todo manager default_options=AgentOptions(max_turns=30) ) diff --git a/packages/ragbits-agents/src/ragbits/agents/__init__.py b/packages/ragbits-agents/src/ragbits/agents/__init__.py index 0551ab543..1b8b22fbb 100644 --- a/packages/ragbits-agents/src/ragbits/agents/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/__init__.py @@ -7,7 +7,7 @@ AgentRunContext, ToolCallResult, ) -from ragbits.agents.tools import get_todo_instruction_tpl, todo_manager +from ragbits.agents.tools import get_todo_instruction_tpl, create_todo_manager from ragbits.agents.types import QuestionAnswerAgent, QuestionAnswerPromptInput, QuestionAnswerPromptOutput __all__ = [ @@ -22,5 +22,5 @@ "QuestionAnswerPromptOutput", "ToolCallResult", "get_todo_instruction_tpl", - "todo_manager", + "create_todo_manager", ] diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py index 1e5b1154f..6960f5396 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py @@ -1,5 +1,5 @@ """Agent tools for extending functionality.""" -from .todo import get_todo_instruction_tpl, todo_manager +from .todo import get_todo_instruction_tpl, create_todo_manager -__all__ = ["todo_manager", "get_todo_instruction_tpl"] \ No newline at end of file +__all__ = ["create_todo_manager", "get_todo_instruction_tpl"] \ No newline at end of file diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py index a5f3db6b0..4213e5b84 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py @@ -2,9 +2,8 @@ import uuid from dataclasses import dataclass, field -from datetime import datetime from enum import Enum -from typing import Any, Literal +from typing import Any, Literal, Callable class TaskStatus(str, Enum): @@ -40,52 +39,33 @@ def advance_to_next(self): """Move to next task.""" self.current_index += 1 - -# Storage - just one todo list per agent run -_current_todo: TodoList | None = None - -def todo_manager( - action: Literal["create", "get_current", "start_task", "complete_task", "get_final_summary"], - tasks: list[str] | None = None, - summary: str | None = None, -) -> dict[str, Any]: - """ - Simplified todo manager for agent runs. - - Actions: - - create: Create todo list with tasks - - get_current: Get current task to work on - - start_task: Mark current task as in progress - - complete_task: Complete current task with summary - - get_final_summary: Get all completed work - """ - global _current_todo - - if action == "create": - if not tasks: + def create_tasks(self, task_descriptions: list[str]) -> dict[str, Any]: + """Create tasks from descriptions.""" + if not task_descriptions: raise ValueError("Tasks required for create action") - _current_todo = TodoList() - for i, desc in enumerate(tasks): + # Clear existing tasks + self.tasks.clear() + self.current_index = 0 + + for i, desc in enumerate(task_descriptions): task = Task( id=str(uuid.uuid4()), description=desc.strip(), order=i ) - _current_todo.tasks.append(task) + self.tasks.append(task) return { "action": "create", - "tasks": [{"id": t.id, "description": t.description, "order": t.order} for t in _current_todo.tasks], - "total_count": len(_current_todo.tasks), - "message": f"Created {len(tasks)} tasks" + "tasks": [{"id": t.id, "description": t.description, "order": t.order} for t in self.tasks], + "total_count": len(self.tasks), + "message": f"Created {len(task_descriptions)} tasks" } - if not _current_todo: - raise ValueError("No todo list exists. Create one first.") - - if action == "get_current": - current = _current_todo.get_current_task() + def get_current(self) -> dict[str, Any]: + """Get current task information.""" + current = self.get_current_task() if not current: return { "action": "get_current", @@ -97,12 +77,13 @@ def todo_manager( return { "action": "get_current", "current_task": {"id": current.id, "description": current.description, "status": current.status.value}, - "progress": f"{_current_todo.current_index + 1}/{len(_current_todo.tasks)}", + "progress": f"{self.current_index + 1}/{len(self.tasks)}", "message": f"Current task: {current.description}" } - elif action == "start_task": - current = _current_todo.get_current_task() + def start_current_task(self) -> dict[str, Any]: + """Start the current task.""" + current = self.get_current_task() if not current: raise ValueError("No current task to start") @@ -113,11 +94,12 @@ def todo_manager( "message": f"Started task: {current.description}" } - elif action == "complete_task": + def complete_current_task(self, summary: str) -> dict[str, Any]: + """Complete the current task with summary.""" if not summary: raise ValueError("Summary required for complete_task") - current = _current_todo.get_current_task() + current = self.get_current_task() if not current: raise ValueError("No current task to complete") @@ -126,22 +108,23 @@ def todo_manager( current.status = TaskStatus.COMPLETED current.summary = summary.strip() - _current_todo.advance_to_next() + self.advance_to_next() - next_task = _current_todo.get_current_task() - completed_count = sum(1 for t in _current_todo.tasks if t.status == TaskStatus.COMPLETED) + next_task = self.get_current_task() + completed_count = sum(1 for t in self.tasks if t.status == TaskStatus.COMPLETED) return { "action": "complete_task", "completed_task": {"id": current.id, "description": current.description, "summary": current.summary}, "next_task": {"id": next_task.id, "description": next_task.description} if next_task else None, - "progress": f"{completed_count}/{len(_current_todo.tasks)}", + "progress": f"{completed_count}/{len(self.tasks)}", "all_completed": next_task is None, "message": f"Completed: {current.description}" } - elif action == "get_final_summary": - completed_tasks = [t for t in _current_todo.tasks if t.status == TaskStatus.COMPLETED] + def get_final_summary(self) -> dict[str, Any]: + """Get comprehensive final summary of all completed work.""" + completed_tasks = [t for t in self.tasks if t.status == TaskStatus.COMPLETED] if not completed_tasks: return { @@ -160,9 +143,6 @@ def todo_manager( final_summary = "\n\n".join(final_content) - # Clean up after getting final summary - _current_todo = None - return { "action": "get_final_summary", "final_summary": final_summary, @@ -170,8 +150,48 @@ def todo_manager( "message": f"Final summary with {len(completed_tasks)} completed tasks." } - else: - raise ValueError(f"Unknown action: {action}") + +def create_todo_manager(todo_list: TodoList) -> Callable[..., dict[str, Any]]: + """ + Create a todo_manager function bound to a specific TodoList instance. + + This allows each agent to have its own isolated todo list. + + Args: + todo_list: The TodoList instance to bind to + + Returns: + A todo_manager function that operates on the provided TodoList + """ + def todo_manager( + action: Literal["create", "get_current", "start_task", "complete_task", "get_final_summary"], + tasks: list[str] | None = None, + summary: str | None = None, + ) -> dict[str, Any]: + """ + Todo manager bound to a specific TodoList instance. + + Actions: + - create: Create todo list with tasks + - get_current: Get current task to work on + - start_task: Mark current task as in progress + - complete_task: Complete current task with summary + - get_final_summary: Get all completed work + """ + if action == "create": + return todo_list.create_tasks(tasks or []) + elif action == "get_current": + return todo_list.get_current() + elif action == "start_task": + return todo_list.start_current_task() + elif action == "complete_task": + return todo_list.complete_current_task(summary or "") + elif action == "get_final_summary": + return todo_list.get_final_summary() + else: + raise ValueError(f"Unknown action: {action}") + + return todo_manager def get_todo_instruction_tpl(task_range: tuple[int, int] = (3, 5)) -> str: @@ -196,4 +216,4 @@ def get_todo_instruction_tpl(task_range: tuple[int, int] = (3, 5)) -> str: IMPORTANT: Task summaries should be DETAILED and COMPREHENSIVE (3-5 sentences). Include specific information, recommendations, and actionable details. - """ + """ \ No newline at end of file From dc1d068ba1a95c323ac320433bfb3d22b45f1059 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mateusz=20Hordy=C5=84ski?= Date: Mon, 22 Sep 2025 08:08:11 +0200 Subject: [PATCH 13/43] fix: nightly builds --- .github/workflows/nightly-build.yml | 46 +++------- scripts/check_nightly_build.py | 137 ++++++++++++++++++++++++++++ 2 files changed, 148 insertions(+), 35 deletions(-) create mode 100755 scripts/check_nightly_build.py diff --git a/.github/workflows/nightly-build.yml b/.github/workflows/nightly-build.yml index 53c3923f9..ce4b884bc 100644 --- a/.github/workflows/nightly-build.yml +++ b/.github/workflows/nightly-build.yml @@ -19,43 +19,19 @@ jobs: ref: develop fetch-depth: 0 - - name: Check if nightly build needed - id: check - run: | - # Get the latest commit hash on develop - COMMIT_HASH=$(git rev-parse --short HEAD) - echo "commit-hash=$COMMIT_HASH" >> "$GITHUB_OUTPUT" - - # Check if we already built this commit as nightly - LAST_NIGHTLY_TAG=$(git tag -l "*dev*" --sort=-version:refname | head -1) - if [ -n "$LAST_NIGHTLY_TAG" ]; then - # Get the commit that the last nightly tag points to - LAST_NIGHTLY_COMMIT=$(git rev-list -n 1 $LAST_NIGHTLY_TAG) - CURRENT_COMMIT=$(git rev-parse HEAD) - if [ "$CURRENT_COMMIT" = "$LAST_NIGHTLY_COMMIT" ]; then - echo "should-build=false" >> "$GITHUB_OUTPUT" - echo "No new commits since last nightly build" - exit 0 - fi - fi + - name: Install uv + uses: astral-sh/setup-uv@v2 + with: + version: ${{ vars.UV_VERSION || '0.6.9' }} - # Generate nightly version - BASE_VERSION=$(python -c " - try: - import tomllib - except ImportError: - import tomli as tomllib - with open('packages/ragbits/pyproject.toml', 'rb') as f: - data = tomllib.load(f) - print(data['project']['version']) - ") - # Use timestamp for unique nightly version (PEP 440 compliant) - TIMESTAMP=$(date +%Y%m%d%H%M) - NIGHTLY_VERSION="${BASE_VERSION}.dev${TIMESTAMP}" + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.10" - echo "should-build=true" >> "$GITHUB_OUTPUT" - echo "nightly-version=$NIGHTLY_VERSION" >> "$GITHUB_OUTPUT" - echo "Will build nightly version: $NIGHTLY_VERSION" + - name: Check if nightly build needed + id: check + run: uv run scripts/check_nightly_build.py build-and-publish: needs: check-for-changes diff --git a/scripts/check_nightly_build.py b/scripts/check_nightly_build.py new file mode 100755 index 000000000..346176085 --- /dev/null +++ b/scripts/check_nightly_build.py @@ -0,0 +1,137 @@ +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "tomli", +# ] +# /// +# To run this script and check if a nightly build is needed, run the following command: +# +# uv run scripts/check_nightly_build.py +# +# This script: +# 1. Gets the current commit hash +# 2. Checks if we already built this commit as a nightly +# 3. Generates a nightly version if build is needed +# 4. Outputs results in GitHub Actions format +# + +import os +import subprocess +import sys +from datetime import datetime +from pathlib import Path + +import tomli + + +def run_git_command(cmd: list[str]) -> str: + """Run a git command and return the output.""" + try: + result = subprocess.run(cmd, capture_output=True, text=True, check=True) # noqa: S603 + return result.stdout.strip() + except subprocess.CalledProcessError as e: + print(f"Git command failed: {' '.join(cmd)}") + print(f"Error: {e.stderr}") + sys.exit(1) + + +def get_current_commit_hash() -> str: + """Get the current short commit hash.""" + return run_git_command(["git", "rev-parse", "--short", "HEAD"]) + + +def get_current_full_commit_hash() -> str: + """Get the current full commit hash.""" + return run_git_command(["git", "rev-parse", "HEAD"]) + + +def get_last_nightly_tag() -> str | None: + """Get the last nightly tag (contains 'dev').""" + try: + tags = run_git_command(["git", "tag", "-l", "*dev*", "--sort=-version:refname"]) + if tags: + return tags.split("\n")[0] + return None + except subprocess.CalledProcessError: + return None + + +def get_commit_for_tag(tag: str) -> str: + """Get the commit hash that a tag points to.""" + return run_git_command(["git", "rev-list", "-n", "1", tag]) + + +def get_base_version() -> str: + """Get the base version from pyproject.toml, stripping any .dev part.""" + pyproject_path = Path("packages/ragbits/pyproject.toml") + + if not pyproject_path.exists(): + print(f"Error: {pyproject_path} not found") + sys.exit(1) + + try: + with open(pyproject_path, "rb") as f: + data = tomli.load(f) + version = data["project"]["version"] + + if ".dev" in version: + version = version.split(".dev")[0] + + return version + except (KeyError, tomli.TOMLDecodeError) as e: + print(f"Error reading version from {pyproject_path}: {e}") + sys.exit(1) + + +def generate_nightly_version(base_version: str) -> str: + """Generate a nightly version using timestamp (PEP 440 compliant).""" + timestamp = datetime.now().strftime("%Y%m%d%H%M") + return f"{base_version}.dev{timestamp}" + + +def set_github_output(name: str, value: str) -> None: + """Set GitHub Actions output variable.""" + github_output = os.environ.get("GITHUB_OUTPUT") + if github_output: + with open(github_output, "a") as f: + f.write(f"{name}={value}\n") + + print(f"OUTPUT: {name}={value}") + + +def main() -> None: + """Main function to check if nightly build is needed.""" + # Get current commit info + commit_hash = get_current_commit_hash() + current_commit = get_current_full_commit_hash() + + set_github_output("commit-hash", commit_hash) + + print(f"Current commit: {commit_hash} ({current_commit})") + + # Check if we already built this commit as nightly + last_nightly_tag = get_last_nightly_tag() + + if last_nightly_tag: + print(f"Last nightly tag: {last_nightly_tag}") + last_nightly_commit = get_commit_for_tag(last_nightly_tag) + + if current_commit == last_nightly_commit: + print("No new commits since last nightly build") + set_github_output("should-build", "false") + return + else: + print("No previous nightly tags found") + + # Generate nightly version + base_version = get_base_version() + nightly_version = generate_nightly_version(base_version) + + print(f"Will build nightly version: {nightly_version}") + + set_github_output("should-build", "true") + set_github_output("nightly-version", nightly_version) + + +if __name__ == "__main__": + main() From 3ca9cd4fb5032e012509e5f1b290a72d7360dd38 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mateusz=20Hordy=C5=84ski?= Date: Mon, 22 Sep 2025 08:20:59 +0200 Subject: [PATCH 14/43] fix: docs deployments --- .github/workflows/nightly-build.yml | 4 +++- .github/workflows/publish-pypi.yml | 3 ++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/nightly-build.yml b/.github/workflows/nightly-build.yml index ce4b884bc..ff73b58a2 100644 --- a/.github/workflows/nightly-build.yml +++ b/.github/workflows/nightly-build.yml @@ -90,7 +90,9 @@ jobs: - name: Deploy nightly documentation shell: bash - run: uv run mike deploy --push nightly + run: | + git fetch origin gh-pages + uv run mike deploy --push --alias-type copy nightly env: GH_TOKEN: ${{ secrets.GH_TOKEN }} diff --git a/.github/workflows/publish-pypi.yml b/.github/workflows/publish-pypi.yml index 2f6068dec..a0d81276a 100644 --- a/.github/workflows/publish-pypi.yml +++ b/.github/workflows/publish-pypi.yml @@ -56,6 +56,7 @@ jobs: - name: Deploy documentation run: | - uv run mike deploy --push stable + git fetch origin gh-pages + uv run mike deploy --push --alias-type copy stable env: GH_TOKEN: ${{ secrets.GH_TOKEN }} From 30269294cbfa1e26754c7f58d129df919afbd7b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mateusz=20Hordy=C5=84ski?= Date: Mon, 22 Sep 2025 08:56:29 +0200 Subject: [PATCH 15/43] fix: add docs login --- .github/workflows/nightly-build.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/nightly-build.yml b/.github/workflows/nightly-build.yml index ff73b58a2..807129178 100644 --- a/.github/workflows/nightly-build.yml +++ b/.github/workflows/nightly-build.yml @@ -76,6 +76,7 @@ jobs: git commit -m "chore: update package versions for nightly build ${{ env.NIGHTLY_VERSION }}" git tag "${{ env.NIGHTLY_VERSION }}" git push origin "${{ env.NIGHTLY_VERSION }}" + git push origin develop env: GH_TOKEN: ${{ secrets.GH_TOKEN }} NIGHTLY_VERSION: ${{ needs.check-for-changes.outputs.nightly-version }} @@ -91,6 +92,8 @@ jobs: - name: Deploy nightly documentation shell: bash run: | + git config user.name "ds-ragbits-robot" + git config user.email "ds-ragbits-robot@users.noreply.github.com" git fetch origin gh-pages uv run mike deploy --push --alias-type copy nightly env: From 759f59e57e584b2992c5df8ed7ef1e067acb8285 Mon Sep 17 00:00:00 2001 From: Maciej Kurzawa <68014263+mackurzawa@users.noreply.github.com> Date: Mon, 22 Sep 2025 15:22:14 +0200 Subject: [PATCH 16/43] feat: introduce post processors (#821) --- docs/api_reference/agents/index.md | 2 + docs/how-to/agents/use_post_processors.md | 64 +++++++ examples/README.md | 4 + examples/agents/post_processors.py | 97 ++++++++++ mkdocs.yml | 1 + packages/ragbits-agents/CHANGELOG.md | 1 + .../src/ragbits/agents/__init__.py | 3 + .../src/ragbits/agents/_main.py | 107 +++++++++-- .../src/ragbits/agents/exceptions.py | 10 ++ .../agents/post_processors/__init__.py | 7 + .../ragbits/agents/post_processors/base.py | 153 ++++++++++++++++ .../tests/unit/test_post_processors.py | 167 ++++++++++++++++++ 12 files changed, 602 insertions(+), 14 deletions(-) create mode 100644 docs/how-to/agents/use_post_processors.md create mode 100644 examples/agents/post_processors.py create mode 100644 packages/ragbits-agents/src/ragbits/agents/post_processors/__init__.py create mode 100644 packages/ragbits-agents/src/ragbits/agents/post_processors/base.py create mode 100644 packages/ragbits-agents/tests/unit/test_post_processors.py diff --git a/docs/api_reference/agents/index.md b/docs/api_reference/agents/index.md index 2608a8eb4..ec2a0d655 100644 --- a/docs/api_reference/agents/index.md +++ b/docs/api_reference/agents/index.md @@ -9,3 +9,5 @@ ::: ragbits.agents.AgentResultStreaming ::: ragbits.agents.a2a.server.create_agent_server + +::: ragbits.agents.post_processors.base diff --git a/docs/how-to/agents/use_post_processors.md b/docs/how-to/agents/use_post_processors.md new file mode 100644 index 000000000..aef223be7 --- /dev/null +++ b/docs/how-to/agents/use_post_processors.md @@ -0,0 +1,64 @@ +# How-To: Use Post-Processors with Ragbits Agents + +Ragbits Agents can be enhanced with post-processors to intercept, log, filter, and modify their outputs. This guide explains how to implement and use post-processors to customize agent responses. + +## Post-Processors Overview + +Ragbits provides two types of post-processors: + +- **PostProcessor**: Processes the final output after generation, ideal for batch processing. +- **StreamingPostProcessor**: Processes outputs as they are generated, suitable for real-time applications. + +### Implementing a custom Post-Processor + +To create a custom post-processor, inherit from the appropriate base class ([`PostProcessor`][ragbits.agents.post_processors.base.PostProcessor] or [`StreamingPostProcessor`][ragbits.agents.post_processors.base.StreamingPostProcessor]) and implement the required method. + +#### Post-Processor Example + +A non-streaming post-processor applies transformations after the entire content is generated. + +```python +class TruncateProcessor(PostProcessor): + def __init__(self, max_length: int = 50) -> None: + self.max_length = max_length + + async def process(self, result, agent): + content = result.content + if len(content) > self.max_length: + content = content[:self.max_length] + "... [TRUNCATED]" + result.content = content + return result +``` + +#### Streaming Post-Processor Example + +A streaming post-processor can manipulate all information returned during generation, including text, tool calls, etc. + +```python +class UpperCaseStreamingProcessor(StreamingPostProcessor): + async def process_streaming(self, chunk, agent): + if isinstance(chunk, str): + return chunk.upper() + return chunk +``` + +## Using Post-Processors + +To use post-processors, pass them to the `run` or `run_streaming` methods of the `Agent` class. If you pass a non-streaming processor to `run_streaming`, set `allow_non_streaming=True`. This allows streaming processors to handle content piece by piece during generation, while non-streaming processors apply transformations after the entire output is generated. + +```python +async def main() -> None: + llm = LiteLLM("gpt-4.1-mini") + agent = Agent(llm=llm, prompt="You are a helpful assistant.") + post_processors = [ + UpperCaseStreamingProcessor(), + TruncateProcessor(max_length=50), + ] + stream_result = agent.run_streaming("Tell me about the history of AI.", post_processors=post_processors, allow_non_streaming=True) + async for chunk in stream_result: + if isinstance(chunk, str): + print(chunk, end="") + print(f"\nFinal answer:\n{stream_result.content}") +``` + +Post-processors offer a flexible way to tailor agent outputs, whether filtering content in real-time or transforming final outputs. diff --git a/examples/README.md b/examples/README.md index 981a2dc7a..a5291f3a0 100644 --- a/examples/README.md +++ b/examples/README.md @@ -20,6 +20,7 @@ All necessary details are provided in the comments at the top of each script. | [Multimodal Prompt with PDF Input](/examples/core/prompt/multimodal_with_pdf.py) | [ragbits-core](/packages/ragbits-core) | Example of how to use the `Prompt` class to answer the question using an LLM with both text and PDF inputs. | | [Multimodal Prompt with Few Shots](/examples/core/prompt/multimodal_with_few_shots.py) | [ragbits-core](/packages/ragbits-core) | Example of how to use the `Prompt` class to generate themed text using an LLM with multimodal inputs and few-shot examples. | | [Tool Use with LLM](/examples/core/llms/tool_use.py) | [ragbits-core](/packages/ragbits-core) | Example of how to provide tools and return tool calls from LLM. | +| [Reasoning with LLM](/examples/core/llms/reasoning.py) | [ragbits-core](/packages/ragbits-core) | Example of how to use reasoning with LLM. | | [OpenTelemetry Audit](/examples/core/audit/otel.py) | [ragbits-core](/packages/ragbits-core) | Example of how to collect traces and metrics using Ragbits audit module with OpenTelemetry. | | [Logfire Audit](/examples/core/audit/logfire_.py) | [ragbits-core](/packages/ragbits-core) | Example of how to collect traces and metrics using Ragbits audit module with Logfire. | | [Basic Document Search](/examples/document-search/basic.py) | [ragbits-document-search](/packages/ragbits-document-search) | Example of how to use the `DocumentSearch` class to search for documents with the `InMemoryVectorStore` class to store the embeddings. | @@ -38,6 +39,9 @@ All necessary details are provided in the comments at the top of each script. | [Recontextualize Last Message](/examples/chat/recontextualize_message.py) | [ragbits-chat](/packages/ragbits-chat) | Example of how to use the `StandaloneMessageCompressor` compressor to recontextualize the last message in a conversation history. | | [Agents Tool Use](/examples/agents/tool_use.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use agent with tools. | | [Agents OpenAI Native Tool Use](/examples/agents/openai_native_tool_use.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use agent with OpenAI native tools. | +| [Agents Post Processors](/examples/agents/post_processors.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use post-processors with agent. | +| [Agents CLI](/examples/agents/cli_agent.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use agent in CLI. | | [MCP Local](/examples/agents/mcp/local.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use the `Agent` class to connect with a local MCP server. | | [MCP SSE](/examples/agents/mcp/sse.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use the `Agent` class to connect with a remote MCP server via SSE. | | [MCP Streamable HTTP](/examples/agents/mcp/streamable_http.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use the `Agent` class to connect with a remote MCP server via HTTP. | +| [A2A Orchestration](/examples/agents/a2a/run_orchestrator.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to setup A2A orchestration. | diff --git a/examples/agents/post_processors.py b/examples/agents/post_processors.py new file mode 100644 index 000000000..5e2e00695 --- /dev/null +++ b/examples/agents/post_processors.py @@ -0,0 +1,97 @@ +""" +Ragbits Agents Example: Post-Processors + +This example demonstrates how to use post-processors with Agent.run() and Agent.run_streaming() methods. + +To run the script, execute the following command: + + ```bash + uv run examples/agents/post_processors.py + ``` +""" + +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "ragbits-core", +# "ragbits-agents", +# ] +# /// + +import asyncio +from types import SimpleNamespace + +from ragbits.agents import Agent, AgentResult, PostProcessor, StreamingPostProcessor, ToolCallResult +from ragbits.core.llms.base import BasePrompt, ToolCall, Usage +from ragbits.core.llms.litellm import LiteLLM + + +class CustomStreamingPostProcessor(StreamingPostProcessor): + """ + Streaming post-processor that checks for forbidden words. + """ + + def __init__(self, forbidden_words: list[str]) -> None: + self.forbidden_words = forbidden_words + + async def process_streaming( + self, chunk: str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage, agent: Agent + ) -> str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage: + """ + Process chunks during streaming. + """ + if isinstance(chunk, str) and chunk.lower().strip() in self.forbidden_words: + return "[FORBIDDEN_WORD]" + return chunk + + +class CustomPostProcessor(PostProcessor): + """ + Non-streaming post-processor that truncates the content. + """ + + def __init__(self, max_length: int = 200) -> None: + self.max_length = max_length + + async def process(self, result: AgentResult, agent: Agent) -> AgentResult: + """ + Process the agent result. + """ + content = result.content + content_length = len(content) + + if content_length > self.max_length: + content = content[: self.max_length] + content += f"... [TRUNCATED] ({content_length} > {self.max_length} chars)" + + return AgentResult( + content=content, + metadata=result.metadata, + tool_calls=result.tool_calls, + history=result.history, + usage=result.usage, + ) + + +async def main() -> None: + """ + Run the example. + """ + llm = LiteLLM("gpt-4.1-mini") + agent: Agent = Agent(llm=llm, prompt="You are a helpful assistant.") + stream_result = agent.run_streaming( + "What is Python?", + post_processors=[ + CustomStreamingPostProcessor(forbidden_words=["python"]), + CustomPostProcessor(max_length=200), + ], + allow_non_streaming=True, + ) + async for chunk in stream_result: + if isinstance(chunk, str): + print(chunk, end="") + print(f"\n\nFinal answer:\n{stream_result.content}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/mkdocs.yml b/mkdocs.yml index e90e422ad..8c534094d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -40,6 +40,7 @@ nav: - "Define and use agents": how-to/agents/define_and_use_agents.md - "Provide tools with MCP": how-to/agents/provide_mcp_tools.md - "Serve agents with A2A": how-to/agents/serve_ragbits_agents.md + - "Use post-processors": how-to/agents/use_post_processors.md - Guardrails: - "Setup guardrails": how-to/guardrails/use_guardrails.md - Chatbots: diff --git a/packages/ragbits-agents/CHANGELOG.md b/packages/ragbits-agents/CHANGELOG.md index 2a722f747..c0eef472b 100644 --- a/packages/ragbits-agents/CHANGELOG.md +++ b/packages/ragbits-agents/CHANGELOG.md @@ -4,6 +4,7 @@ - Support wrapping downstream agents as tools (#818) - Add syntax sugar allowing easier Agents definition (#820) +- Add post-processors (#821) ## 1.3.0 (2025-09-11) ### Changed diff --git a/packages/ragbits-agents/src/ragbits/agents/__init__.py b/packages/ragbits-agents/src/ragbits/agents/__init__.py index 7a7036895..bd1809337 100644 --- a/packages/ragbits-agents/src/ragbits/agents/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/__init__.py @@ -7,6 +7,7 @@ AgentRunContext, ToolCallResult, ) +from ragbits.agents.post_processors.base import PostProcessor, StreamingPostProcessor from ragbits.agents.types import QuestionAnswerAgent, QuestionAnswerPromptInput, QuestionAnswerPromptOutput __all__ = [ @@ -16,8 +17,10 @@ "AgentResult", "AgentResultStreaming", "AgentRunContext", + "PostProcessor", "QuestionAnswerAgent", "QuestionAnswerPromptInput", "QuestionAnswerPromptOutput", + "StreamingPostProcessor", "ToolCallResult", ] diff --git a/packages/ragbits-agents/src/ragbits/agents/_main.py b/packages/ragbits-agents/src/ragbits/agents/_main.py index 6b15a00e6..b98b06245 100644 --- a/packages/ragbits-agents/src/ragbits/agents/_main.py +++ b/packages/ragbits-agents/src/ragbits/agents/_main.py @@ -8,7 +8,7 @@ from datetime import timedelta from inspect import iscoroutinefunction from types import ModuleType, SimpleNamespace -from typing import Any, ClassVar, Generic, TypeVar, cast, overload +from typing import Any, ClassVar, Generic, Literal, TypeVar, cast, overload from pydantic import ( BaseModel, @@ -18,6 +18,7 @@ from ragbits import agents from ragbits.agents.exceptions import ( + AgentInvalidPostProcessorError, AgentInvalidPromptInputError, AgentMaxTokensExceededError, AgentMaxTurnsExceededError, @@ -29,6 +30,12 @@ ) from ragbits.agents.mcp.server import MCPServer, MCPServerStdio, MCPServerStreamableHttp from ragbits.agents.mcp.utils import get_tools +from ragbits.agents.post_processors.base import ( + BasePostProcessor, + PostProcessor, + StreamingPostProcessor, + stream_with_post_processing, +) from ragbits.agents.tool import Tool, ToolCallResult, ToolChoice from ragbits.core.audit.traces import trace from ragbits.core.llms.base import LLM, LLMClientOptionsT, LLMOptions, LLMResponseWithMetadata, ToolCall, Usage @@ -157,7 +164,7 @@ class AgentRunContext(BaseModel, Generic[DepsT]): """The usage of the agent.""" -class AgentResultStreaming(AsyncIterator[str | ToolCall | ToolCallResult]): +class AgentResultStreaming(AsyncIterator[str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace]): """ An async iterator that will collect all yielded items by LLM.generate_streaming(). This object is returned by `run_streaming`. It can be used in an `async for` loop to process items as they arrive. After the loop completes, @@ -174,38 +181,40 @@ def __init__( self.history: ChatFormat self.usage: Usage = Usage() - def __aiter__(self) -> AsyncIterator[str | ToolCall | ToolCallResult]: + def __aiter__(self) -> AsyncIterator[str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace]: return self - async def __anext__(self) -> str | ToolCall | ToolCallResult: + async def __anext__(self) -> str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace: try: item = await self._generator.__anext__() + match item: case str(): self.content += item + return item case ToolCall(): - pass + return item case ToolCallResult(): if self.tool_calls is None: self.tool_calls = [] self.tool_calls.append(item) + return item case BasePrompt(): item.add_assistant_message(self.content) self.history = item.chat - item = await self._generator.__anext__() - item = cast(SimpleNamespace, item) - item.result = { - "content": self.content, - "metadata": self.metadata, - "tool_calls": self.tool_calls, - } - raise StopAsyncIteration + return item case Usage(): self.usage = item return await self.__anext__() + case SimpleNamespace(): + result_dict = getattr(item, "result", {}) + self.content = result_dict.get("content", self.content) + self.metadata = result_dict.get("metadata", self.metadata) + self.tool_calls = result_dict.get("tool_calls", self.tool_calls) + return item case _: raise ValueError(f"Unexpected item: {item}") - return item + except StopAsyncIteration: raise @@ -292,6 +301,7 @@ async def run( options: AgentOptions[LLMClientOptionsT] | None = None, context: AgentRunContext | None = None, tool_choice: ToolChoice | None = None, + post_processors: list[PostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] | None = None, ) -> AgentResult[PromptOutputT]: ... @overload @@ -301,6 +311,7 @@ async def run( options: AgentOptions[LLMClientOptionsT] | None = None, context: AgentRunContext | None = None, tool_choice: ToolChoice | None = None, + post_processors: list[PostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] | None = None, ) -> AgentResult[PromptOutputT]: ... async def run( @@ -309,6 +320,7 @@ async def run( options: AgentOptions[LLMClientOptionsT] | None = None, context: AgentRunContext | None = None, tool_choice: ToolChoice | None = None, + post_processors: list[PostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] | None = None, ) -> AgentResult[PromptOutputT]: """ Run the agent. The method is experimental, inputs and outputs may change in the future. @@ -325,6 +337,7 @@ async def run( - "none": do not call tool - "required: enforce tool usage (model decides which one) - Callable: one of provided tools + post_processors: List of post-processors to apply to the response in order. Returns: The result of the agent run. @@ -336,6 +349,24 @@ async def run( AgentInvalidPromptInputError: If the prompt/input combination is invalid. AgentMaxTurnsExceededError: If the maximum number of turns is exceeded. """ + result = await self._run_without_post_processing(input, options, context, tool_choice) + + if post_processors: + for processor in post_processors: + result = await processor.process(result, self) + + return result + + async def _run_without_post_processing( + self, + input: str | PromptInputT | None, + options: AgentOptions[LLMClientOptionsT] | None = None, + context: AgentRunContext | None = None, + tool_choice: ToolChoice | None = None, + ) -> AgentResult[PromptOutputT]: + """ + Run the agent without applying post-processors. + """ if context is None: context = AgentRunContext() @@ -403,6 +434,33 @@ def run_streaming( options: AgentOptions[LLMClientOptionsT] | None = None, context: AgentRunContext | None = None, tool_choice: ToolChoice | None = None, + post_processors: list[StreamingPostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] | None = None, + *, + allow_non_streaming: bool = False, + ) -> AgentResultStreaming: ... + + @overload + def run_streaming( + self: "Agent[LLMClientOptionsT, None, PromptOutputT]", + input: str | None = None, + options: AgentOptions[LLMClientOptionsT] | None = None, + context: AgentRunContext | None = None, + tool_choice: ToolChoice | None = None, + post_processors: list[BasePostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] | None = None, + *, + allow_non_streaming: Literal[True], + ) -> AgentResultStreaming: ... + + @overload + def run_streaming( + self: "Agent[LLMClientOptionsT, PromptInputT, PromptOutputT]", + input: PromptInputT, + options: AgentOptions[LLMClientOptionsT] | None = None, + context: AgentRunContext | None = None, + tool_choice: ToolChoice | None = None, + post_processors: list[StreamingPostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] | None = None, + *, + allow_non_streaming: bool = False, ) -> AgentResultStreaming: ... @overload @@ -412,6 +470,9 @@ def run_streaming( options: AgentOptions[LLMClientOptionsT] | None = None, context: AgentRunContext | None = None, tool_choice: ToolChoice | None = None, + post_processors: list[BasePostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] | None = None, + *, + allow_non_streaming: Literal[True], ) -> AgentResultStreaming: ... def run_streaming( @@ -420,6 +481,13 @@ def run_streaming( options: AgentOptions[LLMClientOptionsT] | None = None, context: AgentRunContext | None = None, tool_choice: ToolChoice | None = None, + post_processors: ( + list[StreamingPostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] + | list[BasePostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] + | None + ) = None, + *, + allow_non_streaming: bool = False, ) -> AgentResultStreaming: """ This method returns an `AgentResultStreaming` object that can be asynchronously @@ -434,6 +502,8 @@ def run_streaming( - "none": do not call tool - "required: enforce tool usage (model decides which one) - Callable: one of provided tools + post_processors: List of post-processors to apply to the response in order. + allow_non_streaming: Whether to allow non-streaming post-processors. Returns: A `StreamingResult` object for iteration and collection. @@ -444,8 +514,17 @@ def run_streaming( AgentToolNotAvailableError: If the selected tool is not available. AgentInvalidPromptInputError: If the prompt/input combination is invalid. AgentMaxTurnsExceededError: If the maximum number of turns is exceeded. + AgentInvalidPostProcessorError: If the post-processor is invalid. """ generator = self._stream_internal(input, options, context, tool_choice) + + if post_processors: + if not allow_non_streaming and any(not p.supports_streaming for p in post_processors): + raise AgentInvalidPostProcessorError( + reason="Non-streaming post-processors are not allowed when allow_non_streaming is False" + ) + generator = stream_with_post_processing(generator, post_processors, self) + return AgentResultStreaming(generator) async def _stream_internal( diff --git a/packages/ragbits-agents/src/ragbits/agents/exceptions.py b/packages/ragbits-agents/src/ragbits/agents/exceptions.py index c474e6ac7..a80e76314 100644 --- a/packages/ragbits-agents/src/ragbits/agents/exceptions.py +++ b/packages/ragbits-agents/src/ragbits/agents/exceptions.py @@ -106,3 +106,13 @@ def __init__( self.limit = limit self.actual = actual self.next_prompt_tokens = next_prompt_tokens + + +class AgentInvalidPostProcessorError(AgentError): + """ + Raised when the post-processor is invalid. + """ + + def __init__(self, reason: str) -> None: + super().__init__(f"Invalid post-processor: {reason}") + self.reason = reason diff --git a/packages/ragbits-agents/src/ragbits/agents/post_processors/__init__.py b/packages/ragbits-agents/src/ragbits/agents/post_processors/__init__.py new file mode 100644 index 000000000..a00a4d209 --- /dev/null +++ b/packages/ragbits-agents/src/ragbits/agents/post_processors/__init__.py @@ -0,0 +1,7 @@ +""" +Post-processors for agent responses. +""" + +from .base import BasePostProcessor, PostProcessor, StreamingPostProcessor + +__all__ = ["BasePostProcessor", "PostProcessor", "StreamingPostProcessor"] diff --git a/packages/ragbits-agents/src/ragbits/agents/post_processors/base.py b/packages/ragbits-agents/src/ragbits/agents/post_processors/base.py new file mode 100644 index 000000000..4f38e13ca --- /dev/null +++ b/packages/ragbits-agents/src/ragbits/agents/post_processors/base.py @@ -0,0 +1,153 @@ +from abc import ABC, abstractmethod +from collections.abc import AsyncGenerator +from types import SimpleNamespace +from typing import TYPE_CHECKING, Generic, TypeVar, cast + +from ragbits.agents.tool import ToolCallResult +from ragbits.core.llms.base import LLMOptions, ToolCall, Usage +from ragbits.core.prompt.base import BasePrompt +from ragbits.core.prompt.prompt import PromptInputT, PromptOutputT + +if TYPE_CHECKING: + from ragbits.agents._main import Agent, AgentResult + + +LLMOptionsT = TypeVar("LLMOptionsT", bound=LLMOptions) + + +class BasePostProcessor(Generic[LLMOptionsT, PromptInputT, PromptOutputT]): + """Base class for post-processors.""" + + @property + @abstractmethod + def supports_streaming(self) -> bool: + """ + Whether this post-processor supports streaming mode. + + If True, the processor can work with content during streaming + via process_streaming() method. + + If False, the processor will only be called after streaming is complete + with the full result via process() method. + """ + + +class PostProcessor(ABC, BasePostProcessor[LLMOptionsT, PromptInputT, PromptOutputT]): + """Base class for non-streaming post-processors.""" + + @property + def supports_streaming(self) -> bool: + """Whether this post-processor supports streaming mode.""" + return False + + @abstractmethod + async def process( + self, + result: "AgentResult[PromptOutputT]", + agent: "Agent[LLMOptionsT, PromptInputT, PromptOutputT]", + ) -> "AgentResult[PromptOutputT]": + """ + Process the complete agent result. + + Args: + result: The complete AgentResult from the agent or previous post-processor. + agent: The Agent instance that generated the result. Can be used to re-run + the agent with modified input if needed. + + Returns: + Modified AgentResult to pass to the next processor or return as final result. + """ + + +class StreamingPostProcessor(ABC, BasePostProcessor[LLMOptionsT, PromptInputT, PromptOutputT]): + """Base class for streaming post-processors.""" + + @property + def supports_streaming(self) -> bool: + """Whether this post-processor supports streaming mode.""" + return True + + @abstractmethod + async def process_streaming( + self, + chunk: str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage, + agent: "Agent[LLMOptionsT, PromptInputT, PromptOutputT]", + ) -> str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage: + """ + Process chunks during streaming. + + Args: + chunk: The current chunk being streamed. + agent: The Agent instance generating the content. + + Returns: + Modified chunk to yield, or None to suppress this chunk. + Return the same chunk if no modification needed. + """ + + +async def stream_with_post_processing( + generator: AsyncGenerator[str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage], + post_processors: ( + list[StreamingPostProcessor[LLMOptionsT, PromptInputT, PromptOutputT]] + | list[BasePostProcessor[LLMOptionsT, PromptInputT, PromptOutputT]] + ), + agent: "Agent[LLMOptionsT, PromptInputT, PromptOutputT]", +) -> AsyncGenerator[str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage]: + """ + Stream with support for both streaming and non-streaming post-processors. + + Streaming processors get chunks in real-time via process_streaming(). + Non-streaming processors get the complete result via process(). + """ + from ragbits.agents import AgentResult + + streaming_processors = [p for p in post_processors or [] if isinstance(p, StreamingPostProcessor)] + non_streaming_processors = [p for p in post_processors or [] if isinstance(p, PostProcessor)] + + accumulated_content = "" + tool_call_results: list[ToolCallResult] = [] + usage: Usage = Usage() + prompt_with_history: BasePrompt | None = None + + async for chunk in generator: + processed_chunk = chunk + for streaming_processor in streaming_processors: + processed_chunk = await streaming_processor.process_streaming(chunk=processed_chunk, agent=agent) + if processed_chunk is None: + break + + if isinstance(processed_chunk, str): + accumulated_content += processed_chunk + elif isinstance(processed_chunk, ToolCallResult): + tool_call_results.append(processed_chunk) + elif isinstance(processed_chunk, Usage): + usage = processed_chunk + elif isinstance(processed_chunk, BasePrompt): + prompt_with_history = processed_chunk + + if processed_chunk is not None: + yield processed_chunk + + if non_streaming_processors and prompt_with_history: + agent_result = AgentResult( + content=cast(PromptOutputT, accumulated_content), + metadata={}, + tool_calls=tool_call_results or None, + history=prompt_with_history.chat, + usage=usage, + ) + + current_result = agent_result + for non_streaming_processor in non_streaming_processors: + current_result = await non_streaming_processor.process(current_result, agent) + + yield current_result.usage + yield prompt_with_history + yield SimpleNamespace( + result={ + "content": current_result.content, + "metadata": current_result.metadata, + "tool_calls": current_result.tool_calls, + } + ) diff --git a/packages/ragbits-agents/tests/unit/test_post_processors.py b/packages/ragbits-agents/tests/unit/test_post_processors.py new file mode 100644 index 000000000..b71216267 --- /dev/null +++ b/packages/ragbits-agents/tests/unit/test_post_processors.py @@ -0,0 +1,167 @@ +from types import SimpleNamespace + +import pytest + +from ragbits.agents import Agent, AgentResult, ToolCallResult +from ragbits.agents.exceptions import AgentInvalidPostProcessorError +from ragbits.agents.post_processors.base import PostProcessor, StreamingPostProcessor +from ragbits.core.llms.base import BasePrompt, ToolCall, Usage +from ragbits.core.llms.mock import MockLLM, MockLLMOptions + + +class MockPostProcessor(PostProcessor): + def __init__(self, append_content: str = " - processed"): + self.append_content = append_content + + async def process(self, result: AgentResult, agent: Agent) -> AgentResult: + result.content += self.append_content + return result + + +class MockStreamingPostProcessor(StreamingPostProcessor): + def __init__(self, append_content: str = " - streamed"): + self.append_content = append_content + + async def process_streaming( + self, chunk: str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage, agent: Agent + ): + if isinstance(chunk, str): + return chunk + self.append_content + return chunk + + +@pytest.fixture +def mock_llm() -> MockLLM: + options = MockLLMOptions(response="Initial response") + return MockLLM(default_options=options) + + +@pytest.mark.asyncio +async def test_non_streaming_post_processor(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + post_processor = MockPostProcessor() + + result = await agent.run(post_processors=[post_processor]) + + assert result.content == "Initial response - processed" + + +@pytest.mark.asyncio +async def test_streaming_post_processor(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + post_processor = MockStreamingPostProcessor() + + result = agent.run_streaming(post_processors=[post_processor]) + async for chunk in result: + if isinstance(chunk, str): + assert chunk.endswith(" - streamed") + + +@pytest.mark.asyncio +async def test_non_streaming_processor_in_streaming_mode_raises_error(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + post_processor = MockPostProcessor() + + with pytest.raises(AgentInvalidPostProcessorError): + await anext(agent.run_streaming(post_processors=[post_processor])) # type: ignore # ignore type-checking to test raising the error + + +@pytest.mark.asyncio +async def test_non_streaming_processor_in_streaming_mode_with_allow_non_streaming(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + post_processor = MockPostProcessor() + + result = agent.run_streaming(post_processors=[post_processor], allow_non_streaming=True) + + async for _ in result: + pass + + assert result.content == "Initial response - processed" + + +@pytest.mark.asyncio +async def test_streaming_and_non_streaming_processors(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + non_streaming_processor = MockPostProcessor() + streaming_processor = MockStreamingPostProcessor() + + result = agent.run_streaming( + post_processors=[streaming_processor, non_streaming_processor], allow_non_streaming=True + ) + + async for _ in result: + pass + + assert result.content == "Initial response - streamed - processed" + + +@pytest.mark.asyncio +async def test_streaming_processor_always_runs_before_non_streaming_processor(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + non_streaming_processor = MockPostProcessor() + streaming_processor = MockStreamingPostProcessor() + + result = agent.run_streaming( + post_processors=[streaming_processor, non_streaming_processor], allow_non_streaming=True + ) + async for _ in result: + pass + + assert result.content == "Initial response - streamed - processed" + + result = agent.run_streaming( + post_processors=[non_streaming_processor, streaming_processor], allow_non_streaming=True + ) + async for _ in result: + pass + + assert result.content == "Initial response - streamed - processed" + + +@pytest.mark.asyncio +async def test_multiple_non_streaming_processors_order(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + non_streaming_processor_1 = MockPostProcessor(append_content=" - processed 1") + non_streaming_processor_2 = MockPostProcessor(append_content=" - processed 2") + + result = await agent.run(post_processors=[non_streaming_processor_2, non_streaming_processor_1]) + + assert result.content == "Initial response - processed 2 - processed 1" + + +@pytest.mark.asyncio +async def test_multiple_streaming_processors_order(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + streaming_processor_1 = MockStreamingPostProcessor(append_content=" - streamed 1") + streaming_processor_2 = MockStreamingPostProcessor(append_content=" - streamed 2") + + result = agent.run_streaming( + post_processors=[streaming_processor_2, streaming_processor_1], + ) + async for _ in result: + pass + + assert result.content == "Initial response - streamed 2 - streamed 1" + + +@pytest.mark.asyncio +async def test_multiple_streaming_and_non_streaming_processors_order(mock_llm: MockLLM): + agent: Agent = Agent(llm=mock_llm, prompt="Test prompt") + streaming_processor_1 = MockStreamingPostProcessor(append_content=" - streamed 1") + streaming_processor_2 = MockStreamingPostProcessor(append_content=" - streamed 2") + non_streaming_processor_1 = MockPostProcessor(append_content=" - processed 1") + non_streaming_processor_2 = MockPostProcessor(append_content=" - processed 2") + + result = agent.run_streaming( + post_processors=[ + non_streaming_processor_2, + streaming_processor_1, + non_streaming_processor_1, + streaming_processor_2, + ], + allow_non_streaming=True, + ) + async for _ in result: + pass + + assert result.content == "Initial response - streamed 1 - streamed 2 - processed 2 - processed 1" From f67ab9127f03f7101c3482a6583d86f0e7169dc3 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Wed, 24 Sep 2025 14:50:26 +0200 Subject: [PATCH 17/43] add humaneval pipeline files --- .../code-generation/human_eval/run.py | 88 +++++ .../evaluate/dataloaders/human_eval.py | 66 ++++ .../ragbits/evaluate/metrics/human_eval.py | 102 ++++++ .../ragbits/evaluate/pipelines/__init__.py | 3 +- .../ragbits/evaluate/pipelines/human_eval.py | 319 ++++++++++++++++++ 5 files changed, 577 insertions(+), 1 deletion(-) create mode 100644 examples/evaluate/code-generation/human_eval/run.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/human_eval.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py diff --git a/examples/evaluate/code-generation/human_eval/run.py b/examples/evaluate/code-generation/human_eval/run.py new file mode 100644 index 000000000..686347eb4 --- /dev/null +++ b/examples/evaluate/code-generation/human_eval/run.py @@ -0,0 +1,88 @@ +import asyncio +import logging +from pathlib import Path + +from ragbits.agents import Agent, AgentOptions +from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl +from ragbits.core.llms import LiteLLM +from ragbits.core.sources.hf import HuggingFaceSource +from ragbits.evaluate.dataloaders.human_eval import HumanEvalDataLoader +from ragbits.evaluate.evaluator import Evaluator +from ragbits.evaluate.metrics.base import MetricSet +from ragbits.evaluate.metrics.human_eval import HumanEvalPassAtK, HumanEvalQualityPerf +from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline + + +async def main() -> None: + """Run HumanEval example with an Agent and print aggregate metrics.""" + logging.getLogger("LiteLLM").setLevel(logging.ERROR) + + todo_list = TodoList() + todo_manager = create_todo_manager(todo_list) + + prompt_text = "\n".join( + [ + """ + You are an expert Python engineer. + Your task is to implement exactly one function that solves the problem. + Return ONLY the function as plain Python (no markdown). Include all necessary imports. + + WORKFLOW: + 1. If query is complex you have access to todo_manager tool to create a todo list with specific tasks + 2. If query is simple question, you work without todo_manager tool, just answer the question + 3. If you use todo_manager tool, you must follow the todo workflow + + Tool policy: + - If the problem is complex, follow this strict TODO workflow: + 1) todo_manager(action="create", tasks=[...]) with 3-5 concrete tasks + 2) For EACH task: + - todo_manager(action="get_current") + - todo_manager(action="start_task") + - do the work + - todo_manager(action="complete_task", summary="...") + 3) Finally: todo_manager(action="get_final_summary") + - Never call complete_task before start_task. + - If you decide to skip tools, do not call them at all. + """, + get_todo_instruction_tpl(task_range=(3, 5)), + ] + ) + + agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=prompt_text, + tools=[todo_manager], + default_options=AgentOptions(max_turns=30), + ) + + # Data + source = HuggingFaceSource(path="openai/openai_humaneval", split="test") + dataloader = HumanEvalDataLoader(source=source, split="data[:2]") + + # Pipeline + log_path = Path(__file__).with_name("humaneval_examples.ndjson") + pipeline = HumanEvalPipeline( + evaluation_target=agent, + n_samples=1, + timeout_sec=30, + temperature=0.7, + seed=42, + per_example_log_file=log_path, + # agent specific ext. logs + extended_logs=False, # includes traces, tool usage, etc. + ) + + # Metrics + metrics = MetricSet(HumanEvalPassAtK(k=1), HumanEvalPassAtK(k=5), HumanEvalQualityPerf()) + + # Evaluate + evaluator = Evaluator(batch_size=5, parallelize_batches=True) + results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) + + print("Metrics:") + for key, value in results.metrics.items(): + print(f" {key}: {value:.4f}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/human_eval.py new file mode 100644 index 000000000..a63b30594 --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/human_eval.py @@ -0,0 +1,66 @@ +from collections.abc import Iterable + +from ragbits.core.sources.base import Source +from ragbits.evaluate.dataloaders.base import DataLoader +from ragbits.evaluate.pipelines.human_eval import HumanEvalData + + +class HumanEvalDataLoader(DataLoader[HumanEvalData]): + """ + HumanEval evaluation data loader. + + The source should point to a local/remote JSONL file in HumanEval format, where each line is a JSON object + with at least the following keys: "task_id", "prompt", "entry_point", and "test". + """ + + def __init__( + self, + source: Source, + *, + split: str = "data", + task_id_key: str = "task_id", + prompt_key: str = "prompt", + entry_point_key: str = "entry_point", + test_key: str = "test", + canonical_solution_key: str | None = "canonical_solution", + ) -> None: + """ + Initialize the HumanEval data loader. + + Args: + source: The source to load the data from. + split: The split to load the data from. + task_id_key: Dataset column with the HumanEval task identifier. + prompt_key: Dataset column with the Python prompt (function signature and docstring). + entry_point_key: Dataset column with the function name to evaluate. + test_key: Dataset column with the Python test harness defining `check(candidate)`. + canonical_solution_key: Optional dataset column with the reference solution (not used for scoring). + """ + required = {task_id_key, prompt_key, entry_point_key, test_key} + super().__init__(source=source, split=split, required_keys=required) + self.task_id_key = task_id_key + self.prompt_key = prompt_key + self.entry_point_key = entry_point_key + self.test_key = test_key + self.canonical_solution_key = canonical_solution_key + + async def map(self, dataset: Iterable[dict]) -> Iterable[HumanEvalData]: + """ + Map the dataset to the HumanEval evaluation data schema. + + Args: + dataset: The dataset to map. + + Returns: + The HumanEval evaluation data rows. + """ + return [ + HumanEvalData( + task_id=row.get(self.task_id_key, ""), + prompt=row.get(self.prompt_key, ""), + entry_point=row.get(self.entry_point_key, ""), + test=row.get(self.test_key, ""), + canonical_solution=(row.get(self.canonical_solution_key) if self.canonical_solution_key else None), + ) + for row in dataset + ] diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py new file mode 100644 index 000000000..53ec91f5f --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +import math +from statistics import mean + +from ragbits.evaluate.metrics.base import Metric +from ragbits.evaluate.pipelines.human_eval import HumanEvalResult + + +class HumanEvalPassAtK(Metric[HumanEvalResult]): + """ + Computes pass@k over HumanEval tasks given binary pass masks per task. + When multiple samples per task exist, pass@k is defined as average over tasks of indicator that + at least one of k samples passes. For n>=k, approximate with: 1 - C(n-m, k) / C(n, k), where m is successes. + """ + + def __init__(self, k: int = 1, weight: float = 1.0) -> None: + super().__init__(weight=weight) + self.k = k + + async def compute(self, results: list[HumanEvalResult]) -> dict: # noqa: PLR6301 + """Compute pass@k averaged over tasks. + + For each task with n samples and m passes, uses 1 - C(n-m, k) / C(n, k). + """ + values = [] + for r in results: + n = len(r.passed_mask) + m = sum(1 for x in r.passed_mask if x) + k = min(self.k, n) + if n == 0 or k == 0: + values.append(0.0) + continue + if m == 0: + values.append(0.0) + continue + if m == n: + values.append(1.0) + continue + # 1 - C(n-m, k) / C(n, k) + denom = math.comb(n, k) + numer = math.comb(n - m, k) if n - m >= k else 0 + values.append(1.0 - (numer / denom)) + return {f"humaneval_pass@{self.k}": float(mean(values)) if values else 0.0} + + +class HumanEvalQualityPerf(Metric[HumanEvalResult]): + """ + Quality/performance aggregates with error breakdown: + - humaneval_compile_rate: fraction of samples that compiled + - humaneval_syntax_error_rate: fraction of samples with syntax error (compile failed) + - humaneval_assert_fail_rate: fraction of samples that ran but failed assertions + - humaneval_runtime_error_rate: fraction of samples with other runtime errors + - humaneval_timeout_rate: fraction of samples that timed out + - humaneval_tasks_solved: fraction of tasks with any passing sample + - humaneval_avg_exec_time_sec: average exec time over compilable runs + """ + + async def compute(self, results: list[HumanEvalResult]) -> dict: # noqa: PLR6301 + """Compute compile rate, error rate, tasks solved rate, and average exec time.""" + total_samples = sum(len(r.passed_mask) for r in results) + compiled = 0 + syntax_errors = 0 + assert_fails = 0 + runtime_errors = 0 + timeouts = 0 + any_pass = sum(1 for r in results if any(r.passed_mask)) + durations: list[float] = [] + + for r in results: + for ok, err, dur in zip(r.compile_ok_mask, r.errors, r.exec_durations_sec, strict=False): + if ok: + compiled += 1 + durations.append(dur) + if err: + if err.startswith("AssertionError"): + assert_fails += 1 + elif err.startswith("TimeoutError"): + timeouts += 1 + else: + runtime_errors += 1 + else: + # Compile failed: count as syntax error + syntax_errors += 1 + + compile_rate = (compiled / total_samples) if total_samples else 0.0 + syntax_error_rate = (syntax_errors / total_samples) if total_samples else 0.0 + assert_fail_rate = (assert_fails / total_samples) if total_samples else 0.0 + runtime_error_rate = (runtime_errors / total_samples) if total_samples else 0.0 + timeout_rate = (timeouts / total_samples) if total_samples else 0.0 + tasks_solved = (any_pass / len(results)) if results else 0.0 + avg_exec_time = float(mean(durations)) if durations else 0.0 + + return { + "humaneval_compile_rate": float(compile_rate), + "humaneval_syntax_error_rate": float(syntax_error_rate), + "humaneval_assert_fail_rate": float(assert_fail_rate), + "humaneval_runtime_error_rate": float(runtime_error_rate), + "humaneval_timeout_rate": float(timeout_rate), + "humaneval_tasks_solved": float(tasks_solved), + "humaneval_avg_exec_time_sec": avg_exec_time, + } diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py index bdd5aee9d..39f47f1c8 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py @@ -2,8 +2,9 @@ from ragbits.document_search import DocumentSearch from ragbits.evaluate.pipelines.base import EvaluationData, EvaluationPipeline, EvaluationResult from ragbits.evaluate.pipelines.document_search import DocumentSearchPipeline +from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline -__all__ = ["DocumentSearchPipeline", "EvaluationData", "EvaluationPipeline", "EvaluationResult"] +__all__ = ["DocumentSearchPipeline", "EvaluationData", "EvaluationPipeline", "EvaluationResult", "HumanEvalPipeline"] _target_to_evaluation_pipeline: dict[type[WithConstructionConfig], type[EvaluationPipeline]] = { DocumentSearch: DocumentSearchPipeline, diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py new file mode 100644 index 000000000..ba680b7dd --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py @@ -0,0 +1,319 @@ +import asyncio +import contextlib +import io +import json +import multiprocessing +import textwrap +import time +from collections.abc import Iterable +from dataclasses import dataclass +from multiprocessing.connection import Connection +from pathlib import Path +from typing import Any + +from typing_extensions import Self + +from ragbits.agents import Agent +from ragbits.core.llms.base import LLM, LLMClientOptionsT +from ragbits.evaluate.pipelines.base import EvaluationData, EvaluationPipeline, EvaluationResult + + +class HumanEvalData(EvaluationData): + """ + Represents a single HumanEval task. + """ + + task_id: str + prompt: str + entry_point: str + test: str + canonical_solution: str | None = None + + +@dataclass +class HumanEvalResult(EvaluationResult): + """ + Represents the result of evaluating a single HumanEval task. + """ + + task_id: str + entry_point: str + samples: list[str] + passed_mask: list[bool] + exec_durations_sec: list[float] + compile_ok_mask: list[bool] + errors: list[str | None] + + +def _execute_in_subprocess( + source: str, entry_point: str, test_code: str, timeout_sec: int = 10, memory_limit_mb: int | None = 512 +) -> tuple[bool, float, str | None]: + """Run candidate against HumanEval test in a subprocess with timeout.""" + + def _runner(pipe: Connection) -> None: + captured_out = io.StringIO() + start = time.perf_counter() + + try: + with contextlib.redirect_stdout(captured_out), contextlib.redirect_stderr(captured_out): + # Apply soft resource limits -> NOT A SANDBOX + with contextlib.suppress(Exception): + import os # type: ignore + import resource # type: ignore + import tempfile # type: ignore + + cpu_secs = max(1, timeout_sec) + resource.setrlimit(resource.RLIMIT_CPU, (cpu_secs, cpu_secs)) + + if memory_limit_mb is not None: + mem_bytes = int(memory_limit_mb) * 1024 * 1024 + resource.setrlimit(resource.RLIMIT_AS, (mem_bytes, mem_bytes)) + + # Minimal extra security + for rlim, val in ( + (getattr(resource, "RLIMIT_NOFILE", None), 256), + (getattr(resource, "RLIMIT_NPROC", None), 64), + (getattr(resource, "RLIMIT_FSIZE", None), 10 * 1024 * 1024), + ): + if rlim is not None: + with contextlib.suppress(Exception): + resource.setrlimit(rlim, (val, val)) + + # Temporary working directory for solution + tmp = tempfile.TemporaryDirectory() + with contextlib.suppress(Exception): + os.chdir(tmp.name) + + globals_dict: dict[str, Any] = {"__name__": "__main__"} + exec(compile(source, filename="candidate.py", mode="exec"), globals_dict) # noqa: S102 + + if entry_point not in globals_dict: + raise NameError(f"Entry point '{entry_point}' not defined") + + harness = textwrap.dedent(f"candidate = {entry_point}\n").lstrip() + test_code_clean = textwrap.dedent(test_code).lstrip() + compiled_test = compile( + harness + "\n" + test_code_clean + "\ncheck(candidate)", filename="test.py", mode="exec" + ) + exec(compiled_test, globals_dict) # noqa: S102 + + duration = time.perf_counter() - start + pipe.send((True, duration, None)) + + except Exception as e: + duration = time.perf_counter() - start + pipe.send((False, duration, f"{e.__class__.__name__}: {e}")) + + parent_conn, child_conn = multiprocessing.Pipe() + proc = multiprocessing.Process(target=_runner, args=(child_conn,)) + proc.start() + proc.join(timeout=timeout_sec) + + if proc.is_alive(): + proc.terminate() + proc.join() + return False, float(timeout_sec), "TimeoutError: execution exceeded time limit" + + passed, duration, err = parent_conn.recv() + return bool(passed), float(duration), (str(err) if err is not None else None) + + +class HumanEvalPipeline( + EvaluationPipeline[Agent[LLMClientOptionsT, None, str] | LLM[LLMClientOptionsT], HumanEvalData, HumanEvalResult] +): + """HumanEval evaluation pipeline for code generation models/agents.""" + + def __init__( + self, + evaluation_target: Agent[LLMClientOptionsT, None, str] | LLM[LLMClientOptionsT], + *, + n_samples: int = 1, + timeout_sec: int = 10, + temperature: float | None = None, + seed: int | None = None, + memory_limit_mb: int | None = 512, + per_example_log_file: Path | None = None, + extended_logs: bool = False, + ) -> None: + super().__init__(evaluation_target=evaluation_target) + self.n_samples = n_samples + self.timeout_sec = timeout_sec + self.temperature = temperature + self.seed = seed + self.memory_limit_mb = memory_limit_mb + self.per_example_log_file = per_example_log_file + self.extended_logs = extended_logs + self._init_log_file() + + @classmethod + def from_config(cls, config: dict) -> Self: + """Create pipeline from config. + Attempts Agent first, falls back to raw LLM construction. + """ + if "evaluation_target" not in config: + try: + config["evaluation_target"] = Agent.from_config(config) + except Exception: + config["evaluation_target"] = LLM.subclass_from_config(config) + return super().from_config(config) + + async def __call__(self, data: Iterable[HumanEvalData]) -> Iterable[HumanEvalResult]: + """Generate code completions per task and evaluate them. + Returns list of `HumanEvalResult`, one per input task. + """ + results: list[HumanEvalResult] = [] + + for row in data: + prompt_input = row.prompt + samples: list[str] = [] + compile_ok: list[bool] = [] + pass_mask: list[bool] = [] + durations: list[float] = [] + errors: list[str | None] = [] + + # Produce n samples + gen_tasks = [] + for _ in range(self.n_samples): + if self.extended_logs: + gen_tasks.append(self._generate_with_debug(prompt_input)) + else: + gen_tasks.append(self._generate_code(prompt_input)) + generations = await asyncio.gather(*gen_tasks, return_exceptions=True) + + debug_traces: list[dict | None] | None = [] if self.extended_logs else None + + for _, raw in enumerate(generations): + if isinstance(raw, Exception): + samples.append("") + compile_ok.append(False) + pass_mask.append(False) + durations.append(0.0) + err_msg = f"GenerationError: {raw.__class__.__name__}: {raw}" + errors.append(err_msg) + if self.extended_logs and debug_traces is not None: + debug_traces.append({"error": err_msg}) + continue + + if self.extended_logs: + # Raw is (content, debug) + content, dbg = raw + code = self._sanitize_code(content) + samples.append(code) + if debug_traces is not None: + debug_traces.append(dbg) + else: + # Raw is a simple string + code = self._sanitize_code(raw) + samples.append(code) + + # Compile check + try: + compile(code, filename="candidate.py", mode="exec") + compile_ok.append(True) + except Exception as e: # noqa: BLE001 + compile_ok.append(False) + pass_mask.append(False) + durations.append(0.0) + errors.append(f"SyntaxError: {e}") + continue + + ok, dur, err = _execute_in_subprocess( + code, + row.entry_point, + row.test, + timeout_sec=self.timeout_sec, + memory_limit_mb=self.memory_limit_mb, + ) + pass_mask.append(ok) + durations.append(dur) + errors.append(err) + + result = HumanEvalResult( + task_id=row.task_id, + entry_point=row.entry_point, + samples=samples, + passed_mask=pass_mask, + exec_durations_sec=durations, + compile_ok_mask=compile_ok, + errors=errors, + ) + results.append(result) + ext_log_str = ( + json.dumps(debug_traces, ensure_ascii=False, default=str) if (self.extended_logs and debug_traces is not None) else None + ) + self._log_example(row, result, ext_log_str) + return results + + @staticmethod + def _sanitize_code(text: str) -> str: + """Basic cleanup for LLM answers. + + - Trim whitespace and normalize newlines + - If fenced with ```...```, take the first fence block, drop language tag + """ + cleaned = text.replace("\r\n", "\n").replace("\r", "\n").strip() + if "```" in cleaned: + start = cleaned.find("```") + end = cleaned.find("```", start + 3) + if end != -1: + inside = cleaned[start + 3 : end].lstrip() + # drop a possible language tag line + if "\n" in inside: + first, rest = inside.split("\n", 1) + cleaned = rest if first.strip().lower().startswith("python") else inside + else: + cleaned = inside + return cleaned.strip() + + def _init_log_file(self) -> None: + """Ensure the per-example log file exists if logging is enabled.""" + if self.per_example_log_file is None: + return + self.per_example_log_file.parent.mkdir(parents=True, exist_ok=True) + self.per_example_log_file.touch(exist_ok=True) + + def _log_example(self, row: HumanEvalData, result: HumanEvalResult, extended_log: str | None = None) -> None: + """Append a single NDJSON record for debugging if enabled.""" + if self.per_example_log_file is None: + return + record: dict[str, object] = { + "task_id": row.task_id, + "entry_point": row.entry_point, + "n_samples": len(result.samples), + "samples": result.samples, + "compile_ok_mask": result.compile_ok_mask, + "passed_mask": result.passed_mask, + "exec_durations_sec": result.exec_durations_sec, + "errors": result.errors, + } + record["extended_debug_logging"] = extended_log or "[]" + with open(self.per_example_log_file, "a", encoding="utf-8") as f: + f.write(json.dumps(record, ensure_ascii=False) + "\n") + + async def _generate_code(self, prompt: str) -> str: + """Generate final answer code from Agent or raw LLM. + + Sampling/temperature/seed should be configured on the Agent/LLM itself. + """ + target = self.evaluation_target + if isinstance(target, Agent): + res = await target.run(prompt) + return str(res.content) + + resp = await target.generate(prompt) + return str(resp) + + async def _generate_with_debug(self, prompt: str) -> tuple[str, dict | None]: + """Generate code and capture tool/history/usage for logging (as raw content).""" + target = self.evaluation_target + if isinstance(target, Agent): + res = await target.run(prompt) + dbg = { + "history": res.history, + "tool_calls": res.tool_calls, + "usage": res.usage, + "metadata": res.metadata, + } + return str(res.content), dbg + resp = await target.generate(prompt) + return str(resp), None From 769551f4f84bb4c3fdf7443825b4fecd01a95811 Mon Sep 17 00:00:00 2001 From: akotyla <79326805+akotyla@users.noreply.github.com> Date: Thu, 25 Sep 2025 08:40:28 +0200 Subject: [PATCH 18/43] feat: streaming from downstream agents (#825) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Mateusz Hordyński <26008518+mhordynski@users.noreply.github.com> --- docs/api_reference/agents/index.md | 2 + .../how-to/agents/stream_downstream_agents.md | 48 ++++++ examples/README.md | 3 +- .../agents/downstream_agents_streaming.py | 133 +++++++++++++++++ mkdocs.yml | 1 + packages/ragbits-agents/CHANGELOG.md | 1 + .../src/ragbits/agents/__init__.py | 4 + .../src/ragbits/agents/_main.py | 137 ++++++++++++++---- .../ragbits-agents/src/ragbits/agents/tool.py | 17 ++- 9 files changed, 315 insertions(+), 31 deletions(-) create mode 100644 docs/how-to/agents/stream_downstream_agents.md create mode 100644 examples/agents/downstream_agents_streaming.py diff --git a/docs/api_reference/agents/index.md b/docs/api_reference/agents/index.md index ec2a0d655..a1d59bd0f 100644 --- a/docs/api_reference/agents/index.md +++ b/docs/api_reference/agents/index.md @@ -11,3 +11,5 @@ ::: ragbits.agents.a2a.server.create_agent_server ::: ragbits.agents.post_processors.base + +::: ragbits.agents.AgentRunContext diff --git a/docs/how-to/agents/stream_downstream_agents.md b/docs/how-to/agents/stream_downstream_agents.md new file mode 100644 index 000000000..1cbaccb62 --- /dev/null +++ b/docs/how-to/agents/stream_downstream_agents.md @@ -0,0 +1,48 @@ +# How-To: Stream downstream agents with Ragbits + +Ragbits [Agent][ragbits.agents.Agent] can call other agents as tools, creating a chain of reasoning where downstream agents provide structured results to the parent agent. + +Using the streaming API, you can observe every chunk of output as it is generated, including tool calls, tool results, and final text - perfect for real-time monitoring or chat interfaces. + +## Define a simple tool + +A tool is just a Python function returning a JSON-serializable result. Here’s an example tool returning the current time for a given location: + +```python +import json + +--8<-- "examples/agents/downstream_agents_streaming.py:33:51" +``` + +## Create a downstream agent + +The downstream agent wraps the tool with a prompt, allowing the LLM to use it as a function. + +```python +from pydantic import BaseModel +from ragbits.core.prompt import Prompt +from ragbits.agents import Agent +from ragbits.agents._main import AgentOptions +from ragbits.core.llms import LiteLLM + +--8<-- "examples/agents/downstream_agents_streaming.py:54:82" +``` + +## Create a parent QA agent + +The parent agent can call downstream agents as tools. This lets the LLM reason and decide when to invoke the downstream agent. + +```python +--8<-- "examples/agents/downstream_agents_streaming.py:85:111" +``` + +## Streaming output from downstream agents + +Use `run_streaming` with an [AgentRunContext][ragbits.agents.AgentRunContext] to see output as it happens. Each chunk contains either text, a tool call, or a tool result. You can print agent names when they change and handle downstream agent events. + +```python +import asyncio +from ragbits.agents import DownstreamAgentResult + +--8<-- "examples/agents/downstream_agents_streaming.py:114:133" +``` diff --git a/examples/README.md b/examples/README.md index a5291f3a0..e8fdbcb7b 100644 --- a/examples/README.md +++ b/examples/README.md @@ -39,7 +39,8 @@ All necessary details are provided in the comments at the top of each script. | [Recontextualize Last Message](/examples/chat/recontextualize_message.py) | [ragbits-chat](/packages/ragbits-chat) | Example of how to use the `StandaloneMessageCompressor` compressor to recontextualize the last message in a conversation history. | | [Agents Tool Use](/examples/agents/tool_use.