diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..58628f5 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,98 @@ +name: CI + +on: + pull_request: + push: + branches: + - main + workflow_dispatch: + +permissions: + contents: read + +jobs: + frontend: + name: Frontend + runs-on: ubuntu-latest + timeout-minutes: 10 + + steps: + - name: Check Out Repository + uses: actions/checkout@v6 + + - name: Set Up pnpm + uses: pnpm/action-setup@v4 + + - name: Set Up Node.js + uses: actions/setup-node@v6 + with: + node-version: 22 + cache: pnpm + cache-dependency-path: frontend/pnpm-lock.yaml + + - name: Install Frontend Dependencies + working-directory: frontend + run: pnpm install --frozen-lockfile + + - name: Lint Frontend + working-directory: frontend + run: pnpm lint + + - name: Build Frontend + working-directory: frontend + run: pnpm build + + backend: + name: Backend + runs-on: ubuntu-latest + timeout-minutes: 15 + env: + PAPERCHAT_DATABASE_URL: postgresql+psycopg://paperchat:paperchat@127.0.0.1:5433/paperchat + + steps: + - name: Check Out Repository + uses: actions/checkout@v6 + + - name: Set Up uv + uses: astral-sh/setup-uv@v7 + with: + python-version: "3.12" + enable-cache: true + + - name: Install Backend Dependencies + working-directory: backend + run: uv sync --locked --group dev + + - name: Validate Compose Configuration + run: docker compose -f compose.yaml config + + - name: Start Local Postgres Runtime + run: ./scripts/postgres-up.sh + + - name: Apply Backend Migrations + working-directory: backend + run: uv run alembic upgrade head + + - name: Lint Backend + working-directory: backend + run: uv run ruff check + + - name: Check Backend Formatting + working-directory: backend + run: uv run ruff format --check + + - name: Type Check Backend + working-directory: backend + run: uv run ty check + + - name: Test Backend + working-directory: backend + run: uv run pytest + + - name: Show Postgres Logs On Failure + if: failure() + run: docker compose -f compose.yaml logs postgres + + - name: Stop Local Postgres Runtime + if: always() + run: docker compose -f compose.yaml down -v diff --git a/.gitignore b/.gitignore index 8cfbf91..6907411 100644 --- a/.gitignore +++ b/.gitignore @@ -9,15 +9,16 @@ specs *.so # Python files # -/evaluation/data* -/evaluation/results* .env .venv +backend/.venv dist build *.whl *.pytest_cache __pycache__ +frontend/dist +frontend/node_modules # OS generated files # ###################### diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e9f2526..fda1989 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -12,10 +12,17 @@ repos: - id: check-merge-conflict - id: check-executables-have-shebangs - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.15.1" + - repo: local hooks: - id: ruff - args: [--exit-non-zero-on-fix, --config=backend/pyproject.toml] + name: ruff (legacy alias) + entry: /bin/zsh -lc 'cd backend && uv run ruff check' + language: system + files: ^backend/ + pass_filenames: false - id: ruff-format - args: [--config=backend/pyproject.toml] + name: ruff format + entry: /bin/zsh -lc 'cd backend && uv run ruff format --check' + language: system + files: ^backend/ + pass_filenames: false diff --git a/.python-version b/.python-version deleted file mode 100644 index 24ee5b1..0000000 --- a/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.13 diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 6d6e9cd..0000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2025 Kyle Puhger - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/README.md b/README.md index da10cae..378930f 100644 --- a/README.md +++ b/README.md @@ -1,55 +1,56 @@ -

- PaperChat logo -

+# PaperChat ---- +PaperChat is a local web app for grounded chat over user-owned PDFs. -Paperchat is an AI-powered assistant that helps you take your research to the next level by enabling you to chat with your research library. +The live code paths are: -## Installation +- `frontend/` for the React app +- `backend/` for the local Python API +- `compose.yaml` for the local Postgres + `pgvector` runtime -The easiest and fastest way to install PaperChat is using [uv](https://github.com/astral-sh/uv): +## Local Development -```bash -uv tool install git+https://github.com/kpeez/paperchat.git -``` - -This will install PaperChat and make it available as a command-line tool. - -To launch PaperChat, run: +Start the database: ```bash -paperchat +./scripts/postgres-up.sh ``` -Alternatively, you can install PaperChat into your Python environment (requires Python 3.12 or newer): +Start the backend: ```bash -pip install git+https://github.com/kpeez/paperchat.git +cd backend +uv sync --locked --group dev +uv run alembic upgrade head +uv run paperchat-backend ``` -and then launch PaperChat by running: +Start the frontend in a second shell: ```bash -paperchat +cd frontend +pnpm install --frozen-lockfile +pnpm dev ``` -## Quick Start - -1. Launch PaperChat: `paperchat` -2. Upload a PDF document using the sidebar -3. Wait for processing to complete -4. Start asking questions about your document! +## Verification -## Settings +Backend: -PaperChat uses your own model API keys to connect to the AI providers. If you have API keys set as environment variables, they will be automatically detected. Otherwise, you can set them in the settings page. To get API keys, you can sign up for a free account on the providers' websites: +```bash +cd backend +uv run ruff check +uv run ruff format --check +uv run ty check +uv run pytest +``` -- [Gemini](https://aistudio.google.com/apikey) (recommended) - - This is the recommend provider since you can access models for free (e.g., `gemini-2.5-pro-exp-03-25` and `gemini-2.0-flash`), and their text-embedding model is currently the best available on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). -- [OpenAI](https://platform.openai.com/api-keys) -- [Anthropic](https://console.anthropic.com/settings/keys) +Frontend: -## Troubleshooting +```bash +cd frontend +pnpm lint +pnpm build +``` -- Report issues on our [GitHub Issues page](https://github.com/kpeez/paperchat/issues) +Runtime details are documented in [`docs/local-runtime.md`](docs/local-runtime.md). diff --git a/app.py b/app.py deleted file mode 100644 index 519f35f..0000000 --- a/app.py +++ /dev/null @@ -1,107 +0,0 @@ -import os - -import streamlit as st - -from paperchat.ui import ( - check_model_config_changes, - refresh_model_state, - render_api_key_setup, - render_main_content, - render_settings_page, - render_sidebar, - set_css_styles, -) -from paperchat.utils.api_keys import get_api_key -from paperchat.utils.config import DEFAULT_MAX_TOKENS, DEFAULT_MODEL, DEFAULT_PROVIDER - -os.environ["TOKENIZERS_PARALLELISM"] = "false" - -st.set_page_config( - page_title="PaperChat - PDF Chat", - page_icon="📚", - layout="wide", - initial_sidebar_state="expanded", -) - - -set_css_styles() - - -def initialize_document_state() -> None: - """Initialize document-related session state variables.""" - if "filenames" not in st.session_state: - # stores hash -> original filename string - st.session_state.filenames = {} - if "active_document_hash" not in st.session_state: - # hash of the currently selected doc for chatting - st.session_state.active_document_hash = None - if "document_sources" not in st.session_state: - # stores hash -> source info ("vector_store" or "session") - st.session_state.document_sources = {} - - -def initialize_model_state() -> None: - """Initialize model and settings-related session state variables.""" - if "config" not in st.session_state: - st.session_state.config = { - "provider_name": DEFAULT_PROVIDER, - "model_id": DEFAULT_MODEL, - "temperature": 0.7, - "max_tokens": DEFAULT_MAX_TOKENS, - "top_k": 5, - } - - if "provider_settings" not in st.session_state: - provider_name = st.session_state.config.get("provider_name", DEFAULT_PROVIDER) - st.session_state.provider_settings = { - provider_name: { - "model": st.session_state.config.get("model_id", DEFAULT_MODEL), - "temperature": st.session_state.config.get("temperature", 0.7), - } - } - - if "settings" not in st.session_state: - st.session_state.settings = {"top_k": st.session_state.config.get("top_k", 5)} - - if "llm_manager" not in st.session_state or "rag_pipeline" not in st.session_state: - refresh_model_state() - - -def initialize_session() -> None: - """Initialize all session state variables and components in one place.""" - if "messages" not in st.session_state: - st.session_state.messages = [] - - initialize_document_state() - initialize_model_state() - - if "show_api_setup" not in st.session_state: - st.session_state.show_api_setup = False - if "show_settings" not in st.session_state: - st.session_state.show_settings = False - - -def main() -> None: - """Main entry point for the Streamlit app.""" - initialize_session() - check_model_config_changes() - provider_name = st.session_state.config.get("provider_name", DEFAULT_PROVIDER) - provider_key = get_api_key(provider_name) - - if not provider_key or st.session_state.show_api_setup: - render_api_key_setup() - st.stop() - - st.title("🤖 PaperChat - Chat with your research library") - if st.session_state.show_settings: - render_settings_page(on_save_callback=lambda: refresh_model_state()) - st.stop() - - with st.sidebar: - render_sidebar() - - render_main_content() - - -if __name__ == "__main__": - main() diff --git a/backend/alembic.ini b/backend/alembic.ini new file mode 100644 index 0000000..bd2f0bf --- /dev/null +++ b/backend/alembic.ini @@ -0,0 +1,38 @@ +[alembic] +script_location = alembic +prepend_sys_path = . +sqlalchemy.url = postgresql+psycopg://paperchat:paperchat@127.0.0.1:5433/paperchat + +[post_write_hooks] + +[loggers] +keys = root,sqlalchemy,alembic + +[handlers] +keys = console + +[formatters] +keys = generic + +[logger_root] +level = WARN +handlers = console + +[logger_sqlalchemy] +level = WARN +handlers = +qualname = sqlalchemy.engine + +[logger_alembic] +level = INFO +handlers = +qualname = alembic + +[handler_console] +class = StreamHandler +args = (sys.stderr,) +level = NOTSET +formatter = generic + +[formatter_generic] +format = %(levelname)-5.5s [%(name)s] %(message)s diff --git a/backend/alembic/env.py b/backend/alembic/env.py new file mode 100644 index 0000000..63954c4 --- /dev/null +++ b/backend/alembic/env.py @@ -0,0 +1,74 @@ +"""Alembic environment configuration for backend schema migrations.""" + +from __future__ import annotations + +import os +from logging.config import fileConfig + +from sqlalchemy import engine_from_config, pool + +from alembic import context +from paperchat_backend.db.schema import Base + +config = context.config + +if config.config_file_name is not None: + fileConfig(config.config_file_name) + +target_metadata = Base.metadata + + +def _database_url() -> str: + database_url = os.environ.get( + "PAPERCHAT_DATABASE_URL", + config.get_main_option("sqlalchemy.url"), + ) + if database_url is None: + msg = "sqlalchemy.url must be configured for Alembic migrations." + raise RuntimeError(msg) + + return database_url + + +def run_migrations_offline() -> None: + """Run migrations without creating an Engine.""" + context.configure( + url=_database_url(), + target_metadata=target_metadata, + literal_binds=True, + compare_type=True, + compare_server_default=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online() -> None: + """Run migrations using a live database connection.""" + configuration = config.get_section(config.config_ini_section, {}) + configuration["sqlalchemy.url"] = _database_url() + + connectable = engine_from_config( + configuration, + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + compare_type=True, + compare_server_default=True, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/backend/alembic/script.py.mako b/backend/alembic/script.py.mako new file mode 100644 index 0000000..40c6b2b --- /dev/null +++ b/backend/alembic/script.py.mako @@ -0,0 +1,18 @@ +"""${message}""" + +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + + +def upgrade() -> None: + ${upgrades if upgrades else "pass"} + + +def downgrade() -> None: + ${downgrades if downgrades else "pass"} diff --git a/backend/alembic/versions/0001_initial_schema.py b/backend/alembic/versions/0001_initial_schema.py new file mode 100644 index 0000000..d780546 --- /dev/null +++ b/backend/alembic/versions/0001_initial_schema.py @@ -0,0 +1,174 @@ +"""Create the initial backend database schema.""" + +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +from alembic import op +from paperchat_backend.db.schema import ( + DOCUMENT_STATUS_LENGTH, + MESSAGE_ROLE_LENGTH, + Vector, +) + +revision = "0001_initial_schema" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade() -> None: + op.execute("CREATE EXTENSION IF NOT EXISTS pgcrypto") + op.execute("CREATE EXTENSION IF NOT EXISTS vector") + + op.create_table( + "documents", + sa.Column( + "id", + postgresql.UUID(as_uuid=True), + nullable=False, + server_default=sa.text("gen_random_uuid()"), + ), + sa.Column("content_hash", sa.String(length=128), nullable=False), + sa.Column("original_filename", sa.Text(), nullable=False), + sa.Column("display_name", sa.Text(), nullable=False), + sa.Column("file_path", sa.Text(), nullable=False), + sa.Column( + "status", + sa.String(length=DOCUMENT_STATUS_LENGTH), + nullable=False, + server_default=sa.text("'pending'"), + ), + sa.Column( + "created_at", + sa.DateTime(timezone=True), + nullable=False, + server_default=sa.func.now(), + ), + sa.Column( + "updated_at", + sa.DateTime(timezone=True), + nullable=False, + server_default=sa.func.now(), + ), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint("content_hash"), + ) + op.create_index("ix_documents_status", "documents", ["status"], unique=False) + + op.create_table( + "document_chunks", + sa.Column( + "id", + postgresql.UUID(as_uuid=True), + nullable=False, + server_default=sa.text("gen_random_uuid()"), + ), + sa.Column("document_id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("chunk_index", sa.Integer(), nullable=False), + sa.Column("text", sa.Text(), nullable=False), + sa.Column("page_numbers", postgresql.ARRAY(sa.Integer()), nullable=False), + sa.Column("headings", postgresql.ARRAY(sa.Text()), nullable=False), + sa.Column("embedding", Vector(), nullable=False), + sa.Column( + "created_at", + sa.DateTime(timezone=True), + nullable=False, + server_default=sa.func.now(), + ), + sa.ForeignKeyConstraint(["document_id"], ["documents.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint( + "document_id", + "chunk_index", + name="uq_document_chunks_document_id_chunk_index", + ), + ) + op.create_index( + "ix_document_chunks_document_id", "document_chunks", ["document_id"], unique=False + ) + + op.create_table( + "conversations", + sa.Column( + "id", + postgresql.UUID(as_uuid=True), + nullable=False, + server_default=sa.text("gen_random_uuid()"), + ), + sa.Column("title", sa.Text(), nullable=True), + sa.Column( + "created_at", + sa.DateTime(timezone=True), + nullable=False, + server_default=sa.func.now(), + ), + sa.Column( + "updated_at", + sa.DateTime(timezone=True), + nullable=False, + server_default=sa.func.now(), + ), + sa.PrimaryKeyConstraint("id"), + ) + + op.create_table( + "messages", + sa.Column( + "id", + postgresql.UUID(as_uuid=True), + nullable=False, + server_default=sa.text("gen_random_uuid()"), + ), + sa.Column("conversation_id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("message_index", sa.Integer(), nullable=False), + sa.Column( + "role", + sa.String(length=MESSAGE_ROLE_LENGTH), + nullable=False, + ), + sa.Column("content", sa.Text(), nullable=False), + sa.Column("citations", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.Column( + "created_at", + sa.DateTime(timezone=True), + nullable=False, + server_default=sa.func.now(), + ), + sa.ForeignKeyConstraint(["conversation_id"], ["conversations.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint( + "conversation_id", + "message_index", + name="uq_messages_conversation_id_message_index", + ), + ) + op.create_index("ix_messages_conversation_id", "messages", ["conversation_id"], unique=False) + + op.create_table( + "app_state", + sa.Column("key", sa.String(length=64), nullable=False), + sa.Column( + "value", + postgresql.JSONB(astext_type=sa.Text()), + nullable=False, + server_default=sa.text("'{}'::jsonb"), + ), + sa.Column( + "updated_at", + sa.DateTime(timezone=True), + nullable=False, + server_default=sa.func.now(), + ), + sa.PrimaryKeyConstraint("key"), + ) + + +def downgrade() -> None: + op.drop_table("app_state") + op.drop_index("ix_messages_conversation_id", table_name="messages") + op.drop_table("messages") + op.drop_table("conversations") + op.drop_index("ix_document_chunks_document_id", table_name="document_chunks") + op.drop_table("document_chunks") + op.drop_index("ix_documents_status", table_name="documents") + op.drop_table("documents") diff --git a/backend/paperchat_backend/__init__.py b/backend/paperchat_backend/__init__.py index e69de29..3dc1f76 100644 --- a/backend/paperchat_backend/__init__.py +++ b/backend/paperchat_backend/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/backend/paperchat_backend/api/bootstrap.py b/backend/paperchat_backend/api/bootstrap.py index 659b86a..8e3a758 100644 --- a/backend/paperchat_backend/api/bootstrap.py +++ b/backend/paperchat_backend/api/bootstrap.py @@ -1,12 +1,7 @@ from fastapi import APIRouter -from paperchat_backend.models.bootstrap import ( - BootstrapError, - BootstrapResponse, - BootstrapStatus, - CheckResult, - HealthResponse, -) +from paperchat_backend.models.bootstrap import BootstrapResponse, HealthResponse +from paperchat_backend.services import build_bootstrap_response router = APIRouter() @@ -18,29 +13,4 @@ def health() -> HealthResponse: @router.get("/api/bootstrap") def bootstrap() -> BootstrapResponse: - checks = { - "backend": CheckResult(ok=True), - "database": CheckResult(ok=True), - "docker": CheckResult(ok=True), - "migrations": CheckResult(ok=True), - } - - errors: list[BootstrapError] = [] - for name, check in checks.items(): - if not check.ok: - errors.append( - BootstrapError( - code=f"{name}_failed", - message=check.message or f"{name} check failed", - action=f"Check {name} configuration", - ) - ) - - status = BootstrapStatus.ready if all(c.ok for c in checks.values()) else BootstrapStatus.error - - return BootstrapResponse( - app_version="0.1.0", - status=status, - checks=checks, - errors=errors, - ) + return build_bootstrap_response() diff --git a/backend/paperchat_backend/config.py b/backend/paperchat_backend/config.py new file mode 100644 index 0000000..21a28dc --- /dev/null +++ b/backend/paperchat_backend/config.py @@ -0,0 +1,45 @@ +import os +from functools import lru_cache +from pathlib import Path + +from sqlalchemy import URL + +DEFAULT_DB_HOST = "127.0.0.1" +DEFAULT_DB_NAME = "paperchat" +DEFAULT_DB_PASSWORD = "paperchat" +DEFAULT_DB_PORT = 5433 +DEFAULT_DB_USER = "paperchat" +DOCKER_BINARY = "docker" +DOCKER_SERVICE = "postgres" +BACKEND_ROOT = Path(__file__).resolve().parents[1] +REPO_ROOT = Path(__file__).resolve().parents[2] +ALEMBIC_INI_PATH = BACKEND_ROOT / "alembic.ini" +ALEMBIC_DIR = BACKEND_ROOT / "alembic" +COMPOSE_FILE = REPO_ROOT / "compose.yaml" + + +def _read_port(name: str, default: int) -> int: + raw_value = os.getenv(name) + if raw_value is None: + return default + + try: + return int(raw_value) + except ValueError as error: + msg = f"{name} must be an integer, got {raw_value!r}." + raise ValueError(msg) from error + + +@lru_cache +def get_database_url() -> str: + if database_url := os.getenv("PAPERCHAT_DATABASE_URL"): + return database_url + + return URL.create( + "postgresql+psycopg", + username=os.getenv("PAPERCHAT_DB_USER", DEFAULT_DB_USER), + password=os.getenv("PAPERCHAT_DB_PASSWORD", DEFAULT_DB_PASSWORD), + host=os.getenv("PAPERCHAT_DB_HOST", DEFAULT_DB_HOST), + port=_read_port("PAPERCHAT_DB_PORT", DEFAULT_DB_PORT), + database=os.getenv("PAPERCHAT_DB_NAME", DEFAULT_DB_NAME), + ).render_as_string(hide_password=False) diff --git a/backend/paperchat_backend/db/__init__.py b/backend/paperchat_backend/db/__init__.py new file mode 100644 index 0000000..019dbdc --- /dev/null +++ b/backend/paperchat_backend/db/__init__.py @@ -0,0 +1,21 @@ +"""Database schema package for the PaperChat backend.""" + +from paperchat_backend.db.schema import ( + AppState, + Base, + Conversation, + Document, + DocumentChunk, + Message, + Vector, +) + +__all__ = [ + "AppState", + "Base", + "Conversation", + "Document", + "DocumentChunk", + "Message", + "Vector", +] diff --git a/backend/paperchat_backend/db/engine.py b/backend/paperchat_backend/db/engine.py new file mode 100644 index 0000000..0748a14 --- /dev/null +++ b/backend/paperchat_backend/db/engine.py @@ -0,0 +1,10 @@ +from functools import lru_cache + +from sqlalchemy import Engine, create_engine + +from paperchat_backend.config import get_database_url + + +@lru_cache +def get_engine() -> Engine: + return create_engine(get_database_url(), pool_pre_ping=True) diff --git a/backend/paperchat_backend/db/schema.py b/backend/paperchat_backend/db/schema.py new file mode 100644 index 0000000..72fa125 --- /dev/null +++ b/backend/paperchat_backend/db/schema.py @@ -0,0 +1,175 @@ +"""Database schema for the local Postgres persistence contract.""" + +from __future__ import annotations + +from datetime import datetime +from typing import Any +from uuid import UUID + +from sqlalchemy import DateTime, ForeignKey, Index, Integer, String, Text, UniqueConstraint, func +from sqlalchemy.dialects.postgresql import ARRAY, JSONB +from sqlalchemy.dialects.postgresql import UUID as PostgresUUID +from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column +from sqlalchemy.sql import expression +from sqlalchemy.types import UserDefinedType + +DOCUMENT_STATUS_LENGTH = 32 +MESSAGE_ROLE_LENGTH = 16 + + +class Base(DeclarativeBase): + """Base metadata for backend database tables.""" + + +class Vector(UserDefinedType[Any]): + """Minimal SQLAlchemy type for pgvector-backed columns.""" + + cache_ok = True + + def __init__(self, dimensions: int | None = None) -> None: + self.dimensions = dimensions + + def get_col_spec(self, **_: Any) -> str: + if self.dimensions is None: + return "vector" + return f"vector({self.dimensions})" + + +class TimestampMixin: + """Shared timestamp columns for mutable tables.""" + + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ) + updated_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ) + + +class Document(TimestampMixin, Base): + """Local document metadata and ingestion lifecycle state.""" + + __tablename__ = "documents" + + id: Mapped[UUID] = mapped_column( + PostgresUUID(as_uuid=True), + primary_key=True, + server_default=expression.text("gen_random_uuid()"), + ) + content_hash: Mapped[str] = mapped_column(String(128), nullable=False, unique=True) + original_filename: Mapped[str] = mapped_column(Text, nullable=False) + display_name: Mapped[str] = mapped_column(Text, nullable=False) + file_path: Mapped[str] = mapped_column(Text, nullable=False) + status: Mapped[str] = mapped_column( + String(DOCUMENT_STATUS_LENGTH), + nullable=False, + server_default=expression.text("'pending'"), + ) + + __table_args__ = (Index("ix_documents_status", "status"),) + + +class DocumentChunk(Base): + """Chunk rows linked to a durable document id.""" + + __tablename__ = "document_chunks" + + id: Mapped[UUID] = mapped_column( + PostgresUUID(as_uuid=True), + primary_key=True, + server_default=expression.text("gen_random_uuid()"), + ) + document_id: Mapped[UUID] = mapped_column( + PostgresUUID(as_uuid=True), + ForeignKey("documents.id", ondelete="CASCADE"), + nullable=False, + ) + chunk_index: Mapped[int] = mapped_column(Integer, nullable=False) + text: Mapped[str] = mapped_column(Text, nullable=False) + page_numbers: Mapped[list[int]] = mapped_column(ARRAY(Integer()), nullable=False) + headings: Mapped[list[str]] = mapped_column(ARRAY(Text()), nullable=False) + embedding: Mapped[Any] = mapped_column(Vector(), nullable=False) + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ) + + __table_args__ = ( + UniqueConstraint( + "document_id", + "chunk_index", + name="uq_document_chunks_document_id_chunk_index", + ), + Index("ix_document_chunks_document_id", "document_id"), + ) + + +class Conversation(TimestampMixin, Base): + """Chat conversation metadata.""" + + __tablename__ = "conversations" + + id: Mapped[UUID] = mapped_column( + PostgresUUID(as_uuid=True), + primary_key=True, + server_default=expression.text("gen_random_uuid()"), + ) + title: Mapped[str | None] = mapped_column(Text, nullable=True) + + +class Message(Base): + """Persisted chat messages for a local conversation.""" + + __tablename__ = "messages" + + id: Mapped[UUID] = mapped_column( + PostgresUUID(as_uuid=True), + primary_key=True, + server_default=expression.text("gen_random_uuid()"), + ) + conversation_id: Mapped[UUID] = mapped_column( + PostgresUUID(as_uuid=True), + ForeignKey("conversations.id", ondelete="CASCADE"), + nullable=False, + ) + message_index: Mapped[int] = mapped_column(Integer, nullable=False) + role: Mapped[str] = mapped_column(String(MESSAGE_ROLE_LENGTH), nullable=False) + content: Mapped[str] = mapped_column(Text, nullable=False) + citations: Mapped[dict[str, Any] | None] = mapped_column(JSONB, nullable=True) + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ) + + __table_args__ = ( + UniqueConstraint( + "conversation_id", + "message_index", + name="uq_messages_conversation_id_message_index", + ), + Index("ix_messages_conversation_id", "conversation_id"), + ) + + +class AppState(Base): + """Small key-value store for runtime and schema markers.""" + + __tablename__ = "app_state" + + key: Mapped[str] = mapped_column(String(64), primary_key=True) + value: Mapped[dict[str, Any]] = mapped_column( + JSONB, + nullable=False, + server_default=expression.text("'{}'::jsonb"), + ) + updated_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), + nullable=False, + server_default=func.now(), + ) diff --git a/backend/paperchat_backend/main.py b/backend/paperchat_backend/main.py index f4d551e..a6defd0 100644 --- a/backend/paperchat_backend/main.py +++ b/backend/paperchat_backend/main.py @@ -1,4 +1,5 @@ import os +from typing import Any, cast import uvicorn from fastapi import FastAPI @@ -10,7 +11,7 @@ def create_app() -> FastAPI: app = FastAPI() app.add_middleware( - CORSMiddleware, + cast(Any, CORSMiddleware), allow_origins=["http://localhost:5173", "http://127.0.0.1:5173"], allow_credentials=True, allow_methods=["*"], diff --git a/backend/paperchat_backend/services/__init__.py b/backend/paperchat_backend/services/__init__.py new file mode 100644 index 0000000..6802b2c --- /dev/null +++ b/backend/paperchat_backend/services/__init__.py @@ -0,0 +1,3 @@ +from paperchat_backend.services.bootstrap import build_bootstrap_response + +__all__ = ["build_bootstrap_response"] diff --git a/backend/paperchat_backend/services/bootstrap.py b/backend/paperchat_backend/services/bootstrap.py new file mode 100644 index 0000000..208f647 --- /dev/null +++ b/backend/paperchat_backend/services/bootstrap.py @@ -0,0 +1,227 @@ +import subprocess +from collections.abc import Sequence +from dataclasses import dataclass +from shutil import which + +from alembic.config import Config +from alembic.runtime.migration import MigrationContext +from alembic.script import ScriptDirectory +from alembic.util.exc import CommandError +from sqlalchemy import text +from sqlalchemy.exc import SQLAlchemyError + +from paperchat_backend import __version__ +from paperchat_backend.config import ( + ALEMBIC_DIR, + ALEMBIC_INI_PATH, + COMPOSE_FILE, + DOCKER_BINARY, + DOCKER_SERVICE, + get_database_url, +) +from paperchat_backend.db.engine import get_engine +from paperchat_backend.models.bootstrap import ( + BootstrapError, + BootstrapResponse, + BootstrapStatus, + CheckResult, +) + +CHECK_ACTIONS = { + "docker": "Install Docker Desktop and start the local PaperChat database container.", + "database": "Start the local Postgres container and confirm the configured port is reachable.", + "migrations": "Run the backend migrations so the database revision matches the application.", + "pgvector": "Recreate the local database with the pgvector image or install the vector extension.", +} +DATABASE_BLOCKED_MESSAGE = "Database must be reachable before this check can run." +DOCKER_COMMAND_TIMEOUT_SECONDS = 5 + + +@dataclass(frozen=True, slots=True) +class CompletedCheck: + name: str + result: CheckResult + + +def _completed_check(name: str, *, ok: bool, message: str) -> CompletedCheck: + return CompletedCheck(name=name, result=CheckResult(ok=ok, message=message)) + + +def build_bootstrap_response() -> BootstrapResponse: + checks = {"backend": CheckResult(ok=True, message=f"Backend {__version__} is running.")} + + for check in (_check_docker_runtime(), _check_database()): + checks[check.name] = check.result + + if checks["database"].ok: + for check in (_check_migrations(), _check_pgvector()): + checks[check.name] = check.result + else: + for name in ("migrations", "pgvector"): + checks[name] = CheckResult(ok=False, message=DATABASE_BLOCKED_MESSAGE) + + errors = [ + BootstrapError( + code=f"{name}_failed", + message=check.message or f"{name} check failed.", + action=CHECK_ACTIONS[name], + ) + for name, check in checks.items() + if name != "backend" and not check.ok + ] + + return BootstrapResponse( + app_version=__version__, + status=BootstrapStatus.ready if not errors else BootstrapStatus.error, + checks=checks, + errors=errors, + ) + + +def _check_docker_runtime() -> CompletedCheck: + if not COMPOSE_FILE.is_file(): + return _completed_check( + "docker", + ok=False, + message=f"Compose file not found at {COMPOSE_FILE}.", + ) + + docker_binary = which(DOCKER_BINARY) + if docker_binary is None: + return _completed_check("docker", ok=False, message="Docker CLI is not installed.") + + version_result = _run_docker_command([docker_binary, "compose", "version"]) + if isinstance(version_result, CompletedCheck): + return version_result + + compose_command = [docker_binary, "compose", "-f", str(COMPOSE_FILE)] + ps_result = _run_docker_command([*compose_command, "ps", "--services", "--status", "running"]) + if isinstance(ps_result, CompletedCheck): + return ps_result + + if DOCKER_SERVICE not in ps_result.stdout.split(): + return _completed_check( + "docker", + ok=False, + message=f"Compose service '{DOCKER_SERVICE}' is not running.", + ) + + return _completed_check("docker", ok=True, message="Docker runtime is ready.") + + +def _check_database() -> CompletedCheck: + try: + with get_engine().connect() as connection: + connection.execute(text("SELECT 1")) + except SQLAlchemyError as error: + return _completed_check( + "database", + ok=False, + message=str(error.__cause__ or error), + ) + + return _completed_check("database", ok=True, message="Connected to Postgres.") + + +def _check_migrations() -> CompletedCheck: + message: str | None = None + head_revision: str | None = None + + if not ALEMBIC_INI_PATH.is_file(): + message = f"Alembic config not found at {ALEMBIC_INI_PATH}." + elif not ALEMBIC_DIR.is_dir(): + message = f"Alembic scripts not found at {ALEMBIC_DIR}." + else: + try: + alembic_config = Config(str(ALEMBIC_INI_PATH)) + alembic_config.set_main_option("script_location", str(ALEMBIC_DIR)) + alembic_config.set_main_option("sqlalchemy.url", get_database_url()) + head_revision = ScriptDirectory.from_config(alembic_config).get_current_head() + except CommandError as error: + message = str(error) + + if message is None and head_revision is None: + message = "No Alembic head revision is defined." + + if message is None: + try: + with get_engine().connect() as connection: + current_revision = MigrationContext.configure(connection).get_current_revision() + except SQLAlchemyError as error: + message = str(error.__cause__ or error) + else: + if current_revision != head_revision: + message = ( + f"Database revision {current_revision or 'none'} does not match " + f"head {head_revision}." + ) + + if message is not None: + return _completed_check("migrations", ok=False, message=message) + + return _completed_check( + "migrations", ok=True, message=f"Migration revision {head_revision} is current." + ) + + +def _check_pgvector() -> CompletedCheck: + try: + with get_engine().connect() as connection: + extension_version = connection.scalar( + text("SELECT extversion FROM pg_extension WHERE extname = 'vector'") + ) + except SQLAlchemyError as error: + return _completed_check( + "pgvector", + ok=False, + message=str(error.__cause__ or error), + ) + + if extension_version is None: + return _completed_check( + "pgvector", + ok=False, + message="pgvector extension is not installed.", + ) + + return _completed_check( + "pgvector", + ok=True, + message=f"pgvector extension {extension_version} is installed.", + ) + + +def _run_command(command: Sequence[str]) -> subprocess.CompletedProcess[str]: + return subprocess.run( + command, + check=False, + capture_output=True, + text=True, + timeout=DOCKER_COMMAND_TIMEOUT_SECONDS, + ) + + +def _run_docker_command( + command: Sequence[str], +) -> subprocess.CompletedProcess[str] | CompletedCheck: + try: + result = _run_command(command) + except subprocess.TimeoutExpired: + return _completed_check("docker", ok=False, message="Docker command timed out.") + + if result.returncode == 0: + return result + + return _completed_check( + "docker", + ok=False, + message=_command_error_message(result.stderr, result.stdout), + ) + + +def _command_error_message(stderr: str, stdout: str) -> str: + message = stderr.strip() or stdout.strip() + if message: + return message + + return "Docker command failed without output." diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 8b115f1..9bce894 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -3,7 +3,10 @@ name = "paperchat-backend" version = "0.1.0" requires-python = ">=3.12" dependencies = [ + "alembic>=1.16.5", "fastapi", + "psycopg[binary]>=3.2.12", + "sqlalchemy>=2.0.44", "uvicorn[standard]", ] @@ -15,6 +18,7 @@ dev = [ "pytest>=8.3.4", "httpx", "ruff>=0.11.0", + "ty>=0.0.20", ] [build-system] diff --git a/backend/tests/test_bootstrap.py b/backend/tests/test_bootstrap.py index 5adfeec..b90fb1e 100644 --- a/backend/tests/test_bootstrap.py +++ b/backend/tests/test_bootstrap.py @@ -1,34 +1,185 @@ +from typing import Any, cast + +import pytest from fastapi.testclient import TestClient from paperchat_backend.main import create_app +from paperchat_backend.models.bootstrap import CheckResult +from paperchat_backend.services import bootstrap as bootstrap_service client = TestClient(create_app()) -def test_bootstrap_returns_200(): +def make_check(name: str, *, ok: bool, message: str) -> bootstrap_service.CompletedCheck: + return bootstrap_service.CompletedCheck(name=name, result=CheckResult(ok=ok, message=message)) + + +DEFAULT_READY_CHECKS = { + "docker": make_check("docker", ok=True, message="Docker runtime is ready."), + "database": make_check("database", ok=True, message="Connected to Postgres."), + "migrations": make_check( + "migrations", + ok=True, + message="Migration revision 0001_initial_schema is current.", + ), + "pgvector": make_check("pgvector", ok=True, message="pgvector extension 0.8.1 is installed."), +} + + +def patch_bootstrap_checks( + monkeypatch: pytest.MonkeyPatch, + *, + docker: bootstrap_service.CompletedCheck | None = None, + database: bootstrap_service.CompletedCheck | None = None, + migrations: bootstrap_service.CompletedCheck | None = None, + pgvector: bootstrap_service.CompletedCheck | None = None, +) -> None: + checks = { + **DEFAULT_READY_CHECKS, + "docker": docker or DEFAULT_READY_CHECKS["docker"], + "database": database or DEFAULT_READY_CHECKS["database"], + "migrations": migrations or DEFAULT_READY_CHECKS["migrations"], + "pgvector": pgvector or DEFAULT_READY_CHECKS["pgvector"], + } + monkeypatch.setattr( + bootstrap_service, + "_check_docker_runtime", + lambda: checks["docker"], + ) + monkeypatch.setattr( + bootstrap_service, + "_check_database", + lambda: checks["database"], + ) + monkeypatch.setattr( + bootstrap_service, + "_check_migrations", + lambda: checks["migrations"], + ) + monkeypatch.setattr( + bootstrap_service, + "_check_pgvector", + lambda: checks["pgvector"], + ) + + +def get_bootstrap_data( + monkeypatch: pytest.MonkeyPatch, + **checks: bootstrap_service.CompletedCheck | None, +) -> dict[str, Any]: + patch_bootstrap_checks(monkeypatch, **checks) + return cast(dict[str, Any], client.get("/api/bootstrap").json()) + + +def test_bootstrap_returns_200(monkeypatch: pytest.MonkeyPatch): + patch_bootstrap_checks(monkeypatch) response = client.get("/api/bootstrap") assert response.status_code == 200 -def test_bootstrap_has_valid_structure(): - data = client.get("/api/bootstrap").json() +def test_bootstrap_has_valid_structure(monkeypatch: pytest.MonkeyPatch): + data = get_bootstrap_data(monkeypatch) assert "app_version" in data assert "status" in data assert "checks" in data assert "errors" in data -def test_backend_check_is_ok(): - data = client.get("/api/bootstrap").json() +def test_backend_check_is_ok(monkeypatch: pytest.MonkeyPatch): + data = get_bootstrap_data(monkeypatch) assert data["checks"]["backend"]["ok"] is True -def test_status_is_ready(): - data = client.get("/api/bootstrap").json() +def test_status_is_ready(monkeypatch: pytest.MonkeyPatch): + data = get_bootstrap_data(monkeypatch) assert data["status"] == "ready" -def test_all_expected_checks_present(): - data = client.get("/api/bootstrap").json() - expected = {"backend", "database", "docker", "migrations"} +def test_all_expected_checks_present(monkeypatch: pytest.MonkeyPatch): + data = get_bootstrap_data(monkeypatch) + expected = {"backend", "database", "docker", "migrations", "pgvector"} assert set(data["checks"].keys()) == expected + + +@pytest.mark.parametrize( + ("check_patch", "expected_checks", "expected_errors"), + [ + ( + {"docker": make_check("docker", ok=False, message="Docker is unavailable")}, + { + "docker": {"ok": False, "message": "Docker is unavailable"}, + }, + { + "code": "docker_failed", + "message": "Docker is unavailable", + "action": bootstrap_service.CHECK_ACTIONS["docker"], + }, + ), + ( + { + "database": make_check( + "database", + ok=False, + message="Database connection failed", + ) + }, + { + "database": {"ok": False, "message": "Database connection failed"}, + "migrations": { + "ok": False, + "message": bootstrap_service.DATABASE_BLOCKED_MESSAGE, + }, + "pgvector": { + "ok": False, + "message": bootstrap_service.DATABASE_BLOCKED_MESSAGE, + }, + }, + { + "code": "database_failed", + "message": "Database connection failed", + "action": bootstrap_service.CHECK_ACTIONS["database"], + }, + ), + ( + {"migrations": make_check("migrations", ok=False, message="Pending migrations")}, + { + "migrations": {"ok": False, "message": "Pending migrations"}, + }, + { + "code": "migrations_failed", + "message": "Pending migrations", + "action": bootstrap_service.CHECK_ACTIONS["migrations"], + }, + ), + ( + { + "pgvector": make_check( + "pgvector", + ok=False, + message="pgvector extension is missing", + ) + }, + { + "pgvector": {"ok": False, "message": "pgvector extension is missing"}, + }, + { + "code": "pgvector_failed", + "message": "pgvector extension is missing", + "action": bootstrap_service.CHECK_ACTIONS["pgvector"], + }, + ), + ], +) +def test_bootstrap_reports_storage_failure_paths( + monkeypatch: pytest.MonkeyPatch, + check_patch: dict[str, bootstrap_service.CompletedCheck], + expected_checks: dict[str, dict[str, str | bool | None]], + expected_errors: dict[str, str], +): + data = get_bootstrap_data(monkeypatch, **check_patch) + + assert data["status"] != "ready" + for check_name, expected_check in expected_checks.items(): + assert data["checks"][check_name] == expected_check + + assert expected_errors in data["errors"] diff --git a/backend/uv.lock b/backend/uv.lock index f2d5c7f..5c54122 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -2,6 +2,20 @@ version = 1 revision = 3 requires-python = ">=3.12" +[[package]] +name = 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[[package]] name = "uvicorn" version = "0.41.0" diff --git a/compose.yaml b/compose.yaml new file mode 100644 index 0000000..5ad5a61 --- /dev/null +++ b/compose.yaml @@ -0,0 +1,26 @@ +services: + postgres: + image: pgvector/pgvector:pg17 + restart: unless-stopped + environment: + POSTGRES_DB: paperchat + POSTGRES_USER: paperchat + POSTGRES_PASSWORD: paperchat + PGDATA: /var/lib/postgresql/data/pgdata + ports: + - "127.0.0.1:5433:5432" + volumes: + - paperchat_postgres_data:/var/lib/postgresql/data + healthcheck: + test: + [ + "CMD-SHELL", + "pg_isready -h 127.0.0.1 -U $$POSTGRES_USER -d $$POSTGRES_DB || exit 1", + ] + interval: 5s + timeout: 5s + retries: 12 + start_period: 5s + +volumes: + paperchat_postgres_data: diff --git a/docs/assets/logo.png b/docs/assets/logo.png deleted file mode 100644 index 95c1afe..0000000 Binary files a/docs/assets/logo.png and /dev/null differ diff --git a/docs/local-runtime.md b/docs/local-runtime.md new file mode 100644 index 0000000..91652a9 --- /dev/null +++ b/docs/local-runtime.md @@ -0,0 +1,41 @@ +# Local Runtime + +PaperChat PR 2 introduces a single supported local database runtime: + +- Docker-managed Postgres +- `pgvector` enabled through the `pgvector/pgvector` image +- host binding on `127.0.0.1:5433` +- named Docker volume `paperchat_postgres_data` + +## Start + +```bash +./scripts/postgres-up.sh +``` + +This starts the `postgres` service from [`../compose.yaml`](../compose.yaml) +and waits for the container healthcheck to report healthy. + +## Status + +```bash +./scripts/postgres-status.sh +``` + +This shows the Compose service state and the current Docker health status. + +## Stop + +```bash +./scripts/postgres-down.sh +``` + +This stops the Postgres service but leaves the named volume in place. + +## Connection Contract + +- host: `127.0.0.1` +- port: `5433` +- database: `paperchat` +- user: `paperchat` +- password: `paperchat` diff --git a/evaluation/calculate_metrics.py b/evaluation/calculate_metrics.py deleted file mode 100644 index e0ae8cb..0000000 --- a/evaluation/calculate_metrics.py +++ /dev/null @@ -1,177 +0,0 @@ -"""Calculates BEIR evaluation metrics from saved retrieval results.""" - -import json -import logging -from pathlib import Path -from typing import Annotated - -import typer -from beir.datasets.data_loader import GenericDataLoader -from beir.retrieval.evaluation import EvaluateRetrieval -from rich.console import Console -from rich.table import Table - -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") - -app = typer.Typer(help="Calculate BEIR evaluation metrics from retrieval results.") -console = Console() - - -def load_qrels(data_path: Path, split: str = "test") -> dict[str, dict[str, int]]: - """Loads the qrels (relevance judgments) for a given dataset split.""" - if not data_path.exists(): - raise FileNotFoundError( - f"BEIR dataset not found at {data_path}. Run download_data.py first." - ) - logging.info(f"Loading qrels from: {data_path}") - try: - _, _, qrels = GenericDataLoader(data_folder=str(data_path)).load(split=split) - logging.info(f"Loaded {len(qrels)} queries with relevance judgements.") - return qrels - except Exception as e: - logging.error(f"Failed to load qrels from {data_path}: {e}", exc_info=True) - raise - - -def load_results(results_path: Path) -> dict[str, dict[str, float]]: - """Loads the retrieval results from a JSON file.""" - if not results_path.exists(): - raise FileNotFoundError(f"Results file not found at {results_path}") - logging.info(f"Loading retrieval results from: {results_path}") - with open(results_path) as f: - results = json.load(f) - logging.info(f"Loaded results for {len(results)} queries.") - return results - - -def display_metrics(metrics: dict[str, dict[str, float]], title: str = "Retrieval Metrics") -> None: - """Displays the calculated metrics in a formatted table.""" - table = Table(title=title) - table.add_column("Metric", style="cyan", no_wrap=True) - if metrics: - first_metric_dict = next(iter(metrics.values()), None) - if first_metric_dict: - first_metric_keys = list(first_metric_dict.keys()) - for k_header in first_metric_keys: - table.add_column(k_header, style="magenta") - - for metric_name, k_values in metrics.items(): - row = [metric_name] - for k_header in first_metric_keys: - value = k_values.get(k_header, float("nan")) - row.append(f"{value:.4f}") - table.add_row(*row) - - console.print(table) - - -@app.command() -def main( - results_path: Annotated[ - Path, - typer.Argument( - help="Path to the JSON file containing retrieval results.", - exists=True, - file_okay=True, - dir_okay=False, - readable=True, - resolve_path=True, - ), - ], - dataset: Annotated[ - str, - typer.Option( - "--dataset", - "-d", - help="Name of the BEIR dataset evaluated (e.g., 'scidocs'). Used to find qrels.", - ), - ] = "scidocs", - data_dir: Annotated[ - Path, - typer.Option("--data-dir", help="Directory containing BEIR datasets.", resolve_path=True), - ] = Path(__file__).parent / "datasets", - split: Annotated[str, typer.Option("--split", help="Dataset split used for qrels.")] = "test", - metrics_ks_arg: Annotated[ - list[int] | None, - typer.Option( - "--k", - help="Values of k for recall/precision metrics. Defaults to [1, 3, 5, 10, 100, 1000].", - ), - ] = None, - output_file: Annotated[ - Path | None, - typer.Option( - "--output", - "-o", - help="Optional path to save calculated metrics as JSON.", - resolve_path=True, - ), - ] = None, -) -> None: - """ - Calculates and displays BEIR retrieval metrics (NDCG, MAP, Recall, P) for given results. - """ - metrics_ks = metrics_ks_arg if metrics_ks_arg is not None else [1, 3, 5, 10, 100, 1000] - - dataset_path = data_dir / dataset - model_name = results_path.stem.replace(f"{dataset}_", "").replace("_results", "") - title = f"Retrieval Metrics: [{dataset}/{split}] - Model: [{model_name}]" - - try: - logging.info(f"Calculating metrics for results: {results_path}") - logging.info(f"Using qrels for dataset: {dataset}, split: {split}") - - qrels = load_qrels(dataset_path, split) - results = load_results(results_path) - - if not qrels: - typer.echo(typer.style("Error: Qrels are empty.", fg=typer.colors.RED)) - raise typer.Exit(code=1) - if not results: - typer.echo(typer.style("Error: Results are empty.", fg=typer.colors.RED)) - raise typer.Exit(code=1) - - common_queries = set(results.keys()) & set(qrels.keys()) - if len(common_queries) < len(results): - logging.warning( - f"Results contain {len(results) - len(common_queries)} queries not found in qrels. Evaluating only on {len(common_queries)} common queries." - ) - results = {qid: results[qid] for qid in common_queries} - if not common_queries: - typer.echo( - typer.style( - "Error: No common queries between results and qrels.", fg=typer.colors.RED - ) - ) - raise typer.Exit(code=1) - - evaluator = EvaluateRetrieval() - ndcg, map_score, recall, precision = evaluator.evaluate(qrels, results, metrics_ks) - - all_metrics = { - "NDCG": ndcg, - "MAP": map_score, - "Recall": recall, - "Precision": precision, - } - - display_metrics(all_metrics, title=title) - - if output_file: - output_file.parent.mkdir(parents=True, exist_ok=True) - with open(output_file, "w") as f: - json.dump(all_metrics, f, indent=4) - typer.echo(typer.style(f"Metrics saved to {output_file}", fg=typer.colors.GREEN)) - - except FileNotFoundError as e: - logging.error(f"File loading error: {e}") - typer.echo(typer.style(f"Error: {e}", fg=typer.colors.RED)) - raise typer.Exit(code=1) from e - except Exception as e: - logging.error(f"An unexpected error occurred: {e}", exc_info=True) - typer.echo(typer.style(f"An unexpected error occurred: {e}", fg=typer.colors.RED)) - raise typer.Exit(code=1) from e - - -if __name__ == "__main__": - app() diff --git a/evaluation/download_beir.py b/evaluation/download_beir.py deleted file mode 100644 index dac7d53..0000000 --- a/evaluation/download_beir.