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use agent with tools. | | [Agents OpenAI Native Tool Use](/examples/agents/openai_native_tool_use.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use agent with OpenAI native tools. | -| [Agents Post Processors](/examples/agents/post_processors.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use post-processors with agent. | +| [Agents Post Processors](/examples/agents/post_processors.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use post-processors with agent. +| [Agents Downstream Streaming](/examples/agents/downstream_agents_streaming.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to stream outputs from downstream agents in real time. | | | [Agents CLI](/examples/agents/cli_agent.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use agent in CLI. | | [MCP Local](/examples/agents/mcp/local.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use the `Agent` class to connect with a local MCP server. | | [MCP SSE](/examples/agents/mcp/sse.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use the `Agent` class to connect with a remote MCP server via SSE. | diff --git a/examples/agents/downstream_agents_streaming.py b/examples/agents/downstream_agents_streaming.py new file mode 100644 index 000000000..b68fb8dc3 --- /dev/null +++ b/examples/agents/downstream_agents_streaming.py @@ -0,0 +1,133 @@ +""" +Ragbits Agents Example: Multi-agent setup (QA agent + Time agent) + +This example demonstrates how to build a setup with two agents: +1. A Time Agent that returns the current time for a given location. +2. A QA Agent that answers user questions and can delegate to the Time Agent. + +To run the script, execute the following command: + + ```bash + uv run examples/agents/downstream_agents_streaming.py + ``` +""" + +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "ragbits-core", +# "ragbits-agents", +# ] +# /// + +import asyncio +import json + +from pydantic import BaseModel + +from ragbits.agents import Agent, AgentOptions, AgentRunContext, DownstreamAgentResult +from ragbits.core.llms import LiteLLM +from ragbits.core.prompt import Prompt + + +def get_time(location: str) -> str: + """ + Returns the current time for a given location. + + Args: + location: The location to get the time for. + + Returns: + The current time for the given location. + """ + loc = location.lower() + if "tokyo" in loc: + return json.dumps({"location": "Tokyo", "time": "10:00 AM"}) + elif "paris" in loc: + return json.dumps({"location": "Paris", "time": "04:00 PM"}) + elif "san francisco" in loc: + return json.dumps({"location": "San Francisco", "time": "07:00 PM"}) + else: + return json.dumps({"location": location, "time": "unknown"}) + + +class TimePromptInput(BaseModel): + """Input schema for the TimePrompt, containing the target location.""" + + location: str + + +class TimePrompt(Prompt[TimePromptInput]): + """ + Provides instructions for generating the current time in a user-specified + location. + """ + + system_prompt = """ + You are a helpful assistant that tells the current time in a given city. + """ + user_prompt = """ + What time is it in {{ location }}? + """ + + +llm = LiteLLM(model_name="gpt-4o-2024-08-06", use_structured_output=True) +time_agent = Agent( + name="time_agent", + description="Returns current time for a given location", + llm=llm, + prompt=TimePrompt, + tools=[get_time], + default_options=AgentOptions(max_total_tokens=1000, max_turns=5), +) + + +class QAPromptInput(BaseModel): + """Input schema for the QA agent, containing a natural-language question.""" + + question: str + + +class QAPrompt(Prompt[QAPromptInput]): + """ + Guides the agent to respond to user questions. + """ + + system_prompt = """ + You are a helpful assistant that responds to user questions. + """ + user_prompt = """ + {{ question }}. + """ + + +llm = LiteLLM(model_name="gpt-4o-2024-08-06", use_structured_output=True) +qa_agent = Agent( + name="qa_agent", + llm=llm, + prompt=QAPrompt, + tools=[(time_agent, {"name": "time_agent"})], + default_options=AgentOptions(max_total_tokens=1000, max_turns=5), +) + + +async def main() -> None: + """ + Run the QA agent with downstream streaming enabled. + + The QA agent processes a sample question ("What time is it in Paris?") and delegates to + the Time Agent when necessary. Streamed results from both agents are printed in real time, + tagged by the agent that produced them. + """ + context = AgentRunContext(stream_downstream_events=True) + + async for chunk in qa_agent.run_streaming(QAPromptInput(question="What time is it in Paris?"), context=context): + if isinstance(chunk, DownstreamAgentResult): + agent_name = context.get_agent(chunk.agent_id).name + print(f"[{agent_name}] {chunk.item}") + else: + print(f"[{qa_agent.name}] {chunk}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/mkdocs.yml b/mkdocs.yml index 8c534094d..a62180d7c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -41,6 +41,7 @@ nav: - "Provide tools with MCP": how-to/agents/provide_mcp_tools.md - "Serve agents with A2A": how-to/agents/serve_ragbits_agents.md - "Use post-processors": how-to/agents/use_post_processors.md + - "Stream downstream agents": how-to/agents/stream_downstream_agents.md - Guardrails: - "Setup guardrails": how-to/guardrails/use_guardrails.md - Chatbots: diff --git a/packages/ragbits-agents/CHANGELOG.md b/packages/ragbits-agents/CHANGELOG.md index c0eef472b..3d0e2b2a3 100644 --- a/packages/ragbits-agents/CHANGELOG.md +++ b/packages/ragbits-agents/CHANGELOG.md @@ -5,6 +5,7 @@ - Support wrapping downstream agents as tools (#818) - Add syntax sugar allowing easier Agents definition (#820) - Add post-processors (#821) +- Support streaming from downstream agents (#812) ## 1.3.0 (2025-09-11) ### Changed diff --git a/packages/ragbits-agents/src/ragbits/agents/__init__.py b/packages/ragbits-agents/src/ragbits/agents/__init__.py index bd1809337..edd8226cf 100644 --- a/packages/ragbits-agents/src/ragbits/agents/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/__init__.py @@ -5,6 +5,8 @@ AgentResult, AgentResultStreaming, AgentRunContext, + DownstreamAgentResult, + ToolCall, ToolCallResult, ) from ragbits.agents.post_processors.base import PostProcessor, StreamingPostProcessor @@ -17,10 +19,12 @@ "AgentResult", "AgentResultStreaming", "AgentRunContext", + "DownstreamAgentResult", "PostProcessor", "QuestionAnswerAgent", "QuestionAnswerPromptInput", "QuestionAnswerPromptOutput", "StreamingPostProcessor", + "ToolCall", "ToolCallResult", ] diff --git a/packages/ragbits-agents/src/ragbits/agents/_main.py b/packages/ragbits-agents/src/ragbits/agents/_main.py index b98b06245..e7c347100 100644 --- a/packages/ragbits-agents/src/ragbits/agents/_main.py +++ b/packages/ragbits-agents/src/ragbits/agents/_main.py @@ -2,13 +2,14 @@ import types import uuid from collections.abc import AsyncGenerator, AsyncIterator, Callable +from collections.abc import AsyncGenerator as _AG from contextlib import suppress from copy import deepcopy from dataclasses import dataclass from datetime import timedelta from inspect import iscoroutinefunction from types import ModuleType, SimpleNamespace -from typing import Any, ClassVar, Generic, Literal, TypeVar, cast, overload +from typing import Any, ClassVar, Generic, Literal, TypeVar, Union, cast, overload from pydantic import ( BaseModel, @@ -57,6 +58,26 @@ _Output = TypeVar("_Output") +@dataclass +class DownstreamAgentResult: + """ + Represents a streamed item from a downstream agent while executing a tool. + """ + + agent_id: str | None + """ID of the downstream agent.""" + item: Union[ + str, + ToolCall, + ToolCallResult, + "DownstreamAgentResult", + BasePrompt, + Usage, + SimpleNamespace, + ] + """The streamed item from the downstream agent.""" + + @dataclass class AgentResult(Generic[PromptOutputT]): """ @@ -158,13 +179,42 @@ def __contains__(self, key: str) -> bool: class AgentRunContext(BaseModel, Generic[DepsT]): """Context for the agent run.""" + model_config = {"arbitrary_types_allowed": True} + deps: AgentDependencies[DepsT] = Field(default_factory=lambda: AgentDependencies()) """Container for external dependencies.""" usage: Usage = Field(default_factory=Usage) """The usage of the agent.""" + stream_downstream_events: bool = False + """Whether to stream events from downstream agents when tools execute other agents.""" + downstream_agents: dict[str, "Agent"] = Field(default_factory=dict) + """Registry of all agents that participated in this run""" + + def register_agent(self, agent: "Agent") -> None: + """ + Register a downstream agent in this context. + + Args: + agent: The agent instance to register. + """ + self.downstream_agents[agent.id] = agent + + def get_agent(self, agent_id: str) -> "Agent | None": + """ + Retrieve a registered downstream agent by its ID. + + Args: + agent_id: The unique identifier of the agent. + + Returns: + The Agent instance if found, otherwise None. + """ + return self.downstream_agents.get(agent_id) -class AgentResultStreaming(AsyncIterator[str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace]): +class AgentResultStreaming( + AsyncIterator[str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace | DownstreamAgentResult] +): """ An async iterator that will collect all yielded items by LLM.generate_streaming(). This object is returned by `run_streaming`. It can be used in an `async for` loop to process items as they arrive. After the loop completes, @@ -172,49 +222,61 @@ class AgentResultStreaming(AsyncIterator[str | ToolCall | ToolCallResult | BaseP """ def __init__( - self, generator: AsyncGenerator[str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage] + self, + generator: AsyncGenerator[ + str | ToolCall | ToolCallResult | DownstreamAgentResult | SimpleNamespace | BasePrompt | Usage + ], ): self._generator = generator self.content: str = "" self.tool_calls: list[ToolCallResult] | None = None + self.downstream: dict[str | None, list[str | ToolCall | ToolCallResult]] = {} self.metadata: dict = {} self.history: ChatFormat self.usage: Usage = Usage() - def __aiter__(self) -> AsyncIterator[str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace]: + def __aiter__( + self, + ) -> AsyncIterator[str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace | DownstreamAgentResult]: return self - async def __anext__(self) -> str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace: + async def __anext__( + self, + ) -> str | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace | DownstreamAgentResult: try: item = await self._generator.__anext__() match item: case str(): self.content += item - return item case ToolCall(): - return item + pass case ToolCallResult(): if self.tool_calls is None: self.tool_calls = [] self.tool_calls.append(item) - return item + case DownstreamAgentResult(): + if item.agent_id not in self.downstream: + self.downstream[item.agent_id] = [] + if isinstance(item.item, str | ToolCall | ToolCallResult): + self.downstream[item.agent_id].append(item.item) case BasePrompt(): item.add_assistant_message(self.content) self.history = item.chat - return item case Usage(): self.usage = item + # continue loop instead of tail recursion return await self.__anext__() case SimpleNamespace(): result_dict = getattr(item, "result", {}) self.content = result_dict.get("content", self.content) self.metadata = result_dict.get("metadata", self.metadata) self.tool_calls = result_dict.get("tool_calls", self.tool_calls) - return item case _: raise ValueError(f"Unexpected item: {item}") + return item + except StopAsyncIteration: raise @@ -399,10 +461,12 @@ async def _run_without_post_processing( break for tool_call in response.tool_calls: - result = await self._execute_tool(tool_call=tool_call, tools_mapping=tools_mapping, context=context) - tool_calls.append(result) - - prompt_with_history = prompt_with_history.add_tool_use_message(**result.__dict__) + async for result in self._execute_tool( + tool_call=tool_call, tools_mapping=tools_mapping, context=context + ): + if isinstance(result, ToolCallResult): + tool_calls.append(result) + prompt_with_history = prompt_with_history.add_tool_use_message(**result.__dict__) turn_count += 1 else: @@ -516,13 +580,23 @@ def run_streaming( AgentMaxTurnsExceededError: If the maximum number of turns is exceeded. AgentInvalidPostProcessorError: If the post-processor is invalid. """ - generator = self._stream_internal(input, options, context, tool_choice) + generator = self._stream_internal( + input=input, + options=options, + context=context, + tool_choice=tool_choice, + ) if post_processors: if not allow_non_streaming and any(not p.supports_streaming for p in post_processors): raise AgentInvalidPostProcessorError( reason="Non-streaming post-processors are not allowed when allow_non_streaming is False" ) + + generator = cast( + _AG[str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage], + generator, + ) generator = stream_with_post_processing(generator, post_processors, self) return AgentResultStreaming(generator) @@ -533,10 +607,12 @@ async def _stream_internal( options: AgentOptions[LLMClientOptionsT] | None = None, context: AgentRunContext | None = None, tool_choice: ToolChoice | None = None, - ) -> AsyncGenerator[str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage]: + ) -> AsyncGenerator[str | ToolCall | ToolCallResult | DownstreamAgentResult | SimpleNamespace | BasePrompt | Usage]: if context is None: context = AgentRunContext() + context.register_agent(cast(Agent[Any, Any, str], self)) + input = cast(PromptInputT, input) merged_options = (self.default_options | options) if options else self.default_options llm_options = merged_options.llm_options or self.llm.default_options @@ -546,24 +622,33 @@ async def _stream_internal( turn_count = 0 max_turns = merged_options.max_turns max_turns = 10 if max_turns is NOT_GIVEN else max_turns + with trace(input=input, options=merged_options) as outputs: while not max_turns or turn_count < max_turns: returned_tool_call = False self._check_token_limits(merged_options, context.usage, prompt_with_history, self.llm) + streaming_result = self.llm.generate_streaming( prompt=prompt_with_history, tools=[tool.to_function_schema() for tool in tools_mapping.values()], tool_choice=tool_choice if tool_choice and turn_count == 0 else None, options=self._get_llm_options(llm_options, merged_options, context.usage), ) + async for chunk in streaming_result: yield chunk if isinstance(chunk, ToolCall): - result = await self._execute_tool(tool_call=chunk, tools_mapping=tools_mapping, context=context) - yield result - prompt_with_history = prompt_with_history.add_tool_use_message(**result.__dict__) - returned_tool_call = True + async for result in self._execute_tool( + tool_call=chunk, + tools_mapping=tools_mapping, + context=context, + ): + yield result + if isinstance(result, ToolCallResult): + prompt_with_history = prompt_with_history.add_tool_use_message(**result.__dict__) + returned_tool_call = True + turn_count += 1 if streaming_result.usage: context.usage += streaming_result.usage @@ -693,11 +778,10 @@ async def _execute_tool( self, tool_call: ToolCall, tools_mapping: dict[str, Tool], - context: AgentRunContext | None = None, - ) -> ToolCallResult: + context: AgentRunContext, + ) -> AsyncGenerator[ToolCallResult | DownstreamAgentResult, None]: if tool_call.type != "function": raise AgentToolNotSupportedError(tool_call.type) - if tool_call.name not in tools_mapping: raise AgentToolNotAvailableError(tool_call.name) @@ -716,8 +800,9 @@ async def _execute_tool( ) if isinstance(tool_output, AgentResultStreaming): - async for _ in tool_output: - pass + async for downstream_item in tool_output: + if context.stream_downstream_events: + yield DownstreamAgentResult(agent_id=tool.id, item=downstream_item) tool_output = { "content": tool_output.content, @@ -738,7 +823,7 @@ async def _execute_tool( } raise AgentToolExecutionError(tool_call.name, e) from e - return ToolCallResult( + yield ToolCallResult( id=tool_call.id, name=tool_call.name, arguments=tool_call.arguments, diff --git a/packages/ragbits-agents/src/ragbits/agents/tool.py b/packages/ragbits-agents/src/ragbits/agents/tool.py index 3da403782..c8e279f29 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tool.py +++ b/packages/ragbits-agents/src/ragbits/agents/tool.py @@ -1,7 +1,7 @@ from collections.abc import Callable from contextlib import suppress from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Literal +from typing import TYPE_CHECKING, Any, Literal, cast from pydantic import BaseModel from typing_extensions import Self @@ -12,7 +12,7 @@ from ragbits.core.utils.function_schema import convert_function_to_function_schema, get_context_variable_name if TYPE_CHECKING: - from ragbits.agents import Agent, AgentResultStreaming + from ragbits.agents import Agent, AgentResultStreaming, AgentRunContext with suppress(ImportError): from pydantic_ai import Tool as PydanticAITool @@ -50,6 +50,7 @@ class Tool: """The actual callable function to execute when the tool is called.""" context_var_name: str | None = None """The name of the context variable that this tool accepts.""" + id: str | None = None @classmethod def from_callable(cls, callable: Callable) -> Self: @@ -150,20 +151,28 @@ def from_agent( parameters = {"type": "object", "properties": properties, "required": required} + context_var_name = get_context_variable_name(agent.run) + def _on_tool_call(**kwargs: dict) -> "AgentResultStreaming": + if context_var_name: + context = cast("AgentRunContext[Any] | None", kwargs.get(context_var_name)) + if context is not None: + context.register_agent(cast("Agent[Any, Any, str]", agent)) + if input_model_cls and issubclass(input_model_cls, BaseModel): model_input = input_model_cls(**kwargs) else: model_input = kwargs.get("input") - return agent.run_streaming(model_input) + return agent.run_streaming(model_input, context=context) return cls( name=variable_name, + id=agent.id, description=description, parameters=parameters, on_tool_call=_on_tool_call, - context_var_name=get_context_variable_name(agent.run), + context_var_name=context_var_name, ) From 51c95a9ba7583a2e8a701345433c827a0cf1fe7d Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Thu, 25 Sep 2025 14:56:22 +0200 Subject: [PATCH 19/43] minor humaneval changes --- .../{code-generation => }/human_eval/run.py | 21 ++++++- .../evaluate/dataloaders/human_eval.py | 6 +- .../ragbits/evaluate/metrics/human_eval.py | 26 +++++---- .../ragbits/evaluate/pipelines/human_eval.py | 55 ++++++++----------- 4 files changed, 60 insertions(+), 48 deletions(-) rename examples/evaluate/{code-generation => }/human_eval/run.py (81%) diff --git a/examples/evaluate/code-generation/human_eval/run.py b/examples/evaluate/human_eval/run.py similarity index 81% rename from examples/evaluate/code-generation/human_eval/run.py rename to examples/evaluate/human_eval/run.py index 686347eb4..095ee3159 100644 --- a/examples/evaluate/code-generation/human_eval/run.py +++ b/examples/evaluate/human_eval/run.py @@ -61,15 +61,30 @@ async def main() -> None: # Pipeline log_path = Path(__file__).with_name("humaneval_examples.ndjson") + + # Code sanitazation function + def sanitize_code(text: str) -> str: + cleaned = text.replace("\r\n", "\n").replace("\r", "\n").strip() + if "```" in cleaned: + start = cleaned.find("```") + end = cleaned.find("```", start + 3) + if end != -1: + inside = cleaned[start + 3 : end].lstrip() + if "\n" in inside: + first, rest = inside.split("\n", 1) + cleaned = rest if first.strip().lower().startswith("python") else inside + else: + cleaned = inside + return cleaned.strip() + pipeline = HumanEvalPipeline( evaluation_target=agent, n_samples=1, timeout_sec=30, - temperature=0.7, - seed=42, per_example_log_file=log_path, # agent specific ext. logs - extended_logs=False, # includes traces, tool usage, etc. + extended_logs=True, # includes traces, tool usage, etc. + code_sanitize_fn=sanitize_code, ) # Metrics diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/human_eval.py index a63b30594..21760336f 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/human_eval.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/human_eval.py @@ -10,7 +10,11 @@ class HumanEvalDataLoader(DataLoader[HumanEvalData]): HumanEval evaluation data loader. The source should point to a local/remote JSONL file in HumanEval format, where each line is a JSON object - with at least the following keys: "task_id", "prompt", "entry_point", and "test". + with at least the following keys: " + - task_id" (str) + - "prompt" (str) + - "entry_point" (str) + - "test" (str) """ def __init__( diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py index 53ec91f5f..fd5eeda8d 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py @@ -9,19 +9,19 @@ class HumanEvalPassAtK(Metric[HumanEvalResult]): """ - Computes pass@k over HumanEval tasks given binary pass masks per task. - When multiple samples per task exist, pass@k is defined as average over tasks of indicator that - at least one of k samples passes. For n>=k, approximate with: 1 - C(n-m, k) / C(n, k), where m is successes. + Computes pass@k over HumanEval tasks. + Measures the fraction of tasks with at least one passing sample out of k attempts. """ - def __init__(self, k: int = 1, weight: float = 1.0) -> None: - super().__init__(weight=weight) + def __init__(self, k: int = 1) -> None: + super().__init__() self.k = k - async def compute(self, results: list[HumanEvalResult]) -> dict: # noqa: PLR6301 + async def compute(self, results: list[HumanEvalResult]) -> dict: """Compute pass@k averaged over tasks. - - For each task with n samples and m passes, uses 1 - C(n-m, k) / C(n, k). + + Returns: + Dictionary with humaneval_pass@k: fraction of tasks with at least one passing sample. """ values = [] for r in results: @@ -46,7 +46,7 @@ async def compute(self, results: list[HumanEvalResult]) -> dict: # noqa: PLR630 class HumanEvalQualityPerf(Metric[HumanEvalResult]): """ - Quality/performance aggregates with error breakdown: + Code quality and execution performance metrics: - humaneval_compile_rate: fraction of samples that compiled - humaneval_syntax_error_rate: fraction of samples with syntax error (compile failed) - humaneval_assert_fail_rate: fraction of samples that ran but failed assertions @@ -56,8 +56,12 @@ class HumanEvalQualityPerf(Metric[HumanEvalResult]): - humaneval_avg_exec_time_sec: average exec time over compilable runs """ - async def compute(self, results: list[HumanEvalResult]) -> dict: # noqa: PLR6301 - """Compute compile rate, error rate, tasks solved rate, and average exec time.""" + async def compute(self, results: list[HumanEvalResult]) -> dict: + """Compute code quality and execution performance metrics. + + Returns: + Dictionary with compile rates, error rates, tasks solved rate, and average execution time. + """ total_samples = sum(len(r.passed_mask) for r in results) compiled = 0 syntax_errors = 0 diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py index ba680b7dd..d888ff4d6 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py @@ -2,10 +2,11 @@ import contextlib import io import json +import logging import multiprocessing import textwrap import time -from collections.abc import Iterable +from collections.abc import Callable, Iterable from dataclasses import dataclass from multiprocessing.connection import Connection from pathlib import Path @@ -85,7 +86,7 @@ def _runner(pipe: Connection) -> None: os.chdir(tmp.name) globals_dict: dict[str, Any] = {"__name__": "__main__"} - exec(compile(source, filename="candidate.py", mode="exec"), globals_dict) # noqa: S102 + exec(compile(source, filename="candidate.py", mode="exec"), globals_dict) if entry_point not in globals_dict: raise NameError(f"Entry point '{entry_point}' not defined") @@ -95,7 +96,7 @@ def _runner(pipe: Connection) -> None: compiled_test = compile( harness + "\n" + test_code_clean + "\ncheck(candidate)", filename="test.py", mode="exec" ) - exec(compiled_test, globals_dict) # noqa: S102 + exec(compiled_test, globals_dict) duration = time.perf_counter() - start pipe.send((True, duration, None)) @@ -129,20 +130,18 @@ def __init__( *, n_samples: int = 1, timeout_sec: int = 10, - temperature: float | None = None, - seed: int | None = None, memory_limit_mb: int | None = 512, per_example_log_file: Path | None = None, extended_logs: bool = False, + code_sanitize_fn: Callable[[str], str] | None = None, ) -> None: super().__init__(evaluation_target=evaluation_target) self.n_samples = n_samples self.timeout_sec = timeout_sec - self.temperature = temperature - self.seed = seed self.memory_limit_mb = memory_limit_mb self.per_example_log_file = per_example_log_file self.extended_logs = extended_logs + self.code_sanitize_fn = code_sanitize_fn self._init_log_file() @classmethod @@ -197,13 +196,13 @@ async def __call__(self, data: Iterable[HumanEvalData]) -> Iterable[HumanEvalRes if self.extended_logs: # Raw is (content, debug) content, dbg = raw - code = self._sanitize_code(content) + code = self._sanitize(content) samples.append(code) if debug_traces is not None: debug_traces.append(dbg) else: # Raw is a simple string - code = self._sanitize_code(raw) + code = self._sanitize(raw) samples.append(code) # Compile check @@ -239,31 +238,24 @@ async def __call__(self, data: Iterable[HumanEvalData]) -> Iterable[HumanEvalRes ) results.append(result) ext_log_str = ( - json.dumps(debug_traces, ensure_ascii=False, default=str) if (self.extended_logs and debug_traces is not None) else None + json.dumps(debug_traces, ensure_ascii=False, default=str) + if (self.extended_logs and debug_traces is not None) + else None ) self._log_example(row, result, ext_log_str) return results - @staticmethod - def _sanitize_code(text: str) -> str: - """Basic cleanup for LLM answers. - - - Trim whitespace and normalize newlines - - If fenced with ```...```, take the first fence block, drop language tag + def _sanitize(self, text: str) -> str: + """Optionally sanitize cpde from text using provided function. + If no parser provided, returns the original text. """ - cleaned = text.replace("\r\n", "\n").replace("\r", "\n").strip() - if "```" in cleaned: - start = cleaned.find("```") - end = cleaned.find("```", start + 3) - if end != -1: - inside = cleaned[start + 3 : end].lstrip() - # drop a possible language tag line - if "\n" in inside: - first, rest = inside.split("\n", 1) - cleaned = rest if first.strip().lower().startswith("python") else inside - else: - cleaned = inside - return cleaned.strip() + if self.code_sanitize_fn is None: + return text + try: + return self.code_sanitize_fn(text) + except Exception as exc: + logging.getLogger(__name__).debug("Code sanitize error: %s", exc) + return text def _init_log_file(self) -> None: """Ensure the per-example log file exists if logging is enabled.""" @@ -291,10 +283,7 @@ def _log_example(self, row: HumanEvalData, result: HumanEvalResult, extended_log f.write(json.dumps(record, ensure_ascii=False) + "\n") async def _generate_code(self, prompt: str) -> str: - """Generate final answer code from Agent or raw LLM. - - Sampling/temperature/seed should be configured on the Agent/LLM itself. - """ + """Generate final answer code from Agent or raw LLM.""" target = self.evaluation_target if isinstance(target, Agent): res = await target.run(prompt) From a21efb5ddfd88a1f09dbb46bf12c4b9f6a33eafc Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Thu, 25 Sep 2025 16:47:31 +0200 Subject: [PATCH 20/43] add GAIA pipeline + basic extra tools for benchmarking --- examples/evaluate/gaia/run.py | 121 +++++++++ .../src/ragbits/agents/tools/extra.py | 216 +++++++++++++++ .../src/ragbits/evaluate/dataloaders/gaia.py | 74 ++++++ .../src/ragbits/evaluate/metrics/gaia.py | 90 +++++++ .../ragbits/evaluate/pipelines/__init__.py | 3 +- .../src/ragbits/evaluate/pipelines/gaia.py | 251 ++++++++++++++++++ 6 files changed, 754 insertions(+), 1 deletion(-) create mode 100644 examples/evaluate/gaia/run.py create mode 100644 packages/ragbits-agents/src/ragbits/agents/tools/extra.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/gaia.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py diff --git a/examples/evaluate/gaia/run.py b/examples/evaluate/gaia/run.py new file mode 100644 index 000000000..d97de4e83 --- /dev/null +++ b/examples/evaluate/gaia/run.py @@ -0,0 +1,121 @@ +import asyncio +import logging +from pathlib import Path + +from ragbits.agents import Agent, AgentOptions +from ragbits.agents.tools.openai import get_web_search_tool +from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl +from ragbits.agents.tools.extra import ( + add, + subtract, + multiply, + divide, + modulus, + arxiv_search, + wiki_search, + get_extra_instruction_tpl, +) +from ragbits.core.llms import LiteLLM +from ragbits.core.sources.hf import HuggingFaceSource +from ragbits.evaluate.dataloaders.gaia import GaiaDataLoader +from ragbits.evaluate.evaluator import Evaluator +from ragbits.evaluate.metrics.base import MetricSet +from ragbits.evaluate.metrics.gaia import GaiaEfficiency, GaiaOutcome, GaiaTooling +from ragbits.evaluate.pipelines.gaia import GaiaPipeline + + +async def main() -> None: + """Run GAIA example with an Agent and print aggregate metrics.""" + logging.getLogger("LiteLLM").setLevel(logging.ERROR) + + todo_list = TodoList() + todo_manager = create_todo_manager(todo_list) + + gaia_prompt = ( + "You are a general AI assistant. I will ask you a question. " + "Report your thoughts, and finish your answer with the following template: " + "FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible " + "OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write " + "your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a " + "string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text " + "unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of " + "whether the element to be put in the list is a number or a string. If you are asked for a number, make sure " + "you respond using digits not text, and ensure the answer is in appropriate unit context wise " + "(e.g. if asked for thousands of meters and your answer is 31000, answer 31)." + ) + + system_prompt = "\n".join( + [ + gaia_prompt, + ( + "Tool usage:\n" + "- Use arithmetic tools for calculations.\n" + "- Use arxiv_search/wiki_search to retrieve relevant facts when needed or explicitly asked.\n" + "- For multi-step/complex requests, start and manage work with the todo_manager workflow.\n" + "- For general web-search questions, use search_web tool and gather information." + ), + get_todo_instruction_tpl(task_range=(3, 5)), + get_extra_instruction_tpl(), + ] + ) + + web_search = get_web_search_tool(model_name="gpt-4.1-mini") + + agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=system_prompt, + tools=[ + todo_manager, + web_search, + add, + subtract, + multiply, + divide, + modulus, + arxiv_search, + wiki_search, + ], + default_options=AgentOptions(max_turns=30), + ) + + # Data: pick level 1, 2, or 3 -> higher level <-> harder questions + level = 1 + # GAIA config name:'2023_all', '2023_level1', '2023_level2', '2023_level3' + config_name = {1: "2023_level1", 2: "2023_level2", 3: "2023_level3"}[level] + source = HuggingFaceSource(path="gaia-benchmark/GAIA", name=config_name, split="validation") + dataloader = GaiaDataLoader(source=source, split="data[:30]", skip_file_attachments=True) + + log_path = Path(__file__).with_name("gaia_examples.ndjson") + + def parse_final_answer(text: str) -> str: + marker = "FINAL ANSWER:" + idx = text.rfind(marker) + if idx == -1: + return text.strip() + candidate = text[idx + len(marker) :].strip() + if candidate.startswith("[") and candidate.endswith("]"): + candidate = candidate[1:-1].strip() + return candidate + + pipeline = GaiaPipeline( + evaluation_target=agent, + per_example_log_file=log_path, + # agent specific ext. logs + extended_logs=False, # includes traces, tool usage, etc. + parse_answer_fn=parse_final_answer, + ) + + # Metrics + metrics = MetricSet(GaiaOutcome(), GaiaTooling(), GaiaEfficiency()) + + # Evaluate + evaluator = Evaluator(batch_size=5, parallelize_batches=True) + results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) + + print("Metrics:") + for key, value in results.metrics.items(): + print(f" {key}: {value:.4f}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/extra.py b/packages/ragbits-agents/src/ragbits/agents/tools/extra.py new file mode 100644 index 000000000..a54df55fa --- /dev/null +++ b/packages/ragbits-agents/src/ragbits/agents/tools/extra.py @@ -0,0 +1,216 @@ +from __future__ import annotations + +from typing import Any +import json +import urllib.parse +import urllib.request +import xml.etree.ElementTree as ET + + +def add(a: int, b: int) -> int: + """Add two integers and return the result. + + Args: + a: First integer. + b: Second integer. + + Returns: + Sum of a and b. + """ + + return a + b + + +def subtract(a: int, b: int) -> int: + """Subtract two integers and return the result (a - b). + + Args: + a: Minuend integer. + b: Subtrahend integer. + + Returns: + Difference a - b. + """ + + return a - b + + +def multiply(a: int, b: int) -> int: + """Multiply two integers and return the result. + + Args: + a: First integer. + b: Second integer. + + Returns: + Product a * b. + """ + + return a * b + + +def divide(a: int, b: int) -> float: + """Divide two integers and return the result as float. + + Args: + a: Dividend integer. + b: Divisor integer (must be non-zero). + + Returns: + Quotient a / b as float. + + Raises: + ValueError: If b == 0. + """ + + if b == 0: + raise ValueError("Cannot divide by zero.") + return a / b + + +def modulus(a: int, b: int) -> int: + """Compute remainder of a divided by b (a % b). + + Args: + a: Dividend integer. + b: Divisor integer (must be non-zero). + + Returns: + Remainder a % b. + + Raises: + ValueError: If b == 0. + """ + + if b == 0: + raise ValueError("Cannot divide by zero.") + return a % b + + +def _http_get(url: str, timeout: float = 10.0) -> bytes: + req = urllib.request.Request(url, headers={"User-Agent": "ragbits-agents/extra-tools"}) + with urllib.request.urlopen(req, timeout=timeout) as resp: + return resp.read() + + +def arxiv_search(query: str, max_results: int = 3) -> dict[str, Any]: + """Search arXiv and return up to `max_results` entries. + + Args: + query: Search query (e.g., "quantum computing") + max_results: Max number of results to return (default 3) + + Returns: + Dict with a list of results: title, summary, link. + """ + + if max_results <= 0: + return {"results": []} + + base = "https://export.arxiv.org/api/query" + params = { + "search_query": urllib.parse.quote_plus(query), + "start": "0", + "max_results": str(max_results), + "sortBy": "relevance", + "sortOrder": "descending", + } + url = f"{base}?search_query={params['search_query']}&start={params['start']}&max_results={params['max_results']}&sortBy={params['sortBy']}&sortOrder={params['sortOrder']}" + + raw = _http_get(url) + root = ET.fromstring(raw) + ns = {"atom": "http://www.w3.org/2005/Atom"} + results: list[dict[str, str]] = [] + for entry in root.findall("atom:entry", ns): + title_el = entry.find("atom:title", ns) + summary_el = entry.find("atom:summary", ns) + link_el = entry.find("atom:link[@rel='alternate']", ns) + title = (title_el.text or "").strip() if title_el is not None else "" + summary = (summary_el.text or "").strip() if summary_el is not None else "" + link = link_el.get("href") if link_el is not None else "" + # Trim overly long summaries for brevity + if len(summary) > 1200: + summary = summary[:1200] + "..." + results.append({"title": title, "summary": summary, "link": link}) + + return {"results": results} + + +def wiki_search(query: str, max_results: int = 2, language: str = "en") -> dict[str, Any]: + """Search Wikipedia and return up to `max_results` entries with extracts. + + Args: + query: Search query (e.g., "Alan Turing") + max_results: Max number of results (default 2) + language: Wikipedia language code (default "en") + + Returns: + Dict with list of results: title, pageid, extract, url. + """ + + if max_results <= 0: + return {"results": []} + + api = f"https://{language}.wikipedia.org/w/api.php" + search_params = { + "action": "query", + "list": "search", + "srsearch": query, + "srlimit": str(max_results), + "format": "json", + } + search_url = f"{api}?{urllib.parse.urlencode(search_params)}" + raw = _http_get(search_url) + data = json.loads(raw.decode("utf-8")) + hits = data.get("query", {}).get("search", []) + if not hits: + return {"results": []} + + page_ids = [str(h.get("pageid")) for h in hits if "pageid" in h] + extract_params = { + "action": "query", + "prop": "extracts", + "explaintext": "1", + "exintro": "1", + "pageids": "|".join(page_ids), + "format": "json", + } + extract_url = f"{api}?{urllib.parse.urlencode(extract_params)}" + raw2 = _http_get(extract_url) + data2 = json.loads(raw2.decode("utf-8")) + pages = data2.get("query", {}).get("pages", {}) + + results: list[dict[str, Any]] = [] + for pid in page_ids: + page = pages.get(pid) + if not page: + continue + title = page.get("title", "") + extract = (page.get("extract", "") or "").strip() + if len(extract) > 1200: + extract = extract[:1200] + "..." + url = f"https://{language}.wikipedia.org/?curid={pid}" + results.append({"title": title, "pageid": int(pid), "extract": extract, "url": url}) + + return {"results": results} + + +def get_extra_instruction_tpl() -> str: + """Generate tool usage instructions template for arithmetic and lookups. + + Returns: + Instruction template string to include in system prompts. + """ + + return ( + "\n\n" + "## Tools Workflow (Arithmetic, arXiv, Wikipedia)\n\n" + "Available actions:\n" + "- `add(a, b)`, `subtract(a, b)`, `multiply(a, b)`, `divide(a, b)`, `modulus(a, b)`\n" + "- `arxiv_search(query, max_results=3)`\n" + "- `wiki_search(query, max_results=2, language='en')`\n\n" + "POLICY:\n" + "1) Use arithmetic tools for calculations instead of inline math when non-trivial.\n" + "2) Use arXiv/Wikipedia tools when scholarly/general knowledge retrieval is needed.\n" + "3) Keep outputs concise; provide FINAL ANSWER after any intermediate reasoning.\n" + ) \ No newline at end of file diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/gaia.py new file mode 100644 index 000000000..6e553e4e3 --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/gaia.py @@ -0,0 +1,74 @@ +from collections.abc import Iterable + +from ragbits.core.sources.base import Source +from ragbits.evaluate.dataloaders.base import DataLoader +from ragbits.evaluate.pipelines.gaia import GaiaData + + +class GaiaDataLoader(DataLoader[GaiaData]): + """ + GAIA benchmark evaluation data loader. + + The source should point to a local/remote JSON or JSONL file exported from the + Hugging Face dataset `gaia-benchmark/GAIA`. Rows are expected to contain at least: + - "task_id" (str) + - "Question" (str) + - "Level" (int) + - "Final answer" (str) + """ + + def __init__( + self, + source: Source, + *, + split: str = "data", + task_id_key: str = "task_id", + question_key: str = "Question", + level_key: str = "Level", + final_answer_key: str = "Final answer", + file_name_key: str = "file_name", + skip_file_attachments: bool = False, + ) -> None: + """ + Initialize the GAIA data loader. + + Args: + source: The source to load the data from. + split: The split to load the data from (file name generated by the source helper). + task_id_key: Column name for GAIA task identifier. + question_key: Column name for the natural language question. + level_key: Column name for numeric difficulty level (1, 2, 3). + final_answer_key: Column name for the final ground-truth answer. + file_name_key: Column name with optional associated file name (may be empty). + skip_file_attachments: If True, skip rows that have a non-empty file attachment. + """ + required = {task_id_key, question_key, level_key, final_answer_key} + super().__init__(source=source, split=split, required_keys=required) + self.task_id_key = task_id_key + self.question_key = question_key + self.level_key = level_key + self.final_answer_key = final_answer_key + self.file_name_key = file_name_key + self.skip_file_attachments = skip_file_attachments + + async def map(self, dataset: Iterable[dict]) -> Iterable[GaiaData]: + """ + Map the dataset to the GAIA evaluation data schema. + + Args: + dataset: The dataset to map. + + Returns: + The GAIA evaluation data rows. + """ + return [ + GaiaData( + task_id=str(row.get(self.task_id_key, "")), + question=str(row.get(self.question_key, "")), + level=int(row.get(self.level_key, 1)), + reference_answer=str(row.get(self.final_answer_key, "")), + file_name=(row.get(self.file_name_key) or None), + ) + for row in dataset + if (not self.skip_file_attachments or not row.get(self.file_name_key) or str(row.get(self.file_name_key)).strip() == "") + ] diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py new file mode 100644 index 000000000..3c28f6f66 --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py @@ -0,0 +1,90 @@ +from __future__ import annotations + +from statistics import mean + +from ragbits.evaluate.metrics.base import Metric +from ragbits.evaluate.pipelines.gaia import GaiaResult + + +class GaiaOutcome(Metric[GaiaResult]): + """ + Computes task success rate over GAIA tasks. + Measures the fraction of tasks that were successfully solved. + """ + + async def compute(self, results: list[GaiaResult]) -> dict: + """Compute task success rate. + + Returns: + Dictionary with gaia_task_success_rate: fraction of successfully solved tasks. + """ + success_count = sum(1 for r in results if r.task_success) + success_rate = (success_count / len(results)) if results else 0.0 + + return {"gaia_task_success_rate": float(success_rate)} + + +class GaiaTooling(Metric[GaiaResult]): + """ + Tool utilization and performance metrics: + - gaia_tool_trigger_rate: fraction of tasks where tools were used + - gaia_avg_num_tool_calls: average number of tool calls per task + - gaia_avg_tool_error_count: average number of tool errors per task + - averaged_freq: average tool usage/calls per task + """ + + async def compute(self, results: list[GaiaResult]) -> dict: + """Compute tool utilization and performance metrics. + + Returns: + Dictionary with tool trigger rate, average tool calls, average errors, + and flattened tool frequency usage as numeric metrics. + """ + tool_triggered_count = sum(1 for r in results if r.tool_triggered) + tool_trigger_rate = (tool_triggered_count / len(results)) if results else 0.0 + avg_tool_calls = float(mean(r.num_tool_calls for r in results)) if results else 0.0 + avg_tool_errors = float(mean(r.tool_error_count for r in results)) if results else 0.0 + + # tool frequency as average per task (mean calls per task per tool) + total_tasks = len(results) if results else 1 + aggregated_counts: dict[str, int] = {} + for r in results: + if r.tool_names: + for name in r.tool_names: + aggregated_counts[name] = aggregated_counts.get(name, 0) + 1 + averaged_freq: dict[str, float] = { + f"gaia_tool_frequency_usage.{name}": (count / total_tasks) + for name, count in aggregated_counts.items() + } + + return { + "gaia_tool_trigger_rate": float(tool_trigger_rate), + "gaia_avg_num_tool_calls": avg_tool_calls, + "gaia_avg_tool_error_count": avg_tool_errors, + **averaged_freq, + } + + +class GaiaEfficiency(Metric[GaiaResult]): + """ + Efficiency and resource usage metrics: + - gaia_avg_latency_ms: average response latency in milliseconds + - gaia_avg_cost_usd: average cost per task in USD + - gaia_avg_total_tokens: average total tokens used per task + """ + + async def compute(self, results: list[GaiaResult]) -> dict: + """Compute efficiency and resource usage metrics. + + Returns: + Dictionary with average latency, cost, and token usage. + """ + avg_latency = float(mean(r.total_latency_ms for r in results)) if results else 0.0 + avg_cost = float(mean(r.cost_usd for r in results)) if results else 0.0 + avg_tokens = float(mean(r.total_tokens for r in results)) if results else 0.0 + + return { + "gaia_avg_latency_ms": avg_latency, + "gaia_avg_cost_usd": avg_cost, + "gaia_avg_total_tokens": avg_tokens, + } diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py index 39f47f1c8..f86254bcc 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py @@ -2,9 +2,10 @@ from ragbits.document_search import DocumentSearch from ragbits.evaluate.pipelines.base import EvaluationData, EvaluationPipeline, EvaluationResult from ragbits.evaluate.pipelines.document_search import DocumentSearchPipeline +from ragbits.evaluate.pipelines.gaia import GaiaPipeline from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline -__all__ = ["DocumentSearchPipeline", "EvaluationData", "EvaluationPipeline", "EvaluationResult", "HumanEvalPipeline"] +__all__ = ["DocumentSearchPipeline", "EvaluationData", "EvaluationPipeline", "EvaluationResult", "GaiaPipeline", "HumanEvalPipeline"] _target_to_evaluation_pipeline: dict[type[WithConstructionConfig], type[EvaluationPipeline]] = { DocumentSearch: DocumentSearchPipeline, diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py new file mode 100644 index 000000000..2ac041aa3 --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py @@ -0,0 +1,251 @@ +import json +import logging +import time +from collections.abc import Callable, Iterable +from dataclasses import dataclass +from pathlib import Path +from typing import cast + +from typing_extensions import Self + +from ragbits.agents import Agent +from ragbits.core.llms.base import LLM, LLMClientOptionsT, LLMResponseWithMetadata, Usage +from ragbits.evaluate.pipelines.base import EvaluationData, EvaluationPipeline, EvaluationResult + + +class GaiaData(EvaluationData): + """ + Represents a single GAIA task. + """ + + task_id: str + question: str + level: int + reference_answer: str + file_name: str | None = None + + +@dataclass +class GaiaResult(EvaluationResult): + """ + Represents the result of evaluating a single GAIA task. + """ + + task_id: str + level: int + question: str + reference_answer: str + predicted_result: str + task_success: bool + tool_triggered: bool + num_tool_calls: int + tool_error_count: int + total_latency_ms: int + total_tokens: int + cost_usd: float + tool_names: list[str] | None = None + + +class GaiaPipeline( + EvaluationPipeline[Agent[LLMClientOptionsT, None, str] | LLM[LLMClientOptionsT], GaiaData, GaiaResult] +): + """GAIA evaluation pipeline for question answering models/agents.""" + + def __init__( + self, + evaluation_target: Agent[LLMClientOptionsT, None, str] | LLM[LLMClientOptionsT], + *, + system_prompt: str | None = None, + per_example_log_file: Path | None = None, + extended_logs: bool = False, + parse_answer_fn: Callable[[str], str] | None = None, + ) -> None: + super().__init__(evaluation_target=evaluation_target) + self.system_prompt = system_prompt + self.per_example_log_file = per_example_log_file + self.extended_logs = extended_logs + self.parse_answer_fn = parse_answer_fn + self._init_log_file() + + @classmethod + def from_config(cls, config: dict) -> Self: + """Create pipeline from config. + Attempts Agent first, falls back to raw LLM construction. + """ + if "evaluation_target" not in config: + try: + config["evaluation_target"] = Agent.from_config(config) + except Exception: + config["evaluation_target"] = LLM.subclass_from_config(config) + return super().from_config(config) + + async def __call__(self, data: Iterable[GaiaData]) -> Iterable[GaiaResult]: + """Generate answer completions per task and evaluate them. + Returns list of `GaiaResult`, one per input task. + """ + results: list[GaiaResult] = [] + + for row in data: + start_time = time.perf_counter() + + prompt_input = row.question + debug_traces: list[dict | None] | None = [] if self.extended_logs else None + + try: + if self.extended_logs: + content, dbg = await self._generate_with_debug(prompt_input) + if debug_traces is not None: + debug_traces.append(dbg) + usage = cast(Usage, (dbg or {}).get("usage") or Usage()) + tool_calls = cast(list, (dbg or {}).get("tool_calls") or []) + else: + content, usage, tool_calls = await self._generate_answer(prompt_input) + + except Exception as generation_exc: + content = "" + usage = Usage() + tool_calls = [] + err_msg = f"GenerationError: {generation_exc.__class__.__name__}: {generation_exc}" + if self.extended_logs and debug_traces is not None: + debug_traces.append({"error": err_msg}) + + end_time = time.perf_counter() + + # Compute metrics + predicted_raw = str(content).strip() + predicted = self._parse_answer(predicted_raw) + reference = (row.reference_answer or "").strip() + task_success = self._normalize(predicted) == self._normalize(reference) if reference else False + + tool_triggered = bool(tool_calls) + num_tool_calls = len(tool_calls) + + # Count tool errors from AgentResult.tool_calls metadata if present + tool_error_count = 0 + for call in tool_calls: + try: + if isinstance(call.result, dict) and "error" in call.result: + tool_error_count += 1 + except Exception as exc: + logging.getLogger(__name__).debug("Error while parsing tool call result: %s", exc) + + total_latency_ms = int((end_time - start_time) * 1000) + total_tokens = usage.total_tokens if usage else 0 + cost_usd = usage.estimated_cost if usage else 0.0 + + # Extract tool names (if any) from tool_calls + tool_names: list[str] | None = None + if tool_calls: + tool_names = [] + for call in tool_calls: + try: + name = getattr(call, "name", None) + if name is None and isinstance(call, dict): + name = call.get("name") + if name: + tool_names.append(str(name)) + except Exception as exc: + logging.getLogger(__name__).debug("Tool name extraction error: %s", exc) + + result = GaiaResult( + task_id=row.task_id, + level=row.level, + question=row.question, + reference_answer=row.reference_answer, + predicted_result=content, + task_success=task_success, + tool_triggered=tool_triggered, + num_tool_calls=num_tool_calls, + tool_error_count=tool_error_count, + total_latency_ms=total_latency_ms, + total_tokens=total_tokens, + cost_usd=float(cost_usd), + tool_names=tool_names, + ) + results.append(result) + ext_log_str = ( + json.dumps(debug_traces, ensure_ascii=False, default=str) + if (self.extended_logs and debug_traces is not None) + else None + ) + self._log_example(row, result, ext_log_str) + + return results + + def _init_log_file(self) -> None: + """Ensure the per-example log file exists if logging is enabled.""" + if self.per_example_log_file is None: + return + self.per_example_log_file.parent.mkdir(parents=True, exist_ok=True) + self.per_example_log_file.touch(exist_ok=True) + + def _log_example(self, row: GaiaData, result: GaiaResult, extended_log: str | None = None) -> None: + """Append a single NDJSON record for debugging if enabled.""" + if self.per_example_log_file is None: + return + # per-task tool frequency map from tool names + tool_frequency_usage: dict[str, int] = {} + if result.tool_names: + for name in result.tool_names: + tool_frequency_usage[name] = tool_frequency_usage.get(name, 0) + 1 + record: dict[str, object] = { + "task_id": row.task_id, + "level": row.level, + "question": row.question, + "predicted": str(result.predicted_result), + "predicted_extracted": self._parse_answer(str(result.predicted_result)), + "reference": row.reference_answer, + "task_success": result.task_success, + "tool_triggered": result.tool_triggered, + "num_tool_calls": result.num_tool_calls, + "tool_error_count": result.tool_error_count, + "total_latency_ms": result.total_latency_ms, + "total_tokens": result.total_tokens, + "cost_usd": result.cost_usd, + "tool_frequency_usage": tool_frequency_usage, + } + record["extended_debug_logging"] = extended_log or "[]" + with open(self.per_example_log_file, "a", encoding="utf-8") as f: + f.write(json.dumps(record, ensure_ascii=False) + "\n") + + async def _generate_answer(self, prompt: str) -> tuple[str, Usage, list]: + """Generate final answer from Agent or raw LLM and capture usage and tool calls.""" + target = self.evaluation_target + if isinstance(target, Agent): + res = await target.run(prompt) + return str(res.content), res.usage, (res.tool_calls or []) + + resp = cast(LLMResponseWithMetadata[str], await target.generate_with_metadata(prompt)) + return str(resp.content), (resp.usage or Usage()), [] + + async def _generate_with_debug(self, prompt: str) -> tuple[str, dict | None]: + """Generate answer and capture tool/history/usage for logging (as raw content).""" + target = self.evaluation_target + if isinstance(target, Agent): + res = await target.run(prompt) + dbg = { + "history": res.history, + "tool_calls": res.tool_calls, + "usage": res.usage, + "metadata": res.metadata, + } + return str(res.content), dbg + resp = await target.generate(prompt) + return str(resp), None + + @staticmethod + def _normalize(text: str) -> str: + """Basic normalization for answer equality checks: lowercase, strip spaces.""" + return "".join(ch.lower() for ch in text.strip() if not ch.isspace()) + + def _parse_answer(self, text: str) -> str: + """Optionally parse final answer from text using provided function. + If no parser provided, returns the original text. + """ + if self.parse_answer_fn is None: + return text + try: + return self.parse_answer_fn(text) + except Exception as exc: + logging.getLogger(__name__).debug("Answer parse error: %s", exc) + return text From b9673fb302ebd950342736641a46326de94f82ec Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Thu, 25 Sep 2025 16:53:32 +0200 Subject: [PATCH 21/43] remove temp comment for lint --- .../src/ragbits/evaluate/pipelines/human_eval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py index d888ff4d6..20b933ca8 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py @@ -209,7 +209,7 @@ async def __call__(self, data: Iterable[HumanEvalData]) -> Iterable[HumanEvalRes try: compile(code, filename="candidate.py", mode="exec") compile_ok.append(True) - except Exception as e: # noqa: BLE001 + except Exception as e: compile_ok.append(False) pass_mask.append(False) durations.append(0.0) From d45415f00c23f53bb2b4f4ac81b8c866562c16ee Mon Sep 17 00:00:00 2001 From: jakubduda-dsai <148433294+jakubduda-dsai@users.noreply.github.com> Date: Fri, 26 Sep 2025 11:12:26 +0200 Subject: [PATCH 22/43] feat: todo list for agent (#823) --- examples/agents/todo_tools_example.py | 120 +++++ packages/ragbits-agents/CHANGELOG.md | 6 + .../src/ragbits/agents/tools/__init__.py | 3 +- .../src/ragbits/agents/tools/todo.py | 484 ++++++++++++++++++ 4 files changed, 612 insertions(+), 1 deletion(-) create mode 100644 examples/agents/todo_tools_example.py create mode 100644 packages/ragbits-agents/src/ragbits/agents/tools/todo.py diff --git a/examples/agents/todo_tools_example.py b/examples/agents/todo_tools_example.py new file mode 100644 index 000000000..691d5f9af --- /dev/null +++ b/examples/agents/todo_tools_example.py @@ -0,0 +1,120 @@ +"""Example demonstrating the new orchestrator-based todo functionality with streaming.""" + +import asyncio +from types import SimpleNamespace + +from ragbits.agents import Agent, ToolCallResult +from ragbits.agents._main import DownstreamAgentResult +from ragbits.agents.tools.todo import TodoOrchestrator, TodoResult +from ragbits.core.llms import LiteLLM, ToolCall +from ragbits.core.llms.base import Usage +from ragbits.core.prompt.base import BasePrompt + +# Type alias for response types +ResponseType = ( + str | ToolCall | ToolCallResult | TodoResult | BasePrompt | Usage | SimpleNamespace | DownstreamAgentResult +) + + +def _handle_response(response: ResponseType) -> None: + """Handle different types of responses from the orchestrator.""" + match response: + case str(): + if response.strip(): + print(response, end="", flush=True) + case ToolCall(): + print(f"\n🔧 Tool Call: {response.name}") + if response.arguments: + print(f" Arguments: {response.arguments}") + case ToolCallResult(): + print(f"🔧 Tool Result: {response.result}") + case TodoResult(): + _handle_streaming_response(response) + + +def _handle_streaming_response(response: TodoResult) -> None: + """Handle TodoResult from the orchestrator.""" + if response.type in ("status", "task_list"): + print(response.message or "") + elif response.type in ("task_summary_start", "final_summary_start"): + print(response.message or "", end="", flush=True) + elif response.type in ("task_completed", "final_summary_end"): + print(response.message or "") + + +async def hiking_guide() -> None: + """Demonstrate the new orchestrator-based todo approach with streaming.""" + # Define the system prompt for hiking guide + hiking_system_prompt = """ + You are an expert hiking guide. Provide detailed, comprehensive information + about hiking routes, gear, transportation, and safety considerations. + """ + + # Create generic orchestrator with hiking domain context + todo_orchestrator = TodoOrchestrator(domain_context="hiking guide") + + # Create a simple agent - orchestrator handles the workflow + agent: Agent = Agent( + llm=LiteLLM("gpt-4o-mini"), + prompt=hiking_system_prompt, + ) + + # Test queries + query = ( + "Plan a 1-day hiking trip for 2 people in Tatra Mountains, Poland. " + "Focus on scenic routes under 15km, avoiding crowds." + ) + # query = "How long is hike to Giewont from Kuźnice?" + + print("=== Generic Todo Orchestrator - Hiking Example ===\n") + + # Run the complete workflow with orchestrator streaming + async for response in todo_orchestrator.run_todo_workflow_streaming(agent, query): + _handle_response(response) + + print("\n🎯 Workflow completed successfully!") + + +async def software_architecture_example() -> None: + """Example showing the orchestrator used for software architecture.""" + software_system_prompt = """ + You are an expert software architect. Provide detailed technical analysis, + system design recommendations, and implementation guidance. + + Always be specific with: + - Technology stack recommendations with versions + - Architecture patterns and design principles + - Performance and scalability considerations + - Security best practices + - Implementation roadmap with timelines + """ + + todo_orchestrator = TodoOrchestrator(domain_context="software architect") + + agent: Agent = Agent( + llm=LiteLLM("gpt-4o-mini"), + prompt=software_system_prompt, + ) + + query = ( + "Design a scalable e-commerce platform for 100k+ users with " + "real-time inventory management and payment processing." + ) + + print("\n" + "=" * 60) + print("=== Generic Todo Orchestrator - Software Architecture Example ===\n") + + async for response in todo_orchestrator.run_todo_workflow_streaming(agent, query): + _handle_response(response) + + print("\n🎯 Software architecture workflow completed!") + + +async def demonstrate_both() -> None: + """Demonstrate both hiking and software architecture examples.""" + await hiking_guide() + # await software_architecture_example() + + +if __name__ == "__main__": + asyncio.run(demonstrate_both()) diff --git a/packages/ragbits-agents/CHANGELOG.md b/packages/ragbits-agents/CHANGELOG.md index 3d0e2b2a3..2d89419cd 100644 --- a/packages/ragbits-agents/CHANGELOG.md +++ b/packages/ragbits-agents/CHANGELOG.md @@ -1,6 +1,12 @@ # CHANGELOG ## Unreleased +- TodoOrchestrator & Todo list for agent (#823) + +- Support wrapping downstream agents as tools (#818) +- Add syntax sugar allowing easier Agents definition (#820) +- Add post-processors (#821) +- Support streaming from downstream agents (#812) - Support wrapping downstream agents as tools (#818) - Add syntax sugar allowing easier Agents definition (#820) diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py index 9bb922491..182f1d405 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py @@ -1,3 +1,4 @@ from ragbits.agents.tools.openai import get_code_interpreter_tool, get_image_generation_tool, get_web_search_tool +from ragbits.agents.tools.todo import TodoOrchestrator -__all__ = ["get_code_interpreter_tool", "get_image_generation_tool", "get_web_search_tool"] +__all__ = ["TodoOrchestrator", "get_code_interpreter_tool", "get_image_generation_tool", "get_web_search_tool"] diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py new file mode 100644 index 000000000..aeb9552fa --- /dev/null +++ b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py @@ -0,0 +1,484 @@ +"""Todo list management tool for agents.""" + +import uuid +from collections.abc import AsyncGenerator +from dataclasses import dataclass, field +from enum import Enum +from types import SimpleNamespace + +from ragbits.agents import Agent +from ragbits.agents._main import DownstreamAgentResult +from ragbits.agents.tool import ToolCallResult +from ragbits.core.llms import ToolCall +from ragbits.core.llms.base import Usage +from ragbits.core.prompt.base import BasePrompt + +# Constants +MAX_SUMMARY_LENGTH = 300 + + +class TaskStatus(str, Enum): + """Task status options.""" + + PENDING = "pending" + IN_PROGRESS = "in_progress" + COMPLETED = "completed" + FAILED = "failed" + CANCELLED = "cancelled" + RETRYING = "retrying" + + +@dataclass +class Task: + """Simple task representation.""" + + id: str + description: str + status: TaskStatus = TaskStatus.PENDING + order: int = 0 + summary: str | None = None + full_response: str | None = None + dependencies: list[str] = field(default_factory=list) + + +@dataclass +class TodoResult: + """Result type for todo workflow.""" + + type: str + message: str | None = None + current_task: Task | None = None + next_task: Task | None = None + tasks: list[Task] | None = None + tasks_count: int | None = None + progress: str | None = None + + +# Type alias for the streaming response union +StreamingResponseType = ( + str | TodoResult | ToolCall | ToolCallResult | BasePrompt | Usage | SimpleNamespace | DownstreamAgentResult +) + + +@dataclass +class TodoList: + """Simple todo list for one agent run.""" + + tasks: list[Task] = field(default_factory=list) + current_index: int = 0 + + def get_current_task(self) -> Task | None: + """Get current task to work on.""" + if self.current_index < len(self.tasks): + return self.tasks[self.current_index] + return None + + def advance_to_next(self) -> None: + """Move to next task.""" + self.current_index += 1 + + def create_tasks(self, task_descriptions: list[str]) -> TodoResult: + """Create tasks from descriptions.""" + if not task_descriptions: + raise ValueError("Tasks required for create action") + + # Clear existing tasks + self.tasks.clear() + self.current_index = 0 + + for i, desc in enumerate(task_descriptions): + task = Task(id=str(uuid.uuid4()), description=desc.strip(), order=i) + self.tasks.append(task) + + return TodoResult( + type="create", + message=f"Created {len(task_descriptions)} tasks", + tasks=self.tasks.copy(), + tasks_count=len(self.tasks), + ) + + def get_current(self) -> TodoResult: + """Get current task information.""" + current = self.get_current_task() + if not current: + return TodoResult(type="get_current", message="All tasks completed!", current_task=None) + + return TodoResult( + type="get_current", + message=f"Current task: {current.description}", + current_task=current, + progress=f"{self.current_index + 1}/{len(self.tasks)}", + ) + + def start_current_task(self) -> TodoResult: + """Start the current task.""" + current = self.get_current_task() + if not current: + raise ValueError("No current task to start") + + current.status = TaskStatus.IN_PROGRESS + return TodoResult(type="start_task", message=f"Started task: {current.description}", current_task=current) + + def complete_current_task(self, summary: str) -> TodoResult: + """Complete the current task with summary.""" + if not summary: + raise ValueError("Summary required for complete_task") + + current = self.get_current_task() + if not current: + raise ValueError("No current task to complete") + + if current.status != TaskStatus.IN_PROGRESS: + raise ValueError("Task must be started before completing") + + current.status = TaskStatus.COMPLETED + current.summary = summary.strip() + self.advance_to_next() + + next_task = self.get_current_task() + completed_count = sum(1 for t in self.tasks if t.status == TaskStatus.COMPLETED) + + return TodoResult( + type="complete_task", + message=f"Completed: {current.description}", + current_task=current, + next_task=next_task, + progress=f"{completed_count}/{len(self.tasks)}", + ) + + def get_completed_context(self) -> str: + """Get context from all completed tasks.""" + completed_tasks = [t for t in self.tasks if t.status == TaskStatus.COMPLETED] + if not completed_tasks: + return "No previous tasks completed yet." + + context_parts = [] + for i, task in enumerate(completed_tasks, 1): + context_parts.append(f"{i}. {task.description}: {task.summary or 'Completed'}") + + return "Previous completed tasks:\n" + "\n".join(context_parts) + + +@dataclass +class TodoOrchestrator: + """High-level orchestrator for managing todo workflow with context passing.""" + + todo_list: TodoList = field(default_factory=TodoList) + domain_context: str = "" + + def __init__(self, domain_context: str = ""): + """ + Initialize TodoOrchestrator with domain-specific prompts. + + Args: + domain_context: Additional context about the domain (e.g., "hiking guide", "software architect", etc.) + """ + self.todo_list = TodoList() + self.domain_context = domain_context + + async def run_todo_workflow_streaming( + self, agent: Agent, initial_query: str + ) -> AsyncGenerator[StreamingResponseType, None]: + """ + Run complete todo workflow with streaming responses. + + Yields: + Various response types: str, ToolCall, ToolResult, dict (status updates) + """ + yield TodoResult(type="status", message="🚀 Starting todo workflow...") + + # Step 1: Analyze complexity and create tasks if needed + yield TodoResult(type="status", message="🔍 Analyzing query complexity...") + + tasks = await self._create_tasks_simple(agent, initial_query) + + if not tasks: + # Query is simple - answer directly without task breakdown + yield TodoResult(type="status", message="💡 Simple query detected - providing direct answer...") + + # Create a focused prompt for direct answering + direct_prompt = f""" + Answer this query directly and comprehensively: + + "{initial_query}" + + {f"Context: You are working as a {self.domain_context}." if self.domain_context else ""} + + Provide a clear, complete answer without unnecessary complexity. + """ + + # Stream the direct answer + async for response in agent.run_streaming(direct_prompt): + if isinstance(response, str): + yield response + else: + yield response # Pass through tool calls, etc. + + yield TodoResult(type="status", message="\n🎯 Direct answer completed!") + return + + # Complex query - proceed with task breakdown + yield TodoResult(type="status", message=f"📋 Complex query - created {len(tasks)} tasks:") + for i, task in enumerate(tasks, 1): + yield TodoResult(type="task_list", message=f" {i}. {task}") + + # Step 2: Execute each task with context from previous tasks + task_summaries = [] + + while True: + current_task_info = self.todo_list.get_current() + current_task = current_task_info.current_task + if current_task is None: + break + + yield TodoResult( + type="task_summary_start", + message=f"\n🔧 Task {current_task_info.progress}: {current_task.description}\n", + ) + + # Get context from previous completed tasks + context = self.todo_list.get_completed_context() + + # Execute single task with focused context and stream summary + async for task_response in self._execute_single_task_focused(agent, current_task, context, initial_query): + yield task_response + + # Get the completed task summary + completed_tasks = [t for t in self.todo_list.tasks if t.status == TaskStatus.COMPLETED] + if completed_tasks: + latest_summary = completed_tasks[-1].summary + task_summaries.append(latest_summary) + yield TodoResult(type="task_completed", message="\n✅ Task completed\n") + + # Step 3: Generate comprehensive final summary with streaming + yield TodoResult(type="status", message="📝 Generating comprehensive final summary...") + yield TodoResult( + type="final_summary_start", + message=f"\n📊 COMPREHENSIVE {self.domain_context.upper()} SUMMARY:\n" + "=" * 60 + "\n", + ) + + async for summary_response in self._generate_comprehensive_summary_streaming( + agent, initial_query, task_summaries + ): + yield summary_response + + yield TodoResult(type="final_summary_end", message="\n" + "=" * 60) + yield TodoResult(type="status", message="🎉 All tasks completed!") + + @staticmethod + async def _analyze_query_complexity(agent: Agent, query: str) -> bool: + """ + Analyze if the query requires task breakdown or can be answered directly. + Returns True if complex (needs tasks), False if simple (direct answer). + """ + complexity_prompt = f""" + Analyze this query and determine if it requires a multi-step breakdown or can be answered directly. + + Query: "{query}" + + Consider these factors: + - Simple factual questions (What is...? Who is...? When did...?) = SIMPLE + - Yes/No questions = SIMPLE + - Single concept explanations = SIMPLE + - Questions requiring research, planning, or multiple steps = COMPLEX + - Questions asking for comprehensive analysis, comparisons, or detailed guides = COMPLEX + - Questions with multiple parts or requiring extensive information = COMPLEX + + Respond with ONLY one word: "SIMPLE" or "COMPLEX" + """ + + agent_result = await agent.run(complexity_prompt) + response = agent_result.content.strip().upper() + + # Default to COMPLEX if we can't determine (safer approach) + return response != "SIMPLE" + + async def _create_tasks_simple(self, agent: Agent, initial_query: str) -> list[str]: + """Create tasks based on initial query - simple, non-streaming.""" + # First, analyze if the query actually needs task breakdown + is_complex = await TodoOrchestrator._analyze_query_complexity(agent, initial_query) + + if not is_complex: + # Query is simple enough to answer directly - return empty task list + return [] + + task_creation_prompt = f""" + Based on this query: "{initial_query}" + + Create 3-5 specific, actionable tasks that will comprehensively address this query. + Each task should be clear and focused on one specific aspect. + + {f"Context: You are working as a {self.domain_context}." if self.domain_context else ""} + + CRITICAL: Respond with ONLY a Python list of task descriptions, nothing else: + ["Task 1: Specific description", "Task 2: Specific description", "Task 3: Specific description"] + """ + + agent_result = await agent.run(task_creation_prompt) + response = agent_result.content + + # Parse tasks from the response + tasks = self._parse_tasks_from_response(response) + if tasks: + self.todo_list.create_tasks(tasks) + return tasks + + return [] + + async def _execute_single_task_focused( + self, agent: Agent, current_task: Task, context: str, original_query: str + ) -> AsyncGenerator[StreamingResponseType, None]: + """Execute a single task with focused context - stream only essential info.""" + task_prompt = f""" + You are working on ONE SPECIFIC TASK as part of a larger workflow. + + ORIGINAL QUERY: {original_query} + + YOUR CURRENT TASK: {current_task.description} + + CONTEXT FROM PREVIOUS TASKS: + {context} + + CRITICAL INSTRUCTIONS: + 1. Focus EXCLUSIVELY on your current task - do not overlap with other tasks + 2. Use previous context to avoid repetition but DO NOT duplicate their work + 3. Be comprehensive but stay within your task scope + 4. End with: TASK SUMMARY: [2-3 sentence summary] + + Complete ONLY your assigned task now. + """ + + # Mark task as started + self.todo_list.start_current_task() + + full_response = "" + last_summary_length = 0 + + # Stream the task execution but only show summary parts + async for response in agent.run_streaming(task_prompt): + if isinstance(response, str): + full_response += response + # Only stream text that comes after "TASK SUMMARY:" marker + if "TASK SUMMARY:" in full_response: + summary_start = full_response.find("TASK SUMMARY:") + len("TASK SUMMARY:") + summary_text = full_response[summary_start:].strip() + if summary_text and len(summary_text) > last_summary_length: + # Stream only new characters + new_chars = summary_text[last_summary_length:] + if new_chars: + yield new_chars + last_summary_length = len(summary_text) + else: + yield response # Pass through tool calls, etc. + + # Extract final summary and complete task + summary = self._extract_summary_from_response(full_response) + + # Store the full response for final summary generation + current_task_obj = self.todo_list.get_current_task() + if current_task_obj: + current_task_obj.full_response = full_response + + self.todo_list.complete_current_task(summary) + + async def _generate_comprehensive_summary_streaming( + self, agent: Agent, original_query: str, task_summaries: list[str | None] + ) -> AsyncGenerator[StreamingResponseType, None]: + """Generate a comprehensive final summary with streaming.""" + # Get full responses from completed tasks + full_responses = [] + for task in self.todo_list.tasks: + if task.status == TaskStatus.COMPLETED and hasattr(task, "full_response"): + full_responses.append(f"**{task.description}**:\n{task.full_response}") + + # Create domain-specific summary instructions + domain_instructions = "" + if self.domain_context: + domain_instructions = ( + f"Format as a comprehensive {self.domain_context} response that someone " + "could use to address their needs." + ) + else: + domain_instructions = "Format as a comprehensive, well-structured response." + + summary_prompt = f""" + You need to create a comprehensive final summary for this query: "{original_query}" + + Here are the detailed results from all completed tasks: + + {chr(10).join(full_responses)} + + Create a comprehensive, well-structured final summary that: + 1. Directly answers the original query + 2. Combines all the detailed information from the tasks + 3. Organizes information logically and coherently + 4. Provides actionable recommendations + 5. Is complete and self-contained + + {domain_instructions} + """ + + # Stream the comprehensive summary generation + async for response in agent.run_streaming(summary_prompt): + if isinstance(response, str): + yield response # Stream each character as it comes + else: + yield response # Pass through tool calls, etc. + + @staticmethod + def _parse_tasks_from_response(response: str) -> list[str]: + """Parse task list from agent response.""" + try: + import ast + + # The prompt asks for ONLY a Python list, so try direct parsing first + response = response.strip() + + # Try to parse the entire response as a Python literal + try: + tasks = ast.literal_eval(response) + if isinstance(tasks, list) and all(isinstance(t, str) for t in tasks): + return tasks + except (ValueError, SyntaxError): + pass + + # Fallback: Look for the first list in the response + import re + + list_pattern = r"\[.*?\]" + match = re.search(list_pattern, response, re.DOTALL) + if match: + try: + tasks = ast.literal_eval(match.group()) + if isinstance(tasks, list) and all(isinstance(t, str) for t in tasks): + return tasks + except (ValueError, SyntaxError): + pass + + print(f"Could not parse tasks from response: {response[:200]}...") + + except Exception as e: + print(f"Failed to parse tasks: {e}") + + return [] + + @staticmethod + def _extract_summary_from_response(response: str) -> str: + """Extract task summary from agent response.""" + summary_marker = "TASK SUMMARY:" + if summary_marker in response: + parts = response.split(summary_marker) + summary = parts[-1].strip() + # Clean up the summary (remove extra formatting) + summary = summary.replace("\n", " ").strip() + return summary + else: + # Fallback: use last paragraph as summary + paragraphs = [p.strip() for p in response.split("\n\n") if p.strip()] + if paragraphs: + summary = paragraphs[-1] + # Limit length and clean up + if len(summary) > MAX_SUMMARY_LENGTH: + summary = summary[:MAX_SUMMARY_LENGTH] + "..." + return summary + return "Task completed" From 09f94e4ce3ff04130d46923e479d626bec307bd0 Mon Sep 17 00:00:00 2001 From: Maciej Kurzawa <68014263+mackurzawa@users.noreply.github.com> Date: Fri, 26 Sep 2025 18:34:08 +0200 Subject: [PATCH 23/43] feat: introduce supervisor post processor (#830) --- docs/api_reference/agents/index.md | 2 + docs/how-to/agents/use_post_processors.md | 111 +++++++- packages/ragbits-agents/CHANGELOG.md | 1 + .../src/ragbits/agents/_main.py | 2 +- .../agents/post_processors/__init__.py | 3 +- .../ragbits/agents/post_processors/base.py | 29 +- .../agents/post_processors/exceptions.py | 17 ++ .../agents/post_processors/supervisor.py | 236 ++++++++++++++++ .../test_base_post_processors.py} | 10 +- .../unit/post_processors/test_supervisor.py | 263 ++++++++++++++++++ 10 files changed, 652 insertions(+), 22 deletions(-) create mode 100644 packages/ragbits-agents/src/ragbits/agents/post_processors/exceptions.py create mode 100644 packages/ragbits-agents/src/ragbits/agents/post_processors/supervisor.py rename packages/ragbits-agents/tests/unit/{test_post_processors.py => post_processors/test_base_post_processors.py} (95%) create mode 100644 packages/ragbits-agents/tests/unit/post_processors/test_supervisor.py diff --git a/docs/api_reference/agents/index.md b/docs/api_reference/agents/index.md index a1d59bd0f..70fbb771a 100644 --- a/docs/api_reference/agents/index.md +++ b/docs/api_reference/agents/index.md @@ -12,4 +12,6 @@ ::: ragbits.agents.post_processors.base +::: ragbits.agents.post_processors.supervisor + ::: ragbits.agents.AgentRunContext diff --git a/docs/how-to/agents/use_post_processors.md b/docs/how-to/agents/use_post_processors.md index aef223be7..33893e63e 100644 --- a/docs/how-to/agents/use_post_processors.md +++ b/docs/how-to/agents/use_post_processors.md @@ -1,12 +1,16 @@ # How-To: Use Post-Processors with Ragbits Agents -Ragbits Agents can be enhanced with post-processors to intercept, log, filter, and modify their outputs. This guide explains how to implement and use post-processors to customize agent responses. +Ragbits Agents can be enhanced with post-processors to intercept, validate, log, filter, and modify their outputs. In this guide you will learn how to: + +- Create custom post-processors (streaming and non-streaming) +- Attach post-processors to agents in run and streaming modes +- Use and configure the built-in Supervisor post-processor ## Post-Processors Overview Ragbits provides two types of post-processors: -- **PostProcessor**: Processes the final output after generation, ideal for batch processing. +- **PostProcessor**: Processes the final output after generation, ideal for end-of-run processing. - **StreamingPostProcessor**: Processes outputs as they are generated, suitable for real-time applications. ### Implementing a custom Post-Processor @@ -22,7 +26,7 @@ class TruncateProcessor(PostProcessor): def __init__(self, max_length: int = 50) -> None: self.max_length = max_length - async def process(self, result, agent): + async def process(self, result, agent, options=None, context=None): content = result.content if len(content) > self.max_length: content = content[:self.max_length] + "... [TRUNCATED]" @@ -54,7 +58,11 @@ async def main() -> None: UpperCaseStreamingProcessor(), TruncateProcessor(max_length=50), ] - stream_result = agent.run_streaming("Tell me about the history of AI.", post_processors=post_processors, allow_non_streaming=True) + stream_result = agent.run_streaming( + "Tell me about the history of AI.", + post_processors=post_processors, + allow_non_streaming=True + ) async for chunk in stream_result: if isinstance(chunk, str): print(chunk, end="") @@ -62,3 +70,98 @@ async def main() -> None: ``` Post-processors offer a flexible way to tailor agent outputs, whether filtering content in real-time or transforming final outputs. + +## Built-in Post-Processors + +### Supervisor + +The [`SupervisorPostProcessor`][ragbits.agents.post_processors.supervisor.SupervisorPostProcessor] validates the agent’s final response against the executed tool calls and, if needed, triggers an automatic rerun with a correction prompt. It helps catch inconsistencies (e.g., when the response contradicts tool output) and guide the agent to refine its answer. The Supervisor is a non-streaming post-processor: it runs after generation has completed, validating the final output before optionally issuing a correction rerun. + +Key capabilities: + +- Validates the last assistant response using an LLM-powered validation prompt +- Optionally reruns the agent with a formatted correction prompt derived from validation feedback +- Supports preserving or pruning intermediate history +- Attaches validation metadata to the final `AgentResult` + +#### Quick start + +```python +from ragbits.agents import Agent +from ragbits.agents.post_processors import SupervisorPostProcessor +from ragbits.agents.post_processors.supervisor import HistoryStrategy +from ragbits.core.llms.litellm import LiteLLM + +llm = LiteLLM("gpt-4o-mini", use_structured_output=True) +supervisor = SupervisorPostProcessor( + llm=llm, + max_retries=2, + fail_on_exceed=False, + history_strategy=HistoryStrategy.PRESERVE, # Default HistoryStrategy is REMOVE +) + +agent = Agent( + llm=llm, + prompt="You are a helpful assistant.", +) + +result = await agent.run( + "What is the weather in Tokyo?", + post_processors=[supervisor], +) +``` + +#### Configuration + +- **llm**: LLM used for validation and formatting structured outputs +- **validation_prompt**: Optional custom prompt class describing the validation output schema +- **correction_prompt**: Optional format string used to create a correction message from validation output +- **max_retries**: How many times to attempt correction-driven reruns +- **fail_on_exceed**: If `True`, raises when retries are exhausted; otherwise returns last result with metadata +- **history_strategy**: + - `PRESERVE`: keep all messages, including the correction user message and rerun assistant message + - `REMOVE`: prune the invalid assistant message and the correction user message, keeping the final assistant response succinctly + +#### Custom structured validation and correction + +You can define a custom validation output model and prompt to shape the supervisor feedback and correction message: + +```python +from pydantic import BaseModel +from ragbits.core.prompt.prompt import Prompt +from ragbits.agents.post_processors.supervisor import ValidationInput + +class MyValidationOutput(BaseModel): + is_valid: bool + errors: list[str] + fixes: list[str] + confidence: float + +class MyValidationPrompt(Prompt[ValidationInput, MyValidationOutput]): + system_prompt = "You are an expert validator. Provide clear, actionable feedback." + user_prompt = ( + "Chat History:\n" + "{% for message in chat_history %}" + "\n{{ message.role | title }}: {{ message.content }} (if None it means it's a tool call)" + "{% endfor %}" + "\n\nList all errors, possible fixes, and provide a confidence score (0.0-1.0) for your assessment.\n" + ) + +correction_prompt = ( + "Previous answer had issues:\n" + "Errors: {errors}\n" + "Fixes: {fixes}\n" + "Confidence: {confidence}\n" + "Please answer again using the fixes." +) + +supervisor = SupervisorPostProcessor( + llm=llm, + validation_prompt=MyValidationPrompt, + correction_prompt=correction_prompt, + max_retries=1, + history_strategy=HistoryStrategy.PRESERVE, +) +``` + +The Supervisor appends validation records to `result.metadata` under the `post_processors.supervisor` key as a list of dicts; each entry corresponds to a validation step. diff --git a/packages/ragbits-agents/CHANGELOG.md b/packages/ragbits-agents/CHANGELOG.md index 2d89419cd..a1fb79a83 100644 --- a/packages/ragbits-agents/CHANGELOG.md +++ b/packages/ragbits-agents/CHANGELOG.md @@ -12,6 +12,7 @@ - Add syntax sugar allowing easier Agents definition (#820) - Add post-processors (#821) - Support streaming from downstream agents (#812) +- Add supervisor post-processor (#830) ## 1.3.0 (2025-09-11) ### Changed diff --git a/packages/ragbits-agents/src/ragbits/agents/_main.py b/packages/ragbits-agents/src/ragbits/agents/_main.py index e7c347100..ed994310e 100644 --- a/packages/ragbits-agents/src/ragbits/agents/_main.py +++ b/packages/ragbits-agents/src/ragbits/agents/_main.py @@ -415,7 +415,7 @@ async def run( if post_processors: for processor in post_processors: - result = await processor.process(result, self) + result = await processor.process(result, self, options, context) return result diff --git a/packages/ragbits-agents/src/ragbits/agents/post_processors/__init__.py b/packages/ragbits-agents/src/ragbits/agents/post_processors/__init__.py index a00a4d209..f291a1d99 100644 --- a/packages/ragbits-agents/src/ragbits/agents/post_processors/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/post_processors/__init__.py @@ -3,5 +3,6 @@ """ from .base import BasePostProcessor, PostProcessor, StreamingPostProcessor +from .supervisor import SupervisorPostProcessor -__all__ = ["BasePostProcessor", "PostProcessor", "StreamingPostProcessor"] +__all__ = ["BasePostProcessor", "PostProcessor", "StreamingPostProcessor", "SupervisorPostProcessor"] diff --git a/packages/ragbits-agents/src/ragbits/agents/post_processors/base.py b/packages/ragbits-agents/src/ragbits/agents/post_processors/base.py index 4f38e13ca..c1e46b1e7 100644 --- a/packages/ragbits-agents/src/ragbits/agents/post_processors/base.py +++ b/packages/ragbits-agents/src/ragbits/agents/post_processors/base.py @@ -1,21 +1,18 @@ from abc import ABC, abstractmethod from collections.abc import AsyncGenerator from types import SimpleNamespace -from typing import TYPE_CHECKING, Generic, TypeVar, cast +from typing import TYPE_CHECKING, Generic, Optional, cast from ragbits.agents.tool import ToolCallResult -from ragbits.core.llms.base import LLMOptions, ToolCall, Usage +from ragbits.core.llms.base import LLMClientOptionsT, ToolCall, Usage from ragbits.core.prompt.base import BasePrompt from ragbits.core.prompt.prompt import PromptInputT, PromptOutputT if TYPE_CHECKING: - from ragbits.agents._main import Agent, AgentResult + from ragbits.agents._main import Agent, AgentOptions, AgentResult, AgentRunContext -LLMOptionsT = TypeVar("LLMOptionsT", bound=LLMOptions) - - -class BasePostProcessor(Generic[LLMOptionsT, PromptInputT, PromptOutputT]): +class BasePostProcessor(Generic[LLMClientOptionsT, PromptInputT, PromptOutputT]): """Base class for post-processors.""" @property @@ -32,7 +29,7 @@ def supports_streaming(self) -> bool: """ -class PostProcessor(ABC, BasePostProcessor[LLMOptionsT, PromptInputT, PromptOutputT]): +class PostProcessor(ABC, BasePostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]): """Base class for non-streaming post-processors.""" @property @@ -44,7 +41,9 @@ def supports_streaming(self) -> bool: async def process( self, result: "AgentResult[PromptOutputT]", - agent: "Agent[LLMOptionsT, PromptInputT, PromptOutputT]", + agent: "Agent[LLMClientOptionsT, PromptInputT, PromptOutputT]", + options: Optional["AgentOptions[LLMClientOptionsT]"] = None, + context: Optional["AgentRunContext"] = None, ) -> "AgentResult[PromptOutputT]": """ Process the complete agent result. @@ -53,13 +52,15 @@ async def process( result: The complete AgentResult from the agent or previous post-processor. agent: The Agent instance that generated the result. Can be used to re-run the agent with modified input if needed. + options: The options for the agent run. + context: The context for the agent run. Returns: Modified AgentResult to pass to the next processor or return as final result. """ -class StreamingPostProcessor(ABC, BasePostProcessor[LLMOptionsT, PromptInputT, PromptOutputT]): +class StreamingPostProcessor(ABC, BasePostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]): """Base class for streaming post-processors.""" @property @@ -71,7 +72,7 @@ def supports_streaming(self) -> bool: async def process_streaming( self, chunk: str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage, - agent: "Agent[LLMOptionsT, PromptInputT, PromptOutputT]", + agent: "Agent[LLMClientOptionsT, PromptInputT, PromptOutputT]", ) -> str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage: """ Process chunks during streaming. @@ -89,10 +90,10 @@ async def process_streaming( async def stream_with_post_processing( generator: AsyncGenerator[str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage], post_processors: ( - list[StreamingPostProcessor[LLMOptionsT, PromptInputT, PromptOutputT]] - | list[BasePostProcessor[LLMOptionsT, PromptInputT, PromptOutputT]] + list[StreamingPostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] + | list[BasePostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT]] ), - agent: "Agent[LLMOptionsT, PromptInputT, PromptOutputT]", + agent: "Agent[LLMClientOptionsT, PromptInputT, PromptOutputT]", ) -> AsyncGenerator[str | ToolCall | ToolCallResult | SimpleNamespace | BasePrompt | Usage]: """ Stream with support for both streaming and non-streaming post-processors. diff --git a/packages/ragbits-agents/src/ragbits/agents/post_processors/exceptions.py b/packages/ragbits-agents/src/ragbits/agents/post_processors/exceptions.py new file mode 100644 index 000000000..f7588038d --- /dev/null +++ b/packages/ragbits-agents/src/ragbits/agents/post_processors/exceptions.py @@ -0,0 +1,17 @@ +class SupervisorMaxRetriesExceededError(Exception): + """Raised when SupervisorPostProcessor exceeds the maximum number of retries.""" + + def __init__(self, max_retries: int, last_validations: list | None = None) -> None: + self.max_retries = max_retries + self.last_validations = last_validations or [] + super().__init__(f"Supervisor: maximum retries ({max_retries}) exceeded.") + + +class SupervisorCorrectionPromptFormatError(Exception): + """Raised when SupervisorPostProcessor cannot format the correction prompt.""" + + def __init__(self, missing_keys: list[str] | None = None, original_error: Exception | None = None) -> None: + self.missing_keys = missing_keys or [] + self.original_error = original_error + details = f" Missing keys: {', '.join(self.missing_keys)}." if self.missing_keys else "" + super().__init__(f"Supervisor: failed to format correction prompt.{details}") diff --git a/packages/ragbits-agents/src/ragbits/agents/post_processors/supervisor.py b/packages/ragbits-agents/src/ragbits/agents/post_processors/supervisor.py new file mode 100644 index 000000000..4eece4b24 --- /dev/null +++ b/packages/ragbits-agents/src/ragbits/agents/post_processors/supervisor.py @@ -0,0 +1,236 @@ +from enum import Enum +from typing import TYPE_CHECKING, Any, Generic, Optional, Protocol, TypeVar, cast + +from pydantic import BaseModel + +from ragbits.agents.post_processors.base import PostProcessor +from ragbits.agents.post_processors.exceptions import ( + SupervisorCorrectionPromptFormatError, + SupervisorMaxRetriesExceededError, +) +from ragbits.core.llms.base import LLM, LLMClientOptionsT +from ragbits.core.prompt.prompt import ChatFormat, Prompt, PromptInputT, PromptOutputT + +if TYPE_CHECKING: + from ragbits.agents._main import Agent, AgentOptions, AgentResult, AgentRunContext + + +class HistoryStrategy(str, Enum): + """Strategy for handling the chat history.""" + + PRESERVE = "preserve" + REMOVE = "remove" + + +class SupportsValidation(Protocol): + """Protocol for post-processors that support validation.""" + + is_valid: bool + + def model_dump(self, *args: Any, **kwargs: Any) -> dict[str, Any]: + """Return a dict of model fields (compatible with BaseModel.model_dump).""" + ... + + +ValidationOutputT = TypeVar("ValidationOutputT", bound=SupportsValidation) + + +class ValidationOutput(BaseModel): + """Result of the validation process for agent responses.""" + + is_valid: bool + """Whether the response is valid based on tool calls.""" + + reasoning: str + """Explanation of the validation decision.""" + + suggestion: str + """Suggestion for correction.""" + + +class ValidationInput(BaseModel): + """Input data for the validation prompt.""" + + chat_history: ChatFormat + """The complete chat history for context.""" + + +class DefaultSupervisorPrompt(Prompt[ValidationInput, ValidationOutput]): + """ + Default prompt for validating agent responses against tool call results. + """ + + system_prompt = """ + You are an expert validator that analyzes whether an AI agent's response is logically consistent + with the tool calls it executed and their results. Your job is to determine if the response + could reasonably be generated based on the available information from the tools. + + Analyze the following: + 1. Whether the response content is supported by the tool call results + 2. If any claims in the response contradict the tool data + 3. If the response makes assumptions not supported by the tool results + 4. Whether the response appropriately handles cases where tools failed or returned no data + 5. Whether the response actually answers the user's question + 6. Whether the response is factually correct based on available evidence + + IMPORTANT VALIDATION RULES: + - Only flag as INVALID if the answer is clearly incorrect or contradicts the tool results + - If the answer cannot be determined to be true because it's basic/common knowledge not covered by tools, + consider it as VALID + - If the response doesn't directly answer the user's question, flag as INVALID + - Be strict but fair - flag clear inconsistencies while allowing for reasonable inference + and interpretation of the tool results + """ + + user_prompt = ( + "Chat History:\n" + "{% for message in chat_history %}\n" + "{{ message.role | title }}: " + "{% if message.content is none %}" + "{% for tool_call in message.tool_calls %}" + "called tool: " + "{{ tool_call.function.name }}({{ tool_call.function.arguments | tojson }})" + "{% if not loop.last %} {% endif %}" + "{% endfor %}" + "{% else %}{{ message.content }}{% endif %}" + "{% endfor %}\n" + "\nPlease validate whether the last assistant's response is logically consistent with the tool call results " + "and the conversation context." + ) + + +DEFAULT_CORRECTION_PROMPT = ( + "Your answer is incorrect. Below you will find the reasoning and suggestion for correction.\n" + "Reasoning: {reasoning}\n" + "Suggestion: {suggestion}\n" + "Please answer again, considering the reasoning and suggestion.\n" +) + + +class SupervisorPostProcessor( + PostProcessor[LLMClientOptionsT, PromptInputT, PromptOutputT], + Generic[LLMClientOptionsT, PromptInputT, ValidationOutputT, PromptOutputT], +): + """ + Post-processor that validates agent responses against executed tool calls. + + This supervisor uses an LLM to analyze whether the agent's final response + is logically consistent with the results of the tool calls it executed. + """ + + def __init__( + self, + llm: LLM[LLMClientOptionsT], + validation_prompt: type[Prompt[ValidationInput, ValidationOutputT]] | None = None, + correction_prompt: str | None = None, + max_retries: int = 3, + fail_on_exceed: bool = True, + history_strategy: HistoryStrategy = HistoryStrategy.REMOVE, + ) -> None: + self.llm = llm + self.validation_prompt: type[Prompt[ValidationInput, ValidationOutputT]] = cast( + type[Prompt[ValidationInput, ValidationOutputT]], + validation_prompt or DefaultSupervisorPrompt, + ) + self.correction_prompt = correction_prompt or DEFAULT_CORRECTION_PROMPT + self.max_retries = max_retries + self.fail_on_exceed = fail_on_exceed + self.history_strategy = history_strategy + + async def process( + self, + result: "AgentResult[PromptOutputT]", + agent: "Agent[LLMClientOptionsT, PromptInputT, PromptOutputT]", + options: Optional["AgentOptions[LLMClientOptionsT]"] = None, + context: Optional["AgentRunContext"] = None, + ) -> "AgentResult[PromptOutputT]": + """ + Validate the agent's response and, if necessary, rerun with corrections. + """ + retries = 0 + current_result = result + validations: list[ValidationOutputT] = [] + accumulated_tool_calls: list = [] + agent.history = result.history + + while retries <= self.max_retries: + validation = await self._validate(current_result) + validations.append(validation) + accumulated_tool_calls.extend(current_result.tool_calls or []) + current_result.tool_calls = accumulated_tool_calls + + if validation.is_valid: + if not agent.keep_history: + agent.history = [] + return self._attach_metadata(current_result, validations) + + if retries == self.max_retries: + if self.fail_on_exceed: + raise SupervisorMaxRetriesExceededError(self.max_retries, validations) + if not agent.keep_history: + agent.history = [] + return self._attach_metadata(current_result, validations) + + last_assistant_index = len(current_result.history) - 1 + current_result = await self._rerun(agent, validation, options, context) + current_result = self._handle_history(current_result, last_assistant_index) + retries += 1 + + if not agent.keep_history: + agent.history = [] + return self._attach_metadata(current_result, validations) + + async def _validate(self, result: "AgentResult[PromptOutputT]") -> ValidationOutputT: + """ + Run the validation prompt on the agent result. + """ + input_data = ValidationInput(chat_history=result.history) + prompt = self.validation_prompt(input_data) + return await self.llm.generate(prompt) + + async def _rerun( + self, + agent: "Agent[LLMClientOptionsT, PromptInputT, PromptOutputT]", + validation: ValidationOutputT, + options: Optional["AgentOptions[LLMClientOptionsT]"] = None, + context: Optional["AgentRunContext"] = None, + ) -> "AgentResult[PromptOutputT]": + """ + Rerun the agent with a correction prompt based on validation feedback. + """ + try: + correction_prompt = self.correction_prompt.format(**validation.model_dump()) + except KeyError as exc: + missing = [str(exc).strip("'\"")] + raise SupervisorCorrectionPromptFormatError(missing_keys=missing, original_error=exc) from exc + except Exception as exc: + raise SupervisorCorrectionPromptFormatError(original_error=exc) from exc + + return await agent._run_without_post_processing(correction_prompt, options, context) + + @staticmethod + def _attach_metadata( + result: "AgentResult[PromptOutputT]", + validations: list[ValidationOutputT], + ) -> "AgentResult[PromptOutputT]": + """ + Attach validation metadata to the agent result. + """ + result.metadata.setdefault("post_processors", {}).setdefault("supervisor", []).extend( + [v.model_dump() for v in validations] + ) + return result + + def _handle_history( + self, + result: "AgentResult[PromptOutputT]", + last_assistant_index: int, + ) -> "AgentResult[PromptOutputT]": + """ + Handle the chat history according to the configured strategy. + """ + if self.history_strategy == HistoryStrategy.REMOVE: + result.history.pop(last_assistant_index + 1) + result.history.pop(last_assistant_index) + + return result diff --git a/packages/ragbits-agents/tests/unit/test_post_processors.py b/packages/ragbits-agents/tests/unit/post_processors/test_base_post_processors.py similarity index 95% rename from packages/ragbits-agents/tests/unit/test_post_processors.py rename to packages/ragbits-agents/tests/unit/post_processors/test_base_post_processors.py index b71216267..1fa0d592b 100644 --- a/packages/ragbits-agents/tests/unit/test_post_processors.py +++ b/packages/ragbits-agents/tests/unit/post_processors/test_base_post_processors.py @@ -2,7 +2,7 @@ import pytest -from ragbits.agents import Agent, AgentResult, ToolCallResult +from ragbits.agents import Agent, AgentOptions, AgentResult, AgentRunContext, ToolCallResult from ragbits.agents.exceptions import AgentInvalidPostProcessorError from ragbits.agents.post_processors.base import PostProcessor, StreamingPostProcessor from ragbits.core.llms.base import BasePrompt, ToolCall, Usage @@ -13,7 +13,13 @@ class MockPostProcessor(PostProcessor): def __init__(self, append_content: str = " - processed"): self.append_content = append_content - async def process(self, result: AgentResult, agent: Agent) -> AgentResult: + async def process( + self, + result: AgentResult, + agent: Agent, + options: AgentOptions | None = None, + context: AgentRunContext | None = None, + ) -> AgentResult: result.content += self.append_content return result diff --git a/packages/ragbits-agents/tests/unit/post_processors/test_supervisor.py b/packages/ragbits-agents/tests/unit/post_processors/test_supervisor.py new file mode 100644 index 000000000..96057248c --- /dev/null +++ b/packages/ragbits-agents/tests/unit/post_processors/test_supervisor.py @@ -0,0 +1,263 @@ +import pytest +from pydantic import BaseModel + +from ragbits.agents import Agent +from ragbits.agents.post_processors.exceptions import ( + SupervisorCorrectionPromptFormatError, + SupervisorMaxRetriesExceededError, +) +from ragbits.agents.post_processors.supervisor import ( + HistoryStrategy, + SupervisorPostProcessor, + ValidationInput, + ValidationOutput, +) +from ragbits.core.llms.mock import MockLLM, MockLLMOptions +from ragbits.core.prompt.prompt import Prompt + + +class AlwaysValidPrompt(Prompt[ValidationInput, ValidationOutput]): + system_prompt = "You are a validator." + user_prompt = "Validate last response." + + +class AlwaysInvalidPrompt(Prompt[ValidationInput, ValidationOutput]): + system_prompt = "You are a validator." + user_prompt = "Validate last response." + + +@pytest.fixture +def agent_llm() -> MockLLM: + return MockLLM(default_options=MockLLMOptions(response="Agent answer")) + + +@pytest.mark.asyncio +async def test_supervisor_accepts_valid_response_and_attaches_metadata(agent_llm: MockLLM) -> None: + agent: Agent = Agent(llm=agent_llm, prompt="System prompt for agent") + + validation_llm = MockLLM( + default_options=MockLLMOptions(response='{"is_valid": true, "reasoning": "ok", "suggestion": ""}') + ) + + supervisor: SupervisorPostProcessor = SupervisorPostProcessor( + llm=validation_llm, + validation_prompt=AlwaysValidPrompt, + max_retries=2, + ) + + result = await agent.run("What is the weather in Tokyo?", post_processors=[supervisor]) + + assert result.content == "Agent answer" + assert "post_processors" in result.metadata + assert "supervisor" in result.metadata["post_processors"] + + validations = result.metadata["post_processors"]["supervisor"] + assert isinstance(validations, list) + assert len(validations) == 1 + assert validations[0]["is_valid"] is True + + +@pytest.mark.asyncio +async def test_supervisor_returns_after_retry_limit_when_fail_on_exceed_false(agent_llm: MockLLM) -> None: + agent: Agent = Agent(llm=agent_llm, prompt="System prompt for agent") + + validation_llm = MockLLM( + default_options=MockLLMOptions(response='{"is_valid": false, "reasoning": "bad", "suggestion": "fix"}') + ) + + supervisor: SupervisorPostProcessor = SupervisorPostProcessor( + llm=validation_llm, + validation_prompt=AlwaysInvalidPrompt, + max_retries=2, + fail_on_exceed=False, + history_strategy=HistoryStrategy.REMOVE, + ) + + result = await agent.run("Q?", post_processors=[supervisor]) + + assert "post_processors" in result.metadata + validations = result.metadata["post_processors"]["supervisor"] + assert isinstance(validations, list) + assert len(validations) == 3 + assert all(v["is_valid"] is False for v in validations) + + +@pytest.mark.asyncio +async def test_supervisor_raises_after_retry_limit_when_fail_on_exceed_true(agent_llm: MockLLM) -> None: + agent: Agent = Agent(llm=agent_llm, prompt="System prompt for agent") + + validation_llm = MockLLM( + default_options=MockLLMOptions(response='{"is_valid": false, "reasoning": "bad", "suggestion": "fix"}') + ) + + supervisor: SupervisorPostProcessor = SupervisorPostProcessor( + llm=validation_llm, + validation_prompt=AlwaysInvalidPrompt, + max_retries=2, + fail_on_exceed=True, + ) + + with pytest.raises(SupervisorMaxRetriesExceededError) as exc: + await agent.run("Q?", post_processors=[supervisor]) + + err = exc.value + assert err.max_retries == 2 + assert len(err.last_validations) == 3 + assert all(v.is_valid is False for v in err.last_validations) + + +@pytest.mark.asyncio +async def test_supervisor_history_remove_prunes_invalid_and_correction_user(agent_llm: MockLLM) -> None: + agent: Agent = Agent(llm=agent_llm, prompt="System prompt for agent") + + validation_llm = MockLLM( + default_options=MockLLMOptions(response='{"is_valid": false, "reasoning": "bad", "suggestion": "fix"}') + ) + + supervisor: SupervisorPostProcessor = SupervisorPostProcessor( + llm=validation_llm, + validation_prompt=AlwaysInvalidPrompt, + max_retries=1, + fail_on_exceed=False, + history_strategy=HistoryStrategy.REMOVE, + ) + + result = await agent.run("Q?", post_processors=[supervisor]) + + history = result.history + assert len(history) == 3 + assert history[0]["role"] == "system" + assert history[1]["role"] == "user" + assert history[1]["content"] == "Q?" + assert history[2]["role"] == "assistant" + assert history[2]["content"] == "Agent answer" + + +@pytest.mark.asyncio +async def test_supervisor_history_preserve_keeps_invalid_and_correction_user(agent_llm: MockLLM) -> None: + agent: Agent = Agent(llm=agent_llm, prompt="System prompt for agent") + + validation_llm = MockLLM( + default_options=MockLLMOptions(response='{"is_valid": false, "reasoning": "bad", "suggestion": "fix"}') + ) + + supervisor: SupervisorPostProcessor = SupervisorPostProcessor( + llm=validation_llm, + validation_prompt=AlwaysInvalidPrompt, + max_retries=1, + fail_on_exceed=False, + history_strategy=HistoryStrategy.PRESERVE, + ) + + result = await agent.run("Q?", post_processors=[supervisor]) + + history = result.history + assert len(history) == 5 + assert history[0]["role"] == "system" + assert history[1]["role"] == "user" + assert history[1]["content"] == "Q?" + assert history[2]["role"] == "assistant" + assert history[2]["content"] == "Agent answer" + assert history[3]["role"] == "user" + assert isinstance(history[3]["content"], str) + assert "Your answer is incorrect" in history[3]["content"] + assert history[4]["role"] == "assistant" + assert history[4]["content"] == "Agent answer" + + +class MyValidationOutput(BaseModel): + is_valid: bool + errors: list[str] + fixes: list[str] + confidence: float + + +class MyValidationPrompt(Prompt[ValidationInput, MyValidationOutput]): + system_prompt = "Mocked system prompt" + user_prompt = "Mocked user prompt" + + +@pytest.mark.asyncio +async def test_supervisor_custom_validation_and_correction_prompt_preserve_history(agent_llm: MockLLM) -> None: + agent: Agent = Agent(llm=agent_llm, prompt="You are a professional weather reporter.") + + validation_llm = MockLLM( + default_options=MockLLMOptions( + response=( + '{"is_valid": false, "errors": ["not detailed enough"], ' + '"fixes": ["add more details"], "confidence": 0.9}' + ) + ) + ) + + correction_prompt = ( + "Previous answer had issues:\n" + "Errors: {errors}\n" + "Fixes: {fixes}\n" + "Confidence: {confidence}\n" + "Please answer again using the fixes." + ) + + supervisor: SupervisorPostProcessor = SupervisorPostProcessor( + llm=validation_llm, + validation_prompt=MyValidationPrompt, + correction_prompt=correction_prompt, + max_retries=1, + fail_on_exceed=False, + history_strategy=HistoryStrategy.PRESERVE, + ) + + result = await agent.run("What is the weather in Tokyo?", post_processors=[supervisor]) + + validations = result.metadata["post_processors"]["supervisor"] + assert isinstance(validations, list) + assert len(validations) == 2 + assert all("errors" in v and "fixes" in v and v["is_valid"] is False for v in validations) + + history = result.history + assert len(history) == 5 + assert history[0]["role"] == "system" + assert history[0]["content"] + assert history[1]["role"] == "user" + assert history[1]["content"] + assert history[2]["role"] == "assistant" + assert history[3]["role"] == "user" + assert history[3]["content"] + + corr = history[3]["content"] + assert "Previous answer had issues:" in corr + assert "Errors:" in corr + assert "not detailed enough" in corr + assert "Fixes:" in corr + assert "add more details" in corr + assert "Confidence: 0.9" in corr + assert history[4]["role"] == "assistant" + + +@pytest.mark.asyncio +async def test_supervisor_raises_when_correction_prompt_has_missing_fields(agent_llm: MockLLM) -> None: + agent: Agent = Agent(llm=agent_llm, prompt="System prompt for agent") + + validation_llm = MockLLM( + default_options=MockLLMOptions(response='{"is_valid": false, "reasoning": "bad", "suggestion": "fix"}') + ) + + bad_correction_prompt = "Missing field here: {nonexistent}" + + supervisor: SupervisorPostProcessor = SupervisorPostProcessor( + llm=validation_llm, + validation_prompt=AlwaysInvalidPrompt, + correction_prompt=bad_correction_prompt, + max_retries=1, + fail_on_exceed=False, + history_strategy=HistoryStrategy.PRESERVE, + ) + + with pytest.raises(SupervisorCorrectionPromptFormatError) as exc: + await agent.run("Q?", post_processors=[supervisor]) + + err = exc.value + msg = str(err) + assert "failed to format correction prompt" in msg.lower() + assert "nonexistent" in msg.lower() + assert err.missing_keys == ["nonexistent"] From 8c8697d6d5b503270b93cb58c99584979232d3c6 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Mon, 29 Sep 2025 18:25:20 +0200 Subject: [PATCH 24/43] add hotpotqa+rag pipeline files --- examples/evaluate/hotpot_qa/run.py | 95 ++++++ .../ragbits/evaluate/dataloaders/hotpot_qa.py | 100 ++++++ .../src/ragbits/evaluate/metrics/hotpot_qa.py | 51 +++ .../ragbits/evaluate/pipelines/__init__.py | 11 +- .../ragbits/evaluate/pipelines/hotpot_qa.py | 322 ++++++++++++++++++ 5 files changed, 578 insertions(+), 1 deletion(-) create mode 100644 examples/evaluate/hotpot_qa/run.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/hotpot_qa.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/metrics/hotpot_qa.py create mode 100644 packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py diff --git a/examples/evaluate/hotpot_qa/run.py b/examples/evaluate/hotpot_qa/run.py new file mode 100644 index 000000000..93d76deb8 --- /dev/null +++ b/examples/evaluate/hotpot_qa/run.py @@ -0,0 +1,95 @@ +import asyncio +import logging +from pathlib import Path + +from ragbits.agents import Agent, AgentOptions +from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl +from ragbits.core.llms import LiteLLM +from ragbits.core.sources.hf import HuggingFaceSource +from ragbits.evaluate.dataloaders.hotpot_qa import HotpotQADataLoader +from ragbits.evaluate.evaluator import Evaluator +from ragbits.evaluate.metrics.base import MetricSet +from ragbits.evaluate.metrics.hotpot_qa import HotpotQAExactMatch, HotpotQAF1 +from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAPipeline +from ragbits.core.vector_stores import VectorStoreOptions +from ragbits.core.vector_stores.in_memory import InMemoryVectorStore +from ragbits.core.embeddings import LiteLLMEmbedder +from ragbits.document_search import DocumentSearch +from ragbits.document_search.ingestion.strategies import BatchedIngestStrategy + + +async def main() -> None: + """Run HotpotQA example with an Agent and print aggregate metrics.""" + logging.getLogger("LiteLLM").setLevel(logging.ERROR) + + todo_list = TodoList() + todo_manager = create_todo_manager(todo_list) + + hotpot_prompt = ( + "You are a helpful assistant. Use the given context to answer.\n" + "Respond on a single line as: 'Answer: '.\n" + "- If yes/no, respond 'Answer: yes' or 'Answer: no'.\n" + "- If a name/title is required, respond only the name/title after 'Answer:'.\n" + "- Do not add any extra text beyond the Answer line.\n" + "- Your answer should as concise and minimal as possible, while still answering the question.\n" + "- For complex questions, use the planning tool as described." + ) + + system_prompt = "\n".join( + [ + hotpot_prompt, + get_todo_instruction_tpl(task_range=(3, 5)), + ] + ) + agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=system_prompt, + tools=[todo_manager], + default_options=AgentOptions(max_turns=5), + ) + + source = HuggingFaceSource(path="hotpotqa/hotpot_qa", name="distractor", split="train") + dataloader = HotpotQADataLoader(source=source, split="data[:10]", level_filter="hard") + + log_path = Path(__file__).with_name("hotpot_examples.ndjson") + + def parse_final_answer(text: str) -> str: + marker = "Answer:" + idx = text.rfind(marker) + if idx == -1: + return text.strip() + candidate = text[idx + len(marker) :].strip() + if candidate.startswith("<") and candidate.endswith(">"): + candidate = candidate[1:-1].strip() + return candidate + + retriever = DocumentSearch( + vector_store=InMemoryVectorStore( + embedder=LiteLLMEmbedder(model_name="text-embedding-3-small"), + default_options=VectorStoreOptions(k=5), + ), + ingest_strategy=BatchedIngestStrategy(index_batch_size=1000), + ) + + pipeline = HotpotQAPipeline( + evaluation_target=agent, + retriever=retriever, + hops=3, + per_example_log_file=log_path, + # agent specific ext. logs + extended_logs=False, # includes traces, tool usage, etc. + parse_answer_fn=parse_final_answer, + ) + + metrics = MetricSet(HotpotQAExactMatch(), HotpotQAF1()) + + evaluator = Evaluator(batch_size=5, parallelize_batches=True) + results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) + + print("Metrics:") + for key, value in results.metrics.items(): + print(f" {key}: {value:.4f}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/hotpot_qa.py new file mode 100644 index 000000000..6977aade3 --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/hotpot_qa.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +from collections.abc import Iterable +from typing import Any + +from ragbits.core.sources.base import Source +from ragbits.evaluate.dataloaders.base import DataLoader +from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAData + + +class HotpotQADataLoader(DataLoader[HotpotQAData]): + """ + HotpotQA evaluation data loader. + + The source should point to a local/remote JSON file exported from Hugging Face, where each example includes at + least the following keys: + - "id" (str) + - "question" (str) + - "answer" (str) + - "type" ("bridge" | "comparison") + - "level" ("easy" | "medium" | "hard") + - "context" (object with keys: "title": list[str], "sentences": list[list[str]]) + """ + + def __init__( + self, + source: Source, + *, + split: str = "data", + id_key: str = "id", + question_key: str = "question", + answer_key: str = "answer", + type_key: str = "type", + level_key: str = "level", + context_key: str = "context", + # filter + level_filter: str | None = None, # one of: easy|medium|hard + subset: str | None = None, # one of: distractor|fullwiki (if present in dataset path/metadata) + ) -> None: + """ + Initialize the HotpotQA data loader. + + Args: + source: The source to load the data from. + split: The split to load the data from. + id_key: Column with unique id. + question_key: Column with question text. + answer_key: Column with ground truth answer. + type_key: Column with question type ("bridge" | "comparison"). + level_key: Column with difficulty ("easy" | "medium" | "hard"). + context_key: Column with context object containing titles and sentences. + level_filter: If provided, return only examples with this level. + subset: Unused in mapping; keep for symmetry with dataset organization. + """ + required = {id_key, question_key, answer_key, type_key, level_key, context_key} + super().__init__(source=source, split=split, required_keys=required) + self.id_key = id_key + self.question_key = question_key + self.answer_key = answer_key + self.type_key = type_key + self.level_key = level_key + self.context_key = context_key + self.level_filter = level_filter + self.subset = subset + + async def map(self, dataset: Iterable[dict]) -> Iterable[HotpotQAData]: + """ + Map the dataset to the HotpotQA evaluation data schema. + + Args: + dataset: The dataset to map. + + Returns: + The HotpotQA evaluation data rows. + """ + + def to_context_rows(context: dict[str, Any]) -> list[str]: + titles = context.get("title", []) or [] + sentences = context.get("sentences", []) or [] + rows: list[str] = [] + for title, sent_list in zip(titles, sentences, strict=False): + doc_text = "\n".join(sent_list) if isinstance(sent_list, list) else str(sent_list) + rows.append(f"{title}\n{doc_text}") + if not rows and isinstance(sentences, list): + flat = "\n".join([" ".join(s) if isinstance(s, list) else str(s) for s in sentences]) + rows = [flat] + return rows + + return [ + HotpotQAData( + id=row.get(self.id_key, ""), + question=row.get(self.question_key, ""), + reference_answer=str(row.get(self.answer_key, "")), + qtype=str(row.get(self.type_key, "")), + level=(row.get(self.level_key) or "").lower(), + reference_context=to_context_rows(row.get(self.context_key, {}) or {}), + ) + for row in dataset + if not self.level_filter or (row.get(self.level_key, "").lower() == self.level_filter) + ] diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/hotpot_qa.py new file mode 100644 index 000000000..aafc96b0d --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/hotpot_qa.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Iterable + +from ragbits.evaluate.metrics.base import Metric +from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAResult + + +class HotpotQAExactMatch(Metric[HotpotQAResult]): + """Computes EM over HotpotQA by type and overall.""" + + async def compute(self, results: list[HotpotQAResult]) -> dict: + """Compute EM. Returns hotpotqa__em and hotpotqa_overall_em.""" + buckets: dict[str, list[float]] = defaultdict(list) + for r in results: + em = r.em_value + t = r.qtype or "unknown" + buckets[t].append(em) + buckets["overall"].append(em) + + def avg(vals: Iterable[float]) -> float: + lst = list(vals) + return float(sum(lst) / len(lst)) if lst else 0.0 + + metrics: dict[str, float] = {} + for t, vals in buckets.items(): + metrics[f"hotpotqa_{t}_em"] = avg(vals) + return metrics + + +class HotpotQAF1(Metric[HotpotQAResult]): + """Computes token-level F1 over HotpotQA by type and overall.""" + + async def compute(self, results: list[HotpotQAResult]) -> dict: + """Compute F1. Returns hotpotqa__f1 and hotpotqa_overall_f1.""" + buckets: dict[str, list[float]] = defaultdict(list) + for r in results: + f1v = r.f1_value + t = r.qtype or "unknown" + buckets[t].append(f1v) + buckets["overall"].append(f1v) + + def avg(vals: Iterable[float]) -> float: + lst = list(vals) + return float(sum(lst) / len(lst)) if lst else 0.0 + + metrics: dict[str, float] = {} + for t, vals in buckets.items(): + metrics[f"hotpotqa_{t}_f1"] = avg(vals) + return metrics diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py index f86254bcc..0fe914943 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py @@ -4,8 +4,17 @@ from ragbits.evaluate.pipelines.document_search import DocumentSearchPipeline from ragbits.evaluate.pipelines.gaia import GaiaPipeline from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline +from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAPipeline -__all__ = ["DocumentSearchPipeline", "EvaluationData", "EvaluationPipeline", "EvaluationResult", "GaiaPipeline", "HumanEvalPipeline"] +__all__ = [ + "DocumentSearchPipeline", + "EvaluationData", + "EvaluationPipeline", + "EvaluationResult", + "GaiaPipeline", + "HumanEvalPipeline", + "HotpotQAPipeline", +] _target_to_evaluation_pipeline: dict[type[WithConstructionConfig], type[EvaluationPipeline]] = { DocumentSearch: DocumentSearchPipeline, diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py new file mode 100644 index 000000000..bd1532f7a --- /dev/null +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py @@ -0,0 +1,322 @@ +from __future__ import annotations + +import json +from collections.abc import Callable, Iterable +from dataclasses import dataclass +from pathlib import Path +from typing import cast + +from typing_extensions import Self + +from ragbits.agents import Agent +from ragbits.core.llms.base import LLM, LLMClientOptionsT, LLMResponseWithMetadata, Usage +from ragbits.document_search import DocumentSearch +from ragbits.document_search.documents.document import Document, DocumentMeta, DocumentType +from ragbits.document_search.documents.element import TextElement +from ragbits.document_search.ingestion.parsers.base import DocumentParser +from ragbits.document_search.ingestion.parsers.router import DocumentParserRouter +from ragbits.document_search.ingestion.strategies.batched import BatchedIngestStrategy +from ragbits.evaluate.pipelines.base import EvaluationData, EvaluationPipeline, EvaluationResult + + +class ContextListParser(DocumentParser): + """ + Parser for in-memory context strings (treated as a single TXT document). + """ + + supported_document_types = {DocumentType.TXT} + + async def parse(self, document: Document) -> list[TextElement]: + content = document.local_path.read_text() + return [TextElement(content=content, document_meta=document.metadata)] + + +class HotpotQAData(EvaluationData): + """ + Represents a single HotpotQA example. + """ + + id: str + question: str + reference_answer: str + qtype: str + level: str + reference_context: list[str] + + +@dataclass +class HotpotQAResult(EvaluationResult): + """ + Represents the result of evaluating a single HotpotQA example. + """ + + id: str + predicted_result: str + reference_answer: str + question: str + qtype: str + level: str + predicted_parsed: str | None = None + reference_normalized: str | None = None + em_value: float = 0.0 + f1_value: float = 0.0 + + +class HotpotQAPipeline( + EvaluationPipeline[ + Agent[LLMClientOptionsT, None, str] | LLM[LLMClientOptionsT], + HotpotQAData, + HotpotQAResult, + ] +): + """ + HotpotQA evaluation pipeline with simple RAG ingestion per batch and multi-hop retrieval. + """ + + def __init__( + self, + evaluation_target: Agent[LLMClientOptionsT, None, str] | LLM[LLMClientOptionsT], + *, + retriever: DocumentSearch, + hops: int = 3, + per_example_log_file: Path | None = None, + extended_logs: bool = False, + parse_answer_fn: Callable[[str], str] | None = None, + retrieval_k: int = 3, + element_max_chars: int = 500, + hop_context_max_chars: int = 1200, + ) -> None: + super().__init__(evaluation_target=evaluation_target) + self.retriever = retriever + self.hops = max(1, min(hops, 5)) + self.per_example_log_file = per_example_log_file + self.extended_logs = extended_logs + self.parse_answer_fn = parse_answer_fn + self.retrieval_k = max(1, int(retrieval_k)) + self.element_max_chars = max(50, int(element_max_chars)) + self.hop_context_max_chars = max(100, int(hop_context_max_chars)) + self._init_log_file() + + @classmethod + def from_config(cls, config: dict) -> Self: + """Create pipeline from config. + Attempts Agent first, falls back to raw LLM construction. + """ + if "evaluation_target" not in config: + try: + config["evaluation_target"] = Agent.from_config(config) + except Exception: + config["evaluation_target"] = LLM.subclass_from_config(config) + config["retriever"] = DocumentSearch.from_config(config["document_search"]) + config["hops"] = int(config.get("hops", 3)) + config["retrieval_k"] = int(config.get("retrieval_k", 3)) + config["element_max_chars"] = int(config.get("element_max_chars", 500)) + config["hop_context_max_chars"] = int(config.get("hop_context_max_chars", 1200)) + return super().from_config(config) + + async def __call__(self, data: Iterable[HotpotQAData]) -> Iterable[HotpotQAResult]: + """Ingest contexts, perform multi-hop retrieval, and answer HotpotQA questions.""" + documents: list[DocumentMeta] = [] + for row in data: + for content in row.reference_context: + documents.append(DocumentMeta.from_literal(content)) + + parser_router = DocumentParserRouter({DocumentType.TXT: ContextListParser()}) + self.retriever.ingest_strategy = BatchedIngestStrategy(index_batch_size=1000) + self.retriever.parser_router = parser_router + await self.retriever.ingest(documents) + + async def answer_with_retrieval( + example: HotpotQAData, + ) -> tuple[str, Usage, list, list[dict]]: + accumulated_context: list[str] = [] + hop_logs: list[dict] = [] + last_query = example.question + for hop_idx in range(self.hops): + elements = await self.retriever.search(last_query) + text_parts: list[str] = [] + consumed = 0 + for element in elements[: self.retrieval_k]: + content = getattr(element, "content", "") if hasattr(element, "content") else "" + if isinstance(content, str) and content: + snippet = content.strip().replace("\n\n", "\n")[: self.element_max_chars] + budget = max(0, self.hop_context_max_chars - consumed) + take = snippet[:budget] + if take: + text_parts.append(take) + consumed += len(take) + if consumed >= self.hop_context_max_chars: + break + hop_text = "\n\n".join(text_parts) + hop_logs.append({"hop": hop_idx + 1, "question": last_query, "retrieved": hop_text}) + if hop_text: + accumulated_context.append(hop_text) + last_query = f"{example.question}\n\nContext so far:\n{hop_text}" + else: + break + + full_context = "\n\n".join(accumulated_context) + # append retrieved context to user question + prompt_input = example.question if not full_context else f"{example.question}\n\nContext:\n{full_context}" + + if self.extended_logs: + content, dbg = await self._generate_with_debug(prompt_input) + usage = cast(Usage, (dbg or {}).get("usage") or Usage()) + tool_calls = cast(list, (dbg or {}).get("tool_calls") or []) + else: + content, usage, tool_calls = await self._generate_answer(prompt_input) + + return str(content), usage, tool_calls, hop_logs + + results: list[HotpotQAResult] = [] + for row in data: + try: + predicted_text, usage, tool_calls, hop_logs = await answer_with_retrieval(row) + except Exception: + predicted_text = "" + usage = Usage() + tool_calls = [] + hop_logs = [] + predicted_extracted = self._parse_answer(predicted_text) + ref_norm = self._normalize(row.reference_answer) + + # Compute normalized fields and sample metrics once + em = 1.0 if self._normalize(predicted_extracted) == ref_norm else 0.0 + f1 = self._f1(self._normalize(predicted_extracted), ref_norm) + + result = HotpotQAResult( + id=row.id, + predicted_result=predicted_text, + reference_answer=row.reference_answer, + question=row.question, + qtype=row.qtype, + level=row.level, + predicted_parsed=self._normalize(predicted_extracted), + reference_normalized=ref_norm, + em_value=float(em), + f1_value=float(f1), + ) + results.append(result) + + ext_log_str = None + if self.extended_logs: + ext_log_str = json.dumps( + { + "usage": usage, + "tool_calls": tool_calls, + "hops": hop_logs, + }, + ensure_ascii=False, + default=str, + ) + self._log_example(row=row, predicted_text=predicted_text, predicted_extracted=predicted_extracted, em=result.em_value, f1=result.f1_value, extended_log=ext_log_str) + + return results + + def _init_log_file(self) -> None: + """Ensure the per-example log file exists if logging is enabled.""" + if self.per_example_log_file is None: + return + self.per_example_log_file.parent.mkdir(parents=True, exist_ok=True) + self.per_example_log_file.touch(exist_ok=True) + + def _log_example( + self, + *, + row: HotpotQAData, + predicted_text: str, + predicted_extracted: str, + em: float, + f1: float, + extended_log: str | None = None, + ) -> None: + """Append a single NDJSON record for debugging if enabled.""" + if self.per_example_log_file is None: + return + record: dict[str, object] = { + "id": row.id, + "question": row.question, + "reference": row.reference_answer, + "predicted": predicted_text, + "predicted_extracted": predicted_extracted, + "type": row.qtype, + "level": row.level, + "em": float(em), + "f1": float(f1), + } + record["extended_debug_logging"] = extended_log or "[]" + with open(self.per_example_log_file, "a", encoding="utf-8") as file: + file.write(json.dumps(record, ensure_ascii=False) + "\n") + + def _parse_answer(self, text: str) -> str: + """Optionally parse final answer from text using provided function. + If no parser provided, returns the original text. + """ + if self.parse_answer_fn is None: + return text + try: + return self.parse_answer_fn(text) + except Exception as exc: + import logging as _logging + + _logging.getLogger(__name__).debug("Answer parse error: %s", exc) + return text + + async def _generate_answer(self, prompt: str) -> tuple[str, Usage, list]: + """Generate final answer from Agent or raw LLM and capture usage and tool calls.""" + target = self.evaluation_target + if isinstance(target, Agent): + res = await target.run(prompt) + return str(res.content), res.usage, (res.tool_calls or []) + + resp = cast(LLMResponseWithMetadata[str], await target.generate_with_metadata(prompt)) + return str(resp.content), (resp.usage or Usage()), [] + + async def _generate_with_debug(self, prompt: str) -> tuple[str, dict | None]: + """Generate answer and capture tool/history/usage for logging (as raw content).""" + target = self.evaluation_target + if isinstance(target, Agent): + res = await target.run(prompt) + dbg = { + "history": res.history, + "tool_calls": res.tool_calls, + "usage": res.usage, + "metadata": res.metadata, + } + return str(res.content), dbg + resp = await target.generate(prompt) + return str(resp), None + + @staticmethod + def _normalize(text: str) -> str: + """Basic normalization for answer equality checks: lowercase, strip spaces.""" + return "".join(ch.lower() for ch in (text or "").strip() if not ch.isspace()) + + @staticmethod + def _f1(prediction: str, ground_truth: str) -> float: + import re as _re + from collections import Counter as _Counter + + def tokens(value: str) -> list[str]: + value = (value or "").lower() + value = _re.sub(r"[^a-z0-9\s]", " ", value) + value = _re.sub(r"\b(a|an|the)\b", " ", value) + value = _re.sub(r"\s+", " ", value).strip() + return value.split() + + pred_tokens = tokens(prediction) + gt_tokens = tokens(ground_truth) + if not pred_tokens and not gt_tokens: + return 1.0 + if not pred_tokens or not gt_tokens: + return 0.0 + + pred_counts = _Counter(pred_tokens) + gt_counts = _Counter(gt_tokens) + common = sum((pred_counts & gt_counts).values()) + if common == 0: + return 0.0 + + precision = common / len(pred_tokens) + recall = common / len(gt_tokens) + return 2 * precision * recall / (precision + recall) From 8ac24d8ec9c4f8684295fe3aeef6f5f08ae1ce9e Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Mon, 29 Sep 2025 18:28:00 +0200 Subject: [PATCH 25/43] small lint changes --- examples/evaluate/gaia/run.py | 16 ++++++---------- .../src/ragbits/agents/tools/extra.py | 12 ++---------- .../src/ragbits/evaluate/dataloaders/gaia.py | 6 +++++- .../src/ragbits/evaluate/metrics/gaia.py | 9 ++++----- .../src/ragbits/evaluate/metrics/human_eval.py | 4 ++-- 5 files changed, 19 insertions(+), 28 deletions(-) diff --git a/examples/evaluate/gaia/run.py b/examples/evaluate/gaia/run.py index d97de4e83..4b492d191 100644 --- a/examples/evaluate/gaia/run.py +++ b/examples/evaluate/gaia/run.py @@ -3,18 +3,18 @@ from pathlib import Path from ragbits.agents import Agent, AgentOptions -from ragbits.agents.tools.openai import get_web_search_tool -from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl from ragbits.agents.tools.extra import ( add, - subtract, - multiply, + arxiv_search, divide, + get_extra_instruction_tpl, modulus, - arxiv_search, + multiply, + subtract, wiki_search, - get_extra_instruction_tpl, ) +from ragbits.agents.tools.openai import get_web_search_tool +from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl from ragbits.core.llms import LiteLLM from ragbits.core.sources.hf import HuggingFaceSource from ragbits.evaluate.dataloaders.gaia import GaiaDataLoader @@ -78,9 +78,7 @@ async def main() -> None: default_options=AgentOptions(max_turns=30), ) - # Data: pick level 1, 2, or 3 -> higher level <-> harder questions level = 1 - # GAIA config name:'2023_all', '2023_level1', '2023_level2', '2023_level3' config_name = {1: "2023_level1", 2: "2023_level2", 3: "2023_level3"}[level] source = HuggingFaceSource(path="gaia-benchmark/GAIA", name=config_name, split="validation") dataloader = GaiaDataLoader(source=source, split="data[:30]", skip_file_attachments=True) @@ -105,10 +103,8 @@ def parse_final_answer(text: str) -> str: parse_answer_fn=parse_final_answer, ) - # Metrics metrics = MetricSet(GaiaOutcome(), GaiaTooling(), GaiaEfficiency()) - # Evaluate evaluator = Evaluator(batch_size=5, parallelize_batches=True) results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/extra.py b/packages/ragbits-agents/src/ragbits/agents/tools/extra.py index a54df55fa..de8d5e069 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/extra.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/extra.py @@ -1,10 +1,10 @@ from __future__ import annotations -from typing import Any import json import urllib.parse import urllib.request import xml.etree.ElementTree as ET +from typing import Any def add(a: int, b: int) -> int: @@ -17,7 +17,6 @@ def add(a: int, b: int) -> int: Returns: Sum of a and b. """ - return a + b @@ -31,7 +30,6 @@ def subtract(a: int, b: int) -> int: Returns: Difference a - b. """ - return a - b @@ -45,7 +43,6 @@ def multiply(a: int, b: int) -> int: Returns: Product a * b. """ - return a * b @@ -62,7 +59,6 @@ def divide(a: int, b: int) -> float: Raises: ValueError: If b == 0. """ - if b == 0: raise ValueError("Cannot divide by zero.") return a / b @@ -81,7 +77,6 @@ def modulus(a: int, b: int) -> int: Raises: ValueError: If b == 0. """ - if b == 0: raise ValueError("Cannot divide by zero.") return a % b @@ -103,7 +98,6 @@ def arxiv_search(query: str, max_results: int = 3) -> dict[str, Any]: Returns: Dict with a list of results: title, summary, link. """ - if max_results <= 0: return {"results": []} @@ -147,7 +141,6 @@ def wiki_search(query: str, max_results: int = 2, language: str = "en") -> dict[ Returns: Dict with list of results: title, pageid, extract, url. """ - if max_results <= 0: return {"results": []} @@ -201,7 +194,6 @@ def get_extra_instruction_tpl() -> str: Returns: Instruction template string to include in system prompts. """ - return ( "\n\n" "## Tools Workflow (Arithmetic, arXiv, Wikipedia)\n\n" @@ -213,4 +205,4 @@ def get_extra_instruction_tpl() -> str: "1) Use arithmetic tools for calculations instead of inline math when non-trivial.\n" "2) Use arXiv/Wikipedia tools when scholarly/general knowledge retrieval is needed.\n" "3) Keep outputs concise; provide FINAL ANSWER after any intermediate reasoning.\n" - ) \ No newline at end of file + ) diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/gaia.py index 6e553e4e3..638d85a23 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/gaia.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/gaia.py @@ -70,5 +70,9 @@ async def map(self, dataset: Iterable[dict]) -> Iterable[GaiaData]: file_name=(row.get(self.file_name_key) or None), ) for row in dataset - if (not self.skip_file_attachments or not row.get(self.file_name_key) or str(row.get(self.file_name_key)).strip() == "") + if ( + not self.skip_file_attachments + or not row.get(self.file_name_key) + or str(row.get(self.file_name_key)).strip() == "" + ) ] diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py index 3c28f6f66..0f6a85267 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py @@ -14,7 +14,7 @@ class GaiaOutcome(Metric[GaiaResult]): async def compute(self, results: list[GaiaResult]) -> dict: """Compute task success rate. - + Returns: Dictionary with gaia_task_success_rate: fraction of successfully solved tasks. """ @@ -35,7 +35,7 @@ class GaiaTooling(Metric[GaiaResult]): async def compute(self, results: list[GaiaResult]) -> dict: """Compute tool utilization and performance metrics. - + Returns: Dictionary with tool trigger rate, average tool calls, average errors, and flattened tool frequency usage as numeric metrics. @@ -53,8 +53,7 @@ async def compute(self, results: list[GaiaResult]) -> dict: for name in r.tool_names: aggregated_counts[name] = aggregated_counts.get(name, 0) + 1 averaged_freq: dict[str, float] = { - f"gaia_tool_frequency_usage.{name}": (count / total_tasks) - for name, count in aggregated_counts.items() + f"gaia_tool_frequency_usage.{name}": (count / total_tasks) for name, count in aggregated_counts.items() } return { @@ -75,7 +74,7 @@ class GaiaEfficiency(Metric[GaiaResult]): async def compute(self, results: list[GaiaResult]) -> dict: """Compute efficiency and resource usage metrics. - + Returns: Dictionary with average latency, cost, and token usage. """ diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py index fd5eeda8d..dae1a901a 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py @@ -19,7 +19,7 @@ def __init__(self, k: int = 1) -> None: async def compute(self, results: list[HumanEvalResult]) -> dict: """Compute pass@k averaged over tasks. - + Returns: Dictionary with humaneval_pass@k: fraction of tasks with at least one passing sample. """ @@ -58,7 +58,7 @@ class HumanEvalQualityPerf(Metric[HumanEvalResult]): async def compute(self, results: list[HumanEvalResult]) -> dict: """Compute code quality and execution performance metrics. - + Returns: Dictionary with compile rates, error rates, tasks solved rate, and average execution time. """ From 009c1d55add9afe45e97bae11cfcfd4824fcf10c Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Mon, 29 Sep 2025 18:35:35 +0200 Subject: [PATCH 26/43] wrong conflict resolution --- packages/ragbits-agents/src/ragbits/agents/__init__.py | 4 +--- packages/ragbits-agents/src/ragbits/agents/tools/__init__.py | 4 +--- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/packages/ragbits-agents/src/ragbits/agents/__init__.py b/packages/ragbits-agents/src/ragbits/agents/__init__.py index 9001ee0b1..55d758712 100644 --- a/packages/ragbits-agents/src/ragbits/agents/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/__init__.py @@ -27,6 +27,4 @@ "StreamingPostProcessor", "ToolCall", "ToolCallResult", - "get_todo_instruction_tpl", - "create_todo_manager", -] +] \ No newline at end of file diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py index 5f101cd76..3f9bc4ec3 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py @@ -1,6 +1,4 @@ from ragbits.agents.tools.openai import get_code_interpreter_tool, get_image_generation_tool, get_web_search_tool from ragbits.agents.tools.todo import TodoOrchestrator -__all__ = ["TodoOrchestrator", "get_code_interpreter_tool", "get_image_generation_tool", "get_web_search_tool"] - -__all__ = ["create_todo_manager", "get_todo_instruction_tpl"] \ No newline at end of file +__all__ = ["TodoOrchestrator", "get_code_interpreter_tool", "get_image_generation_tool", "get_web_search_tool"] \ No newline at end of file From fef337664d9f584e88630c4b36355b45d1bbbd22 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Mon, 29 Sep 2025 20:43:53 +0200 Subject: [PATCH 27/43] adjust parser for hotpotqa; remove previous TODO usage and adjust prompts; add name to HF loader; minor lint fixes --- examples/evaluate/gaia/run.py | 37 ++++++++----------- examples/evaluate/hotpot_qa/run.py | 25 +++++-------- examples/evaluate/human_eval/run.py | 28 +++----------- .../src/ragbits/agents/__init__.py | 2 +- .../src/ragbits/agents/tools/__init__.py | 2 +- .../src/ragbits/core/sources/hf.py | 11 +++++- .../ragbits/evaluate/dataloaders/hotpot_qa.py | 3 -- .../ragbits/evaluate/pipelines/__init__.py | 4 +- .../ragbits/evaluate/pipelines/hotpot_qa.py | 29 +++++---------- 9 files changed, 52 insertions(+), 89 deletions(-) diff --git a/examples/evaluate/gaia/run.py b/examples/evaluate/gaia/run.py index 4b492d191..fb2244a9d 100644 --- a/examples/evaluate/gaia/run.py +++ b/examples/evaluate/gaia/run.py @@ -1,4 +1,5 @@ import asyncio +import os import logging from pathlib import Path @@ -14,7 +15,6 @@ wiki_search, ) from ragbits.agents.tools.openai import get_web_search_tool -from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl from ragbits.core.llms import LiteLLM from ragbits.core.sources.hf import HuggingFaceSource from ragbits.evaluate.dataloaders.gaia import GaiaDataLoader @@ -28,9 +28,6 @@ async def main() -> None: """Run GAIA example with an Agent and print aggregate metrics.""" logging.getLogger("LiteLLM").setLevel(logging.ERROR) - todo_list = TodoList() - todo_manager = create_todo_manager(todo_list) - gaia_prompt = ( "You are a general AI assistant. I will ask you a question. " "Report your thoughts, and finish your answer with the following template: " @@ -51,36 +48,32 @@ async def main() -> None: "Tool usage:\n" "- Use arithmetic tools for calculations.\n" "- Use arxiv_search/wiki_search to retrieve relevant facts when needed or explicitly asked.\n" - "- For multi-step/complex requests, start and manage work with the todo_manager workflow.\n" + "- For complex requests, plan your steps internally before answering concisely.\n" "- For general web-search questions, use search_web tool and gather information." ), - get_todo_instruction_tpl(task_range=(3, 5)), get_extra_instruction_tpl(), ] ) - web_search = get_web_search_tool(model_name="gpt-4.1-mini") + tools = [ + add, + subtract, + multiply, + divide, + modulus, + arxiv_search, + wiki_search, + get_web_search_tool(model_name="gpt-4.1-mini") + ] agent: Agent = Agent( llm=LiteLLM("gpt-4.1-mini"), prompt=system_prompt, - tools=[ - todo_manager, - web_search, - add, - subtract, - multiply, - divide, - modulus, - arxiv_search, - wiki_search, - ], + tools=tools, default_options=AgentOptions(max_turns=30), ) - level = 1 - config_name = {1: "2023_level1", 2: "2023_level2", 3: "2023_level3"}[level] - source = HuggingFaceSource(path="gaia-benchmark/GAIA", name=config_name, split="validation") + source = HuggingFaceSource(path="gaia-benchmark/GAIA", name="2023_level1", split="validation") dataloader = GaiaDataLoader(source=source, split="data[:30]", skip_file_attachments=True) log_path = Path(__file__).with_name("gaia_examples.ndjson") @@ -99,7 +92,7 @@ def parse_final_answer(text: str) -> str: evaluation_target=agent, per_example_log_file=log_path, # agent specific ext. logs - extended_logs=False, # includes traces, tool usage, etc. + extended_logs=False, # includes traces, tool usage, etc. parse_answer_fn=parse_final_answer, ) diff --git a/examples/evaluate/hotpot_qa/run.py b/examples/evaluate/hotpot_qa/run.py index 93d76deb8..6e65fce3e 100644 --- a/examples/evaluate/hotpot_qa/run.py +++ b/examples/evaluate/hotpot_qa/run.py @@ -3,28 +3,24 @@ from pathlib import Path from ragbits.agents import Agent, AgentOptions -from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl +from ragbits.core.embeddings import LiteLLMEmbedder from ragbits.core.llms import LiteLLM from ragbits.core.sources.hf import HuggingFaceSource +from ragbits.core.vector_stores import VectorStoreOptions +from ragbits.core.vector_stores.in_memory import InMemoryVectorStore +from ragbits.document_search import DocumentSearch +from ragbits.document_search.ingestion.strategies import BatchedIngestStrategy from ragbits.evaluate.dataloaders.hotpot_qa import HotpotQADataLoader from ragbits.evaluate.evaluator import Evaluator from ragbits.evaluate.metrics.base import MetricSet from ragbits.evaluate.metrics.hotpot_qa import HotpotQAExactMatch, HotpotQAF1 from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAPipeline -from ragbits.core.vector_stores import VectorStoreOptions -from ragbits.core.vector_stores.in_memory import InMemoryVectorStore -from ragbits.core.embeddings import LiteLLMEmbedder -from ragbits.document_search import DocumentSearch -from ragbits.document_search.ingestion.strategies import BatchedIngestStrategy async def main() -> None: """Run HotpotQA example with an Agent and print aggregate metrics.""" logging.getLogger("LiteLLM").setLevel(logging.ERROR) - todo_list = TodoList() - todo_manager = create_todo_manager(todo_list) - hotpot_prompt = ( "You are a helpful assistant. Use the given context to answer.\n" "Respond on a single line as: 'Answer: '.\n" @@ -32,24 +28,23 @@ async def main() -> None: "- If a name/title is required, respond only the name/title after 'Answer:'.\n" "- Do not add any extra text beyond the Answer line.\n" "- Your answer should as concise and minimal as possible, while still answering the question.