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Download a BEIR dataset (e.g., scidocs) for evaluation using Typer.""" - -import logging -from pathlib import Path -from typing import Annotated - -import typer -from beir import util -from beir.datasets.data_loader import GenericDataLoader - -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") - -app = typer.Typer(help="BEIR Dataset Download Utility") - - -def download_beir_dataset( - dataset_name: str, output_dir: Path, data_path: Path | None = None -) -> Path: - """ - Downloads the specified BEIR dataset using beir.util and returns the path. - - Args: - dataset_name: The name of the BEIR dataset (e.g., "scidocs"). - output_dir: The directory to save the downloaded dataset. - data_path: The specific path where the dataset was extracted (optional, for logging). - - Returns: - The path to the downloaded dataset directory. - - Raises: - Exception: If the download fails. - """ - output_dir.mkdir(parents=True, exist_ok=True) - url = f"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{dataset_name}.zip" - extracted_path_str = util.download_and_unzip(url, str(output_dir)) - extracted_path = Path(extracted_path_str) - logging.info(f"Dataset '{dataset_name}' downloaded and extracted to: {extracted_path}") - return extracted_path - - -@app.command() -def main( - dataset: Annotated[ - str, - typer.Option( - "--dataset", - "-d", - help="Name of the BEIR dataset to download (e.g., 'scidocs', 'nfcorpus').", - ), - ] = "scidocs", - output_dir: Annotated[ - Path, - typer.Option( - "--output-dir", - "-o", - help="Directory to save the downloaded dataset.", - resolve_path=True, - ), - ] = Path(__file__).parent / "datasets", -) -> None: - """ - Downloads and verifies a specified BEIR dataset. - """ - logging.info(f"Starting download for dataset: {dataset}") - try: - data_path = download_beir_dataset(dataset, output_dir) - logging.info(f"Verifying dataset integrity by loading '{dataset}/test' split...") - corpus, queries, qrels = GenericDataLoader(data_folder=str(data_path)).load(split="test") - logging.info( - f"Successfully downloaded and verified dataset '{dataset}'. " - f"Corpus size: {len(corpus)}, Queries size: {len(queries)}, Qrels size: {len(qrels)}" - ) - typer.echo( - typer.style( - f"Dataset '{dataset}' downloaded successfully to {data_path}", - fg=typer.colors.GREEN, - ) - ) - except Exception as e: - logging.error(f"Failed to download or verify dataset '{dataset}': {e}", exc_info=True) - raise typer.Exit(code=1) from e - - -if __name__ == "__main__": - app() diff --git a/evaluation/run_evaluation.py b/evaluation/run_evaluation.py deleted file mode 100644 index f6d7b42..0000000 --- a/evaluation/run_evaluation.py +++ /dev/null @@ -1,215 +0,0 @@ -"""Coordinates the BEIR evaluation workflow: download, retrieve, calculate, summarize.""" - -import csv -import datetime -import json -import logging -import subprocess -import sys -from pathlib import Path -from typing import Annotated, Any - -import typer - -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") - -BASE_EVAL_DIR = Path(__file__).parent -DEFAULT_DATA_DIR = BASE_EVAL_DIR / "datasets" -DEFAULT_RESULTS_DIR = BASE_EVAL_DIR / "results" -DEFAULT_DB_DIR = BASE_EVAL_DIR / "data" -DEFAULT_SUMMARY_FILE = DEFAULT_RESULTS_DIR / "evaluation_summary.csv" - -app = typer.Typer(help="Run the full BEIR evaluation workflow.") - - -def _run_command(command: list[str], step_name: str) -> None: - """Helper function to run a command as a subprocess and handle errors.""" - logging.info(f"Starting step: {step_name}...") - try: - subprocess.run( - command, - check=True, - capture_output=True, - text=True, - cwd=BASE_EVAL_DIR.parent, - ) - logging.info(f"{step_name} completed successfully.") - except subprocess.CalledProcessError as e: - logging.error(f"{step_name} failed with exit code {e.returncode}") - logging.error(f"Stdout:\n{e.stdout}") - logging.error(f"Stderr:\n{e.stderr}") - raise typer.Exit(code=e.returncode) from e - except Exception as e: - logging.error(f"An unexpected error occurred during {step_name}: {e}", exc_info=True) - raise typer.Exit(code=1) from e - - -def _append_to_summary( - summary_file: Path, run_info: dict[str, Any], metrics: dict[str, dict[str, float]] -) -> None: - """Appends run information and key metrics to the summary CSV file.""" - summary_file.parent.mkdir(parents=True, exist_ok=True) - header = ["timestamp", "dataset", "split", "model_id", "metric", "value"] - key_metrics_to_log = {"NDCG@10", "Recall@100", "Recall@1000", "MAP@1000", "P@10"} - - rows_to_write = [] - file_exists = summary_file.is_file() - - for _metric_group_name, metric_values in metrics.items(): - for metric_name_at_k, value in metric_values.items(): - if metric_name_at_k in key_metrics_to_log: - row = run_info.copy() - row["metric"] = metric_name_at_k - row["value"] = f"{value:.4f}" - rows_to_write.append(row) - - if not rows_to_write: - logging.warning("No key metrics found to log in the summary file.") - return - - try: - with open(summary_file, mode="a", newline="", encoding="utf-8") as csvfile: - writer = csv.DictWriter(csvfile, fieldnames=header) - if not file_exists: - writer.writeheader() - writer.writerows(rows_to_write) - logging.info(f"Successfully appended results to {summary_file}") - except Exception as e: - logging.error( - f"Failed to append results to summary file {summary_file}: {e}", exc_info=True - ) - - -@app.command() -def run( - model_id: Annotated[ - str, - typer.Option("--model-id", "-m", help="Embedding model identifier (required)."), - ], - dataset: Annotated[ - str, - typer.Option("--dataset", "-d", help="Name of the BEIR dataset."), - ] = "scidocs", - data_dir: Annotated[ - Path, - typer.Option("--data-dir", help="Directory containing BEIR datasets.", resolve_path=True), - ] = DEFAULT_DATA_DIR, - results_dir: Annotated[ - Path, - typer.Option("--results-dir", help="Directory to save results.", resolve_path=True), - ] = DEFAULT_RESULTS_DIR, - db_dir: Annotated[ - Path, - typer.Option( - "--db-dir", help="Base directory for evaluation databases.", resolve_path=True - ), - ] = DEFAULT_DB_DIR, - split: Annotated[str, typer.Option("--split", help="Dataset split.")] = "test", - metrics_ks_arg: Annotated[ - list[int] | None, - typer.Option("--k", help="Values of k for metrics. Defaults to [1, 3, 5, 10, 100, 1000]."), - ] = None, - skip_ingestion: Annotated[ - bool, - typer.Option("--skip-ingestion", help="Skip ingestion if collection exists."), - ] = False, - force_download: Annotated[ - bool, - typer.Option("--force-download", help="Force download dataset even if it exists."), - ] = False, - summary_file: Annotated[ - Path, - typer.Option("--summary-file", help="Path to the CSV summary file.", resolve_path=True), - ] = DEFAULT_SUMMARY_FILE, -) -> None: - """Runs the full BEIR evaluation workflow and logs results to a summary file.""" - - timestamp = datetime.datetime.now().isoformat() - dataset_path = data_dir / dataset - sanitized_model_name = model_id.replace("/", "_") - results_json_path = results_dir / f"{dataset}_{sanitized_model_name}_results.json" - metrics_ks = metrics_ks_arg if metrics_ks_arg is not None else [1, 3, 5, 10, 100, 1000] - metrics_ks_str = [str(k) for k in metrics_ks] - - if not dataset_path.exists() or force_download: - logging.info(f"Dataset '{dataset}' not found at {data_dir} or download forced.") - download_cmd = [ - sys.executable, - str(BASE_EVAL_DIR / "download_beir.py"), - "--dataset", - dataset, - "--output-dir", - str(data_dir), - ] - _run_command(download_cmd, "Data Download") - else: - logging.info(f"Dataset '{dataset}' found at {data_dir}. Skipping download.") - - retrieval_cmd = [ - sys.executable, - str(BASE_EVAL_DIR / "run_retrieval.py"), - "--model-id", - model_id, - "--dataset", - dataset, - "--data-dir", - str(data_dir), - "--results-dir", - str(results_dir), - "--db-dir", - str(db_dir), - "--split", - split, - ] - if skip_ingestion: - retrieval_cmd.append("--skip-ingestion") - - _run_command(retrieval_cmd, "Retrieval Execution") - - calc_cmd = [ - sys.executable, - str(BASE_EVAL_DIR / "calculate_metrics.py"), - str(results_json_path), - "--dataset", - dataset, - "--data-dir", - str(data_dir), - "--split", - split, - ] - for k in metrics_ks_str: - calc_cmd.extend(["--k", k]) - metrics_json_output = results_dir / f"{dataset}_{sanitized_model_name}_metrics.json" - calc_cmd.extend(["--output", str(metrics_json_output)]) - - _run_command(calc_cmd, "Metrics Calculation") - - try: - with open(metrics_json_output) as f: - calculated_metrics = json.load(f) - - run_info = { - "timestamp": timestamp, - "dataset": dataset, - "split": split, - "model_id": model_id, - } - _append_to_summary(summary_file, run_info, calculated_metrics) - - except FileNotFoundError as e: - logging.error( - f"Metrics JSON file not found: {metrics_json_output}. Cannot append to summary." - ) - raise typer.Exit(code=1) from e - except Exception as e: - logging.error(f"Failed to read metrics JSON or append to summary: {e}", exc_info=True) - raise typer.Exit(code=1) from e - - logging.info("Evaluation workflow complete.") - typer.echo( - typer.style("Evaluation workflow finished successfully!", fg=typer.colors.BRIGHT_GREEN) - ) - - -if __name__ == "__main__": - app() diff --git a/evaluation/run_retrieval.py b/evaluation/run_retrieval.py deleted file mode 100644 index 44dce90..0000000 --- a/evaluation/run_retrieval.py +++ /dev/null @@ -1,320 +0,0 @@ -"""Runs retrieval on a BEIR dataset and saves results using Typer.""" - -import json -import logging -from pathlib import Path -from typing import Annotated, Any - -import typer -from beir.datasets.data_loader import GenericDataLoader -from tqdm import tqdm - -from paperchat.core import VectorStore, get_embedding_model - -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") -DEFAULT_EMBEDDING_BATCH_SIZE = 32 -BASE_EVAL_DIR = Path(__file__).parent -DEFAULT_DATA_DIR = BASE_EVAL_DIR / "datasets" -DEFAULT_RESULTS_DIR = BASE_EVAL_DIR / "results" -DEFAULT_DB_DIR = BASE_EVAL_DIR / "data" - -app = typer.Typer(help="Run BEIR retrieval evaluation and save results.") - - -def load_beir_dataset( - data_path: Path, split: str = "test" -) -> tuple[dict[str, dict[str, str]], dict[str, str]]: - """ - Loads the corpus and queries from a downloaded BEIR dataset folder. - - Args: - data_path: Path to the BEIR dataset directory (e.g., evaluation/datasets/scidocs). - split: The dataset split to load (e.g., "test", "train", "dev"). - - Returns: - A tuple containing the corpus and queries dictionaries. - """ - if not data_path.exists(): - raise FileNotFoundError( - f"BEIR dataset not found at {data_path}. Run download_data.py first." - ) - logging.info(f"Loading BEIR dataset from: {data_path}") - corpus, queries, _ = GenericDataLoader(data_folder=str(data_path)).load(split=split) - logging.info(f"Loaded corpus with {len(corpus)} documents and {len(queries)} queries.") - return corpus, queries - - -def prepare_and_embed_corpus( - corpus: dict[str, dict[str, str]], embed_fn: Any, batch_size: int -) -> list[dict[str, Any]]: - """ - Formats BEIR corpus and generates embeddings for direct Milvus insertion. - - Args: - corpus: The BEIR corpus dictionary {doc_id: {'title': ..., 'text': ...}}. - embed_fn: The embedding function instance. - batch_size: Batch size for embedding generation. - - Returns: - A list of dictionaries ready for client.insert, including embeddings. - """ - doc_ids = list(corpus.keys()) - texts_to_embed = [ - (corpus[doc_id].get("title", "") + " " + corpus[doc_id].get("text", "")).strip() - for doc_id in doc_ids - ] - - all_embeddings = [] - logging.info( - f"Generating embeddings for {len(texts_to_embed)} documents in batches of {batch_size}..." - ) - for i in tqdm(range(0, len(texts_to_embed), batch_size), desc="Embedding Corpus"): - batch_texts = texts_to_embed[i : i + batch_size] - embeddings = embed_fn.encode_documents(batch_texts) - if hasattr(embeddings, "tolist"): - all_embeddings.extend(embeddings.tolist()) - else: - all_embeddings.extend(embeddings) - - if len(all_embeddings) != len(doc_ids): - raise RuntimeError("Mismatch between number of documents and generated embeddings.") - - data_for_milvus = [] - for i, doc_id in enumerate(doc_ids): - data_for_milvus.append( - { - "text": texts_to_embed[i], - "embedding": all_embeddings[i], - "source": doc_id, - "chunk_id": f"{doc_id}_chunk0", - "label": [], - "page_numbers": [], - "headings": corpus[doc_id].get("title", ""), - } - ) - - return data_for_milvus - - -def run_retrieval( - vector_store: VectorStore, queries: dict[str, str], k: int = 100 -) -> dict[str, dict[str, float]]: - """ - Runs retrieval for all queries against the vector store. - - Args: - vector_store: The initialized VectorStore. - queries: The dictionary of queries {query_id: query_text}. - k: The number of documents to retrieve for each query. - - Returns: - A dictionary containing the retrieval results in BEIR format: - {query_id: {beir_doc_id1: score1, beir_doc_id2: score2, ...}} - """ - results: dict[str, dict[str, float]] = {} - logging.info(f"Running retrieval for {len(queries)} queries...") - - output_fields = ["source"] - - for query_id, query_text in tqdm(queries.items(), desc="Retrieving"): - try: - retrieved_hits = vector_store.search(query_text, top_k=k, output_fields=output_fields) - query_results = {} - for hit in retrieved_hits: - original_doc_id = hit.get("entity", {}).get("source") - if original_doc_id: - score = hit.get("distance", 0.0) - query_results[original_doc_id] = float(score) - else: - logging.warning( - f"Retrieved hit for query {query_id} is missing 'source' field. Hit: {hit}" - ) - results[query_id] = query_results - except Exception as e: - logging.error(f"Error retrieving results for query_id {query_id}: {e}", exc_info=True) - results[query_id] = {} - logging.info("Retrieval finished.") - return results - - -def save_results(results: dict[str, dict[str, float]], output_path: Path) -> None: - """Saves the retrieval results to a JSON file.""" - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w") as f: - json.dump(results, f, indent=4) - logging.info(f"Retrieval results saved to: {output_path}") - - -def _ingest_corpus_if_needed( - vector_store: VectorStore, - corpus: dict[str, dict[str, str]], - embedding_model: Any, - batch_size: int, -) -> None: - """Handles the logic for checking, dropping, creating, and ingesting into a collection automatically.""" - expected_doc_count = len(corpus) - collection_name = vector_store.collection_name - needs_ingestion = True - - if vector_store.client.has_collection(collection_name): - vector_store.client.load_collection(collection_name) - current_doc_count = vector_store.client.num_entities(collection_name) - logging.info(f"Collection '{collection_name}' exists with {current_doc_count} entities.") - - if current_doc_count == expected_doc_count: - logging.info( - f"Collection appears complete ({current_doc_count}/{expected_doc_count}). Skipping ingestion." - ) - needs_ingestion = False - else: - logging.warning( - f"Collection '{collection_name}' exists but is incomplete ({current_doc_count}/{expected_doc_count}). " - f"Dropping and re-creating for full ingestion." - ) - vector_store.client.release_collection(collection_name) - vector_store.client.drop_collection(collection_name) - vector_store._get_or_create_collection(collection_name, vector_store.main_schema) - vector_store._build_indices() - else: - logging.info(f"Collection '{collection_name}' does not exist. Creating and ingesting.") - vector_store._get_or_create_collection(collection_name, vector_store.main_schema) - vector_store._build_indices() - - if needs_ingestion: - logging.info(f"Preparing and embedding corpus for '{collection_name}'...") - data_to_insert = prepare_and_embed_corpus(corpus, embedding_model, batch_size) - logging.info(f"Ingesting {len(data_to_insert)} documents into '{collection_name}'...") - insert_result = vector_store.client.insert( - collection_name=collection_name, data=data_to_insert - ) - insert_count = insert_result.get("insert_count", 0) - if insert_count != len(data_to_insert): - raise RuntimeError( - f"Mismatch in document insertion count. Expected {len(data_to_insert)}, got {insert_count}." - ) - logging.info(f"Ingestion complete and flushed for '{collection_name}'.") - - -def run_evaluation_pipeline( - dataset: str, - data_dir: Path, - db_dir: Path, - split: str, - embedding_model_id: str, - results_dir: Path, - top_k: int, - batch_size: int, -) -> Path | None: - """Runs the full pipeline: load, init, ingest, retrieve, save. Returns results file path or None.""" - dataset_path = data_dir / dataset - sanitized_model_name = embedding_model_id.replace("/", "_") - results_file = results_dir / f"{dataset}_{sanitized_model_name}_results.json" - - if results_file.exists(): - logging.info(f"Results file already exists: {results_file}. Skipping retrieval.") - typer.echo( - typer.style( - f"Skipping retrieval, results found at {results_file}", fg=typer.colors.YELLOW - ) - ) - return results_file - - db_dir.mkdir(parents=True, exist_ok=True) - eval_db_path = str(db_dir / f"evaluation_{dataset}_{sanitized_model_name}.db") - logging.info(f"Using evaluation database: {eval_db_path}") - - vector_store = None - try: - corpus, queries = load_beir_dataset(dataset_path, split) - - logging.info(f"Initializing VectorStore for model: {embedding_model_id}") - embedding_model = get_embedding_model(embedding_model_id) - vector_store = VectorStore( - db_path=eval_db_path, - model_identifier=embedding_model_id, - ) - - _ingest_corpus_if_needed( - vector_store=vector_store, - corpus=corpus, - embedding_model=embedding_model, - batch_size=batch_size, - ) - - results = run_retrieval(vector_store, queries, k=top_k) - save_results(results, results_file) - typer.echo(typer.style(f"Retrieval results saved to {results_file}", fg=typer.colors.GREEN)) - return results_file - - except FileNotFoundError as e: - logging.error(f"Data loading error: {e}") - raise typer.Exit(code=1) from e - except Exception as e: - logging.error(f"An unexpected error occurred in pipeline: {e}", exc_info=True) - raise typer.Exit(code=1) from e - finally: - if vector_store and hasattr(vector_store, "client") and vector_store.client: - try: - logging.info("Milvus client cleanup check.") - except Exception as disconnect_e: - logging.warning(f"Error during Milvus cleanup attempt: {disconnect_e}") - - -@app.command() -def main( - embedding_model_id: Annotated[ - str, - typer.Option("--model-id", "-m", help="Embedding model identifier (required)."), - ], - dataset: Annotated[ - str, - typer.Option("--dataset", "-d", help="Name of the BEIR dataset."), - ] = "scidocs", - data_dir: Annotated[ - Path, - typer.Option("--data-dir", help="Directory containing BEIR datasets.", resolve_path=True), - ] = DEFAULT_DATA_DIR, - split: Annotated[str, typer.Option("--split", help="Dataset split.")] = "test", - results_dir: Annotated[ - Path, - typer.Option("--results-dir", help="Directory to save results.", resolve_path=True), - ] = DEFAULT_RESULTS_DIR, - db_dir: Annotated[ - Path, - typer.Option( - "--db-dir", help="Base directory for evaluation databases.", resolve_path=True - ), - ] = DEFAULT_DB_DIR, - top_k: Annotated[int, typer.Option("--top-k", help="Number of documents to retrieve.")] = 100, - batch_size: Annotated[ - int, typer.Option("--batch-size", help="Batch size for embedding generation.") - ] = DEFAULT_EMBEDDING_BATCH_SIZE, -) -> None: - """ - Runs BEIR retrieval evaluation using dataset-specific DBs. - """ - try: - results_path = run_evaluation_pipeline( - dataset=dataset, - data_dir=data_dir, - db_dir=db_dir, - split=split, - embedding_model_id=embedding_model_id, - results_dir=results_dir, - top_k=top_k, - batch_size=batch_size, - ) - if results_path: - logging.info("Retrieval pipeline completed successfully.") - else: - logging.info("Retrieval step skipped as results already exist.") - except typer.Exit: - logging.error("Retrieval pipeline failed.") - raise - except Exception as e: - logging.error(f"Unexpected error in main: {e}", exc_info=True) - raise typer.Exit(code=1) from e - - -if __name__ == "__main__": - app() diff --git a/frontend/package.json b/frontend/package.json index 6c11f40..15f8b62 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -3,6 +3,7 @@ "private": true, "version": "0.0.0", "type": "module", + "packageManager": "pnpm@10.30.3", "scripts": { "dev": "vite", "build": "tsc -b && vite build", diff --git a/paperchat/__init__.py b/paperchat/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/paperchat/cli.py b/paperchat/cli.py deleted file mode 100644 index d994121..0000000 --- a/paperchat/cli.py +++ /dev/null @@ -1,9 +0,0 @@ -import subprocess -import sys -from pathlib import Path - - -def main(): - app_path = Path(__file__).parent.parent / "app.py" - args = ["streamlit", "run", str(app_path)] + sys.argv[1:] - subprocess.run(args, check=False) diff --git a/paperchat/core/__init__.py b/paperchat/core/__init__.py deleted file mode 100644 index 63c21a3..0000000 --- a/paperchat/core/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .embeddings import get_embedding_model -from .ingestion import process_pdf_document -from .rag_pipeline import RAGPipeline -from .vector_store import VectorStore - -__all__ = ["RAGPipeline", "VectorStore", "get_embedding_model", "process_pdf_document"] diff --git a/paperchat/core/embeddings.py b/paperchat/core/embeddings.py deleted file mode 100644 index fa825c8..0000000 --- a/paperchat/core/embeddings.py +++ /dev/null @@ -1,134 +0,0 @@ -"""Load text embedding models from Milvus""" - -from typing import Any - -from pymilvus.model import DefaultEmbeddingFunction, dense - -from paperchat.utils.api_keys import get_api_key -from paperchat.utils.logging import get_component_logger - -logger = get_component_logger("embeddings") - -PROVIDERS_REQUIRING_KEYS = {"openai", "gemini"} -SUPPORTED_EMBEDDING_MODELS = { - "openai/text-embedding-3-small": { - "class": dense.OpenAIEmbeddingFunction, - "init_args": {"model_name": "text-embedding-3-small"}, - "dimensions": 1536, - }, - "openai/text-embedding-3-large": { - "class": dense.OpenAIEmbeddingFunction, - "init_args": {"model_name": "text-embedding-3-large"}, - "dimensions": 3072, - }, - "gemini/gemini-embedding-exp-03-07": { - "class": dense.GeminiEmbeddingFunction, - "init_args": {"model_name": "gemini-embedding-exp-03-07"}, - "dimensions": 3072, - }, - "gemini/models/text-embedding-004": { - "class": dense.GeminiEmbeddingFunction, - "init_args": {"model_name": "models/text-embedding-004"}, - "dimensions": 768, - }, -} - - -def list_embedding_models() -> list[dict[str, Any]]: - """List all supported embedding models, indicating if they require an API key.""" - available_models = [] - for identifier, config in SUPPORTED_EMBEDDING_MODELS.items(): - try: - provider = identifier.split("/")[0] - except IndexError: - logger.warning(f"Skipping invalid embedding model identifier: {identifier}") - continue - - api_key_present_or_not_required = True - if provider in PROVIDERS_REQUIRING_KEYS and get_api_key(provider) is None: - api_key_present_or_not_required = False - - if api_key_present_or_not_required: - available_models.append( - { - "id": identifier, - "name": identifier, - "dimensions": config.get("dimensions"), - "provider": provider, - } - ) - return available_models - - -def get_embedding_model(model_identifier: str) -> Any: - """ - Get an initialized embedding function instance from Milvus. - - Args: - model_identifier: The unique identifier ('provider/model_name'). - - Returns: - An initialized embedding function instance from pymilvus.model. - """ - if model_identifier == "pymilvus/default": - try: - logger.info( - "[get_embedding_model] Using generic default. Instantiating DefaultEmbeddingFunction()" - ) - embedding_function = DefaultEmbeddingFunction() - if not ( - hasattr(embedding_function, "dim") - and isinstance(embedding_function.dim, int) - and embedding_function.dim > 0 - ): - raise AttributeError( - "DefaultEmbeddingFunction instance lacks a valid .dim attribute." - ) - logger.info( - f"[get_embedding_model] Successfully instantiated pymilvus default: {type(embedding_function)} with dim {embedding_function.dim}" - ) - return embedding_function - except Exception as e: - logger.error(f"Error instantiating DefaultEmbeddingFunction: {e!s}", exc_info=True) - raise RuntimeError("Failed to instantiate the pymilvus default embedding model.") from e - - if "/" not in model_identifier: - raise ValueError( - f"Invalid model identifier format: '{model_identifier}'. Expected 'provider/model_name'." - ) - - if model_identifier not in SUPPORTED_EMBEDDING_MODELS: - available = [m["id"] for m in list_embedding_models()] - raise ValueError( - f"Unsupported embedding model identifier: '{model_identifier}'. " - f"Available models (with keys): {available}" - ) - - config = SUPPORTED_EMBEDDING_MODELS[model_identifier] - provider = model_identifier.split("/")[0] - model_class = config["class"] - init_args = config["init_args"].copy() - - if provider in PROVIDERS_REQUIRING_KEYS: - api_key = get_api_key(provider) - if not api_key: - raise ValueError( - f"API key for provider '{provider}' not found, required by '{model_identifier}'" - ) - if provider in ["openai", "gemini"]: - init_args["api_key"] = api_key - - try: - embedding_function = model_class(**init_args) - - return embedding_function - - except Exception as e: - logger.error( - f"Error instantiating {model_class.__name__} for {model_identifier}: {e!s}", - exc_info=True, - ) - raise RuntimeError( - f"Failed to instantiate embedding model '{model_identifier}' for provider '{provider}'. " - f"Check API keys, model availability, and pymilvus installation." - ) from e diff --git a/paperchat/core/ingestion.py b/paperchat/core/ingestion.py deleted file mode 100644 index fe9b29b..0000000 --- a/paperchat/core/ingestion.py +++ /dev/null @@ -1,87 +0,0 @@ -import datetime -from pathlib import Path -from typing import Any - -from docling.chunking import DocChunk, HybridChunker -from docling.datamodel.document import DoclingDocument -from docling.document_converter import DocumentConverter -from pymilvus import model - - -def parse_pdf(pdf_path: str | Path) -> DoclingDocument: - """Convert a PDF file to a Docling document. - - Args: - pdf_path: Path to the PDF file (can be string or Path object) - - Returns: - Parsed Docling document - """ - converter = DocumentConverter() - pdf_path = Path(pdf_path).resolve() - result = converter.convert(str(pdf_path)) - - return result.document - - -def extract_page_numbers(chunk: DocChunk) -> list[int]: - """Extract page numbers from a DocChunk. - - Args: - chunk: DocChunk to extract page numbers from - - Returns: - List of page numbers - """ - page_numbers = set() - for item in chunk.meta.doc_items: - for prov in item.prov: - page_numbers.add(prov.page_no) - - return sorted(page_numbers) - - -def process_pdf_document( - pdf_path: str | Path, - embed_fn: Any | None = None, - max_tokens_per_chunk: int = 1024, -) -> list[dict[str, Any]]: - """Process a PDF document into a list of dictionaries ready for vector database insertion. - - This function handles the document processing pipeline and returns data in a format - ready to be directly inserted into a vector database. - - Args: - pdf_path: Path to the PDF file - embed_fn: Function to use for embedding generation, defaults to Milvus DefaultEmbeddingFunction - max_tokens_per_chunk: Maximum number of tokens per chunk - - Returns: - List of dictionaries with fields matching the Milvus schema, ready for direct insertion - """ - if embed_fn is None: - embed_fn = model.DefaultEmbeddingFunction() - - pdf_file = Path(pdf_path) - pdf_doc = parse_pdf(pdf_file) - chunker = HybridChunker(max_tokens=max_tokens_per_chunk, merge_peers=True) - pdf_chunks = list(chunker.chunk(pdf_doc)) - - texts = [chunker.contextualize(chunk) for chunk in pdf_chunks] - embeddings = embed_fn.encode_documents(texts) - - data_entries = [] - for i, (chunk, embedding) in enumerate(zip(pdf_chunks, embeddings, strict=True)): - data_entry = { - "chunk_id": f"chunk_{i:02d}", - "embedding": embedding, - "text": chunk.text, - "label": [item.label for item in chunk.meta.doc_items or []], - "source": pdf_file.stem, - "page_numbers": extract_page_numbers(chunk), - "headings": ", ".join(chunk.meta.headings).lower() if chunk.meta.headings else "", - "timestamp": datetime.datetime.now().isoformat(), - } - data_entries.append(data_entry) - - return data_entries diff --git a/paperchat/core/rag_pipeline.py b/paperchat/core/rag_pipeline.py deleted file mode 100644 index 24c9d11..0000000 --- a/paperchat/core/rag_pipeline.py +++ /dev/null @@ -1,97 +0,0 @@ -from typing import Any, Generator - -from ..llms.manager import LLMManager -from ..utils.formatting import format_context -from ..utils.settings import get_active_embedding_model -from .vector_store import DEFAULT_SIMILARITY_THRESHOLD, VectorStore - - -class RAGPipeline: - """ - Orchestrates the Retrieval-Augmented Generation (RAG) pipeline. - - Handles document processing, retrieval of relevant chunks, and generation - of responses using a configured LLM. - """ - - def __init__(self, llm_manager: LLMManager): - """ - Initializes the RAG pipeline with necessary components. - - Args: - llm_manager: An instance of LLMManager to handle LLM interactions. - """ - self.llm_manager = llm_manager - active_model = get_active_embedding_model() - self.vector_store = VectorStore(model_identifier=active_model) - - def retrieve( - self, - query: str, - top_k: int = 5, - threshold: float = DEFAULT_SIMILARITY_THRESHOLD, - filter_expression: str | None = None, - ) -> list[dict[str, Any]]: - """ - Retrieves relevant document chunks based on the query. - - Args: - query: The user's query text. - top_k: The number of chunks to retrieve. - threshold: The minimum similarity score to return. - filter_expression: A filter expression to apply to the query. - Returns: - A list of retrieved document chunk dictionaries with metadata. - """ - results = self.vector_store.search( - query_text=query, - top_k=top_k, - threshold=threshold, - filter_expression=filter_expression, - ) - return results - - def generate( - self, query: str, retrieved_results: list[dict[str, Any]], stream: bool = False - ) -> str | Generator[str, None, None]: - """ - Generates a response using the LLM based on the query and retrieved context. - - Args: - query: The user's query text. - retrieved_results: List of document text and metadata retrieved as context. - stream: Whether to stream the response. - - Returns: - The generated response (str or Generator). - """ - context = format_context(retrieved_results, include_metadata=True) - - if stream: - return self.llm_manager.generate_streaming_response(query, context) - else: - return self.llm_manager.generate_response(query, context) - - def run( - self, - query: str, - top_k: int = 5, - stream: bool = False, - ) -> tuple[str | Generator[str, None, None], list[dict[str, Any]]]: - """ - Executes the full RAG pipeline: process/ensure doc data, retrieve, generate. - - Args: - query: The user's query. - document_data: Optional pre-processed document data. - pdf_path: Optional path to a PDF file (used if document_data is None). - top_k: Number of chunks to retrieve. - stream: Whether to stream the LLM response. - - Returns: - A tuple containing the generated response and the list of retrieved chunks. - """ - retrieved_chunks = self.retrieve(query, top_k) - response = self.generate(query, retrieved_chunks, stream) - - return response, retrieved_chunks diff --git a/paperchat/core/vector_store.py b/paperchat/core/vector_store.py deleted file mode 100644 index 0455c8a..0000000 --- a/paperchat/core/vector_store.py +++ /dev/null @@ -1,486 +0,0 @@ -"""Vector storage implementation using Milvus.""" - -import re -from datetime import datetime -from pathlib import Path -from typing import Any - -from pymilvus import ( - CollectionSchema, - DataType, - FieldSchema, - MilvusClient, -) - -from paperchat.core.embeddings import get_embedding_model - -from ..utils.logging import get_component_logger -from .ingestion import ( - process_pdf_document, -) - -DEFAULT_SIMILARITY_TOP_K = 5 -DEFAULT_SIMILARITY_THRESHOLD = 0.2 - - -class VectorStore: - """ - Handles interactions with a Milvus vector store collection for document chunks. - - Attributes: - db_path (str): Path to the Milvus Lite database file. - collection_name (str): Name of the collection managed by this instance (dynamic based on model). - model_identifier (str): Identifier of the embedding model being used. - metadata_collection_name (str): Name of the shared metadata collection. - client (MilvusClient): The initialized Milvus client. - _embed_fn (Any): The loaded embedding function instance. - embedding_dim (int): Dimension of the vectors, derived from the model. - main_schema (CollectionSchema): The schema used for the main collection. - metadata_schema (CollectionSchema): The schema used for the metadata collection. - """ - - METADATA_COLLECTION_NAME = "paperchat_metadata" - VECTOR_DB_DIR = str(Path.home() / ".paperchat" / "paperchat.db") - - def __init__( - self, - db_path: str = VECTOR_DB_DIR, - model_identifier: str = "pymilvus/default", - ): - """ - Initializes the VectorStore, connects to Milvus, determines the correct - data collection based on the model_identifier, and ensures both the - model-specific data collection and the shared metadata collection exist. - """ - self.db_path = db_path - self.model_identifier = model_identifier - self.logger = get_component_logger("VectorStore") - - try: - self.logger.info(f"Loading embedding model: {model_identifier}") - self._embed_fn = get_embedding_model(model_identifier) - self.embedding_dim = self._embed_fn.dim - self.logger.info(f"Using embedding dimension from _embed_fn.dim: {self.embedding_dim}") - - except ( - ValueError, - RuntimeError, - AttributeError, - ) as e: - self.logger.exception( - f"Failed to load embedding model '{model_identifier}' or get its dimension: {e}" - ) - raise - - if self.model_identifier == "pymilvus/default": - sanitized_suffix = sanitize_model_identifier(self._embed_fn.model_name) - self.logger.info( - f"Default model resolved to: '{self._embed_fn.model_name}'. Using sanitized name for collection suffix." - ) - else: - sanitized_suffix = sanitize_model_identifier(self.model_identifier) - self.collection_name = f"paperchat_docs_{sanitized_suffix}" - self.logger.info(f"Using data collection: {self.collection_name}") - - self.metadata_collection_name = self.METADATA_COLLECTION_NAME - self.logger.info(f"Using metadata collection: {self.metadata_collection_name}") - - try: - self.client = MilvusClient(uri=self.db_path) - self.logger.info(f"Connected to Milvus: {self.db_path}") - except Exception as e: - self.logger.exception(f"Failed to initialize MilvusClient: {e}") - raise - - self.main_schema = self._define_main_schema() - self.metadata_schema = self._define_metadata_schema() - self._get_or_create_collection(self.collection_name, self.main_schema) - self._get_or_create_collection(self.metadata_collection_name, self.metadata_schema) - self._build_indices() - - def add_document(self, pdf_path: str | Path, source_id: str, max_tokens: int = 1024) -> bool: - """ - Processes a PDF document, adds chunks to the main collection, - and adds metadata to the metadata collection. Skips if the document - (based on source) already exists in the metadata collection. - - Args: - pdf_path: Path to the PDF file. - source_id: The unique identifier (e.g., original filename stem) for this document. - max_tokens: Maximum number of tokens per chunk. - - Returns: - True if the document was successfully processed and added (or already existed), - False if an error occurred during processing or insertion. - """ - pdf_file = Path(pdf_path) - self.logger.info(f"Processing request for document: {pdf_file.name} (ID: {source_id})") - - try: - # 1. check metadata existence - existing = self.client.query( - collection_name=self.metadata_collection_name, - filter=f'source == "{source_id}"', - output_fields=["source"], - limit=1, - ) - - if existing: - self.logger.info(f"Document ID '{source_id}' already exists in metadata. Skipping.") - return True - - # 2. process pdf into chunks (if new) - self.logger.info(f"Document ID '{source_id}' not found. Processing PDF...") - pdf_data = process_pdf_document( - pdf_path, embed_fn=self._embed_fn, max_tokens_per_chunk=max_tokens - ) - - for chunk in pdf_data: - chunk["source"] = source_id - - # 3. insert chunks into main collection - self.logger.info(f"Inserting {len(pdf_data)} chunks into '{self.collection_name}'") - chunk_result = self.client.insert(collection_name=self.collection_name, data=pdf_data) - insert_count = chunk_result.get("insert_count", 0) - if insert_count != len(pdf_data): - self.logger.error( - f"Mismatch in chunk insertion count for {source_id}. Expected {len(pdf_data)}, got {insert_count}. IDs: {chunk_result.get('primary_keys')}" - ) - # Decide on rollback or error state? For now, log error and continue to metadata attempt, but return False later. - # Consider deleting inserted chunks if possible? client.delete might be complex with specific IDs. - return False - - self.logger.info(f"Successfully inserted {insert_count} chunks for {source_id}") - - # 4. insert metadata entry - iso_timestamp = datetime.now().isoformat() - metadata_entry = [ - { - "source": source_id, - "original_filename": pdf_file.name, - "date_added": iso_timestamp, - } - ] - self.logger.info( - f"Inserting metadata for '{source_id}' into '{self.metadata_collection_name}'" - ) - meta_result = self.client.insert( - collection_name=self.metadata_collection_name, data=metadata_entry - ) - meta_insert_count = meta_result.get("insert_count", 0) - - if meta_insert_count != 1: - self.logger.error( - f"Failed to insert metadata for {source_id}. Result: {meta_result}" - ) - return False - - self.logger.info(f"Successfully added document '{source_id}'") - return True - - except Exception as e: - self.logger.exception(f"Failed processing/adding document {source_id}: {e}") - return False - - def search( - self, - query_text: str, - top_k: int = DEFAULT_SIMILARITY_TOP_K, - threshold: float = DEFAULT_SIMILARITY_THRESHOLD, - output_fields: list[str] | None = None, - filter_expression: str | None = None, - ) -> list[dict[str, Any]]: - """ - Searches the main collection for text chunks similar to the query text. - - Args: - query_text: The text to search for. - top_k: The maximum number of results to return. - threshold: The minimum similarity score to return. - output_fields: List of field names to include in the results. - Defaults to main schema fields except embedding. - filter_expression: Milvus filter expression string (e.g., "source == 'doc1.pdf'"). - - Returns: - A list of dictionaries representing the retrieved chunks, ordered by similarity. - Returns an empty list if no results are found or an error occurs. - """ - if output_fields is None: - output_fields = [f.name for f in self.main_schema.fields if f.name != "embedding"] - - try: - self.logger.debug(f"Loading collection '{self.collection_name}' for search.") - query_embedding = self._embed_fn.encode_queries([query_text])[0] - except Exception as e: - self.logger.exception(f"Failed to generate query embedding or load collection: {e}") - return [] - - search_params = { - "nprobe": 10, - "metric_type": "COSINE", - "radius": threshold, - "range_filter": 1.0, - } - - try: - raw_results = self.client.search( - collection_name=self.collection_name, - data=[query_embedding], - search_params=search_params, - limit=top_k, - output_fields=output_fields, - filter=filter_expression, - ) - results = raw_results[0] - - except Exception as e: - self.logger.exception(f"Search failed: {e}") - results = [] - - return results - - def list_documents(self) -> list[dict[str, Any]]: - """ - Lists all documents by querying the metadata collection. - - Returns: - A list of dictionaries, each containing metadata for one document - (source, original_filename, date_added). - Returns an empty list if there are no documents or an error occurs. - """ - self.logger.debug(f"Listing documents from '{self.metadata_collection_name}'") - try: - results = self.client.query( - collection_name=self.metadata_collection_name, - output_fields=["source", "original_filename", "date_added"], - limit=100, - ) - self.logger.info(f"Found {len(results)} documents in metadata.") - return results - - except Exception as e: - self.logger.exception(f"Failed to list documents from metadata: {e}") - return [] - - def remove_document(self, source_id: str) -> tuple[int, int]: - """ - Removes a document and its associated data from both the metadata - and main document collections based on the source identifier. - - Args: - source_id: The unique identifier (e.g., file stem) of the document to delete. - - Returns: - A tuple containing: - (number of metadata entries deleted, number of chunks deleted). - Typically (1, N) on success, (0, 0) if not found, or potentially - (0, N) or (1, 0) if deletion from one collection fails. - """ - self.logger.info(f"Attempting to remove document with ID: '{source_id}'") - metadata_deleted_count = 0 - chunks_deleted_count = 0 - - # step 1: remove from metadata - try: - self.logger.debug( - f"Removing metadata entry for '{source_id}' from '{self.metadata_collection_name}'" - ) - meta_del_result = self.client.delete( - collection_name=self.metadata_collection_name, - filter=f'source == "{source_id}"', - ) - if hasattr(meta_del_result, "delete_count"): - metadata_deleted_count = meta_del_result.delete_count - elif hasattr(meta_del_result, "primary_keys"): - metadata_deleted_count = len(meta_del_result.primary_keys) - else: - metadata_deleted_count = 1 if meta_del_result else 0 - - self.logger.info( - f"Removed {metadata_deleted_count} entries from metadata for '{source_id}'" - ) - except Exception as e: - self.logger.exception( - f"Failed to remove from metadata collection for '{source_id}': {e}" - ) - - # step 2: remove from main collection - try: - self.logger.debug(f"Removing chunks for '{source_id}' from '{self.collection_name}'") - chunk_del_result = self.client.delete( - collection_name=self.collection_name, - filter=f'source == "{source_id}"', - ) - if hasattr(chunk_del_result, "delete_count"): - chunks_deleted_count = chunk_del_result.delete_count - elif hasattr(chunk_del_result, "primary_keys"): - chunks_deleted_count = len(chunk_del_result.primary_keys) - else: - chunks_deleted_count = 0 - - self.logger.info( - f"Removed {chunks_deleted_count} chunks from main collection for '{source_id}'" - ) - except Exception as e: - self.logger.exception( - f"Failed to remove chunks from main collection for '{source_id}': {e}" - ) - - self.logger.info( - f"Removal complete for '{source_id}'. Metadata removed: {metadata_deleted_count}, Chunks removed: {chunks_deleted_count}" - ) - return metadata_deleted_count, chunks_deleted_count - - def _define_main_schema(self) -> CollectionSchema: - """Defines the Milvus collection schema for document chunks.""" - fields = [ - FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True), - FieldSchema( - name="chunk_id", - dtype=DataType.VARCHAR, - max_length=64, - description="Original chunk identifier", - ), - FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=self.embedding_dim), - FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=32768), - FieldSchema( - name="label", dtype=DataType.ARRAY, element_type=DataType.VARCHAR, max_length=64 - ), - FieldSchema(name="page_numbers", dtype=DataType.ARRAY, element_type=DataType.INT16), - FieldSchema(name="headings", dtype=DataType.VARCHAR, max_length=512), - FieldSchema( - name="source", - dtype=DataType.VARCHAR, - max_length=512, - description="Source document filename stem (e.g., pdf_file.stem)", - ), - ] - return CollectionSchema( - fields, description="Document Chunks Collection", enable_dynamic_field=True - ) - - def _define_metadata_schema(self) -> CollectionSchema: - """Defines the Milvus collection schema for document metadata.""" - fields = [ - FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True), - FieldSchema( - name="source", - dtype=DataType.VARCHAR, - max_length=512, - description="Unique source identifier/filename stem (e.g., pdf_file.stem)", - ), - FieldSchema( - name="original_filename", - dtype=DataType.VARCHAR, - max_length=1024, - description="Original filename of the source document", - ), - FieldSchema( - name="date_added", - dtype=DataType.VARCHAR, - max_length=64, - description="ISO 8601 timestamp when the document was