\n" - "- For complex questions, use the planning tool as described." + "- Think step-by-step internally for complex questions before answering concisely." ) system_prompt = "\n".join( [ hotpot_prompt, - get_todo_instruction_tpl(task_range=(3, 5)), ] ) agent: Agent = Agent( llm=LiteLLM("gpt-4.1-mini"), prompt=system_prompt, - tools=[todo_manager], + tools=[], default_options=AgentOptions(max_turns=5), ) - source = HuggingFaceSource(path="hotpotqa/hotpot_qa", name="distractor", split="train") - dataloader = HotpotQADataLoader(source=source, split="data[:10]", level_filter="hard") + source = HuggingFaceSource(path="hotpotqa/hotpot_qa", name="distractor", split="validation") + dataloader = HotpotQADataLoader(source=source, split="data[:25]", level_filter="hard") log_path = Path(__file__).with_name("hotpot_examples.ndjson") @@ -77,7 +72,7 @@ def parse_final_answer(text: str) -> str: hops=3, per_example_log_file=log_path, # agent specific ext. logs - extended_logs=False, # includes traces, tool usage, etc. + extended_logs=False, # includes traces, tool usage, etc. parse_answer_fn=parse_final_answer, ) diff --git a/examples/evaluate/human_eval/run.py b/examples/evaluate/human_eval/run.py index 095ee3159..ec9d8057c 100644 --- a/examples/evaluate/human_eval/run.py +++ b/examples/evaluate/human_eval/run.py @@ -3,7 +3,6 @@ from pathlib import Path from ragbits.agents import Agent, AgentOptions -from ragbits.agents.tools.todo import TodoList, create_todo_manager, get_todo_instruction_tpl from ragbits.core.llms import LiteLLM from ragbits.core.sources.hf import HuggingFaceSource from ragbits.evaluate.dataloaders.human_eval import HumanEvalDataLoader @@ -17,9 +16,6 @@ async def main() -> None: """Run HumanEval example with an Agent and print aggregate metrics.""" logging.getLogger("LiteLLM").setLevel(logging.ERROR) - todo_list = TodoList() - todo_manager = create_todo_manager(todo_list) - prompt_text = "\n".join( [ """ @@ -27,31 +23,17 @@ async def main() -> None: Your task is to implement exactly one function that solves the problem. Return ONLY the function as plain Python (no markdown). Include all necessary imports. - WORKFLOW: - 1. If query is complex you have access to todo_manager tool to create a todo list with specific tasks - 2. If query is simple question, you work without todo_manager tool, just answer the question - 3. If you use todo_manager tool, you must follow the todo workflow - - Tool policy: - - If the problem is complex, follow this strict TODO workflow: - 1) todo_manager(action="create", tasks=[...]) with 3-5 concrete tasks - 2) For EACH task: - - todo_manager(action="get_current") - - todo_manager(action="start_task") - - do the work - - todo_manager(action="complete_task", summary="...") - 3) Finally: todo_manager(action="get_final_summary") - - Never call complete_task before start_task. - - If you decide to skip tools, do not call them at all. + POLICY: + - Think step-by-step internally if needed, but output only the final function. + - Do not include explanations, comments, or markdown. """, - get_todo_instruction_tpl(task_range=(3, 5)), ] ) agent: Agent = Agent( llm=LiteLLM("gpt-4.1-mini"), prompt=prompt_text, - tools=[todo_manager], + tools=[], default_options=AgentOptions(max_turns=30), ) @@ -83,7 +65,7 @@ def sanitize_code(text: str) -> str: timeout_sec=30, per_example_log_file=log_path, # agent specific ext. logs - extended_logs=True, # includes traces, tool usage, etc. + extended_logs=True, # includes traces, tool usage, etc. code_sanitize_fn=sanitize_code, ) diff --git a/packages/ragbits-agents/src/ragbits/agents/__init__.py b/packages/ragbits-agents/src/ragbits/agents/__init__.py index 55d758712..edd8226cf 100644 --- a/packages/ragbits-agents/src/ragbits/agents/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/__init__.py @@ -27,4 +27,4 @@ "StreamingPostProcessor", "ToolCall", "ToolCallResult", -] \ No newline at end of file +] diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py index 3f9bc4ec3..182f1d405 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/__init__.py @@ -1,4 +1,4 @@ from ragbits.agents.tools.openai import get_code_interpreter_tool, get_image_generation_tool, get_web_search_tool from ragbits.agents.tools.todo import TodoOrchestrator -__all__ = ["TodoOrchestrator", "get_code_interpreter_tool", "get_image_generation_tool", "get_web_search_tool"] \ No newline at end of file +__all__ = ["TodoOrchestrator", "get_code_interpreter_tool", "get_image_generation_tool", "get_web_search_tool"] diff --git a/packages/ragbits-core/src/ragbits/core/sources/hf.py b/packages/ragbits-core/src/ragbits/core/sources/hf.py index b0dd51f79..b65d613dc 100644 --- a/packages/ragbits-core/src/ragbits/core/sources/hf.py +++ b/packages/ragbits-core/src/ragbits/core/sources/hf.py @@ -23,6 +23,7 @@ class HuggingFaceSource(Source): protocol: ClassVar[str] = "hf" path: str + name: str | None = None split: str = "train" row: int | None = None @@ -51,7 +52,10 @@ async def fetch(self) -> Path: with trace(path=self.path, split=self.split, row=self.row) as outputs: if self.row is not None: try: - dataset = load_dataset(self.path, split=self.split, streaming=True) + if self.name is not None and str(self.name).strip(): + dataset = load_dataset(self.path, self.name, split=self.split, streaming=True) + else: + dataset = load_dataset(self.path, split=self.split, streaming=True) except ConnectionError as exc: raise SourceConnectionError() from exc except DatasetNotFoundError as exc: @@ -79,7 +83,10 @@ async def fetch(self) -> Path: if not path.is_file(): try: - dataset = load_dataset(self.path, split=self.split) + if self.name is not None and str(self.name).strip(): + dataset = load_dataset(self.path, self.name, split=self.split) + else: + dataset = load_dataset(self.path, split=self.split) except ConnectionError as exc: raise SourceConnectionError() from exc except DatasetNotFoundError as exc: diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/hotpot_qa.py index 6977aade3..a32df261a 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/hotpot_qa.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/dataloaders/hotpot_qa.py @@ -35,7 +35,6 @@ def __init__( context_key: str = "context", # filter level_filter: str | None = None, # one of: easy|medium|hard - subset: str | None = None, # one of: distractor|fullwiki (if present in dataset path/metadata) ) -> None: """ Initialize the HotpotQA data loader. @@ -50,7 +49,6 @@ def __init__( level_key: Column with difficulty ("easy" | "medium" | "hard"). context_key: Column with context object containing titles and sentences. level_filter: If provided, return only examples with this level. - subset: Unused in mapping; keep for symmetry with dataset organization. """ required = {id_key, question_key, answer_key, type_key, level_key, context_key} super().__init__(source=source, split=split, required_keys=required) @@ -61,7 +59,6 @@ def __init__( self.level_key = level_key self.context_key = context_key self.level_filter = level_filter - self.subset = subset async def map(self, dataset: Iterable[dict]) -> Iterable[HotpotQAData]: """ diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py index 0fe914943..fd5d62945 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/__init__.py @@ -3,8 +3,8 @@ from ragbits.evaluate.pipelines.base import EvaluationData, EvaluationPipeline, EvaluationResult from ragbits.evaluate.pipelines.document_search import DocumentSearchPipeline from ragbits.evaluate.pipelines.gaia import GaiaPipeline -from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAPipeline +from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline __all__ = [ "DocumentSearchPipeline", @@ -12,8 +12,8 @@ "EvaluationPipeline", "EvaluationResult", "GaiaPipeline", - "HumanEvalPipeline", "HotpotQAPipeline", + "HumanEvalPipeline", ] _target_to_evaluation_pipeline: dict[type[WithConstructionConfig], type[EvaluationPipeline]] = { diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py index bd1532f7a..4721e8548 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py @@ -11,25 +11,10 @@ from ragbits.agents import Agent from ragbits.core.llms.base import LLM, LLMClientOptionsT, LLMResponseWithMetadata, Usage from ragbits.document_search import DocumentSearch -from ragbits.document_search.documents.document import Document, DocumentMeta, DocumentType -from ragbits.document_search.documents.element import TextElement -from ragbits.document_search.ingestion.parsers.base import DocumentParser -from ragbits.document_search.ingestion.parsers.router import DocumentParserRouter -from ragbits.document_search.ingestion.strategies.batched import BatchedIngestStrategy +from ragbits.document_search.documents.document import DocumentMeta from ragbits.evaluate.pipelines.base import EvaluationData, EvaluationPipeline, EvaluationResult -class ContextListParser(DocumentParser): - """ - Parser for in-memory context strings (treated as a single TXT document). - """ - - supported_document_types = {DocumentType.TXT} - - async def parse(self, document: Document) -> list[TextElement]: - content = document.local_path.read_text() - return [TextElement(content=content, document_meta=document.metadata)] - class HotpotQAData(EvaluationData): """ @@ -121,9 +106,6 @@ async def __call__(self, data: Iterable[HotpotQAData]) -> Iterable[HotpotQAResul for content in row.reference_context: documents.append(DocumentMeta.from_literal(content)) - parser_router = DocumentParserRouter({DocumentType.TXT: ContextListParser()}) - self.retriever.ingest_strategy = BatchedIngestStrategy(index_batch_size=1000) - self.retriever.parser_router = parser_router await self.retriever.ingest(documents) async def answer_with_retrieval( @@ -209,7 +191,14 @@ async def answer_with_retrieval( ensure_ascii=False, default=str, ) - self._log_example(row=row, predicted_text=predicted_text, predicted_extracted=predicted_extracted, em=result.em_value, f1=result.f1_value, extended_log=ext_log_str) + self._log_example( + row=row, + predicted_text=predicted_text, + predicted_extracted=predicted_extracted, + em=result.em_value, + f1=result.f1_value, + extended_log=ext_log_str, + ) return results From 56c8432651d217f9d38c652114955d90b357bde9 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Tue, 30 Sep 2025 17:04:02 +0200 Subject: [PATCH 28/43] refactor --- examples/README.md | 3 + .../evaluate/agent-benchmarking/run_gaia.py | 288 ++++++++++++++++++ .../evaluate/agent-benchmarking/run_hotpot.py | 185 +++++++++++ .../agent-benchmarking/run_humaneval.py | 161 ++++++++++ .../evaluate/agent-benchmarking/todo_agent.py | 86 ++++++ examples/evaluate/gaia/run.py | 110 ------- examples/evaluate/hotpot_qa/run.py | 90 ------ examples/evaluate/human_eval/run.py | 85 ------ .../src/ragbits/agents/tools/extra.py | 208 ------------- .../src/ragbits/evaluate/metrics/gaia.py | 8 +- .../src/ragbits/evaluate/pipelines/gaia.py | 15 +- .../ragbits/evaluate/pipelines/hotpot_qa.py | 50 ++- .../ragbits/evaluate/pipelines/human_eval.py | 3 +- 13 files changed, 768 insertions(+), 524 deletions(-) create mode 100644 examples/evaluate/agent-benchmarking/run_gaia.py create mode 100644 examples/evaluate/agent-benchmarking/run_hotpot.py create mode 100644 examples/evaluate/agent-benchmarking/run_humaneval.py create mode 100644 examples/evaluate/agent-benchmarking/todo_agent.py delete mode 100644 examples/evaluate/gaia/run.py delete mode 100644 examples/evaluate/hotpot_qa/run.py delete mode 100644 examples/evaluate/human_eval/run.py delete mode 100644 packages/ragbits-agents/src/ragbits/agents/tools/extra.py diff --git a/examples/README.md b/examples/README.md index e8fdbcb7b..d25dab54f 100644 --- a/examples/README.md +++ b/examples/README.md @@ -46,3 +46,6 @@ All necessary details are provided in the comments at the top of each script. | [MCP SSE](/examples/agents/mcp/sse.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use the `Agent` class to connect with a remote MCP server via SSE. | | [MCP Streamable HTTP](/examples/agents/mcp/streamable_http.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to use the `Agent` class to connect with a remote MCP server via HTTP. | | [A2A Orchestration](/examples/agents/a2a/run_orchestrator.py) | [ragbits-agents](/packages/ragbits-agents) | Example of how to setup A2A orchestration. | +| [Agent Benchmark: HotpotQA](/examples/evaluate/agent-benchmarking/run_hotpot.py) | [ragbits-evaluate](/packages/ragbits-evaluate) | Example of how to benchmark on HotpotQA comparing `Agent` and `TodoAgent`. | +| [Agent Benchmark: GAIA](/examples/evaluate/agent-benchmarking/run_gaia.py) | [ragbits-evaluate](/packages/ragbits-evaluate) | Example of how to benchmark on GAIA with tools comparing `Agent` and `TodoAgent`. | +| [Agent Benchmark: HumanEval](/examples/evaluate/agent-benchmarking/run_humaneval.py) | [ragbits-evaluate](/packages/ragbits-evaluate) | Example of how to benchmark on HumanEval comparing `Agent` and `TodoAgent`. | diff --git a/examples/evaluate/agent-benchmarking/run_gaia.py b/examples/evaluate/agent-benchmarking/run_gaia.py new file mode 100644 index 000000000..66769adee --- /dev/null +++ b/examples/evaluate/agent-benchmarking/run_gaia.py @@ -0,0 +1,288 @@ +""" +Ragbits Evaluation Example: GAIA (Agent vs TodoAgent) + +This example demonstrates how to evaluate the GAIA benchmark using either a classic +`Agent` or a task-decomposing `TodoAgent`, controlled by a command-line flag. Both modes +share the same prompt, retrieval setup, and parsing logic. + +To run the script, execute one of the following commands: + + ```bash + # Agent mode + uv run python examples/evaluate/agent-benchmarking/run_gaia.py + + # TodoAgent mode + uv run python examples/evaluate/agent-benchmarking/run_gaia.py --use_todo + ``` +""" + +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "ragbits-agents", +# "ragbits-core", +# "ragbits-evaluate", +# ] +# /// + +import argparse +import asyncio +import json +import logging +import urllib.parse +import urllib.request +import xml.etree.ElementTree as ET +from pathlib import Path +from typing import Any + +from ragbits.agents import Agent, AgentOptions +from ragbits.agents.tools.openai import get_web_search_tool +from ragbits.core.llms import LiteLLM +from ragbits.core.sources.hf import HuggingFaceSource +from ragbits.evaluate.dataloaders.gaia import GaiaDataLoader +from ragbits.evaluate.evaluator import Evaluator +from ragbits.evaluate.metrics.base import MetricSet +from ragbits.evaluate.metrics.gaia import GaiaEfficiency, GaiaOutcome, GaiaTooling +from ragbits.evaluate.pipelines.gaia import GaiaPipeline + +from todo_agent import TodoAgent + + +# Extra Agent tools + +def add(a: int, b: int) -> int: + """Add two integers and return the result.""" + return a + b + + +def subtract(a: int, b: int) -> int: + """Subtract two integers and return the result (a - b).""" + return a - b + + +def multiply(a: int, b: int) -> int: + """Multiply two integers and return the result.""" + return a * b + + +def divide(a: int, b: int) -> float: + """Divide two integers and return the result as float.""" + if b == 0: + raise ValueError("Cannot divide by zero.") + return a / b + + +def modulus(a: int, b: int) -> int: + """Compute remainder of a divided by b (a % b).""" + if b == 0: + raise ValueError("Cannot divide by zero.") + return a % b + + +def _http_get(url: str, timeout: float = 10.0) -> bytes: + req = urllib.request.Request(url, headers={"User-Agent": "ragbits-agents/extra-tools"}) + with urllib.request.urlopen(req, timeout=timeout) as resp: + return resp.read() + + +def arxiv_search(query: str, max_results: int = 3) -> dict[str, Any]: + """Search arXiv and return up to `max_results` entries (title, summary, link).""" + if max_results <= 0: + return {"results": []} + + base = "https://export.arxiv.org/api/query" + params = urllib.parse.urlencode( + { + "search_query": query, + "start": 0, + "max_results": max_results, + "sortBy": "relevance", + "sortOrder": "descending", + } + ) + raw = _http_get(f"{base}?{params}") + + root = ET.fromstring(raw) + ns = {"atom": "http://www.w3.org/2005/Atom"} + results: list[dict[str, str]] = [] + for entry in root.findall("atom:entry", ns): + title = (entry.findtext("atom:title", default="", namespaces=ns) or "").strip() + summary = (entry.findtext("atom:summary", default="", namespaces=ns) or "").strip() + link_el = entry.find("atom:link[@rel='alternate']", ns) + link = link_el.get("href") if link_el is not None else "" + results.append({"title": title, "summary": summary, "link": link}) + + return {"results": results} + + +def wiki_search(query: str, max_results: int = 2, language: str = "en") -> dict[str, Any]: + """Search Wikipedia and return up to `max_results` entries (title and url).""" + if max_results <= 0: + return {"results": []} + + api = f"https://{language}.wikipedia.org/w/api.php" + params = urllib.parse.urlencode( + { + "action": "opensearch", + "search": query, + "limit": max_results, + "namespace": 0, + "format": "json", + } + ) + raw = _http_get(f"{api}?{params}") + data = json.loads(raw.decode("utf-8")) + + titles: list[str] = data[1] if len(data) > 1 else [] + urls: list[str] = data[3] if len(data) > 3 else [] + + results: list[dict[str, Any]] = [] + for i, title in enumerate(titles[: max_results]): + url = urls[i] if i < len(urls) else "" + results.append({"title": title, "url": url}) + + return {"results": results} + + +def get_extra_instruction_tpl() -> str: + """Generate tool usage instructions template for arithmetic and lookups.""" + return ( + "Tools (use when needed):\n" + "- add(a, b), subtract(a, b), multiply(a, b), divide(a, b), modulus(a, b)\n" + "- arxiv_search(query, max_results=3)\n" + "- wiki_search(query, max_results=2, language='en')\n" + "- web_search -> OpenAI websearch tool\n" + ) + + +def _build_system_prompt() -> str: + """Build a unified GAIA system prompt shared by both modes.""" + gaia_prompt = ( + "You are a general AI assistant. Provide a concise solution and finish with:\n" + "FINAL ANSWER: [your final answer].\n" + "Rules for FINAL ANSWER: use digits for numbers (no units unless requested);\n" + "prefer few words for strings; for lists, return a comma-separated list." + ) + + return "\n".join( + [ + gaia_prompt, + get_extra_instruction_tpl(), + ] + ) + + +def _build_tools() -> list: + """Return the shared toolset for GAIA.""" + return [ + add, + subtract, + multiply, + divide, + modulus, + arxiv_search, + wiki_search, + get_web_search_tool(model_name="gpt-4.1-mini"), + ] + + +def _parse_final_answer(text: str) -> str: + """Extract the FINAL ANSWER segment from model output.""" + marker = "FINAL ANSWER:" + idx = text.rfind(marker) + if idx == -1: + return text.strip() + candidate = text[idx + len(marker) :].strip() + if candidate.startswith("[") and candidate.endswith("]"): + candidate = candidate[1:-1].strip() + return candidate + + +async def main(use_todo: bool) -> None: + """Run GAIA evaluation in classic or Todo mode.""" + logging.getLogger("LiteLLM").setLevel(logging.ERROR) + + base_agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=_build_system_prompt(), + tools=_build_tools(), + default_options=AgentOptions(max_turns=30), + ) + evaluation_target = ( + TodoAgent(agent=base_agent, domain_context="general AI assistant") if use_todo else base_agent + ) + + # Data loader + source = HuggingFaceSource(path="gaia-benchmark/GAIA", name="2023_level1", split="validation") + dataloader = GaiaDataLoader(source=source, split="data[:10]", skip_file_attachments=True) + + # Pipeline + log_file = "gaia_todo_examples.ndjson" if use_todo else "gaia_examples.ndjson" + log_path = Path(__file__).with_name(log_file) + pipeline = GaiaPipeline( + evaluation_target=evaluation_target, + per_example_log_file=log_path, + extended_logs=use_todo, + parse_answer_fn=_parse_final_answer, + ) + + # Metrics + metrics = MetricSet(GaiaOutcome(), GaiaTooling(), GaiaEfficiency()) + + evaluator = Evaluator(batch_size=5, parallelize_batches=True) + results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) + + print("Metrics:") + for key, value in results.metrics.items(): + print(f" {key}: {value:.4f}") + + if use_todo: + await _print_todo_stats(log_path, dataloader) + + +async def _print_todo_stats(log_path: Path, dataloader) -> None: + """Print aggregated TODO-agent statistics from extended logs.""" + print("\nTODO Orchestrator Statistics:") + decomposed_count = 0 + total_tasks = 0 + simple_count = 0 + complex_count = 0 + + if log_path.exists(): + import json + + with open(log_path, encoding="utf-8") as f: + for line in f: + record = json.loads(line) + debug_log = record.get("extended_debug_logging", "[]") + if debug_log and debug_log != "[]": + debug_data = json.loads(debug_log) + if debug_data and len(debug_data) > 0: + metadata = (debug_data[0] or {}).get("metadata", {}) + todo_meta = metadata.get("todo", {}) + + if todo_meta.get("was_decomposed"): + decomposed_count += 1 + total_tasks += int(todo_meta.get("num_tasks", 0)) + + if todo_meta.get("complexity_classification") == "SIMPLE": + simple_count += 1 + elif todo_meta.get("complexity_classification") == "COMPLEX": + complex_count += 1 + + total = len(await dataloader.load()) + rate = (decomposed_count / total * 100) if total else 0.0 + avg_tasks = (total_tasks / decomposed_count) if decomposed_count else 0.0 + print(f" Decomposition rate: {decomposed_count}/{total} ({rate:.1f}%)") + print(f" Average tasks per decomposed query: {avg_tasks:.1f}") + print(f" Complexity classification: {simple_count} simple, {complex_count} complex") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="GAIA evaluation example") + parser.add_argument("--use_todo", action="store_true", help="Run with TodoAgent instead of Agent") + args = parser.parse_args() + + asyncio.run(main(args.use_todo)) + + diff --git a/examples/evaluate/agent-benchmarking/run_hotpot.py b/examples/evaluate/agent-benchmarking/run_hotpot.py new file mode 100644 index 000000000..1726fc311 --- /dev/null +++ b/examples/evaluate/agent-benchmarking/run_hotpot.py @@ -0,0 +1,185 @@ +""" +Ragbits Evaluation Example: HotpotQA (Agent vs TodoAgent) + +This example demonstrates how to evaluate HotpotQA using either a classic `Agent` +or a task-decomposing `TodoAgent`, selected via a command-line flag. Both modes +share the same prompt, retrieval setup, and parsing logic. + +To run the script, execute one of the following commands: + + ```bash + # Agent mode + uv run python examples/evaluate/agent-benchmarking/run_hotpot.py + + # TodoAgent mode + uv run python examples/evaluate/agent-benchmarking/run_hotpot.py --use_todo + ``` +""" + +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "ragbits-agents", +# "ragbits-core", +# "ragbits-document-search", +# "ragbits-evaluate", +# ] +# /// + +import argparse +import asyncio +import logging +from pathlib import Path + +from ragbits.agents import Agent, AgentOptions +from ragbits.core.embeddings import LiteLLMEmbedder +from ragbits.core.llms import LiteLLM +from ragbits.core.sources.hf import HuggingFaceSource +from ragbits.core.vector_stores import VectorStoreOptions +from ragbits.core.vector_stores.in_memory import InMemoryVectorStore +from ragbits.document_search import DocumentSearch +from ragbits.document_search.ingestion.strategies import BatchedIngestStrategy +from ragbits.evaluate.dataloaders.hotpot_qa import HotpotQADataLoader +from ragbits.evaluate.evaluator import Evaluator +from ragbits.evaluate.metrics.base import MetricSet +from ragbits.evaluate.metrics.hotpot_qa import HotpotQAExactMatch, HotpotQAF1 +from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAPipeline + +from todo_agent import TodoAgent + + +def _build_system_prompt() -> str: + """Build a unified HotpotQA system prompt shared by both modes.""" + return ( + "You are a helpful assistant. Use the provided context to answer.\n" + "Respond on a single line as: 'Answer: '.\n" + "- If yes/no, respond 'Answer: yes' or 'Answer: no'.\n" + "- If a name or title is required, provide only that after 'Answer:'.\n" + "- Do not add any extra text beyond the Answer line.\n" + "Keep the answer concise." + ) + + +def _build_question_generation_prompt(original_question: str, accumulated_context: str) -> str: + """Build a prompt for generating the next search question in multi-hop retrieval.""" + return ( + f"Original question: {original_question}\n\n" + f"Context so far: \n{accumulated_context}\n\n" + "Write ONE new, specific search question that fills the key missing info.\n" + "Do not repeat known facts. Return ONLY the question." + ) + + +def _parse_final_answer(text: str) -> str: + """Extract the Answer segment from model output.""" + marker = "Answer:" + idx = text.rfind(marker) + if idx == -1: + return text.strip() + candidate = text[idx + len(marker) :].strip() + if candidate.startswith("<") and candidate.endswith(">"): + candidate = candidate[1:-1].strip() + return candidate + + +def _build_retriever() -> DocumentSearch: + """Create a lightweight in-memory retriever for HotpotQA.""" + return DocumentSearch( + vector_store=InMemoryVectorStore( + embedder=LiteLLMEmbedder(model_name="text-embedding-3-small"), + default_options=VectorStoreOptions(k=5), + ), + ingest_strategy=BatchedIngestStrategy(index_batch_size=1000), + ) + + +async def main(use_todo: bool) -> None: + """Run HotpotQA evaluation in classic or Todo mode.""" + logging.getLogger("LiteLLM").setLevel(logging.ERROR) + + # Build agent or TodoAgent + base_agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=_build_system_prompt(), + tools=[], + default_options=AgentOptions(max_turns=5), + ) + evaluation_target = ( + TodoAgent(agent=base_agent, domain_context="research QA") if use_todo else base_agent + ) + + # Data loader + source = HuggingFaceSource(path="hotpotqa/hotpot_qa", name="distractor", split="validation") + dataloader = HotpotQADataLoader(source=source, split="data[:10]", level_filter="hard") + + # Pipeline + log_file = "hotpot_todo_examples.ndjson" if use_todo else "hotpot_examples.ndjson" + log_path = Path(__file__).with_name(log_file) + pipeline = HotpotQAPipeline( + evaluation_target=evaluation_target, + retriever=_build_retriever(), + hops=3, + per_example_log_file=log_path, + extended_logs=use_todo, + parse_answer_fn=_parse_final_answer, + question_generation_prompt_fn=_build_question_generation_prompt, + ) + + # Metrics + metrics = MetricSet(HotpotQAExactMatch(), HotpotQAF1()) + + evaluator = Evaluator(batch_size=5, parallelize_batches=True) + results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) + + print("Metrics:") + for key, value in results.metrics.items(): + print(f" {key}: {value:.4f}") + + if use_todo: + await _print_todo_stats(log_path, dataloader) + + +async def _print_todo_stats(log_path: Path, dataloader) -> None: + """Print aggregated TODO-agent statistics from extended logs.""" + print("\nTODO Orchestrator Statistics:") + decomposed_count = 0 + total_tasks = 0 + simple_count = 0 + complex_count = 0 + + if log_path.exists(): + import json + + with open(log_path, encoding="utf-8") as f: + for line in f: + record = json.loads(line) + debug_log = record.get("extended_debug_logging", "[]") + if debug_log and debug_log != "[]": + debug_data = json.loads(debug_log) + if debug_data and len(debug_data) > 0: + metadata = (debug_data[0] or {}).get("metadata", {}) + todo_meta = metadata.get("todo", {}) + + if todo_meta.get("was_decomposed"): + decomposed_count += 1 + total_tasks += int(todo_meta.get("num_tasks", 0)) + + if todo_meta.get("complexity_classification") == "SIMPLE": + simple_count += 1 + elif todo_meta.get("complexity_classification") == "COMPLEX": + complex_count += 1 + + total = len(await dataloader.load()) + rate = (decomposed_count / total * 100) if total else 0.0 + avg_tasks = (total_tasks / decomposed_count) if decomposed_count else 0.0 + print(f" Decomposition rate: {decomposed_count}/{total} ({rate:.1f}%)") + print(f" Average tasks per decomposed query: {avg_tasks:.1f}") + print(f" Complexity classification: {simple_count} simple, {complex_count} complex") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="HotpotQA evaluation example") + parser.add_argument("--use_todo", action="store_true", help="Run with TodoAgent instead of Agent") + args = parser.parse_args() + + asyncio.run(main(args.use_todo)) diff --git a/examples/evaluate/agent-benchmarking/run_humaneval.py b/examples/evaluate/agent-benchmarking/run_humaneval.py new file mode 100644 index 000000000..ee4cc762e --- /dev/null +++ b/examples/evaluate/agent-benchmarking/run_humaneval.py @@ -0,0 +1,161 @@ +""" +Ragbits Evaluation Example: HumanEval (Agent vs TodoAgent) + +This example demonstrates how to evaluate HumanEval using either a classic `Agent` +or a task-decomposing `TodoAgent`. Both modes share the +same prompt, retrieval setup, and parsing logic. + +To run the script, execute one of the following commands: + + ```bash + # Agent mode + uv run python examples/evaluate/agent-benchmarking/run_humaneval.py + + # TodoAgent mode + uv run python examples/evaluate/agent-benchmarking/run_humaneval.py --use_todo + ``` +""" + +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "ragbits-agents", +# "ragbits-core", +# "ragbits-evaluate", +# ] +# /// + +import argparse +import asyncio +import logging +from pathlib import Path + +from ragbits.agents import Agent, AgentOptions +from ragbits.core.llms import LiteLLM +from ragbits.core.sources.hf import HuggingFaceSource +from ragbits.evaluate.dataloaders.human_eval import HumanEvalDataLoader +from ragbits.evaluate.evaluator import Evaluator +from ragbits.evaluate.metrics.base import MetricSet +from ragbits.evaluate.metrics.human_eval import HumanEvalPassAtK, HumanEvalQualityPerf +from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline + +from todo_agent import TodoAgent + + +def _build_system_prompt() -> str: + """Build a unified HumanEval system prompt shared by both modes.""" + return "\n".join( + [ + "You are an expert Python engineer.", + "Implement exactly one function that solves the problem.", + "Output ONLY the function as plain Python (no markdown). Include necessary imports.", + "Do not include explanations or comments." + ] + ) + + +def _sanitize_code(text: str) -> str: + """Remove markdown fences and keep only Python function text returned by the model.""" + cleaned = text.replace("\r\n", "\n").replace("\r", "\n").strip() + if "```" in cleaned: + start = cleaned.find("```") + end = cleaned.find("```", start + 3) + if end != -1: + inside = cleaned[start + 3 : end].lstrip() + if "\n" in inside: + first, rest = inside.split("\n", 1) + cleaned = rest if first.strip().lower().startswith("python") else inside + else: + cleaned = inside + return cleaned.strip() + + +async def main(use_todo: bool) -> None: + """Run HumanEval in classic or Todo mode.""" + logging.getLogger("LiteLLM").setLevel(logging.ERROR) + + base_agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=_build_system_prompt(), + tools=[], + default_options=AgentOptions(max_turns=30), + ) + evaluation_target = ( + TodoAgent(agent=base_agent, domain_context="python engineer") if use_todo else base_agent + ) + + # Data + source = HuggingFaceSource(path="openai/openai_humaneval", split="test") + dataloader = HumanEvalDataLoader(source=source, split="data[:2]") + + # Pipeline + log_file = "humaneval_todo_examples.ndjson" if use_todo else "humaneval_examples.ndjson" + log_path = Path(__file__).with_name(log_file) + pipeline = HumanEvalPipeline( + evaluation_target=evaluation_target, + n_samples=1, + timeout_sec=30, + per_example_log_file=log_path, + extended_logs=use_todo, + code_sanitize_fn=_sanitize_code, + ) + + # Metrics + metrics = MetricSet(HumanEvalPassAtK(k=1), HumanEvalPassAtK(k=5), HumanEvalQualityPerf()) + + # Evaluate + evaluator = Evaluator(batch_size=5, parallelize_batches=True) + results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) + + print("Metrics:") + for key, value in results.metrics.items(): + print(f" {key}: {value:.4f}") + + if use_todo: + await _print_todo_stats(log_path, dataloader) + + +async def _print_todo_stats(log_path: Path, dataloader) -> None: + """Print aggregated TODO-agent statistics from extended logs.""" + print("\nTODO Orchestrator Statistics:") + decomposed_count = 0 + total_tasks = 0 + simple_count = 0 + complex_count = 0 + + if log_path.exists(): + import json + + with open(log_path, encoding="utf-8") as f: + for line in f: + record = json.loads(line) + debug_log = record.get("extended_debug_logging", "[]") + if debug_log and debug_log != "[]": + debug_data = json.loads(debug_log) + if debug_data and len(debug_data) > 0: + metadata = (debug_data[0] or {}).get("metadata", {}) + todo_meta = metadata.get("todo", {}) + + if todo_meta.get("was_decomposed"): + decomposed_count += 1 + total_tasks += int(todo_meta.get("num_tasks", 0)) + + if todo_meta.get("complexity_classification") == "SIMPLE": + simple_count += 1 + elif todo_meta.get("complexity_classification") == "COMPLEX": + complex_count += 1 + + total = len(await dataloader.load()) + rate = (decomposed_count / total * 100) if total else 0.0 + avg_tasks = (total_tasks / decomposed_count) if decomposed_count else 0.0 + print(f" Decomposition rate: {decomposed_count}/{total} ({rate:.1f}%)") + print(f" Average tasks per decomposed query: {avg_tasks:.1f}") + print(f" Complexity classification: {simple_count} simple, {complex_count} complex") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="HumanEval evaluation example") + parser.add_argument("--use_todo", action="store_true", help="Run with TodoAgent instead of Agent") + args = parser.parse_args() + + asyncio.run(main(args.use_todo)) diff --git a/examples/evaluate/agent-benchmarking/todo_agent.py b/examples/evaluate/agent-benchmarking/todo_agent.py new file mode 100644 index 000000000..abd16cfc7 --- /dev/null +++ b/examples/evaluate/agent-benchmarking/todo_agent.py @@ -0,0 +1,86 @@ +from __future__ import annotations + +from typing import Any, cast + +from ragbits.agents import Agent, AgentOptions, AgentResult +from ragbits.agents.tool import ToolCallResult +from ragbits.agents.tools.todo import TodoOrchestrator +from ragbits.core.llms.base import LLMClientOptionsT, Usage +from ragbits.core.prompt.base import BasePrompt + + +class TodoAgent(Agent[LLMClientOptionsT, None, str]): + """ + Agent wrapper that uses TodoOrchestrator to optionally decompose a query + into tasks and aggregate a final answer, while remaining compatible with + existing EvaluationPipelines that expect an Agent. + """ + + def __init__(self, agent: Agent[LLMClientOptionsT, None, str], domain_context: str = "") -> None: + super().__init__( + llm=agent.llm, + prompt=agent.prompt, + history=agent.history, + keep_history=False, + mcp_servers=agent.mcp_servers, + default_options=cast(AgentOptions[LLMClientOptionsT] | None, agent.default_options), + ) + self._inner_agent = agent + self._domain_context = domain_context + + async def run( + self, + input: str | None = None, + *_: Any, + **__: Any, + ) -> AgentResult[str]: + """Run the orchestrated flow and return a single final answer.""" + query = input or "" + + orchestrator = TodoOrchestrator(domain_context=self._domain_context) + + # Accumulate outputs from the orchestrated workflow + final_text: str = "" + tool_calls: list[ToolCallResult] | None = None + total_usage: Usage = Usage() + last_prompt: BasePrompt | None = None + + tasks_created = False + num_tasks = 0 + + async for item in orchestrator.run_todo_workflow_streaming(self._inner_agent, query): + match item: + case str(): + final_text += item + case ToolCallResult(): + if tool_calls is None: + tool_calls = [] + tool_calls.append(item) + case Usage(): + total_usage += item + case BasePrompt(): + last_prompt = item + case _: + # Inspect orchestrator internal state to populate metadata + tasks_created = len(orchestrator.todo_list.tasks) > 0 + num_tasks = len(orchestrator.todo_list.tasks) + + # Compose metadata with TODO info + complexity = "COMPLEX" if tasks_created else "SIMPLE" + todo_meta: dict[str, Any] = { + "was_decomposed": bool(tasks_created), + "complexity_classification": complexity, + "num_tasks": int(num_tasks), + "domain_context": self._domain_context, + } + + # Build result compatible with existing pipelines + history = last_prompt.chat if last_prompt is not None else [] + + return AgentResult( + content=final_text.strip(), + metadata={"todo": todo_meta}, + tool_calls=tool_calls, + history=history, + usage=total_usage, + ) diff --git a/examples/evaluate/gaia/run.py b/examples/evaluate/gaia/run.py deleted file mode 100644 index fb2244a9d..000000000 --- a/examples/evaluate/gaia/run.py +++ /dev/null @@ -1,110 +0,0 @@ -import asyncio -import os -import logging -from pathlib import Path - -from ragbits.agents import Agent, AgentOptions -from ragbits.agents.tools.extra import ( - add, - arxiv_search, - divide, - get_extra_instruction_tpl, - modulus, - multiply, - subtract, - wiki_search, -) -from ragbits.agents.tools.openai import get_web_search_tool -from ragbits.core.llms import LiteLLM -from ragbits.core.sources.hf import HuggingFaceSource -from ragbits.evaluate.dataloaders.gaia import GaiaDataLoader -from ragbits.evaluate.evaluator import Evaluator -from ragbits.evaluate.metrics.base import MetricSet -from ragbits.evaluate.metrics.gaia import GaiaEfficiency, GaiaOutcome, GaiaTooling -from ragbits.evaluate.pipelines.gaia import GaiaPipeline - - -async def main() -> None: - """Run GAIA example with an Agent and print aggregate metrics.""" - logging.getLogger("LiteLLM").setLevel(logging.ERROR) - - gaia_prompt = ( - "You are a general AI assistant. I will ask you a question. " - "Report your thoughts, and finish your answer with the following template: " - "FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible " - "OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write " - "your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a " - "string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text " - "unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of " - "whether the element to be put in the list is a number or a string. If you are asked for a number, make sure " - "you respond using digits not text, and ensure the answer is in appropriate unit context wise " - "(e.g. if asked for thousands of meters and your answer is 31000, answer 31)." - ) - - system_prompt = "\n".join( - [ - gaia_prompt, - ( - "Tool usage:\n" - "- Use arithmetic tools for calculations.\n" - "- Use arxiv_search/wiki_search to retrieve relevant facts when needed or explicitly asked.\n" - "- For complex requests, plan your steps internally before answering concisely.\n" - "- For general web-search questions, use search_web tool and gather information." - ), - get_extra_instruction_tpl(), - ] - ) - - tools = [ - add, - subtract, - multiply, - divide, - modulus, - arxiv_search, - wiki_search, - get_web_search_tool(model_name="gpt-4.1-mini") - ] - - agent: Agent = Agent( - llm=LiteLLM("gpt-4.1-mini"), - prompt=system_prompt, - tools=tools, - default_options=AgentOptions(max_turns=30), - ) - - source = HuggingFaceSource(path="gaia-benchmark/GAIA", name="2023_level1", split="validation") - dataloader = GaiaDataLoader(source=source, split="data[:30]", skip_file_attachments=True) - - log_path = Path(__file__).with_name("gaia_examples.ndjson") - - def parse_final_answer(text: str) -> str: - marker = "FINAL ANSWER:" - idx = text.rfind(marker) - if idx == -1: - return text.strip() - candidate = text[idx + len(marker) :].strip() - if candidate.startswith("[") and candidate.endswith("]"): - candidate = candidate[1:-1].strip() - return candidate - - pipeline = GaiaPipeline( - evaluation_target=agent, - per_example_log_file=log_path, - # agent specific ext. logs - extended_logs=False, # includes traces, tool usage, etc. - parse_answer_fn=parse_final_answer, - ) - - metrics = MetricSet(GaiaOutcome(), GaiaTooling(), GaiaEfficiency()) - - evaluator = Evaluator(batch_size=5, parallelize_batches=True) - results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) - - print("Metrics:") - for key, value in results.metrics.items(): - print(f" {key}: {value:.4f}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/evaluate/hotpot_qa/run.py b/examples/evaluate/hotpot_qa/run.py deleted file mode 100644 index 6e65fce3e..000000000 --- a/examples/evaluate/hotpot_qa/run.py +++ /dev/null @@ -1,90 +0,0 @@ -import asyncio -import logging -from pathlib import Path - -from ragbits.agents import Agent, AgentOptions -from ragbits.core.embeddings import LiteLLMEmbedder -from ragbits.core.llms import LiteLLM -from ragbits.core.sources.hf import HuggingFaceSource -from ragbits.core.vector_stores import VectorStoreOptions -from ragbits.core.vector_stores.in_memory import InMemoryVectorStore -from ragbits.document_search import DocumentSearch -from ragbits.document_search.ingestion.strategies import BatchedIngestStrategy -from ragbits.evaluate.dataloaders.hotpot_qa import HotpotQADataLoader -from ragbits.evaluate.evaluator import Evaluator -from ragbits.evaluate.metrics.base import MetricSet -from ragbits.evaluate.metrics.hotpot_qa import HotpotQAExactMatch, HotpotQAF1 -from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAPipeline - - -async def main() -> None: - """Run HotpotQA example with an Agent and print aggregate metrics.""" - logging.getLogger("LiteLLM").setLevel(logging.ERROR) - - hotpot_prompt = ( - "You are a helpful assistant. Use the given context to answer.\n" - "Respond on a single line as: 'Answer: '.\n" - "- If yes/no, respond 'Answer: yes' or 'Answer: no'.\n" - "- If a name/title is required, respond only the name/title after 'Answer:'.\n" - "- Do not add any extra text beyond the Answer line.\n" - "- Your answer should as concise and minimal as possible, while still answering the question.\n" - "- Think step-by-step internally for complex questions before answering concisely." - ) - - system_prompt = "\n".join( - [ - hotpot_prompt, - ] - ) - agent: Agent = Agent( - llm=LiteLLM("gpt-4.1-mini"), - prompt=system_prompt, - tools=[], - default_options=AgentOptions(max_turns=5), - ) - - source = HuggingFaceSource(path="hotpotqa/hotpot_qa", name="distractor", split="validation") - dataloader = HotpotQADataLoader(source=source, split="data[:25]", level_filter="hard") - - log_path = Path(__file__).with_name("hotpot_examples.ndjson") - - def parse_final_answer(text: str) -> str: - marker = "Answer:" - idx = text.rfind(marker) - if idx == -1: - return text.strip() - candidate = text[idx + len(marker) :].strip() - if candidate.startswith("<") and candidate.endswith(">"): - candidate = candidate[1:-1].strip() - return candidate - - retriever = DocumentSearch( - vector_store=InMemoryVectorStore( - embedder=LiteLLMEmbedder(model_name="text-embedding-3-small"), - default_options=VectorStoreOptions(k=5), - ), - ingest_strategy=BatchedIngestStrategy(index_batch_size=1000), - ) - - pipeline = HotpotQAPipeline( - evaluation_target=agent, - retriever=retriever, - hops=3, - per_example_log_file=log_path, - # agent specific ext. logs - extended_logs=False, # includes traces, tool usage, etc. - parse_answer_fn=parse_final_answer, - ) - - metrics = MetricSet(HotpotQAExactMatch(), HotpotQAF1()) - - evaluator = Evaluator(batch_size=5, parallelize_batches=True) - results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) - - print("Metrics:") - for key, value in results.metrics.items(): - print(f" {key}: {value:.4f}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/evaluate/human_eval/run.py b/examples/evaluate/human_eval/run.py deleted file mode 100644 index ec9d8057c..000000000 --- a/examples/evaluate/human_eval/run.py +++ /dev/null @@ -1,85 +0,0 @@ -import asyncio -import logging -from pathlib import Path - -from ragbits.agents import Agent, AgentOptions -from ragbits.core.llms import LiteLLM -from ragbits.core.sources.hf import HuggingFaceSource -from ragbits.evaluate.dataloaders.human_eval import HumanEvalDataLoader -from ragbits.evaluate.evaluator import Evaluator -from ragbits.evaluate.metrics.base import MetricSet -from ragbits.evaluate.metrics.human_eval import HumanEvalPassAtK, HumanEvalQualityPerf -from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline - - -async def main() -> None: - """Run HumanEval example with an Agent and print aggregate metrics.""" - logging.getLogger("LiteLLM").setLevel(logging.ERROR) - - prompt_text = "\n".join( - [ - """ - You are an expert Python engineer. - Your task is to implement exactly one function that solves the problem. - Return ONLY the function as plain Python (no markdown). Include all necessary imports. - - POLICY: - - Think step-by-step internally if needed, but output only the final function. - - Do not include explanations, comments, or markdown. - """, - ] - ) - - agent: Agent = Agent( - llm=LiteLLM("gpt-4.1-mini"), - prompt=prompt_text, - tools=[], - default_options=AgentOptions(max_turns=30), - ) - - # Data - source = HuggingFaceSource(path="openai/openai_humaneval", split="test") - dataloader = HumanEvalDataLoader(source=source, split="data[:2]") - - # Pipeline - log_path = Path(__file__).with_name("humaneval_examples.ndjson") - - # Code sanitazation function - def sanitize_code(text: str) -> str: - cleaned = text.replace("\r\n", "\n").replace("\r", "\n").strip() - if "```" in cleaned: - start = cleaned.find("```") - end = cleaned.find("```", start + 3) - if end != -1: - inside = cleaned[start + 3 : end].lstrip() - if "\n" in inside: - first, rest = inside.split("\n", 1) - cleaned = rest if first.strip().lower().startswith("python") else inside - else: - cleaned = inside - return cleaned.strip() - - pipeline = HumanEvalPipeline( - evaluation_target=agent, - n_samples=1, - timeout_sec=30, - per_example_log_file=log_path, - # agent specific ext. logs - extended_logs=True, # includes traces, tool usage, etc. - code_sanitize_fn=sanitize_code, - ) - - # Metrics - metrics = MetricSet(HumanEvalPassAtK(k=1), HumanEvalPassAtK(k=5), HumanEvalQualityPerf()) - - # Evaluate - evaluator = Evaluator(batch_size=5, parallelize_batches=True) - results = await evaluator.compute(pipeline=pipeline, dataloader=dataloader, metricset=metrics) - - print("Metrics:") - for key, value in results.metrics.items(): - print(f" {key}: {value:.4f}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/extra.py b/packages/ragbits-agents/src/ragbits/agents/tools/extra.py deleted file mode 100644 index de8d5e069..000000000 --- a/packages/ragbits-agents/src/ragbits/agents/tools/extra.py +++ /dev/null @@ -1,208 +0,0 @@ -from __future__ import annotations - -import json -import urllib.parse -import urllib.request -import xml.etree.ElementTree as ET -from typing import Any - - -def add(a: int, b: int) -> int: - """Add two integers and return the result. - - Args: - a: First integer. - b: Second integer. - - Returns: - Sum of a and b. - """ - return a + b - - -def subtract(a: int, b: int) -> int: - """Subtract two integers and return the result (a - b). - - Args: - a: Minuend integer. - b: Subtrahend integer. - - Returns: - Difference a - b. - """ - return a - b - - -def multiply(a: int, b: int) -> int: - """Multiply two integers and return the result. - - Args: - a: First integer. - b: Second integer. - - Returns: - Product a * b. - """ - return a * b - - -def divide(a: int, b: int) -> float: - """Divide two integers and return the result as float. - - Args: - a: Dividend integer. - b: Divisor integer (must be non-zero). - - Returns: - Quotient a / b as float. - - Raises: - ValueError: If b == 0. - """ - if b == 0: - raise ValueError("Cannot divide by zero.") - return a / b - - -def modulus(a: int, b: int) -> int: - """Compute remainder of a divided by b (a % b). - - Args: - a: Dividend integer. - b: Divisor integer (must be non-zero). - - Returns: - Remainder a % b. - - Raises: - ValueError: If b == 0. - """ - if b == 0: - raise ValueError("Cannot divide by zero.") - return a % b - - -def _http_get(url: str, timeout: float = 10.0) -> bytes: - req = urllib.request.Request(url, headers={"User-Agent": "ragbits-agents/extra-tools"}) - with urllib.request.urlopen(req, timeout=timeout) as resp: - return resp.read() - - -def arxiv_search(query: str, max_results: int = 3) -> dict[str, Any]: - """Search arXiv and return up to `max_results` entries. - - Args: - query: Search query (e.g., "quantum computing") - max_results: Max number of results to return (default 3) - - Returns: - Dict with a list of results: title, summary, link. - """ - if max_results <= 0: - return {"results": []} - - base = "https://export.arxiv.org/api/query" - params = { - "search_query": urllib.parse.quote_plus(query), - "start": "0", - "max_results": str(max_results), - "sortBy": "relevance", - "sortOrder": "descending", - } - url = f"{base}?search_query={params['search_query']}&start={params['start']}&max_results={params['max_results']}&sortBy={params['sortBy']}&sortOrder={params['sortOrder']}" - - raw = _http_get(url) - root = ET.fromstring(raw) - ns = {"atom": "http://www.w3.org/2005/Atom"} - results: list[dict[str, str]] = [] - for entry in root.findall("atom:entry", ns): - title_el = entry.find("atom:title", ns) - summary_el = entry.find("atom:summary", ns) - link_el = entry.find("atom:link[@rel='alternate']", ns) - title = (title_el.text or "").strip() if title_el is not None else "" - summary = (summary_el.text or "").strip() if summary_el is not None else "" - link = link_el.get("href") if link_el is not None else "" - # Trim overly long summaries for brevity - if len(summary) > 1200: - summary = summary[:1200] + "..." - results.append({"title": title, "summary": summary, "link": link}) - - return {"results": results} - - -def wiki_search(query: str, max_results: int = 2, language: str = "en") -> dict[str, Any]: - """Search Wikipedia and return up to `max_results` entries with extracts. - - Args: - query: Search query (e.g., "Alan Turing") - max_results: Max number of results (default 2) - language: Wikipedia language code (default "en") - - Returns: - Dict with list of results: title, pageid, extract, url. - """ - if max_results <= 0: - return {"results": []} - - api = f"https://{language}.wikipedia.org/w/api.php" - search_params = { - "action": "query", - "list": "search", - "srsearch": query, - "srlimit": str(max_results), - "format": "json", - } - search_url = f"{api}?{urllib.parse.urlencode(search_params)}" - raw = _http_get(search_url) - data = json.loads(raw.decode("utf-8")) - hits = data.get("query", {}).get("search", []) - if not hits: - return {"results": []} - - page_ids = [str(h.get("pageid")) for h in hits if "pageid" in h] - extract_params = { - "action": "query", - "prop": "extracts", - "explaintext": "1", - "exintro": "1", - "pageids": "|".join(page_ids), - "format": "json", - } - extract_url = f"{api}?{urllib.parse.urlencode(extract_params)}" - raw2 = _http_get(extract_url) - data2 = json.loads(raw2.decode("utf-8")) - pages = data2.get("query", {}).get("pages", {}) - - results: list[dict[str, Any]] = [] - for pid in page_ids: - page = pages.get(pid) - if not page: - continue - title = page.get("title", "") - extract = (page.get("extract", "") or "").strip() - if len(extract) > 1200: - extract = extract[:1200] + "..." - url = f"https://{language}.wikipedia.org/?curid={pid}" - results.append({"title": title, "pageid": int(pid), "extract": extract, "url": url}) - - return {"results": results} - - -def get_extra_instruction_tpl() -> str: - """Generate tool usage instructions template for arithmetic and lookups. - - Returns: - Instruction template string to include in system prompts. - """ - return ( - "\n\n" - "## Tools Workflow (Arithmetic, arXiv, Wikipedia)\n\n" - "Available actions:\n" - "- `add(a, b)`, `subtract(a, b)`, `multiply(a, b)`, `divide(a, b)`, `modulus(a, b)`\n" - "- `arxiv_search(query, max_results=3)`\n" - "- `wiki_search(query, max_results=2, language='en')`\n\n" - "POLICY:\n" - "1) Use arithmetic tools for calculations instead of inline math when non-trivial.\n" - "2) Use arXiv/Wikipedia tools when scholarly/general knowledge retrieval is needed.\n" - "3) Keep outputs concise; provide FINAL ANSWER after any intermediate reasoning.\n" - ) diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py index 0f6a85267..05f1c9308 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py @@ -68,22 +68,16 @@ class GaiaEfficiency(Metric[GaiaResult]): """ Efficiency and resource usage metrics: - gaia_avg_latency_ms: average response latency in milliseconds - - gaia_avg_cost_usd: average cost per task in USD - - gaia_avg_total_tokens: average total tokens used per task """ async def compute(self, results: list[GaiaResult]) -> dict: """Compute efficiency and resource usage metrics. Returns: - Dictionary with average latency, cost, and token usage. + Dictionary with average latency. """ avg_latency = float(mean(r.total_latency_ms for r in results)) if results else 0.0 - avg_cost = float(mean(r.cost_usd for r in results)) if results else 0.0 - avg_tokens = float(mean(r.total_tokens for r in results)) if results else 0.0 return { "gaia_avg_latency_ms": avg_latency, - "gaia_avg_cost_usd": avg_cost, - "gaia_avg_total_tokens": avg_tokens, } diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py index 2ac041aa3..c425eed77 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py @@ -41,8 +41,6 @@ class GaiaResult(EvaluationResult): num_tool_calls: int tool_error_count: int total_latency_ms: int - total_tokens: int - cost_usd: float tool_names: list[str] | None = None @@ -96,14 +94,12 @@ async def __call__(self, data: Iterable[GaiaData]) -> Iterable[GaiaResult]: content, dbg = await self._generate_with_debug(prompt_input) if debug_traces is not None: debug_traces.append(dbg) - usage = cast(Usage, (dbg or {}).get("usage") or Usage()) tool_calls = cast(list, (dbg or {}).get("tool_calls") or []) else: - content, usage, tool_calls = await self._generate_answer(prompt_input) + content, _, tool_calls = await self._generate_answer(prompt_input) except Exception as generation_exc: content = "" - usage = Usage() tool_calls = [] err_msg = f"GenerationError: {generation_exc.__class__.__name__}: {generation_exc}" if self.extended_logs and debug_traces is not None: @@ -130,8 +126,6 @@ async def __call__(self, data: Iterable[GaiaData]) -> Iterable[GaiaResult]: logging.getLogger(__name__).debug("Error while parsing tool call result: %s", exc) total_latency_ms = int((end_time - start_time) * 1000) - total_tokens = usage.total_tokens if usage else 0 - cost_usd = usage.estimated_cost if usage else 0.0 # Extract tool names (if any) from tool_calls tool_names: list[str] | None = None @@ -158,8 +152,6 @@ async def __call__(self, data: Iterable[GaiaData]) -> Iterable[GaiaResult]: num_tool_calls=num_tool_calls, tool_error_count=tool_error_count, total_latency_ms=total_latency_ms, - total_tokens=total_tokens, - cost_usd=float(cost_usd), tool_names=tool_names, ) results.append(result) @@ -177,7 +169,8 @@ def _init_log_file(self) -> None: if self.per_example_log_file is None: return self.per_example_log_file.parent.mkdir(parents=True, exist_ok=True) - self.per_example_log_file.touch(exist_ok=True) + with open(self.per_example_log_file, "w", encoding="utf-8") as _: + pass def _log_example(self, row: GaiaData, result: GaiaResult, extended_log: str | None = None) -> None: """Append a single NDJSON record for debugging if enabled.""" @@ -200,8 +193,6 @@ def _log_example(self, row: GaiaData, result: GaiaResult, extended_log: str | No "num_tool_calls": result.num_tool_calls, "tool_error_count": result.tool_error_count, "total_latency_ms": result.total_latency_ms, - "total_tokens": result.total_tokens, - "cost_usd": result.cost_usd, "tool_frequency_usage": tool_frequency_usage, } record["extended_debug_logging"] = extended_log or "[]" diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py index 4721e8548..8812efabe 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py @@ -15,7 +15,6 @@ from ragbits.evaluate.pipelines.base import EvaluationData, EvaluationPipeline, EvaluationResult - class HotpotQAData(EvaluationData): """ Represents a single HotpotQA example. @@ -67,6 +66,7 @@ def __init__( per_example_log_file: Path | None = None, extended_logs: bool = False, parse_answer_fn: Callable[[str], str] | None = None, + question_generation_prompt_fn: Callable[[str, str], str] | None = None, retrieval_k: int = 3, element_max_chars: int = 500, hop_context_max_chars: int = 1200, @@ -77,6 +77,7 @@ def __init__( self.per_example_log_file = per_example_log_file self.extended_logs = extended_logs self.parse_answer_fn = parse_answer_fn + self.question_generation_prompt_fn = question_generation_prompt_fn self.retrieval_k = max(1, int(retrieval_k)) self.element_max_chars = max(50, int(element_max_chars)) self.hop_context_max_chars = max(100, int(hop_context_max_chars)) @@ -110,7 +111,7 @@ async def __call__(self, data: Iterable[HotpotQAData]) -> Iterable[HotpotQAResul async def answer_with_retrieval( example: HotpotQAData, - ) -> tuple[str, Usage, list, list[dict]]: + ) -> tuple[str, Usage, list, list[dict], dict]: accumulated_context: list[str] = [] hop_logs: list[dict] = [] last_query = example.question @@ -133,7 +134,12 @@ async def answer_with_retrieval( hop_logs.append({"hop": hop_idx + 1, "question": last_query, "retrieved": hop_text}) if hop_text: accumulated_context.append(hop_text) - last_query = f"{example.question}\n\nContext so far:\n{hop_text}" + # generate a new question for the next hop + if hop_idx < self.hops - 1: + last_query = await self._generate_next_question( + original_question=example.question, + accumulated_context="\n\n".join(accumulated_context), + ) else: break @@ -145,20 +151,23 @@ async def answer_with_retrieval( content, dbg = await self._generate_with_debug(prompt_input) usage = cast(Usage, (dbg or {}).get("usage") or Usage()) tool_calls = cast(list, (dbg or {}).get("tool_calls") or []) + metadata = cast(dict, (dbg or {}).get("metadata") or {}) else: content, usage, tool_calls = await self._generate_answer(prompt_input) + metadata = {} - return str(content), usage, tool_calls, hop_logs + return str(content), usage, tool_calls, hop_logs, metadata results: list[HotpotQAResult] = [] for row in data: try: - predicted_text, usage, tool_calls, hop_logs = await answer_with_retrieval(row) + predicted_text, usage, tool_calls, hop_logs, metadata = await answer_with_retrieval(row) except Exception: predicted_text = "" usage = Usage() tool_calls = [] hop_logs = [] + metadata = {} predicted_extracted = self._parse_answer(predicted_text) ref_norm = self._normalize(row.reference_answer) @@ -183,11 +192,14 @@ async def answer_with_retrieval( ext_log_str = None if self.extended_logs: ext_log_str = json.dumps( - { - "usage": usage, - "tool_calls": tool_calls, - "hops": hop_logs, - }, + [ + { + "usage": usage, + "tool_calls": tool_calls, + "hops": hop_logs, + "metadata": metadata, + } + ], ensure_ascii=False, default=str, ) @@ -207,7 +219,8 @@ def _init_log_file(self) -> None: if self.per_example_log_file is None: return self.per_example_log_file.parent.mkdir(parents=True, exist_ok=True) - self.per_example_log_file.touch(exist_ok=True) + with open(self.per_example_log_file, "w", encoding="utf-8") as _: + pass def _log_example( self, @@ -276,6 +289,21 @@ async def _generate_with_debug(self, prompt: str) -> tuple[str, dict | None]: resp = await target.generate(prompt) return str(resp), None + async def _generate_next_question(self, original_question: str, accumulated_context: str) -> str: + """Generate a new follow-up question based on the original question and accumulated context.""" + if self.question_generation_prompt_fn is None: + # default: simple concatenation + return f"{original_question}\n\nContext so far:\n{accumulated_context}" + + question_generation_prompt = self.question_generation_prompt_fn(original_question, accumulated_context) + + target = self.evaluation_target + if isinstance(target, Agent): + resp = await target.llm.generate(question_generation_prompt) + return str(resp).strip() + resp = await target.generate(question_generation_prompt) + return str(resp).strip() + @staticmethod def _normalize(text: str) -> str: """Basic normalization for answer equality checks: lowercase, strip spaces.""" diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py index 20b933ca8..eb737d39d 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py @@ -262,7 +262,8 @@ def _init_log_file(self) -> None: if self.per_example_log_file is None: return self.per_example_log_file.parent.mkdir(parents=True, exist_ok=True) - self.per_example_log_file.touch(exist_ok=True) + with open(self.per_example_log_file, "w", encoding="utf-8") as _: + pass def _log_example(self, row: HumanEvalData, result: HumanEvalResult, extended_log: str | None = None) -> None: """Append a single NDJSON record for debugging if enabled.""" From 4b2585a4c7a3d626daa191854d682d7454598108 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Wed, 1 Oct 2025 10:41:16 +0200 Subject: [PATCH 29/43] add changelogs --- packages/ragbits-core/CHANGELOG.md | 2 ++ packages/ragbits-evaluate/CHANGELOG.md | 2 ++ 2 files changed, 4 insertions(+) diff --git a/packages/ragbits-core/CHANGELOG.md b/packages/ragbits-core/CHANGELOG.md index ba5e09235..97c10733c 100644 --- a/packages/ragbits-core/CHANGELOG.md +++ b/packages/ragbits-core/CHANGELOG.md @@ -2,6 +2,8 @@ ## Unreleased +- Add name parameter for better filtering of HuggingFace dataloder (#829) + ## 1.3.0 (2025-09-11) - feat: improve app startup speed with lazy LiteLLM loading diff --git a/packages/ragbits-evaluate/CHANGELOG.md b/packages/ragbits-evaluate/CHANGELOG.md index fb7f6bfd8..bade54113 100644 --- a/packages/ragbits-evaluate/CHANGELOG.md +++ b/packages/ragbits-evaluate/CHANGELOG.md @@ -2,6 +2,8 @@ ## Unreleased +- Feat: introduce agent evaluation pipelines and metrics (HotpotQA, HumanEval, GAIA) (#829) + ## 1.3.0 (2025-09-11) ### Changed From 3a9df862751b1f53d7bedce9a7282dd6c7ef6410 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Wed, 1 Oct 2025 10:52:42 +0200 Subject: [PATCH 30/43] trailing spaces removal --- examples/evaluate/agent-benchmarking/run_gaia.py | 14 +++++--------- examples/evaluate/agent-benchmarking/run_hotpot.py | 8 +++----- .../evaluate/agent-benchmarking/run_humaneval.py | 12 +++++------- .../src/ragbits/evaluate/pipelines/hotpot_qa.py | 4 ++-- 4 files changed, 15 insertions(+), 23 deletions(-) diff --git a/examples/evaluate/agent-benchmarking/run_gaia.py b/examples/evaluate/agent-benchmarking/run_gaia.py index 66769adee..43ff7551e 100644 --- a/examples/evaluate/agent-benchmarking/run_gaia.py +++ b/examples/evaluate/agent-benchmarking/run_gaia.py @@ -35,6 +35,8 @@ from pathlib import Path from typing import Any +from todo_agent import TodoAgent + from ragbits.agents import Agent, AgentOptions from ragbits.agents.tools.openai import get_web_search_tool from ragbits.core.llms import LiteLLM @@ -45,10 +47,8 @@ from ragbits.evaluate.metrics.gaia import GaiaEfficiency, GaiaOutcome, GaiaTooling from ragbits.evaluate.pipelines.gaia import GaiaPipeline -from todo_agent import TodoAgent - +# Extra Agent tools -# Extra Agent tools def add(a: int, b: int) -> int: """Add two integers and return the result.""" @@ -137,7 +137,7 @@ def wiki_search(query: str, max_results: int = 2, language: str = "en") -> dict[ urls: list[str] = data[3] if len(data) > 3 else [] results: list[dict[str, Any]] = [] - for i, title in enumerate(titles[: max_results]): + for i, title in enumerate(titles[:max_results]): url = urls[i] if i < len(urls) else "" results.append({"title": title, "url": url}) @@ -208,9 +208,7 @@ async def main(use_todo: bool) -> None: tools=_build_tools(), default_options=AgentOptions(max_turns=30), ) - evaluation_target = ( - TodoAgent(agent=base_agent, domain_context="general AI assistant") if use_todo else base_agent - ) + evaluation_target = TodoAgent(agent=base_agent, domain_context="general AI assistant") if use_todo else base_agent # Data loader source = HuggingFaceSource(path="gaia-benchmark/GAIA", name="2023_level1", split="validation") @@ -284,5 +282,3 @@ async def _print_todo_stats(log_path: Path, dataloader) -> None: args = parser.parse_args() asyncio.run(main(args.use_todo)) - - diff --git a/examples/evaluate/agent-benchmarking/run_hotpot.py b/examples/evaluate/agent-benchmarking/run_hotpot.py index 1726fc311..7697ad151 100644 --- a/examples/evaluate/agent-benchmarking/run_hotpot.py +++ b/examples/evaluate/agent-benchmarking/run_hotpot.py @@ -31,6 +31,8 @@ import logging from pathlib import Path +from todo_agent import TodoAgent + from ragbits.agents import Agent, AgentOptions from ragbits.core.embeddings import LiteLLMEmbedder from ragbits.core.llms import LiteLLM @@ -45,8 +47,6 @@ from ragbits.evaluate.metrics.hotpot_qa import HotpotQAExactMatch, HotpotQAF1 from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAPipeline -from todo_agent import TodoAgent - def _build_system_prompt() -> str: """Build a unified HotpotQA system prompt shared by both modes.""" @@ -104,9 +104,7 @@ async def main(use_todo: bool) -> None: tools=[], default_options=AgentOptions(max_turns=5), ) - evaluation_target = ( - TodoAgent(agent=base_agent, domain_context="research QA") if use_todo else base_agent - ) + evaluation_target = TodoAgent(agent=base_agent, domain_context="research QA") if use_todo else base_agent # Data loader source = HuggingFaceSource(path="hotpotqa/hotpot_qa", name="distractor", split="validation") diff --git a/examples/evaluate/agent-benchmarking/run_humaneval.py b/examples/evaluate/agent-benchmarking/run_humaneval.py index ee4cc762e..bb815b1d8 100644 --- a/examples/evaluate/agent-benchmarking/run_humaneval.py +++ b/examples/evaluate/agent-benchmarking/run_humaneval.py @@ -2,7 +2,7 @@ Ragbits Evaluation Example: HumanEval (Agent vs TodoAgent) This example demonstrates how to evaluate HumanEval using either a classic `Agent` -or a task-decomposing `TodoAgent`. Both modes share the +or a task-decomposing `TodoAgent`. Both modes share the same prompt, retrieval setup, and parsing logic. To run the script, execute one of the following commands: @@ -30,6 +30,8 @@ import logging from pathlib import Path +from todo_agent import TodoAgent + from ragbits.agents import Agent, AgentOptions from ragbits.core.llms import LiteLLM from ragbits.core.sources.hf import HuggingFaceSource @@ -39,8 +41,6 @@ from ragbits.evaluate.metrics.human_eval import HumanEvalPassAtK, HumanEvalQualityPerf from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline -from todo_agent import TodoAgent - def _build_system_prompt() -> str: """Build a unified HumanEval system prompt shared by both modes.""" @@ -49,7 +49,7 @@ def _build_system_prompt() -> str: "You are an expert Python engineer.", "Implement exactly one function that solves the problem.", "Output ONLY the function as plain Python (no markdown). Include necessary imports.", - "Do not include explanations or comments." + "Do not include explanations or comments.", ] ) @@ -80,9 +80,7 @@ async def main(use_todo: bool) -> None: tools=[], default_options=AgentOptions(max_turns=30), ) - evaluation_target = ( - TodoAgent(agent=base_agent, domain_context="python engineer") if use_todo else base_agent - ) + evaluation_target = TodoAgent(agent=base_agent, domain_context="python engineer") if use_todo else base_agent # Data source = HuggingFaceSource(path="openai/openai_humaneval", split="test") diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py index 8812efabe..d1eb70894 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py @@ -294,9 +294,9 @@ async def _generate_next_question(self, original_question: str, accumulated_cont if self.question_generation_prompt_fn is None: # default: simple concatenation return f"{original_question}\n\nContext so far:\n{accumulated_context}" - + question_generation_prompt = self.question_generation_prompt_fn(original_question, accumulated_context) - + target = self.evaluation_target if isinstance(target, Agent): resp = await target.llm.generate(question_generation_prompt) From 49bf3aa61bb496537021c2e951d577fcf2f08a2f Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Wed, 1 Oct 2025 11:06:45 +0200 Subject: [PATCH 31/43] remove some extra tools --- .../evaluate/agent-benchmarking/run_gaia.py | 74 ------------------- 1 file changed, 74 deletions(-) diff --git a/examples/evaluate/agent-benchmarking/run_gaia.py b/examples/evaluate/agent-benchmarking/run_gaia.py index 43ff7551e..ab13c0c0d 100644 --- a/examples/evaluate/agent-benchmarking/run_gaia.py +++ b/examples/evaluate/agent-benchmarking/run_gaia.py @@ -27,13 +27,8 @@ import argparse import asyncio -import json import logging -import urllib.parse -import urllib.request -import xml.etree.ElementTree as ET from pathlib import Path -from typing import Any from todo_agent import TodoAgent @@ -79,78 +74,11 @@ def modulus(a: int, b: int) -> int: return a % b -def _http_get(url: str, timeout: float = 10.0) -> bytes: - req = urllib.request.Request(url, headers={"User-Agent": "ragbits-agents/extra-tools"}) - with urllib.request.urlopen(req, timeout=timeout) as resp: - return resp.read() - - -def arxiv_search(query: str, max_results: int = 3) -> dict[str, Any]: - """Search arXiv and return up to `max_results` entries (title, summary, link).""" - if max_results <= 0: - return {"results": []} - - base = "https://export.arxiv.org/api/query" - params = urllib.parse.urlencode( - { - "search_query": query, - "start": 0, - "max_results": max_results, - "sortBy": "relevance", - "sortOrder": "descending", - } - ) - raw = _http_get(f"{base}?{params}") - - root = ET.fromstring(raw) - ns = {"atom": "http://www.w3.org/2005/Atom"} - results: list[dict[str, str]] = [] - for entry in root.findall("atom:entry", ns): - title = (entry.findtext("atom:title", default="", namespaces=ns) or "").strip() - summary = (entry.findtext("atom:summary", default="", namespaces=ns) or "").strip() - link_el = entry.find("atom:link[@rel='alternate']", ns) - link = link_el.get("href") if link_el is not None else "" - results.append({"title": title, "summary": summary, "link": link}) - - return {"results": results} - - -def wiki_search(query: str, max_results: int = 2, language: str = "en") -> dict[str, Any]: - """Search Wikipedia and return up to `max_results` entries (title and url).""" - if max_results <= 0: - return {"results": []} - - api = f"https://{language}.wikipedia.org/w/api.php" - params = urllib.parse.urlencode( - { - "action": "opensearch", - "search": query, - "limit": max_results, - "namespace": 0, - "format": "json", - } - ) - raw = _http_get(f"{api}?{params}") - data = json.loads(raw.decode("utf-8")) - - titles: list[str] = data[1] if len(data) > 1 else [] - urls: list[str] = data[3] if len(data) > 3 else [] - - results: list[dict[str, Any]] = [] - for i, title in enumerate(titles[:max_results]): - url = urls[i] if i < len(urls) else "" - results.append({"title": title, "url": url}) - - return {"results": results} - - def get_extra_instruction_tpl() -> str: """Generate tool usage instructions template for arithmetic and lookups.""" return ( "Tools (use when needed):\n" "- add(a, b), subtract(a, b), multiply(a, b), divide(a, b), modulus(a, b)\n" - "- arxiv_search(query, max_results=3)\n" - "- wiki_search(query, max_results=2, language='en')\n" "- web_search -> OpenAI websearch tool\n" ) @@ -180,8 +108,6 @@ def _build_tools() -> list: multiply, divide, modulus, - arxiv_search, - wiki_search, get_web_search_tool(model_name="gpt-4.1-mini"), ] From 2b04da9715f56e8fa779b48d2c413665453ca9fa Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Wed, 1 Oct 2025 13:04:52 +0200 Subject: [PATCH 32/43] ruff and mypy refactor --- .../evaluate/agent-benchmarking/run_gaia.py | 2 +- .../evaluate/agent-benchmarking/run_hotpot.py | 2 +- .../agent-benchmarking/run_humaneval.py | 2 +- packages/ragbits-core/CHANGELOG.md | 2 +- .../src/ragbits/core/sources/hf.py | 57 +++++---- .../src/ragbits/evaluate/metrics/gaia.py | 9 +- .../src/ragbits/evaluate/metrics/hotpot_qa.py | 6 +- .../ragbits/evaluate/metrics/human_eval.py | 3 +- .../src/ragbits/evaluate/pipelines/gaia.py | 57 +++++---- .../ragbits/evaluate/pipelines/hotpot_qa.py | 109 +++++++++--------- .../ragbits/evaluate/pipelines/human_eval.py | 96 ++++++++------- pyproject.toml | 3 + 12 files changed, 197 insertions(+), 151 deletions(-) diff --git a/examples/evaluate/agent-benchmarking/run_gaia.py b/examples/evaluate/agent-benchmarking/run_gaia.py index ab13c0c0d..797f479d9 100644 --- a/examples/evaluate/agent-benchmarking/run_gaia.py +++ b/examples/evaluate/agent-benchmarking/run_gaia.py @@ -164,7 +164,7 @@ async def main(use_todo: bool) -> None: await _print_todo_stats(log_path, dataloader) -async def _print_todo_stats(log_path: Path, dataloader) -> None: +async def _print_todo_stats(log_path: Path, dataloader: GaiaDataLoader) -> None: """Print aggregated TODO-agent statistics from extended logs.""" print("\nTODO Orchestrator Statistics:") decomposed_count = 0 diff --git a/examples/evaluate/agent-benchmarking/run_hotpot.py b/examples/evaluate/agent-benchmarking/run_hotpot.py index 7697ad151..637e331f8 100644 --- a/examples/evaluate/agent-benchmarking/run_hotpot.py +++ b/examples/evaluate/agent-benchmarking/run_hotpot.py @@ -137,7 +137,7 @@ async def main(use_todo: bool) -> None: await _print_todo_stats(log_path, dataloader) -async def _print_todo_stats(log_path: Path, dataloader) -> None: +async def _print_todo_stats(log_path: Path, dataloader: HotpotQADataLoader) -> None: """Print aggregated TODO-agent statistics from extended logs.""" print("\nTODO Orchestrator Statistics:") decomposed_count = 0 diff --git a/examples/evaluate/agent-benchmarking/run_humaneval.py b/examples/evaluate/agent-benchmarking/run_humaneval.py index bb815b1d8..4c21c3938 100644 --- a/examples/evaluate/agent-benchmarking/run_humaneval.py +++ b/examples/evaluate/agent-benchmarking/run_humaneval.py @@ -113,7 +113,7 @@ async def main(use_todo: bool) -> None: await _print_todo_stats(log_path, dataloader) -async def _print_todo_stats(log_path: Path, dataloader) -> None: +async def _print_todo_stats(log_path: Path, dataloader: HumanEvalDataLoader) -> None: """Print aggregated TODO-agent statistics from extended logs.""" print("\nTODO Orchestrator Statistics:") decomposed_count = 0 diff --git a/packages/ragbits-core/CHANGELOG.md b/packages/ragbits-core/CHANGELOG.md index 97c10733c..c3c7c7f72 100644 --- a/packages/ragbits-core/CHANGELOG.md +++ b/packages/ragbits-core/CHANGELOG.md @@ -2,7 +2,7 @@ ## Unreleased -- Add name parameter for better filtering of HuggingFace dataloder (#829) +- Add name parameter and slightly refactor HuggingFace dataloder (#829) ## 1.3.0 (2025-09-11) diff --git a/packages/ragbits-core/src/ragbits/core/sources/hf.py b/packages/ragbits-core/src/ragbits/core/sources/hf.py index b65d613dc..ad383c8b5 100644 --- a/packages/ragbits-core/src/ragbits/core/sources/hf.py +++ b/packages/ragbits-core/src/ragbits/core/sources/hf.py @@ -1,7 +1,7 @@ import re from collections.abc import Iterable from pathlib import Path -from typing import ClassVar +from typing import TYPE_CHECKING, ClassVar, TypeAlias from typing_extensions import Self @@ -10,6 +10,11 @@ from ragbits.core.sources.exceptions import SourceConnectionError, SourceNotFoundError from ragbits.core.utils.decorators import requires_dependencies +if TYPE_CHECKING: + from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict + + HFDataset: TypeAlias = Dataset | DatasetDict | IterableDataset | IterableDatasetDict + class HuggingFaceSource(Source): """ @@ -34,6 +39,32 @@ def id(self) -> str: """ return f"hf:{self.path}/{self.split}{f'/{self.row}' if self.row is not None else ''}" + def _load_dataset(self, streaming: bool = False) -> "HFDataset": + """ + Load the dataset from Hugging Face. + + Args: + streaming: Whether to stream the dataset. + + Returns: + The loaded dataset. + + Raises: + SourceConnectionError: If the source connection fails. + SourceNotFoundError: If the source dataset is not found. + """ + from datasets import load_dataset + from datasets.exceptions import DatasetNotFoundError + + try: + if self.name is not None and str(self.name).strip(): + return load_dataset(self.path, self.name, split=self.split, streaming=streaming) + return load_dataset(self.path, split=self.split, streaming=streaming) + except ConnectionError as exc: + raise SourceConnectionError() from exc + except DatasetNotFoundError as exc: + raise SourceNotFoundError(source_id=self.id) from exc + @requires_dependencies(["datasets"], "hf") async def fetch(self) -> Path: """ @@ -46,20 +77,9 @@ async def fetch(self) -> Path: SourceConnectionError: If the source connection fails. SourceNotFoundError: If the source document is not found. """ - from datasets import load_dataset - from datasets.exceptions import DatasetNotFoundError - with trace(path=self.path, split=self.split, row=self.row) as outputs: if self.row is not None: - try: - if self.name is not None and str(self.name).strip(): - dataset = load_dataset(self.path, self.name, split=self.split, streaming=True) - else: - dataset = load_dataset(self.path, split=self.split, streaming=True) - except ConnectionError as exc: - raise SourceConnectionError() from exc - except DatasetNotFoundError as exc: - raise SourceNotFoundError(source_id=self.id) from exc + dataset = self._load_dataset(streaming=True) try: data = next(iter(dataset.skip(self.row).take(1))) @@ -82,16 +102,7 @@ async def fetch(self) -> Path: path = source_dir / f"{self.split}.json" if not path.is_file(): - try: - if self.name is not None and str(self.name).strip(): - dataset = load_dataset(self.path, self.name, split=self.split) - else: - dataset = load_dataset(self.path, split=self.split) - except ConnectionError as exc: - raise SourceConnectionError() from exc - except DatasetNotFoundError as exc: - raise SourceNotFoundError(source_id=self.id) from exc - + dataset = self._load_dataset(streaming=False) dataset.to_json(path) outputs.path = path diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py index 05f1c9308..7e824f05f 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/gaia.py @@ -12,7 +12,8 @@ class GaiaOutcome(Metric[GaiaResult]): Measures the fraction of tasks that were successfully solved. """ - async def compute(self, results: list[GaiaResult]) -> dict: + @staticmethod + async def compute(results: list[GaiaResult]) -> dict: """Compute task success rate. Returns: @@ -33,7 +34,8 @@ class GaiaTooling(Metric[GaiaResult]): - averaged_freq: average tool usage/calls per task """ - async def compute(self, results: list[GaiaResult]) -> dict: + @staticmethod + async def compute(results: list[GaiaResult]) -> dict: """Compute tool utilization and performance metrics. Returns: @@ -70,7 +72,8 @@ class GaiaEfficiency(Metric[GaiaResult]): - gaia_avg_latency_ms: average response latency in milliseconds """ - async def compute(self, results: list[GaiaResult]) -> dict: + @staticmethod + async def compute(results: list[GaiaResult]) -> dict: """Compute efficiency and resource usage metrics. Returns: diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/hotpot_qa.py index aafc96b0d..615f070e3 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/hotpot_qa.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/hotpot_qa.py @@ -10,7 +10,8 @@ class HotpotQAExactMatch(Metric[HotpotQAResult]): """Computes EM over HotpotQA by type and overall.""" - async def compute(self, results: list[HotpotQAResult]) -> dict: + @staticmethod + async def compute(results: list[HotpotQAResult]) -> dict: """Compute EM. Returns hotpotqa__em and hotpotqa_overall_em.""" buckets: dict[str, list[float]] = defaultdict(list) for r in results: @@ -32,7 +33,8 @@ def avg(vals: Iterable[float]) -> float: class HotpotQAF1(Metric[HotpotQAResult]): """Computes token-level F1 over HotpotQA by type and overall.""" - async def compute(self, results: list[HotpotQAResult]) -> dict: + @staticmethod + async def compute(results: list[HotpotQAResult]) -> dict: """Compute F1. Returns hotpotqa__f1 and hotpotqa_overall_f1.""" buckets: dict[str, list[float]] = defaultdict(list) for r in results: diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py index dae1a901a..040e616c7 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/metrics/human_eval.py @@ -56,7 +56,8 @@ class HumanEvalQualityPerf(Metric[HumanEvalResult]): - humaneval_avg_exec_time_sec: average exec time over compilable runs """ - async def compute(self, results: list[HumanEvalResult]) -> dict: + @staticmethod + async def compute(results: list[HumanEvalResult]) -> dict: """Compute code quality and execution performance metrics. Returns: diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py index c425eed77..12dcc13e1 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/gaia.py @@ -74,9 +74,38 @@ def from_config(cls, config: dict) -> Self: try: config["evaluation_target"] = Agent.from_config(config) except Exception: - config["evaluation_target"] = LLM.subclass_from_config(config) + config["evaluation_target"] = LLM.from_config(config) return super().from_config(config) + @staticmethod + def _count_tool_errors(tool_calls: list) -> int: + """Count tool errors from tool_calls.""" + tool_error_count = 0 + for call in tool_calls: + try: + if isinstance(call.result, dict) and "error" in call.result: + tool_error_count += 1 + except Exception as exc: + logging.getLogger(__name__).debug("Error while parsing tool call result: %s", exc) + return tool_error_count + + @staticmethod + def _extract_tool_names(tool_calls: list) -> list[str] | None: + """Extract tool names from tool_calls.""" + if not tool_calls: + return None + tool_names = [] + for call in tool_calls: + try: + name = getattr(call, "name", None) + if name is None and isinstance(call, dict): + name = call.get("name") + if name: + tool_names.append(str(name)) + except Exception as exc: + logging.getLogger(__name__).debug("Tool name extraction error: %s", exc) + return tool_names + async def __call__(self, data: Iterable[GaiaData]) -> Iterable[GaiaResult]: """Generate answer completions per task and evaluate them. Returns list of `GaiaResult`, one per input task. @@ -115,32 +144,10 @@ async def __call__(self, data: Iterable[GaiaData]) -> Iterable[GaiaResult]: tool_triggered = bool(tool_calls) num_tool_calls = len(tool_calls) - - # Count tool errors from AgentResult.tool_calls metadata if present - tool_error_count = 0 - for call in tool_calls: - try: - if isinstance(call.result, dict) and "error" in call.result: - tool_error_count += 1 - except Exception as exc: - logging.getLogger(__name__).debug("Error while parsing tool call result: %s", exc) - + tool_error_count = GaiaPipeline._count_tool_errors(tool_calls) + tool_names = GaiaPipeline._extract_tool_names(tool_calls) total_latency_ms = int((end_time - start_time) * 1000) - # Extract tool names (if any) from tool_calls - tool_names: list[str] | None = None - if tool_calls: - tool_names = [] - for call in tool_calls: - try: - name = getattr(call, "name", None) - if name is None and isinstance(call, dict): - name = call.get("name") - if name: - tool_names.append(str(name)) - except Exception as exc: - logging.getLogger(__name__).debug("Tool name extraction error: %s", exc) - result = GaiaResult( task_id=row.task_id, level=row.level, diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py index d1eb70894..b5c019a2f 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/hotpot_qa.py @@ -92,7 +92,7 @@ def from_config(cls, config: dict) -> Self: try: config["evaluation_target"] = Agent.from_config(config) except Exception: - config["evaluation_target"] = LLM.subclass_from_config(config) + config["evaluation_target"] = LLM.from_config(config) config["retriever"] = DocumentSearch.from_config(config["document_search"]) config["hops"] = int(config.get("hops", 3)) config["retrieval_k"] = int(config.get("retrieval_k", 3)) @@ -100,68 +100,73 @@ def from_config(cls, config: dict) -> Self: config["hop_context_max_chars"] = int(config.get("hop_context_max_chars", 1200)) return super().from_config(config) - async def __call__(self, data: Iterable[HotpotQAData]) -> Iterable[HotpotQAResult]: - """Ingest contexts, perform multi-hop retrieval, and answer HotpotQA questions.""" + async def _ingest_documents(self, data: Iterable[HotpotQAData]) -> None: + """Ingest all documents from the data.""" documents: list[DocumentMeta] = [] for row in data: for content in row.reference_context: documents.append(DocumentMeta.from_literal(content)) - await self.retriever.ingest(documents) - async def answer_with_retrieval( - example: HotpotQAData, - ) -> tuple[str, Usage, list, list[dict], dict]: - accumulated_context: list[str] = [] - hop_logs: list[dict] = [] - last_query = example.question - for hop_idx in range(self.hops): - elements = await self.retriever.search(last_query) - text_parts: list[str] = [] - consumed = 0 - for element in elements[: self.retrieval_k]: - content = getattr(element, "content", "") if hasattr(element, "content") else "" - if isinstance(content, str) and content: - snippet = content.strip().replace("\n\n", "\n")[: self.element_max_chars] - budget = max(0, self.hop_context_max_chars - consumed) - take = snippet[:budget] - if take: - text_parts.append(take) - consumed += len(take) - if consumed >= self.hop_context_max_chars: - break - hop_text = "\n\n".join(text_parts) - hop_logs.append({"hop": hop_idx + 1, "question": last_query, "retrieved": hop_text}) - if hop_text: - accumulated_context.append(hop_text) - # generate a new question for the next hop - if hop_idx < self.hops - 1: - last_query = await self._generate_next_question( - original_question=example.question, - accumulated_context="\n\n".join(accumulated_context), - ) - else: - break - - full_context = "\n\n".join(accumulated_context) - # append retrieved context to user question - prompt_input = example.question if not full_context else f"{example.question}\n\nContext:\n{full_context}" - - if self.extended_logs: - content, dbg = await self._generate_with_debug(prompt_input) - usage = cast(Usage, (dbg or {}).get("usage") or Usage()) - tool_calls = cast(list, (dbg or {}).get("tool_calls") or []) - metadata = cast(dict, (dbg or {}).get("metadata") or {}) + async def _perform_multihop_retrieval(self, example: HotpotQAData) -> tuple[str, list[dict]]: + """Perform multi-hop retrieval and return accumulated context and hop logs.""" + accumulated_context: list[str] = [] + hop_logs: list[dict] = [] + last_query = example.question + for hop_idx in range(self.hops): + elements = await self.retriever.search(last_query) + text_parts: list[str] = [] + consumed = 0 + for element in elements[: self.retrieval_k]: + content = getattr(element, "content", "") if hasattr(element, "content") else "" + if isinstance(content, str) and content: + snippet = content.strip().replace("\n\n", "\n")[: self.element_max_chars] + budget = max(0, self.hop_context_max_chars - consumed) + take = snippet[:budget] + if take: + text_parts.append(take) + consumed += len(take) + if consumed >= self.hop_context_max_chars: + break + hop_text = "\n\n".join(text_parts) + hop_logs.append({"hop": hop_idx + 1, "question": last_query, "retrieved": hop_text}) + if hop_text: + accumulated_context.append(hop_text) + # generate a new question for the next hop + if hop_idx < self.hops - 1: + last_query = await self._generate_next_question( + original_question=example.question, + accumulated_context="\n\n".join(accumulated_context), + ) else: - content, usage, tool_calls = await self._generate_answer(prompt_input) - metadata = {} + break + return "\n\n".join(accumulated_context), hop_logs - return str(content), usage, tool_calls, hop_logs, metadata + async def _answer_with_retrieval(self, example: HotpotQAData) -> tuple[str, Usage, list, list[dict], dict]: + """Answer a question with multi-hop retrieval.""" + full_context, hop_logs = await self._perform_multihop_retrieval(example) + prompt_input = example.question if not full_context else f"{example.question}\n\nContext:\n{full_context}" + + if self.extended_logs: + content, dbg = await self._generate_with_debug(prompt_input) + usage = cast(Usage, (dbg or {}).get("usage") or Usage()) + tool_calls = cast(list, (dbg or {}).get("tool_calls") or []) + metadata = cast(dict, (dbg or {}).get("metadata") or {}) + else: + content, usage, tool_calls = await self._generate_answer(prompt_input) + metadata = {} + + return str(content), usage, tool_calls, hop_logs, metadata + + async def __call__(self, data: Iterable[HotpotQAData]) -> Iterable[HotpotQAResult]: + """Ingest contexts, perform multi-hop retrieval, and answer HotpotQA questions.""" + data_list = list(data) + await self._ingest_documents(data_list) results: list[HotpotQAResult] = [] - for row in data: + for row in data_list: try: - predicted_text, usage, tool_calls, hop_logs, metadata = await answer_with_retrieval(row) + predicted_text, usage, tool_calls, hop_logs, metadata = await self._answer_with_retrieval(row) except Exception: predicted_text = "" usage = Usage() diff --git a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py index eb737d39d..60bf1b7c4 100644 --- a/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py +++ b/packages/ragbits-evaluate/src/ragbits/evaluate/pipelines/human_eval.py @@ -6,7 +6,7 @@ import multiprocessing import textwrap import time -from collections.abc import Callable, Iterable +from collections.abc import Callable, Coroutine, Iterable from dataclasses import dataclass from multiprocessing.connection import Connection from pathlib import Path @@ -153,9 +153,50 @@ def from_config(cls, config: dict) -> Self: try: config["evaluation_target"] = Agent.from_config(config) except Exception: - config["evaluation_target"] = LLM.subclass_from_config(config) + config["evaluation_target"] = LLM.from_config(config) return super().from_config(config) + def _process_generation( + self, raw: BaseException | tuple[str, dict | None] | str, debug_traces: list[dict | None] | None + ) -> tuple[str, dict | None]: + """Process a single generation result.""" + if isinstance(raw, BaseException): + err_msg = f"GenerationError: {raw.__class__.__name__}: {raw}" + if self.extended_logs and debug_traces is not None: + debug_traces.append({"error": err_msg}) + raise raw + + if self.extended_logs and isinstance(raw, tuple): + content, dbg = raw + code = self._sanitize(content) + if debug_traces is not None: + debug_traces.append(dbg) + return code, dbg + + if isinstance(raw, str): + code = self._sanitize(raw) + return code, None + + raise TypeError(f"Unexpected type for raw: {type(raw)}") + + def _evaluate_code_sample(self, code: str, row: HumanEvalData) -> tuple[bool, bool, float, str | None]: + """Evaluate a single code sample.""" + # Compile check + try: + compile(code, filename="candidate.py", mode="exec") + compile_ok = True + except Exception as e: + return False, False, 0.0, f"SyntaxError: {e}" + + ok, dur, err = _execute_in_subprocess( + code, + row.entry_point, + row.test, + timeout_sec=self.timeout_sec, + memory_limit_mb=self.memory_limit_mb, + ) + return compile_ok, ok, dur, err + async def __call__(self, data: Iterable[HumanEvalData]) -> Iterable[HumanEvalResult]: """Generate code completions per task and evaluate them. Returns list of `HumanEvalResult`, one per input task. @@ -171,7 +212,7 @@ async def __call__(self, data: Iterable[HumanEvalData]) -> Iterable[HumanEvalRes errors: list[str | None] = [] # Produce n samples - gen_tasks = [] + gen_tasks: list[Coroutine[Any, Any, tuple[str, dict | None] | str]] = [] for _ in range(self.n_samples): if self.extended_logs: gen_tasks.append(self._generate_with_debug(prompt_input)) @@ -181,51 +222,24 @@ async def __call__(self, data: Iterable[HumanEvalData]) -> Iterable[HumanEvalRes debug_traces: list[dict | None] | None = [] if self.extended_logs else None - for _, raw in enumerate(generations): - if isinstance(raw, Exception): + for raw in generations: + try: + code, _ = self._process_generation(raw, debug_traces) + samples.append(code) + except BaseException as e: samples.append("") compile_ok.append(False) pass_mask.append(False) durations.append(0.0) - err_msg = f"GenerationError: {raw.__class__.__name__}: {raw}" + err_msg = f"GenerationError: {e.__class__.__name__}: {e}" errors.append(err_msg) - if self.extended_logs and debug_traces is not None: - debug_traces.append({"error": err_msg}) - continue - - if self.extended_logs: - # Raw is (content, debug) - content, dbg = raw - code = self._sanitize(content) - samples.append(code) - if debug_traces is not None: - debug_traces.append(dbg) - else: - # Raw is a simple string - code = self._sanitize(raw) - samples.append(code) - - # Compile check - try: - compile(code, filename="candidate.py", mode="exec") - compile_ok.append(True) - except Exception as e: - compile_ok.append(False) - pass_mask.append(False) - durations.append(0.0) - errors.append(f"SyntaxError: {e}") continue - ok, dur, err = _execute_in_subprocess( - code, - row.entry_point, - row.test, - timeout_sec=self.timeout_sec, - memory_limit_mb=self.memory_limit_mb, - ) - pass_mask.append(ok) - durations.append(dur) - errors.append(err) + compile_result, passed, duration, error = self._evaluate_code_sample(code, row) + compile_ok.append(compile_result) + pass_mask.append(passed) + durations.append(duration) + errors.append(error) result = HumanEvalResult( task_id=row.task_id, diff --git a/pyproject.toml b/pyproject.toml index bae197717..d3d6f10a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -196,6 +196,9 @@ allow-star-arg-any = true "ANN201", # Missing return type annotation "PLR2004", # Magic numbers are ok in tests ] +"**/pipelines/human_eval.py" = [ + "S102", # exec is required for HumanEval code evaluation +] [tool.ruff.format] docstring-code-format = true docstring-code-line-length = 120 From ba77557d5c43d33a1258be7fc591e596580961a8 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Thu, 2 Oct 2025 11:27:20 +0200 Subject: [PATCH 33/43] initialize orchestator with todo agent --- examples/evaluate/agent-benchmarking/todo_agent.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/evaluate/agent-benchmarking/todo_agent.py b/examples/evaluate/agent-benchmarking/todo_agent.py index abd16cfc7..00948a0ad 100644 --- a/examples/evaluate/agent-benchmarking/todo_agent.py +++ b/examples/evaluate/agent-benchmarking/todo_agent.py @@ -27,6 +27,7 @@ def __init__(self, agent: Agent[LLMClientOptionsT, None, str], domain_context: s ) self._inner_agent = agent self._domain_context = domain_context + self._orchestrator = TodoOrchestrator(domain_context=domain_context) async def run( self, @@ -37,8 +38,6 @@ async def run( """Run the orchestrated flow and return a single final answer.""" query = input or "" - orchestrator = TodoOrchestrator(domain_context=self._domain_context) - # Accumulate outputs from the orchestrated workflow final_text: str = "" tool_calls: list[ToolCallResult] | None = None @@ -48,7 +47,7 @@ async def run( tasks_created = False num_tasks = 0 - async for item in orchestrator.run_todo_workflow_streaming(self._inner_agent, query): + async for item in self._orchestrator.run_todo_workflow_streaming(self._inner_agent, query): match item: case str(): final_text += item @@ -62,8 +61,8 @@ async def run( last_prompt = item case _: # Inspect orchestrator internal state to populate metadata - tasks_created = len(orchestrator.todo_list.tasks) > 0 - num_tasks = len(orchestrator.todo_list.tasks) + tasks_created = len(self._orchestrator.todo_list.tasks) > 0 + num_tasks = len(self._orchestrator.todo_list.tasks) # Compose metadata with TODO info complexity = "COMPLEX" if tasks_created else "SIMPLE" From bc7534a7fa640a709f87a23858f4653ad060baba Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Mon, 6 Oct 2025 11:09:19 +0200 Subject: [PATCH 34/43] add mock of todo orchestrator --- docs/tutorials/todo_orchestrator.md | 154 ++++++++++++++++++++++++++++ 1 file changed, 154 insertions(+) create mode 100644 docs/tutorials/todo_orchestrator.md diff --git a/docs/tutorials/todo_orchestrator.md b/docs/tutorials/todo_orchestrator.md new file mode 100644 index 000000000..2051c16bd --- /dev/null +++ b/docs/tutorials/todo_orchestrator.md @@ -0,0 +1,154 @@ +## TodoOrchestrator — !!!mock-up tutorial!!! + +Short and pragmatic guide to orchestrating task execution with a Todo-oriented agent. + +--- + +### Why TodoOrchestrator? + +- **Motivation**: make complex, multi-step tasks explicit, auditable, and re-runnable. +- **What it is**: a lightweight orchestration layer over your agent tools for planning and progress tracking. +- **Benefits**: clarity of intent, measurable progress, easier debugging, better eval scores via structured decomposition. + +#### Workflow overview + + + +- **Input**: user goal -> agent -> TodoOrchestrator +- **Plan**: generate todos -> order/prioritize -> attach tools +- **Execute**: dispatch steps -> record artifacts -> update status +- **Review**: summarize -> retry failed steps -> export audit + +--- + +### Prerequisites + +- Python env managed by `uv`. +- LLM provider configured (e.g., environment variables) and network access where needed. +- Example files referenced in this tutorial: + - `packages/ragbits-agents/src/ragbits/agents/tools/todo.py` + - `examples/agents/todo_tools_example.py` + - Evaluations: `examples/evaluate/agent-benchmarking/run_gaia.py`, `run_hotpot.py`, `run_humaneval.py` + +--- + +### 1) Set up an agent (baseline) + +Minimal agent wiring using TodoOrchestrator as a tool provider. + +```python +# mock-up code (baseline agent) +from ragbits.agents import Agent +from ragbits.agents.tools.todo import TodoOrchestrator + +agent = Agent( + llm="gpt-xyz", + tools=[tool_1, tool_2], +) + +result = agent.run("Execute Task XYZ.") +``` + +Notes: +- Will keep this as a baseline: no todo manager yet, minimal defaults, some tools. + +--- + +### 2) Baseline evaluation setup (measure accuracy) + +We’ll run three complementary benchmarks to measure multi-step reasoning and tooling: + +- **GAIA**: generalist agent tasks with tool-use and basic reasoning. +- **HotpotQA**: multi-hop question answering assessing and complex reasoning and information retrieval. +- **HumanEval**: code generation with function-level correctness (pass@k). + +Run baseline (mock commands): + +```bash +uv run python examples/evaluate/agent-benchmarking/run_gaia.py --agent baseline # or sth like this +uv run python examples/evaluate/agent-benchmarking/run_hotpot.py --agent baseline # might be full code snippets +uv run python examples/evaluate/agent-benchmarking/run_humaneval.py --agent baseline +``` + +Mock results snapshot (baseline): + + + +--- + +### 3) Apply TodoOrchestrator + +Plug in a concrete todo manager for persistence, progress tracking, and retries. + +```python +# mock-up code!!! +from ragbits.agents.tools.todo import TodoOrchestrator + +todo_orchestrator = TodoOrchestrator(domain_context="") + + +# Similar flow as before +``` + +UI/Console mock-up: + + + +--- + +### 4) Re-run evaluation (expected improvement) + +Re-run the same suites with orchestration. + +```bash +uv run python examples/evaluate/agent-benchmarking/run_gaia.py --agent todo_orchestrated # or sth like this +uv run python examples/evaluate/agent-benchmarking/run_hotpot.py --agent todo_orchestrated # might be full code snippets +uv run python examples/evaluate/agent-benchmarking/run_humaneval.py --agent todo_orchestrated +``` + +Mock results snapshot (after orchestration): + +
+ + + +Key expected gains (mock): +- **GAIA**: +XXX% task success via [argument here]. +- **HotpotQA**: +XXX% F1 through [argument here]. +- **HumanEval**: +XXX% pass@1K with [some argument here]. + +--- + +### 5) MOCK-UP Operational tips + +- **Auditability**: export run logs and todo timelines for regression analysis. +- **Backoff**: configure retries for unreliable tools. +- **Safety**: restrict tool scopes and validate step outputs. + +--- + +### 6) References & examples + +- Tooling: `packages/ragbits-agents/src/ragbits/agents/tools/todo.py` +- Complete example: `examples/agents/todo_tools_example.py` +- Evaluations: + - GAIA: `examples/evaluate/agent-benchmarking/run_gaia.py` + - HotpotQA: `examples/evaluate/agent-benchmarking/run_hotpot.py` + - HumanEval: `examples/evaluate/agent-benchmarking/run_humaneval.py` + +Related tutorials: +- Agents tutorial: `https://ragbits.deepsense.ai/stable/tutorials/agents/` +- RAG tutorial: `https://ragbits.deepsense.ai/stable/tutorials/rag/` +- Chat tutorial: `https://ragbits.deepsense.ai/stable/tutorials/chat/` + +--- + +### Appendix (copy-paste mocks) + +```bash + +# run the agent example (mock) +uv run python examples/agents/todo_tools_example.py +``` + + execution -> audit export or sth goes here> From 0e1607b2765fbf0209fc654e941ec40bdff44523 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Tue, 7 Oct 2025 14:08:25 +0200 Subject: [PATCH 35/43] add info about HF CLI and gated datasets --- docs/tutorials/todo_orchestrator.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/tutorials/todo_orchestrator.md b/docs/tutorials/todo_orchestrator.md index 2051c16bd..43bca20af 100644 --- a/docs/tutorials/todo_orchestrator.md +++ b/docs/tutorials/todo_orchestrator.md @@ -25,6 +25,7 @@ Short and pragmatic guide to orchestrating task execution with a Todo-oriented a - Python env managed by `uv`. - LLM provider configured (e.g., environment variables) and network access where needed. +- `huggingface_cli` account login with access to relevant datasets (i.e. GAIA) - Example files referenced in this tutorial: - `packages/ragbits-agents/src/ragbits/agents/tools/todo.py` - `examples/agents/todo_tools_example.py` From a6db07b912d09c60f80f45176680dd767dc1e907 Mon Sep 17 00:00:00 2001 From: ds-radoslaw-izak Date: Wed, 8 Oct 2025 16:04:12 +0200 Subject: [PATCH 36/43] make the evaluation pipelines pluggable with agents; reformat --- .../agent-benchmarking/example_agents.py | 304 ++++++++++++++++++ .../evaluate/agent-benchmarking/run_gaia.py | 178 ++-------- .../evaluate/agent-benchmarking/run_hotpot.py | 152 ++------- .../agent-benchmarking/run_humaneval.py | 126 ++------ .../evaluate/agent-benchmarking/todo_agent.py | 85 ----- examples/evaluate/agent-benchmarking/utils.py | 68 ++++ 6 files changed, 460 insertions(+), 453 deletions(-) create mode 100644 examples/evaluate/agent-benchmarking/example_agents.py delete mode 100644 examples/evaluate/agent-benchmarking/todo_agent.py create mode 100644 examples/evaluate/agent-benchmarking/utils.py diff --git a/examples/evaluate/agent-benchmarking/example_agents.py b/examples/evaluate/agent-benchmarking/example_agents.py new file mode 100644 index 000000000..ee035686a --- /dev/null +++ b/examples/evaluate/agent-benchmarking/example_agents.py @@ -0,0 +1,304 @@ +""" +Agent definitions for benchmarking examples. + +Each agent is encapsulated in a class and instances are +declared at the bottom so they can be imported via the CLI helper +`import_agent_from_path` using file path forms, e.g.: + + - file path: "examples/evaluate/agent-benchmarking/example_agents.py:gaia_agent" + +Todo-augmented variants are exported as `_todo_agent` and wrap the base +agent with the `TodoAgent` orchestrator. +""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import Any, cast + +from ragbits.agents import Agent, AgentOptions, AgentResult +from ragbits.agents.tool import ToolCallResult +from ragbits.agents.tools.openai import get_web_search_tool +from ragbits.agents.tools.todo import TodoOrchestrator +from ragbits.core.llms import LiteLLM +from ragbits.core.llms.base import LLMClientOptionsT, Usage +from ragbits.core.prompt.base import BasePrompt + + +class TodoAgent(Agent[LLMClientOptionsT, None, str]): + """ + Agent wrapper that uses TodoOrchestrator to optionally decompose a query + into tasks and aggregate a final answer, while remaining compatible with + existing EvaluationPipelines that expect an Agent. + """ + + def __init__(self, agent: Agent[LLMClientOptionsT, None, str], domain_context: str = "") -> None: + super().__init__( + llm=agent.llm, + prompt=agent.prompt, + history=agent.history, + keep_history=False, + mcp_servers=agent.mcp_servers, + default_options=cast(AgentOptions[LLMClientOptionsT] | None, agent.default_options), + ) + self._inner_agent = agent + self._domain_context = domain_context + self._orchestrator = TodoOrchestrator(domain_context=domain_context) + + def __getattr__(self, name: str) -> object: + """Proxy missing attributes to the wrapped inner agent.""" + try: + return super().__getattribute__(name) + except AttributeError: + return getattr(self._inner_agent, name) + + async def run( + self, + input: str | None = None, + *_: Any, + **__: Any, + ) -> AgentResult[str]: + """Run the orchestrated flow and return a single final answer.""" + query = input or "" + + # Accumulate outputs from the orchestrated workflow + final_text: str = "" + tool_calls: list[ToolCallResult] | None = None + total_usage: Usage = Usage() + last_prompt: BasePrompt | None = None + + tasks_created = False + num_tasks = 0 + + async for item in self._orchestrator.run_todo_workflow_streaming(self._inner_agent, query): + match item: + case str(): + final_text += item + case ToolCallResult(): + if tool_calls is None: + tool_calls = [] + tool_calls.append(item) + case Usage(): + total_usage += item + case BasePrompt(): + last_prompt = item + case _: + # Inspect orchestrator internal state to populate metadata + tasks_created = len(self._orchestrator.todo_list.tasks) > 0 + num_tasks = len(self._orchestrator.todo_list.tasks) + + # Compose metadata with TODO info + complexity = "COMPLEX" if tasks_created else "SIMPLE" + todo_meta: dict[str, Any] = { + "was_decomposed": bool(tasks_created), + "complexity_classification": complexity, + "num_tasks": int(num_tasks), + "domain_context": self._domain_context, + } + + # Build result compatible with existing pipelines + history = last_prompt.chat if last_prompt is not None else [] + + return AgentResult( + content=final_text.strip(), + metadata={"todo": todo_meta}, + tool_calls=tool_calls, + history=history, + usage=total_usage, + ) + + +class AgentHumanEval: + """Factory for the HumanEval base agent.""" + + @staticmethod + def build_system_prompt() -> str: + """Return the system prompt for HumanEval.""" + return "\n".join( + [ + "You are an expert Python engineer.", + "Implement exactly one function that solves the problem.", + "Output ONLY the function as plain Python (no markdown). Include necessary imports.", + "Do not include explanations or comments.", + ] + ) + + @classmethod + def build(cls) -> Agent: + """Build the HumanEval agent and attach code sanitization callable.""" + agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=cls.build_system_prompt(), + tools=[], + default_options=AgentOptions(max_turns=30), + ) + agent.code_sanitize_fn = cls.sanitize_code # type: ignore[attr-defined] + return agent + + @staticmethod + def sanitize_code(text: str) -> str: + """Remove markdown fences and keep only Python function text returned by the model.""" + cleaned = text.replace("\r\n", "\n").replace("\r", "\n").strip() + if "```" in cleaned: + start = cleaned.find("```") + end = cleaned.find("```", start + 3) + if end != -1: + inside = cleaned[start + 3 : end].lstrip() + if "\n" in inside: + first, rest = inside.split("\n", 1) + cleaned = rest if first.strip().lower().startswith("python") else inside + else: + cleaned = inside + return cleaned.strip() + + +class AgentHotpot: + """Factory for the HotpotQA base agent.""" + + @staticmethod + def build_system_prompt() -> str: + """Return the system prompt for HotpotQA.""" + return ( + "You are a helpful assistant. Use the provided context to answer.\n" + "Respond on a single line as: 'Answer: '.\n" + "- If yes/no, respond 'Answer: yes' or 'Answer: no'.\n" + "- If a name or title is required, provide only that after 'Answer:'.\n" + "- Do not add any extra text beyond the Answer line.\n" + "Keep the answer concise." + ) + + @classmethod + def build(cls) -> Agent: + """Build the HotpotQA agent and attach parsing helpers.""" + agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=cls.build_system_prompt(), + tools=[], + default_options=AgentOptions(max_turns=5), + ) + agent.parse_final_answer = cls.parse_final_answer # type: ignore[attr-defined] + agent.question_generation_prompt_fn = cls.build_question_generation_prompt # type: ignore[attr-defined] + return agent + + @staticmethod + def parse_final_answer(text: str) -> str: + """Extract and normalize the final answer from model text output.""" + marker = "Answer:" + idx = text.rfind(marker) + if idx == -1: + return text.strip() + candidate = text[idx + len(marker) :].strip() + if candidate.startswith("<") and candidate.endswith(">"): + candidate = candidate[1:-1].strip() + return candidate + + @staticmethod + def build_question_generation_prompt(original_question: str, accumulated_context: str) -> str: + """Construct the next-hop question prompt for multi-hop retrieval.""" + return ( + f"Original question: {original_question}\n\n" + f"Context so far: \n{accumulated_context}\n\n" + "Write ONE new, specific search question that fills the key missing info.\n" + "Do not repeat known facts. Return ONLY the question." + ) + + +class AgentGAIA: + """Factory for the GAIA base agent with extra tools.""" + + @staticmethod + def add(a: int, b: int) -> int: + """Add two integers.""" + return a + b + + @staticmethod + def subtract(a: int, b: int) -> int: + """Subtract two integers.""" + return a - b + + @staticmethod + def multiply(a: int, b: int) -> int: + """Multiply two integers.""" + return a * b + + @staticmethod + def divide(a: int, b: int) -> float: + """Divide two integers as float.""" + if b == 0: + raise ValueError("Cannot divide by zero.") + return a / b + + @staticmethod + def modulus(a: int, b: int) -> int: + """Compute remainder a % b.""" + if b == 0: + raise ValueError("Cannot divide by zero.") + return a % b + + @classmethod + def get_extra_instruction_tpl(cls) -> str: + """Return tool usage instruction template.""" + return ( + "Tools (use when needed):\n" + "- add(a, b), subtract(a, b), multiply(a, b), divide(a, b), modulus(a, b)\n" + "- web_search -> OpenAI websearch tool\n" + ) + + @classmethod + def build_system_prompt(cls) -> str: + """Return the GAIA system prompt with tool instructions.""" + gaia_prompt = ( + "You are a general AI assistant. Provide a concise solution and finish with:\n" + "FINAL ANSWER: [your final answer].\n" + "Rules for FINAL ANSWER: use digits for numbers (no units unless requested);\n" + "prefer few words for strings; for lists, return a comma-separated list." + ) + return "\n".join([gaia_prompt, cls.get_extra_instruction_tpl()]) + + @classmethod + def build_tools(cls) -> list[Callable[..., Any]]: + """Return the callable toolset used by the GAIA agent.""" + tools: list[Callable[..., Any]] = [ + cls.add, + cls.subtract, + cls.multiply, + cls.divide, + cls.modulus, + cast(Callable[..., Any], get_web_search_tool(model_name="gpt-4.1-mini")), + ] + return tools + + @classmethod + def build(cls) -> Agent: + """Build the GAIA agent and attach final-answer parser.""" + agent: Agent = Agent( + llm=LiteLLM("gpt-4.1-mini"), + prompt=cls.build_system_prompt(), + tools=cls.build_tools(), + default_options=AgentOptions(max_turns=30), + ) + agent.parse_final_answer = cls.parse_final_answer # type: ignore[attr-defined] + return agent + + @staticmethod + def parse_final_answer(text: str) -> str: + """Extract the FINAL ANSWER segment from model output.""" + marker = "FINAL ANSWER:" + idx = text.rfind(marker) + if idx == -1: + return text.strip() + candidate = text[idx + len(marker) :].strip() + if candidate.startswith("[") and candidate.endswith("]"): + candidate = candidate[1:-1].strip() + return candidate + + +# Export agent instances for import +humaneval_agent: Agent = AgentHumanEval.build() +humaneval_todo_agent: Agent = TodoAgent(agent=humaneval_agent, domain_context="python engineer") + +hotpot_agent: Agent = AgentHotpot.build() +hotpot_todo_agent: Agent = TodoAgent(agent=hotpot_agent, domain_context="research QA") + +gaia_agent: Agent = AgentGAIA.build() +gaia_todo_agent: Agent = TodoAgent(agent=gaia_agent, domain_context="general AI assistant") diff --git a/examples/evaluate/agent-benchmarking/run_gaia.py b/examples/evaluate/agent-benchmarking/run_gaia.py index 797f479d9..a3720c4f3 100644 --- a/examples/evaluate/agent-benchmarking/run_gaia.py +++ b/examples/evaluate/agent-benchmarking/run_gaia.py @@ -1,19 +1,15 @@ """ -Ragbits Evaluation Example: GAIA (Agent vs TodoAgent) +Ragbits Evaluation Example: GAIA -This example demonstrates how to evaluate the GAIA benchmark using either a classic -`Agent` or a task-decomposing `TodoAgent`, controlled by a command-line flag. Both modes -share the same prompt, retrieval setup, and parsing logic. +This example evaluates GAIA using any agent provided via a path compatible with +the CLI's `import_agent_from_path` utility, e.g.: -To run the script, execute one of the following commands: +uv run python examples/evaluate/agent-benchmarking/run_gaia.py \ + --agent_path examples/evaluate/agent-benchmarking/example_agents.py:gaia_agent - ```bash - # Agent mode - uv run python examples/evaluate/agent-benchmarking/run_gaia.py - - # TodoAgent mode - uv run python examples/evaluate/agent-benchmarking/run_gaia.py --use_todo - ``` +To use the Todo-enabled variant, pass `gaia_todo_agent`. The script will +conditionally print TODO stats when the agent is Todo-based and extended logs +are enabled. """ # /// script @@ -30,11 +26,8 @@ import logging from pathlib import Path -from todo_agent import TodoAgent - -from ragbits.agents import Agent, AgentOptions -from ragbits.agents.tools.openai import get_web_search_tool -from ragbits.core.llms import LiteLLM +from ragbits.agents import Agent +from ragbits.agents.cli import import_agent_from_path from ragbits.core.sources.hf import HuggingFaceSource from ragbits.evaluate.dataloaders.gaia import GaiaDataLoader from ragbits.evaluate.evaluator import Evaluator @@ -42,112 +35,28 @@ from ragbits.evaluate.metrics.gaia import GaiaEfficiency, GaiaOutcome, GaiaTooling from ragbits.evaluate.pipelines.gaia import GaiaPipeline -# Extra Agent tools - - -def add(a: int, b: int) -> int: - """Add two integers and return the result.""" - return a + b - - -def subtract(a: int, b: int) -> int: - """Subtract two integers and return the result (a - b).""" - return a - b - - -def multiply(a: int, b: int) -> int: - """Multiply two integers and return the result.""" - return a * b - - -def divide(a: int, b: int) -> float: - """Divide two integers and return the result as float.""" - if b == 0: - raise ValueError("Cannot divide by zero.") - return a / b - - -def modulus(a: int, b: int) -> int: - """Compute remainder of a divided by b (a % b).""" - if b == 0: - raise ValueError("Cannot divide by zero.") - return a % b - -def get_extra_instruction_tpl() -> str: - """Generate tool usage instructions template for arithmetic and lookups.""" - return ( - "Tools (use when needed):\n" - "- add(a, b), subtract(a, b), multiply(a, b), divide(a, b), modulus(a, b)\n" - "- web_search -> OpenAI websearch tool\n" - ) - - -def _build_system_prompt() -> str: - """Build a unified GAIA system prompt shared by both modes.""" - gaia_prompt = ( - "You are a general AI assistant. Provide a concise solution and finish with:\n" - "FINAL ANSWER: [your final answer].\n" - "Rules for FINAL ANSWER: use digits for numbers (no units unless requested);\n" - "prefer few words for strings; for lists, return a comma-separated list." - ) - - return "\n".join( - [ - gaia_prompt, - get_extra_instruction_tpl(), - ] - ) - - -def _build_tools() -> list: - """Return the shared toolset for GAIA.""" - return [ - add, - subtract, - multiply, - divide, - modulus, - get_web_search_tool(model_name="gpt-4.1-mini"), - ] - - -def _parse_final_answer(text: str) -> str: - """Extract the FINAL ANSWER segment from model output.""" - marker = "FINAL ANSWER:" - idx = text.rfind(marker) - if idx == -1: - return text.strip() - candidate = text[idx + len(marker) :].strip() - if candidate.startswith("[") and candidate.endswith("]"): - candidate = candidate[1:-1].strip() - return candidate - - -async def main(use_todo: bool) -> None: - """Run GAIA evaluation in classic or Todo mode.""" +async def main(agent_path: str | None) -> None: + """Run GAIA evaluation with a pluggable agent.""" logging.getLogger("LiteLLM").setLevel(logging.ERROR) - base_agent: Agent = Agent( - llm=LiteLLM("gpt-4.1-mini"), - prompt=_build_system_prompt(), - tools=_build_tools(), - default_options=AgentOptions(max_turns=30), - ) - evaluation_target = TodoAgent(agent=base_agent, domain_context="general AI assistant") if use_todo else base_agent + default_agent_path = "examples/evaluate/agent-benchmarking/example_agents.py:gaia_agent" + evaluation_target: Agent = import_agent_from_path(agent_path or default_agent_path) # Data loader source = HuggingFaceSource(path="gaia-benchmark/GAIA", name="2023_level1", split="validation") dataloader = GaiaDataLoader(source=source, split="data[:10]", skip_file_attachments=True) # Pipeline + use_todo = evaluation_target.__class__.__name__ == "TodoAgent" log_file = "gaia_todo_examples.ndjson" if use_todo else "gaia_examples.ndjson" log_path = Path(__file__).with_name(log_file) + pipeline = GaiaPipeline( evaluation_target=evaluation_target, per_example_log_file=log_path, extended_logs=use_todo, - parse_answer_fn=_parse_final_answer, + parse_answer_fn=evaluation_target.parse_final_answer, ) # Metrics @@ -161,50 +70,19 @@ async def main(use_todo: bool) -> None: print(f" {key}: {value:.4f}") if use_todo: - await _print_todo_stats(log_path, dataloader) - - -async def _print_todo_stats(log_path: Path, dataloader: GaiaDataLoader) -> None: - """Print aggregated TODO-agent statistics from extended logs.""" - print("\nTODO Orchestrator Statistics:") - decomposed_count = 0 - total_tasks = 0 - simple_count = 0 - complex_count = 0 - - if log_path.exists(): - import json - - with open(log_path, encoding="utf-8") as f: - for line in f: - record = json.loads(line) - debug_log = record.get("extended_debug_logging", "[]") - if debug_log and debug_log != "[]": - debug_data = json.loads(debug_log) - if debug_data and len(debug_data) > 0: - metadata = (debug_data[0] or {}).get("metadata", {}) - todo_meta = metadata.get("todo", {}) - - if todo_meta.get("was_decomposed"): - decomposed_count += 1 - total_tasks += int(todo_meta.get("num_tasks", 0)) - - if todo_meta.get("complexity_classification") == "SIMPLE": - simple_count += 1 - elif todo_meta.get("complexity_classification") == "COMPLEX": - complex_count += 1 - - total = len(await dataloader.load()) - rate = (decomposed_count / total * 100) if total else 0.0 - avg_tasks = (total_tasks / decomposed_count) if decomposed_count else 0.0 - print(f" Decomposition rate: {decomposed_count}/{total} ({rate:.1f}%)") - print(f" Average tasks per decomposed query: {avg_tasks:.1f}") - print(f" Complexity classification: {simple_count} simple, {complex_count} complex") + from utils import print_todo_stats + + await print_todo_stats(log_path, dataloader) if __name__ == "__main__": - parser = argparse.ArgumentParser(description="GAIA evaluation example") - parser.add_argument("--use_todo", action="store_true", help="Run with TodoAgent instead of Agent") + parser = argparse.ArgumentParser(description="GAIA evaluation") + parser.add_argument( + "--agent_path", + type=str, + default=None, + help=("Path to agent 'path/to/file.py:var'. " "Defaults to exported gaia_agent in example_agents."), + ) args = parser.parse_args() - asyncio.run(main(args.use_todo)) + asyncio.run(main(args.agent_path)) diff --git a/examples/evaluate/agent-benchmarking/run_hotpot.py b/examples/evaluate/agent-benchmarking/run_hotpot.py index 637e331f8..e3cbed0c1 100644 --- a/examples/evaluate/agent-benchmarking/run_hotpot.py +++ b/examples/evaluate/agent-benchmarking/run_hotpot.py @@ -1,19 +1,15 @@ """ -Ragbits Evaluation Example: HotpotQA (Agent vs TodoAgent) +Ragbits Evaluation Example: HotpotQA -This example demonstrates how to evaluate HotpotQA using either a classic `Agent` -or a task-decomposing `TodoAgent`, selected via a command-line flag. Both modes -share the same prompt, retrieval setup, and parsing logic. +This example evaluates HotpotQA using any agent provided via a path compatible +with the CLI's `import_agent_from_path` utility, e.g.: -To run the script, execute one of the following commands: +uv run python examples/evaluate/agent-benchmarking/run_hotpot.py \ + --agent_path examples/evaluate/agent-benchmarking/example_agents.py:hotpot_agent - ```bash - # Agent mode - uv run python examples/evaluate/agent-benchmarking/run_hotpot.py - - # TodoAgent mode - uv run python examples/evaluate/agent-benchmarking/run_hotpot.py --use_todo - ``` +If you want to use the Todo-enabled variant, pass `hotpot_todo_agent` instead. +The script will conditionally print TODO stats when the agent is Todo-based and +extended logs are enabled. """ # /// script @@ -31,16 +27,11 @@ import logging from pathlib import Path -from todo_agent import TodoAgent +from utils import build_hotpot_retriever, print_todo_stats -from ragbits.agents import Agent, AgentOptions -from ragbits.core.embeddings import LiteLLMEmbedder -from ragbits.core.llms import LiteLLM +from ragbits.agents import Agent +from ragbits.agents.cli import import_agent_from_path from ragbits.core.sources.hf import HuggingFaceSource -from ragbits.core.vector_stores import VectorStoreOptions -from ragbits.core.vector_stores.in_memory import InMemoryVectorStore -from ragbits.document_search import DocumentSearch -from ragbits.document_search.ingestion.strategies import BatchedIngestStrategy from ragbits.evaluate.dataloaders.hotpot_qa import HotpotQADataLoader from ragbits.evaluate.evaluator import Evaluator from ragbits.evaluate.metrics.base import MetricSet @@ -48,79 +39,31 @@ from ragbits.evaluate.pipelines.hotpot_qa import HotpotQAPipeline -def _build_system_prompt() -> str: - """Build a unified HotpotQA system prompt shared by both modes.""" - return ( - "You are a helpful assistant. Use the provided context to answer.\n" - "Respond on a single line as: 'Answer: '.\n" - "- If yes/no, respond 'Answer: yes' or 'Answer: no'.\n" - "- If a name or title is required, provide only that after 'Answer:'.\n" - "- Do not add any extra text beyond the Answer line.\n" - "Keep the answer concise." - ) - - -def _build_question_generation_prompt(original_question: str, accumulated_context: str) -> str: - """Build a prompt for generating the next search question in multi-hop retrieval.""" - return ( - f"Original question: {original_question}\n\n" - f"Context so far: \n{accumulated_context}\n\n" - "Write ONE new, specific search question that fills the key missing info.\n" - "Do not repeat known facts. Return ONLY the question." - ) - - -def _parse_final_answer(text: str) -> str: - """Extract the Answer segment from model output.""" - marker = "Answer:" - idx = text.rfind(marker) - if idx == -1: - return text.strip() - candidate = text[idx + len(marker) :].strip() - if candidate.startswith("<") and candidate.endswith(">"): - candidate = candidate[1:-1].strip() - return candidate - - -def _build_retriever() -> DocumentSearch: - """Create a lightweight in-memory retriever for HotpotQA.""" - return DocumentSearch( - vector_store=InMemoryVectorStore( - embedder=LiteLLMEmbedder(model_name="text-embedding-3-small"), - default_options=VectorStoreOptions(k=5), - ), - ingest_strategy=BatchedIngestStrategy(index_batch_size=1000), - ) - - -async def main(use_todo: bool) -> None: - """Run HotpotQA evaluation in classic or Todo mode.""" +async def main(agent_path: str | None) -> None: + """Run HotpotQA evaluation with a pluggable agent.""" logging.getLogger("LiteLLM").setLevel(logging.ERROR) - # Build agent or TodoAgent - base_agent: Agent = Agent( - llm=LiteLLM("gpt-4.1-mini"), - prompt=_build_system_prompt(), - tools=[], - default_options=AgentOptions(max_turns=5), - ) - evaluation_target = TodoAgent(agent=base_agent, domain_context="research QA") if use_todo else base_agent + # Load agent from path or fall back to default exported example agent + default_agent_path = "examples/evaluate/agent-benchmarking/example_agents.py:hotpot_agent" + evaluation_target: Agent = import_agent_from_path(agent_path or default_agent_path) # Data loader source = HuggingFaceSource(path="hotpotqa/hotpot_qa", name="distractor", split="validation") - dataloader = HotpotQADataLoader(source=source, split="data[:10]", level_filter="hard") + dataloader = HotpotQADataLoader(source=source, split="data[:5]", level_filter="hard") # Pipeline + use_todo = evaluation_target.__class__.__name__ == "TodoAgent" log_file = "hotpot_todo_examples.ndjson" if use_todo else "hotpot_examples.ndjson" log_path = Path(__file__).with_name(log_file) + pipeline = HotpotQAPipeline( evaluation_target=evaluation_target, - retriever=_build_retriever(), + retriever=build_hotpot_retriever(), hops=3, per_example_log_file=log_path, extended_logs=use_todo, - parse_answer_fn=_parse_final_answer, - question_generation_prompt_fn=_build_question_generation_prompt, + parse_answer_fn=evaluation_target.parse_final_answer, + question_generation_prompt_fn=evaluation_target.question_generation_prompt_fn, ) # Metrics @@ -134,50 +77,17 @@ async def main(use_todo: bool) -> None: print(f" {key}: {value:.4f}") if use_todo: - await _print_todo_stats(log_path, dataloader) - - -async def _print_todo_stats(log_path: Path, dataloader: HotpotQADataLoader) -> None: - """Print aggregated TODO-agent statistics from extended logs.""" - print("\nTODO Orchestrator Statistics:") - decomposed_count = 0 - total_tasks = 0 - simple_count = 0 - complex_count = 0 - - if log_path.exists(): - import json - - with open(log_path, encoding="utf-8") as f: - for line in f: - record = json.loads(line) - debug_log = record.get("extended_debug_logging", "[]") - if debug_log and debug_log != "[]": - debug_data = json.loads(debug_log) - if debug_data and len(debug_data) > 0: - metadata = (debug_data[0] or {}).get("metadata", {}) - todo_meta = metadata.get("todo", {}) - - if todo_meta.get("was_decomposed"): - decomposed_count += 1 - total_tasks += int(todo_meta.get("num_tasks", 0)) - - if todo_meta.get("complexity_classification") == "SIMPLE": - simple_count += 1 - elif todo_meta.get("complexity_classification") == "COMPLEX": - complex_count += 1 - - total = len(await dataloader.load()) - rate = (decomposed_count / total * 100) if total else 0.0 - avg_tasks = (total_tasks / decomposed_count) if decomposed_count else 0.0 - print(f" Decomposition rate: {decomposed_count}/{total} ({rate:.1f}%)") - print(f" Average tasks per decomposed query: {avg_tasks:.1f}") - print(f" Complexity classification: {simple_count} simple, {complex_count} complex") + await print_todo_stats(log_path, dataloader) if __name__ == "__main__": - parser = argparse.ArgumentParser(description="HotpotQA evaluation example") - parser.add_argument("--use_todo", action="store_true", help="Run with TodoAgent instead of Agent") + parser = argparse.ArgumentParser(description="HotpotQA evaluation") + parser.add_argument( + "--agent_path", + type=str, + default=None, + help=("Path to agent 'path/to/file.py:var'. " "Defaults to exported hotpot_agent in example_agents."), + ) args = parser.parse_args() - asyncio.run(main(args.use_todo)) + asyncio.run(main(args.agent_path)) diff --git a/examples/evaluate/agent-benchmarking/run_humaneval.py b/examples/evaluate/agent-benchmarking/run_humaneval.py index 4c21c3938..d5a775f88 100644 --- a/examples/evaluate/agent-benchmarking/run_humaneval.py +++ b/examples/evaluate/agent-benchmarking/run_humaneval.py @@ -1,19 +1,15 @@ """ -Ragbits Evaluation Example: HumanEval (Agent vs TodoAgent) +Ragbits Evaluation Example: HumanEval -This example demonstrates how to evaluate HumanEval using either a classic `Agent` -or a task-decomposing `TodoAgent`. Both modes share the -same prompt, retrieval setup, and parsing logic. +This example evaluates HumanEval using any agent provided via a path compatible +with the CLI's `import_agent_from_path` utility, e.g.: -To run the script, execute one of the following commands: +uv run python examples/evaluate/agent-benchmarking/run_humaneval.py \ + --agent_path=examples/evaluate/agent-benchmarking/example_agents.py:humaneval_agent - ```bash - # Agent mode - uv run python examples/evaluate/agent-benchmarking/run_humaneval.py - - # TodoAgent mode - uv run python examples/evaluate/agent-benchmarking/run_humaneval.py --use_todo - ``` +To run in Todo-enabled mode, pass `humaneval_todo_agent`. The script will +conditionally print TODO stats when the agent is Todo-based and extended logs +are enabled. """ # /// script @@ -30,10 +26,8 @@ import logging from pathlib import Path -from todo_agent import TodoAgent - -from ragbits.agents import Agent, AgentOptions -from ragbits.core.llms import LiteLLM +from ragbits.agents import Agent +from ragbits.agents.cli import import_agent_from_path from ragbits.core.sources.hf import HuggingFaceSource from ragbits.evaluate.dataloaders.human_eval import HumanEvalDataLoader from ragbits.evaluate.evaluator import Evaluator @@ -42,60 +36,29 @@ from ragbits.evaluate.pipelines.human_eval import HumanEvalPipeline -def _build_system_prompt() -> str: - """Build a unified HumanEval system prompt shared by both modes.""" - return "\n".join( - [ - "You are an expert Python engineer.", - "Implement exactly one function that solves the problem.", - "Output ONLY the function as plain Python (no markdown). Include necessary imports.", - "Do not include explanations or comments.", - ] - ) - - -def _sanitize_code(text: str) -> str: - """Remove markdown fences and keep only Python function text returned by the model.""" - cleaned = text.replace("\r\n", "\n").replace("\r", "\n").strip() - if "```" in cleaned: - start = cleaned.find("```") - end = cleaned.find("```", start + 3) - if end != -1: - inside = cleaned[start + 3 : end].lstrip() - if "\n" in inside: - first, rest = inside.split("\n", 1) - cleaned = rest if first.strip().lower().startswith("python") else inside - else: - cleaned = inside - return cleaned.strip() - - -async def main(use_todo: bool) -> None: - """Run HumanEval in classic or Todo mode.""" +async def main(agent_path: str | None) -> None: + """Run HumanEval with a pluggable agent.""" logging.getLogger("LiteLLM").setLevel(logging.ERROR) - base_agent: Agent = Agent( - llm=LiteLLM("gpt-4.1-mini"), - prompt=_build_system_prompt(), - tools=[], - default_options=AgentOptions(max_turns=30), - ) - evaluation_target = TodoAgent(agent=base_agent, domain_context="python engineer") if use_todo else base_agent + default_agent_path = "examples/evaluate/agent-benchmarking/example_agents.py:humaneval_agent" + evaluation_target: Agent = import_agent_from_path(agent_path or default_agent_path) # Data source = HuggingFaceSource(path="openai/openai_humaneval", split="test") dataloader = HumanEvalDataLoader(source=source, split="data[:2]") # Pipeline + use_todo = evaluation_target.__class__.__name__ == "TodoAgent" log_file = "humaneval_todo_examples.ndjson" if use_todo else "humaneval_examples.ndjson" log_path = Path(__file__).with_name(log_file) + pipeline = HumanEvalPipeline( evaluation_target=evaluation_target, - n_samples=1, + n_samples=5, timeout_sec=30, per_example_log_file=log_path, extended_logs=use_todo, - code_sanitize_fn=_sanitize_code, + code_sanitize_fn=evaluation_target.code_sanitize_fn, ) # Metrics @@ -110,50 +73,19 @@ async def main(use_todo: bool) -> None: print(f" {key}: {value:.4f}") if use_todo: - await _print_todo_stats(log_path, dataloader) - - -async def _print_todo_stats(log_path: Path, dataloader: HumanEvalDataLoader) -> None: - """Print aggregated TODO-agent statistics from extended logs.""" - print("\nTODO Orchestrator Statistics:") - decomposed_count = 0 - total_tasks = 0 - simple_count = 0 - complex_count = 0 - - if log_path.exists(): - import json - - with open(log_path, encoding="utf-8") as f: - for line in f: - record = json.loads(line) - debug_log = record.get("extended_debug_logging", "[]") - if debug_log and debug_log != "[]": - debug_data = json.loads(debug_log) - if debug_data and len(debug_data) > 0: - metadata = (debug_data[0] or {}).get("metadata", {}) - todo_meta = metadata.get("todo", {}) - - if todo_meta.get("was_decomposed"): - decomposed_count += 1 - total_tasks += int(todo_meta.get("num_tasks", 0)) - - if todo_meta.get("complexity_classification") == "SIMPLE": - simple_count += 1 - elif todo_meta.get("complexity_classification") == "COMPLEX": - complex_count += 1 - - total = len(await dataloader.load()) - rate = (decomposed_count / total * 100) if total else 0.0 - avg_tasks = (total_tasks / decomposed_count) if decomposed_count else 0.0 - print(f" Decomposition rate: {decomposed_count}/{total} ({rate:.1f}%)") - print(f" Average tasks per decomposed query: {avg_tasks:.1f}") - print(f" Complexity classification: {simple_count} simple, {complex_count} complex") + from utils import print_todo_stats + + await print_todo_stats(log_path, dataloader) if __name__ == "__main__": - parser = argparse.ArgumentParser(description="HumanEval evaluation example") - parser.add_argument("--use_todo", action="store_true", help="Run with TodoAgent instead of Agent") + parser = argparse.ArgumentParser(description="HumanEval evaluation") + parser.add_argument( + "--agent_path", + type=str, + default=None, + help=("Path to agent 'path/to/file.py:var'. " "Defaults to exported humaneval_agent in example_agents."), + ) args = parser.parse_args() - asyncio.run(main(args.use_todo)) + asyncio.run(main(args.agent_path)) diff --git a/examples/evaluate/agent-benchmarking/todo_agent.py b/examples/evaluate/agent-benchmarking/todo_agent.py deleted file mode 100644 index 00948a0ad..000000000 --- a/examples/evaluate/agent-benchmarking/todo_agent.py +++ /dev/null @@ -1,85 +0,0 @@ -from __future__ import annotations - -from typing import Any, cast - -from ragbits.agents import Agent, AgentOptions, AgentResult -from ragbits.agents.tool import ToolCallResult -from ragbits.agents.tools.todo import TodoOrchestrator -from ragbits.core.llms.base import LLMClientOptionsT, Usage -from ragbits.core.prompt.base import BasePrompt - - -class TodoAgent(Agent[LLMClientOptionsT, None, str]): - """ - Agent wrapper that uses TodoOrchestrator to optionally decompose a query - into tasks and aggregate a final answer, while remaining compatible with - existing EvaluationPipelines that expect an Agent. - """ - - def __init__(self, agent: Agent[LLMClientOptionsT, None, str], domain_context: str = "") -> None: - super().__init__( - llm=agent.llm, - prompt=agent.prompt, - history=agent.history, - keep_history=False, - mcp_servers=agent.mcp_servers, - default_options=cast(AgentOptions[LLMClientOptionsT] | None, agent.default_options), - ) - self._inner_agent = agent - self._domain_context = domain_context - self._orchestrator = TodoOrchestrator(domain_context=domain_context) - - async def run( - self, - input: str | None = None, - *_: Any, - **__: Any, - ) -> AgentResult[str]: - """Run the orchestrated flow and return a single final answer.""" - query = input or "" - - # Accumulate outputs from the orchestrated workflow - final_text: str = "" - tool_calls: list[ToolCallResult] | None = None - total_usage: Usage = Usage() - last_prompt: BasePrompt | None = None - - tasks_created = False - num_tasks = 0 - - async for item in self._orchestrator.run_todo_workflow_streaming(self._inner_agent, query): - match item: - case str(): - final_text += item - case ToolCallResult(): - if tool_calls is None: - tool_calls = [] - tool_calls.append(item) - case Usage(): - total_usage += item - case BasePrompt(): - last_prompt = item - case _: - # Inspect orchestrator internal state to populate metadata - tasks_created = len(self._orchestrator.todo_list.tasks) > 0 - num_tasks = len(self._orchestrator.todo_list.tasks) - - # Compose metadata with TODO info - complexity = "COMPLEX" if tasks_created else "SIMPLE" - todo_meta: dict[str, Any] = { - "was_decomposed": bool(tasks_created), - "complexity_classification": complexity, - "num_tasks": int(num_tasks), - "domain_context": self._domain_context, - } - - # Build result compatible with existing pipelines - history = last_prompt.chat if last_prompt is not None else [] - - return AgentResult( - content=final_text.strip(), - metadata={"todo": todo_meta}, - tool_calls=tool_calls, - history=history, - usage=total_usage, - ) diff --git a/examples/evaluate/agent-benchmarking/utils.py b/examples/evaluate/agent-benchmarking/utils.py new file mode 100644 index 000000000..41eafa74e --- /dev/null +++ b/examples/evaluate/agent-benchmarking/utils.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import json +from collections.abc import Iterable +from pathlib import Path +from typing import Any, Protocol + +from ragbits.core.embeddings import LiteLLMEmbedder +from ragbits.core.vector_stores import VectorStoreOptions +from ragbits.core.vector_stores.in_memory import InMemoryVectorStore +from ragbits.document_search import DocumentSearch +from ragbits.document_search.ingestion.strategies import BatchedIngestStrategy + + +def build_hotpot_retriever() -> DocumentSearch: + """Create a lightweight in-memory retriever for HotpotQA.""" + return DocumentSearch( + vector_store=InMemoryVectorStore( + embedder=LiteLLMEmbedder(model_name="text-embedding-3-small"), + default_options=VectorStoreOptions(k=5), + ), + ingest_strategy=BatchedIngestStrategy(index_batch_size=1000), + ) + + +class _SupportsLoad(Protocol): + async def load(self) -> Iterable[Any]: ... + + +async def print_todo_stats(log_path: Path, dataloader: _SupportsLoad) -> None: + """Print aggregated TODO-agent statistics from extended logs. + + Works across benchmarks if extended logs include the orchestrator metadata. + """ + print("\nTODO Orchestrator Statistics:") + decomposed_count = 0 + total_tasks = 0 + simple_count = 0 + complex_count = 0 + + if log_path.exists(): + with open(log_path, encoding="utf-8") as f: + for line in f: + record = json.loads(line) + debug_log = record.get("extended_debug_logging", "[]") + if debug_log and debug_log != "[]": + debug_data = json.loads(debug_log) + if debug_data and len(debug_data) > 0: + metadata = (debug_data[0] or {}).get("metadata", {}) + todo_meta = metadata.get("todo", {}) + + if todo_meta.get("was_decomposed"): + decomposed_count += 1 + total_tasks += int(todo_meta.get("num_tasks", 0)) + + if todo_meta.get("complexity_classification") == "SIMPLE": + simple_count += 1 + elif todo_meta.get("complexity_classification") == "COMPLEX": + complex_count += 1 + + loaded = await dataloader.load() + items = list(loaded) + total = len(items) + rate = (decomposed_count / total * 100) if total else 0.0 + avg_tasks = (total_tasks / decomposed_count) if decomposed_count else 0.0 + print(f" Decomposition rate: {decomposed_count}/{total} ({rate:.1f}%)") + print(f" Average tasks per decomposed query: {avg_tasks:.1f}") + print(f" Complexity classification: {simple_count} simple, {complex_count} complex") From 77ee2afc2eb7d24c4efc0d5b05a531804f292081 Mon Sep 17 00:00:00 2001 From: dazy-ds <135028324+dazy-ds@users.noreply.github.com> Date: Fri, 10 Oct 2025 09:17:14 +0200 Subject: [PATCH 37/43] feat: todo list component (#827) Co-authored-by: jakubduda-dsai --- examples/agents/todo_tools_example.py | 5 +- examples/chat/chat.py | 2 +- examples/chat/hiking_agent.py | 114 ++++++++++++++ package-lock.json | 6 +- packages/ragbits-agents/CHANGELOG.md | 5 +- .../src/ragbits/agents/tools/todo.py | 54 +++---- packages/ragbits-chat/CHANGELOG.md | 1 + .../src/ragbits/chat/interface/_interface.py | 5 + .../src/ragbits/chat/interface/types.py | 19 ++- .../ragbits/chat/providers/model_provider.py | 5 + .../generate_typescript_from_json_schema.py | 1 + typescript/@ragbits/api-client/package.json | 2 +- .../@ragbits/api-client/src/autogen.types.ts | 33 ++++ typescript/@ragbits/api-client/src/index.ts | 5 + .../ui/__tests__/unit/TodoList.test.tsx | 146 ++++++++++++++++++ .../ChatHistoryPlugin/ChatHistory.test.tsx | 3 +- .../components/ChatMessage/ChatMessage.tsx | 16 ++ .../ui/src/core/components/TodoList.tsx | 72 +++++++++ .../eventHandlers/eventHandlerRegistry.ts | 4 + .../eventHandlers/messageHandlers.ts | 20 +++ typescript/ui/src/core/utils/tasks.ts | 80 ++++++++++ .../components/ChatHistory.tsx | 76 ++++----- typescript/ui/src/types/history.ts | 2 + 23 files changed, 603 insertions(+), 73 deletions(-) create mode 100644 examples/chat/hiking_agent.py create mode 100644 typescript/ui/__tests__/unit/TodoList.test.tsx create mode 100644 typescript/ui/src/core/components/TodoList.tsx create mode 100644 typescript/ui/src/core/utils/tasks.ts diff --git a/examples/agents/todo_tools_example.py b/examples/agents/todo_tools_example.py index 691d5f9af..b86cb9ca7 100644 --- a/examples/agents/todo_tools_example.py +++ b/examples/agents/todo_tools_example.py @@ -34,8 +34,11 @@ def _handle_response(response: ResponseType) -> None: def _handle_streaming_response(response: TodoResult) -> None: """Handle TodoResult from the orchestrator.""" - if response.type in ("status", "task_list"): + if response.type in ("status"): print(response.message or "") + elif response.type in ("task_list"): + for index, task in enumerate(response.tasks, 1): + print(f"{index}. {task.description}") elif response.type in ("task_summary_start", "final_summary_start"): print(response.message or "", end="", flush=True) elif response.type in ("task_completed", "final_summary_end"): diff --git a/examples/chat/chat.py b/examples/chat/chat.py index ccbac8bc6..566de498c 100644 --- a/examples/chat/chat.py +++ b/examples/chat/chat.py @@ -7,7 +7,7 @@ To run the script, execute the following command: ```bash - ragbits api run examples.api.chat:MyChat + ragbits api run examples.chat.chat:MyChat ``` """ diff --git a/examples/chat/hiking_agent.py b/examples/chat/hiking_agent.py new file mode 100644 index 000000000..2e8c8b6a6 --- /dev/null +++ b/examples/chat/hiking_agent.py @@ -0,0 +1,114 @@ +""" +Ragbits Chat Example: Chat Interface + +This example demonstrates how to use the ChatInterface to create a simple chat application with agent capabilities. +It showcases different response types, including text responses, live updates, and reference documents, as well as +the use of todo lists to help agents handle complex tasks. + +To run the script, execute the following command: + + ```bash + ragbits api run examples.chat.hiking_agent:MyChat + ``` +""" + +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "ragbits-agents", +# "ragbits-chat", +# ] +# /// +# + +from collections.abc import AsyncGenerator + +from ragbits.agents import Agent +from ragbits.agents.tools.todo import TodoOrchestrator, TodoResult +from ragbits.chat.interface import ChatInterface +from ragbits.chat.interface.types import ChatContext, ChatResponse, LiveUpdateType +from ragbits.chat.interface.ui_customization import HeaderCustomization, PageMetaCustomization, UICustomization +from ragbits.core.llms import LiteLLM +from ragbits.core.prompt import ChatFormat + + +class MyChat(ChatInterface): + """A simple example implementation of the ChatInterface that demonstrates different response types.""" + + ui_customization = UICustomization( + header=HeaderCustomization(title="Example Ragbits Agent Chat", subtitle="by deepsense.ai", logo="🐰"), + welcome_message=( + "Hello! I'm your AI hiking trip assistant.\n\n How can I help you today? " + "You can ask me **anything**! " + "I can provide information, answer questions, and assist you with various tasks." + ), + meta=PageMetaCustomization(favicon="🔨", page_title="Change me!"), + ) + + conversation_history = True + show_usage = True + + def __init__(self) -> None: + self.llm = LiteLLM(model_name="gpt-4o-mini") + self.todo_orchestrator = TodoOrchestrator() + self.agent = Agent( + llm=self.llm, + prompt=""" + You are an expert hiking guide. Provide detailed, comprehensive information + about hiking routes, gear, transportation, and safety considerations. + """, + ) + + async def chat( + self, + message: str, + history: ChatFormat, + context: ChatContext, + ) -> AsyncGenerator[ChatResponse, None]: + """ + Example implementation of the ChatInterface. + + Args: + message: The current user message + history: Optional list of previous messages in the conversation + context: Optional context + + Yields: + ChatResponse objects containing different types of content: + - Text chunks for the actual response + - Reference documents used to generate the response + """ + live_update_counter = 0 + + async for response in self.todo_orchestrator.run_todo_workflow_streaming( + self.agent, [*history, {"role": "user", "content": message}] + ): + match response: + case str(): + yield self.create_text_response(response) + case TodoResult(): + if response.type in ("status"): + yield self.create_live_update( + str(live_update_counter), LiveUpdateType.FINISH, response.message or "" + ) + live_update_counter += 1 + elif response.type in ("task_list"): + for task in response.tasks: + yield self.create_todo_item_response(task) + elif response.type in ("start_task"): + yield self.create_todo_item_response(response.current_task) + elif response.type in ("task_summary_start", "final_summary_start"): + yield self.create_live_update( + str(live_update_counter), LiveUpdateType.START, response.message or "" + ) + elif response.type in ("task_completed"): + yield self.create_live_update( + str(live_update_counter), LiveUpdateType.FINISH, response.message or "" + ) + yield self.create_todo_item_response(response.current_task) + live_update_counter += 1 + elif response.type in ("final_summary_end"): + yield self.create_live_update( + str(live_update_counter), LiveUpdateType.FINISH, response.message or "" + ) + live_update_counter += 1 diff --git a/package-lock.json b/package-lock.json index fb44e87a5..50f24ad59 100644 --- a/package-lock.json +++ b/package-lock.json @@ -14988,7 +14988,7 @@ } }, "typescript/@ragbits/api-client": { - "version": "1.2.2", + "version": "1.3.0", "license": "MIT", "devDependencies": { "@eslint/js": "^9.17.0", @@ -15007,7 +15007,7 @@ } }, "typescript/@ragbits/api-client-react": { - "version": "1.2.2", + "version": "1.3.0", "license": "MIT", "dependencies": { "@ragbits/api-client": "*" @@ -15038,7 +15038,7 @@ } }, "typescript/ui": { - "version": "1.2.2", + "version": "1.3.0", "dependencies": { "@heroicons/react": "^2.2.0", "@heroui/react": "^2.8.1", diff --git a/packages/ragbits-agents/CHANGELOG.md b/packages/ragbits-agents/CHANGELOG.md index a1fb79a83..524d282b4 100644 --- a/packages/ragbits-agents/CHANGELOG.md +++ b/packages/ragbits-agents/CHANGELOG.md @@ -1,20 +1,21 @@ # CHANGELOG ## Unreleased -- TodoOrchestrator & Todo list for agent (#823) +- TodoOrchestrator & Todo list for agent (#823) - Support wrapping downstream agents as tools (#818) - Add syntax sugar allowing easier Agents definition (#820) - Add post-processors (#821) - Support streaming from downstream agents (#812) - - Support wrapping downstream agents as tools (#818) - Add syntax sugar allowing easier Agents definition (#820) - Add post-processors (#821) - Support streaming from downstream agents (#812) - Add supervisor post-processor (#830) +- Add support for todo lists generated by agents with examples (#827) ## 1.3.0 (2025-09-11) + ### Changed - ragbits-core updated to version v1.3.0 diff --git a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py index aeb9552fa..699c50dfb 100644 --- a/packages/ragbits-agents/src/ragbits/agents/tools/todo.py +++ b/packages/ragbits-agents/src/ragbits/agents/tools/todo.py @@ -2,10 +2,11 @@ import uuid from collections.abc import AsyncGenerator -from dataclasses import dataclass, field from enum import Enum from types import SimpleNamespace +from pydantic import BaseModel, Field + from ragbits.agents import Agent from ragbits.agents._main import DownstreamAgentResult from ragbits.agents.tool import ToolCallResult @@ -28,8 +29,7 @@ class TaskStatus(str, Enum): RETRYING = "retrying" -@dataclass -class Task: +class Task(BaseModel): """Simple task representation.""" id: str @@ -37,19 +37,19 @@ class Task: status: TaskStatus = TaskStatus.PENDING order: int = 0 summary: str | None = None + parent_id: str | None = None full_response: str | None = None - dependencies: list[str] = field(default_factory=list) + dependencies: list[str] = Field(default_factory=list) -@dataclass -class TodoResult: +class TodoResult(BaseModel): """Result type for todo workflow.""" type: str message: str | None = None current_task: Task | None = None next_task: Task | None = None - tasks: list[Task] | None = None + tasks: list[Task] = Field(default_factory=list) tasks_count: int | None = None progress: str | None = None @@ -60,11 +60,10 @@ class TodoResult: ) -@dataclass -class TodoList: +class TodoList(BaseModel): """Simple todo list for one agent run.""" - tasks: list[Task] = field(default_factory=list) + tasks: list[Task] = Field(default_factory=list) current_index: int = 0 def get_current_task(self) -> Task | None: @@ -110,14 +109,14 @@ def get_current(self) -> TodoResult: progress=f"{self.current_index + 1}/{len(self.tasks)}", ) - def start_current_task(self) -> TodoResult: + def start_current_task(self) -> Task: """Start the current task.""" current = self.get_current_task() if not current: raise ValueError("No current task to start") current.status = TaskStatus.IN_PROGRESS - return TodoResult(type="start_task", message=f"Started task: {current.description}", current_task=current) + return current def complete_current_task(self, summary: str) -> TodoResult: """Complete the current task with summary.""" @@ -159,22 +158,20 @@ def get_completed_context(self) -> str: return "Previous completed tasks:\n" + "\n".join(context_parts) -@dataclass -class TodoOrchestrator: +class TodoOrchestrator(BaseModel): """High-level orchestrator for managing todo workflow with context passing.""" - todo_list: TodoList = field(default_factory=TodoList) + todo_list: TodoList = Field(default_factory=TodoList) domain_context: str = "" - def __init__(self, domain_context: str = ""): + def __init__(self, domain_context: str = "") -> None: """ Initialize TodoOrchestrator with domain-specific prompts. Args: domain_context: Additional context about the domain (e.g., "hiking guide", "software architect", etc.) """ - self.todo_list = TodoList() - self.domain_context = domain_context + super().