added", - ), - ] - return CollectionSchema( - fields, description="Document Metadata Collection", enable_dynamic_field=False - ) - - def _get_or_create_collection(self, collection_name: str, schema: CollectionSchema) -> None: - """Creates the specified collection if it doesn't exist.""" - try: - collection_exists = self.client.has_collection(collection_name) - if not collection_exists: - self.logger.info(f"Creating collection '{collection_name}'") - self.client.create_collection(collection_name=collection_name, schema=schema) - else: - self.logger.info(f"Collection '{collection_name}' already exists.") - if collection_name == self.collection_name: - self._check_collection_dimension(collection_name) - - except Exception as e: - self.logger.exception(f"Failed to check/create collection '{collection_name}': {e}") - raise - - def _check_collection_dimension(self, collection_name: str) -> None: - """Verify the dimension of the existing collection's embedding field.""" - try: - collection_info = self.client.describe_collection(collection_name) - for field in collection_info["fields"]: - if field["name"] == "embedding": - existing_dim = field["params"]["dim"] - if existing_dim != self.embedding_dim: - error_msg = ( - f"Dimension mismatch for collection '{collection_name}'! " - f"Expected (from model '{self.model_identifier}'): {self.embedding_dim}. " - f"Found in existing collection: {existing_dim}. " - f"Must drop and recreate collection or use matching model." - ) - self.logger.critical(error_msg) - raise ValueError(error_msg) - return # Found the embedding field - self.logger.warning( - f"Could not find 'embedding' field in existing collection '{collection_name}' description." - ) - except Exception as e: - self.logger.exception( - f"Failed to check dimension for existing collection '{collection_name}': {e}" - ) - # Decide if this should be a critical error? For now, just log. - - def _build_indices(self) -> None: - """Creates indices for both main and metadata collections if they don't exist.""" - try: - self.logger.info( - f"Ensuring vector index exists on 'embedding' for '{self.collection_name}'" - ) - index_params = self.client.prepare_index_params() - index_params.add_index( - field_name="embedding", - index_type="IVF_FLAT", - metric_type="COSINE", - params={"nlist": 128}, - ) - self.client.create_index( - collection_name=self.collection_name, index_params=index_params - ) - self.logger.info(f"Vector index ensured for '{self.collection_name}'") - except Exception as e: - self.logger.warning(f"Could not ensure vector index for '{self.collection_name}': {e}") - - # metadata db index - metadata_index_field = "source" - try: - metadata_index_params = self.client.prepare_index_params() - metadata_index_params.add_index(field_name=metadata_index_field) - self.logger.info( - f"Ensuring scalar index exists on '{metadata_index_field}' for '{self.metadata_collection_name}'" - ) - self.client.create_index( - collection_name=self.metadata_collection_name, index_params=metadata_index_params - ) - self.logger.info(f"Scalar index ensured for '{self.metadata_collection_name}'") - except Exception as e: - self.logger.warning( - f"Could not ensure scalar index for '{self.metadata_collection_name}': {e}" - ) - - -def sanitize_model_identifier(model_identifier: str) -> str: - """ - Sanitizes a model identifier string to be suitable for a Milvus collection name suffix. - - Replaces potentially problematic characters like '/', '.', '-' with underscores '_' - and ensures the name starts and ends with alphanumeric characters. - """ - sanitized = re.sub(r"[^a-zA-Z0-9_]", "_", model_identifier) - sanitized = sanitized.strip("_") - if not sanitized: - return "default_model" - if not re.match(r"^[a-zA-Z_]", sanitized): - sanitized = "_" + sanitized - max_len = 200 # Milvus char limit for collection names is 255 - if len(sanitized) > max_len: - sanitized = sanitized[:max_len] - - return sanitized diff --git a/paperchat/llms/__init__.py b/paperchat/llms/__init__.py deleted file mode 100644 index c83a7f3..0000000 --- a/paperchat/llms/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -LLM provider interface for multiple AI model providers. -""" - -from .anthropic import AnthropicAdapter -from .gemini import GeminiAdapter -from .manager import LLMManager -from .openai import OpenAIAdapter - -__all__ = [ - "AnthropicAdapter", - "GeminiAdapter", - "LLMManager", - "OpenAIAdapter", -] diff --git a/paperchat/llms/anthropic.py b/paperchat/llms/anthropic.py deleted file mode 100644 index 49a6bf6..0000000 --- a/paperchat/llms/anthropic.py +++ /dev/null @@ -1,106 +0,0 @@ -""" -Anthropic provider implementation. -""" - -from typing import Any, Generator - -from anthropic import Anthropic - -from ..utils.api_keys import get_api_key -from ..utils.config import DEFAULT_SYSTEM_PROMPT - - -class AnthropicAdapter: - """Anthropic provider implementation.""" - - @staticmethod - def list_models(client: Any | None = None) -> list[dict[str, Any]]: - """List available models from Anthropic.""" - return [ - {"id": "claude-3-7-sonnet-20250219"}, - {"id": "claude-3-5-sonnet-20241022"}, - {"id": "claude-3-5-haiku-20241022"}, - {"id": "claude-3-opus-20240229"}, - ] - - @staticmethod - def get_client(api_key: str | None = None) -> Anthropic: - """Get an Anthropic client with appropriate API key.""" - if not api_key: - api_key = get_api_key("anthropic") - if not api_key: - return None - - return Anthropic(api_key=api_key) - - @staticmethod - def build_prompt(query: str, context: str) -> dict[str, Any]: - """Build a prompt for the Anthropic API.""" - return { - "system": DEFAULT_SYSTEM_PROMPT, - "messages": [ - { - "role": "user", - "content": f"Here is the relevant context from the paper:\n\n{context}\n\nQuestion: {query}", - } - ], - } - - @staticmethod - def generate_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: Anthropic | None = None, - ) -> str: - """Generate a response from Anthropic.""" - if client is None: - client = AnthropicAdapter.get_client() - - if client is None: - return "Error: Unable to create Anthropic client. Please check your API key." - - prompt = AnthropicAdapter.build_prompt(query, context) - - try: - message = client.messages.create( - model=model, - system=prompt["system"], - messages=prompt["messages"], - temperature=temperature, - max_tokens=max_tokens, - ) - return message.content[0].text - - except Exception as e: - return f"Error generating response: {e!s}" - - @staticmethod - def generate_streaming_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: Anthropic | None = None, - ) -> Generator[str, None, None]: - """Generate a streaming response from Anthropic.""" - if client is None: - client = AnthropicAdapter.get_client() - prompt = AnthropicAdapter.build_prompt(query, context) - - try: - with client.messages.stream( - model=model, - system=prompt["system"], - messages=prompt["messages"], - temperature=temperature, - max_tokens=max_tokens, - ) as stream: - for text in stream.text_stream: - yield text - - except Exception as e: - yield f"Error generating response: {e!s}" diff --git a/paperchat/llms/common.py b/paperchat/llms/common.py deleted file mode 100644 index 94b58d9..0000000 --- a/paperchat/llms/common.py +++ /dev/null @@ -1,183 +0,0 @@ -""" -Common protocols and interfaces for LLM providers. -""" - -import logging -from typing import Any, Generator, Protocol, Type, runtime_checkable - -from ..utils.config import DEFAULT_PROVIDER -from .anthropic import AnthropicAdapter -from .gemini import GeminiAdapter -from .openai import OpenAIAdapter - -logger = logging.getLogger(__name__) - - -@runtime_checkable -class LLMProvider(Protocol): - """Protocol defining the interface for LLM providers.""" - - @staticmethod - def get_client(api_key: str | None = None) -> Any: - """Get a client for this provider.""" - ... - - @staticmethod - def build_prompt(query: str, context: str) -> Any: - """Build a provider-specific prompt.""" - ... - - @staticmethod - def generate_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: Any | None = None, - ) -> str: - """Generate a response from the LLM.""" - ... - - @staticmethod - def generate_streaming_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: Any | None = None, - ) -> Generator[str, None, None]: - """Generate a streaming response from the LLM.""" - ... - - @staticmethod - def list_models(client: Any | None = None) -> list[dict[str, Any]]: - """List available models from the provider.""" - ... - - -# registry of available LLM providers -PROVIDERS: dict[str, Type[LLMProvider]] = { - "openai": OpenAIAdapter, - "anthropic": AnthropicAdapter, - "gemini": GeminiAdapter, -} - - -def get_provider(provider_name: str = DEFAULT_PROVIDER) -> Type[LLMProvider]: - """ - Get the provider class for the specified provider name. - - Args: - provider_name: Name of the provider to use - - Returns: - Provider class - - Raises: - ValueError: If the provider is not supported - """ - provider = PROVIDERS.get(provider_name) - if provider is None: - supported = ", ".join(PROVIDERS.keys()) - raise ValueError(f"Unsupported provider: {provider_name}. Supported providers: {supported}") - - return provider - - -def get_client(provider_name: str = DEFAULT_PROVIDER, api_key: str | None = None) -> Any: - """Get a client for the specified provider.""" - provider = get_provider(provider_name) - - return provider.get_client(api_key) - - -def generate_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: Any | None = None, - provider_name: str = DEFAULT_PROVIDER, -) -> str: - """ - Generate a response using the specified provider. - - Args: - query: User's question - context: Context from retrieved document chunks - model: Model to use - temperature: Temperature parameter (0-1) - max_tokens: Maximum tokens in response - client: Optional client instance - provider_name: Name of the provider to use - - Returns: - Generated response - """ - provider = get_provider(provider_name) - - return provider.generate_response( - query=query, - context=context, - model=model, - temperature=temperature, - max_tokens=max_tokens, - client=client, - ) - - -def generate_streaming_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: Any | None = None, - provider_name: str = DEFAULT_PROVIDER, -) -> Generator[str, None, None]: - """ - Generate a streaming response using the specified provider. - - Args: - query: User's question - context: Context from retrieved document chunks - model: Model to use - temperature: Temperature parameter (0-1) - max_tokens: Maximum tokens in response - client: Optional client instance - provider_name: Name of the provider to use - - Yields: - Chunks of the generated response - """ - provider = get_provider(provider_name) - - yield from provider.generate_streaming_response( - query=query, - context=context, - model=model, - temperature=temperature, - max_tokens=max_tokens, - client=client, - ) - - -def register_provider(name: str, provider_class: Type[LLMProvider]) -> None: - """Register a new LLM provider.""" - PROVIDERS[name] = provider_class - - -def list_available_providers() -> list[str]: - """List all available provider names.""" - - return list(PROVIDERS.keys()) - - -def list_llm_models(provider_name: str = DEFAULT_PROVIDER) -> list[dict[str, Any]]: - """List LLM models available from the specified provider.""" - provider = get_provider(provider_name) - - return provider.list_models() diff --git a/paperchat/llms/gemini.py b/paperchat/llms/gemini.py deleted file mode 100644 index 840644d..0000000 --- a/paperchat/llms/gemini.py +++ /dev/null @@ -1,106 +0,0 @@ -""" -Gemini provider implementation. -""" - -from typing import Any, Generator - -from google import genai -from google.genai import types - -from ..utils.api_keys import get_api_key -from ..utils.config import DEFAULT_SYSTEM_PROMPT - - -class GeminiAdapter: - """Gemini provider implementation.""" - - @staticmethod - def list_models(client: Any | None = None) -> list[dict[str, Any]]: - """List available models from Gemini.""" - return [ - {"id": "gemini-2.5-pro-exp-03-25"}, - {"id": "gemini-2.5-flash-preview-04-17"}, - {"id": "gemini-2.0-flash"}, - {"id": "gemini-2.5-pro-preview-03-25"}, - ] - - @staticmethod - def get_client(api_key: str | None = None) -> Any: - """Get a Gemini client with appropriate API key.""" - if not api_key: - api_key = get_api_key("gemini") - if not api_key: - return None - - return genai.Client(api_key=api_key) - - @staticmethod - def build_prompt(query: str, context: str) -> str: - """Build a prompt for the Gemini API.""" - return f"Here is the relevant context from the paper:\n\n{context}\n\nQuestion: {query}" - - @staticmethod - def generate_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: Any | None = None, - ) -> str: - """Generate a response from Gemini.""" - if client is None: - client = GeminiAdapter.get_client() - if client is None: - return "Error: Unable to create Gemini client. Please check your API key." - - prompt = GeminiAdapter.build_prompt(query, context) - - try: - response = client.models.generate_content( - model=model, - contents=prompt, - config=types.GenerateContentConfig( - temperature=temperature, - max_output_tokens=max_tokens, - system_instruction=DEFAULT_SYSTEM_PROMPT, - ), - ) - return response.text - - except Exception as e: - return f"Error generating response: {e!s}" - - @staticmethod - def generate_streaming_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: Any | None = None, - ) -> Generator[str, None, None]: - """Generate a streaming response from Gemini.""" - if client is None: - client = GeminiAdapter.get_client() - if client is None: - yield "Error: Unable to create Gemini client. Please check your API key." - return - - prompt = GeminiAdapter.build_prompt(query, context) - - try: - for chunk in client.models.generate_content_stream( - model=model, - contents=prompt, - config=types.GenerateContentConfig( - temperature=temperature, - max_output_tokens=max_tokens, - system_instruction=DEFAULT_SYSTEM_PROMPT, - ), - ): - if chunk.text: - yield chunk.text - - except Exception as e: - yield f"Error generating response: {e!s}" diff --git a/paperchat/llms/manager.py b/paperchat/llms/manager.py deleted file mode 100644 index e1e312c..0000000 --- a/paperchat/llms/manager.py +++ /dev/null @@ -1,63 +0,0 @@ -from typing import Any, Generator - -from .common import get_provider - - -class LLMManager: - """Manager class for handling LLM providers and clients.""" - - def __init__(self, config: dict[str, Any], api_key: str | None = None): - """Initialize the LLM manager with a configuration dictionary. - - Args: - config: Dictionary with 'provider_name', 'model_id', 'temperature', 'max_tokens' - api_key: Optional API key for the provider - """ - self.config = config - self._api_key = api_key - self._client = None - - @property - def client(self) -> Any: - """Get or create the LLM client.""" - if self._client is None: - self._client = self._create_client() - return self._client - - def _create_client(self) -> Any: - """Create a new client for the configured provider.""" - provider = get_provider(self.config["provider_name"]) - return provider.get_client(self._api_key) - - def generate_response(self, query: str, context: str) -> str: - """Generate a response from the LLM.""" - provider = get_provider(self.config["provider_name"]) - return provider.generate_response( - query=query, - context=context, - model=self.config["model_id"], - temperature=self.config["temperature"], - max_tokens=self.config["max_tokens"], - client=self.client, - ) - - def generate_streaming_response(self, query: str, context: str) -> Generator[str, None, None]: - """Generate a streaming response from the LLM.""" - provider = get_provider(self.config["provider_name"]) - yield from provider.generate_streaming_response( - query=query, - context=context, - model=self.config["model_id"], - temperature=self.config["temperature"], - max_tokens=self.config["max_tokens"], - client=self.client, - ) - - def update_config(self, **kwargs) -> None: - """Update the configuration with new values.""" - provider_changed = "provider_name" in kwargs - - self.config.update(kwargs) - - if provider_changed: - self._client = None diff --git a/paperchat/llms/openai.py b/paperchat/llms/openai.py deleted file mode 100644 index f7f49d5..0000000 --- a/paperchat/llms/openai.py +++ /dev/null @@ -1,95 +0,0 @@ -""" -OpenAI provider implementation. -""" - -from typing import Any, Generator - -from openai import OpenAI - -from ..utils.api_keys import get_api_key -from ..utils.config import DEFAULT_SYSTEM_PROMPT - - -class OpenAIAdapter: - """OpenAI provider implementation.""" - - @staticmethod - def list_models(client: Any | None = None) -> list[dict[str, Any]]: - """List available models from OpenAI.""" - return [{"id": "gpt-4.1"}, {"id": "gpt-4.1-mini"}, {"id": "gpt-4.1-nano"}] - - @staticmethod - def get_client(api_key: str | None = None) -> OpenAI: - """Get an OpenAI client with appropriate API key.""" - if not api_key: - api_key = get_api_key("openai") - if not api_key: - return None - - return OpenAI(api_key=api_key) - - @staticmethod - def build_prompt(query: str, context: str) -> list[dict[str, str]]: - """Build a prompt for the OpenAI API.""" - return [ - {"role": "system", "content": DEFAULT_SYSTEM_PROMPT}, - { - "role": "user", - "content": f"Here is the relevant context from the paper:\n\n{context}\n\nQuestion: {query}", - }, - ] - - @staticmethod - def generate_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: OpenAI | None = None, - ) -> str: - """Generate a response from OpenAI.""" - if client is None: - client = OpenAIAdapter.get_client() - if client is None: - return "Error: Unable to create OpenAI client. Please check your API key." - - messages = OpenAIAdapter.build_prompt(query, context) - try: - completion = client.chat.completions.create( - model=model, messages=messages, temperature=temperature, max_tokens=max_tokens - ) - return completion.choices[0].message.content - - except Exception as e: - return f"Error generating response: {e!s}" - - @staticmethod - def generate_streaming_response( - query: str, - context: str, - model: str, - temperature: float, - max_tokens: int, - client: OpenAI | None = None, - ) -> Generator[str, None, None]: - """Generate a streaming response from OpenAI.""" - if client is None: - client = OpenAIAdapter.get_client() - - messages = OpenAIAdapter.build_prompt(query, context) - try: - stream = client.chat.completions.create( - model=model, - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - stream=True, - ) - - for chunk in stream: - if chunk.choices and chunk.choices[0].delta.content: - yield chunk.choices[0].delta.content - - except Exception as e: - yield f"Error generating response: {e!s}" diff --git a/paperchat/ui/__init__.py b/paperchat/ui/__init__.py deleted file mode 100644 index cc2ba02..0000000 --- a/paperchat/ui/__init__.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -UI components for the Streamlit application. - -This module exports UI rendering components that can be used -to build and display different parts of the Streamlit application. -""" - -from .api_settings import render_api_key_manager -from .chat import render_api_key_setup, render_main_content -from .common import ( - check_model_config_changes, - refresh_model_state, - render_page_header, - set_css_styles, -) -from .document import render_sidebar -from .settings import ( - render_api_key_table, - render_settings_page, -) - -__all__ = [ - # Common UI components - "check_model_config_changes", - "refresh_model_state", - # API Settings - "render_api_key_manager", - # Chat UI - "render_api_key_setup", - # Settings UI - "render_api_key_table", - "render_main_content", - "render_page_header", - "render_settings_page", - # Document UI - "render_sidebar", - "set_css_styles", -] diff --git a/paperchat/ui/api_settings.py b/paperchat/ui/api_settings.py deleted file mode 100644 index c4703b3..0000000 --- a/paperchat/ui/api_settings.py +++ /dev/null @@ -1,177 +0,0 @@ -""" -API key settings UI components for Streamlit. -""" - -from typing import Callable, Optional - -import streamlit as st - -from paperchat.utils.api_keys import ( - get_api_key, - get_provider_display_name, - remove_api_key, - set_api_key, -) - - -def render_api_key_form(provider: str, on_save_callback: Optional[Callable] = None) -> bool: - """ - Render an API key configuration form for a provider and handle the form submission. - - Args: - provider: Provider ID (e.g., 'openai') - on_save_callback: Function to call when settings are saved - - Returns: - True if form was submitted, False otherwise - """ - provider_name = get_provider_display_name(provider) - - form_key = f"{provider}_api_key_form" - - with st.form(form_key): - api_key = st.text_input( - f"{provider_name} API Key", - type="password", - value=get_api_key(provider) or "", - placeholder=f"Enter your {provider_name} API key", - help=f"API key for {provider_name} services. This will be securely stored on your device.", - ) - - submitted = st.form_submit_button("Save") - - if submitted: - # Check for empty value - if not api_key: - st.error("API key cannot be empty.") - return False - - # Save the API key - success, error_msg = set_api_key(provider, api_key) - if success: - st.success(f"{provider_name} API key saved successfully!") - if on_save_callback: - on_save_callback() - return True - else: - st.error(f"Failed to save API key: {error_msg}") - return False - - return False - - -def render_api_key_section(provider: str, on_save_callback: Optional[Callable] = None) -> bool: - """ - Render a provider API key section with current status and edit form. - - Args: - provider: Provider ID - on_save_callback: Function to call when settings are saved - - Returns: - True if API key is configured, False otherwise - """ - provider_name = get_provider_display_name(provider) - api_key = get_api_key(provider) - is_configured = api_key is not None - - col1, col2 = st.columns([3, 1]) - - with col1: - if is_configured: - st.success(f"{provider_name} API key is configured") - else: - st.info(f"No {provider_name} API key configured") - - with col2: - if is_configured and st.button(f"Remove {provider} Key", key=f"remove_{provider}_key"): - success, message = remove_api_key(provider) - if success: - st.success(message) - if on_save_callback: - on_save_callback() - st.rerun() - else: - st.error(message) - return is_configured - - # Render the form if not configured or edit button pressed - if not is_configured or st.button(f"Edit {provider} Key", key=f"edit_{provider}_key"): - render_api_key_form(provider, on_save_callback) - - return is_configured - - -def render_api_key_manager(provider: str, on_save_callback: Optional[Callable] = None) -> bool: - """ - Render a complete API key management UI for a provider. - - Args: - provider: Provider ID (e.g., 'openai') - on_save_callback: Function to call when settings are saved - - Returns: - True if API key is configured, False otherwise - """ - provider_name = get_provider_display_name(provider) - api_key = get_api_key(provider) - is_configured = api_key is not None - - st.subheader(f"{provider_name} API Key") - - if is_configured: - st.success(f"{provider_name} API key is configured") - - col1, col2 = st.columns(2) - with col1: - if st.button("Edit API Key", key=f"edit_{provider}_api_key"): - st.session_state[f"show_{provider}_edit"] = True - with col2: - if st.button("Remove API Key", key=f"remove_{provider}_api_key"): - success, message = remove_api_key(provider) - if success: - st.success(message) - if on_save_callback: - on_save_callback() - st.rerun() - else: - st.error(message) - else: - st.info(f"No {provider_name} API key configured") - st.session_state[f"show_{provider}_edit"] = True - - if st.session_state.get(f"show_{provider}_edit", False): - render_api_key_form(provider, on_save_callback) - - return is_configured - - -def render_api_keys_section(on_save_callback: Optional[Callable] = None) -> None: - """ - Render a section showing all available API providers and their configuration status. - - Args: - on_save_callback: Function to call when settings are saved - """ - from paperchat.utils.api_keys import get_available_providers - - st.subheader("API Keys") - st.markdown("Configure API keys for language model providers:") - - for provider in get_available_providers(): - with st.expander(get_provider_display_name(provider), expanded=True): - render_api_key_section(provider, on_save_callback) - if provider == "openai": - st.caption( - "Get an OpenAI API key from [OpenAI API Keys](https://platform.openai.com/api-keys)" - ) - elif provider == "anthropic": - st.caption( - "Get an Anthropic API key from [Anthropic Console](https://console.anthropic.com/)" - ) - elif provider == "gemini": - st.caption( - "Get a Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey)" - ) - - st.divider() diff --git a/paperchat/ui/chat.py b/paperchat/ui/chat.py deleted file mode 100644 index 213ee19..0000000 --- a/paperchat/ui/chat.py +++ /dev/null @@ -1,209 +0,0 @@ -""" -Chat interface UI components for Streamlit. -""" - -from typing import Generator - -import streamlit as st - -from ..utils.formatting import ( - format_response_with_citations, - format_retrieved_chunks_for_display, - process_citations, -) -from .common import check_model_config_changes, render_page_header - - -def render_api_key_setup() -> None: - """Render the API key setup screen when no keys are configured.""" - from paperchat.ui.api_settings import render_api_key_manager - - render_page_header("Welcome to PaperChat 🔑", "Configure your API key to get started") - - st.markdown(""" - To use PaperChat, you'll need at least one API key from a supported provider. - Please configure an API key below to continue. - """) - - tab1, tab2 = st.tabs(["OpenAI", "Anthropic"]) - - with tab1: - openai_configured = render_api_key_manager("openai", on_save_callback=lambda: st.rerun()) - st.markdown(""" - **Need an OpenAI API key?