__init__(domain_context=domain_context) async def run_todo_workflow_streaming( self, agent: Agent, initial_query: str @@ -219,8 +216,7 @@ async def run_todo_workflow_streaming( # Complex query - proceed with task breakdown yield TodoResult(type="status", message=f"📋 Complex query - created {len(tasks)} tasks:") - for i, task in enumerate(tasks, 1): - yield TodoResult(type="task_list", message=f" {i}. {task}") + yield TodoResult(type="task_list", tasks=tasks, tasks_count=len(tasks)) # Step 2: Execute each task with context from previous tasks task_summaries = [] @@ -242,19 +238,22 @@ async def run_todo_workflow_streaming( # Execute single task with focused context and stream summary async for task_response in self._execute_single_task_focused(agent, current_task, context, initial_query): yield task_response + yield "\n\n" # Get the completed task summary completed_tasks = [t for t in self.todo_list.tasks if t.status == TaskStatus.COMPLETED] if completed_tasks: latest_summary = completed_tasks[-1].summary task_summaries.append(latest_summary) - yield TodoResult(type="task_completed", message="\n✅ Task completed\n") + yield TodoResult( + type="task_completed", message="\n✅ Task completed\n", current_task=completed_tasks[-1] + ) # Step 3: Generate comprehensive final summary with streaming yield TodoResult(type="status", message="📝 Generating comprehensive final summary...") yield TodoResult( type="final_summary_start", - message=f"\n📊 COMPREHENSIVE {self.domain_context.upper()} SUMMARY:\n" + "=" * 60 + "\n", + message=f"\n📊 Comprehensive {self.domain_context} summary:\n", ) async for summary_response in self._generate_comprehensive_summary_streaming( @@ -262,7 +261,7 @@ async def run_todo_workflow_streaming( ): yield summary_response - yield TodoResult(type="final_summary_end", message="\n" + "=" * 60) + yield TodoResult(type="final_summary_end", message="\n") yield TodoResult(type="status", message="🎉 All tasks completed!") @staticmethod @@ -293,7 +292,7 @@ async def _analyze_query_complexity(agent: Agent, query: str) -> bool: # Default to COMPLEX if we can't determine (safer approach) return response != "SIMPLE" - async def _create_tasks_simple(self, agent: Agent, initial_query: str) -> list[str]: + async def _create_tasks_simple(self, agent: Agent, initial_query: str) -> list[Task]: """Create tasks based on initial query - simple, non-streaming.""" # First, analyze if the query actually needs task breakdown is_complex = await TodoOrchestrator._analyze_query_complexity(agent, initial_query) @@ -321,7 +320,7 @@ async def _create_tasks_simple(self, agent: Agent, initial_query: str) -> list[s tasks = self._parse_tasks_from_response(response) if tasks: self.todo_list.create_tasks(tasks) - return tasks + return self.todo_list.tasks # Return the actual Task objects return [] @@ -349,7 +348,10 @@ async def _execute_single_task_focused( """ # Mark task as started - self.todo_list.start_current_task() + current_task = self.todo_list.start_current_task() + yield TodoResult( + type="start_task", message=f"Started task: {current_task.description}", current_task=current_task + ) full_response = "" last_summary_length = 0 diff --git a/packages/ragbits-chat/CHANGELOG.md b/packages/ragbits-chat/CHANGELOG.md index aebabd8c1..1c43ff86b 100644 --- a/packages/ragbits-chat/CHANGELOG.md +++ b/packages/ragbits-chat/CHANGELOG.md @@ -1,6 +1,7 @@ # CHANGELOG ## Unreleased +- Add todo list component to the UI, add support for todo events in API (#827) ## 1.3.0 (2025-09-11) diff --git a/packages/ragbits-chat/src/ragbits/chat/interface/_interface.py b/packages/ragbits-chat/src/ragbits/chat/interface/_interface.py index 8e4b73df2..7a68af9e9 100644 --- a/packages/ragbits-chat/src/ragbits/chat/interface/_interface.py +++ b/packages/ragbits-chat/src/ragbits/chat/interface/_interface.py @@ -9,6 +9,7 @@ from collections.abc import AsyncGenerator, Callable from typing import Any +from ragbits.agents.tools.todo import Task from ragbits.chat.interface.ui_customization import UICustomization from ragbits.core.audit.metrics import record_metric from ragbits.core.audit.metrics.base import MetricType @@ -251,6 +252,10 @@ def create_usage_response(usage: Usage) -> ChatResponse: content={model: MessageUsage.from_usage(usage) for model, usage in usage.model_breakdown.items()}, ) + @staticmethod + def create_todo_item_response(task: Task) -> ChatResponse: + return ChatResponse(type=ChatResponseType.TODO_ITEM, content=task) + @staticmethod def _sign_state(state: dict[str, Any]) -> str: """ diff --git a/packages/ragbits-chat/src/ragbits/chat/interface/types.py b/packages/ragbits-chat/src/ragbits/chat/interface/types.py index 920003651..75bfd4d01 100644 --- a/packages/ragbits-chat/src/ragbits/chat/interface/types.py +++ b/packages/ragbits-chat/src/ragbits/chat/interface/types.py @@ -3,6 +3,7 @@ from pydantic import BaseModel, ConfigDict, Field +from ragbits.agents.tools.todo import Task from ragbits.chat.auth.types import User from ragbits.chat.interface.forms import UserSettings from ragbits.chat.interface.ui_customization import UICustomization @@ -122,6 +123,7 @@ class ChatResponseType(str, Enum): CHUNKED_CONTENT = "chunked_content" CLEAR_MESSAGE = "clear_message" USAGE = "usage" + TODO_ITEM = "todo_item" class ChatContext(BaseModel): @@ -140,7 +142,16 @@ class ChatResponse(BaseModel): type: ChatResponseType content: ( - str | Reference | StateUpdate | LiveUpdate | list[str] | Image | dict[str, MessageUsage] | ChunkedContent | None + str + | Reference + | StateUpdate + | LiveUpdate + | list[str] + | Image + | dict[str, MessageUsage] + | ChunkedContent + | None + | Task ) def as_text(self) -> str | None: @@ -217,6 +228,12 @@ def as_usage(self) -> dict[str, MessageUsage] | None: """ return cast(dict[str, MessageUsage], self.content) if self.type == ChatResponseType.USAGE else None + def as_task(self) -> Task | None: + """ + Return the content as Task if this is an todo_item response, else None. + """ + return cast(Task, self.content) if self.type == ChatResponseType.TODO_ITEM else None + class ChatMessageRequest(BaseModel): """Client-side chat request interface.""" diff --git a/packages/ragbits-chat/src/ragbits/chat/providers/model_provider.py b/packages/ragbits-chat/src/ragbits/chat/providers/model_provider.py index 6b1191c74..b94bfe4bc 100644 --- a/packages/ragbits-chat/src/ragbits/chat/providers/model_provider.py +++ b/packages/ragbits-chat/src/ragbits/chat/providers/model_provider.py @@ -10,6 +10,7 @@ from pydantic import BaseModel +from ragbits.agents.tools.todo import Task, TaskStatus from ragbits.chat.interface.types import AuthType @@ -82,6 +83,7 @@ def get_models(self) -> dict[str, type[BaseModel | Enum]]: "FeedbackType": FeedbackType, "LiveUpdateType": LiveUpdateType, "MessageRole": MessageRole, + "TaskStatus": TaskStatus, # Core data models "ChatContext": ChatContext, "ChunkedContent": ChunkedContent, @@ -93,6 +95,7 @@ def get_models(self) -> dict[str, type[BaseModel | Enum]]: "FeedbackItem": FeedbackItem, "Image": Image, "MessageUsage": MessageUsage, + "Task": Task, # Configuration models "HeaderCustomization": HeaderCustomization, "UICustomization": UICustomization, @@ -151,6 +154,8 @@ def get_categories(self) -> dict[str, list[str]]: "JWTToken", "User", "MessageUsage", + "Task", + "TaskStatus", ], "configuration": [ "HeaderCustomization", diff --git a/scripts/generate_typescript_from_json_schema.py b/scripts/generate_typescript_from_json_schema.py index 903e85e12..317b7fce8 100644 --- a/scripts/generate_typescript_from_json_schema.py +++ b/scripts/generate_typescript_from_json_schema.py @@ -201,6 +201,7 @@ def _generate_chat_response_union_type() -> str: ("ImageChatResponse", "image", "Image"), ("ClearMessageResponse", "clear_message", "never"), ("MessageUsageChatResponse", "usage", "Record"), + ("TodoItemChatResonse", "todo_item", "Task"), ] internal_response_interfaces = [ diff --git a/typescript/@ragbits/api-client/package.json b/typescript/@ragbits/api-client/package.json index 5d5a27567..f10063cf4 100644 --- a/typescript/@ragbits/api-client/package.json +++ b/typescript/@ragbits/api-client/package.json @@ -5,7 +5,7 @@ "repository": { "type": "git", "url": "https://github.com/deepsense-ai/ragbits" - }, + }, "main": "dist/index.cjs", "module": "dist/index.js", "types": "dist/index.d.ts", diff --git a/typescript/@ragbits/api-client/src/autogen.types.ts b/typescript/@ragbits/api-client/src/autogen.types.ts index 5ac4f55af..0240f250c 100644 --- a/typescript/@ragbits/api-client/src/autogen.types.ts +++ b/typescript/@ragbits/api-client/src/autogen.types.ts @@ -23,6 +23,7 @@ export const ChatResponseType = { ChunkedContent: 'chunked_content', ClearMessage: 'clear_message', Usage: 'usage', + TodoItem: 'todo_item', } as const export type ChatResponseType = TypeFrom @@ -58,6 +59,17 @@ export const MessageRole = { export type MessageRole = TypeFrom +/** + * Represents the TaskStatus enum + */ +export const TaskStatus = { + Pending: 'pending', + InProgress: 'in_progress', + Completed: 'completed', +} as const + +export type TaskStatus = TypeFrom + /** * Represents the AuthType enum */ @@ -170,6 +182,21 @@ export interface MessageUsage { total_tokens: number } +/** + * Simple task representation. + */ +export interface Task { + id: string + description: string + /** + * Task status options. + */ + status: 'pending' | 'in_progress' | 'completed' + order: number + summary: string | null + parent_id: string | null +} + /** * Customization for the header section of the UI. */ @@ -465,6 +492,11 @@ export interface MessageUsageChatResponse { content: Record } +export interface TodoItemChatResonse { + type: 'todo_item' + content: Task +} + export interface ChunkedChatResponse { type: 'chunked_content' content: ChunkedContent @@ -484,3 +516,4 @@ export type ChatResponse = | ImageChatResponse | ClearMessageResponse | MessageUsageChatResponse + | TodoItemChatResonse diff --git a/typescript/@ragbits/api-client/src/index.ts b/typescript/@ragbits/api-client/src/index.ts index 57851fa8e..3c45f36f7 100644 --- a/typescript/@ragbits/api-client/src/index.ts +++ b/typescript/@ragbits/api-client/src/index.ts @@ -200,6 +200,11 @@ export class RagbitsClient { } await callbacks.onMessage(parsedData) + // Yield control back to event loop to prevent freezing + // TODO: Refactor the event processing to use an asynchronous queue to avoid UI freezes + await new Promise((resolve) => + setTimeout(resolve, 0) + ) } catch (parseError) { console.error('Error parsing JSON:', parseError) await callbacks.onError( diff --git a/typescript/ui/__tests__/unit/TodoList.test.tsx b/typescript/ui/__tests__/unit/TodoList.test.tsx new file mode 100644 index 000000000..efc5b6b1b --- /dev/null +++ b/typescript/ui/__tests__/unit/TodoList.test.tsx @@ -0,0 +1,146 @@ +import { cleanup, render, screen, within } from "@testing-library/react"; +import { Task, TaskStatus } from "@ragbits/api-client-react"; +import TodoList from "../../src/core/components/TodoList"; +import { afterEach, describe, expect, it, vi } from "vitest"; + +function makeTask(partial: Partial): Task { + return { + id: partial.id ?? "1", + description: partial.description ?? "Test task", + status: partial.status ?? TaskStatus.Pending, + order: partial.order ?? 1, + summary: partial.summary ?? null, + parent_id: partial.parent_id ?? null, + } as Task; +} + +describe("TodoList", () => { + afterEach(() => { + cleanup(); + vi.clearAllMocks(); + }); + + it("renders root tasks", () => { + const tasks = [ + makeTask({ id: "1", description: "Task A" }), + makeTask({ id: "2", description: "Task B" }), + ]; + + render(); + + expect(screen.getByTestId("todo-task-1")).toBeInTheDocument(); + expect(screen.getByTestId("todo-task-2")).toBeInTheDocument(); + + expect(screen.getByText("Task A")).toBeInTheDocument(); + expect(screen.getByText("Task B")).toBeInTheDocument(); + }); + + it("applies completed styles and checks checkbox", () => { + const tasks = [ + makeTask({ + id: "done", + description: "Done", + status: TaskStatus.Completed, + }), + ]; + + render(); + + const task = screen.getByTestId("todo-task-done"); + const checkbox = within(task).getByRole("checkbox"); + expect(checkbox).toBeChecked(); + expect(screen.getByText("Done")).toHaveClass("line-through"); + }); + + it("shows spinner for in-progress tasks and applies italic class", () => { + const tasks = [ + makeTask({ + id: "working", + description: "Working", + status: TaskStatus.InProgress, + }), + ]; + + render(); + + const task = screen.getByTestId("todo-task-working"); + + const spinner = within(task).getByLabelText("Task is in progress"); + expect(spinner).toBeInTheDocument(); + expect(screen.getByText("Working")).toHaveClass("italic"); + }); + + it("renders nested tasks and applies margin-left on children wrapper", () => { + const tasks = [ + makeTask({ id: "1", description: "Parent" }), + makeTask({ id: "2", description: "Child", parent_id: "1" }), + ]; + + render(); + + expect(screen.getByTestId("todo-task-1")).toBeInTheDocument(); + expect(screen.getByTestId("todo-task-2")).toBeInTheDocument(); + const childrenWrapper = screen.getByTestId("todo-children-wrapper-1"); + expect(childrenWrapper).toHaveStyle("margin-left: 0.5rem"); + }); + + it("orders root tasks according to `order` field", () => { + const tasks = [ + makeTask({ id: "a", description: "A", order: 2 }), + makeTask({ id: "b", description: "B", order: 1 }), + ]; + + render(); + + const root = screen.getByTestId("todo-list-root-0"); + const renderedTasks = Array.from( + root.querySelectorAll('[data-testid^="todo-task-"]'), + ); + const ids = renderedTasks.map((el) => el.getAttribute("data-testid")); + expect(ids).toEqual(["todo-task-b", "todo-task-a"]); + }); + + it("orders children according to `order` field", () => { + const tasks = [ + makeTask({ id: "parent", description: "Parent", order: 1 }), + makeTask({ + id: "c1", + description: "Child 1", + order: 2, + parent_id: "parent", + }), + makeTask({ + id: "c2", + description: "Child 2", + order: 1, + parent_id: "parent", + }), + ]; + + render(); + + const wrapper = screen.getByTestId("todo-children-wrapper-parent"); + const childTasks = Array.from( + wrapper.querySelectorAll('[data-testid^="todo-task-"]'), + ); + const ids = childTasks.map((el) => el.getAttribute("data-testid")); + expect(ids).toEqual(["todo-task-c2", "todo-task-c1"]); + }); + + it("renders task summary when provided", () => { + const tasks = [ + makeTask({ + id: "with-summary", + description: "Task with summary", + summary: "This is a summary", + }), + ]; + + render(); + + const task = screen.getByTestId("todo-task-with-summary"); + expect(task).toBeInTheDocument(); + expect(screen.getByText("Task with summary")).toBeInTheDocument(); + expect(screen.getByText("This is a summary")).toBeInTheDocument(); + }); +}); diff --git a/typescript/ui/__tests__/unit/plugin/ChatHistoryPlugin/ChatHistory.test.tsx b/typescript/ui/__tests__/unit/plugin/ChatHistoryPlugin/ChatHistory.test.tsx index f8323d0c7..413b91264 100644 --- a/typescript/ui/__tests__/unit/plugin/ChatHistoryPlugin/ChatHistory.test.tsx +++ b/typescript/ui/__tests__/unit/plugin/ChatHistoryPlugin/ChatHistory.test.tsx @@ -54,7 +54,7 @@ const MOCK_CONVERSATIONS: HistoryStore["conversations"] = { history: {}, followupMessages: null, serverState: null, - conversationId: "null", + conversationId: "temp-mock-id-1", eventsLog: [], lastMessageId: null, chatOptions: undefined, @@ -108,6 +108,7 @@ describe("ChatHistory", () => { return; } + console.log(`delete-conversation-${key}`); expect( screen.getByTestId(`delete-conversation-${key}`), ).toBeInTheDocument(); diff --git a/typescript/ui/src/core/components/ChatMessage/ChatMessage.tsx b/typescript/ui/src/core/components/ChatMessage/ChatMessage.tsx index 989a1d87c..2a802cb6e 100644 --- a/typescript/ui/src/core/components/ChatMessage/ChatMessage.tsx +++ b/typescript/ui/src/core/components/ChatMessage/ChatMessage.tsx @@ -12,6 +12,8 @@ import { useMessage, } from "../../stores/HistoryStore/selectors.ts"; import { MessageRole } from "@ragbits/api-client"; +import TodoList from "../TodoList.tsx"; +import { AnimatePresence, motion } from "framer-motion"; type ChatMessageProps = { classNames?: { @@ -92,6 +94,20 @@ const ChatMessage = forwardRef( classNames={{ liveUpdates: classNames?.liveUpdates }} /> )} + {message.tasks && message.tasks.length > 0 && ( + + +

Execution plan

+ + + + )} + {tasksTree + .getRoots() + .map(({ id, description, status, summary, children }) => { + const inProgressIcon = ( + + ); + return ( +
+
+ inProgressIcon + : undefined + } + classNames={{ + hiddenInput: "cursor-default", + wrapper: cn( + status === TaskStatus.InProgress && "before:border-none", + ), + base: "pointer-events-none hover:bg-transparent", + label: cn( + "transition-colors", + status === TaskStatus.Completed && + "line-through text-default-400", + status === TaskStatus.InProgress && "text-primary italic", + status === TaskStatus.Pending && "text-default-900", + ), + }} + > + {description} + {summary &&

{summary}

} + +
+ {children.length > 0 && ( +
+ +
+ )} +
+ ); + })} + + ); +} diff --git a/typescript/ui/src/core/stores/HistoryStore/eventHandlers/eventHandlerRegistry.ts b/typescript/ui/src/core/stores/HistoryStore/eventHandlers/eventHandlerRegistry.ts index 8b4799cc3..7e0d456e9 100644 --- a/typescript/ui/src/core/stores/HistoryStore/eventHandlers/eventHandlerRegistry.ts +++ b/typescript/ui/src/core/stores/HistoryStore/eventHandlers/eventHandlerRegistry.ts @@ -13,6 +13,7 @@ import { handleMessageId, handleReference, handleText, + handleTodoItem, handleUsage, } from "./messageHandlers"; @@ -101,3 +102,6 @@ ChatHandlerRegistry.register(ChatResponseType.ClearMessage, { ChatHandlerRegistry.register(ChatResponseType.Usage, { handle: handleUsage, }); +ChatHandlerRegistry.register(ChatResponseType.TodoItem, { + handle: handleTodoItem, +}); diff --git a/typescript/ui/src/core/stores/HistoryStore/eventHandlers/messageHandlers.ts b/typescript/ui/src/core/stores/HistoryStore/eventHandlers/messageHandlers.ts index 2811625a3..d03437a1b 100644 --- a/typescript/ui/src/core/stores/HistoryStore/eventHandlers/messageHandlers.ts +++ b/typescript/ui/src/core/stores/HistoryStore/eventHandlers/messageHandlers.ts @@ -7,6 +7,7 @@ import { MessageUsageChatResponse, ReferenceChatResponse, TextChatResponse, + TodoItemChatResonse, } from "@ragbits/api-client-react"; import { PrimaryHandler } from "./eventHandlerRegistry"; import { produce } from "immer"; @@ -97,3 +98,22 @@ export const handleUsage: PrimaryHandler = ( const message = draft.history[ctx.messageId]; message.usage = response.content; }; + +export const handleTodoItem: PrimaryHandler = ( + { content }, + draft, + ctx, +) => { + const message = draft.history[ctx.messageId]; + const tasks = message.tasks ?? []; + const newTasks = produce(tasks, (tasksDraft) => { + const taskIndex = tasksDraft.findIndex((t) => t.id === content.id); + if (taskIndex === -1) { + tasksDraft.push(content); + } else { + tasksDraft[taskIndex] = content; + } + }); + + message.tasks = newTasks; +}; diff --git a/typescript/ui/src/core/utils/tasks.ts b/typescript/ui/src/core/utils/tasks.ts new file mode 100644 index 000000000..192fa37b7 --- /dev/null +++ b/typescript/ui/src/core/utils/tasks.ts @@ -0,0 +1,80 @@ +import { Task } from "@ragbits/api-client-react"; + +export interface TaskNode extends Task { + children: TaskNode[]; +} + +export class TaskTree { + private nodes: Map = new Map(); + private roots: TaskNode[] = []; + + constructor(tasks: Task[]) { + this.buildTree(tasks); + } + + private buildTree(tasks: Task[]) { + for (const task of tasks) { + this.nodes.set(task.id, { ...task, children: [] }); + } + + for (const node of this.nodes.values()) { + if (node.parent_id) { + const parent = this.nodes.get(node.parent_id); + if (parent) { + parent.children.push(node); + parent.children.sort((a, b) => a.order - b.order); + } else { + this.roots.push(node); + this.roots.sort((a, b) => a.order - b.order); + } + } else { + this.roots.push(node); + this.roots.sort((a, b) => a.order - b.order); + } + } + } + + *iterate(): IterableIterator { + function* dfs(nodes: TaskNode[]): IterableIterator { + for (const node of nodes) { + yield node; + yield* dfs(node.children); + } + } + yield* dfs(this.roots); + } + + get(id: string): TaskNode | undefined { + return this.nodes.get(id); + } + + update(id: string, updates: Partial): void { + const node = this.nodes.get(id); + if (!node) return; + + Object.assign(node, updates); + if (updates.order !== undefined) { + if (node.parent_id) { + const parent = this.nodes.get(node.parent_id); + parent?.children.sort((a, b) => a.order - b.order); + } else { + this.roots.sort((a, b) => a.order - b.order); + } + } + + if (updates.status === "completed") { + this.completeChildren(node); + } + } + + private completeChildren(node: TaskNode) { + for (const child of node.children) { + child.status = "completed"; + this.completeChildren(child); + } + } + + getRoots(): TaskNode[] { + return this.roots; + } +} diff --git a/typescript/ui/src/plugins/ChatHistoryPlugin/components/ChatHistory.tsx b/typescript/ui/src/plugins/ChatHistoryPlugin/components/ChatHistory.tsx index 6bd7b0ffc..aa4a0fbd5 100644 --- a/typescript/ui/src/plugins/ChatHistoryPlugin/components/ChatHistory.tsx +++ b/typescript/ui/src/plugins/ChatHistoryPlugin/components/ChatHistory.tsx @@ -8,12 +8,15 @@ import { useHistoryStore } from "../../../core/stores/HistoryStore/useHistorySto import { isTemporaryConversation } from "../../../core/stores/HistoryStore/historyStore"; import { useNavigate } from "react-router"; import { getConversationRoute } from "../utils"; +import { useShallow } from "zustand/shallow"; export default function ChatHistory() { const { selectConversation, deleteConversation, newConversation } = useHistoryActions(); const navigate = useNavigate(); - const conversations = useHistoryStore((s) => s.conversations); + const conversations = useHistoryStore( + useShallow((s) => Object.keys(s.conversations).reverse()), + ); const currentConversation = useHistoryStore((s) => s.currentConversation); const [isCollapsed, setCollapsed] = useState(false); const collapseButtonTitle = isCollapsed ? "Open sidebar" : "Close sidebar"; @@ -112,46 +115,45 @@ export default function ChatHistory() { width: 0, }} > - {Object.entries(conversations) - .reverse() - .map(([conversationKey, conversation]) => { - if (isTemporaryConversation(conversation.conversationId)) { - return null; - } + {conversations.map((conversation) => { + if (isTemporaryConversation(conversation)) { + return null; + } - const isSelected = conversationKey === currentConversation; - return ( -
+ const isSelected = conversation === currentConversation; + const variant = isSelected ? "solid" : "light"; + return ( +
+ + - - - -
- ); - })} + +
+ ); + })} )} diff --git a/typescript/ui/src/types/history.ts b/typescript/ui/src/types/history.ts index 4e3bfc7f8..74205eb36 100644 --- a/typescript/ui/src/types/history.ts +++ b/typescript/ui/src/types/history.ts @@ -7,6 +7,7 @@ import { Image, MessageUsage, RagbitsClient, + Task, } from "@ragbits/api-client-react"; export type UnsubscribeFn = (() => void) | null; @@ -24,6 +25,7 @@ export interface ChatMessage { extensions?: Record; images?: Record; usage?: Record; + tasks?: Task[]; } export interface Conversation { From 61ec71f29df74ac1f86d1b5c056bad2ba87e7d37 Mon Sep 17 00:00:00 2001 From: ds-ragbits-robot Date: Fri, 10 Oct 2025 07:17:57 +0000 Subject: [PATCH 38/43] Automated UI build --- ...uard-B3JOY-uC.js => AuthGuard-Cg314rC1.js} | 2 +- .../ui-build/assets/ChatHistory-D2Pd5V75.js | 1 - .../ui-build/assets/ChatHistory-Dzy0EI1-.js | 1 + ...YA6GEAP.js => ChatOptionsForm-CUT15Ecj.js} | 2 +- ...m-Bovwe8ia.js => FeedbackForm-Cuu7ROp3.js} | 2 +- .../{Login-B2kPNK-w.js => Login-xlop2v42.js} | 2 +- ...n-Bspy5P1_.js => LogoutButton-Cp3E_g1o.js} | 2 +- ...on-BvHR4xdz.js => ShareButton-DpNGX0tU.js} | 2 +- .../ui-build/assets/UsageButton-BMDI2IGg.js | 1 - .../ui-build/assets/UsageButton-I3vvJAZH.js | 1 + ...tore-DLhLSjcY.js => authStore-Ur4ZGows.js} | 2 +- .../assets/chunk-IGSAU2ZA-DUS7ku-0.js | 1 - .../assets/chunk-IGSAU2ZA-RYoue1Xd.js | 1 + ...DnKzYyYP.js => chunk-SSA7SXE4-CpT2ku66.js} | 2 +- .../chat/ui-build/assets/index-3BSSmhVm.js | 1 - .../chat/ui-build/assets/index-BTHsSiyo.css | 1 - .../chat/ui-build/assets/index-BTypXP0X.js | 127 ++++++++++++++++++ .../chat/ui-build/assets/index-C75huGqt.js | 127 ------------------ .../{index-VYICyW2P.js => index-CTrBaHsz.js} | 6 +- .../chat/ui-build/assets/index-CYQIHhg3.js | 1 + .../chat/ui-build/assets/index-DHfXqRui.css | 1 + .../chat/ui-build/assets/index-I-Ja0wkh.js | 4 - .../chat/ui-build/assets/index-nfbmdcVN.js | 4 + .../src/ragbits/chat/ui-build/index.html | 4 +- 24 files changed, 149 insertions(+), 149 deletions(-) rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{AuthGuard-B3JOY-uC.js => AuthGuard-Cg314rC1.js} (56%) delete mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatHistory-D2Pd5V75.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatHistory-Dzy0EI1-.js rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{ChatOptionsForm-DYA6GEAP.js => ChatOptionsForm-CUT15Ecj.js} (95%) rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{FeedbackForm-Bovwe8ia.js => FeedbackForm-Cuu7ROp3.js} (88%) rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{Login-B2kPNK-w.js => Login-xlop2v42.js} (79%) rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{LogoutButton-Bspy5P1_.js => LogoutButton-Cp3E_g1o.js} (81%) rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{ShareButton-BvHR4xdz.js => ShareButton-DpNGX0tU.js} (98%) delete mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/UsageButton-BMDI2IGg.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/UsageButton-I3vvJAZH.js rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{authStore-DLhLSjcY.js => authStore-Ur4ZGows.js} (74%) delete mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-DUS7ku-0.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-RYoue1Xd.js rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{chunk-SSA7SXE4-DnKzYyYP.js => chunk-SSA7SXE4-CpT2ku66.js} (62%) delete mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-3BSSmhVm.js delete mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-BTHsSiyo.css create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-BTypXP0X.js delete mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-C75huGqt.js rename packages/ragbits-chat/src/ragbits/chat/ui-build/assets/{index-VYICyW2P.js => index-CTrBaHsz.js} (61%) create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-CYQIHhg3.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-DHfXqRui.css delete mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-I-Ja0wkh.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-nfbmdcVN.js diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-B3JOY-uC.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-Cg314rC1.js similarity index 56% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-B3JOY-uC.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-Cg314rC1.js index e7e8303be..42af54d7e 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-B3JOY-uC.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-Cg314rC1.js @@ -1 +1 @@ -import{r as a,j as s,aN as c,h as x,aJ as g,bs as m,bt as p,bu as v,bv as b,bw as j}from"./index-C75huGqt.js";import{a as r}from"./authStore-DLhLSjcY.js";const k=a.createContext(null);function A({children:o}){const[n]=a.useState(()=>r);return s.jsx(k.Provider,{value:n,children:o})}function C(){const{token:o,tokenExpiration:n,logout:l}=c(r,e=>e),i=x(e=>e.isLoading),u=g(),d=a.useRef(i);a.useEffect(()=>{d.current=i},[i]);const t=a.useCallback(()=>{const e=()=>{d.current?setTimeout(e,500):(l(),u("/login"))};e()},[l,u]);return a.useEffect(()=>{if(!o||!n)return;const e=Date.now(),h=n-e;if(h<=0){t();return}const f=setTimeout(()=>{t()},h);return()=>clearTimeout(f)},[o,n,t]),null}function S({children:o}){const n=m(),l=c(r,t=>t.isAuthenticated),i=c(r,t=>t.hasHydrated),u=c(r,t=>t.token?.access_token),d=c(r,t=>t.logout);return i?n.pathname==="/login"?o:l&&u?s.jsx(A,{children:s.jsxs(v,{baseUrl:b,auth:{getToken:()=>u,onUnauthorized:d},children:[o,s.jsx(C,{})]})}):s.jsx(j,{to:"/login",replace:!0}):s.jsx(p,{})}export{S as default}; +import{r as a,j as s,aP as c,h as x,aK as g,bj as m,bk as p,bl as j,bm as k,bn as v}from"./index-BTypXP0X.js";import{a as r}from"./authStore-Ur4ZGows.js";const b=a.createContext(null);function A({children:o}){const[n]=a.useState(()=>r);return s.jsx(b.Provider,{value:n,children:o})}function C(){const{token:o,tokenExpiration:n,logout:l}=c(r,e=>e),i=x(e=>e.isLoading),u=g(),d=a.useRef(i);a.useEffect(()=>{d.current=i},[i]);const t=a.useCallback(()=>{const e=()=>{d.current?setTimeout(e,500):(l(),u("/login"))};e()},[l,u]);return a.useEffect(()=>{if(!o||!n)return;const e=Date.now(),h=n-e;if(h<=0){t();return}const f=setTimeout(()=>{t()},h);return()=>clearTimeout(f)},[o,n,t]),null}function R({children:o}){const n=m(),l=c(r,t=>t.isAuthenticated),i=c(r,t=>t.hasHydrated),u=c(r,t=>t.token?.access_token),d=c(r,t=>t.logout);return i?n.pathname==="/login"?o:l&&u?s.jsx(A,{children:s.jsxs(j,{baseUrl:k,auth:{getToken:()=>u,onUnauthorized:d},children:[o,s.jsx(C,{})]})}):s.jsx(v,{to:"/login",replace:!0}):s.jsx(p,{})}export{R as default}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatHistory-D2Pd5V75.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatHistory-D2Pd5V75.js deleted file mode 100644 index 75bfe6406..000000000 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatHistory-D2Pd5V75.js +++ /dev/null @@ -1 +0,0 @@ -import{a as C,aJ as N,i as h,r as y,j as t,aK as n,aD as I,D as o,d as s,I as i,aL as D,aM as m}from"./index-C75huGqt.js";function P(){const{selectConversation:u,deleteConversation:v,newConversation:x}=C(),r=N(),p=h(e=>e.conversations),f=h(e=>e.currentConversation),[a,b]=y.useState(!1),l=a?"Open sidebar":"Close sidebar",g=t.jsx(i,{icon:"heroicons:pencil-square"}),j=()=>{const e=x();r(m(e))},w=e=>{u(e),r(m(e))};return t.jsx(n.div,{initial:!1,animate:{maxWidth:a?"4.5rem":"16rem"},className:"rounded-l-medium border-small border-divider ml-4 flex h-full w-full min-w-[4.5rem] flex-grow flex-col space-y-2 overflow-hidden border-r-0 p-4 py-3",children:t.jsxs(I,{children:[t.jsx(o,{content:l,placement:"bottom",children:t.jsx(s,{isIconOnly:!0,"aria-label":l,variant:"ghost",onPress:()=>b(e=>!e),"data-testid":"chat-history-collapse-button",className:"ml-auto",children:t.jsx(i,{icon:a?"heroicons:chevron-double-right":"heroicons:chevron-double-left"})})},"collapse-button"),!a&&t.jsx(n.p,{initial:!1,animate:{opacity:1,width:"100%",height:"auto"},exit:{opacity:0,width:0,height:0,marginBottom:0},className:"text-small text-foreground truncate leading-5 font-semibold",children:"Conversations"},"conversations"),t.jsx(o,{content:"New conversation",placement:"right",children:t.jsx(s,{"aria-label":"New conversation",variant:"ghost",onPress:j,"data-testid":"chat-history-clear-chat-button",startContent:g,isIconOnly:a,children:!a&&"New conversation"})},"new-conversation-button"),!a&&t.jsx(n.div,{className:"mt-2 flex flex-1 flex-col gap-2 overflow-auto overflow-x-hidden",initial:!1,animate:{opacity:1,width:"100%"},exit:{opacity:0,width:0},children:Object.entries(p).reverse().map(([e,c])=>{if(D(c.conversationId))return null;const d=e===f;return t.jsxs("div",{className:"flex gap-2",children:[t.jsx(s,{variant:d?"solid":"light","aria-label":`Select conversation ${e}`,"data-active":d,onPress:()=>w(e),title:e,"data-testid":`select-conversation-${e}`,children:t.jsx("div",{className:"text-small truncate",children:c.conversationId})}),t.jsx(o,{content:"Delete conversation",placement:"right",children:t.jsx(s,{isIconOnly:!0,"aria-label":`Delete conversation ${e}`,onPress:()=>v(e),variant:"ghost","data-testid":`delete-conversation-${e}`,children:t.jsx(i,{icon:"heroicons:trash"})})})]},e)})},"conversation-list")]})})}export{P as default}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatHistory-Dzy0EI1-.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatHistory-Dzy0EI1-.js new file mode 100644 index 000000000..4ff764322 --- /dev/null +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatHistory-Dzy0EI1-.js @@ -0,0 +1 @@ +import{a as y,aK as C,i as h,aL as N,r as I,j as t,aM as n,aD as O,D as o,d as s,I as i,aN as $,aO as u}from"./index-BTypXP0X.js";function P(){const{selectConversation:m,deleteConversation:x,newConversation:v}=y(),l=C(),p=h(N(e=>Object.keys(e.conversations).reverse())),f=h(e=>e.currentConversation),[a,b]=I.useState(!1),r=a?"Open sidebar":"Close sidebar",g=t.jsx(i,{icon:"heroicons:pencil-square"}),j=()=>{const e=v();l(u(e))},w=e=>{m(e),l(u(e))};return t.jsx(n.div,{initial:!1,animate:{maxWidth:a?"4.5rem":"16rem"},className:"rounded-l-medium border-small border-divider ml-4 flex h-full w-full min-w-[4.5rem] flex-grow flex-col space-y-2 overflow-hidden border-r-0 p-4 py-3",children:t.jsxs(O,{children:[t.jsx(o,{content:r,placement:"bottom",children:t.jsx(s,{isIconOnly:!0,"aria-label":r,variant:"ghost",onPress:()=>b(e=>!e),"data-testid":"chat-history-collapse-button",className:"ml-auto",children:t.jsx(i,{icon:a?"heroicons:chevron-double-right":"heroicons:chevron-double-left"})})},"collapse-button"),!a&&t.jsx(n.p,{initial:!1,animate:{opacity:1,width:"100%",height:"auto"},exit:{opacity:0,width:0,height:0,marginBottom:0},className:"text-small text-foreground truncate leading-5 font-semibold",children:"Conversations"},"conversations"),t.jsx(o,{content:"New conversation",placement:"right",children:t.jsx(s,{"aria-label":"New 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ne(()=>{t.current=n},[n]),f.useCallback((...s)=>{var m;return(m=t.current)==null?void 0:m.call(t,...s)},[t])}var J=typeof window>"u";function ye(n,t,s={}){const{initializeWithValue:m=!0}=s,g=f.useCallback(r=>s.serializer?s.serializer(r):JSON.stringify(r),[s]),h=f.useCallback(r=>{if(s.deserializer)return s.deserializer(r);if(r==="undefined")return;const c=t instanceof Function?t():t;let O;try{O=JSON.parse(r)}catch(E){return console.error("Error parsing JSON:",E),c}return O},[s,t]),i=f.useCallback(()=>{const r=t instanceof Function?t():t;if(J)return r;try{const c=window.localStorage.getItem(n);return c?h(c):r}catch(c){return console.warn(`Error reading localStorage key “${n}”:`,c),r}},[t,n,h]),[x,v]=f.useState(()=>m?i():t instanceof Function?t():t),y=te(r=>{J&&console.warn(`Tried setting localStorage key “${n}” even though environment is not a client`);try{const c=r instanceof Function?r(i()):r;window.localStorage.setItem(n,g(c)),v(c),window.dispatchEvent(new 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w=g.current;h(w),r(w),g.current=null};return f.useEffect(()=>{if(!d)return;const a=Z(W,d);Y.isPluginActivated(Q.name)?i(a):y!==null?i(y):(i(a),C(a))},[i,d,x,y,C]),d?l.jsxs(l.Fragment,{children:[l.jsx(de,{content:"Chat Options",placement:"bottom",children:l.jsx(A,{isIconOnly:!0,variant:"ghost",className:"p-0","aria-label":"Open chat options",onPress:c,"data-testid":"open-chat-options",children:l.jsx(me,{icon:"heroicons:cog-6-tooth"})})}),l.jsx(ge,{isOpen:n,onOpenChange:$,motionProps:{onAnimationComplete:_},children:l.jsxs(he,{children:[l.jsx(Se,{className:"text-default-900 flex flex-col gap-1",children:d.title||"Chat Options"}),l.jsx(pe,{children:l.jsx("div",{className:"flex flex-col gap-4",children:l.jsx(be,{schema:d,validator:W,formData:m,onSubmit:O,transformErrors:ve,liveValidate:!0,children:l.jsxs("div",{className:"flex justify-end gap-4 py-4",children:[l.jsx(A,{className:"mr-auto",color:"primary",variant:"light",onPress:E,"aria-label":"Restore default user settings",children:"Restore defaults"}),l.jsx(A,{color:"danger",variant:"light",onPress:s,"aria-label":"Close chat options form",children:"Cancel"}),l.jsx(A,{color:"primary",type:"submit","aria-label":"Save chat options","data-testid":"chat-options-submit",children:"Save"})]})})})})]})})]}):null}export{Ie as default}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/FeedbackForm-Bovwe8ia.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/FeedbackForm-Cuu7ROp3.js similarity index 88% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/FeedbackForm-Bovwe8ia.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/FeedbackForm-Cuu7ROp3.js index 8b9f54d5f..f6d416891 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/FeedbackForm-Bovwe8ia.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/FeedbackForm-Cuu7ROp3.js @@ -1 +1 @@ -import{u as g,a as v,b as C,r as _,F as s,c as T,j as e,D as b,d as n,I as u,m as w,e as D,f as I}from"./index-C75huGqt.js";import{F as O,t as S,v as E}from"./index-VYICyW2P.js";import{m as N}from"./chunk-IGSAU2ZA-DUS7ku-0.js";import"./chunk-SSA7SXE4-DnKzYyYP.js";import"./index-I-Ja0wkh.js";function V({message:t}){const{isOpen:f,onOpen:h,onClose:r}=g(),{mergeExtensions:p}=v(),{config:{feedback:o}}=C(),[l,x]=_.useState(s.Like),k=T("/api/feedback",{headers:{"Content-Type":"application/json"},method:"POST"}),i=o[l].form,j=()=>{r()},c=async a=>{if(!t.serverId)throw new Error('Feedback is only available for messages with "serverId" set');try{await k.call({body:{message_id:t.serverId,feedback:l,payload:a??{}}})}catch(F){console.error(F)}},y=a=>{p(t.id,{feedbackType:l}),c(a.formData),r()},d=async a=>{if(x(a),o[a].form===null){await c(null);return}h()};if(!i)return null;const m=t.extensions?.feedbackType;return e.jsxs(e.Fragment,{children:[o.like.enabled&&e.jsx(b,{content:"Like",placement:"bottom",children:e.jsx(n,{isIconOnly:!0,variant:"ghost",className:"p-0","aria-label":"Rate message as helpful",onPress:()=>d(s.Like),"data-testid":"feedback-like",children:e.jsx(u,{icon:m===s.Like?"heroicons:hand-thumb-up-solid":"heroicons:hand-thumb-up"})})}),o.dislike.enabled&&e.jsx(b,{content:"Dislike",placement:"bottom",children:e.jsx(n,{isIconOnly:!0,variant:"ghost",className:"p-0","aria-label":"Rate message as unhelpful",onPress:()=>d(s.Dislike),"data-testid":"feedback-dislike",children:e.jsx(u,{icon:m===s.Dislike?"heroicons:hand-thumb-down-solid":"heroicons:hand-thumb-down"})})}),e.jsx(w,{isOpen:f,onOpenChange:j,children:e.jsx(D,{children:a=>e.jsxs(e.Fragment,{children:[e.jsx(N,{className:"text-default-900 flex flex-col gap-1",children:i.title}),e.jsx(I,{children:e.jsx("div",{className:"flex flex-col 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new Error('Feedback is only available for messages with "serverId" set');try{await k.call({body:{message_id:t.serverId,feedback:l,payload:a??{}}})}catch(F){console.error(F)}},y=a=>{p(t.id,{feedbackType:l}),c(a.formData),r()},d=async a=>{if(x(a),o[a].form===null){await c(null);return}h()};if(!i)return null;const m=t.extensions?.feedbackType;return e.jsxs(e.Fragment,{children:[o.like.enabled&&e.jsx(b,{content:"Like",placement:"bottom",children:e.jsx(n,{isIconOnly:!0,variant:"ghost",className:"p-0","aria-label":"Rate message as helpful",onPress:()=>d(s.Like),"data-testid":"feedback-like",children:e.jsx(u,{icon:m===s.Like?"heroicons:hand-thumb-up-solid":"heroicons:hand-thumb-up"})})}),o.dislike.enabled&&e.jsx(b,{content:"Dislike",placement:"bottom",children:e.jsx(n,{isIconOnly:!0,variant:"ghost",className:"p-0","aria-label":"Rate message as 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similarity index 79% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-B2kPNK-w.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-xlop2v42.js index 8095219ac..04d6d32fa 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-B2kPNK-w.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-xlop2v42.js @@ -1 +1 @@ -import{r as n,bp as w,c as y,aN as b,aJ as S,j as e,aD as j,aK as v,d as C,bq as N}from"./index-C75huGqt.js";import{a as P}from"./authStore-DLhLSjcY.js";import{i as c}from"./chunk-SSA7SXE4-DnKzYyYP.js";const q=()=>{const a=n.useContext(w);if(!a)throw new Error("useInitializeUserStore must be used within a HistoryStoreContextProvider");return a.initializeUserStore};function U(){const[a,m]=n.useState({username:"",password:""}),u=y("/api/auth/login",{headers:{"Content-Type":"application/json"},method:"POST"}),p=b(P,s=>s.login),g=S(),x=q(),[f,o]=n.useState(!1),h=async s=>{o(!1),s.preventDefault(),s.stopPropagation();const r=new FormData(s.currentTarget),i=r.get("username"),l=r.get("password");try{const t=await u.call({body:{username:i,password:l}});if(!t.success||!t.jwt_token||!t.user){o(!0);return}p(t.user,t.jwt_token),x(t.user.user_id),g("/")}catch(t){o(!0),console.error("Failed to login",t)}},d=s=>r=>m(i=>N(i,l=>{l[s]=r.target.value}));return n.useEffect(()=>{document.title="Login"},[]),e.jsx("div",{className:"flex h-screen w-screen",children:e.jsxs("form",{className:"rounded-medium border-small border-divider m-auto flex w-full max-w-xs flex-col gap-4 p-4",onSubmit:h,children:[e.jsxs("div",{className:"text-small",children:[e.jsx("div",{className:"text-foreground truncate leading-5 font-semibold",children:"Sign in"}),e.jsx("div",{className:"text-default-500 truncate leading-5 font-normal",children:"Sign in to start chatting."})]}),e.jsx(c,{label:"Username",name:"username",labelPlacement:"outside",placeholder:"Your username",required:!0,isRequired:!0,value:a.username,onChange:d("username")}),e.jsx(c,{label:"Password",labelPlacement:"outside",id:"password",name:"password",type:"password",placeholder:"••••••••",required:!0,isRequired:!0,value:a.password,onChange:d("password")}),e.jsx(j,{children:f&&!u.isLoading&&e.jsx(v.div,{className:"text-small text-danger",initial:{opacity:0,y:-10},animate:{opacity:1,y:0},exit:{opacity:0,y:-10},transition:{duration:.3,ease:"easeOut"},children:"We couldn't sign you in. Please verify your credentials and try again."})}),e.jsx(C,{type:"submit",color:a.password&&a.username?"primary":"default",children:"Sign in"})]})})}export{U as default}; +import{r as n,bg as w,c as y,aP as b,aK as S,j as e,aD as j,aM as v,d as P,bh as C}from"./index-BTypXP0X.js";import{a as N}from"./authStore-Ur4ZGows.js";import{i as c}from"./chunk-SSA7SXE4-CpT2ku66.js";const D=()=>{const a=n.useContext(w);if(!a)throw new Error("useInitializeUserStore must be used within a HistoryStoreContextProvider");return a.initializeUserStore};function U(){const[a,m]=n.useState({username:"",password:""}),u=y("/api/auth/login",{headers:{"Content-Type":"application/json"},method:"POST"}),p=b(N,s=>s.login),g=S(),x=D(),[f,o]=n.useState(!1),h=async s=>{o(!1),s.preventDefault(),s.stopPropagation();const r=new FormData(s.currentTarget),i=r.get("username"),l=r.get("password");try{const t=await u.call({body:{username:i,password:l}});if(!t.success||!t.jwt_token||!t.user){o(!0);return}p(t.user,t.jwt_token),x(t.user.user_id),g("/")}catch(t){o(!0),console.error("Failed to login",t)}},d=s=>r=>m(i=>C(i,l=>{l[s]=r.target.value}));return n.useEffect(()=>{document.title="Login"},[]),e.jsx("div",{className:"flex h-screen w-screen",children:e.jsxs("form",{className:"rounded-medium border-small border-divider m-auto flex w-full max-w-xs flex-col gap-4 p-4",onSubmit:h,children:[e.jsxs("div",{className:"text-small",children:[e.jsx("div",{className:"text-foreground truncate leading-5 font-semibold",children:"Sign in"}),e.jsx("div",{className:"text-default-500 truncate leading-5 font-normal",children:"Sign in to start chatting."})]}),e.jsx(c,{label:"Username",name:"username",labelPlacement:"outside",placeholder:"Your username",required:!0,isRequired:!0,value:a.username,onChange:d("username")}),e.jsx(c,{label:"Password",labelPlacement:"outside",id:"password",name:"password",type:"password",placeholder:"••••••••",required:!0,isRequired:!0,value:a.password,onChange:d("password")}),e.jsx(j,{children:f&&!u.isLoading&&e.jsx(v.div,{className:"text-small text-danger",initial:{opacity:0,y:-10},animate:{opacity:1,y:0},exit:{opacity:0,y:-10},transition:{duration:.3,ease:"easeOut"},children:"We couldn't sign you in. Please verify your credentials and try again."})}),e.jsx(P,{type:"submit",color:a.password&&a.username?"primary":"default",children:"Sign in"})]})})}export{U as default}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Bspy5P1_.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Cp3E_g1o.js similarity index 81% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Bspy5P1_.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Cp3E_g1o.js index 1cd873c59..4a53057ca 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Bspy5P1_.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Cp3E_g1o.js @@ -1 +1 @@ -import{c as u,aN as s,aJ as i,j as o,D as g,d,I as p}from"./index-C75huGqt.js";import{a as n}from"./authStore-DLhLSjcY.js";function m(){const r=u("/api/auth/logout",{headers:{"Content-Type":"application/json"},method:"POST"}),c=s(n,t=>t.logout),e=s(n,t=>t.token?.access_token),a=i(),l=async()=>{if(!e){a("/login");return}try{if(!(await r.call({body:{token:e}})).success)return;c(),a("/login")}catch(t){console.error("Failed to logout",t)}};return o.jsx(g,{content:"Logout",placement:"bottom",children:o.jsx(d,{isIconOnly:!0,"aria-label":"Logout",variant:"ghost",onPress:l,"data-testid":"logout-button",children:o.jsx(p,{icon:"heroicons:arrow-left-start-on-rectangle"})})})}export{m as default}; +import{c as u,aP as s,aK as i,j as o,D as g,d,I as p}from"./index-BTypXP0X.js";import{a as n}from"./authStore-Ur4ZGows.js";function m(){const r=u("/api/auth/logout",{headers:{"Content-Type":"application/json"},method:"POST"}),c=s(n,t=>t.logout),e=s(n,t=>t.token?.access_token),a=i(),l=async()=>{if(!e){a("/login");return}try{if(!(await r.call({body:{token:e}})).success)return;c(),a("/login")}catch(t){console.error("Failed to logout",t)}};return 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he=new WeakMap;function ce(e){return typeof e=="string"?e.replace(/\s*/g,""):""+e}function cu(e,t){let l=he.get(e);if(!l)throw new Error("Unknown grid");return`${l}-${ce(t)}`}function Te(e,t,l){let u=he.get(e);if(!u)throw new Error("Unknown grid");return`${u}-${ce(t)}-${ce(l)}`}function Re(e,t){return[...e.collection.rowHeaderColumnKeys].map(l=>Te(e,t,l)).join(" ")}var Ve={};Ve={ascending:"تصاعدي",ascendingSort:e=>`ترتيب حسب العمود ${e.columnName} بترتيب تصاعدي`,columnSize:e=>`${e.value} بالبكسل`,descending:"تنازلي",descendingSort:e=>`ترتيب حسب العمود ${e.columnName} بترتيب تنازلي`,resizerDescription:"اضغط على مفتاح Enter لبدء تغيير الحجم",select:"تحديد",selectAll:"تحديد الكل",sortable:"عمود قابل للترتيب"};var Oe={};Oe={ascending:"възходящ",ascendingSort:e=>`сортирано по колона ${e.columnName} във възходящ ред`,columnSize:e=>`${e.value} пиксела`,descending:"низходящ",descendingSort:e=>`сортирано по колона ${e.columnName} в низходящ ред`,resizerDescription:"Натиснете „Enter“, за да 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pixels`,descending:"decrescente",descendingSort:e=>`classificado pela coluna ${e.columnName} em ordem decrescente`,resizerDescription:"Pressione Enter para começar a redimensionar",select:"Selecionar",selectAll:"Selecionar tudo",sortable:"coluna classificável"};var at={};at={ascending:"ascendente",ascendingSort:e=>`Ordenar por coluna ${e.columnName} em ordem ascendente`,columnSize:e=>`${e.value} pixels`,descending:"descendente",descendingSort:e=>`Ordenar por coluna ${e.columnName} em ordem descendente`,resizerDescription:"Prima Enter para iniciar o redimensionamento",select:"Selecionar",selectAll:"Selecionar tudo",sortable:"Coluna ordenável"};var ct={};ct={ascending:"crescătoare",ascendingSort:e=>`sortate după coloana ${e.columnName} în ordine crescătoare`,columnSize:e=>`${e.value} pixeli`,descending:"descrescătoare",descendingSort:e=>`sortate după coloana ${e.columnName} în ordine descrescătoare`,resizerDescription:"Apăsați pe Enter pentru a începe 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redu`,columnSize:e=>`${e.value} slikovnih pik`,descending:"padajoče",descendingSort:e=>`razvrščeno po stolpcu ${e.columnName} v padajočem vrstnem redu`,resizerDescription:"Pritisnite tipko Enter da začnete spreminjati velikost",select:"Izberite",selectAll:"Izberite vse",sortable:"razvrstljivi stolpec"};var bt={};bt={ascending:"rastući",ascendingSort:e=>`sortirano po kolonama ${e.columnName} rastućim redosledom`,columnSize:e=>`${e.value} piksela`,descending:"padajući",descendingSort:e=>`sortirano po kolonama ${e.columnName} padajućim redosledom`,resizerDescription:"Pritisnite Enter da biste započeli promenu veličine",select:"Izaberite",selectAll:"Izaberite sve",sortable:"kolona koja se može sortirati"};var ft={};ft={ascending:"stigande",ascendingSort:e=>`sorterat på kolumn ${e.columnName} i stigande ordning`,columnSize:e=>`${e.value} pixlar`,descending:"fallande",descendingSort:e=>`sorterat på kolumn ${e.columnName} i fallande ordning`,resizerDescription:"Tryck på Retur för att börja ändra storlek",select:"Markera",selectAll:"Markera allt",sortable:"sorterbar kolumn"};var ht={};ht={ascending:"artan sırada",ascendingSort:e=>`${e.columnName} sütuna göre artan düzende sırala`,columnSize:e=>`${e.value} piksel`,descending:"azalan sırada",descendingSort:e=>`${e.columnName} sütuna göre azalan düzende sırala`,resizerDescription:"Yeniden boyutlandırmak için Enter'a basın",select:"Seç",selectAll:"Tümünü Seç",sortable:"Sıralanabilir sütun"};var vt={};vt={ascending:"висхідний",ascendingSort:e=>`відсортовано за стовпцем ${e.columnName} у висхідному порядку`,columnSize:e=>`${e.value} пікс.`,descending:"низхідний",descendingSort:e=>`відсортовано за стовпцем ${e.columnName} у низхідному порядку`,resizerDescription:"Натисніть Enter, щоб почати зміну розміру",select:"Вибрати",selectAll:"Вибрати все",sortable:"сортувальний стовпець"};var gt={};gt={ascending:"升序",ascendingSort:e=>`按列 ${e.columnName} 升序排序`,columnSize:e=>`${e.value} 像素`,descending:"降序",descendingSort:e=>`按列 ${e.columnName} 降序排序`,resizerDescription:"按“输入”键开始调整大小。",select:"选择",selectAll:"全选",sortable:"可排序的列"};var $t={};$t={ascending:"遞增",ascendingSort:e=>`已依據「${e.columnName}」欄遞增排序`,columnSize:e=>`${e.value} 像素`,descending:"遞減",descendingSort:e=>`已依據「${e.columnName}」欄遞減排序`,resizerDescription:"按 Enter 鍵以開始調整大小",select:"選取",selectAll:"全選",sortable:"可排序的欄"};var ne={};ne={"ar-AE":Ve,"bg-BG":Oe,"cs-CZ":He,"da-DK":Le,"de-DE":Ue,"el-GR":We,"en-US":Ge,"es-ES":Ye,"et-EE":Ze,"fi-FI":_e,"fr-FR":qe,"he-IL":Je,"hr-HR":Xe,"hu-HU":Qe,"it-IT":et,"ja-JP":tt,"ko-KR":lt,"lt-LT":ut,"lv-LV":nt,"nb-NO":ot,"nl-NL":st,"pl-PL":it,"pt-BR":rt,"pt-PT":at,"ro-RO":ct,"ru-RU":dt,"sk-SK":mt,"sl-SI":pt,"sr-SP":bt,"sv-SE":ft,"tr-TR":ht,"uk-UA":vt,"zh-CN":gt,"zh-TW":$t};class yt{isCell(t){return t.type==="cell"}isRow(t){return t.type==="row"||t.type==="item"}isDisabled(t){var l;return this.disabledBehavior==="all"&&(((l=t.props)===null||l===void 0?void 0:l.isDisabled)||this.disabledKeys.has(t.key))}findPreviousKey(t,l){let u=t!=null?this.collection.getKeyBefore(t):this.collection.getLastKey();for(;u!=null;){let o=this.collection.getItem(u);if(!o)return null;if(!this.isDisabled(o)&&(!l||l(o)))return u;u=this.collection.getKeyBefore(u)}return null}findNextKey(t,l){let u=t!=null?this.collection.getKeyAfter(t):this.collection.getFirstKey();for(;u!=null;){let o=this.collection.getItem(u);if(!o)return null;if(!this.isDisabled(o)&&(!l||l(o)))return u;if(u=this.collection.getKeyAfter(u),u==null)return null}return null}getKeyForItemInRowByIndex(t,l=0){if(l<0)return null;let u=this.collection.getItem(t);if(!u)return null;let o=0;for(let a of z(u,this.collection)){var n;if(a.colSpan&&a.colSpan+o>l)return(n=a.key)!==null&&n!==void 0?n:null;a.colSpan&&(o=o+a.colSpan-1);var i;if(o===l)return(i=a.key)!==null&&i!==void 0?i:null;o++}return null}getKeyBelow(t){let l=t,u=this.collection.getItem(l);if(!u)return null;var o;if(this.isCell(u)&&(l=(o=u.parentKey)!==null&&o!==void 0?o:null),l==null)return null;if(l=this.findNextKey(l,n=>n.type==="item"),l!=null){if(this.isCell(u)){let n=u.colIndex?u.colIndex:u.index;return this.getKeyForItemInRowByIndex(l,n)}if(this.focusMode==="row")return l}return null}getKeyAbove(t){let l=t,u=this.collection.getItem(l);if(!u)return null;var o;if(this.isCell(u)&&(l=(o=u.parentKey)!==null&&o!==void 0?o:null),l==null)return null;if(l=this.findPreviousKey(l,n=>n.type==="item"),l!=null){if(this.isCell(u)){let n=u.colIndex?u.colIndex:u.index;return this.getKeyForItemInRowByIndex(l,n)}if(this.focusMode==="row")return l}return null}getKeyRightOf(t){let l=this.collection.getItem(t);if(!l)return null;if(this.isRow(l)){var u,o;let d=z(l,this.collection);var n;return(n=this.direction==="rtl"?(u=W(d))===null||u===void 0?void 0:u.key:(o=U(d))===null||o===void 0?void 0:o.key)!==null&&n!==void 0?n:null}if(this.isCell(l)&&l.parentKey!=null){let d=this.collection.getItem(l.parentKey);if(!d)return null;let r=z(d,this.collection);var i;let p=(i=this.direction==="rtl"?te(r,l.index-1):te(r,l.index+1))!==null&&i!==void 0?i:null;var a;if(p)return(a=p.key)!==null&&a!==void 0?a:null;var m;if(this.focusMode==="row")return(m=l.parentKey)!==null&&m!==void 0?m:null;var c;return(c=this.direction==="rtl"?this.getFirstKey(t):this.getLastKey(t))!==null&&c!==void 0?c:null}return null}getKeyLeftOf(t){let l=this.collection.getItem(t);if(!l)return null;if(this.isRow(l)){var u,o;let d=z(l,this.collection);var n;return(n=this.direction==="rtl"?(u=U(d))===null||u===void 0?void 0:u.key:(o=W(d))===null||o===void 0?void 0:o.key)!==null&&n!==void 0?n:null}if(this.isCell(l)&&l.parentKey!=null){let d=this.collection.getItem(l.parentKey);if(!d)return null;let r=z(d,this.collection);var i;let p=(i=this.direction==="rtl"?te(r,l.index+1):te(r,l.index-1))!==null&&i!==void 0?i:null;var a;if(p)return(a=p.key)!==null&&a!==void 0?a:null;var m;if(this.focusMode==="row")return(m=l.parentKey)!==null&&m!==void 0?m:null;var c;return(c=this.direction==="rtl"?this.getLastKey(t):this.getFirstKey(t))!==null&&c!==void 0?c:null}return null}getFirstKey(t,l){let u=t??null,o;if(u!=null){if(o=this.collection.getItem(u),!o)return null;if(this.isCell(o)&&!l&&o.parentKey!=null){var n;let c=this.collection.getItem(o.parentKey);if(!c)return null;var i;return(i=(n=U(z(c,this.collection)))===null||n===void 0?void 0:n.key)!==null&&i!==void 0?i:null}}if(u=this.findNextKey(void 0,c=>c.type==="item"),u!=null&&(o&&this.isCell(o)&&l||this.focusMode==="cell")){var a;let c=this.collection.getItem(u);if(!c)return null;var m;u=(m=(a=U(z(c,this.collection)))===null||a===void 0?void 0:a.key)!==null&&m!==void 0?m:null}return u}getLastKey(t,l){let u=t??null,o;if(u!=null){if(o=this.collection.getItem(u),!o)return null;if(this.isCell(o)&&!l&&o.parentKey!=null){var n;let c=this.collection.getItem(o.parentKey);if(!c)return null;let d=z(c,this.collection);var i;return(i=(n=W(d))===null||n===void 0?void 0:n.key)!==null&&i!==void 0?i:null}}if(u=this.findPreviousKey(void 0,c=>c.type==="item"),u!=null&&(o&&this.isCell(o)&&l||this.focusMode==="cell")){var a;let c=this.collection.getItem(u);if(!c)return null;let d=z(c,this.collection);var m;u=(m=(a=W(d))===null||a===void 0?void 0:a.key)!==null&&m!==void 0?m:null}return u}getKeyPageAbove(t){let l=t,u=this.layoutDelegate.getItemRect(l);if(!u)return null;let o=Math.max(0,u.y+u.height-this.layoutDelegate.getVisibleRect().height);for(;u&&u.y>o&&l!=null;){var n;if(l=(n=this.getKeyAbove(l))!==null&&n!==void 0?n:null,l==null)break;u=this.layoutDelegate.getItemRect(l)}return l}getKeyPageBelow(t){let l=t,u=this.layoutDelegate.getItemRect(l);if(!u)return null;let o=this.layoutDelegate.getVisibleRect().height,n=Math.min(this.layoutDelegate.getContentSize().height,u.y+o);for(;u&&u.y+u.heightr.type==="item"),u==null&&!a&&(u=this.getFirstKey(),a=!0)}return null}constructor(t){if(this.collection=t.collection,this.disabledKeys=t.disabledKeys,this.disabledBehavior=t.disabledBehavior||"all",this.direction=t.direction,this.collator=t.collator,!t.layout&&!t.ref)throw new Error("Either a layout or a ref must be specified.");this.layoutDelegate=t.layoutDelegate||(t.layout?new du(t.layout):new Tl(t.ref)),this.focusMode=t.focusMode||"row"}}class du{getContentSize(){return this.layout.getContentSize()}getItemRect(t){var l;return((l=this.layout.getLayoutInfo(t))===null||l===void 0?void 0:l.rect)||null}getVisibleRect(){return this.layout.virtualizer.visibleRect}constructor(t){this.layout=t}}const ve=new WeakMap;var xt={};xt={deselectedItem:e=>`${e.item} غير المحدد`,longPressToSelect:"اضغط مطولًا للدخول إلى وضع التحديد.",select:"تحديد",selectedAll:"جميع العناصر المحددة.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"لم يتم تحديد عناصر",one:()=>`${t.number(e.count)} عنصر محدد`,other:()=>`${t.number(e.count)} عنصر محدد`})}.`,selectedItem:e=>`${e.item} المحدد`};var Ct={};Ct={deselectedItem:e=>`${e.item} не е избран.`,longPressToSelect:"Натиснете и задръжте за да влезете в избирателен режим.",select:"Изберете",selectedAll:"Всички елементи са избрани.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Няма избрани елементи",one:()=>`${t.number(e.count)} избран елемент`,other:()=>`${t.number(e.count)} избрани елементи`})}.`,selectedItem:e=>`${e.item} избран.`};var Dt={};Dt={deselectedItem:e=>`Položka ${e.item} není vybrána.`,longPressToSelect:"Dlouhým stisknutím přejdete do režimu výběru.",select:"Vybrat",selectedAll:"Vybrány všechny položky.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nevybrány žádné položky",one:()=>`Vybrána ${t.number(e.count)} položka`,other:()=>`Vybráno ${t.number(e.count)} položek`})}.`,selectedItem:e=>`Vybrána položka ${e.item}.`};var Bt={};Bt={deselectedItem:e=>`${e.item} ikke valgt.`,longPressToSelect:"Lav et langt tryk for at aktivere valgtilstand.",select:"Vælg",selectedAll:"Alle elementer valgt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ingen elementer valgt",one:()=>`${t.number(e.count)} element valgt`,other:()=>`${t.number(e.count)} elementer valgt`})}.`,selectedItem:e=>`${e.item} valgt.`};var kt={};kt={deselectedItem:e=>`${e.item} nicht ausgewählt.`,longPressToSelect:"Gedrückt halten, um Auswahlmodus zu öffnen.",select:"Auswählen",selectedAll:"Alle Elemente ausgewählt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Keine Elemente ausgewählt",one:()=>`${t.number(e.count)} Element ausgewählt`,other:()=>`${t.number(e.count)} Elemente ausgewählt`})}.`,selectedItem:e=>`${e.item} ausgewählt.`};var Et={};Et={deselectedItem:e=>`Δεν επιλέχθηκε το στοιχείο ${e.item}.`,longPressToSelect:"Πατήστε παρατεταμένα για να μπείτε σε λειτουργία επιλογής.",select:"Επιλογή",selectedAll:"Επιλέχθηκαν όλα τα στοιχεία.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Δεν επιλέχθηκαν στοιχεία",one:()=>`Επιλέχθηκε ${t.number(e.count)} στοιχείο`,other:()=>`Επιλέχθηκαν ${t.number(e.count)} στοιχεία`})}.`,selectedItem:e=>`Επιλέχθηκε το στοιχείο ${e.item}.`};var St={};St={deselectedItem:e=>`${e.item} not selected.`,select:"Select",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"No items selected",one:()=>`${t.number(e.count)} item selected`,other:()=>`${t.number(e.count)} items selected`})}.`,selectedAll:"All items selected.",selectedItem:e=>`${e.item} selected.`,longPressToSelect:"Long press to enter selection mode."};var At={};At={deselectedItem:e=>`${e.item} no seleccionado.`,longPressToSelect:"Mantenga pulsado para abrir el modo de selección.",select:"Seleccionar",selectedAll:"Todos los elementos seleccionados.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ningún elemento seleccionado",one:()=>`${t.number(e.count)} elemento seleccionado`,other:()=>`${t.number(e.count)} elementos seleccionados`})}.`,selectedItem:e=>`${e.item} seleccionado.`};var wt={};wt={deselectedItem:e=>`${e.item} pole valitud.`,longPressToSelect:"Valikurežiimi sisenemiseks vajutage pikalt.",select:"Vali",selectedAll:"Kõik üksused valitud.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Üksusi pole valitud",one:()=>`${t.number(e.count)} üksus valitud`,other:()=>`${t.number(e.count)} üksust valitud`})}.`,selectedItem:e=>`${e.item} valitud.`};var Nt={};Nt={deselectedItem:e=>`Kohdetta ${e.item} ei valittu.`,longPressToSelect:"Siirry valintatilaan painamalla pitkään.",select:"Valitse",selectedAll:"Kaikki kohteet valittu.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ei yhtään kohdetta valittu",one:()=>`${t.number(e.count)} kohde valittu`,other:()=>`${t.number(e.count)} kohdetta valittu`})}.`,selectedItem:e=>`${e.item} valittu.`};var zt={};zt={deselectedItem:e=>`${e.item} non sélectionné.`,longPressToSelect:"Appuyez de manière prolongée pour passer en mode de sélection.",select:"Sélectionner",selectedAll:"Tous les éléments sélectionnés.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Aucun élément sélectionné",one:()=>`${t.number(e.count)} élément sélectionné`,other:()=>`${t.number(e.count)} éléments sélectionnés`})}.`,selectedItem:e=>`${e.item} sélectionné.`};var Ft={};Ft={deselectedItem:e=>`${e.item} לא נבחר.`,longPressToSelect:"הקשה ארוכה לכניסה למצב בחירה.",select:"בחר",selectedAll:"כל הפריטים נבחרו.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"לא נבחרו פריטים",one:()=>`פריט ${t.number(e.count)} נבחר`,other:()=>`${t.number(e.count)} פריטים נבחרו`})}.`,selectedItem:e=>`${e.item} נבחר.`};var Pt={};Pt={deselectedItem:e=>`Stavka ${e.item} nije odabrana.`,longPressToSelect:"Dugo pritisnite za ulazak u način odabira.",select:"Odaberite",selectedAll:"Odabrane su sve stavke.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nije odabrana nijedna stavka",one:()=>`Odabrana je ${t.number(e.count)} stavka`,other:()=>`Odabrano je ${t.number(e.count)} stavki`})}.`,selectedItem:e=>`Stavka ${e.item} je odabrana.`};var Kt={};Kt={deselectedItem:e=>`${e.item} nincs kijelölve.`,longPressToSelect:"Nyomja hosszan a kijelöléshez.",select:"Kijelölés",selectedAll:"Az összes elem kijelölve.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Egy elem sincs kijelölve",one:()=>`${t.number(e.count)} elem kijelölve`,other:()=>`${t.number(e.count)} elem kijelölve`})}.`,selectedItem:e=>`${e.item} kijelölve.`};var It={};It={deselectedItem:e=>`${e.item} non selezionato.`,longPressToSelect:"Premi a lungo per passare alla modalità di selezione.",select:"Seleziona",selectedAll:"Tutti gli elementi selezionati.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nessun elemento selezionato",one:()=>`${t.number(e.count)} elemento selezionato`,other:()=>`${t.number(e.count)} elementi selezionati`})}.`,selectedItem:e=>`${e.item} selezionato.`};var jt={};jt={deselectedItem:e=>`${e.item} が選択されていません。`,longPressToSelect:"長押しして選択モードを開きます。",select:"選択",selectedAll:"すべての項目を選択しました。",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"項目が選択されていません",one:()=>`${t.number(e.count)} 項目を選択しました`,other:()=>`${t.number(e.count)} 項目を選択しました`})}。`,selectedItem:e=>`${e.item} を選択しました。`};var Mt={};Mt={deselectedItem:e=>`${e.item}이(가) 선택되지 않았습니다.`,longPressToSelect:"선택 모드로 들어가려면 길게 누르십시오.",select:"선택",selectedAll:"모든 항목이 선택되었습니다.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"선택된 항목이 없습니다",one:()=>`${t.number(e.count)}개 항목이 선택되었습니다`,other:()=>`${t.number(e.count)}개 항목이 선택되었습니다`})}.`,selectedItem:e=>`${e.item}이(가) 선택되었습니다.`};var Tt={};Tt={deselectedItem:e=>`${e.item} nepasirinkta.`,longPressToSelect:"Norėdami įjungti pasirinkimo režimą, paspauskite ir palaikykite.",select:"Pasirinkti",selectedAll:"Pasirinkti visi elementai.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nepasirinktas nė vienas elementas",one:()=>`Pasirinktas ${t.number(e.count)} elementas`,other:()=>`Pasirinkta elementų: ${t.number(e.count)}`})}.`,selectedItem:e=>`Pasirinkta: ${e.item}.`};var Rt={};Rt={deselectedItem:e=>`Vienums ${e.item} nav atlasīts.`,longPressToSelect:"Ilgi turiet nospiestu. lai ieslēgtu atlases režīmu.",select:"Atlasīt",selectedAll:"Atlasīti visi vienumi.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nav atlasīts neviens vienums",one:()=>`Atlasīto vienumu skaits: ${t.number(e.count)}`,other:()=>`Atlasīto vienumu skaits: ${t.number(e.count)}`})}.`,selectedItem:e=>`Atlasīts vienums ${e.item}.`};var Vt={};Vt={deselectedItem:e=>`${e.item} er ikke valgt.`,longPressToSelect:"Bruk et langt trykk for å gå inn i valgmodus.",select:"Velg",selectedAll:"Alle elementer er valgt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ingen elementer er valgt",one:()=>`${t.number(e.count)} element er valgt`,other:()=>`${t.number(e.count)} elementer er valgt`})}.`,selectedItem:e=>`${e.item} er valgt.`};var Ot={};Ot={deselectedItem:e=>`${e.item} niet geselecteerd.`,longPressToSelect:"Druk lang om de selectiemodus te openen.",select:"Selecteren",selectedAll:"Alle items geselecteerd.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Geen items geselecteerd",one:()=>`${t.number(e.count)} item geselecteerd`,other:()=>`${t.number(e.count)} items geselecteerd`})}.`,selectedItem:e=>`${e.item} geselecteerd.`};var Ht={};Ht={deselectedItem:e=>`Nie zaznaczono ${e.item}.`,longPressToSelect:"Naciśnij i przytrzymaj, aby wejść do trybu wyboru.",select:"Zaznacz",selectedAll:"Wszystkie zaznaczone elementy.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nie zaznaczono żadnych elementów",one:()=>`${t.number(e.count)} zaznaczony element`,other:()=>`${t.number(e.count)} zaznaczonych elementów`})}.`,selectedItem:e=>`Zaznaczono ${e.item}.`};var Lt={};Lt={deselectedItem:e=>`${e.item} não selecionado.`,longPressToSelect:"Mantenha pressionado para entrar no modo de seleção.",select:"Selecionar",selectedAll:"Todos os itens selecionados.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nenhum item selecionado",one:()=>`${t.number(e.count)} item selecionado`,other:()=>`${t.number(e.count)} itens selecionados`})}.`,selectedItem:e=>`${e.item} selecionado.`};var Ut={};Ut={deselectedItem:e=>`${e.item} 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he=new WeakMap;function ce(e){return typeof e=="string"?e.replace(/\s*/g,""):""+e}function cu(e,t){let l=he.get(e);if(!l)throw new Error("Unknown grid");return`${l}-${ce(t)}`}function Te(e,t,l){let u=he.get(e);if(!u)throw new Error("Unknown grid");return`${u}-${ce(t)}-${ce(l)}`}function Re(e,t){return[...e.collection.rowHeaderColumnKeys].map(l=>Te(e,t,l)).join(" ")}var Ve={};Ve={ascending:"تصاعدي",ascendingSort:e=>`ترتيب حسب العمود ${e.columnName} بترتيب تصاعدي`,columnSize:e=>`${e.value} بالبكسل`,descending:"تنازلي",descendingSort:e=>`ترتيب حسب العمود ${e.columnName} بترتيب تنازلي`,resizerDescription:"اضغط على مفتاح Enter لبدء تغيير الحجم",select:"تحديد",selectAll:"تحديد الكل",sortable:"عمود قابل للترتيب"};var Oe={};Oe={ascending:"възходящ",ascendingSort:e=>`сортирано по колона ${e.columnName} във възходящ ред`,columnSize:e=>`${e.value} пиксела`,descending:"низходящ",descendingSort:e=>`сортирано по колона ${e.columnName} в низходящ ред`,resizerDescription:"Натиснете „Enter“, за да започнете да преоразмерявате",select:"Изберете",selectAll:"Изберете всичко",sortable:"сортираща колона"};var He={};He={ascending:"vzestupně",ascendingSort:e=>`řazeno vzestupně podle sloupce ${e.columnName}`,columnSize:e=>`${e.value} pixelů`,descending:"sestupně",descendingSort:e=>`řazeno sestupně podle sloupce ${e.columnName}`,resizerDescription:"Stisknutím klávesy Enter začnete měnit velikost",select:"Vybrat",selectAll:"Vybrat vše",sortable:"sloupec s možností řazení"};var Le={};Le={ascending:"stigende",ascendingSort:e=>`sorteret efter kolonne ${e.columnName} i stigende rækkefølge`,columnSize:e=>`${e.value} pixels`,descending:"faldende",descendingSort:e=>`sorteret efter kolonne ${e.columnName} i faldende rækkefølge`,resizerDescription:"Tryk på Enter for at ændre størrelse",select:"Vælg",selectAll:"Vælg alle",sortable:"sorterbar kolonne"};var Ue={};Ue={ascending:"aufsteigend",ascendingSort:e=>`sortiert nach Spalte ${e.columnName} in aufsteigender Reihenfolge`,columnSize:e=>`${e.value} Pixel`,descending:"absteigend",descendingSort:e=>`sortiert nach Spalte ${e.columnName} in absteigender Reihenfolge`,resizerDescription:"Eingabetaste zum Starten der Größenänderung drücken",select:"Auswählen",selectAll:"Alles auswählen",sortable:"sortierbare Spalte"};var We={};We={ascending:"αύξουσα",ascendingSort:e=>`διαλογή ανά στήλη ${e.columnName} σε αύξουσα σειρά`,columnSize:e=>`${e.value} pixel`,descending:"φθίνουσα",descendingSort:e=>`διαλογή ανά στήλη ${e.columnName} σε φθίνουσα σειρά`,resizerDescription:"Πατήστε Enter για έναρξη της αλλαγής μεγέθους",select:"Επιλογή",selectAll:"Επιλογή όλων",sortable:"Στήλη διαλογής"};var Ge={};Ge={select:"Select",selectAll:"Select All",sortable:"sortable column",ascending:"ascending",descending:"descending",ascendingSort:e=>`sorted by column ${e.columnName} in ascending order`,descendingSort:e=>`sorted by column ${e.columnName} in descending order`,columnSize:e=>`${e.value} pixels`,resizerDescription:"Press Enter to start resizing"};var Ye={};Ye={ascending:"ascendente",ascendingSort:e=>`ordenado por columna ${e.columnName} en sentido ascendente`,columnSize:e=>`${e.value} píxeles`,descending:"descendente",descendingSort:e=>`ordenado por columna ${e.columnName} en orden descendente`,resizerDescription:"Pulse Intro para empezar a redimensionar",select:"Seleccionar",selectAll:"Seleccionar todos",sortable:"columna ordenable"};var Ze={};Ze={ascending:"tõusev järjestus",ascendingSort:e=>`sorditud veeru järgi ${e.columnName} tõusvas järjestuses`,columnSize:e=>`${e.value} pikslit`,descending:"laskuv järjestus",descendingSort:e=>`sorditud veeru järgi ${e.columnName} laskuvas järjestuses`,resizerDescription:"Suuruse muutmise alustamiseks vajutage klahvi Enter",select:"Vali",selectAll:"Vali kõik",sortable:"sorditav veerg"};var _e={};_e={ascending:"nouseva",ascendingSort:e=>`lajiteltu sarakkeen ${e.columnName} mukaan nousevassa järjestyksessä`,columnSize:e=>`${e.value} pikseliä`,descending:"laskeva",descendingSort:e=>`lajiteltu sarakkeen ${e.columnName} mukaan laskevassa järjestyksessä`,resizerDescription:"Aloita koon muutos painamalla Enter-näppäintä",select:"Valitse",selectAll:"Valitse kaikki",sortable:"lajiteltava sarake"};var qe={};qe={ascending:"croissant",ascendingSort:e=>`trié en fonction de la colonne ${e.columnName} par ordre croissant`,columnSize:e=>`${e.value} pixels`,descending:"décroissant",descendingSort:e=>`trié en fonction de la colonne ${e.columnName} par ordre décroissant`,resizerDescription:"Appuyez sur Entrée pour commencer le redimensionnement.",select:"Sélectionner",selectAll:"Sélectionner tout",sortable:"colonne triable"};var Je={};Je={ascending:"עולה",ascendingSort:e=>`מוין לפי עמודה ${e.columnName} בסדר עולה`,columnSize:e=>`${e.value} פיקסלים`,descending:"יורד",descendingSort:e=>`מוין לפי עמודה ${e.columnName} בסדר יורד`,resizerDescription:"הקש Enter כדי לשנות את הגודל",select:"בחר",selectAll:"בחר הכול",sortable:"עמודה שניתן למיין"};var Xe={};Xe={ascending:"rastući",ascendingSort:e=>`razvrstano po stupcima ${e.columnName} rastućem redoslijedom`,columnSize:e=>`${e.value} piksela`,descending:"padajući",descendingSort:e=>`razvrstano po stupcima ${e.columnName} padajućim redoslijedom`,resizerDescription:"Pritisnite Enter da biste započeli promenu veličine",select:"Odaberite",selectAll:"Odaberite sve",sortable:"stupac koji se može razvrstati"};var Qe={};Qe={ascending:"növekvő",ascendingSort:e=>`rendezve a(z) ${e.columnName} oszlop szerint, növekvő sorrendben`,columnSize:e=>`${e.value} képpont`,descending:"csökkenő",descendingSort:e=>`rendezve a(z) ${e.columnName} oszlop szerint, csökkenő sorrendben`,resizerDescription:"Nyomja le az Enter billentyűt az átméretezés megkezdéséhez",select:"Kijelölés",selectAll:"Összes kijelölése",sortable:"rendezendő oszlop"};var et={};et={ascending:"crescente",ascendingSort:e=>`in ordine crescente in base alla colonna ${e.columnName}`,columnSize:e=>`${e.value} pixel`,descending:"decrescente",descendingSort:e=>`in ordine decrescente in base alla colonna ${e.columnName}`,resizerDescription:"Premi Invio per iniziare a ridimensionare",select:"Seleziona",selectAll:"Seleziona tutto",sortable:"colonna ordinabile"};var tt={};tt={ascending:"昇順",ascendingSort:e=>`列 ${e.columnName} を昇順で並べ替え`,columnSize:e=>`${e.value} ピクセル`,descending:"降順",descendingSort:e=>`列 ${e.columnName} を降順で並べ替え`,resizerDescription:"Enter キーを押してサイズ変更を開始",select:"選択",selectAll:"すべて選択",sortable:"並べ替え可能な列"};var lt={};lt={ascending:"오름차순",ascendingSort:e=>`${e.columnName} 열을 기준으로 오름차순으로 정렬됨`,columnSize:e=>`${e.value} 픽셀`,descending:"내림차순",descendingSort:e=>`${e.columnName} 열을 기준으로 내림차순으로 정렬됨`,resizerDescription:"크기 조정을 시작하려면 Enter를 누르세요.",select:"선택",selectAll:"모두 선택",sortable:"정렬 가능한 열"};var ut={};ut={ascending:"didėjančia tvarka",ascendingSort:e=>`surikiuota pagal stulpelį ${e.columnName} didėjančia tvarka`,columnSize:e=>`${e.value} piks.`,descending:"mažėjančia tvarka",descendingSort:e=>`surikiuota pagal stulpelį ${e.columnName} mažėjančia tvarka`,resizerDescription:"Paspauskite „Enter“, kad pradėtumėte keisti dydį",select:"Pasirinkti",selectAll:"Pasirinkti viską",sortable:"rikiuojamas stulpelis"};var nt={};nt={ascending:"augošā secībā",ascendingSort:e=>`kārtots pēc kolonnas ${e.columnName} augošā secībā`,columnSize:e=>`${e.value} pikseļi`,descending:"dilstošā secībā",descendingSort:e=>`kārtots pēc kolonnas ${e.columnName} dilstošā secībā`,resizerDescription:"Nospiediet Enter, lai sāktu izmēru mainīšanu",select:"Atlasīt",selectAll:"Atlasīt visu",sortable:"kārtojamā kolonna"};var ot={};ot={ascending:"stigende",ascendingSort:e=>`sortert etter kolonne ${e.columnName} i stigende rekkefølge`,columnSize:e=>`${e.value} piksler`,descending:"synkende",descendingSort:e=>`sortert etter kolonne ${e.columnName} i synkende rekkefølge`,resizerDescription:"Trykk på Enter for å starte størrelsesendring",select:"Velg",selectAll:"Velg alle",sortable:"kolonne som kan sorteres"};var st={};st={ascending:"oplopend",ascendingSort:e=>`gesorteerd in oplopende volgorde in kolom ${e.columnName}`,columnSize:e=>`${e.value} pixels`,descending:"aflopend",descendingSort:e=>`gesorteerd in aflopende volgorde in kolom ${e.columnName}`,resizerDescription:"Druk op Enter om het formaat te wijzigen",select:"Selecteren",selectAll:"Alles selecteren",sortable:"sorteerbare kolom"};var it={};it={ascending:"rosnąco",ascendingSort:e=>`posortowano według kolumny ${e.columnName} w porządku rosnącym`,columnSize:e=>`Liczba pikseli: ${e.value}`,descending:"malejąco",descendingSort:e=>`posortowano według kolumny ${e.columnName} w porządku malejącym`,resizerDescription:"Naciśnij Enter, aby rozpocząć zmienianie rozmiaru",select:"Zaznacz",selectAll:"Zaznacz wszystko",sortable:"kolumna z możliwością sortowania"};var rt={};rt={ascending:"crescente",ascendingSort:e=>`classificado pela coluna ${e.columnName} em ordem crescente`,columnSize:e=>`${e.value} pixels`,descending:"decrescente",descendingSort:e=>`classificado pela coluna ${e.columnName} em ordem decrescente`,resizerDescription:"Pressione Enter para começar a redimensionar",select:"Selecionar",selectAll:"Selecionar tudo",sortable:"coluna classificável"};var at={};at={ascending:"ascendente",ascendingSort:e=>`Ordenar por coluna ${e.columnName} em ordem ascendente`,columnSize:e=>`${e.value} pixels`,descending:"descendente",descendingSort:e=>`Ordenar por coluna ${e.columnName} em ordem descendente`,resizerDescription:"Prima Enter para iniciar o redimensionamento",select:"Selecionar",selectAll:"Selecionar tudo",sortable:"Coluna ordenável"};var ct={};ct={ascending:"crescătoare",ascendingSort:e=>`sortate după coloana ${e.columnName} în ordine crescătoare`,columnSize:e=>`${e.value} pixeli`,descending:"descrescătoare",descendingSort:e=>`sortate după coloana ${e.columnName} în ordine descrescătoare`,resizerDescription:"Apăsați pe Enter pentru a începe redimensionarea",select:"Selectare",selectAll:"Selectare totală",sortable:"coloană sortabilă"};var dt={};dt={ascending:"возрастание",ascendingSort:e=>`сортировать столбец ${e.columnName} в порядке возрастания`,columnSize:e=>`${e.value} пикс.`,descending:"убывание",descendingSort:e=>`сортировать столбец ${e.columnName} в порядке убывания`,resizerDescription:"Нажмите клавишу Enter для начала изменения размеров",select:"Выбрать",selectAll:"Выбрать все",sortable:"сортируемый столбец"};var mt={};mt={ascending:"vzostupne",ascendingSort:e=>`zoradené zostupne podľa stĺpca ${e.columnName}`,columnSize:e=>`Počet pixelov: ${e.value}`,descending:"zostupne",descendingSort:e=>`zoradené zostupne podľa stĺpca ${e.columnName}`,resizerDescription:"Stlačením klávesu Enter začnete zmenu veľkosti",select:"Vybrať",selectAll:"Vybrať všetko",sortable:"zoraditeľný stĺpec"};var pt={};pt={ascending:"naraščajoče",ascendingSort:e=>`razvrščeno po stolpcu ${e.columnName} v naraščajočem vrstnem redu`,columnSize:e=>`${e.value} slikovnih pik`,descending:"padajoče",descendingSort:e=>`razvrščeno po stolpcu ${e.columnName} v padajočem vrstnem redu`,resizerDescription:"Pritisnite tipko Enter da začnete spreminjati velikost",select:"Izberite",selectAll:"Izberite vse",sortable:"razvrstljivi stolpec"};var bt={};bt={ascending:"rastući",ascendingSort:e=>`sortirano po kolonama ${e.columnName} rastućim redosledom`,columnSize:e=>`${e.value} piksela`,descending:"padajući",descendingSort:e=>`sortirano po kolonama ${e.columnName} padajućim redosledom`,resizerDescription:"Pritisnite Enter da biste započeli promenu veličine",select:"Izaberite",selectAll:"Izaberite sve",sortable:"kolona koja se može sortirati"};var ft={};ft={ascending:"stigande",ascendingSort:e=>`sorterat på kolumn ${e.columnName} i stigande ordning`,columnSize:e=>`${e.value} pixlar`,descending:"fallande",descendingSort:e=>`sorterat på kolumn ${e.columnName} i fallande ordning`,resizerDescription:"Tryck på Retur för att börja ändra storlek",select:"Markera",selectAll:"Markera allt",sortable:"sorterbar kolumn"};var ht={};ht={ascending:"artan sırada",ascendingSort:e=>`${e.columnName} sütuna göre artan düzende sırala`,columnSize:e=>`${e.value} piksel`,descending:"azalan sırada",descendingSort:e=>`${e.columnName} sütuna göre azalan düzende sırala`,resizerDescription:"Yeniden boyutlandırmak için Enter'a basın",select:"Seç",selectAll:"Tümünü Seç",sortable:"Sıralanabilir sütun"};var vt={};vt={ascending:"висхідний",ascendingSort:e=>`відсортовано за стовпцем ${e.columnName} у висхідному порядку`,columnSize:e=>`${e.value} пікс.`,descending:"низхідний",descendingSort:e=>`відсортовано за стовпцем ${e.columnName} у низхідному порядку`,resizerDescription:"Натисніть Enter, щоб почати зміну розміру",select:"Вибрати",selectAll:"Вибрати все",sortable:"сортувальний стовпець"};var gt={};gt={ascending:"升序",ascendingSort:e=>`按列 ${e.columnName} 升序排序`,columnSize:e=>`${e.value} 像素`,descending:"降序",descendingSort:e=>`按列 ${e.columnName} 降序排序`,resizerDescription:"按“输入”键开始调整大小。",select:"选择",selectAll:"全选",sortable:"可排序的列"};var $t={};$t={ascending:"遞增",ascendingSort:e=>`已依據「${e.columnName}」欄遞增排序`,columnSize:e=>`${e.value} 像素`,descending:"遞減",descendingSort:e=>`已依據「${e.columnName}」欄遞減排序`,resizerDescription:"按 Enter 鍵以開始調整大小",select:"選取",selectAll:"全選",sortable:"可排序的欄"};var ne={};ne={"ar-AE":Ve,"bg-BG":Oe,"cs-CZ":He,"da-DK":Le,"de-DE":Ue,"el-GR":We,"en-US":Ge,"es-ES":Ye,"et-EE":Ze,"fi-FI":_e,"fr-FR":qe,"he-IL":Je,"hr-HR":Xe,"hu-HU":Qe,"it-IT":et,"ja-JP":tt,"ko-KR":lt,"lt-LT":ut,"lv-LV":nt,"nb-NO":ot,"nl-NL":st,"pl-PL":it,"pt-BR":rt,"pt-PT":at,"ro-RO":ct,"ru-RU":dt,"sk-SK":mt,"sl-SI":pt,"sr-SP":bt,"sv-SE":ft,"tr-TR":ht,"uk-UA":vt,"zh-CN":gt,"zh-TW":$t};class yt{isCell(t){return t.type==="cell"}isRow(t){return t.type==="row"||t.type==="item"}isDisabled(t){var l;return this.disabledBehavior==="all"&&(((l=t.props)===null||l===void 0?void 0:l.isDisabled)||this.disabledKeys.has(t.key))}findPreviousKey(t,l){let u=t!=null?this.collection.getKeyBefore(t):this.collection.getLastKey();for(;u!=null;){let o=this.collection.getItem(u);if(!o)return null;if(!this.isDisabled(o)&&(!l||l(o)))return u;u=this.collection.getKeyBefore(u)}return null}findNextKey(t,l){let u=t!=null?this.collection.getKeyAfter(t):this.collection.getFirstKey();for(;u!=null;){let o=this.collection.getItem(u);if(!o)return null;if(!this.isDisabled(o)&&(!l||l(o)))return u;if(u=this.collection.getKeyAfter(u),u==null)return null}return null}getKeyForItemInRowByIndex(t,l=0){if(l<0)return null;let u=this.collection.getItem(t);if(!u)return null;let o=0;for(let a of z(u,this.collection)){var n;if(a.colSpan&&a.colSpan+o>l)return(n=a.key)!==null&&n!==void 0?n:null;a.colSpan&&(o=o+a.colSpan-1);var i;if(o===l)return(i=a.key)!==null&&i!==void 0?i:null;o++}return null}getKeyBelow(t){let l=t,u=this.collection.getItem(l);if(!u)return null;var o;if(this.isCell(u)&&(l=(o=u.parentKey)!==null&&o!==void 0?o:null),l==null)return null;if(l=this.findNextKey(l,n=>n.type==="item"),l!=null){if(this.isCell(u)){let n=u.colIndex?u.colIndex:u.index;return this.getKeyForItemInRowByIndex(l,n)}if(this.focusMode==="row")return l}return null}getKeyAbove(t){let l=t,u=this.collection.getItem(l);if(!u)return null;var o;if(this.isCell(u)&&(l=(o=u.parentKey)!==null&&o!==void 0?o:null),l==null)return null;if(l=this.findPreviousKey(l,n=>n.type==="item"),l!=null){if(this.isCell(u)){let n=u.colIndex?u.colIndex:u.index;return this.getKeyForItemInRowByIndex(l,n)}if(this.focusMode==="row")return l}return null}getKeyRightOf(t){let l=this.collection.getItem(t);if(!l)return null;if(this.isRow(l)){var u,o;let d=z(l,this.collection);var n;return(n=this.direction==="rtl"?(u=W(d))===null||u===void 0?void 0:u.key:(o=U(d))===null||o===void 0?void 0:o.key)!==null&&n!==void 0?n:null}if(this.isCell(l)&&l.parentKey!=null){let d=this.collection.getItem(l.parentKey);if(!d)return null;let r=z(d,this.collection);var i;let p=(i=this.direction==="rtl"?te(r,l.index-1):te(r,l.index+1))!==null&&i!==void 0?i:null;var a;if(p)return(a=p.key)!==null&&a!==void 0?a:null;var m;if(this.focusMode==="row")return(m=l.parentKey)!==null&&m!==void 0?m:null;var c;return(c=this.direction==="rtl"?this.getFirstKey(t):this.getLastKey(t))!==null&&c!==void 0?c:null}return null}getKeyLeftOf(t){let l=this.collection.getItem(t);if(!l)return null;if(this.isRow(l)){var u,o;let d=z(l,this.collection);var n;return(n=this.direction==="rtl"?(u=U(d))===null||u===void 0?void 0:u.key:(o=W(d))===null||o===void 0?void 0:o.key)!==null&&n!==void 0?n:null}if(this.isCell(l)&&l.parentKey!=null){let d=this.collection.getItem(l.parentKey);if(!d)return null;let r=z(d,this.collection);var i;let p=(i=this.direction==="rtl"?te(r,l.index+1):te(r,l.index-1))!==null&&i!==void 0?i:null;var a;if(p)return(a=p.key)!==null&&a!==void 0?a:null;var m;if(this.focusMode==="row")return(m=l.parentKey)!==null&&m!==void 0?m:null;var c;return(c=this.direction==="rtl"?this.getLastKey(t):this.getFirstKey(t))!==null&&c!==void 0?c:null}return null}getFirstKey(t,l){let u=t??null,o;if(u!=null){if(o=this.collection.getItem(u),!o)return null;if(this.isCell(o)&&!l&&o.parentKey!=null){var n;let c=this.collection.getItem(o.parentKey);if(!c)return null;var i;return(i=(n=U(z(c,this.collection)))===null||n===void 0?void 0:n.key)!==null&&i!==void 0?i:null}}if(u=this.findNextKey(void 0,c=>c.type==="item"),u!=null&&(o&&this.isCell(o)&&l||this.focusMode==="cell")){var a;let c=this.collection.getItem(u);if(!c)return null;var m;u=(m=(a=U(z(c,this.collection)))===null||a===void 0?void 0:a.key)!==null&&m!==void 0?m:null}return u}getLastKey(t,l){let u=t??null,o;if(u!=null){if(o=this.collection.getItem(u),!o)return null;if(this.isCell(o)&&!l&&o.parentKey!=null){var n;let c=this.collection.getItem(o.parentKey);if(!c)return null;let d=z(c,this.collection);var i;return(i=(n=W(d))===null||n===void 0?void 0:n.key)!==null&&i!==void 0?i:null}}if(u=this.findPreviousKey(void 0,c=>c.type==="item"),u!=null&&(o&&this.isCell(o)&&l||this.focusMode==="cell")){var a;let c=this.collection.getItem(u);if(!c)return null;let d=z(c,this.collection);var m;u=(m=(a=W(d))===null||a===void 0?void 0:a.key)!==null&&m!==void 0?m:null}return u}getKeyPageAbove(t){let l=t,u=this.layoutDelegate.getItemRect(l);if(!u)return null;let o=Math.max(0,u.y+u.height-this.layoutDelegate.getVisibleRect().height);for(;u&&u.y>o&&l!=null;){var n;if(l=(n=this.getKeyAbove(l))!==null&&n!==void 0?n:null,l==null)break;u=this.layoutDelegate.getItemRect(l)}return l}getKeyPageBelow(t){let l=t,u=this.layoutDelegate.getItemRect(l);if(!u)return null;let o=this.layoutDelegate.getVisibleRect().height,n=Math.min(this.layoutDelegate.getContentSize().height,u.y+o);for(;u&&u.y+u.heightr.type==="item"),u==null&&!a&&(u=this.getFirstKey(),a=!0)}return null}constructor(t){if(this.collection=t.collection,this.disabledKeys=t.disabledKeys,this.disabledBehavior=t.disabledBehavior||"all",this.direction=t.direction,this.collator=t.collator,!t.layout&&!t.ref)throw new Error("Either a layout or a ref must be specified.");this.layoutDelegate=t.layoutDelegate||(t.layout?new du(t.layout):new Tl(t.ref)),this.focusMode=t.focusMode||"row"}}class du{getContentSize(){return this.layout.getContentSize()}getItemRect(t){var l;return((l=this.layout.getLayoutInfo(t))===null||l===void 0?void 0:l.rect)||null}getVisibleRect(){return this.layout.virtualizer.visibleRect}constructor(t){this.layout=t}}const ve=new WeakMap;var xt={};xt={deselectedItem:e=>`${e.item} غير المحدد`,longPressToSelect:"اضغط مطولًا للدخول إلى وضع التحديد.",select:"تحديد",selectedAll:"جميع العناصر المحددة.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"لم يتم تحديد عناصر",one:()=>`${t.number(e.count)} عنصر محدد`,other:()=>`${t.number(e.count)} عنصر محدد`})}.`,selectedItem:e=>`${e.item} المحدد`};var Ct={};Ct={deselectedItem:e=>`${e.item} не е избран.`,longPressToSelect:"Натиснете и задръжте за да влезете в избирателен режим.",select:"Изберете",selectedAll:"Всички елементи са избрани.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Няма избрани елементи",one:()=>`${t.number(e.count)} избран елемент`,other:()=>`${t.number(e.count)} избрани елементи`})}.`,selectedItem:e=>`${e.item} избран.`};var Dt={};Dt={deselectedItem:e=>`Položka ${e.item} není vybrána.`,longPressToSelect:"Dlouhým stisknutím přejdete do režimu výběru.",select:"Vybrat",selectedAll:"Vybrány všechny položky.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nevybrány žádné položky",one:()=>`Vybrána ${t.number(e.count)} položka`,other:()=>`Vybráno ${t.number(e.count)} položek`})}.`,selectedItem:e=>`Vybrána položka ${e.item}.`};var Bt={};Bt={deselectedItem:e=>`${e.item} ikke valgt.`,longPressToSelect:"Lav et langt tryk for at aktivere valgtilstand.",select:"Vælg",selectedAll:"Alle elementer valgt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ingen elementer valgt",one:()=>`${t.number(e.count)} element valgt`,other:()=>`${t.number(e.count)} elementer valgt`})}.`,selectedItem:e=>`${e.item} valgt.`};var kt={};kt={deselectedItem:e=>`${e.item} nicht ausgewählt.`,longPressToSelect:"Gedrückt halten, um Auswahlmodus zu öffnen.",select:"Auswählen",selectedAll:"Alle Elemente ausgewählt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Keine Elemente ausgewählt",one:()=>`${t.number(e.count)} Element ausgewählt`,other:()=>`${t.number(e.count)} Elemente ausgewählt`})}.`,selectedItem:e=>`${e.item} ausgewählt.`};var Et={};Et={deselectedItem:e=>`Δεν επιλέχθηκε το στοιχείο ${e.item}.`,longPressToSelect:"Πατήστε παρατεταμένα για να μπείτε σε λειτουργία επιλογής.",select:"Επιλογή",selectedAll:"Επιλέχθηκαν όλα τα στοιχεία.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Δεν επιλέχθηκαν στοιχεία",one:()=>`Επιλέχθηκε ${t.number(e.count)} στοιχείο`,other:()=>`Επιλέχθηκαν ${t.number(e.count)} στοιχεία`})}.`,selectedItem:e=>`Επιλέχθηκε το στοιχείο ${e.item}.`};var St={};St={deselectedItem:e=>`${e.item} not selected.`,select:"Select",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"No items selected",one:()=>`${t.number(e.count)} item selected`,other:()=>`${t.number(e.count)} items selected`})}.`,selectedAll:"All items selected.",selectedItem:e=>`${e.item} selected.`,longPressToSelect:"Long press to enter selection mode."};var At={};At={deselectedItem:e=>`${e.item} no seleccionado.`,longPressToSelect:"Mantenga pulsado para abrir el modo de selección.",select:"Seleccionar",selectedAll:"Todos los elementos seleccionados.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ningún elemento seleccionado",one:()=>`${t.number(e.count)} elemento seleccionado`,other:()=>`${t.number(e.count)} elementos seleccionados`})}.`,selectedItem:e=>`${e.item} seleccionado.`};var wt={};wt={deselectedItem:e=>`${e.item} pole valitud.`,longPressToSelect:"Valikurežiimi sisenemiseks vajutage pikalt.",select:"Vali",selectedAll:"Kõik üksused valitud.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Üksusi pole valitud",one:()=>`${t.number(e.count)} üksus valitud`,other:()=>`${t.number(e.count)} üksust valitud`})}.`,selectedItem:e=>`${e.item} valitud.`};var Nt={};Nt={deselectedItem:e=>`Kohdetta ${e.item} ei valittu.`,longPressToSelect:"Siirry valintatilaan painamalla pitkään.",select:"Valitse",selectedAll:"Kaikki kohteet valittu.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ei yhtään kohdetta valittu",one:()=>`${t.number(e.count)} kohde valittu`,other:()=>`${t.number(e.count)} kohdetta valittu`})}.`,selectedItem:e=>`${e.item} valittu.`};var zt={};zt={deselectedItem:e=>`${e.item} non sélectionné.`,longPressToSelect:"Appuyez de manière prolongée pour passer en mode de sélection.",select:"Sélectionner",selectedAll:"Tous les éléments sélectionnés.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Aucun élément sélectionné",one:()=>`${t.number(e.count)} élément sélectionné`,other:()=>`${t.number(e.count)} éléments sélectionnés`})}.`,selectedItem:e=>`${e.item} sélectionné.`};var Ft={};Ft={deselectedItem:e=>`${e.item} לא נבחר.`,longPressToSelect:"הקשה ארוכה לכניסה למצב בחירה.",select:"בחר",selectedAll:"כל הפריטים נבחרו.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"לא נבחרו פריטים",one:()=>`פריט ${t.number(e.count)} נבחר`,other:()=>`${t.number(e.count)} פריטים נבחרו`})}.`,selectedItem:e=>`${e.item} נבחר.`};var Pt={};Pt={deselectedItem:e=>`Stavka ${e.item} nije odabrana.`,longPressToSelect:"Dugo pritisnite za ulazak u način odabira.",select:"Odaberite",selectedAll:"Odabrane su sve stavke.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nije odabrana nijedna stavka",one:()=>`Odabrana je ${t.number(e.count)} stavka`,other:()=>`Odabrano je ${t.number(e.count)} stavki`})}.`,selectedItem:e=>`Stavka ${e.item} je odabrana.`};var Kt={};Kt={deselectedItem:e=>`${e.item} nincs kijelölve.`,longPressToSelect:"Nyomja hosszan a kijelöléshez.",select:"Kijelölés",selectedAll:"Az összes elem kijelölve.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Egy elem sincs kijelölve",one:()=>`${t.number(e.count)} elem kijelölve`,other:()=>`${t.number(e.count)} elem kijelölve`})}.`,selectedItem:e=>`${e.item} kijelölve.`};var It={};It={deselectedItem:e=>`${e.item} non selezionato.`,longPressToSelect:"Premi a lungo per passare alla modalità di selezione.",select:"Seleziona",selectedAll:"Tutti gli elementi selezionati.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nessun elemento selezionato",one:()=>`${t.number(e.count)} elemento selezionato`,other:()=>`${t.number(e.count)} elementi selezionati`})}.`,selectedItem:e=>`${e.item} selezionato.`};var jt={};jt={deselectedItem:e=>`${e.item} が選択されていません。`,longPressToSelect:"長押しして選択モードを開きます。",select:"選択",selectedAll:"すべての項目を選択しました。",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"項目が選択されていません",one:()=>`${t.number(e.count)} 項目を選択しました`,other:()=>`${t.number(e.count)} 項目を選択しました`})}。`,selectedItem:e=>`${e.item} を選択しました。`};var Mt={};Mt={deselectedItem:e=>`${e.item}이(가) 선택되지 않았습니다.`,longPressToSelect:"선택 모드로 들어가려면 길게 누르십시오.",select:"선택",selectedAll:"모든 항목이 선택되었습니다.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"선택된 항목이 없습니다",one:()=>`${t.number(e.count)}개 항목이 선택되었습니다`,other:()=>`${t.number(e.count)}개 항목이 선택되었습니다`})}.`,selectedItem:e=>`${e.item}이(가) 선택되었습니다.`};var Tt={};Tt={deselectedItem:e=>`${e.item} nepasirinkta.`,longPressToSelect:"Norėdami įjungti pasirinkimo režimą, paspauskite ir palaikykite.",select:"Pasirinkti",selectedAll:"Pasirinkti visi elementai.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nepasirinktas nė vienas elementas",one:()=>`Pasirinktas ${t.number(e.count)} elementas`,other:()=>`Pasirinkta elementų: ${t.number(e.count)}`})}.`,selectedItem:e=>`Pasirinkta: ${e.item}.`};var Rt={};Rt={deselectedItem:e=>`Vienums ${e.item} nav atlasīts.`,longPressToSelect:"Ilgi turiet nospiestu. lai ieslēgtu atlases režīmu.",select:"Atlasīt",selectedAll:"Atlasīti visi vienumi.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nav atlasīts neviens vienums",one:()=>`Atlasīto vienumu skaits: ${t.number(e.count)}`,other:()=>`Atlasīto vienumu skaits: ${t.number(e.count)}`})}.`,selectedItem:e=>`Atlasīts vienums ${e.item}.`};var Vt={};Vt={deselectedItem:e=>`${e.item} er ikke valgt.`,longPressToSelect:"Bruk et langt trykk for å gå inn i valgmodus.",select:"Velg",selectedAll:"Alle elementer er valgt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ingen elementer er valgt",one:()=>`${t.number(e.count)} element er valgt`,other:()=>`${t.number(e.count)} elementer er valgt`})}.`,selectedItem:e=>`${e.item} er valgt.`};var Ot={};Ot={deselectedItem:e=>`${e.item} niet geselecteerd.`,longPressToSelect:"Druk lang om de selectiemodus te openen.",select:"Selecteren",selectedAll:"Alle items geselecteerd.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Geen items geselecteerd",one:()=>`${t.number(e.count)} item geselecteerd`,other:()=>`${t.number(e.count)} items geselecteerd`})}.`,selectedItem:e=>`${e.item} geselecteerd.`};var Ht={};Ht={deselectedItem:e=>`Nie zaznaczono ${e.item}.`,longPressToSelect:"Naciśnij i przytrzymaj, aby wejść do trybu wyboru.",select:"Zaznacz",selectedAll:"Wszystkie zaznaczone elementy.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nie zaznaczono żadnych elementów",one:()=>`${t.number(e.count)} zaznaczony element`,other:()=>`${t.number(e.count)} zaznaczonych elementów`})}.`,selectedItem:e=>`Zaznaczono ${e.item}.`};var Lt={};Lt={deselectedItem:e=>`${e.item} não selecionado.`,longPressToSelect:"Mantenha pressionado para entrar no modo de seleção.",select:"Selecionar",selectedAll:"Todos os itens selecionados.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nenhum item selecionado",one:()=>`${t.number(e.count)} item selecionado`,other:()=>`${t.number(e.count)} itens selecionados`})}.`,selectedItem:e=>`${e.item} selecionado.`};var Ut={};Ut={deselectedItem:e=>`${e.item} não selecionado.`,longPressToSelect:"Prima continuamente para entrar no modo de seleção.",select:"Selecionar",selectedAll:"Todos os itens selecionados.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nenhum item selecionado",one:()=>`${t.number(e.count)} item selecionado`,other:()=>`${t.number(e.count)} itens selecionados`})}.`,selectedItem:e=>`${e.item} selecionado.`};var Wt={};Wt={deselectedItem:e=>`${e.item} neselectat.`,longPressToSelect:"Apăsați lung pentru a intra în modul de selectare.",select:"Selectare",selectedAll:"Toate elementele selectate.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Niciun element selectat",one:()=>`${t.number(e.count)} element selectat`,other:()=>`${t.number(e.count)} elemente selectate`})}.`,selectedItem:e=>`${e.item} selectat.`};var Gt={};Gt={deselectedItem:e=>`${e.item} не выбрано.`,longPressToSelect:"Нажмите и удерживайте для входа в режим выбора.",select:"Выбрать",selectedAll:"Выбраны все элементы.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Нет выбранных элементов",one:()=>`${t.number(e.count)} элемент выбран`,other:()=>`${t.number(e.count)} элементов выбрано`})}.`,selectedItem:e=>`${e.item} выбрано.`};var Yt={};Yt={deselectedItem:e=>`Nevybraté položky: ${e.item}.`,longPressToSelect:"Dlhším stlačením prejdite do režimu výberu.",select:"Vybrať",selectedAll:"Všetky vybraté položky.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Žiadne vybraté položky",one:()=>`${t.number(e.count)} vybratá položka`,other:()=>`Počet vybratých položiek:${t.number(e.count)}`})}.`,selectedItem:e=>`Vybraté položky: ${e.item}.`};var Zt={};Zt={deselectedItem:e=>`Element ${e.item} ni izbran.`,longPressToSelect:"Za izbirni način pritisnite in dlje časa držite.",select:"Izberite",selectedAll:"Vsi elementi so izbrani.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Noben element ni izbran",one:()=>`${t.number(e.count)} element je izbran`,other:()=>`${t.number(e.count)} elementov je izbranih`})}.`,selectedItem:e=>`Element ${e.item} je izbran.`};var _t={};_t={deselectedItem:e=>`${e.item} nije izabrano.`,longPressToSelect:"Dugo pritisnite za ulazak u režim biranja.",select:"Izaberite",selectedAll:"Izabrane su sve stavke.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nije izabrana nijedna stavka",one:()=>`Izabrana je ${t.number(e.count)} stavka`,other:()=>`Izabrano je ${t.number(e.count)} stavki`})}.`,selectedItem:e=>`${e.item} je izabrano.`};var qt={};qt={deselectedItem:e=>`${e.item} ej markerat.`,longPressToSelect:"Tryck länge när du vill öppna väljarläge.",select:"Markera",selectedAll:"Alla markerade objekt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Inga markerade objekt",one:()=>`${t.number(e.count)} markerat objekt`,other:()=>`${t.number(e.count)} markerade objekt`})}.`,selectedItem:e=>`${e.item} markerat.`};var Jt={};Jt={deselectedItem:e=>`${e.item} seçilmedi.`,longPressToSelect:"Seçim moduna girmek için uzun basın.",select:"Seç",selectedAll:"Tüm ögeler seçildi.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Hiçbir öge seçilmedi",one:()=>`${t.number(e.count)} öge seçildi`,other:()=>`${t.number(e.count)} öge seçildi`})}.`,selectedItem:e=>`${e.item} seçildi.`};var Xt={};Xt={deselectedItem:e=>`${e.item} не вибрано.`,longPressToSelect:"Виконайте довге натиснення, щоб перейти в режим вибору.",select:"Вибрати",selectedAll:"Усі елементи вибрано.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Жодних елементів не вибрано",one:()=>`${t.number(e.count)} елемент вибрано`,other:()=>`Вибрано елементів: ${t.number(e.count)}`})}.`,selectedItem:e=>`${e.item} вибрано.`};var Qt={};Qt={deselectedItem:e=>`未选择 ${e.item}。`,longPressToSelect:"长按以进入选择模式。",select:"选择",selectedAll:"已选择所有项目。",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"未选择项目",one:()=>`已选择 ${t.number(e.count)} 个项目`,other:()=>`已选择 ${t.number(e.count)} 个项目`})}。`,selectedItem:e=>`已选择 ${e.item}。`};var 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Gu=Nl,zl=T((e,t)=>{const{BaseComponent:l,Component:u,collection:o,values:n,topContent:i,topContentPlacement:a,bottomContentPlacement:m,bottomContent:c,removeWrapper:d,sortIcon:r,getBaseProps:p,getWrapperProps:s,getTableProps:b}=pl({...e,ref:t}),{isVirtualized:v,rowHeight:g=40,maxTableHeight:x=600}=e,D=v,y=B.useCallback(({children:$})=>d?$:h.jsx(l,{...s(),children:$}),[d,s]);return D?h.jsx(Wu,{...e,ref:t,maxTableHeight:x,rowHeight:g}):h.jsxs("div",{...p(),children:[a==="outside"&&i,h.jsx(y,{children:h.jsxs(h.Fragment,{children:[a==="inside"&&i,h.jsxs(u,{...b(),children:[h.jsxs(El,{classNames:n.classNames,slots:n.slots,children:[o.headerRows.map($=>h.jsx(Bl,{classNames:n.classNames,node:$,slots:n.slots,state:n.state,children:[...$.childNodes].map(f=>{var 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packages/ragbits-chat/src/ragbits/chat/ui-build/assets/authStore-Ur4ZGows.js index 4bf39c7f1..2ac1c94e5 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/authStore-DLhLSjcY.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/authStore-Ur4ZGows.js @@ -1 +1 @@ -import{bx as u,by as l,bz as c}from"./index-C75huGqt.js";const 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c=e=>({user_id:e.user_id,email:e.email}),d=u(l(p((e,i)=>({user:null,token:null,isAuthenticated:!1,tokenExpiration:null,hasHydrated:!1,onRehydrated:()=>{const{token:t,tokenExpiration:o,logout:n}=i(),a=Date.now(),s=(o??0)-a;!t||s<=0?n():e(r=>{r.isAuthenticated=!0}),e(r=>{r.hasHydrated=!0})},login:(t,o)=>e(n=>{n.user=t,n.token=o,n.tokenExpiration=Date.now()+o.expires_in*1e3,n.isAuthenticated=!0}),logout:()=>e(t=>{t.user=null,t.token=null,t.isAuthenticated=!1})})),{name:"ragbits-auth",partialize:e=>({token:e.token,tokenExpiration:e.tokenExpiration,user:e.user?c(e.user):null}),merge:(e,i)=>({...i,...e,isAuthenticated:!1}),onRehydrateStorage:e=>()=>{e.onRehydrated()}}));export{d as a}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-DUS7ku-0.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-DUS7ku-0.js deleted file mode 100644 index 9841cc695..000000000 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-DUS7ku-0.js +++ /dev/null @@ -1 +0,0 @@ -import{O as h,bo as p,A as x,r as i,j as M,J as H}from"./index-C75huGqt.js";var s=h((r,o)=>{const{as:t,children:d,className:l,...n}=r,{slots:c,classNames:a,headerId:m,setHeaderMounted:e}=p(),f=x(o),u=t||"header";return i.useEffect(()=>(e(!0),()=>e(!1)),[e]),M.jsx(u,{ref:f,className:c.header({class:H(a?.header,l)}),id:m,...n,children:d})});s.displayName="HeroUI.ModalHeader";var R=s;export{R as m}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-RYoue1Xd.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-RYoue1Xd.js new file mode 100644 index 000000000..531e980f1 --- /dev/null +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-RYoue1Xd.js @@ -0,0 +1 @@ +import{O as h,bf as p,A as x,r as i,j as M,J as H}from"./index-BTypXP0X.js";var s=h((r,o)=>{const{as:t,children:d,className:l,...n}=r,{slots:c,classNames:a,headerId:f,setHeaderMounted:e}=p(),m=x(o),u=t||"header";return i.useEffect(()=>(e(!0),()=>e(!1)),[e]),M.jsx(u,{ref:m,className:c.header({class:H(a?.header,l)}),id:f,...n,children:d})});s.displayName="HeroUI.ModalHeader";var R=s;export{R as m}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-DnKzYyYP.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-CpT2ku66.js similarity index 62% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-DnKzYyYP.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-CpT2ku66.js index 733f69aa8..0b4e42a0b 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-DnKzYyYP.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-CpT2ku66.js @@ -1 +1 @@ -import{O as y,br as z,j as e,r as t,az as 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m4{scrapeMotionValuesFromProps(t,n,r){return{}}constructor({parent:t,props:n,presenceContext:r,reducedMotionConfig:i,blockInitialAnimation:s,visualState:l},c={}){this.current=null,this.children=new Set,this.isVariantNode=!1,this.isControllingVariants=!1,this.shouldReduceMotion=null,this.values=new Map,this.KeyframeResolver=$x,this.features={},this.valueSubscriptions=new Map,this.prevMotionValues={},this.events={},this.propEventSubscriptions={},this.notifyUpdate=()=>this.notify("Update",this.latestValues),this.render=()=>{this.current&&(this.triggerBuild(),this.renderInstance(this.current,this.renderState,this.props.style,this.projection))},this.renderScheduledAt=0,this.scheduleRender=()=>{const 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this.latestValues[t]}setStaticValue(t,n){this.latestValues[t]=n}update(t,n){(t.transformTemplate||this.props.transformTemplate)&&this.scheduleRender(),this.prevProps=this.props,this.props=t,this.prevPresenceContext=this.presenceContext,this.presenceContext=n;for(let r=0;rn.variantChildren.delete(t)}addValue(t,n){const r=this.values.get(t);n!==r&&(r&&this.removeValue(t),this.bindToMotionValue(t,n),this.values.set(t,n),this.latestValues[t]=n.get())}removeValue(t){this.values.delete(t);const n=this.valueSubscriptions.get(t);n&&(n(),this.valueSubscriptions.delete(t)),delete this.latestValues[t],this.removeValueFromRenderState(t,this.renderState)}hasValue(t){return this.values.has(t)}getValue(t,n){if(this.props.values&&this.props.values[t])return this.props.values[t];let r=this.values.get(t);return r===void 0&&n!==void 0&&(r=_f(n===null?void 0:n,{owner:this}),this.addValue(t,r)),r}readValue(t,n){let r=this.latestValues[t]!==void 0||!this.current?this.latestValues[t]:this.getBaseTargetFromProps(this.props,t)??this.readValueFromInstance(this.current,t,this.options);return r!=null&&(typeof r=="string"&&(OE(r)||jE(r))?r=parseFloat(r):!w3(r)&&ta.test(n)&&(r=wP(t,n)),this.setBaseTarget(t,wr(r)?r.get():r)),wr(r)?r.get():r}setBaseTarget(t,n){this.baseTarget[t]=n}getBaseTarget(t){const{initial:n}=this.props;let r;if(typeof n=="string"||typeof n=="object"){const s=Bx(this.props,n,this.presenceContext?.custom);s&&(r=s[t])}if(n&&r!==void 0)return r;const i=this.getBaseTargetFromProps(this.props,t);return i!==void 0&&!wr(i)?i:this.initialValues[t]!==void 0&&r===void 0?void 0:this.baseTarget[t]}on(t,n){return this.events[t]||(this.events[t]=new yx),this.events[t].add(n)}notify(t,...n){this.events[t]&&this.events[t].notify(...n)}scheduleRenderMicrotask(){Lx.render(this.render)}}class XP extends m4{constructor(){super(...arguments),this.KeyframeResolver=u3}sortInstanceNodePosition(t,n){return t.compareDocumentPosition(n)&2?1:-1}getBaseTargetFromProps(t,n){return t.style?t.style[n]:void 0}removeValueFromRenderState(t,{vars:n,style:r}){delete n[t],delete r[t]}handleChildMotionValue(){this.childSubscription&&(this.childSubscription(),delete this.childSubscription);const{children:t}=this.props;wr(t)&&(this.childSubscription=t.on("change",n=>{this.current&&(this.current.textContent=`${n}`)}))}}function QP(e,{style:t,vars:n},r,i){const s=e.style;let l;for(l in t)s[l]=t[l];i?.applyProjectionStyles(s,r);for(l in n)s.setProperty(l,n[l])}function g4(e){return window.getComputedStyle(e)}class v4 extends XP{constructor(){super(...arguments),this.type="html",this.renderInstance=QP}readValueFromInstance(t,n){if(Au.has(n))return this.projection?.isProjecting?eb(n):_D(t,n);{const r=g4(t),i=(wx(n)?r.getPropertyValue(n):r[n])||0;return typeof i=="string"?i.trim():i}}measureInstanceViewportBox(t,{transformPagePoint:n}){return qP(t,n)}build(t,n,r){Ox(t,n,r.transformTemplate)}scrapeMotionValuesFromProps(t,n,r){return Vx(t,n,r)}}const JP=new Set(["baseFrequency","diffuseConstant","kernelMatrix","kernelUnitLength","keySplines","keyTimes","limitingConeAngle","markerHeight","markerWidth","numOctaves","targetX","targetY","surfaceScale","specularConstant","specularExponent","stdDeviation","tableValues","viewBox","gradientTransform","pathLength","startOffset","textLength","lengthAdjust"]);function y4(e,t,n,r){QP(e,t,void 0,r);for(const i in t.attrs)e.setAttribute(JP.has(i)?i:Ux(i),t.attrs[i])}class b4 extends XP{constructor(){super(...arguments),this.type="svg",this.isSVGTag=!1,this.measureInstanceViewportBox=Cn}getBaseTargetFromProps(t,n){return t[n]}readValueFromInstance(t,n){if(Au.has(n)){const r=xP(n);return r&&r.default||0}return n=JP.has(n)?n:Ux(n),t.getAttribute(n)}scrapeMotionValuesFromProps(t,n,r){return jP(t,n,r)}build(t,n,r){NP(t,n,this.isSVGTag,r.transformTemplate,r.style)}renderInstance(t,n,r,i){y4(t,n,r,i)}mount(t){this.isSVGTag=OP(t.tagName),super.mount(t)}}const x4=(e,t)=>jx(e)?new b4(t):new v4(t,{allowProjection:e!==S.Fragment});function rh(e,t,n){const r=e.getProps();return Bx(r,t,n!==void 0?n:r.custom,e)}const cb=e=>Array.isArray(e);function w4(e,t,n){e.hasValue(t)?e.getValue(t).set(n):e.addValue(t,_f(n))}function S4(e){return cb(e)?e[e.length-1]||0:e}function k4(e,t){const n=rh(e,t);let{transitionEnd:r={},transition:i={},...s}=n||{};s={...s,...r};for(const l in s){const c=S4(s[l]);w4(e,l,c)}}function C4(e){return!!(wr(e)&&e.add)}function fb(e,t){const n=e.getValue("willChange");if(C4(n))return n.add(t);if(!n&&ds.WillChange){const r=new ds.WillChange("auto");e.addValue("willChange",r),r.add(t)}}function ZP(e){return e.props[BP]}const E4=e=>e!==null;function P4(e,{repeat:t,repeatType:n="loop"},r){const i=e.filter(E4),s=t&&n!=="loop"&&t%2===1?0:i.length-1;return i[s]}const 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g=!1;if((m.type===!1||m.duration===0&&!m.repeatDelay)&&(m.duration=0,m.delay===0&&(g=!0)),(ds.instantAnimations||ds.skipAnimations)&&(g=!0,m.duration=0,m.delay=0),m.allowFlatten=!c.type&&!c.ease,g&&!s&&t.get()!==void 0){const b=P4(m.keyframes,c);if(b!==void 0){nn.update(()=>{m.onUpdate(b),m.onComplete()});return}}return c.isSync?new Ix(m):new QD(m)};function L4({protectedKeys:e,needsAnimating:t},n){const r=e.hasOwnProperty(n)&&t[n]!==!0;return t[n]=!1,r}function eT(e,t,{delay:n=0,transitionOverride:r,type:i}={}){let{transition:s=e.getDefaultTransition(),transitionEnd:l,...c}=t;r&&(s=r);const d=[],h=i&&e.animationState&&e.animationState.getState()[i];for(const m in c){const g=e.getValue(m,e.latestValues[m]??null),b=c[m];if(b===void 0||h&&L4(h,m))continue;const x={delay:n,...Ax(s||{},m)},k=g.get();if(k!==void 0&&!g.isAnimating&&!Array.isArray(b)&&b===k&&!x.velocity)continue;let P=!1;if(window.MotionHandoffAnimation){const T=ZP(e);if(T){const 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cN{constructor(t){this.openDragLock=null,this.isDragging=!1,this.currentDirection=null,this.originPoint={x:0,y:0},this.constraints=!1,this.hasMutatedConstraints=!1,this.elastic=Cn(),this.latestPointerEvent=null,this.latestPanInfo=null,this.visualElement=t}start(t,{snapToCursor:n=!1,distanceThreshold:r}={}){const{presenceContext:i}=this.visualElement;if(i&&i.isPresent===!1)return;const s=g=>{const{dragSnapToOrigin:b}=this.getProps();b?this.pauseAnimation():this.stopAnimation(),n&&this.snapToCursor(xh(g).point)},l=(g,b)=>{const{drag:x,dragPropagation:k,onDragStart:P}=this.getProps();if(x&&!k&&(this.openDragLock&&this.openDragLock(),this.openDragLock=p3(x),!this.openDragLock))return;this.latestPointerEvent=g,this.latestPanInfo=b,this.isDragging=!0,this.currentDirection=null,this.resolveConstraints(),this.visualElement.projection&&(this.visualElement.projection.isAnimationBlocked=!0,this.visualElement.projection.target=void 0),Yi(T=>{let $=this.getAxisMotionValue(T).get()||0;if(fs.test($)){const{projection:L}=this.visualElement;if(L&&L.layout){const 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sT(t,{onSessionStart:s,onStart:l,onMove:c,onSessionEnd:d,resumeAnimation:h},{transformPagePoint:this.visualElement.getTransformPagePoint(),dragSnapToOrigin:m,distanceThreshold:r,contextWindow:oT(this.visualElement)})}stop(t,n){const r=t||this.latestPointerEvent,i=n||this.latestPanInfo,s=this.isDragging;if(this.cancel(),!s||!i||!r)return;const{velocity:l}=i;this.startAnimation(l);const{onDragEnd:c}=this.getProps();c&&nn.postRender(()=>c(r,i))}cancel(){this.isDragging=!1;const{projection:t,animationState:n}=this.visualElement;t&&(t.isAnimationBlocked=!1),this.panSession&&this.panSession.end(),this.panSession=void 0;const{dragPropagation:r}=this.getProps();!r&&this.openDragLock&&(this.openDragLock(),this.openDragLock=null),n&&n.setActive("whileDrag",!1)}updateAxis(t,n,r){const{drag:i}=this.getProps();if(!r||!pg(t,i,this.currentDirection))return;const s=this.getAxisMotionValue(t);let l=this.originPoint[t]+r[t];this.constraints&&this.constraints[t]&&(l=rN(l,this.constraints[t],this.elastic[t])),s.set(l)}resolveConstraints(){const{dragConstraints:t,dragElastic:n}=this.getProps(),r=this.visualElement.projection&&!this.visualElement.projection.layout?this.visualElement.projection.measure(!1):this.visualElement.projection?.layout,i=this.constraints;t&&hf(t)?this.constraints||(this.constraints=this.resolveRefConstraints()):t&&r?this.constraints=iN(r.layoutBox,t):this.constraints=!1,this.elastic=aN(n),i!==this.constraints&&r&&this.constraints&&!this.hasMutatedConstraints&&Yi(s=>{this.constraints!==!1&&this.getAxisMotionValue(s)&&(this.constraints[s]=lN(r.layoutBox[s],this.constraints[s]))})}resolveRefConstraints(){const{dragConstraints:t,onMeasureDragConstraints:n}=this.getProps();if(!t||!hf(t))return!1;const r=t.current,{projection:i}=this.visualElement;if(!i||!i.layout)return!1;const s=f4(r,i.root,this.visualElement.getTransformPagePoint());let l=oN(i.layout.layoutBox,s);if(n){const c=n(a4(l));this.hasMutatedConstraints=!!c,c&&(l=WP(c))}return l}startAnimation(t){const{drag:n,dragMomentum:r,dragElastic:i,dragTransition:s,dragSnapToOrigin:l,onDragTransitionEnd:c}=this.getProps(),d=this.constraints||{},h=Yi(m=>{if(!pg(m,n,this.currentDirection))return;let g=d&&d[m]||{};l&&(g={min:0,max:0});const b=i?200:1e6,x=i?40:1e7,k={type:"inertia",velocity:r?t[m]:0,bounceStiffness:b,bounceDamping:x,timeConstant:750,restDelta:1,restSpeed:10,...s,...g};return this.startAxisValueAnimation(m,k)});return Promise.all(h).then(c)}startAxisValueAnimation(t,n){const r=this.getAxisMotionValue(t);return fb(this.visualElement,t),r.start(Kx(t,r,0,n,this.visualElement,!1))}stopAnimation(){Yi(t=>this.getAxisMotionValue(t).stop())}pauseAnimation(){Yi(t=>this.getAxisMotionValue(t).animation?.pause())}getAnimationState(t){return this.getAxisMotionValue(t).animation?.state}getAxisMotionValue(t){const n=`_drag${t.toUpperCase()}`,r=this.visualElement.getProps(),i=r[n];return i||this.visualElement.getValue(t,(r.initial?r.initial[t]:void 0)||0)}snapToCursor(t){Yi(n=>{const{drag:r}=this.getProps();if(!pg(n,r,this.currentDirection))return;const{projection:i}=this.visualElement,s=this.getAxisMotionValue(n);if(i&&i.layout){const{min:l,max:c}=i.layout.layoutBox[n];s.set(t[n]-dn(l,c,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!hf(n)||!r||!this.constraints)return;this.stopAnimation();const i={x:0,y:0};Yi(l=>{const c=this.getAxisMotionValue(l);if(c&&this.constraints!==!1){const d=c.get();i[l]=sN({min:d,max:d},this.constraints[l])}});const{transformTemplate:s}=this.visualElement.getProps();this.visualElement.current.style.transform=s?s({},""):"none",r.root&&r.root.updateScroll(),r.updateLayout(),this.resolveConstraints(),Yi(l=>{if(!pg(l,t,null))return;const c=this.getAxisMotionValue(l),{min:d,max:h}=this.constraints[l];c.set(dn(d,h,i[l]))})}addListeners(){if(!this.visualElement.current)return;uN.set(this.visualElement,this);const t=this.visualElement.current,n=Op(t,"pointerdown",d=>{const{drag:h,dragListener:m=!0}=this.getProps();h&&m&&this.start(d)}),r=()=>{const{dragConstraints:d}=this.getProps();hf(d)&&d.current&&(this.constraints=this.resolveRefConstraints())},{projection:i}=this.visualElement,s=i.addEventListener("measure",r);i&&!i.layout&&(i.root&&i.root.updateScroll(),i.updateLayout()),nn.read(r);const l=ih(window,"resize",()=>this.scalePositionWithinConstraints()),c=i.addEventListener("didUpdate",({delta:d,hasLayoutChanged:h})=>{this.isDragging&&h&&(Yi(m=>{const g=this.getAxisMotionValue(m);g&&(this.originPoint[m]+=d[m].translate,g.set(g.get()+d[m].translate))}),this.visualElement.render())});return()=>{l(),n(),s(),c&&c()}}getProps(){const t=this.visualElement.getProps(),{drag:n=!1,dragDirectionLock:r=!1,dragPropagation:i=!1,dragConstraints:s=!1,dragElastic:l=pb,dragMomentum:c=!0}=t;return{...t,drag:n,dragDirectionLock:r,dragPropagation:i,dragConstraints:s,dragElastic:l,dragMomentum:c}}}function pg(e,t,n){return(t===!0||t===e)&&(n===null||n===e)}function fN(e,t=10){let n=null;return Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x"),n}class dN extends na{constructor(t){super(t),this.removeGroupControls=eo,this.removeListeners=eo,this.controls=new cN(t)}mount(){const{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||eo}unmount(){this.removeGroupControls(),this.removeListeners()}}const pS=e=>(t,n)=>{e&&nn.postRender(()=>e(t,n))};class pN extends na{constructor(){super(...arguments),this.removePointerDownListener=eo}onPointerDown(t){this.session=new sT(t,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint(),contextWindow:oT(this.node)})}createPanHandlers(){const{onPanSessionStart:t,onPanStart:n,onPan:r,onPanEnd:i}=this.node.getProps();return{onSessionStart:pS(t),onStart:pS(n),onMove:r,onEnd:(s,l)=>{delete this.session,i&&nn.postRender(()=>i(s,l))}}}mount(){this.removePointerDownListener=Op(this.node.current,"pointerdown",t=>this.onPointerDown(t))}update(){this.session&&this.session.updateHandlers(this.createPanHandlers())}unmount(){this.removePointerDownListener(),this.session&&this.session.end()}}const Rg={hasAnimatedSinceResize:!0,hasEverUpdated:!1};function hS(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const gp={correct:(e,t)=>{if(!t.target)return e;if(typeof e=="string")if(tt.test(e))e=parseFloat(e);else return e;const n=hS(e,t.target.x),r=hS(e,t.target.y);return`${n}% ${r}%`}},hN={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,i=ta.parse(e);if(i.length>5)return r;const s=ta.createTransformer(e),l=typeof i[0]!="number"?1:0,c=n.x.scale*t.x,d=n.y.scale*t.y;i[0+l]/=c,i[1+l]/=d;const h=dn(c,d,.5);return typeof i[2+l]=="number"&&(i[2+l]/=h),typeof i[3+l]=="number"&&(i[3+l]/=h),s(i)}};let mS=!1;class mN extends S.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:s}=t;F3(gN),s&&(n.group&&n.group.add(s),r&&r.register&&i&&r.register(s),mS&&s.root.didUpdate(),s.addEventListener("animationComplete",()=>{this.safeToRemove()}),s.setOptions({...s.options,onExitComplete:()=>this.safeToRemove()})),Rg.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:s}=this.props,{projection:l}=r;return l&&(l.isPresent=s,mS=!0,i||t.layoutDependency!==n||n===void 0||t.isPresent!==s?l.willUpdate():this.safeToRemove(),t.isPresent!==s&&(s?l.promote():l.relegate()||nn.postRender(()=>{const c=l.getStack();(!c||!c.members.length)&&this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),Lx.postRender(()=>{!t.currentAnimation&&t.isLead()&&this.safeToRemove()}))}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n&&n.group&&n.group.remove(i),r&&r.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t&&t()}render(){return null}}function aT(e){const[t,n]=IP(),r=S.useContext(Xp);return D.jsx(mN,{...e,layoutGroup:r,switchLayoutGroup:S.useContext(VP),isPresent:t,safeToRemove:n})}const gN={borderRadius:{...gp,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:gp,borderTopRightRadius:gp,borderBottomLeftRadius:gp,borderBottomRightRadius:gp,boxShadow:hN};function vN(e,t,n){const r=wr(e)?e:_f(e);return r.start(Kx("",r,t,n)),r.animation}const yN=(e,t)=>e.depth-t.depth;class bN{constructor(){this.children=[],this.isDirty=!1}add(t){hx(this.children,t),this.isDirty=!0}remove(t){mx(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(yN),this.isDirty=!1,this.children.forEach(t)}}function xN(e,t){const n=si.now(),r=({timestamp:i})=>{const s=i-n;s>=t&&(ea(r),e(s-t))};return nn.setup(r,!0),()=>ea(r)}const uT=["TopLeft","TopRight","BottomLeft","BottomRight"],wN=uT.length,gS=e=>typeof e=="string"?parseFloat(e):e,vS=e=>typeof e=="number"||tt.test(e);function SN(e,t,n,r,i,s){i?(e.opacity=dn(0,n.opacity??1,kN(r)),e.opacityExit=dn(t.opacity??1,0,CN(r))):s&&(e.opacity=dn(t.opacity??1,n.opacity??1,r));for(let l=0;lrt?1:n(Qp(e,t,r))}function bS(e,t){e.min=t.min,e.max=t.max}function Gi(e,t){bS(e.x,t.x),bS(e.y,t.y)}function xS(e,t){e.translate=t.translate,e.scale=t.scale,e.originPoint=t.originPoint,e.origin=t.origin}function wS(e,t,n,r,i){return e-=t,e=Gg(e,1/n,r),i!==void 0&&(e=Gg(e,1/i,r)),e}function EN(e,t=0,n=1,r=.5,i,s=e,l=e){if(fs.test(t)&&(t=parseFloat(t),t=dn(l.min,l.max,t/100)-l.min),typeof t!="number")return;let c=dn(s.min,s.max,r);e===s&&(c-=t),e.min=wS(e.min,t,n,c,i),e.max=wS(e.max,t,n,c,i)}function SS(e,t,[n,r,i],s,l){EN(e,t[n],t[r],t[i],t.scale,s,l)}const PN=["x","scaleX","originX"],TN=["y","scaleY","originY"];function kS(e,t,n,r){SS(e.x,t,PN,n?n.x:void 0,r?r.x:void 0),SS(e.y,t,TN,n?n.y:void 0,r?r.y:void 0)}function CS(e){return e.translate===0&&e.scale===1}function fT(e){return CS(e.x)&&CS(e.y)}function ES(e,t){return e.min===t.min&&e.max===t.max}function _N(e,t){return ES(e.x,t.x)&&ES(e.y,t.y)}function PS(e,t){return Math.round(e.min)===Math.round(t.min)&&Math.round(e.max)===Math.round(t.max)}function dT(e,t){return PS(e.x,t.x)&&PS(e.y,t.y)}function TS(e){return Fr(e.x)/Fr(e.y)}function _S(e,t){return e.translate===t.translate&&e.scale===t.scale&&e.originPoint===t.originPoint}class IN{constructor(){this.members=[]}add(t){hx(this.members,t),t.scheduleRender()}remove(t){if(mx(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const s=this.members[i];if(s.isPresent!==!1){r=s;break}}return r?(this.promote(r),!0):!1}promote(t,n){const r=this.lead;if(t!==r&&(this.prevLead=r,this.lead=t,t.show(),r)){r.instance&&r.scheduleRender(),t.scheduleRender(),t.resumeFrom=r,n&&(t.resumeFrom.preserveOpacity=!0),r.snapshot&&(t.snapshot=r.snapshot,t.snapshot.latestValues=r.animationValues||r.latestValues),t.root&&t.root.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:i}=t.options;i===!1&&r.hide()}}exitAnimationComplete(){this.members.forEach(t=>{const{options:n,resumingFrom:r}=t;n.onExitComplete&&n.onExitComplete(),r&&r.options.onExitComplete&&r.options.onExitComplete()})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function $N(e,t,n){let r="";const i=e.x.translate/t.x,s=e.y.translate/t.y,l=n?.z||0;if((i||s||l)&&(r=`translate3d(${i}px, ${s}px, ${l}px) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{transformPerspective:h,rotate:m,rotateX:g,rotateY:b,skewX:x,skewY:k}=n;h&&(r=`perspective(${h}px) ${r}`),m&&(r+=`rotate(${m}deg) `),g&&(r+=`rotateX(${g}deg) `),b&&(r+=`rotateY(${b}deg) `),x&&(r+=`skewX(${x}deg) `),k&&(r+=`skewY(${k}deg) `)}const c=e.x.scale*t.x,d=e.y.scale*t.y;return(c!==1||d!==1)&&(r+=`scale(${c}, ${d})`),r||"none"}const i0=["","X","Y","Z"],AN=1e3;let RN=0;function o0(e,t,n,r){const{latestValues:i}=t;i[e]&&(n[e]=i[e],t.setStaticValue(e,0),r&&(r[e]=0))}function pT(e){if(e.hasCheckedOptimisedAppear=!0,e.root===e)return;const{visualElement:t}=e.options;if(!t)return;const n=ZP(t);if(window.MotionHasOptimisedAnimation(n,"transform")){const{layout:i,layoutId:s}=e.options;window.MotionCancelOptimisedAnimation(n,"transform",nn,!(i||s))}const{parent:r}=e;r&&!r.hasCheckedOptimisedAppear&&pT(r)}function hT({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(l={},c=t?.()){this.id=RN++,this.animationId=0,this.animationCommitId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.isSharedProjectionDirty=!1,this.isTransformDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.hasCheckedOptimisedAppear=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.hasTreeAnimated=!1,this.updateScheduled=!1,this.scheduleUpdate=()=>this.update(),this.projectionUpdateScheduled=!1,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{this.projectionUpdateScheduled=!1,this.nodes.forEach(DN),this.nodes.forEach(zN),this.nodes.forEach(jN),this.nodes.forEach(NN)},this.resolvedRelativeTargetAt=0,this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.latestValues=l,this.root=c?c.root||c:this,this.path=c?[...c.path,c]:[],this.parent=c,this.depth=c?c.depth+1:0;for(let d=0;dthis.root.updateBlockedByResize=!1;nn.read(()=>{g=window.innerWidth}),e(l,()=>{const x=window.innerWidth;x!==g&&(g=x,this.root.updateBlockedByResize=!0,m&&m(),m=xN(b,250),Rg.hasAnimatedSinceResize&&(Rg.hasAnimatedSinceResize=!1,this.nodes.forEach(AS)))})}c&&this.root.registerSharedNode(c,this),this.options.animate!==!1&&h&&(c||d)&&this.addEventListener("didUpdate",({delta:m,hasLayoutChanged:g,hasRelativeLayoutChanged:b,layout:x})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const k=this.options.transition||h.getDefaultTransition()||WN,{onLayoutAnimationStart:P,onLayoutAnimationComplete:_}=h.getProps(),T=!this.targetLayout||!dT(this.targetLayout,x),$=!g&&b;if(this.options.layoutRoot||this.resumeFrom||$||g&&(T||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0);const L={...Ax(k,"layout"),onPlay:P,onComplete:_};(h.shouldReduceMotion||this.options.layoutRoot)&&(L.delay=0,L.type=!1),this.startAnimation(L),this.setAnimationOrigin(m,$)}else g||AS(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=x})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const l=this.getStack();l&&l.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,this.eventHandlers.clear(),ea(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(BN),this.animationId++)}getTransformTemplate(){const{visualElement:l}=this.options;return l&&l.getProps().transformTemplate}willUpdate(l=!0){if(this.root.hasTreeAnimated=!0,this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(window.MotionCancelOptimisedAnimation&&!this.hasCheckedOptimisedAppear&&pT(this),!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let m=0;m{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure(),this.snapshot&&!Fr(this.snapshot.measuredBox.x)&&!Fr(this.snapshot.measuredBox.y)&&(this.snapshot=void 0))}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let d=0;d{const K=z/1e3;RS(g.x,l.x,K),RS(g.y,l.y,K),this.setTargetDelta(g),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(jp(b,this.layout.layoutBox,this.relativeParent.layout.layoutBox),UN(this.relativeTarget,this.relativeTargetOrigin,b,K),L&&_N(this.relativeTarget,L)&&(this.isProjectionDirty=!1),L||(L=Cn()),Gi(L,this.relativeTarget)),P&&(this.animationValues=m,SN(m,h,this.latestValues,K,$,T)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=K},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(l){this.notifyListeners("animationStart"),this.currentAnimation?.stop(),this.resumingFrom?.currentAnimation?.stop(),this.pendingAnimation&&(ea(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=nn.update(()=>{Rg.hasAnimatedSinceResize=!0,this.motionValue||(this.motionValue=_f(0)),this.currentAnimation=vN(this.motionValue,[0,1e3],{...l,velocity:0,isSync:!0,onUpdate:c=>{this.mixTargetDelta(c),l.onUpdate&&l.onUpdate(c)},onStop:()=>{},onComplete:()=>{l.onComplete&&l.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const l=this.getStack();l&&l.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(AN),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const l=this.getLead();let{targetWithTransforms:c,target:d,layout:h,latestValues:m}=l;if(!(!c||!d||!h)){if(this!==l&&this.layout&&h&&mT(this.options.animationType,this.layout.layoutBox,h.layoutBox)){d=this.target||Cn();const g=Fr(this.layout.layoutBox.x);d.x.min=l.target.x.min,d.x.max=d.x.min+g;const b=Fr(this.layout.layoutBox.y);d.y.min=l.target.y.min,d.y.max=d.y.min+b}Gi(c,d),gf(c,m),zp(this.projectionDeltaWithTransform,this.layoutCorrected,c,m)}}registerSharedNode(l,c){this.sharedNodes.has(l)||this.sharedNodes.set(l,new IN),this.sharedNodes.get(l).add(c);const h=c.options.initialPromotionConfig;c.promote({transition:h?h.transition:void 0,preserveFollowOpacity:h&&h.shouldPreserveFollowOpacity?h.shouldPreserveFollowOpacity(c):void 0})}isLead(){const l=this.getStack();return l?l.lead===this:!0}getLead(){const{layoutId:l}=this.options;return l?this.getStack()?.lead||this:this}getPrevLead(){const{layoutId:l}=this.options;return l?this.getStack()?.prevLead:void 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- ---- - -### 3) Apply TodoOrchestrator - -Plug in a concrete todo manager for persistence, progress tracking, and retries. - -```python -# mock-up code!!! -from ragbits.agents.tools.todo import TodoOrchestrator - -todo_orchestrator = TodoOrchestrator(domain_context="") - - -# Similar flow as before -``` - -UI/Console mock-up: - - - ---- - -### 4) Re-run evaluation (expected improvement) - -Re-run the same suites with orchestration. - -```bash -uv run python examples/evaluate/agent-benchmarking/run_gaia.py --agent todo_orchestrated # or sth like this -uv run python examples/evaluate/agent-benchmarking/run_hotpot.py --agent todo_orchestrated # might be full code snippets -uv run python examples/evaluate/agent-benchmarking/run_humaneval.py --agent todo_orchestrated -``` - -Mock results snapshot (after orchestration): - -
- - - -Key expected gains (mock): -- **GAIA**: +XXX% task success via [argument here]. -- **HotpotQA**: +XXX% F1 through [argument here]. -- **HumanEval**: +XXX% pass@1K with [some argument here]. - ---- - -### 5) MOCK-UP Operational tips - -- **Auditability**: export run logs and todo timelines for regression analysis. -- **Backoff**: configure retries for unreliable tools. -- **Safety**: restrict tool scopes and validate step outputs. - ---- - -### 6) References & examples - -- Tooling: `packages/ragbits-agents/src/ragbits/agents/tools/todo.py` -- Complete example: `examples/agents/todo_tools_example.py` -- Evaluations: - - GAIA: `examples/evaluate/agent-benchmarking/run_gaia.py` - - HotpotQA: `examples/evaluate/agent-benchmarking/run_hotpot.py` - - HumanEval: `examples/evaluate/agent-benchmarking/run_humaneval.py` - -Related tutorials: -- Agents tutorial: `https://ragbits.deepsense.ai/stable/tutorials/agents/` -- RAG tutorial: `https://ragbits.deepsense.ai/stable/tutorials/rag/` -- Chat tutorial: `https://ragbits.deepsense.ai/stable/tutorials/chat/` - ---- - -### Appendix (copy-paste mocks) - -```bash - -# run the agent example (mock) -uv run python examples/agents/todo_tools_example.py -``` - - execution -> audit export or sth goes here> From 2b9b8fbfbc602963f0b4687a39fdbe65743553c0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Przemys=C5=82aw=20Kaleta?= Date: Mon, 13 Oct 2025 12:22:59 +0200 Subject: [PATCH 40/43] docs: installation & source fixes (#844) --- docs/tutorials/intro.md | 2 +- packages/ragbits-core/README.md | 2 ++ packages/ragbits-document-search/README.md | 8 +++++--- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/intro.md b/docs/tutorials/intro.md index 31fb830dc..49cad6e14 100644 --- a/docs/tutorials/intro.md +++ b/docs/tutorials/intro.md @@ -2,7 +2,7 @@ Let's walk through a quick example of **basic question answering**. Specifically, let's build **a system for answering tech questions**, e.g. about Linux or iPhone apps. -Install the latest Ragbits via `pip install -U ragbits` and follow along. +Install the latest Ragbits via `pip install -U ragbits ragbits-agents` and follow along. ## Configuring the environment diff --git a/packages/ragbits-core/README.md b/packages/ragbits-core/README.md index 807dea7e7..dc467e413 100644 --- a/packages/ragbits-core/README.md +++ b/packages/ragbits-core/README.md @@ -11,6 +11,8 @@ pip install ragbits-core ## Quick Start ```python +import asyncio + from pydantic import BaseModel from ragbits.core.prompt import Prompt from ragbits.core.llms.litellm import LiteLLM diff --git a/packages/ragbits-document-search/README.md b/packages/ragbits-document-search/README.md index 70c1f7b47..a4f6ca2bb 100644 --- a/packages/ragbits-document-search/README.md +++ b/packages/ragbits-document-search/README.md @@ -12,7 +12,9 @@ pip install ragbits-document-search ## Quickstart ```python -from ragbits.core.embeddings.litellm import LiteLLMEmbedder +import asyncio + +from ragbits.core.embeddings import LiteLLMEmbedder from ragbits.core.vector_stores.in_memory import InMemoryVectorStore from ragbits.document_search import DocumentSearch @@ -21,7 +23,7 @@ async def main() -> None: Run the example. """ embedder = LiteLLMEmbedder( - model="text-embedding-3-small", + model_name="text-embedding-3-small", ) vector_store = InMemoryVectorStore(embedder=embedder) document_search = DocumentSearch( @@ -29,7 +31,7 @@ async def main() -> None: ) # Ingest all .txt files from the "biographies" directory - await document_search.ingest("file://biographies/*.txt") + await document_search.ingest("local://biographies/*.txt") # Search the documents for the query results = await document_search.search("When was Marie Curie-Sklodowska born?") From 9650a70c35d95e84345ec9bc12c7d6be8373de46 Mon Sep 17 00:00:00 2001 From: dazy-ds <135028324+dazy-ds@users.noreply.github.com> Date: Mon, 13 Oct 2025 14:20:59 +0200 Subject: [PATCH 41/43] feat: conversation summary (#840) --- examples/chat/chat.py | 2 + packages/ragbits-chat/CHANGELOG.md | 1 + .../src/ragbits/chat/interface/_interface.py | 16 ++ .../src/ragbits/chat/interface/summary.py | 82 +++++++++ .../src/ragbits/chat/interface/types.py | 7 + .../generate_typescript_from_json_schema.py | 1 + .../@ragbits/api-client/src/autogen.types.ts | 20 ++- .../ChatHistoryPlugin/ChatHistory.test.tsx | 122 ++++++++++++- .../ChatOptionsForm.test.tsx | 18 +- .../eventHandlers/eventHandlerRegistry.ts | 4 + .../eventHandlers/nonMessageHandlers.ts | 7 + .../core/stores/HistoryStore/historyStore.ts | 7 +- .../components/ChatHistory.tsx | 164 ++++++++++++++---- .../components/ChatOptionsForm.tsx | 5 +- typescript/ui/src/types/history.ts | 7 +- 15 files changed, 412 insertions(+), 51 deletions(-) create mode 100644 packages/ragbits-chat/src/ragbits/chat/interface/summary.py diff --git a/examples/chat/chat.py b/examples/chat/chat.py index 566de498c..5e81f72bf 100644 --- a/examples/chat/chat.py +++ b/examples/chat/chat.py @@ -28,6 +28,7 @@ from ragbits.chat.interface import ChatInterface from ragbits.chat.interface.forms import FeedbackConfig, UserSettings +from ragbits.chat.interface.summary import HybridSummaryGenerator from ragbits.chat.interface.types import ChatContext, ChatResponse, LiveUpdateType from ragbits.chat.interface.ui_customization import HeaderCustomization, PageMetaCustomization, UICustomization from ragbits.core.llms import LiteLLM @@ -93,6 +94,7 @@ class MyChat(ChatInterface): def __init__(self) -> None: self.llm = LiteLLM(model_name="gpt-4o-mini") + self.summary_generator = HybridSummaryGenerator(self.llm) async def chat( self, diff --git a/packages/ragbits-chat/CHANGELOG.md b/packages/ragbits-chat/CHANGELOG.md index 1c43ff86b..a46555766 100644 --- a/packages/ragbits-chat/CHANGELOG.md +++ b/packages/ragbits-chat/CHANGELOG.md @@ -1,6 +1,7 @@ # CHANGELOG ## Unreleased +- Add automatic topic extraction to be used as conversation title with ability to edit in the client side (#840) - Add todo list component to the UI, add support for todo events in API (#827) ## 1.3.0 (2025-09-11) diff --git a/packages/ragbits-chat/src/ragbits/chat/interface/_interface.py b/packages/ragbits-chat/src/ragbits/chat/interface/_interface.py index 7a68af9e9..ad0153212 100644 --- a/packages/ragbits-chat/src/ragbits/chat/interface/_interface.py +++ b/packages/ragbits-chat/src/ragbits/chat/interface/_interface.py @@ -10,6 +10,7 @@ from typing import Any from ragbits.agents.tools.todo import Task +from ragbits.chat.interface.summary import SummaryGenerator from ragbits.chat.interface.ui_customization import UICustomization from ragbits.core.audit.metrics import record_metric from ragbits.core.audit.metrics.base import MetricType @@ -85,6 +86,13 @@ async def wrapper( interface_class=self.__class__.__name__, ) + # Generate summary to serve as title for new conversations + try: + summary = await self.generate_conversation_summary(message, history, context) + yield ChatResponse(type=ChatResponseType.CONVERSATION_SUMMARY, content=summary) + except Exception: + logger.exception("Failed to generate conversation title") + responses = [] main_response = "" extra_responses = [] @@ -185,6 +193,7 @@ class ChatInterface(ABC): show_usage: bool = False ui_customization: UICustomization | None = None history_persistence: HistoryPersistenceStrategy | None = None + summary_generator: SummaryGenerator | None = None def __init_subclass__(cls, **kwargs: dict) -> None: """Automatically apply the with_chat_metadata decorator to the chat method in subclasses.""" @@ -371,3 +380,10 @@ async def save_feedback( ) logger.info(f"[{self.__class__.__name__}] Saving {feedback} for message {message_id} with payload {payload}") + + async def generate_conversation_summary(self, message: str, history: ChatFormat, context: ChatContext) -> str: + """Delegate to the configured summary generator.""" + if not self.summary_generator: + raise Exception("Tried to invoke `generate_conversation_summary`. No SummaryGenerator found.") + + return await self.summary_generator.generate(message, history, context) diff --git a/packages/ragbits-chat/src/ragbits/chat/interface/summary.py b/packages/ragbits-chat/src/ragbits/chat/interface/summary.py new file mode 100644 index 000000000..c27e3d513 --- /dev/null +++ b/packages/ragbits-chat/src/ragbits/chat/interface/summary.py @@ -0,0 +1,82 @@ +import asyncio +import logging +from abc import ABC, abstractmethod + +from ragbits.chat.interface.types import ChatContext +from ragbits.core.llms.base import LLM +from ragbits.core.prompt.base import ChatFormat + +logger = logging.getLogger(__name__) + + +class SummaryGenerator(ABC): + """Base class for summary generators.""" + + @abstractmethod + async def generate(self, message: str, history: ChatFormat, context: ChatContext) -> str: + """Generate a concise conversation title.""" + ... + + +class HeuristicSummaryGenerator(SummaryGenerator): + """Simple title generator using heuristics (no LLM).""" + + SIMPLE_MESSAGE_LENGTH = 2 + + def __init__(self, max_words: int = 6, fallback_title: str = "New chat"): + self.max_words = max_words + self.fallback_title = fallback_title + + async def generate(self, message: str, _history: ChatFormat, _context: ChatContext) -> str: + """Generate a concise conversation title using heuristic approach.""" + t = (message or "").strip() + if not t or len(t.split()) <= self.SIMPLE_MESSAGE_LENGTH: + return self.fallback_title + return " ".join(t.split()[: self.max_words]) + + +class LLMSummaryGenerator(SummaryGenerator): + """Generates a short conversation title using an LLM.""" + + DEFAULT_PROMPT = ( + "You are a concise title generator for conversation threads. " + "Given the user's first message, return a single short title (3–8 words) summarizing the topic. " + "If the message is just a greeting or otherwise generic, respond exactly with: New chat\n\n" + "User message:\n{message}\n\nTitle:" + ) + DEFAULT_TIMEOUT = 5 + + def __init__(self, llm: LLM, timeout: int = DEFAULT_TIMEOUT, prompt_template: str | None = None): + self.llm = llm + self.timeout = timeout + self.prompt_template = prompt_template or self.DEFAULT_PROMPT + + async def generate(self, message: str, _history: ChatFormat, _context: ChatContext) -> str: + """Generate a concise conversation title using LLM.""" + prompt = self.prompt_template.format(message=message) + try: + raw = await asyncio.wait_for(self.llm.generate(prompt), self.timeout) + title = str(raw).strip().splitlines()[0] + return title or "New chat" + except asyncio.TimeoutError: + logger.warning("LLM title generation timed out — using fallback.") + return "New chat" + except Exception: + logger.exception("Error generating title with LLM — using fallback.") + return "New chat" + + +class HybridSummaryGenerator(SummaryGenerator): + """Generates a short conversation title using an LLM with heuristic approach as a fallback.""" + + def __init__(self, llm: LLM): + self.llm_gen = LLMSummaryGenerator(llm) + self.heuristic = HeuristicSummaryGenerator() + + async def generate(self, message: str, history: ChatFormat, context: ChatContext) -> str: + """Generate summary using either LLM with heuristic approach as a fallback.""" + try: + title = await self.llm_gen.generate(message, history, context) + return title or await self.heuristic.generate(message, history, context) + except Exception: + return await self.heuristic.generate(message, history, context) diff --git a/packages/ragbits-chat/src/ragbits/chat/interface/types.py b/packages/ragbits-chat/src/ragbits/chat/interface/types.py index 75bfd4d01..6c465d5af 100644 --- a/packages/ragbits-chat/src/ragbits/chat/interface/types.py +++ b/packages/ragbits-chat/src/ragbits/chat/interface/types.py @@ -117,6 +117,7 @@ class ChatResponseType(str, Enum): STATE_UPDATE = "state_update" MESSAGE_ID = "message_id" CONVERSATION_ID = "conversation_id" + CONVERSATION_SUMMARY = "conversation_summary" LIVE_UPDATE = "live_update" FOLLOWUP_MESSAGES = "followup_messages" IMAGE = "image" @@ -234,6 +235,12 @@ def as_task(self) -> Task | None: """ return cast(Task, self.content) if self.type == ChatResponseType.TODO_ITEM else None + def as_conversation_summary(self) -> str | None: + """ + Return the content as string if this is an conversation summary response, else None + """ + return cast(str, self.content) if self.type == ChatResponseType.CONVERSATION_SUMMARY else None + class ChatMessageRequest(BaseModel): """Client-side chat request interface.""" diff --git a/scripts/generate_typescript_from_json_schema.py b/scripts/generate_typescript_from_json_schema.py index 317b7fce8..c1d7ec3b3 100644 --- a/scripts/generate_typescript_from_json_schema.py +++ b/scripts/generate_typescript_from_json_schema.py @@ -202,6 +202,7 @@ def _generate_chat_response_union_type() -> str: ("ClearMessageResponse", "clear_message", "never"), ("MessageUsageChatResponse", "usage", "Record"), ("TodoItemChatResonse", "todo_item", "Task"), + ("ConversationSummaryResponse", "conversation_summary", "string"), ] internal_response_interfaces = [ diff --git a/typescript/@ragbits/api-client/src/autogen.types.ts b/typescript/@ragbits/api-client/src/autogen.types.ts index 0240f250c..ebeeb03f8 100644 --- a/typescript/@ragbits/api-client/src/autogen.types.ts +++ b/typescript/@ragbits/api-client/src/autogen.types.ts @@ -17,6 +17,7 @@ export const ChatResponseType = { StateUpdate: 'state_update', MessageId: 'message_id', ConversationId: 'conversation_id', + ConversationSummary: 'conversation_summary', LiveUpdate: 'live_update', FollowupMessages: 'followup_messages', Image: 'image', @@ -66,6 +67,9 @@ export const TaskStatus = { Pending: 'pending', InProgress: 'in_progress', Completed: 'completed', + Failed: 'failed', + Cancelled: 'cancelled', + Retrying: 'retrying', } as const export type TaskStatus = TypeFrom @@ -191,10 +195,18 @@ export interface Task { /** * Task status options. */ - status: 'pending' | 'in_progress' | 'completed' + status: + | 'pending' + | 'in_progress' + | 'completed' + | 'failed' + | 'cancelled' + | 'retrying' order: number summary: string | null parent_id: string | null + full_response: string | null + dependencies: string[] } /** @@ -497,6 +509,11 @@ export interface TodoItemChatResonse { content: Task } +export interface ConversationSummaryResponse { + type: 'conversation_summary' + content: string +} + export interface ChunkedChatResponse { type: 'chunked_content' content: ChunkedContent @@ -517,3 +534,4 @@ export type ChatResponse = | ClearMessageResponse | MessageUsageChatResponse | TodoItemChatResonse + | ConversationSummaryResponse diff --git a/typescript/ui/__tests__/unit/plugin/ChatHistoryPlugin/ChatHistory.test.tsx b/typescript/ui/__tests__/unit/plugin/ChatHistoryPlugin/ChatHistory.test.tsx index 413b91264..a2ecfad24 100644 --- a/typescript/ui/__tests__/unit/plugin/ChatHistoryPlugin/ChatHistory.test.tsx +++ b/typescript/ui/__tests__/unit/plugin/ChatHistoryPlugin/ChatHistory.test.tsx @@ -1,4 +1,4 @@ -import { render, screen } from "@testing-library/react"; +import { render, screen, waitFor } from "@testing-library/react"; import { describe, it, expect, vi, Mock } from "vitest"; vi.mock("../../../../src/core/stores/HistoryStore/useHistoryStore", () => { @@ -7,15 +7,18 @@ vi.mock("../../../../src/core/stores/HistoryStore/useHistoryStore", () => { }; }); +const selectConversationMock = vi.fn(); +const deleteConversationMock = vi.fn(); +const newConversationMock = vi.fn(); +const setConversationPropertiesMock = vi.fn(); + vi.mock("../../../../src/core/stores/HistoryStore/selectors", () => { - const selectConversationMock = vi.fn(); - const deleteConversationMock = vi.fn(); - const newConversationMock = vi.fn(); return { useHistoryActions: () => ({ selectConversation: selectConversationMock, deleteConversation: deleteConversationMock, newConversation: newConversationMock, + setConversationProperties: setConversationPropertiesMock, }), }; }); @@ -98,7 +101,7 @@ describe("ChatHistory", () => { expect(selectedConversation).toHaveAttribute("data-active", "true"); }); - it("renders delete buttons", () => { + it("renders dropdown buttons", () => { const mockConversationsKeys = Object.keys(MOCK_CONVERSATIONS); mockStore(null); render(); @@ -110,7 +113,7 @@ describe("ChatHistory", () => { console.log(`delete-conversation-${key}`); expect( - screen.getByTestId(`delete-conversation-${key}`), + screen.getByTestId(`dropdown-conversation-${key}`), ).toBeInTheDocument(); }); }); @@ -136,12 +139,16 @@ describe("ChatHistory", () => { const selectedKey = Object.keys(MOCK_CONVERSATIONS)[0]; mockStore(null); render(); + const user = userEvent.setup(); + const dropdownButton = screen.getByTestId( + `dropdown-conversation-${selectedKey}`, + ); + await user.click(dropdownButton); const deleteButton = screen.getByTestId( `delete-conversation-${selectedKey}`, ); expect(deleteButton).toBeInTheDocument(); - const user = userEvent.setup(); await user.click(deleteButton); const deleteMock = useHistoryActions().deleteConversation; @@ -159,4 +166,105 @@ describe("ChatHistory", () => { const newConversationMock = useHistoryActions().newConversation; expect(newConversationMock).toHaveBeenCalled(); }); + + it("starts editing when Edit is clicked (input appears and is focused)", async () => { + mockStore(null); + render(); + + const key = Object.keys(MOCK_CONVERSATIONS)[0]; + const user = userEvent.setup(); + + const dropdownButton = screen.getByTestId(`dropdown-conversation-${key}`); + await user.click(dropdownButton); + + const editButton = screen.getByTestId(`edit-conversation-${key}`); + await user.click(editButton); + + const input = await screen.findByDisplayValue( + MOCK_CONVERSATIONS[key].conversationId, + ); + expect(input).toBeInTheDocument(); + + await waitFor(() => { + expect(document.activeElement).toBe(input); + }); + }); + + it("saves edited value on Enter and calls setConversationProperties", async () => { + mockStore(null); + render(); + + const key = Object.keys(MOCK_CONVERSATIONS)[0]; + const user = userEvent.setup(); + + const dropdownButton = screen.getByTestId(`dropdown-conversation-${key}`); + await user.click(dropdownButton); + + const editButton = screen.getByTestId(`edit-conversation-${key}`); + await user.click(editButton); + + const input = await screen.findByDisplayValue( + MOCK_CONVERSATIONS[key].conversationId, + ); + expect(input).toBeInTheDocument(); + + await waitFor(() => { + expect(document.activeElement).toBe(input); + }); + }); + + it("does NOT call setConversationProperties for whitespace-only input", async () => { + mockStore(null); + render(); + + const key = Object.keys(MOCK_CONVERSATIONS)[0]; + const user = userEvent.setup(); + + await user.click(screen.getByTestId(`dropdown-conversation-${key}`)); + await user.click(screen.getByTestId(`edit-conversation-${key}`)); + + const input = await screen.findByDisplayValue( + MOCK_CONVERSATIONS[key].conversationId, + ); + + await user.clear(input); + await user.type(input, " "); + await user.keyboard("{Enter}"); + + await waitFor(() => { + expect(setConversationPropertiesMock).not.toHaveBeenCalled(); + }); + + await waitFor(() => { + expect( + screen.queryByDisplayValue(MOCK_CONVERSATIONS[key].conversationId), + ).not.toBeInTheDocument(); + }); + }); + + it("cancels edit when Escape is pressed and does not call setConversationProperties", async () => { + mockStore(null); + render(); + + const key = Object.keys(MOCK_CONVERSATIONS)[0]; + const user = userEvent.setup(); + + await user.click(screen.getByTestId(`dropdown-conversation-${key}`)); + await user.click(screen.getByTestId(`edit-conversation-${key}`)); + + const input = await screen.findByDisplayValue( + MOCK_CONVERSATIONS[key].conversationId, + ); + + await user.type(input, "temp"); + await user.type(input, "{Escape}"); + + await waitFor(() => { + expect(setConversationPropertiesMock).not.toHaveBeenCalled(); + }); + + await waitFor(() => { + expect(screen.queryByDisplayValue("temp")).not.toBeInTheDocument(); + }); + }); }); diff --git a/typescript/ui/__tests__/unit/plugin/ChatOptionsPlugin/ChatOptionsForm.test.tsx b/typescript/ui/__tests__/unit/plugin/ChatOptionsPlugin/ChatOptionsForm.test.tsx index d4ecd79e5..1474e139c 100644 --- a/typescript/ui/__tests__/unit/plugin/ChatOptionsPlugin/ChatOptionsForm.test.tsx +++ b/typescript/ui/__tests__/unit/plugin/ChatOptionsPlugin/ChatOptionsForm.test.tsx @@ -8,7 +8,7 @@ import { useConfigContext } from "../../../../src/core/contexts/ConfigContext/us vi.mock("../../../../src/core/stores/HistoryStore/selectors", () => ({ useConversationProperty: vi.fn(() => ({})), useHistoryActions: vi.fn(() => ({ - setChatOptions: vi.fn(), + setConversationProperties: vi.fn(), initializeChatOptions: vi.fn(), })), })); @@ -40,11 +40,11 @@ vi.mock("@rjsf/utils/lib/schema/getDefaultFormState", () => ({ getDefaultBasedOnSchemaType: vi.fn(() => ({ example: "default" })), })); -const setChatOptionsMock = vi.fn(); +const setConversationProperitesMock = vi.fn(); const initializeChatOptionsMock = vi.fn(); function mockActions() { (useHistoryActions as Mock).mockReturnValue({ - setChatOptions: setChatOptionsMock, + setConversationProperties: setConversationProperitesMock, initializeChatOptions: initializeChatOptionsMock, }); } @@ -80,7 +80,12 @@ describe("ChatOptionsForm", () => { await user.click(screen.getByTestId("chat-options-submit")); await waitFor(() => { - expect(setChatOptionsMock).toHaveBeenCalledWith({}); + expect(setConversationProperitesMock).toHaveBeenCalledWith( + expect.anything(), + { + chatOptions: {}, + }, + ); }); }); @@ -95,7 +100,10 @@ describe("ChatOptionsForm", () => { await user.click(restoreBtn); await waitFor(() => { - expect(setChatOptionsMock).toHaveBeenCalledWith({ example: "default" }); + expect(setConversationProperitesMock).toHaveBeenCalledWith( + expect.anything(), + { chatOptions: { example: "default" } }, + ); }); }); diff --git a/typescript/ui/src/core/stores/HistoryStore/eventHandlers/eventHandlerRegistry.ts b/typescript/ui/src/core/stores/HistoryStore/eventHandlers/eventHandlerRegistry.ts index 7e0d456e9..b52dcb37f 100644 --- a/typescript/ui/src/core/stores/HistoryStore/eventHandlers/eventHandlerRegistry.ts +++ b/typescript/ui/src/core/stores/HistoryStore/eventHandlers/eventHandlerRegistry.ts @@ -3,6 +3,7 @@ import { Conversation, HistoryStore } from "../../../../types/history"; import { handleAfterConversationId, handleConversationId, + handleConversationSummary, handleFollowupMessages, handleStateUpdate, } from "./nonMessageHandlers"; @@ -105,3 +106,6 @@ ChatHandlerRegistry.register(ChatResponseType.Usage, { ChatHandlerRegistry.register(ChatResponseType.TodoItem, { handle: handleTodoItem, }); +ChatHandlerRegistry.register(ChatResponseType.ConversationSummary, { + handle: handleConversationSummary, +}); diff --git a/typescript/ui/src/core/stores/HistoryStore/eventHandlers/nonMessageHandlers.ts b/typescript/ui/src/core/stores/HistoryStore/eventHandlers/nonMessageHandlers.ts index 3ba45f774..1b2b7c99a 100644 --- a/typescript/ui/src/core/stores/HistoryStore/eventHandlers/nonMessageHandlers.ts +++ b/typescript/ui/src/core/stores/HistoryStore/eventHandlers/nonMessageHandlers.ts @@ -1,5 +1,6 @@ import { ConversationIdChatResponse, + ConversationSummaryResponse, FollowupMessagesChatResponse, StateUpdateChatResponse, } from "@ragbits/api-client-react"; @@ -45,3 +46,9 @@ export const handleFollowupMessages: PrimaryHandler< > = ({ content }, draft) => { draft.followupMessages = content; }; + +export const handleConversationSummary: PrimaryHandler< + ConversationSummaryResponse +> = ({ content }, draft) => { + draft.summary = content; +}; diff --git a/typescript/ui/src/core/stores/HistoryStore/historyStore.ts b/typescript/ui/src/core/stores/HistoryStore/historyStore.ts index 67bbb8059..4d6024186 100644 --- a/typescript/ui/src/core/stores/HistoryStore/historyStore.ts +++ b/typescript/ui/src/core/stores/HistoryStore/historyStore.ts @@ -278,11 +278,10 @@ export const createHistoryStore = immer((set, get) => ({ }), ); }, - setChatOptions: (options) => { - const { currentConversation } = get(); + setConversationProperties: (conversationKey, properties) => { set( - updateConversation(currentConversation, (draft) => { - draft.chatOptions = options; + updateConversation(conversationKey, (draft) => { + Object.assign(draft, properties); }), ); }, diff --git a/typescript/ui/src/plugins/ChatHistoryPlugin/components/ChatHistory.tsx b/typescript/ui/src/plugins/ChatHistoryPlugin/components/ChatHistory.tsx index aa4a0fbd5..0d82c184b 100644 --- a/typescript/ui/src/plugins/ChatHistoryPlugin/components/ChatHistory.tsx +++ b/typescript/ui/src/plugins/ChatHistoryPlugin/components/ChatHistory.tsx @@ -1,7 +1,14 @@ -import { Button } from "@heroui/react"; +import { + Button, + Dropdown, + DropdownItem, + DropdownMenu, + DropdownTrigger, + Input, +} from "@heroui/react"; import { Icon } from "@iconify/react"; import { motion, AnimatePresence } from "framer-motion"; -import { useState } from "react"; +import { useRef, useState } from "react"; import DelayedTooltip from "../../../core/components/DelayedTooltip"; import { useHistoryActions } from "../../../core/stores/HistoryStore/selectors"; import { useHistoryStore } from "../../../core/stores/HistoryStore/useHistoryStore"; @@ -9,19 +16,62 @@ import { isTemporaryConversation } from "../../../core/stores/HistoryStore/histo import { useNavigate } from "react-router"; import { getConversationRoute } from "../utils"; import { useShallow } from "zustand/shallow"; +import { zip } from "lodash"; export default function ChatHistory() { - const { selectConversation, deleteConversation, newConversation } = - useHistoryActions(); + const { + selectConversation, + deleteConversation, + newConversation, + setConversationProperties, + } = useHistoryActions(); const navigate = useNavigate(); const conversations = useHistoryStore( useShallow((s) => Object.keys(s.conversations).reverse()), ); + const summaries = useHistoryStore( + useShallow((s) => + Object.values(s.conversations) + .reverse() + .map((entry) => entry.summary), + ), + ); const currentConversation = useHistoryStore((s) => s.currentConversation); const [isCollapsed, setCollapsed] = useState(false); + const [editingKey, setEditingKey] = useState(null); + const [editValue, setEditValue] = useState(""); + const [ignoreBlur, setIgnoreBlur] = useState(false); + const inputRef = useRef(null); const collapseButtonTitle = isCollapsed ? "Open sidebar" : "Close sidebar"; const newChatIcon = ; + const handleStartEdit = (conversationKey: string, currentSummary: string) => { + setEditingKey(conversationKey); + setEditValue(currentSummary ?? ""); + setIgnoreBlur(true); + setTimeout(() => { + inputRef.current?.focus?.(); + setTimeout(() => setIgnoreBlur(false), 120); + }, 0); + }; + + const handleSaveEdit = (conversationKey: string) => { + if (editingKey !== conversationKey) return; + const trimmed = (editValue || "").trim(); + if (trimmed) { + setConversationProperties(conversationKey, { summary: trimmed }); + } + setEditingKey(null); + setEditValue(""); + setIgnoreBlur(false); + }; + + const handleCancelEdit = () => { + setEditingKey(null); + setEditValue(""); + setIgnoreBlur(false); + }; + const handleNewConversation = () => { const conversationId = newConversation(); navigate(getConversationRoute(conversationId)); @@ -115,42 +165,94 @@ export default function ChatHistory() { width: 0, }} > - {conversations.map((conversation) => { - if (isTemporaryConversation(conversation)) { + {zip(conversations, summaries).map(([conversation, summary]) => { + if (!conversation || isTemporaryConversation(conversation)) { return null; } const isSelected = conversation === currentConversation; + const isEdited = conversation === editingKey; const variant = isSelected ? "solid" : "light"; return ( -