** - 1. Go to [OpenAI API Keys](https://platform.openai.com/api-keys) - 2. Create an account or sign in - 3. Click "Create new secret key" - 4. Copy the key and paste it above - """) - - with tab2: - anthropic_configured = render_api_key_manager( - "anthropic", on_save_callback=lambda: st.rerun() - ) - st.markdown(""" - **Need an Anthropic API key?** - 1. Go to [Anthropic Console](https://console.anthropic.com/) - 2. Create an account or sign in - 3. Navigate to API Keys and create a new key - 4. Copy the key and paste it above - """) - - if (openai_configured or anthropic_configured) and st.button( - "Continue to PaperChat", use_container_width=True - ): - st.session_state.show_api_setup = False - st.rerun() - - -def _get_retrieval_filter() -> str | None: - """Determine the Milvus filter expression based on selected documents.""" - selected_docs = st.session_state.get("selected_documents") - filter_expression = None - - if not selected_docs: - st.info("ℹ️ No specific documents selected. Searching across the entire knowledge base.") - return None - - # build the filter basd on list items - if isinstance(selected_docs, list) and len(selected_docs) > 0: - # construct a Milvus 'in' filter expression - # e.g., source in ["doc1", "doc2"] - formatted_sources = [f'"{doc_name}"' for doc_name in selected_docs] - filter_expression = f"source in [{', '.join(formatted_sources)}]" - else: - st.warning("Selected documents state is invalid. Searching across all documents.") - # fallback to searching all if state is weird - filter_expression = None - - return filter_expression - - -def _handle_rag_query( - user_query: str, filter_expression: str | None, top_k: int -) -> tuple[Generator[str, None, None], list] | tuple[None, None]: - """ - Execute RAG pipeline: retrieve chunks and return generator stream + retrieved chunks. - - Args: - user_query: The user's query string. - filter_expression: Milvus filter expression for retrieval. - top_k: Number of chunks to retrieve. - - Returns: - A tuple containing the LLM response generator and the list of retrieved chunks, - or (None, None) if an error occurs. - """ - try: - rag_pipeline = st.session_state.rag_pipeline - - retrieved_chunks = rag_pipeline.retrieve( - query=user_query, top_k=top_k, filter_expression=filter_expression - ) - - llm_response_stream = rag_pipeline.generate( - query=user_query, - retrieved_results=retrieved_chunks, - stream=True, - ) - return llm_response_stream, retrieved_chunks - - except Exception as e: - error_message = f"Sorry, I encountered an error: {e!s}" - st.error(error_message) - return None, None - - -def render_chat_interface() -> None: - """Render the chat interface.""" - chat_container = st.container() - - with chat_container: - if st.session_state.messages: - for msg in st.session_state.messages: - with st.chat_message(msg["role"]): - if msg["role"] == "assistant": - formatted_content = format_response_with_citations(msg["content"]) - st.markdown(formatted_content, unsafe_allow_html=True) - if msg.get("sources"): - with st.expander("📚 Sources", expanded=False): - sources_md = format_retrieved_chunks_for_display(msg["sources"]) - st.markdown(sources_md, unsafe_allow_html=True) - else: - st.markdown(msg["content"]) - - chat_input_placeholder = "Ask a question..." - user_query = st.chat_input(chat_input_placeholder) - - if user_query: - st.session_state.messages.append({"role": "user", "content": user_query}) - with st.chat_message("user"): - st.markdown(user_query) - - with st.chat_message("assistant"): - response_placeholder = st.empty() - response_placeholder.markdown("Thinking...▌") - - check_model_config_changes() - top_k = st.session_state.config.get("top_k", 5) - - filter_expression = _get_retrieval_filter() - llm_response_stream, retrieved_chunks = _handle_rag_query( - user_query, filter_expression, top_k - ) - - if llm_response_stream and retrieved_chunks is not None: - full_response = response_placeholder.write_stream(llm_response_stream) - - processed_response, cited_chunks_filtered = process_citations( - full_response, retrieved_chunks - ) - - formatted_final_content = format_response_with_citations(processed_response) - response_placeholder.markdown(formatted_final_content, unsafe_allow_html=True) - - message_data = { - "role": "assistant", - "content": processed_response, - "sources": cited_chunks_filtered, - } - st.session_state.messages.append(message_data) - - if cited_chunks_filtered: - with st.expander("📚 Sources", expanded=False): - sources_md = format_retrieved_chunks_for_display(cited_chunks_filtered) - st.markdown(sources_md, unsafe_allow_html=True) - else: - response_placeholder.error("An error occurred while processing your request.") - st.session_state.messages.append( - { - "role": "assistant", - "content": "Sorry, I couldn't process your request due to an error.", - "sources": [], - } - ) - - -def render_main_content() -> None: - """Render the main content area with chat or welcome screen.""" - selected_docs = st.session_state.get("selected_documents") - - if selected_docs and isinstance(selected_docs, list) and len(selected_docs) > 0: - render_chat_interface() - elif selected_docs is not None and isinstance(selected_docs, list) and len(selected_docs) == 0: - st.info( - "Please select one or more documents from the 'Select Chat Context' dropdown in the sidebar to begin chatting." - ) - else: - # this branch covers cases where selected_documents hasn't been initialized yet - # (e.g., on first run or if pipeline failed) - st.markdown(""" - ### 👋 Welcome to PaperChat! - - To get started: - 1. Upload one or more PDFs using the sidebar - 2. Select the document(s) you want to chat with - 3. Ask questions about the document content - - PaperChat will use AI to retrieve relevant information and provide answers based on the selected document(s). - """) diff --git a/paperchat/ui/common.py b/paperchat/ui/common.py deleted file mode 100644 index 1d17661..0000000 --- a/paperchat/ui/common.py +++ /dev/null @@ -1,206 +0,0 @@ -""" -Common UI components. -""" - -import streamlit as st - -from paperchat.core import RAGPipeline -from paperchat.llms import LLMManager -from paperchat.utils.api_keys import get_api_key -from paperchat.utils.config import DEFAULT_MODEL, DEFAULT_PROVIDER - - -def set_css_styles() -> None: - """ - Apply custom CSS styles to improve the UI appearance. - - This function adds custom CSS to the Streamlit app to enhance the appearance - of various UI elements. - """ - st.markdown( - """ - - """, - unsafe_allow_html=True, - ) - - -def refresh_model_state() -> None: - """ - Refresh LLM manager and RAG pipeline with current config settings. - - This function should be called whenever model settings change to ensure - the LLM provider and model are correctly updated. - """ - provider_name = st.session_state.config.get("provider_name", DEFAULT_PROVIDER) - api_key = get_api_key(provider_name) - - st.session_state.llm_manager = LLMManager(config=st.session_state.config, api_key=api_key) - st.session_state.rag_pipeline = RAGPipeline(llm_manager=st.session_state.llm_manager) - - if "previous_config" not in st.session_state: - st.session_state.previous_config = {} - - st.session_state.previous_config = { - "provider_name": provider_name, - "model_id": st.session_state.config.get("model_id", DEFAULT_MODEL), - "temperature": st.session_state.config.get("temperature", 0.7), - } - - -def check_model_config_changes() -> bool: - """ - Check if model configuration has changed and needs refresh. - - Returns: - bool: True if config changed and model state was refreshed - """ - if "previous_config" not in st.session_state: - refresh_model_state() - return True - - prev_config = st.session_state.previous_config - current_config = st.session_state.config - if prev_config.get("provider_name") != current_config.get("provider_name") or prev_config.get( - "model_id" - ) != current_config.get("model_id"): - refresh_model_state() - return True - - return False - - -def render_page_header(title: str, subtitle: str | None = None) -> None: - """ - Render a consistent page header with title and optional subtitle. - - Args: - title: Main page title - subtitle: Optional subtitle text - """ - st.title(title) - if subtitle: - st.markdown(f"**{subtitle}**") - st.divider() diff --git a/paperchat/ui/document.py b/paperchat/ui/document.py deleted file mode 100644 index abb7fd6..0000000 --- a/paperchat/ui/document.py +++ /dev/null @@ -1,454 +0,0 @@ -""" -Document handling UI components for Streamlit. -""" - -import hashlib -import logging -import os -import tempfile -from pathlib import Path - -import streamlit as st - -from paperchat.llms.common import list_llm_models -from paperchat.ui.common import refresh_model_state -from paperchat.ui.settings import ( - update_global_settings, -) -from paperchat.utils.api_keys import get_api_key, get_available_providers - -logger = logging.getLogger("document_ui") - - -def render_compact_settings_ui() -> None: - """Render a compact settings UI for the sidebar.""" - - all_models = [] - active_provider = st.session_state.config.get("provider_name", "") - active_model = st.session_state.config.get("model_id", "") - - for provider in get_available_providers(): - if not get_api_key(provider): - continue - - models = list_llm_models(provider_name=provider) - for model in models: - model_display = f"{model['id']}" - all_models.append( - {"display": model_display, "provider": provider, "model_id": model["id"]} - ) - - if not all_models: - st.warning("No models available. Please configure API keys in settings.") - return - - st.markdown("**Model Selection**") - - selected_index = 0 - for i, model_info in enumerate(all_models): - if model_info["provider"] == active_provider and model_info["model_id"] == active_model: - selected_index = i - break - - selected_model = st.selectbox( - "Select model:", - options=range(len(all_models)), - format_func=lambda i: all_models[i]["display"], - index=selected_index, - ) - - selected_provider = all_models[selected_model]["provider"] - selected_model_id = all_models[selected_model]["model_id"] - - if selected_provider != active_provider or selected_model_id != active_model: - st.session_state.config.update( - { - "provider_name": selected_provider, - "model_id": selected_model_id, - } - ) - - current_temp = st.session_state.config.get("temperature", 0.7) - temperature = st.slider( - "Temperature", - min_value=0.0, - max_value=2.0, - value=current_temp, - step=0.1, - help="Controls randomness: 0=deterministic, 2=creative", - ) - - st.session_state.config["temperature"] = temperature - - st.markdown("---") - st.markdown("**Retrieval settings:**") - top_k = st.slider( - "Chunks to retrieve", - min_value=1, - max_value=10, - value=st.session_state.config.get("top_k", 5), - help="Higher values retrieve more chunks but may include less relevant information", - ) - - update_global_settings(selected_provider, selected_model_id, temperature) - - st.session_state.config["top_k"] = top_k - - -def check_vector_store_for_document(content_hash: str, original_filename: str) -> bool: - """Check if a document exists in the vector store. - - Args: - content_hash: Hash of the document - original_filename: Original filename - - Returns: - True if document was found in vector store, False otherwise - """ - vector_store = st.session_state.vector_store - - exists, doc_info = vector_store.get_document_info(content_hash) - - if exists: - collection_name = doc_info["collection_name"] - logger.debug(f"Found document in registry: {original_filename} -> {collection_name}") - - collections = vector_store.list_collections() - - if collection_name in collections: - info = vector_store.get_collection_info(collection_name) - - if info["count"] > 0: - doc_data = vector_store.get_document_texts(collection_name) - - st.session_state.processed_documents[content_hash] = { - "collection_name": collection_name, - "chunk_texts": doc_data["documents"], - "chunk_metadata": doc_data["metadatas"], - } - - st.session_state.filenames[content_hash] = original_filename - st.session_state.document_sources[content_hash] = "vector_store" - - return True - - return False - - -def _process_uploaded_pdf(file_content: bytes, original_filename: str, content_hash: str) -> None: - """Helper function to process a new PDF file and update state.""" - with st.spinner(f"Processing {original_filename}..."): - pdf_path = None - try: - with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file: - tmp_file.write(file_content) - pdf_path = tmp_file.name - - original_stem = Path(original_filename).stem - success = st.session_state.rag_pipeline.vector_store.add_document( - pdf_path=pdf_path, - source_id=original_stem, - ) - - if success: - st.session_state.filenames[content_hash] = original_filename - st.session_state.document_sources[content_hash] = "vector_store" - st.session_state.messages = [] - - if "selected_documents" in st.session_state and isinstance( - st.session_state.selected_documents, list - ): - if original_stem not in st.session_state.selected_documents: - st.session_state.selected_documents.append(original_stem) - logger.info(f"Added '{original_stem}' to selected documents.") - else: - st.session_state.selected_documents = [original_stem] - logger.info(f"Initialized selected documents with '{original_stem}'.") - - st.success(f"Document processed and added: {original_filename}") - st.rerun() - else: - st.error(f"Failed to process {original_filename}. Check logs.") - - except Exception as e: - logger.exception(f"Error processing uploaded file {original_filename}: {e}") - st.error(f"Error processing document: {e}") - finally: - if pdf_path and os.path.exists(pdf_path): - try: - os.unlink(pdf_path) - except Exception as e: - logger.error(f"Failed to delete temp file {pdf_path}: {e}") - - -def render_upload_section() -> None: - """Render document upload section in the sidebar.""" - st.markdown("Upload a PDF to start chatting with its content.") - - uploaded_file = st.file_uploader( - "Choose a PDF file", type=["pdf"], help="Select a PDF file to upload and process" - ) - - if uploaded_file is not None: - original_filename = uploaded_file.name - file_content = uploaded_file.getvalue() - content_hash = hashlib.md5(file_content).hexdigest() - - # check if already known - if content_hash in st.session_state.filenames: - st.success(f"Document ready: {original_filename}") - # We might re-introduce setting active_document_hash if needed for single-doc focus later - # if st.session_state.active_document_hash != content_hash: - # st.session_state.active_document_hash = content_hash - # st.session_state.messages = [] - # st.rerun() - else: - _process_uploaded_pdf(file_content, original_filename, content_hash) - - -def render_document_list() -> None: - """Render the list of processed documents.""" - if not st.session_state.filenames: - st.info("No document loaded. Please upload a PDF file to start.") - return - - st.header("📚 Loaded Documents") - - doc_items = [ - (st.session_state.filenames[hash_val], hash_val) for hash_val in st.session_state.filenames - ] - - if not doc_items: - st.info("Upload a PDF to begin.") - return - - doc_items.sort() - cols = st.columns([5, 1]) - - with cols[0]: - st.markdown("**Select a document to chat with:**") - - active_hash = st.session_state.active_document_hash - - selected_doc_hash = None - - for filename, file_hash in doc_items: - source_icon = "💾" - tooltip = f"Source: {filename} (Stored)" - - label = f"{source_icon} {filename}" - - button_type = "primary" if file_hash == active_hash else "secondary" - button_key = f"doc_select_{file_hash}" - - if st.button( - label, key=button_key, help=tooltip, type=button_type, use_container_width=True - ): - selected_doc_hash = file_hash - - if selected_doc_hash and selected_doc_hash != active_hash: - st.session_state.active_document_hash = selected_doc_hash - st.session_state.messages = [] - st.rerun() - - -def render_model_selection() -> tuple[str | None, str | None]: - """Render the model selection dropdown in the sidebar.""" - all_models = [] - active_provider = st.session_state.config.get("provider_name", "") - active_model = st.session_state.config.get("model_id", "") - - for provider in get_available_providers(): - if not get_api_key(provider): - continue - - models = list_llm_models(provider_name=provider) - for model in models: - model_display = f"{model['id']}" - all_models.append( - {"display": model_display, "provider": provider, "model_id": model["id"]} - ) - - if not all_models: - return None, None - - selected_index = 0 - for i, model_info in enumerate(all_models): - if model_info["provider"] == active_provider and model_info["model_id"] == active_model: - selected_index = i - break - - selected_model = st.selectbox( - "Model:", - options=range(len(all_models)), - format_func=lambda i: all_models[i]["display"], - index=selected_index, - ) - - selected_provider = all_models[selected_model]["provider"] - selected_model_id = all_models[selected_model]["model_id"] - - if selected_provider != active_provider or selected_model_id != active_model: - st.session_state.config.update( - { - "provider_name": selected_provider, - "model_id": selected_model_id, - } - ) - - temperature = st.session_state.config.get("temperature", 0.7) - update_global_settings(selected_provider, selected_model_id, temperature) - refresh_model_state() - st.success(f"Switched to {selected_provider.capitalize()} model: {selected_model_id}") - - return selected_provider, selected_model_id - - -def render_advanced_settings(selected_provider: str, selected_model_id: str) -> None: - """Render advanced settings like temperature and retrieval settings.""" - with st.expander("Advanced Settings", expanded=False): - if selected_provider and selected_model_id: - current_temp = st.session_state.config.get("temperature", 0.7) - temperature = st.slider( - "Temperature", - min_value=0.0, - max_value=2.0, - value=current_temp, - step=0.1, - help="Controls randomness: 0=deterministic, 2=creative", - ) - - if temperature != current_temp: - st.session_state.config["temperature"] = temperature - update_global_settings(selected_provider, selected_model_id, temperature) - refresh_model_state() - - st.markdown("**Retrieval settings:**") - top_k = st.slider( - "Chunks to retrieve", - min_value=1, - max_value=10, - value=st.session_state.config.get("top_k", 5), - help="Higher values retrieve more chunks but may include less relevant information", - ) - - if "config" in st.session_state: - st.session_state.config["top_k"] = top_k - - -def _render_document_selector(doc_names: list[str]) -> None: - """Renders the document selection checkboxes and syncs state.""" - # Apply custom CSS to shrink checkbox label font size - st.markdown( - """ - - """, - unsafe_allow_html=True, - ) - - # default to all documents - if "selected_documents" not in st.session_state: - st.session_state.selected_documents = doc_names[:] - - with st.expander("Select Chat Context", expanded=True): - st.caption("Check/uncheck boxes to select documents. Default is all selected.") - - col1, col2 = st.columns(2) - select_all_clicked = col1.button("Select All", key="select_all_docs") - deselect_all_clicked = col2.button("Deselect All", key="deselect_all_docs") - - if select_all_clicked and set(st.session_state.selected_documents) != set(doc_names): - st.session_state.selected_documents = doc_names[:] - logger.info("Selected all documents.") - st.rerun() - - if deselect_all_clicked and st.session_state.selected_documents: - st.session_state.selected_documents = [] - logger.info("Deselected all documents.") - st.rerun() - - # render checkboxes based on current session state - if doc_names: - current_ui_selection = [] - for doc_name in doc_names: - is_selected = doc_name in st.session_state.selected_documents - if st.checkbox(doc_name, value=is_selected, key=f"cb_{doc_name}"): - current_ui_selection.append(doc_name) - - # compare UI state with session state and update if checkbox interaction caused change - # this check is important to only trigger rerun on direct checkbox clicks, - # not after button clicks (which already reran). - if set(current_ui_selection) != set(st.session_state.selected_documents): - st.session_state.selected_documents = current_ui_selection - logger.info( - f"Updated selected documents via checkboxes: {st.session_state.selected_documents}" - ) - st.rerun() - else: - st.info("No documents found in the vector store.") - if st.session_state.selected_documents: - st.session_state.selected_documents = [] - # no rerun needed here as it implies doc_names just became empty - - -def render_sidebar() -> None: - """Render the app sidebar.""" - st.header("📚 Sources") - - with st.expander("Add Document", expanded=True): - render_upload_section() - - try: - if "rag_pipeline" in st.session_state and st.session_state.rag_pipeline: - vector_store = st.session_state.rag_pipeline.vector_store - doc_list = vector_store.list_documents() - doc_names = sorted([doc.get("source", "Unknown") for doc in doc_list]) - - _render_document_selector(doc_names) - - else: - st.warning("RAG Pipeline not initialized. Cannot list documents.") - if "selected_documents" not in st.session_state or st.session_state.selected_documents: - st.session_state.selected_documents = [] - - except AttributeError as e: - logger.error(f"Attribute error accessing vector store: {e}") - st.error("Could not access vector store components.") - except Exception as e: - logger.error(f"Failed to list documents from vector store: {e}") - st.error("Could not retrieve document list.") - - st.divider() - - st.header("⚙️ Settings") - - if st.button("Open Settings", key="open_settings"): - st.session_state.show_settings = True - st.rerun() - - selected_provider, selected_model_id = render_model_selection() - render_advanced_settings(selected_provider, selected_model_id) - - st.divider() - - st.header("🔄 Actions") - - col1, col2 = st.columns(2) - with col1: - if st.button("🗑️ Clear Chat"): - st.session_state.messages = [] - st.rerun() - - with col2: - if st.button("🔄 Reset All"): - st.session_state.processed_documents = {} - st.session_state.messages = [] - st.rerun() diff --git a/paperchat/ui/settings.py b/paperchat/ui/settings.py deleted file mode 100644 index 060e9d8..0000000 --- a/paperchat/ui/settings.py +++ /dev/null @@ -1,608 +0,0 @@ -""" -Settings UI components for Streamlit. -""" - -from typing import Callable - -import streamlit as st - -from paperchat.core.embeddings import ( - PROVIDERS_REQUIRING_KEYS, - list_embedding_models, -) -from paperchat.ui.common import render_page_header -from paperchat.utils.api_keys import ( - get_api_key, - get_available_providers, - get_provider_display_name, - mask_api_key, - remove_api_key, - set_api_key, -) -from paperchat.utils.settings import get_active_embedding_model, set_active_embedding_model - -from ..llms.common import list_llm_models - - -def add_api_table_css() -> None: - """ - Add custom CSS for the API key table styling. - """ - st.markdown( - """ - - """, - unsafe_allow_html=True, - ) - - -def handle_api_key_popup(provider: str) -> None: - """ - Handle the API key popup interaction for a provider. - - Args: - provider: Provider ID (e.g., 'openai') - """ - provider_name = get_provider_display_name(provider) - current_key = get_api_key(provider) or "" - - with st.form(key=f"{provider}_popup_form"): - api_key = st.text_input( - label="Edit Provider API Key", - value=current_key, - type="password" if current_key else "default", - placeholder=f"Enter {provider_name} API key...", - key=f"{provider}_popup_input", - ) - - col1, col2 = st.columns(2) - with col1: - save = st.form_submit_button("Save", use_container_width=True) - with col2: - if current_key: - delete = st.form_submit_button("Delete", use_container_width=True) - else: - close_popover = st.form_submit_button("Close", use_container_width=True) - - if save and api_key: - success, error_msg = set_api_key(provider, api_key) - if success: - st.success(f"{provider_name} API key saved successfully!") - st.rerun() - else: - st.error(f"Failed to save API key: {error_msg}") - - if current_key and delete: - success, message = remove_api_key(provider) - if success: - st.success(message) - st.rerun() - else: - st.error(message) - - if not current_key and close_popover: - st.rerun() - - -def render_model_table() -> None: - """ - Render a table of all available models across providers. - """ - st.markdown("#### Available Models") - - all_models = [] - for provider in get_available_providers(): - if not get_api_key(provider): - continue - - models = list_llm_models(provider_name=provider) - for model in models: - all_models.append( - { - "provider": provider, - "provider_display": get_provider_display_name(provider), - "model_id": model["id"], - } - ) - - if not all_models: - st.info("No models available. Please configure API keys first.") - return - - active_provider = st.session_state.config.get("provider_name", "") - active_model = st.session_state.config.get("model_id", "") - - col1, col2, col3 = st.columns([2, 3, 2]) - with col1: - st.markdown("**Provider**") - with col2: - st.markdown("**Model ID**") - with col3: - st.markdown("**Status**") - - st.divider() - - for model_info in all_models: - col1, col2, col3 = st.columns([2, 3, 2]) - - with col1: - st.markdown(f"{model_info['provider_display']}") - - with col2: - st.markdown(f"`{model_info['model_id']}`") - - with col3: - is_selected = ( - model_info["provider"] == active_provider and model_info["model_id"] == active_model - ) - if is_selected: - st.markdown("✅ **Active**") - elif st.button( - "Select", key=f"select_{model_info['provider']}_{model_info['model_id']}" - ): - st.session_state.config["provider_name"] = model_info["provider"] - st.session_state.config["model_id"] = model_info["model_id"] - - st.success(f"Selected {model_info['model_id']} as active model") - st.rerun() - - st.divider() - - -def render_api_key_table() -> None: - """ - Render a table of API providers using Streamlit's column layout for a native look. - This approach allows embedding interactive buttons directly in the table. - """ - providers = get_available_providers() - - if not providers: - st.info("No API providers are configured.") - return - - col1, col2, col3, col4 = st.columns([1, 1, 1, 1]) - with col1: - st.markdown("**ID**") - with col2: - st.markdown("**Type**") - with col3: - st.markdown("**API Key**") - with col4: - st.markdown("**Actions**") - - st.divider() - - for provider in providers: - col1, col2, col3, col4 = st.columns([1, 1, 1, 1]) - - with col1: - st.markdown(f"{provider.lower()}") - - with col2: - st.markdown(f"{get_provider_display_name(provider)}") - - with col3: - api_key = get_api_key(provider) - if api_key: - st.code(mask_api_key(api_key), language=None) - else: - st.markdown("No API Key set") - - with col4, st.popover("⚙️", use_container_width=False): - handle_api_key_popup(provider) - - st.divider() - - -def render_model_selector(provider: str) -> str: - """ - Render model selection dropdown for the specified provider. - - Args: - provider: Provider ID - - Returns: - The selected model ID - """ - st.subheader("2️⃣ Model") - - models = list_llm_models(provider_name=provider) - model_ids = [model["id"] for model in models] - - if not model_ids: - st.warning(f"No models configured for {provider.capitalize()}.") - return "" - - current_settings = st.session_state.provider_settings.get(provider, {}) - current_model = current_settings.get("model", "") - - model_index = model_ids.index(current_model) if current_model in model_ids else 0 - - selected_model = st.selectbox( - f"Select {provider.capitalize()} Model:", - options=model_ids, - index=model_index, - key=f"{provider}_model_selector", - ) - - if "provider_settings" in st.session_state and provider in st.session_state.provider_settings: - st.session_state.provider_settings[provider]["model"] = selected_model - - return selected_model - - -def render_model_settings(provider: str) -> None: - """ - Render model-specific settings UI for a provider. - - Args: - provider: Provider name (e.g., "openai") - """ - provider = provider.lower() - models = list_llm_models(provider_name=provider) - model_ids = [model["id"] for model in models] - - if model_ids: - st.selectbox( - "Default model", - model_ids, - help="Select which model to use by default", - key=f"{provider}_default_model", - ) - - st.slider( - "Temperature", - min_value=0.0, - max_value=2.0, - value=0.7, - step=0.1, - help="Controls randomness in responses: 0=deterministic, 1=creative", - key=f"{provider}_temperature", - ) - - -def render_provider_selector() -> str: - """ - Render provider selection section and return the selected provider. - - Returns: - The selected provider ID - """ - st.subheader("1️⃣ Provider") - - providers = get_available_providers() - configured_providers = [p for p in providers if get_api_key(p)] - - if not configured_providers: - st.warning("No API keys configured. Please add provider API keys in settings.") - return st.session_state.active_provider - - selected_provider = st.radio( - "Select AI Provider:", - options=configured_providers, - format_func=lambda x: x.capitalize(), - index=configured_providers.index(st.session_state.active_provider) - if st.session_state.active_provider in configured_providers - else 0, - key="provider_selector", - horizontal=True, - ) - - if selected_provider != st.session_state.active_provider: - st.session_state.active_provider = selected_provider - st.rerun() - - return selected_provider - - -def render_parameter_settings(provider: str) -> float: - """ - Render common parameter settings (temperature, etc). - - Args: - provider: Provider ID - - Returns: - The selected temperature value - """ - st.subheader("3️⃣ Parameters") - - current_settings = st.session_state.provider_settings.get(provider, {}) - current_temp = current_settings.get("temperature", 0.7) - - temperature = st.slider( - "Temperature", - min_value=0.0, - max_value=2.0, - value=current_temp, - step=0.1, - help="Controls randomness in responses: 0=deterministic, higher=more creative", - key=f"{provider}_temperature", - ) - - if "provider_settings" in st.session_state and provider in st.session_state.provider_settings: - st.session_state.provider_settings[provider]["temperature"] = temperature - - return temperature - - -def update_global_settings(provider: str, model: str, temperature: float) -> None: - """ - Update global settings based on provider selections. - - Args: - provider: Selected provider ID - model: Selected model ID - temperature: Selected temperature value - """ - if "config" not in st.session_state: - st.session_state.config = { - "provider_name": provider, - "model_id": model, - "temperature": temperature, - "max_tokens": 1000, - "top_k": 5, - } - else: - st.session_state.config.update( - { - "model_id": model, - "temperature": temperature, - "provider_name": provider, - } - ) - - -def _render_llm_settings_form_section() -> tuple[str | None, str | None, float]: - """Renders the LLM provider, model, and temperature selectors.""" - st.markdown("### LLM Configuration") - available_llm_providers = get_available_providers() - selected_model_id = None - selected_temperature = 0.7 - - if not available_llm_providers: - st.warning("No LLM providers available. Please configure API keys first.") - current_llm_provider = None - else: - current_llm_provider_name = st.session_state.config.get( - "provider_name", get_available_providers()[0] - ) - if current_llm_provider_name not in available_llm_providers: - current_llm_provider_name = available_llm_providers[0] - - llm_provider_index = available_llm_providers.index(current_llm_provider_name) - current_llm_provider = st.selectbox( - "LLM Provider", - options=available_llm_providers, - index=llm_provider_index, - format_func=get_provider_display_name, - key="llm_provider_selector", - help="Select the Large Language Model provider.", - ) - - if current_llm_provider: - models = list_llm_models(provider_name=current_llm_provider) - model_ids = [m["id"] for m in models] - current_model_id = st.session_state.config.get("model_id", "") - if current_model_id not in model_ids: - current_model_id = model_ids[0] if model_ids else "" - model_index = model_ids.index(current_model_id) if current_model_id in model_ids else 0 - - selected_model_id = st.selectbox( - "Model", - options=model_ids, - index=model_index, - key="model_selector", - help="Select the specific model to use.", - disabled=not model_ids, - ) - - current_temperature = st.session_state.config.get("temperature", 0.7) - selected_temperature = st.slider( - "Temperature", - min_value=0.0, - max_value=1.0, - value=current_temperature, - step=0.05, - key="temperature_slider", - help="Controls randomness. Lower values make responses more deterministic.", - ) - else: - st.selectbox("Model", options=[], disabled=True) - st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, disabled=True) - - return current_llm_provider, selected_model_id, selected_temperature - - -def _render_embedding_settings_form_section() -> str | None: - """Renders the Embedding model selector.""" - st.markdown("### Embedding Configuration") - available_embed_models = list_embedding_models() - embed_model_ids = [m["id"] for m in available_embed_models] - selected_embed_model_id = None - - if not embed_model_ids: - st.warning( - "No Embedding models available. Configure required API keys (e.g., Gemini or OpenAI) or rely on the offline default." - ) - st.info("Offline default: `milvus/all-MiniLM-L6-v2` will be used.") - current_embed_model_id = "milvus/all-MiniLM-L6-v2" - st.selectbox( - "Embedding Model", - options=[current_embed_model_id], - index=0, - key="embedding_model_selector", - help="Select the model used for document embedding and search. Requires API keys for some models.", - disabled=True, - ) - else: - current_embed_model_id = get_active_embedding_model() - if current_embed_model_id not in embed_model_ids: - embed_model_index = 0 - current_embed_model_id = embed_model_ids[0] - else: - embed_model_index = embed_model_ids.index(current_embed_model_id) - - selected_embed_model_id = st.selectbox( - "Embedding Model", - options=embed_model_ids, - index=embed_model_index, - key="embedding_model_selector", - help="Select the model used for document embedding and search. API keys required for OpenAI/Gemini models.", - ) - return selected_embed_model_id - - -def _save_llm_settings( - selected_llm_provider: str | None, - selected_llm_model: str | None, - selected_llm_temp: float, -) -> bool: - """Saves LLM settings to session state and returns True if changed.""" - changed = False - if selected_llm_provider and selected_llm_model: - if st.session_state.config.get("provider_name") != selected_llm_provider: - st.session_state.config["provider_name"] = selected_llm_provider - changed = True - if st.session_state.config.get("model_id") != selected_llm_model: - st.session_state.config["model_id"] = selected_llm_model - changed = True - if st.session_state.config.get("temperature") != selected_llm_temp: - st.session_state.config["temperature"] = selected_llm_temp - changed = True - - if changed: - st.success( - f"LLM settings saved: Provider '{selected_llm_provider}', Model '{selected_llm_model}', Temp {selected_llm_temp:.2f}" - ) - return changed # Return True if anything was actually changed - else: - st.warning("LLM Provider/Model not fully selected. LLM settings not saved.") - return False - - -def _save_embedding_settings(selected_embed_model: str | None) -> bool | None: - """Saves embedding setting after checking API key. Returns True on success, False on key error, None if no model selected.""" - if selected_embed_model is None: - return None # Indicate no action taken - - provider = selected_embed_model.split("/")[0] - key_needed = provider in PROVIDERS_REQUIRING_KEYS - key_present = get_api_key(provider) is not None - - if key_needed and not key_present: - st.error( - f"Cannot save: Embedding model '{selected_embed_model}' requires an API key for '{provider}', which is missing." - ) - return False # Indicate failure due to missing key - - if get_active_embedding_model() != selected_embed_model: - save_attempt = set_active_embedding_model(selected_embed_model) - if save_attempt: - st.success(f"Embedding model saved: '{selected_embed_model}'") - return True # Indicate successful save - else: - st.error("Failed to save embedding model setting.") - return False # Indicate failure during save - else: - return True # Indicate success (no change needed) - - -def _handle_settings_form_submission( - selected_llm_provider: str | None, - selected_llm_model: str | None, - selected_llm_temp: float, - selected_embed_model: str | None, - on_save_callback: Callable | None, -) -> None: - """Handles the logic after the settings form is submitted.""" - llm_settings_changed = _save_llm_settings( - selected_llm_provider, selected_llm_model, selected_llm_temp - ) - embedding_save_status = _save_embedding_settings(selected_embed_model) - - # --- Callback and Rerun Logic --- - # Callback if *any* setting was successfully changed or saved (even if value didn't change) - if (llm_settings_changed or embedding_save_status is True) and on_save_callback: - on_save_callback() - - # Rerun if *any* setting was successfully changed or saved, - # *unless* the only action was an embedding save failure (due to key error). - if llm_settings_changed or embedding_save_status is True: - st.rerun() - # No explicit else needed, as we don't rerun on embedding_save_status == False or None - # if llm_settings_changed is also False. - - -def render_unified_settings_page(on_save_callback: Callable | None = None) -> None: - """Render the unified settings page with LLM and Embedding settings.""" - if st.button("← Back"): - st.session_state.show_settings = False - st.rerun() - - render_page_header("Application Settings", "Configure API Keys, LLM, and Embedding models.") - add_api_table_css() - - st.markdown("### API Key Management") - st.info( - "Enter API keys for the providers you wish to use. Keys are stored securely.", icon="🔑" - ) - render_api_key_table() - - st.divider() - - # LLM and Embedding settings within a form - with st.form(key="unified_settings_form"): - col1, col2 = st.columns(2) - with col1: - # Render LLM settings section - selected_llm_provider, selected_llm_model, selected_llm_temp = ( - _render_llm_settings_form_section() - ) - with col2: - # Render Embedding settings section - selected_embed_model = _render_embedding_settings_form_section() - - submitted = st.form_submit_button("Save Settings", use_container_width=True) - - if submitted: - # Handle form submission logic - _handle_settings_form_submission( - selected_llm_provider, - selected_llm_model, - selected_llm_temp, - selected_embed_model, - on_save_callback, - ) - - -def render_settings_page(on_save_callback: Callable | None = None) -> None: - """Main function to render the settings page content.""" - render_unified_settings_page(on_save_callback) diff --git a/paperchat/utils/__init__.py b/paperchat/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/paperchat/utils/api_keys.py b/paperchat/utils/api_keys.py deleted file mode 100644 index afb1d0e..0000000 --- a/paperchat/utils/api_keys.py +++ /dev/null @@ -1,184 +0,0 @@ -""" -API Key Management Utilities using System Keyring. -""" - -import os -from typing import Final - -import keyring -import keyring.errors - -_KEYRING_SERVICE_NAME: Final[str] = "paperchat" -_PROVIDER_CONFIG: Final[dict[str, dict[str, str | None]]] = { - "openai": {"env_var": "OPENAI_API_KEY", "prefix": "sk-", "name": "OpenAI"}, - "gemini": {"env_var": "GEMINI_API_KEY", "prefix": None, "name": "Gemini"}, - "anthropic": {"env_var": "ANTHROPIC_API_KEY", "prefix": "sk-ant-", "name": "Anthropic"}, -} - - -def _get_keyring_username(provider: str) -> str: - """Generates the username for storing the provider's key in keyring.""" - return f"{provider.lower()}_api_key" - - -def _validate_key(provider: str, key: str) -> tuple[bool, str | None]: - """ - Performs basic validation on the API key format. - - Args: - provider: The name of the provider (e.g., 'openai', 'anthropic') - key: The API key to validate - - Returns: - Tuple of (is_valid, error_message) - - is_valid: True if the key is valid, False otherwise - - error_message: None if valid, error description if invalid - """ - config = _PROVIDER_CONFIG.get(provider.lower()) - if not config: - return True, None # Cannot validate unknown providers - - prefix = config.get("prefix") - provider_name = config.get("name", provider.capitalize()) - - if not key: - return False, "API key cannot be empty." - - if prefix and not key.startswith(prefix): - return False, f"Invalid format. {provider_name} API keys should start with '{prefix}'" - - return True, None - - -def get_api_key(provider: str) -> str | None: - """ - Gets the API key for the specified provider. - - Checks environment variables first, then the system keyring. - - Args: - provider: The name of the provider (e.g., 'openai', 'anthropic'). Case-insensitive. - - Returns: - The API key string, or None if not found or if keyring access fails. - """ - provider_lower = provider.lower() - config = _PROVIDER_CONFIG.get(provider_lower) - env_var_name = config.get("env_var") if config else None - - if env_var_name: - api_key = os.environ.get(env_var_name) - if api_key: - return api_key - - try: - keyring_username = _get_keyring_username(provider_lower) - api_key = keyring.get_password(_KEYRING_SERVICE_NAME, keyring_username) - if api_key: - return api_key - except Exception: - # Just return None to indicate key not found - pass - - return None - - -def set_api_key(provider: str, api_key: str) -> tuple[bool, str | None]: - """ - Securely saves the API key for the specified provider using the system keyring. - - Args: - provider: The name of the provider (e.g., 'openai'). Case-insensitive. - api_key: The API key string to save. - - Returns: - Tuple of (success, error_message) - - success: True if saving was successful, False otherwise - - error_message: None if successful, error description if failed - """ - provider_lower = provider.lower() - is_valid, error_msg = _validate_key(provider_lower, api_key) - - if not is_valid: - return False, error_msg - - try: - keyring_username = _get_keyring_username(provider_lower) - keyring.set_password(_KEYRING_SERVICE_NAME, keyring_username, api_key) - return True, None - except Exception as e: - return False, f"Error saving API key to keyring: {e}" - - -def remove_api_key(provider: str) -> tuple[bool, str | None]: - """ - Removes the API key for the specified provider from the system keyring. - - Note: This does not affect environment variables. - - Args: - provider: The name of the provider (e.g., 'openai'). Case-insensitive. - - Returns: - Tuple of (success, error_message) - - success: True if the key was successfully removed or was not present - - error_message: None if successful, error description if failed - """ - provider_lower = provider.lower() - provider_name = _PROVIDER_CONFIG.get(provider_lower, {}).get("name", provider.capitalize()) - keyring_username = _get_keyring_username(provider_lower) - - try: - # Check if key exists in keyring before attempting delete - current_key = keyring.get_password(_KEYRING_SERVICE_NAME, keyring_username) - if current_key is None: - return True, f"No {provider_name} key found in keyring." - - keyring.delete_password(_KEYRING_SERVICE_NAME, keyring_username) - return