- - +
+ {isEdited ? ( + setEditValue(e.target.value)} + onBlur={() => { + if (ignoreBlur) return; + handleSaveEdit(conversation); + }} + onKeyDown={(e) => { + if (e.key === "Enter") handleSaveEdit(conversation); + if (e.key === "Escape") handleCancelEdit(); + }} + className="flex-1" + data-testid={`input-conversation-${conversation}`} + /> + ) : ( - + )} + + + + + + + } + onPress={() => + handleStartEdit(conversation, summary ?? conversation) + } + data-testid={`edit-conversation-${conversation}`} + > + Edit + + } + onPress={() => deleteConversation(conversation)} + data-testid={`delete-conversation-${conversation}`} + > + Delete conversation + + +
); })} diff --git a/typescript/ui/src/plugins/ChatOptionsPlugin/components/ChatOptionsForm.tsx b/typescript/ui/src/plugins/ChatOptionsPlugin/components/ChatOptionsForm.tsx index bac0a531d..cad006d2a 100644 --- a/typescript/ui/src/plugins/ChatOptionsPlugin/components/ChatOptionsForm.tsx +++ b/typescript/ui/src/plugins/ChatOptionsPlugin/components/ChatOptionsForm.tsx @@ -31,7 +31,8 @@ export default function ChatOptionsForm() { const chatOptions = useConversationProperty((s) => s.chatOptions); // Needed to solve flicker to default settings when the modal is closing const pendingSettingsRef = useRef | null>(null); - const { setChatOptions, initializeChatOptions } = useHistoryActions(); + const { setConversationProperties, initializeChatOptions } = + useHistoryActions(); const currentConversation = useHistoryStore((s) => s.currentConversation); const { config: { user_settings: userSettings }, @@ -82,7 +83,7 @@ export default function ChatOptionsForm() { } const settings = pendingSettingsRef.current; - setChatOptions(settings); + setConversationProperties(currentConversation, { chatOptions: settings }); ensureSyncWithStorage(settings); pendingSettingsRef.current = null; }; diff --git a/typescript/ui/src/types/history.ts b/typescript/ui/src/types/history.ts index 74205eb36..249264577 100644 --- a/typescript/ui/src/types/history.ts +++ b/typescript/ui/src/types/history.ts @@ -38,7 +38,9 @@ export interface Conversation { chatOptions: Record | undefined; isLoading: boolean; abortController: AbortController | null; + summary?: string; } + export interface HistoryStore { conversations: Record; currentConversation: string; @@ -61,7 +63,10 @@ export interface HistoryStore { extensions: Record, ) => void; initializeChatOptions: (defaults: Record) => void; - setChatOptions: (options: Record) => void; + setConversationProperties: ( + conversationKey: string, + properties: Partial, + ) => void; }; primitives: { From 7fa44c52817e21947d9b2db2605031d461903f52 Mon Sep 17 00:00:00 2001 From: ds-ragbits-robot Date: Mon, 13 Oct 2025 12:21:45 +0000 Subject: [PATCH 42/43] Automated UI build --- ...uard-Cg314rC1.js => AuthGuard-CLPypBq0.js} | 2 +- .../ui-build/assets/ChatHistory-C9npR0N7.js | 2 + .../ui-build/assets/ChatHistory-Dzy0EI1-.js | 1 - .../assets/ChatOptionsForm-BmoJGYUy.js | 1 + .../assets/ChatOptionsForm-CUT15Ecj.js | 1 - .../ui-build/assets/FeedbackForm-Cav2SsCB.js | 1 + .../ui-build/assets/FeedbackForm-Cuu7ROp3.js | 1 - .../{Login-xlop2v42.js => Login-Bcduy8Fr.js} | 2 +- ...n-Cp3E_g1o.js => LogoutButton-CgB7xh9T.js} | 2 +- ...on-DpNGX0tU.js => ShareButton-B1XlPNhP.js} | 2 +- .../ui-build/assets/UsageButton-I3vvJAZH.js | 1 - .../ui-build/assets/UsageButton-dxos0384.js | 1 + ...tore-Ur4ZGows.js => authStore-BmdbjFC4.js} | 2 +- .../assets/chunk-IGSAU2ZA-Beuyhkep.js | 1 + .../assets/chunk-IGSAU2ZA-RYoue1Xd.js | 1 - .../assets/chunk-SSA7SXE4-CTW6lkmA.js | 1 + .../assets/chunk-SSA7SXE4-CpT2ku66.js | 1 - .../chat/ui-build/assets/index-CTrBaHsz.js | 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mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-DmyaiLHy.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-IAfGflqy.css delete mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-nfbmdcVN.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/index-qUEoadOF.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/useMenuTriggerState-BKJ8iWug.js create mode 100644 packages/ragbits-chat/src/ragbits/chat/ui-build/assets/useSelectableItem-ChcXAf8h.js diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-Cg314rC1.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-CLPypBq0.js similarity index 56% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-Cg314rC1.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-CLPypBq0.js index 42af54d7e..8ba84a9b9 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-Cg314rC1.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/AuthGuard-CLPypBq0.js @@ -1 +1 @@ -import{r as a,j as s,aP as c,h as x,aK as g,bj as m,bk as p,bl as j,bm as k,bn as v}from"./index-BTypXP0X.js";import{a as r}from"./authStore-Ur4ZGows.js";const b=a.createContext(null);function A({children:o}){const[n]=a.useState(()=>r);return s.jsx(b.Provider,{value:n,children:o})}function C(){const{token:o,tokenExpiration:n,logout:l}=c(r,e=>e),i=x(e=>e.isLoading),u=g(),d=a.useRef(i);a.useEffect(()=>{d.current=i},[i]);const t=a.useCallback(()=>{const e=()=>{d.current?setTimeout(e,500):(l(),u("/login"))};e()},[l,u]);return a.useEffect(()=>{if(!o||!n)return;const e=Date.now(),h=n-e;if(h<=0){t();return}const f=setTimeout(()=>{t()},h);return()=>clearTimeout(f)},[o,n,t]),null}function R({children:o}){const 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h,aL as N,r as I,j as t,aM as n,aD as O,D as o,d as s,I as i,aN as $,aO as u}from"./index-BTypXP0X.js";function P(){const{selectConversation:m,deleteConversation:x,newConversation:v}=y(),l=C(),p=h(N(e=>Object.keys(e.conversations).reverse())),f=h(e=>e.currentConversation),[a,b]=I.useState(!1),r=a?"Open sidebar":"Close sidebar",g=t.jsx(i,{icon:"heroicons:pencil-square"}),j=()=>{const e=v();l(u(e))},w=e=>{m(e),l(u(e))};return t.jsx(n.div,{initial:!1,animate:{maxWidth:a?"4.5rem":"16rem"},className:"rounded-l-medium border-small border-divider ml-4 flex h-full w-full min-w-[4.5rem] flex-grow flex-col space-y-2 overflow-hidden border-r-0 p-4 py-3",children:t.jsxs(O,{children:[t.jsx(o,{content:r,placement:"bottom",children:t.jsx(s,{isIconOnly:!0,"aria-label":r,variant:"ghost",onPress:()=>b(e=>!e),"data-testid":"chat-history-collapse-button",className:"ml-auto",children:t.jsx(i,{icon:a?"heroicons:chevron-double-right":"heroicons:chevron-double-left"})})},"collapse-button"),!a&&t.jsx(n.p,{initial:!1,animate:{opacity:1,width:"100%",height:"auto"},exit:{opacity:0,width:0,height:0,marginBottom:0},className:"text-small text-foreground truncate leading-5 font-semibold",children:"Conversations"},"conversations"),t.jsx(o,{content:"New conversation",placement:"right",children:t.jsx(s,{"aria-label":"New conversation",variant:"ghost",onPress:j,"data-testid":"chat-history-clear-chat-button",startContent:g,isIconOnly:a,children:!a&&"New conversation"})},"new-conversation-button"),!a&&t.jsx(n.div,{className:"mt-2 flex flex-1 flex-col gap-2 overflow-auto 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b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ChatOptionsForm-BmoJGYUy.js @@ -0,0 +1 @@ +import{g as z,r as f,u as ie,h as ce,a as ue,i as le,b as fe,p as Y,C as Q,j as l,D as de,d as A,I as me,m as ge,e as he,f as pe}from"./index-DaG4uPnK.js";import{g as Z,v as W,F as ve,t as be}from"./index-DmyaiLHy.js";import{m as Se}from"./chunk-IGSAU2ZA-Beuyhkep.js";import"./chunk-SSA7SXE4-CTW6lkmA.js";import"./useMenuTriggerState-BKJ8iWug.js";import"./useSelectableItem-ChcXAf8h.js";import"./index-qUEoadOF.js";var B,V;function xe(){if(V)return B;V=1;var n="Expected a function",t=NaN,s="[object Symbol]",m=/^\s+|\s+$/g,g=/^[-+]0x[0-9a-f]+$/i,h=/^0b[01]+$/i,i=/^0o[0-7]+$/i,b=parseInt,S=typeof z=="object"&&z&&z.Object===Object&&z,y=typeof self=="object"&&self&&self.Object===Object&&self,C=S||y||Function("return this")(),d=Object.prototype,r=d.toString,c=Math.max,O=Math.min,E=function(){return C.Date.now()};function $(e,u,v){var T,L,P,I,p,j,R=0,G=!1,D=!1,k=!0;if(typeof e!="function")throw 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N}from"./chunk-IGSAU2ZA-Beuyhkep.js";import"./chunk-SSA7SXE4-CTW6lkmA.js";import"./useMenuTriggerState-BKJ8iWug.js";import"./useSelectableItem-ChcXAf8h.js";import"./index-qUEoadOF.js";function z({message:t}){const{isOpen:f,onOpen:h,onClose:r}=g(),{mergeExtensions:p}=v(),{config:{feedback:o}}=C(),[i,x]=_.useState(s.Like),k=T("/api/feedback",{headers:{"Content-Type":"application/json"},method:"POST"}),l=o[i].form,j=()=>{r()},c=async a=>{if(!t.serverId)throw new Error('Feedback is only available for messages with "serverId" set');try{await k.call({body:{message_id:t.serverId,feedback:i,payload:a??{}}})}catch(F){console.error(F)}},y=a=>{p(t.id,{feedbackType:i}),c(a.formData),r()},d=async a=>{if(x(a),o[a].form===null){await c(null);return}h()};if(!l)return null;const m=t.extensions?.feedbackType;return e.jsxs(e.Fragment,{children:[o.like.enabled&&e.jsx(b,{content:"Like",placement:"bottom",children:e.jsx(n,{isIconOnly:!0,variant:"ghost",className:"p-0","aria-label":"Rate message as 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V({message:t}){const{isOpen:f,onOpen:h,onClose:r}=g(),{mergeExtensions:p}=v(),{config:{feedback:o}}=C(),[l,x]=_.useState(s.Like),k=T("/api/feedback",{headers:{"Content-Type":"application/json"},method:"POST"}),i=o[l].form,j=()=>{r()},c=async a=>{if(!t.serverId)throw new Error('Feedback is only available for messages with "serverId" set');try{await k.call({body:{message_id:t.serverId,feedback:l,payload:a??{}}})}catch(F){console.error(F)}},y=a=>{p(t.id,{feedbackType:l}),c(a.formData),r()},d=async a=>{if(x(a),o[a].form===null){await c(null);return}h()};if(!i)return null;const m=t.extensions?.feedbackType;return e.jsxs(e.Fragment,{children:[o.like.enabled&&e.jsx(b,{content:"Like",placement:"bottom",children:e.jsx(n,{isIconOnly:!0,variant:"ghost",className:"p-0","aria-label":"Rate message as 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form",children:"Cancel"}),e.jsx(n,{color:"primary",type:"submit","aria-label":"Submit feedback","data-testid":"feedback-submit",children:"Submit"})]})})})})]})})})]})}export{V as default}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-xlop2v42.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-Bcduy8Fr.js similarity index 79% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-xlop2v42.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-Bcduy8Fr.js index 04d6d32fa..c780033a1 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-xlop2v42.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/Login-Bcduy8Fr.js @@ -1 +1 @@ -import{r as n,bg as w,c as y,aP as b,aK as S,j as e,aD as j,aM as v,d as P,bh as C}from"./index-BTypXP0X.js";import{a as N}from"./authStore-Ur4ZGows.js";import{i as c}from"./chunk-SSA7SXE4-CpT2ku66.js";const D=()=>{const a=n.useContext(w);if(!a)throw new Error("useInitializeUserStore must be used within a HistoryStoreContextProvider");return a.initializeUserStore};function U(){const[a,m]=n.useState({username:"",password:""}),u=y("/api/auth/login",{headers:{"Content-Type":"application/json"},method:"POST"}),p=b(N,s=>s.login),g=S(),x=D(),[f,o]=n.useState(!1),h=async s=>{o(!1),s.preventDefault(),s.stopPropagation();const r=new FormData(s.currentTarget),i=r.get("username"),l=r.get("password");try{const t=await u.call({body:{username:i,password:l}});if(!t.success||!t.jwt_token||!t.user){o(!0);return}p(t.user,t.jwt_token),x(t.user.user_id),g("/")}catch(t){o(!0),console.error("Failed to login",t)}},d=s=>r=>m(i=>C(i,l=>{l[s]=r.target.value}));return n.useEffect(()=>{document.title="Login"},[]),e.jsx("div",{className:"flex h-screen w-screen",children:e.jsxs("form",{className:"rounded-medium border-small border-divider m-auto flex w-full max-w-xs flex-col gap-4 p-4",onSubmit:h,children:[e.jsxs("div",{className:"text-small",children:[e.jsx("div",{className:"text-foreground truncate leading-5 font-semibold",children:"Sign in"}),e.jsx("div",{className:"text-default-500 truncate leading-5 font-normal",children:"Sign in to start chatting."})]}),e.jsx(c,{label:"Username",name:"username",labelPlacement:"outside",placeholder:"Your username",required:!0,isRequired:!0,value:a.username,onChange:d("username")}),e.jsx(c,{label:"Password",labelPlacement:"outside",id:"password",name:"password",type:"password",placeholder:"••••••••",required:!0,isRequired:!0,value:a.password,onChange:d("password")}),e.jsx(j,{children:f&&!u.isLoading&&e.jsx(v.div,{className:"text-small text-danger",initial:{opacity:0,y:-10},animate:{opacity:1,y:0},exit:{opacity:0,y:-10},transition:{duration:.3,ease:"easeOut"},children:"We couldn't sign you in. Please verify your credentials and try again."})}),e.jsx(P,{type:"submit",color:a.password&&a.username?"primary":"default",children:"Sign in"})]})})}export{U as default}; +import{r as n,bm as w,c as y,aW as b,aE as S,j as e,aq as j,aG as v,d as C,bn as P}from"./index-DaG4uPnK.js";import{a as E}from"./authStore-BmdbjFC4.js";import{i as c}from"./chunk-SSA7SXE4-CTW6lkmA.js";const N=()=>{const a=n.useContext(w);if(!a)throw new Error("useInitializeUserStore must be used within a HistoryStoreContextProvider");return a.initializeUserStore};function U(){const[a,m]=n.useState({username:"",password:""}),u=y("/api/auth/login",{headers:{"Content-Type":"application/json"},method:"POST"}),p=b(E,s=>s.login),g=S(),x=N(),[f,o]=n.useState(!1),h=async s=>{o(!1),s.preventDefault(),s.stopPropagation();const r=new FormData(s.currentTarget),i=r.get("username"),l=r.get("password");try{const t=await u.call({body:{username:i,password:l}});if(!t.success||!t.jwt_token||!t.user){o(!0);return}p(t.user,t.jwt_token),x(t.user.user_id),g("/")}catch(t){o(!0),console.error("Failed to login",t)}},d=s=>r=>m(i=>P(i,l=>{l[s]=r.target.value}));return n.useEffect(()=>{document.title="Login"},[]),e.jsx("div",{className:"flex h-screen w-screen",children:e.jsxs("form",{className:"rounded-medium border-small border-divider m-auto flex w-full max-w-xs flex-col gap-4 p-4",onSubmit:h,children:[e.jsxs("div",{className:"text-small",children:[e.jsx("div",{className:"text-foreground truncate leading-5 font-semibold",children:"Sign in"}),e.jsx("div",{className:"text-default-500 truncate leading-5 font-normal",children:"Sign in to start chatting."})]}),e.jsx(c,{label:"Username",name:"username",labelPlacement:"outside",placeholder:"Your username",required:!0,isRequired:!0,value:a.username,onChange:d("username")}),e.jsx(c,{label:"Password",labelPlacement:"outside",id:"password",name:"password",type:"password",placeholder:"••••••••",required:!0,isRequired:!0,value:a.password,onChange:d("password")}),e.jsx(j,{children:f&&!u.isLoading&&e.jsx(v.div,{className:"text-small text-danger",initial:{opacity:0,y:-10},animate:{opacity:1,y:0},exit:{opacity:0,y:-10},transition:{duration:.3,ease:"easeOut"},children:"We couldn't sign you in. Please verify your credentials and try again."})}),e.jsx(C,{type:"submit",color:a.password&&a.username?"primary":"default",children:"Sign in"})]})})}export{U as default}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Cp3E_g1o.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-CgB7xh9T.js similarity index 81% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Cp3E_g1o.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-CgB7xh9T.js index 4a53057ca..53fddb164 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-Cp3E_g1o.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/LogoutButton-CgB7xh9T.js @@ -1 +1 @@ -import{c as u,aP as s,aK as i,j as o,D as g,d,I as p}from"./index-BTypXP0X.js";import{a as n}from"./authStore-Ur4ZGows.js";function m(){const r=u("/api/auth/logout",{headers:{"Content-Type":"application/json"},method:"POST"}),c=s(n,t=>t.logout),e=s(n,t=>t.token?.access_token),a=i(),l=async()=>{if(!e){a("/login");return}try{if(!(await r.call({body:{token:e}})).success)return;c(),a("/login")}catch(t){console.error("Failed to logout",t)}};return o.jsx(g,{content:"Logout",placement:"bottom",children:o.jsx(d,{isIconOnly:!0,"aria-label":"Logout",variant:"ghost",onPress:l,"data-testid":"logout-button",children:o.jsx(p,{icon:"heroicons:arrow-left-start-on-rectangle"})})})}export{m as default}; +import{c as u,aW as s,aE as i,j as o,D as g,d,I as p}from"./index-DaG4uPnK.js";import{a as n}from"./authStore-BmdbjFC4.js";function m(){const r=u("/api/auth/logout",{headers:{"Content-Type":"application/json"},method:"POST"}),c=s(n,t=>t.logout),e=s(n,t=>t.token?.access_token),a=i(),l=async()=>{if(!e){a("/login");return}try{if(!(await r.call({body:{token:e}})).success)return;c(),a("/login")}catch(t){console.error("Failed to logout",t)}};return o.jsx(g,{content:"Logout",placement:"bottom",children:o.jsx(d,{isIconOnly:!0,"aria-label":"Logout",variant:"ghost",onPress:l,"data-testid":"logout-button",children:o.jsx(p,{icon:"heroicons:arrow-left-start-on-rectangle"})})})}export{m as default}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ShareButton-DpNGX0tU.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ShareButton-B1XlPNhP.js similarity index 80% rename from packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ShareButton-DpNGX0tU.js rename to packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ShareButton-B1XlPNhP.js index c07f73fb3..ecc7a860d 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ShareButton-DpNGX0tU.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/ShareButton-B1XlPNhP.js @@ -1 +1 @@ -import{aI as Gr,u as Jr,r as hr,i as Wr,j as k,D as qr,d as gr,I as Yr,m as Kr,e as Lr,f as Qr,aJ as Vr}from"./index-BTypXP0X.js";import{m as 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tvarka",descendingSort:e=>`surikiuota pagal stulpelį ${e.columnName} mažėjančia tvarka`,resizerDescription:"Paspauskite „Enter“, kad pradėtumėte keisti dydį",select:"Pasirinkti",selectAll:"Pasirinkti viską",sortable:"rikiuojamas stulpelis"};var nt={};nt={ascending:"augošā secībā",ascendingSort:e=>`kārtots pēc kolonnas ${e.columnName} augošā secībā`,columnSize:e=>`${e.value} pikseļi`,descending:"dilstošā secībā",descendingSort:e=>`kārtots pēc kolonnas ${e.columnName} dilstošā secībā`,resizerDescription:"Nospiediet Enter, lai sāktu izmēru mainīšanu",select:"Atlasīt",selectAll:"Atlasīt visu",sortable:"kārtojamā kolonna"};var ot={};ot={ascending:"stigende",ascendingSort:e=>`sortert etter kolonne ${e.columnName} i stigende rekkefølge`,columnSize:e=>`${e.value} piksler`,descending:"synkende",descendingSort:e=>`sortert etter kolonne ${e.columnName} i synkende rekkefølge`,resizerDescription:"Trykk på Enter for å starte størrelsesendring",select:"Velg",selectAll:"Velg alle",sortable:"kolonne som kan sorteres"};var st={};st={ascending:"oplopend",ascendingSort:e=>`gesorteerd in oplopende volgorde in kolom ${e.columnName}`,columnSize:e=>`${e.value} pixels`,descending:"aflopend",descendingSort:e=>`gesorteerd in aflopende volgorde in kolom ${e.columnName}`,resizerDescription:"Druk op Enter om het formaat te wijzigen",select:"Selecteren",selectAll:"Alles selecteren",sortable:"sorteerbare kolom"};var it={};it={ascending:"rosnąco",ascendingSort:e=>`posortowano według kolumny ${e.columnName} w porządku rosnącym`,columnSize:e=>`Liczba pikseli: ${e.value}`,descending:"malejąco",descendingSort:e=>`posortowano według kolumny ${e.columnName} w porządku malejącym`,resizerDescription:"Naciśnij Enter, aby rozpocząć zmienianie rozmiaru",select:"Zaznacz",selectAll:"Zaznacz wszystko",sortable:"kolumna z możliwością sortowania"};var rt={};rt={ascending:"crescente",ascendingSort:e=>`classificado pela coluna ${e.columnName} em ordem crescente`,columnSize:e=>`${e.value} pixels`,descending:"decrescente",descendingSort:e=>`classificado pela coluna ${e.columnName} em ordem decrescente`,resizerDescription:"Pressione Enter para começar a redimensionar",select:"Selecionar",selectAll:"Selecionar tudo",sortable:"coluna classificável"};var at={};at={ascending:"ascendente",ascendingSort:e=>`Ordenar por coluna ${e.columnName} em ordem ascendente`,columnSize:e=>`${e.value} pixels`,descending:"descendente",descendingSort:e=>`Ordenar por coluna ${e.columnName} em ordem descendente`,resizerDescription:"Prima Enter para iniciar o redimensionamento",select:"Selecionar",selectAll:"Selecionar tudo",sortable:"Coluna ordenável"};var ct={};ct={ascending:"crescătoare",ascendingSort:e=>`sortate după coloana ${e.columnName} în ordine crescătoare`,columnSize:e=>`${e.value} pixeli`,descending:"descrescătoare",descendingSort:e=>`sortate după coloana ${e.columnName} în ordine descrescătoare`,resizerDescription:"Apăsați pe Enter pentru a începe redimensionarea",select:"Selectare",selectAll:"Selectare totală",sortable:"coloană sortabilă"};var dt={};dt={ascending:"возрастание",ascendingSort:e=>`сортировать столбец ${e.columnName} в порядке возрастания`,columnSize:e=>`${e.value} пикс.`,descending:"убывание",descendingSort:e=>`сортировать столбец ${e.columnName} в порядке убывания`,resizerDescription:"Нажмите клавишу Enter для начала изменения размеров",select:"Выбрать",selectAll:"Выбрать все",sortable:"сортируемый столбец"};var mt={};mt={ascending:"vzostupne",ascendingSort:e=>`zoradené zostupne podľa stĺpca ${e.columnName}`,columnSize:e=>`Počet pixelov: ${e.value}`,descending:"zostupne",descendingSort:e=>`zoradené zostupne podľa stĺpca ${e.columnName}`,resizerDescription:"Stlačením klávesu Enter začnete zmenu veľkosti",select:"Vybrať",selectAll:"Vybrať všetko",sortable:"zoraditeľný stĺpec"};var pt={};pt={ascending:"naraščajoče",ascendingSort:e=>`razvrščeno po stolpcu ${e.columnName} v naraščajočem vrstnem redu`,columnSize:e=>`${e.value} slikovnih pik`,descending:"padajoče",descendingSort:e=>`razvrščeno po stolpcu ${e.columnName} v padajočem vrstnem redu`,resizerDescription:"Pritisnite tipko Enter da začnete spreminjati velikost",select:"Izberite",selectAll:"Izberite vse",sortable:"razvrstljivi stolpec"};var bt={};bt={ascending:"rastući",ascendingSort:e=>`sortirano po kolonama ${e.columnName} rastućim redosledom`,columnSize:e=>`${e.value} piksela`,descending:"padajući",descendingSort:e=>`sortirano po kolonama ${e.columnName} padajućim redosledom`,resizerDescription:"Pritisnite Enter da biste započeli promenu veličine",select:"Izaberite",selectAll:"Izaberite sve",sortable:"kolona koja se može sortirati"};var ft={};ft={ascending:"stigande",ascendingSort:e=>`sorterat på kolumn ${e.columnName} i stigande ordning`,columnSize:e=>`${e.value} pixlar`,descending:"fallande",descendingSort:e=>`sorterat på kolumn ${e.columnName} i fallande ordning`,resizerDescription:"Tryck på Retur för att börja ändra storlek",select:"Markera",selectAll:"Markera allt",sortable:"sorterbar kolumn"};var ht={};ht={ascending:"artan sırada",ascendingSort:e=>`${e.columnName} sütuna göre artan düzende sırala`,columnSize:e=>`${e.value} piksel`,descending:"azalan sırada",descendingSort:e=>`${e.columnName} sütuna göre azalan düzende sırala`,resizerDescription:"Yeniden boyutlandırmak için Enter'a basın",select:"Seç",selectAll:"Tümünü Seç",sortable:"Sıralanabilir sütun"};var vt={};vt={ascending:"висхідний",ascendingSort:e=>`відсортовано за стовпцем ${e.columnName} у висхідному порядку`,columnSize:e=>`${e.value} пікс.`,descending:"низхідний",descendingSort:e=>`відсортовано за стовпцем ${e.columnName} у низхідному порядку`,resizerDescription:"Натисніть Enter, щоб почати зміну розміру",select:"Вибрати",selectAll:"Вибрати все",sortable:"сортувальний стовпець"};var gt={};gt={ascending:"升序",ascendingSort:e=>`按列 ${e.columnName} 升序排序`,columnSize:e=>`${e.value} 像素`,descending:"降序",descendingSort:e=>`按列 ${e.columnName} 降序排序`,resizerDescription:"按“输入”键开始调整大小。",select:"选择",selectAll:"全选",sortable:"可排序的列"};var $t={};$t={ascending:"遞增",ascendingSort:e=>`已依據「${e.columnName}」欄遞增排序`,columnSize:e=>`${e.value} 像素`,descending:"遞減",descendingSort:e=>`已依據「${e.columnName}」欄遞減排序`,resizerDescription:"按 Enter 鍵以開始調整大小",select:"選取",selectAll:"全選",sortable:"可排序的欄"};var ne={};ne={"ar-AE":Ve,"bg-BG":Oe,"cs-CZ":He,"da-DK":Le,"de-DE":Ue,"el-GR":We,"en-US":Ge,"es-ES":Ye,"et-EE":Ze,"fi-FI":_e,"fr-FR":qe,"he-IL":Je,"hr-HR":Xe,"hu-HU":Qe,"it-IT":et,"ja-JP":tt,"ko-KR":lt,"lt-LT":ut,"lv-LV":nt,"nb-NO":ot,"nl-NL":st,"pl-PL":it,"pt-BR":rt,"pt-PT":at,"ro-RO":ct,"ru-RU":dt,"sk-SK":mt,"sl-SI":pt,"sr-SP":bt,"sv-SE":ft,"tr-TR":ht,"uk-UA":vt,"zh-CN":gt,"zh-TW":$t};class yt{isCell(t){return t.type==="cell"}isRow(t){return t.type==="row"||t.type==="item"}isDisabled(t){var l;return this.disabledBehavior==="all"&&(((l=t.props)===null||l===void 0?void 0:l.isDisabled)||this.disabledKeys.has(t.key))}findPreviousKey(t,l){let u=t!=null?this.collection.getKeyBefore(t):this.collection.getLastKey();for(;u!=null;){let o=this.collection.getItem(u);if(!o)return null;if(!this.isDisabled(o)&&(!l||l(o)))return u;u=this.collection.getKeyBefore(u)}return null}findNextKey(t,l){let u=t!=null?this.collection.getKeyAfter(t):this.collection.getFirstKey();for(;u!=null;){let o=this.collection.getItem(u);if(!o)return null;if(!this.isDisabled(o)&&(!l||l(o)))return u;if(u=this.collection.getKeyAfter(u),u==null)return null}return null}getKeyForItemInRowByIndex(t,l=0){if(l<0)return null;let u=this.collection.getItem(t);if(!u)return null;let o=0;for(let a of z(u,this.collection)){var n;if(a.colSpan&&a.colSpan+o>l)return(n=a.key)!==null&&n!==void 0?n:null;a.colSpan&&(o=o+a.colSpan-1);var i;if(o===l)return(i=a.key)!==null&&i!==void 0?i:null;o++}return null}getKeyBelow(t){let l=t,u=this.collection.getItem(l);if(!u)return null;var o;if(this.isCell(u)&&(l=(o=u.parentKey)!==null&&o!==void 0?o:null),l==null)return null;if(l=this.findNextKey(l,n=>n.type==="item"),l!=null){if(this.isCell(u)){let n=u.colIndex?u.colIndex:u.index;return this.getKeyForItemInRowByIndex(l,n)}if(this.focusMode==="row")return l}return null}getKeyAbove(t){let l=t,u=this.collection.getItem(l);if(!u)return null;var o;if(this.isCell(u)&&(l=(o=u.parentKey)!==null&&o!==void 0?o:null),l==null)return null;if(l=this.findPreviousKey(l,n=>n.type==="item"),l!=null){if(this.isCell(u)){let n=u.colIndex?u.colIndex:u.index;return this.getKeyForItemInRowByIndex(l,n)}if(this.focusMode==="row")return l}return null}getKeyRightOf(t){let l=this.collection.getItem(t);if(!l)return null;if(this.isRow(l)){var u,o;let d=z(l,this.collection);var n;return(n=this.direction==="rtl"?(u=W(d))===null||u===void 0?void 0:u.key:(o=U(d))===null||o===void 0?void 0:o.key)!==null&&n!==void 0?n:null}if(this.isCell(l)&&l.parentKey!=null){let d=this.collection.getItem(l.parentKey);if(!d)return null;let r=z(d,this.collection);var i;let p=(i=this.direction==="rtl"?te(r,l.index-1):te(r,l.index+1))!==null&&i!==void 0?i:null;var a;if(p)return(a=p.key)!==null&&a!==void 0?a:null;var m;if(this.focusMode==="row")return(m=l.parentKey)!==null&&m!==void 0?m:null;var c;return(c=this.direction==="rtl"?this.getFirstKey(t):this.getLastKey(t))!==null&&c!==void 0?c:null}return null}getKeyLeftOf(t){let l=this.collection.getItem(t);if(!l)return null;if(this.isRow(l)){var u,o;let d=z(l,this.collection);var n;return(n=this.direction==="rtl"?(u=U(d))===null||u===void 0?void 0:u.key:(o=W(d))===null||o===void 0?void 0:o.key)!==null&&n!==void 0?n:null}if(this.isCell(l)&&l.parentKey!=null){let d=this.collection.getItem(l.parentKey);if(!d)return null;let r=z(d,this.collection);var i;let p=(i=this.direction==="rtl"?te(r,l.index+1):te(r,l.index-1))!==null&&i!==void 0?i:null;var a;if(p)return(a=p.key)!==null&&a!==void 0?a:null;var m;if(this.focusMode==="row")return(m=l.parentKey)!==null&&m!==void 0?m:null;var c;return(c=this.direction==="rtl"?this.getLastKey(t):this.getFirstKey(t))!==null&&c!==void 0?c:null}return null}getFirstKey(t,l){let u=t??null,o;if(u!=null){if(o=this.collection.getItem(u),!o)return null;if(this.isCell(o)&&!l&&o.parentKey!=null){var n;let c=this.collection.getItem(o.parentKey);if(!c)return null;var i;return(i=(n=U(z(c,this.collection)))===null||n===void 0?void 0:n.key)!==null&&i!==void 0?i:null}}if(u=this.findNextKey(void 0,c=>c.type==="item"),u!=null&&(o&&this.isCell(o)&&l||this.focusMode==="cell")){var a;let c=this.collection.getItem(u);if(!c)return null;var m;u=(m=(a=U(z(c,this.collection)))===null||a===void 0?void 0:a.key)!==null&&m!==void 0?m:null}return u}getLastKey(t,l){let u=t??null,o;if(u!=null){if(o=this.collection.getItem(u),!o)return null;if(this.isCell(o)&&!l&&o.parentKey!=null){var n;let c=this.collection.getItem(o.parentKey);if(!c)return null;let d=z(c,this.collection);var i;return(i=(n=W(d))===null||n===void 0?void 0:n.key)!==null&&i!==void 0?i:null}}if(u=this.findPreviousKey(void 0,c=>c.type==="item"),u!=null&&(o&&this.isCell(o)&&l||this.focusMode==="cell")){var a;let c=this.collection.getItem(u);if(!c)return null;let d=z(c,this.collection);var m;u=(m=(a=W(d))===null||a===void 0?void 0:a.key)!==null&&m!==void 0?m:null}return u}getKeyPageAbove(t){let l=t,u=this.layoutDelegate.getItemRect(l);if(!u)return null;let o=Math.max(0,u.y+u.height-this.layoutDelegate.getVisibleRect().height);for(;u&&u.y>o&&l!=null;){var n;if(l=(n=this.getKeyAbove(l))!==null&&n!==void 0?n:null,l==null)break;u=this.layoutDelegate.getItemRect(l)}return l}getKeyPageBelow(t){let l=t,u=this.layoutDelegate.getItemRect(l);if(!u)return null;let o=this.layoutDelegate.getVisibleRect().height,n=Math.min(this.layoutDelegate.getContentSize().height,u.y+o);for(;u&&u.y+u.heightr.type==="item"),u==null&&!a&&(u=this.getFirstKey(),a=!0)}return null}constructor(t){if(this.collection=t.collection,this.disabledKeys=t.disabledKeys,this.disabledBehavior=t.disabledBehavior||"all",this.direction=t.direction,this.collator=t.collator,!t.layout&&!t.ref)throw new Error("Either a layout or a ref must be specified.");this.layoutDelegate=t.layoutDelegate||(t.layout?new du(t.layout):new Tl(t.ref)),this.focusMode=t.focusMode||"row"}}class du{getContentSize(){return this.layout.getContentSize()}getItemRect(t){var l;return((l=this.layout.getLayoutInfo(t))===null||l===void 0?void 0:l.rect)||null}getVisibleRect(){return this.layout.virtualizer.visibleRect}constructor(t){this.layout=t}}const ve=new WeakMap;var xt={};xt={deselectedItem:e=>`${e.item} غير المحدد`,longPressToSelect:"اضغط مطولًا للدخول إلى وضع التحديد.",select:"تحديد",selectedAll:"جميع العناصر المحددة.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"لم يتم تحديد عناصر",one:()=>`${t.number(e.count)} عنصر محدد`,other:()=>`${t.number(e.count)} عنصر محدد`})}.`,selectedItem:e=>`${e.item} المحدد`};var Ct={};Ct={deselectedItem:e=>`${e.item} не е избран.`,longPressToSelect:"Натиснете и задръжте за да влезете в избирателен режим.",select:"Изберете",selectedAll:"Всички елементи са избрани.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Няма избрани елементи",one:()=>`${t.number(e.count)} избран елемент`,other:()=>`${t.number(e.count)} избрани елементи`})}.`,selectedItem:e=>`${e.item} избран.`};var Dt={};Dt={deselectedItem:e=>`Položka ${e.item} není vybrána.`,longPressToSelect:"Dlouhým stisknutím přejdete do režimu výběru.",select:"Vybrat",selectedAll:"Vybrány všechny položky.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nevybrány žádné položky",one:()=>`Vybrána ${t.number(e.count)} položka`,other:()=>`Vybráno ${t.number(e.count)} položek`})}.`,selectedItem:e=>`Vybrána položka ${e.item}.`};var Bt={};Bt={deselectedItem:e=>`${e.item} ikke valgt.`,longPressToSelect:"Lav et langt tryk for at aktivere valgtilstand.",select:"Vælg",selectedAll:"Alle elementer valgt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ingen elementer valgt",one:()=>`${t.number(e.count)} element valgt`,other:()=>`${t.number(e.count)} elementer valgt`})}.`,selectedItem:e=>`${e.item} valgt.`};var kt={};kt={deselectedItem:e=>`${e.item} nicht ausgewählt.`,longPressToSelect:"Gedrückt halten, um Auswahlmodus zu öffnen.",select:"Auswählen",selectedAll:"Alle Elemente ausgewählt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Keine Elemente ausgewählt",one:()=>`${t.number(e.count)} Element ausgewählt`,other:()=>`${t.number(e.count)} Elemente ausgewählt`})}.`,selectedItem:e=>`${e.item} ausgewählt.`};var Et={};Et={deselectedItem:e=>`Δεν επιλέχθηκε το στοιχείο ${e.item}.`,longPressToSelect:"Πατήστε παρατεταμένα για να μπείτε σε λειτουργία επιλογής.",select:"Επιλογή",selectedAll:"Επιλέχθηκαν όλα τα στοιχεία.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Δεν επιλέχθηκαν στοιχεία",one:()=>`Επιλέχθηκε ${t.number(e.count)} στοιχείο`,other:()=>`Επιλέχθηκαν ${t.number(e.count)} στοιχεία`})}.`,selectedItem:e=>`Επιλέχθηκε το στοιχείο ${e.item}.`};var St={};St={deselectedItem:e=>`${e.item} not selected.`,select:"Select",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"No items selected",one:()=>`${t.number(e.count)} item selected`,other:()=>`${t.number(e.count)} items selected`})}.`,selectedAll:"All items selected.",selectedItem:e=>`${e.item} selected.`,longPressToSelect:"Long press to enter selection mode."};var At={};At={deselectedItem:e=>`${e.item} no seleccionado.`,longPressToSelect:"Mantenga pulsado para abrir el modo de selección.",select:"Seleccionar",selectedAll:"Todos los elementos seleccionados.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ningún elemento seleccionado",one:()=>`${t.number(e.count)} elemento seleccionado`,other:()=>`${t.number(e.count)} elementos seleccionados`})}.`,selectedItem:e=>`${e.item} seleccionado.`};var wt={};wt={deselectedItem:e=>`${e.item} pole valitud.`,longPressToSelect:"Valikurežiimi sisenemiseks vajutage pikalt.",select:"Vali",selectedAll:"Kõik üksused valitud.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Üksusi pole valitud",one:()=>`${t.number(e.count)} üksus valitud`,other:()=>`${t.number(e.count)} üksust valitud`})}.`,selectedItem:e=>`${e.item} valitud.`};var Nt={};Nt={deselectedItem:e=>`Kohdetta ${e.item} ei valittu.`,longPressToSelect:"Siirry valintatilaan painamalla pitkään.",select:"Valitse",selectedAll:"Kaikki kohteet valittu.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ei yhtään kohdetta valittu",one:()=>`${t.number(e.count)} kohde valittu`,other:()=>`${t.number(e.count)} kohdetta valittu`})}.`,selectedItem:e=>`${e.item} valittu.`};var zt={};zt={deselectedItem:e=>`${e.item} non sélectionné.`,longPressToSelect:"Appuyez de manière prolongée pour passer en mode de sélection.",select:"Sélectionner",selectedAll:"Tous les éléments sélectionnés.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Aucun élément sélectionné",one:()=>`${t.number(e.count)} élément sélectionné`,other:()=>`${t.number(e.count)} éléments sélectionnés`})}.`,selectedItem:e=>`${e.item} sélectionné.`};var Ft={};Ft={deselectedItem:e=>`${e.item} לא נבחר.`,longPressToSelect:"הקשה ארוכה לכניסה למצב בחירה.",select:"בחר",selectedAll:"כל הפריטים נבחרו.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"לא נבחרו פריטים",one:()=>`פריט ${t.number(e.count)} נבחר`,other:()=>`${t.number(e.count)} פריטים נבחרו`})}.`,selectedItem:e=>`${e.item} נבחר.`};var Pt={};Pt={deselectedItem:e=>`Stavka ${e.item} nije odabrana.`,longPressToSelect:"Dugo pritisnite za ulazak u način odabira.",select:"Odaberite",selectedAll:"Odabrane su sve stavke.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nije odabrana nijedna stavka",one:()=>`Odabrana je ${t.number(e.count)} stavka`,other:()=>`Odabrano je ${t.number(e.count)} stavki`})}.`,selectedItem:e=>`Stavka ${e.item} je odabrana.`};var Kt={};Kt={deselectedItem:e=>`${e.item} nincs kijelölve.`,longPressToSelect:"Nyomja hosszan a kijelöléshez.",select:"Kijelölés",selectedAll:"Az összes elem kijelölve.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Egy elem sincs kijelölve",one:()=>`${t.number(e.count)} elem kijelölve`,other:()=>`${t.number(e.count)} elem kijelölve`})}.`,selectedItem:e=>`${e.item} kijelölve.`};var It={};It={deselectedItem:e=>`${e.item} non selezionato.`,longPressToSelect:"Premi a lungo per passare alla modalità di selezione.",select:"Seleziona",selectedAll:"Tutti gli elementi selezionati.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nessun elemento selezionato",one:()=>`${t.number(e.count)} elemento selezionato`,other:()=>`${t.number(e.count)} elementi selezionati`})}.`,selectedItem:e=>`${e.item} selezionato.`};var jt={};jt={deselectedItem:e=>`${e.item} が選択されていません。`,longPressToSelect:"長押しして選択モードを開きます。",select:"選択",selectedAll:"すべての項目を選択しました。",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"項目が選択されていません",one:()=>`${t.number(e.count)} 項目を選択しました`,other:()=>`${t.number(e.count)} 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details",onPress:u,children:h.jsx(tu,{icon:"heroicons:information-circle"})})}),h.jsx(lu,{isOpen:l,onOpenChange:n,size:"4xl",children:h.jsx(uu,{children:h.jsxs(h.Fragment,{children:[h.jsx(ru,{className:"text-default-900 flex flex-col gap-1",children:"Usage details"}),h.jsx(nu,{children:h.jsxs(Yu,{"aria-label":"Table with detailed usage statistics for each model used to generate the message.",fullWidth:!0,children:[h.jsx(Ju,{columns:d,children:s=>h.jsx(_u,{children:s.label},s.key)}),h.jsx(tn,{items:r,children:s=>h.jsx(Qu,{className:s.model==="Total"?"border-default-200 border-t-1":"",children:b=>h.jsx(un,{children:ou(s,b)})},s.model)})]})})]})})})]})}export{dn as default}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/UsageButton-dxos0384.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/UsageButton-dxos0384.js new file mode 100644 index 000000000..9d688fcc4 --- /dev/null +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/UsageButton-dxos0384.js 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he=new WeakMap;function ce(e){return typeof e=="string"?e.replace(/\s*/g,""):""+e}function cu(e,t){let l=he.get(e);if(!l)throw new Error("Unknown grid");return`${l}-${ce(t)}`}function Te(e,t,l){let u=he.get(e);if(!u)throw new Error("Unknown grid");return`${u}-${ce(t)}-${ce(l)}`}function Re(e,t){return[...e.collection.rowHeaderColumnKeys].map(l=>Te(e,t,l)).join(" ")}var Ve={};Ve={ascending:"تصاعدي",ascendingSort:e=>`ترتيب حسب العمود ${e.columnName} بترتيب تصاعدي`,columnSize:e=>`${e.value} بالبكسل`,descending:"تنازلي",descendingSort:e=>`ترتيب حسب العمود ${e.columnName} بترتيب تنازلي`,resizerDescription:"اضغط على مفتاح Enter لبدء تغيير الحجم",select:"تحديد",selectAll:"تحديد الكل",sortable:"عمود قابل للترتيب"};var Oe={};Oe={ascending:"възходящ",ascendingSort:e=>`сортирано по колона ${e.columnName} във възходящ ред`,columnSize:e=>`${e.value} пиксела`,descending:"низходящ",descendingSort:e=>`сортирано по колона ${e.columnName} в низходящ ред`,resizerDescription:"Натиснете „Enter“, за да 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som kan sorteres"};var st={};st={ascending:"oplopend",ascendingSort:e=>`gesorteerd in oplopende volgorde in kolom ${e.columnName}`,columnSize:e=>`${e.value} pixels`,descending:"aflopend",descendingSort:e=>`gesorteerd in aflopende volgorde in kolom ${e.columnName}`,resizerDescription:"Druk op Enter om het formaat te wijzigen",select:"Selecteren",selectAll:"Alles selecteren",sortable:"sorteerbare kolom"};var it={};it={ascending:"rosnąco",ascendingSort:e=>`posortowano według kolumny ${e.columnName} w porządku rosnącym`,columnSize:e=>`Liczba pikseli: ${e.value}`,descending:"malejąco",descendingSort:e=>`posortowano według kolumny ${e.columnName} w porządku malejącym`,resizerDescription:"Naciśnij Enter, aby rozpocząć zmienianie rozmiaru",select:"Zaznacz",selectAll:"Zaznacz wszystko",sortable:"kolumna z możliwością sortowania"};var rt={};rt={ascending:"crescente",ascendingSort:e=>`classificado pela coluna ${e.columnName} em ordem crescente`,columnSize:e=>`${e.value} pixels`,descending:"decrescente",descendingSort:e=>`classificado pela coluna ${e.columnName} em ordem decrescente`,resizerDescription:"Pressione Enter para começar a redimensionar",select:"Selecionar",selectAll:"Selecionar tudo",sortable:"coluna classificável"};var at={};at={ascending:"ascendente",ascendingSort:e=>`Ordenar por coluna ${e.columnName} em ordem ascendente`,columnSize:e=>`${e.value} pixels`,descending:"descendente",descendingSort:e=>`Ordenar por coluna ${e.columnName} em ordem descendente`,resizerDescription:"Prima Enter para iniciar o redimensionamento",select:"Selecionar",selectAll:"Selecionar tudo",sortable:"Coluna ordenável"};var ct={};ct={ascending:"crescătoare",ascendingSort:e=>`sortate după coloana ${e.columnName} în ordine crescătoare`,columnSize:e=>`${e.value} pixeli`,descending:"descrescătoare",descendingSort:e=>`sortate după coloana ${e.columnName} în ordine descrescătoare`,resizerDescription:"Apăsați pe Enter pentru a începe redimensionarea",select:"Selectare",selectAll:"Selectare totală",sortable:"coloană sortabilă"};var dt={};dt={ascending:"возрастание",ascendingSort:e=>`сортировать столбец ${e.columnName} в порядке возрастания`,columnSize:e=>`${e.value} пикс.`,descending:"убывание",descendingSort:e=>`сортировать столбец ${e.columnName} в порядке убывания`,resizerDescription:"Нажмите клавишу Enter для начала изменения размеров",select:"Выбрать",selectAll:"Выбрать все",sortable:"сортируемый столбец"};var mt={};mt={ascending:"vzostupne",ascendingSort:e=>`zoradené zostupne podľa stĺpca ${e.columnName}`,columnSize:e=>`Počet pixelov: ${e.value}`,descending:"zostupne",descendingSort:e=>`zoradené zostupne podľa stĺpca ${e.columnName}`,resizerDescription:"Stlačením klávesu Enter začnete zmenu veľkosti",select:"Vybrať",selectAll:"Vybrať všetko",sortable:"zoraditeľný stĺpec"};var pt={};pt={ascending:"naraščajoče",ascendingSort:e=>`razvrščeno po stolpcu ${e.columnName} v naraščajočem vrstnem redu`,columnSize:e=>`${e.value} slikovnih pik`,descending:"padajoče",descendingSort:e=>`razvrščeno po stolpcu ${e.columnName} v padajočem vrstnem redu`,resizerDescription:"Pritisnite tipko Enter da začnete spreminjati velikost",select:"Izberite",selectAll:"Izberite vse",sortable:"razvrstljivi stolpec"};var bt={};bt={ascending:"rastući",ascendingSort:e=>`sortirano po kolonama ${e.columnName} rastućim redosledom`,columnSize:e=>`${e.value} piksela`,descending:"padajući",descendingSort:e=>`sortirano po kolonama ${e.columnName} padajućim redosledom`,resizerDescription:"Pritisnite Enter da biste započeli promenu veličine",select:"Izaberite",selectAll:"Izaberite sve",sortable:"kolona koja se može sortirati"};var ft={};ft={ascending:"stigande",ascendingSort:e=>`sorterat på kolumn ${e.columnName} i stigande ordning`,columnSize:e=>`${e.value} pixlar`,descending:"fallande",descendingSort:e=>`sorterat på kolumn ${e.columnName} i fallande ordning`,resizerDescription:"Tryck på Retur för att börja ändra storlek",select:"Markera",selectAll:"Markera allt",sortable:"sorterbar kolumn"};var ht={};ht={ascending:"artan sırada",ascendingSort:e=>`${e.columnName} sütuna göre artan düzende sırala`,columnSize:e=>`${e.value} piksel`,descending:"azalan sırada",descendingSort:e=>`${e.columnName} sütuna göre azalan düzende sırala`,resizerDescription:"Yeniden boyutlandırmak için Enter'a basın",select:"Seç",selectAll:"Tümünü Seç",sortable:"Sıralanabilir sütun"};var vt={};vt={ascending:"висхідний",ascendingSort:e=>`відсортовано за стовпцем ${e.columnName} у висхідному порядку`,columnSize:e=>`${e.value} пікс.`,descending:"низхідний",descendingSort:e=>`відсортовано за стовпцем ${e.columnName} у низхідному порядку`,resizerDescription:"Натисніть Enter, щоб почати зміну розміру",select:"Вибрати",selectAll:"Вибрати все",sortable:"сортувальний стовпець"};var gt={};gt={ascending:"升序",ascendingSort:e=>`按列 ${e.columnName} 升序排序`,columnSize:e=>`${e.value} 像素`,descending:"降序",descendingSort:e=>`按列 ${e.columnName} 降序排序`,resizerDescription:"按“输入”键开始调整大小。",select:"选择",selectAll:"全选",sortable:"可排序的列"};var $t={};$t={ascending:"遞增",ascendingSort:e=>`已依據「${e.columnName}」欄遞增排序`,columnSize:e=>`${e.value} 像素`,descending:"遞減",descendingSort:e=>`已依據「${e.columnName}」欄遞減排序`,resizerDescription:"按 Enter 鍵以開始調整大小",select:"選取",selectAll:"全選",sortable:"可排序的欄"};var ne={};ne={"ar-AE":Ve,"bg-BG":Oe,"cs-CZ":He,"da-DK":Le,"de-DE":Ue,"el-GR":We,"en-US":Ge,"es-ES":Ye,"et-EE":Ze,"fi-FI":_e,"fr-FR":qe,"he-IL":Je,"hr-HR":Xe,"hu-HU":Qe,"it-IT":et,"ja-JP":tt,"ko-KR":lt,"lt-LT":ut,"lv-LV":nt,"nb-NO":ot,"nl-NL":st,"pl-PL":it,"pt-BR":rt,"pt-PT":at,"ro-RO":ct,"ru-RU":dt,"sk-SK":mt,"sl-SI":pt,"sr-SP":bt,"sv-SE":ft,"tr-TR":ht,"uk-UA":vt,"zh-CN":gt,"zh-TW":$t};class yt{isCell(t){return t.type==="cell"}isRow(t){return t.type==="row"||t.type==="item"}isDisabled(t){var l;return this.disabledBehavior==="all"&&(((l=t.props)===null||l===void 0?void 0:l.isDisabled)||this.disabledKeys.has(t.key))}findPreviousKey(t,l){let u=t!=null?this.collection.getKeyBefore(t):this.collection.getLastKey();for(;u!=null;){let o=this.collection.getItem(u);if(!o)return null;if(!this.isDisabled(o)&&(!l||l(o)))return u;u=this.collection.getKeyBefore(u)}return null}findNextKey(t,l){let u=t!=null?this.collection.getKeyAfter(t):this.collection.getFirstKey();for(;u!=null;){let o=this.collection.getItem(u);if(!o)return null;if(!this.isDisabled(o)&&(!l||l(o)))return u;if(u=this.collection.getKeyAfter(u),u==null)return null}return null}getKeyForItemInRowByIndex(t,l=0){if(l<0)return null;let u=this.collection.getItem(t);if(!u)return null;let o=0;for(let a of z(u,this.collection)){var n;if(a.colSpan&&a.colSpan+o>l)return(n=a.key)!==null&&n!==void 0?n:null;a.colSpan&&(o=o+a.colSpan-1);var i;if(o===l)return(i=a.key)!==null&&i!==void 0?i:null;o++}return null}getKeyBelow(t){let l=t,u=this.collection.getItem(l);if(!u)return null;var o;if(this.isCell(u)&&(l=(o=u.parentKey)!==null&&o!==void 0?o:null),l==null)return null;if(l=this.findNextKey(l,n=>n.type==="item"),l!=null){if(this.isCell(u)){let n=u.colIndex?u.colIndex:u.index;return this.getKeyForItemInRowByIndex(l,n)}if(this.focusMode==="row")return l}return null}getKeyAbove(t){let l=t,u=this.collection.getItem(l);if(!u)return null;var o;if(this.isCell(u)&&(l=(o=u.parentKey)!==null&&o!==void 0?o:null),l==null)return null;if(l=this.findPreviousKey(l,n=>n.type==="item"),l!=null){if(this.isCell(u)){let n=u.colIndex?u.colIndex:u.index;return this.getKeyForItemInRowByIndex(l,n)}if(this.focusMode==="row")return l}return null}getKeyRightOf(t){let l=this.collection.getItem(t);if(!l)return null;if(this.isRow(l)){var u,o;let d=z(l,this.collection);var n;return(n=this.direction==="rtl"?(u=W(d))===null||u===void 0?void 0:u.key:(o=U(d))===null||o===void 0?void 0:o.key)!==null&&n!==void 0?n:null}if(this.isCell(l)&&l.parentKey!=null){let d=this.collection.getItem(l.parentKey);if(!d)return null;let r=z(d,this.collection);var i;let p=(i=this.direction==="rtl"?te(r,l.index-1):te(r,l.index+1))!==null&&i!==void 0?i:null;var a;if(p)return(a=p.key)!==null&&a!==void 0?a:null;var m;if(this.focusMode==="row")return(m=l.parentKey)!==null&&m!==void 0?m:null;var c;return(c=this.direction==="rtl"?this.getFirstKey(t):this.getLastKey(t))!==null&&c!==void 0?c:null}return null}getKeyLeftOf(t){let l=this.collection.getItem(t);if(!l)return null;if(this.isRow(l)){var u,o;let d=z(l,this.collection);var n;return(n=this.direction==="rtl"?(u=U(d))===null||u===void 0?void 0:u.key:(o=W(d))===null||o===void 0?void 0:o.key)!==null&&n!==void 0?n:null}if(this.isCell(l)&&l.parentKey!=null){let d=this.collection.getItem(l.parentKey);if(!d)return null;let r=z(d,this.collection);var i;let p=(i=this.direction==="rtl"?te(r,l.index+1):te(r,l.index-1))!==null&&i!==void 0?i:null;var a;if(p)return(a=p.key)!==null&&a!==void 0?a:null;var m;if(this.focusMode==="row")return(m=l.parentKey)!==null&&m!==void 0?m:null;var c;return(c=this.direction==="rtl"?this.getLastKey(t):this.getFirstKey(t))!==null&&c!==void 0?c:null}return null}getFirstKey(t,l){let u=t??null,o;if(u!=null){if(o=this.collection.getItem(u),!o)return null;if(this.isCell(o)&&!l&&o.parentKey!=null){var n;let c=this.collection.getItem(o.parentKey);if(!c)return null;var i;return(i=(n=U(z(c,this.collection)))===null||n===void 0?void 0:n.key)!==null&&i!==void 0?i:null}}if(u=this.findNextKey(void 0,c=>c.type==="item"),u!=null&&(o&&this.isCell(o)&&l||this.focusMode==="cell")){var a;let c=this.collection.getItem(u);if(!c)return null;var m;u=(m=(a=U(z(c,this.collection)))===null||a===void 0?void 0:a.key)!==null&&m!==void 0?m:null}return u}getLastKey(t,l){let u=t??null,o;if(u!=null){if(o=this.collection.getItem(u),!o)return null;if(this.isCell(o)&&!l&&o.parentKey!=null){var n;let c=this.collection.getItem(o.parentKey);if(!c)return null;let d=z(c,this.collection);var i;return(i=(n=W(d))===null||n===void 0?void 0:n.key)!==null&&i!==void 0?i:null}}if(u=this.findPreviousKey(void 0,c=>c.type==="item"),u!=null&&(o&&this.isCell(o)&&l||this.focusMode==="cell")){var a;let c=this.collection.getItem(u);if(!c)return null;let d=z(c,this.collection);var m;u=(m=(a=W(d))===null||a===void 0?void 0:a.key)!==null&&m!==void 0?m:null}return u}getKeyPageAbove(t){let l=t,u=this.layoutDelegate.getItemRect(l);if(!u)return null;let o=Math.max(0,u.y+u.height-this.layoutDelegate.getVisibleRect().height);for(;u&&u.y>o&&l!=null;){var n;if(l=(n=this.getKeyAbove(l))!==null&&n!==void 0?n:null,l==null)break;u=this.layoutDelegate.getItemRect(l)}return l}getKeyPageBelow(t){let l=t,u=this.layoutDelegate.getItemRect(l);if(!u)return null;let o=this.layoutDelegate.getVisibleRect().height,n=Math.min(this.layoutDelegate.getContentSize().height,u.y+o);for(;u&&u.y+u.heightr.type==="item"),u==null&&!a&&(u=this.getFirstKey(),a=!0)}return null}constructor(t){if(this.collection=t.collection,this.disabledKeys=t.disabledKeys,this.disabledBehavior=t.disabledBehavior||"all",this.direction=t.direction,this.collator=t.collator,!t.layout&&!t.ref)throw new Error("Either a layout or a ref must be specified.");this.layoutDelegate=t.layoutDelegate||(t.layout?new du(t.layout):new Tl(t.ref)),this.focusMode=t.focusMode||"row"}}class du{getContentSize(){return this.layout.getContentSize()}getItemRect(t){var l;return((l=this.layout.getLayoutInfo(t))===null||l===void 0?void 0:l.rect)||null}getVisibleRect(){return this.layout.virtualizer.visibleRect}constructor(t){this.layout=t}}const ve=new WeakMap;var xt={};xt={deselectedItem:e=>`${e.item} غير المحدد`,longPressToSelect:"اضغط مطولًا للدخول إلى وضع التحديد.",select:"تحديد",selectedAll:"جميع العناصر المحددة.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"لم يتم تحديد عناصر",one:()=>`${t.number(e.count)} عنصر محدد`,other:()=>`${t.number(e.count)} عنصر محدد`})}.`,selectedItem:e=>`${e.item} المحدد`};var Ct={};Ct={deselectedItem:e=>`${e.item} не е избран.`,longPressToSelect:"Натиснете и задръжте за да влезете в избирателен режим.",select:"Изберете",selectedAll:"Всички елементи са избрани.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Няма избрани елементи",one:()=>`${t.number(e.count)} избран елемент`,other:()=>`${t.number(e.count)} избрани елементи`})}.`,selectedItem:e=>`${e.item} избран.`};var Dt={};Dt={deselectedItem:e=>`Položka ${e.item} není vybrána.`,longPressToSelect:"Dlouhým stisknutím přejdete do režimu výběru.",select:"Vybrat",selectedAll:"Vybrány všechny položky.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nevybrány žádné položky",one:()=>`Vybrána ${t.number(e.count)} položka`,other:()=>`Vybráno ${t.number(e.count)} položek`})}.`,selectedItem:e=>`Vybrána položka ${e.item}.`};var Bt={};Bt={deselectedItem:e=>`${e.item} ikke valgt.`,longPressToSelect:"Lav et langt tryk for at aktivere valgtilstand.",select:"Vælg",selectedAll:"Alle elementer valgt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ingen elementer valgt",one:()=>`${t.number(e.count)} element valgt`,other:()=>`${t.number(e.count)} elementer valgt`})}.`,selectedItem:e=>`${e.item} valgt.`};var kt={};kt={deselectedItem:e=>`${e.item} nicht ausgewählt.`,longPressToSelect:"Gedrückt halten, um Auswahlmodus zu öffnen.",select:"Auswählen",selectedAll:"Alle Elemente ausgewählt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Keine Elemente ausgewählt",one:()=>`${t.number(e.count)} Element ausgewählt`,other:()=>`${t.number(e.count)} Elemente ausgewählt`})}.`,selectedItem:e=>`${e.item} ausgewählt.`};var Et={};Et={deselectedItem:e=>`Δεν επιλέχθηκε το στοιχείο ${e.item}.`,longPressToSelect:"Πατήστε παρατεταμένα για να μπείτε σε λειτουργία επιλογής.",select:"Επιλογή",selectedAll:"Επιλέχθηκαν όλα τα στοιχεία.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Δεν επιλέχθηκαν στοιχεία",one:()=>`Επιλέχθηκε ${t.number(e.count)} στοιχείο`,other:()=>`Επιλέχθηκαν ${t.number(e.count)} στοιχεία`})}.`,selectedItem:e=>`Επιλέχθηκε το στοιχείο ${e.item}.`};var St={};St={deselectedItem:e=>`${e.item} not selected.`,select:"Select",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"No items selected",one:()=>`${t.number(e.count)} item selected`,other:()=>`${t.number(e.count)} items selected`})}.`,selectedAll:"All items selected.",selectedItem:e=>`${e.item} selected.`,longPressToSelect:"Long press to enter selection mode."};var At={};At={deselectedItem:e=>`${e.item} no seleccionado.`,longPressToSelect:"Mantenga pulsado para abrir el modo de selección.",select:"Seleccionar",selectedAll:"Todos los elementos seleccionados.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ningún elemento seleccionado",one:()=>`${t.number(e.count)} elemento seleccionado`,other:()=>`${t.number(e.count)} elementos seleccionados`})}.`,selectedItem:e=>`${e.item} seleccionado.`};var wt={};wt={deselectedItem:e=>`${e.item} pole valitud.`,longPressToSelect:"Valikurežiimi sisenemiseks vajutage pikalt.",select:"Vali",selectedAll:"Kõik üksused valitud.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Üksusi pole valitud",one:()=>`${t.number(e.count)} üksus valitud`,other:()=>`${t.number(e.count)} üksust valitud`})}.`,selectedItem:e=>`${e.item} valitud.`};var Nt={};Nt={deselectedItem:e=>`Kohdetta ${e.item} ei valittu.`,longPressToSelect:"Siirry valintatilaan painamalla pitkään.",select:"Valitse",selectedAll:"Kaikki kohteet valittu.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ei yhtään kohdetta valittu",one:()=>`${t.number(e.count)} kohde valittu`,other:()=>`${t.number(e.count)} kohdetta valittu`})}.`,selectedItem:e=>`${e.item} valittu.`};var zt={};zt={deselectedItem:e=>`${e.item} non sélectionné.`,longPressToSelect:"Appuyez de manière prolongée pour passer en mode de sélection.",select:"Sélectionner",selectedAll:"Tous les éléments sélectionnés.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Aucun élément sélectionné",one:()=>`${t.number(e.count)} élément sélectionné`,other:()=>`${t.number(e.count)} éléments sélectionnés`})}.`,selectedItem:e=>`${e.item} sélectionné.`};var Ft={};Ft={deselectedItem:e=>`${e.item} לא נבחר.`,longPressToSelect:"הקשה ארוכה לכניסה למצב בחירה.",select:"בחר",selectedAll:"כל הפריטים נבחרו.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"לא נבחרו פריטים",one:()=>`פריט ${t.number(e.count)} נבחר`,other:()=>`${t.number(e.count)} פריטים נבחרו`})}.`,selectedItem:e=>`${e.item} נבחר.`};var Pt={};Pt={deselectedItem:e=>`Stavka ${e.item} nije odabrana.`,longPressToSelect:"Dugo pritisnite za ulazak u način odabira.",select:"Odaberite",selectedAll:"Odabrane su sve stavke.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nije odabrana nijedna stavka",one:()=>`Odabrana je ${t.number(e.count)} stavka`,other:()=>`Odabrano je ${t.number(e.count)} stavki`})}.`,selectedItem:e=>`Stavka ${e.item} je odabrana.`};var Kt={};Kt={deselectedItem:e=>`${e.item} nincs kijelölve.`,longPressToSelect:"Nyomja hosszan a kijelöléshez.",select:"Kijelölés",selectedAll:"Az összes elem kijelölve.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Egy elem sincs kijelölve",one:()=>`${t.number(e.count)} elem kijelölve`,other:()=>`${t.number(e.count)} elem kijelölve`})}.`,selectedItem:e=>`${e.item} kijelölve.`};var It={};It={deselectedItem:e=>`${e.item} non selezionato.`,longPressToSelect:"Premi a lungo per passare alla modalità di selezione.",select:"Seleziona",selectedAll:"Tutti gli elementi selezionati.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nessun elemento selezionato",one:()=>`${t.number(e.count)} elemento selezionato`,other:()=>`${t.number(e.count)} elementi selezionati`})}.`,selectedItem:e=>`${e.item} selezionato.`};var jt={};jt={deselectedItem:e=>`${e.item} が選択されていません。`,longPressToSelect:"長押しして選択モードを開きます。",select:"選択",selectedAll:"すべての項目を選択しました。",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"項目が選択されていません",one:()=>`${t.number(e.count)} 項目を選択しました`,other:()=>`${t.number(e.count)} 項目を選択しました`})}。`,selectedItem:e=>`${e.item} を選択しました。`};var Mt={};Mt={deselectedItem:e=>`${e.item}이(가) 선택되지 않았습니다.`,longPressToSelect:"선택 모드로 들어가려면 길게 누르십시오.",select:"선택",selectedAll:"모든 항목이 선택되었습니다.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"선택된 항목이 없습니다",one:()=>`${t.number(e.count)}개 항목이 선택되었습니다`,other:()=>`${t.number(e.count)}개 항목이 선택되었습니다`})}.`,selectedItem:e=>`${e.item}이(가) 선택되었습니다.`};var Tt={};Tt={deselectedItem:e=>`${e.item} nepasirinkta.`,longPressToSelect:"Norėdami įjungti pasirinkimo režimą, paspauskite ir palaikykite.",select:"Pasirinkti",selectedAll:"Pasirinkti visi elementai.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nepasirinktas nė vienas elementas",one:()=>`Pasirinktas ${t.number(e.count)} elementas`,other:()=>`Pasirinkta elementų: ${t.number(e.count)}`})}.`,selectedItem:e=>`Pasirinkta: ${e.item}.`};var Rt={};Rt={deselectedItem:e=>`Vienums ${e.item} nav atlasīts.`,longPressToSelect:"Ilgi turiet nospiestu. lai ieslēgtu atlases režīmu.",select:"Atlasīt",selectedAll:"Atlasīti visi vienumi.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nav atlasīts neviens vienums",one:()=>`Atlasīto vienumu skaits: ${t.number(e.count)}`,other:()=>`Atlasīto vienumu skaits: ${t.number(e.count)}`})}.`,selectedItem:e=>`Atlasīts vienums ${e.item}.`};var Vt={};Vt={deselectedItem:e=>`${e.item} er ikke valgt.`,longPressToSelect:"Bruk et langt trykk for å gå inn i valgmodus.",select:"Velg",selectedAll:"Alle elementer er valgt.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Ingen elementer er valgt",one:()=>`${t.number(e.count)} element er valgt`,other:()=>`${t.number(e.count)} elementer er valgt`})}.`,selectedItem:e=>`${e.item} er valgt.`};var Ot={};Ot={deselectedItem:e=>`${e.item} niet geselecteerd.`,longPressToSelect:"Druk lang om de selectiemodus te openen.",select:"Selecteren",selectedAll:"Alle items geselecteerd.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Geen items geselecteerd",one:()=>`${t.number(e.count)} item geselecteerd`,other:()=>`${t.number(e.count)} items geselecteerd`})}.`,selectedItem:e=>`${e.item} geselecteerd.`};var Ht={};Ht={deselectedItem:e=>`Nie zaznaczono ${e.item}.`,longPressToSelect:"Naciśnij i przytrzymaj, aby wejść do trybu wyboru.",select:"Zaznacz",selectedAll:"Wszystkie zaznaczone elementy.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nie zaznaczono żadnych elementów",one:()=>`${t.number(e.count)} zaznaczony element`,other:()=>`${t.number(e.count)} zaznaczonych elementów`})}.`,selectedItem:e=>`Zaznaczono ${e.item}.`};var Lt={};Lt={deselectedItem:e=>`${e.item} não selecionado.`,longPressToSelect:"Mantenha pressionado para entrar no modo de seleção.",select:"Selecionar",selectedAll:"Todos os itens selecionados.",selectedCount:(e,t)=>`${t.plural(e.count,{"=0":"Nenhum item selecionado",one:()=>`${t.number(e.count)} item selecionado`,other:()=>`${t.number(e.count)} itens selecionados`})}.`,selectedItem:e=>`${e.item} selecionado.`};var Ut={};Ut={deselectedItem:e=>`${e.item} 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Gu=Nl,zl=T((e,t)=>{const{BaseComponent:l,Component:u,collection:o,values:n,topContent:i,topContentPlacement:a,bottomContentPlacement:m,bottomContent:c,removeWrapper:d,sortIcon:r,getBaseProps:p,getWrapperProps:s,getTableProps:b}=pl({...e,ref:t}),{isVirtualized:v,rowHeight:g=40,maxTableHeight:x=600}=e,D=v,y=B.useCallback(({children:$})=>d?$:h.jsx(l,{...s(),children:$}),[d,s]);return D?h.jsx(Wu,{...e,ref:t,maxTableHeight:x,rowHeight:g}):h.jsxs("div",{...p(),children:[a==="outside"&&i,h.jsx(y,{children:h.jsxs(h.Fragment,{children:[a==="inside"&&i,h.jsxs(u,{...b(),children:[h.jsxs(El,{classNames:n.classNames,slots:n.slots,children:[o.headerRows.map($=>h.jsx(Bl,{classNames:n.classNames,node:$,slots:n.slots,state:n.state,children:[...$.childNodes].map(f=>{var 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e.replace(/(-?)(\d*)\.?(\d*)e([+-]\d+)/,function(t,l,u,o,n){return n<0?l+"0."+Array(1-n-u.length).join("0")+o+u:l+u+o+Array(n-o.length+1).join("0")})}function mn({usage:e}){const t=Object.keys(e),{isOpen:l,onOpen:u,onClose:o}=Xl(),n=()=>{o()},i=Object.keys(e[t[0]]),a=[...we.filter(s=>s==="model"||i.includes(s)),...i.filter(s=>!we.includes(s))],m=i.reduce((s,b)=>({...s,[b]:0}),{}),c=t.reduce((s,b)=>(a.reduce((v,g)=>(g==="model"||(v[g]+=e[b][g]),v),s),s),m),d=[...a.map(s=>({key:s,label:nn[s]??De.words(s).map(De.upperFirst).join(" ")}))],r=[...t.map(s=>({model:s,...e[s]})),{model:"Total",...c}],p=h.jsxs("div",{className:"p-2",children:[h.jsxs("div",{className:"flex flex-col gap-2",children:[c.prompt_tokens!==void 0&&h.jsxs("div",{className:"flex justify-between gap-2",children:[h.jsx("span",{className:"font-semibold",children:"Prompt tokens"}),h.jsx("span",{children:c.prompt_tokens})]}),c.completion_tokens!==void 0&&h.jsxs("div",{className:"flex justify-between 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packages/ragbits-chat/src/ragbits/chat/ui-build/assets/authStore-BmdbjFC4.js index 2ac1c94e5..a90205362 100644 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/authStore-Ur4ZGows.js +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/authStore-BmdbjFC4.js @@ -1 +1 @@ -import{bo as u,bp as l,bq as p}from"./index-BTypXP0X.js";const c=e=>({user_id:e.user_id,email:e.email}),d=u(l(p((e,i)=>({user:null,token:null,isAuthenticated:!1,tokenExpiration:null,hasHydrated:!1,onRehydrated:()=>{const{token:t,tokenExpiration:o,logout:n}=i(),a=Date.now(),s=(o??0)-a;!t||s<=0?n():e(r=>{r.isAuthenticated=!0}),e(r=>{r.hasHydrated=!0})},login:(t,o)=>e(n=>{n.user=t,n.token=o,n.tokenExpiration=Date.now()+o.expires_in*1e3,n.isAuthenticated=!0}),logout:()=>e(t=>{t.user=null,t.token=null,t.isAuthenticated=!1})})),{name:"ragbits-auth",partialize:e=>({token:e.token,tokenExpiration:e.tokenExpiration,user:e.user?c(e.user):null}),merge:(e,i)=>({...i,...e,isAuthenticated:!1}),onRehydrateStorage:e=>()=>{e.onRehydrated()}}));export{d as a}; +import{bu as u,bv as l,bw as c}from"./index-DaG4uPnK.js";const h=e=>({user_id:e.user_id,email:e.email}),d=u(l(c((e,i)=>({user:null,token:null,isAuthenticated:!1,tokenExpiration:null,hasHydrated:!1,onRehydrated:()=>{const{token:t,tokenExpiration:o,logout:n}=i(),a=Date.now(),s=(o??0)-a;!t||s<=0?n():e(r=>{r.isAuthenticated=!0}),e(r=>{r.hasHydrated=!0})},login:(t,o)=>e(n=>{n.user=t,n.token=o,n.tokenExpiration=Date.now()+o.expires_in*1e3,n.isAuthenticated=!0}),logout:()=>e(t=>{t.user=null,t.token=null,t.isAuthenticated=!1})})),{name:"ragbits-auth",partialize:e=>({token:e.token,tokenExpiration:e.tokenExpiration,user:e.user?h(e.user):null}),merge:(e,i)=>({...i,...e,isAuthenticated:!1}),onRehydrateStorage:e=>()=>{e.onRehydrated()}}));export{d as a}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-Beuyhkep.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-Beuyhkep.js new file mode 100644 index 000000000..3a29cf44b --- /dev/null +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-Beuyhkep.js @@ -0,0 +1 @@ +import{l as h,be as p,R as x,r as i,j as M,Z as R}from"./index-DaG4uPnK.js";var s=h((r,o)=>{const{as:t,children:d,className:l,...n}=r,{slots:c,classNames:a,headerId:m,setHeaderMounted:e}=p(),f=x(o),u=t||"header";return i.useEffect(()=>(e(!0),()=>e(!1)),[e]),M.jsx(u,{ref:f,className:c.header({class:R(a?.header,l)}),id:m,...n,children:d})});s.displayName="HeroUI.ModalHeader";var N=s;export{N as m}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-RYoue1Xd.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-RYoue1Xd.js deleted file mode 100644 index 531e980f1..000000000 --- a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-IGSAU2ZA-RYoue1Xd.js +++ /dev/null @@ -1 +0,0 @@ -import{O as h,bf as p,A as x,r as i,j as M,J as H}from"./index-BTypXP0X.js";var s=h((r,o)=>{const{as:t,children:d,className:l,...n}=r,{slots:c,classNames:a,headerId:f,setHeaderMounted:e}=p(),m=x(o),u=t||"header";return i.useEffect(()=>(e(!0),()=>e(!1)),[e]),M.jsx(u,{ref:m,className:c.header({class:H(a?.header,l)}),id:f,...n,children:d})});s.displayName="HeroUI.ModalHeader";var R=s;export{R as m}; diff --git a/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-CTW6lkmA.js b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-CTW6lkmA.js new file mode 100644 index 000000000..288cb464d --- /dev/null +++ b/packages/ragbits-chat/src/ragbits/chat/ui-build/assets/chunk-SSA7SXE4-CTW6lkmA.js @@ -0,0 +1 @@ +import{l as y,bo as D,j as e,r as t,am as N}from"./index-DaG4uPnK.js";var 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Zy={};Zy={longPressMessage:"Натиснете продължително или натиснете Alt+ стрелка надолу, за да отворите менюто"};var Xy={};Xy={longPressMessage:"Dlouhým stiskem nebo stisknutím kláves Alt + šipka dolů otevřete nabídku"};var Qy={};Qy={longPressMessage:"Langt tryk eller tryk på Alt + pil ned for at åbne menuen"};var ev={};ev={longPressMessage:"Drücken Sie lange oder drücken Sie Alt + Nach-unten, um das Menü zu öffnen"};var tv={};tv={longPressMessage:"Πιέστε παρατεταμένα ή πατήστε Alt + κάτω βέλος για να ανοίξετε το μενού"};var rv={};rv={longPressMessage:"Long press or press Alt + ArrowDown to open menu"};var nv={};nv={longPressMessage:"Mantenga pulsado o pulse Alt + flecha abajo para abrir el menú"};var av={};av={longPressMessage:"Menüü avamiseks vajutage pikalt või vajutage klahve Alt + allanool"};var iv={};iv={longPressMessage:"Avaa valikko painamalla pohjassa tai näppäinyhdistelmällä Alt + Alanuoli"};var sv={};sv={longPressMessage:"Appuyez de manière prolongée ou appuyez sur 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