True, f"{provider_name} API key successfully removed from keyring." - except keyring.errors.PasswordDeleteError: - # This specific error often means the password was not found - return True, f"No {provider_name} key found in keyring to delete." - except Exception as e: - return False, f"Error removing {provider_name} API key from keyring: {e}" - - -def get_provider_display_name(provider: str) -> str: - """ - Get the display name for a provider. - - Args: - provider: The provider identifier (e.g., 'openai') - - Returns: - The display name (e.g., 'OpenAI') - """ - provider_lower = provider.lower() - return _PROVIDER_CONFIG.get(provider_lower, {}).get("name", provider.capitalize()) - - -def get_available_providers() -> list[str]: - """ - Get list of all configured providers. - - Returns: - List of provider identifiers - """ - return list(_PROVIDER_CONFIG.keys()) - - -def mask_api_key(api_key: str) -> str: - """ - Create a masked version of an API key for display. - - Args: - api_key: The API key to mask - - Returns: - Masked API key showing only first and last few characters - """ - if not api_key or len(api_key) < 8: - return "****" - - mask_len = max(len(api_key) - 8, 0) - return api_key[:4] + "*" * mask_len + api_key[-4:] diff --git a/paperchat/utils/config.py b/paperchat/utils/config.py deleted file mode 100644 index cd53993..0000000 --- a/paperchat/utils/config.py +++ /dev/null @@ -1,32 +0,0 @@ -import torch - -from .api_keys import get_api_key - -DEVICE = "auto" - - -def get_device(): - if DEVICE == "auto": - return ( - "cuda" - if torch.cuda.is_available() - else "mps" - if torch.backends.mps.is_available() - else "cpu" - ) - return DEVICE - - -# LLM -DEFAULT_PROVIDER = "gemini" if get_api_key("gemini") else "openai" -DEFAULT_MODEL = "gemini-2.0-flash" if get_api_key("gemini") else "gpt-4.1-nano" -DEFAULT_TEMPERATURE = 0.7 -DEFAULT_MAX_TOKENS = 1000 - -DEFAULT_SYSTEM_PROMPT = """ -You are a helpful academic research assistant helping with scientific papers. -Act as an expert in the field of the paper you're discussing. -Answer questions based on your knowledge of the paper and the context provided. -When citing information, refer to the specific section of the paper where it appears. -Be precise, scholarly, and focus on the factual content of the paper. -""" diff --git a/paperchat/utils/formatting.py b/paperchat/utils/formatting.py deleted file mode 100644 index d52dd22..0000000 --- a/paperchat/utils/formatting.py +++ /dev/null @@ -1,164 +0,0 @@ -import re -from typing import Any - - -def format_context(retrieved_results: list[dict[str, Any]], include_metadata: bool = True) -> str: - """ - Format retrieved chunks into a context string for the LLM. - - Args: - retrieved_results: List of retrieval results - include_metadata: Whether to include metadata in the context - - Returns: - Formatted context string - """ - context_parts = [] - - for i, result in enumerate(retrieved_results): - if include_metadata: - text = result["entity"]["text"] - pages = result["entity"].get("page_numbers", []) - page_str = ", ".join(map(str, pages)) if pages else "Unknown" - source = result["entity"]["source"] - context_parts.append(f"[CHUNK {i + 1} | Source: {source} | {page_str}]:\n{text}\n") - else: - context_parts.append(f"[CHUNK {i + 1}]:\n{text}\n") - - return "\n\n".join(context_parts) - - -def process_citations( - raw_response: str, retrieved_chunks: list[dict[str, Any]] -) -> tuple[str, list[dict[str, Any]]]: - """ - Processes citations in the raw LLM response to use sequential footnote-style - references and filters chunks to only include cited ones. - - Finds citations like "Chunk 1", "(see CHUNK 1)", "(Chunks 1, 2)", etc., replaces them - with sequential markers like [1], [2], etc., based on the order of appearance, - and returns the processed response and the list of corresponding cited chunks - in the correct order. - - Args: - raw_response: The raw text response from the LLM. - retrieved_chunks: The list of all retrieved chunk dictionaries. - - Returns: - A tuple containing: - - The response string with sequential citations ([1], [2], ...). - - A list of chunk dictionaries that were actually cited, ordered - by their citation number. - """ - # captures the chunk type (group 1) and the number list (group 2). - citation_pattern = re.compile(r"\b(Chunks?)\s+(\d+(?:\s*(?:,|and)\s*\d+)*)\b", re.IGNORECASE) - - cited_chunk_indices: list[int] = [] - unique_citations: dict[int, int] = {} # map original chunk index -> citation number [1], [2]... - processed_parts: list[str] = [] - last_end = 0 - - for match in citation_pattern.finditer(raw_response): - start, end = match.span() - processed_parts.append(raw_response[last_end:start]) - chunk_nums_str = match.group(2) - chunk_indices_0_based = [] - try: - # use more robust split logic allowing flexible spacing - indices_1_based = [int(n.strip()) for n in re.split(r"\s*(?:and|,)\s*", chunk_nums_str)] - chunk_indices_0_based = [idx - 1 for idx in indices_1_based] - except ValueError: - # if number parsing fails, append the original matched text and skip - processed_parts.append(match.group(0)) - last_end = end - continue - - # validate chunk indices - if not all(0 <= idx < len(retrieved_chunks) for idx in chunk_indices_0_based): - processed_parts.append(match.group(0)) # append original if invalid index - last_end = end - continue - - # generate citation numbers and update tracking - citation_numbers_for_this_match = [] - for original_chunk_index in chunk_indices_0_based: - if original_chunk_index not in unique_citations: - current_citation_num = len(unique_citations) + 1 - unique_citations[original_chunk_index] = current_citation_num - cited_chunk_indices.append(original_chunk_index) - citation_numbers_for_this_match.append(str(current_citation_num)) - else: - citation_numbers_for_this_match.append(str(unique_citations[original_chunk_index])) - - citation_numbers_for_this_match.sort(key=int) - # create the replacement footnote string, e.g., "[1]" or "[1, 2]" - replacement_string = f"[{', '.join(citation_numbers_for_this_match)}]" - processed_parts.append(replacement_string) - - last_end = end - - # append any remaining text after the last match - processed_parts.append(raw_response[last_end:]) - - processed_response = "".join(processed_parts) - - # ensure cited chunks are ordered by their *first* appearance (citation number) - cited_chunks_ordered = [ - retrieved_chunks[i] for i in cited_chunk_indices if 0 <= i < len(retrieved_chunks) - ] - - return processed_response, cited_chunks_ordered - - -def format_response_with_citations(response: str) -> str: - """ - Format the response with highlighted citations. Now expects [1], [2] style. - - Args: - response: Response text from the LLM, potentially with [N] citations. - - Returns: - Response with HTML-formatted citation highlights - """ - # find [1] or [1, 2] style citations - citation_pattern = r"(\[\d+(?:,\s*\d+)*\])" - formatted_response = re.sub( - citation_pattern, - lambda m: f'{m.group(1)}', - response, - ) - return formatted_response - - -def format_retrieved_chunks_for_display(chunks: list[dict[str, Any]]) -> str: - """ - Format retrieved chunks for display in the UI. - Handles the Milvus result structure. - - Args: - chunks: List of retrieved chunk dictionaries from Milvus search. - Expected structure: [{'id': ..., 'distance': ..., 'entity': {...}}] - - Returns: - Markdown-formatted text for displaying chunks in Streamlit - """ - if not chunks: - return "No sources retrieved." - - formatted_text = "### Sources\n\n" - for i, result in enumerate(chunks): - entity = result.get("entity", {}) - text = entity.get("text", "No text found.") - page_numbers = entity.get("page_numbers", []) - source = entity.get("source", "Unknown source") - - if page_numbers: - page_str = ", ".join(map(str, sorted(set(page_numbers)))) - page_info = f"Page(s) {page_str}" - else: - page_info = "Unknown page" - - formatted_text += f"**Source [{i + 1}]** ({source} | {page_info})\n\n" - formatted_text += f"{text}\n\n---\n\n" - - return formatted_text diff --git a/paperchat/utils/logging.py b/paperchat/utils/logging.py deleted file mode 100644 index c5276dc..0000000 --- a/paperchat/utils/logging.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Logging utilities for PaperChat components.""" - -import logging -import logging.handlers -import os -from datetime import date -from pathlib import Path - - -def setup_logger( - logger_name: str, - log_file: str | None = None, - console_level: int = logging.WARNING, - file_level: int = logging.DEBUG, - propagate: bool = False, -) -> logging.Logger: - """Set up a logger with console and optional file handlers. - - Args: - logger_name: Name of the logger - log_file: Path to log file. If None, no file logging is configured. - console_level: Logging level for console output - file_level: Logging level for file output - propagate: Whether to propagate to parent loggers - - Returns: - Configured logger instance - """ - logger = logging.getLogger(logger_name) - logger.setLevel(min(console_level, file_level)) - logger.propagate = propagate - - if logger.handlers: - logger.handlers.clear() - - console_handler = logging.StreamHandler() - console_handler.setLevel(console_level) - console_format = logging.Formatter("%(levelname)s - %(message)s") - console_handler.setFormatter(console_format) - logger.addHandler(console_handler) - - if log_file: - log_dir = os.path.dirname(log_file) - if log_dir: - os.makedirs(log_dir, exist_ok=True) - - file_handler = logging.FileHandler(log_file) - file_handler.setLevel(file_level) - file_format = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - file_handler.setFormatter(file_format) - logger.addHandler(file_handler) - - return logger - - -def get_component_logger(component_name: str) -> logging.Logger: - """Get a logger for a specific component with standard configuration. - - Logs will be stored in date-based subdirectories, with a unique - log file per process execution based on PID. - - Args: - component_name: Name of the component (e.g., 'vector_store', 'embeddings') - - Returns: - Configured logger for the component - """ - today_str = date.today().strftime("%Y-%m-%d") - log_dir_base = Path.home() / ".paperchat" / "logs" - log_dir_dated = log_dir_base / today_str - - os.makedirs(log_dir_dated, exist_ok=True) - - pid = os.getpid() - log_file = log_dir_dated / f"{component_name}_pid{pid}.log" - - return setup_logger( - logger_name=component_name, - log_file=str(log_file), - console_level=logging.WARNING, - file_level=logging.DEBUG, - propagate=False, - ) diff --git a/paperchat/utils/settings.py b/paperchat/utils/settings.py deleted file mode 100644 index 050bcf8..0000000 --- a/paperchat/utils/settings.py +++ /dev/null @@ -1,116 +0,0 @@ -"""Handles loading and saving application settings.""" - -import json -from pathlib import Path -from typing import Any - -from paperchat.core.embeddings import SUPPORTED_EMBEDDING_MODELS - -from .logging import get_component_logger - -DEFAULT_EMBEDDING_MODEL = "pymilvus/default" -SETTINGS_DIR = Path.home() / ".paperchat" -SETTINGS_FILE = SETTINGS_DIR / "settings.json" -EMBEDDING_MODEL_SETTING_KEY = "embedding_model_identifier" - -logger = get_component_logger("Settings") - - -def _ensure_settings_dir_exists() -> None: - """Creates the settings directory if it doesn't exist.""" - SETTINGS_DIR.mkdir(parents=True, exist_ok=True) - - -def load_settings() -> dict[str, Any]: - """Loads settings from the JSON file. - - Returns: - A dictionary containing the settings, or an empty dictionary - if the file doesn't exist or is invalid. - """ - _ensure_settings_dir_exists() - if not SETTINGS_FILE.is_file(): - logger.info(f"Settings file not found ({SETTINGS_FILE}), returning empty settings.") - return {} - try: - with open(SETTINGS_FILE, "r", encoding="utf-8") as f: - settings = json.load(f) - if not isinstance(settings, dict): - logger.warning( - f"Settings file ({SETTINGS_FILE}) does not contain a valid JSON object, returning empty settings." - ) - return {} - logger.info(f"Loaded settings from {SETTINGS_FILE}") - return settings - except json.JSONDecodeError: - logger.exception(f"Error decoding JSON from {SETTINGS_FILE}, returning empty settings.") - return {} - except Exception: - logger.exception(f"Failed to load settings from {SETTINGS_FILE}, returning empty settings.") - return {} - - -def save_settings(settings: dict[str, Any]) -> bool: - """Saves the provided settings dictionary to the JSON file.""" - _ensure_settings_dir_exists() - try: - with open(SETTINGS_FILE, "w", encoding="utf-8") as f: - json.dump(settings, f, indent=4) - logger.info(f"Saved settings to {SETTINGS_FILE}") - return True - except Exception: - logger.exception(f"Failed to save settings to {SETTINGS_FILE}") - return False - - -def get_active_embedding_model() -> str: - """Determines the active embedding model identifier. - - Checks user settings first, then falls back to the application default. - Ensures the chosen model is currently supported. - """ - settings = load_settings() - user_choice = settings.get(EMBEDDING_MODEL_SETTING_KEY) - - if user_choice and isinstance(user_choice, str): - if user_choice in SUPPORTED_EMBEDDING_MODELS: - logger.info(f"Using user-selected embedding model: {user_choice}") - return user_choice - else: - logger.warning( - f"User-selected embedding model '{user_choice}' is not currently supported. " - f"Falling back to default: {DEFAULT_EMBEDDING_MODEL}" - ) - else: - logger.info("No valid user setting found for embedding model.") - - # fallback to default - logger.info(f"Using default embedding model: {DEFAULT_EMBEDDING_MODEL}") - if DEFAULT_EMBEDDING_MODEL not in SUPPORTED_EMBEDDING_MODELS: - logger.critical( - f"Default embedding model '{DEFAULT_EMBEDDING_MODEL}' from config is not in SUPPORTED_EMBEDDING_MODELS! " - "Please check core/embeddings.py and utils/config.py." - ) - # last resort, try to find *any* supported model - fallback = next(iter(SUPPORTED_EMBEDDING_MODELS), None) - if fallback: - logger.warning(f"Falling back to first available supported model: {fallback}") - return fallback - else: - # fatal configuration error - raise RuntimeError("Configuration Error: No supported embedding models found!") - - return DEFAULT_EMBEDDING_MODEL - - -def set_active_embedding_model(model_identifier: str) -> bool: - """Saves the user's chosen embedding model to settings.""" - if model_identifier not in SUPPORTED_EMBEDDING_MODELS: - logger.error( - f"Attempted to set unsupported embedding model '{model_identifier}' in settings." - ) - return False - - settings = load_settings() - settings[EMBEDDING_MODEL_SETTING_KEY] = model_identifier - return save_settings(settings) diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 7b5e202..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,96 +0,0 @@ -[project] -name = "paperchat" -version = "0.1.0" -description = "An AI-powered assistant that enables you to chat with your research library." -authors = [{ name="Kyle Puhger", email="kpuhger@gmail.com" }] -readme = "README.md" -license = { text = "MIT" } -keywords = ["chat", "research"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", -] -requires-python = ">=3.12" -dependencies = [ - "anthropic>=0.50.0", - "docling>=2.31.0", - "google-genai>=1.12.1", - "keyring>=25.6.0", - "openai>=1.76.0", - "pymilvus[model]>=2.5.7", - "streamlit>=1.44.1", - "torch>=2.6.0", -] - -[dependency-groups] -dev = [ - "prek>=0.3.4", - "pytest>=8.3.4", - "ruff>=0.11.0", -] - -[project.optional-dependencies] -eval = [ - "beir>=2.1.0", - "typer>=0.15.2", -] - -[project.scripts] -paperchat = "paperchat.cli:main" - -[project.urls] -"Homepage" = "https://github.com/kpeez/paperchat" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.pytest.ini_options] -testpaths = ["tests"] -addopts = "--log-cli-level=INFO -s --durations=0" - -[tool.ruff] -line-length = 100 -target-version = "py311" - -[tool.ruff.lint] -select = [ - "E", # pycodestyle - "W", - "F", # pyflakes - "PL", # pylint - "I", # isort - "B", # flake8-bugbear - "SIM", # flake8-simplify - "C4", # flake8-comprehensions - "RUF", # ruff -] -fixable = ["ALL"] -ignore = [ - "RUF001", #ambiguous unicode character - "PLR0913", # too many arguments - "PLR0914", # too many local variables - "PLR0917", # too many positional arguments - "PLR2004", # magic number - "PLW2901", # for loop variable overwritten by assignment target - "E501", # line too long - "B008", # function-call-in-default-argument -] - -[tool.ruff.lint.isort] -known-first-party = ["paperchat", "paperchat_backend"] - -[tool.ruff.lint.per-file-ignores] -"__init__.py" = ["E402", "F401", "D104"] - -[tool.ruff.lint.pydocstyle] -convention = "google" - -[tool.ruff.format] -quote-style = "double" -indent-style = "space" -skip-magic-trailing-comma = false -line-ending = "auto" diff --git a/scripts/postgres-down.sh b/scripts/postgres-down.sh new file mode 100755 index 0000000..ab46fbd --- /dev/null +++ b/scripts/postgres-down.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env bash +set -euo pipefail + +repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +if ! command -v docker >/dev/null 2>&1; then + echo "docker is required to stop the PaperChat local Postgres runtime." >&2 + exit 1 +fi + +docker compose -f "$repo_root/compose.yaml" stop postgres diff --git a/scripts/postgres-status.sh b/scripts/postgres-status.sh new file mode 100755 index 0000000..a0f18bd --- /dev/null +++ b/scripts/postgres-status.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +set -euo pipefail + +repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +compose_file="$repo_root/compose.yaml" + +if ! command -v docker >/dev/null 2>&1; then + echo "docker is required to inspect the PaperChat local Postgres runtime." >&2 + exit 1 +fi + +docker compose -f "$compose_file" ps postgres + +container_id="$(docker compose -f "$compose_file" ps -q postgres)" +if [[ -n "$container_id" ]]; then + docker inspect --format 'health={{if .State.Health}}{{.State.Health.Status}}{{else}}{{.State.Status}}{{end}}' "$container_id" +fi diff --git a/scripts/postgres-up.sh b/scripts/postgres-up.sh new file mode 100755 index 0000000..39c753a --- /dev/null +++ b/scripts/postgres-up.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash +set -euo pipefail + +repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +compose_file="$repo_root/compose.yaml" + +if ! command -v docker >/dev/null 2>&1; then + echo "docker is required to start the PaperChat local Postgres runtime." >&2 + exit 1 +fi + +docker compose -f "$compose_file" up -d postgres + +container_id="$(docker compose -f "$compose_file" ps -q postgres)" +if [[ -z "$container_id" ]]; then + echo "postgres container was not created." >&2 + exit 1 +fi + +for _ in {1..30}; do + status="$(docker inspect --format '{{if .State.Health}}{{.State.Health.Status}}{{else}}{{.State.Status}}{{end}}' "$container_id")" + if [[ "$status" == "healthy" ]]; then + echo "postgres is healthy on 127.0.0.1:5433" + exit 0 + fi + sleep 1 +done + +echo "postgres did not become healthy within 30 seconds." >&2 +exit 1 diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/test_api_keys.py b/tests/test_api_keys.py deleted file mode 100644 index 531324d..0000000 --- a/tests/test_api_keys.py +++ /dev/null @@ -1,177 +0,0 @@ -"""Tests for the API key management utilities.""" - -import os -from unittest import mock - -import keyring - -from paperchat.utils.api_keys import ( - _get_keyring_username, - _validate_key, - get_api_key, - get_available_providers, - get_provider_display_name, - mask_api_key, - remove_api_key, - set_api_key, -) - - -def test_get_keyring_username(): - """Test the username generation for keyring storage.""" - assert _get_keyring_username("openai") == "openai_api_key" - assert _get_keyring_username("ANTHROPIC") == "anthropic_api_key" - assert _get_keyring_username("custom") == "custom_api_key" - - -def test_validate_key(): - """Test API key validation logic.""" - is_valid, error = _validate_key("openai", "sk-abcdefghijklmnop") - assert is_valid - assert error is None - - is_valid, error = _validate_key("anthropic", "sk-ant-api12345") - assert is_valid - assert error is None - - is_valid, error = _validate_key("openai", "") - assert not is_valid - assert "cannot be empty" in error - - is_valid, error = _validate_key("anthropic", "not-a-valid-key") - assert not is_valid - assert "should start with" in error - - is_valid, error = _validate_key("custom", "any-key-works") - assert is_valid - assert error is None - - -@mock.patch.dict(os.environ, {"OPENAI_API_KEY": "env-openai-key", "ANTHROPIC_API_KEY": ""}) -def test_get_api_key_from_env(): - """Test retrieving API key from environment variables.""" - assert get_api_key("openai") == "env-openai-key" - - -@mock.patch.dict(os.environ, {"OPENAI_API_KEY": "", "ANTHROPIC_API_KEY": ""}) -@mock.patch("keyring.get_password") -def test_get_api_key_from_keyring(mock_get_password): - """Test retrieving API key from keyring when not in env vars.""" - mock_get_password.return_value = "keyring-anthropic-key" - assert get_api_key("anthropic") == "keyring-anthropic-key" - mock_get_password.assert_called_once() - - -@mock.patch.dict(os.environ, {"OPENAI_API_KEY": "", "ANTHROPIC_API_KEY": ""}) -@mock.patch("keyring.get_password") -def test_get_api_key_not_found(mock_get_password): - """Test behavior when API key is not found anywhere.""" - mock_get_password.return_value = None - assert get_api_key("openai") is None - - -@mock.patch.dict(os.environ, {"OPENAI_API_KEY": "", "ANTHROPIC_API_KEY": ""}) -@mock.patch("keyring.get_password") -def test_get_api_key_keyring_exception(mock_get_password): - """Test handling keyring exception gracefully.""" - mock_get_password.side_effect = Exception("Keyring error") - assert get_api_key("openai") is None - - -@mock.patch("keyring.set_password") -def test_set_api_key_success(mock_set_password): - """Test setting an API key successfully.""" - success, error = set_api_key("openai", "sk-validkey12345") - assert success - assert error is None - mock_set_password.assert_called_once() - - -@mock.patch("keyring.set_password") -def test_set_api_key_invalid(mock_set_password): - """Test setting an invalid API key.""" - success, error = set_api_key("anthropic", "invalid-key") - assert not success - assert "should start with" in error - mock_set_password.assert_not_called() - - -@mock.patch("keyring.set_password") -def test_set_api_key_exception(mock_set_password): - """Test handling exception when setting API key.""" - mock_set_password.side_effect = Exception("Set password error") - success, error = set_api_key("openai", "sk-validkey12345") - assert not success - assert "Error saving" in error - - -@mock.patch("keyring.get_password") -@mock.patch("keyring.delete_password") -def test_remove_api_key_success(mock_delete, mock_get): - """Test removing an API key successfully.""" - mock_get.return_value = "some-key" - success, message = remove_api_key("openai") - assert success - assert "successfully removed" in message - mock_delete.assert_called_once() - - -@mock.patch("keyring.get_password") -@mock.patch("keyring.delete_password") -def test_remove_api_key_not_found(mock_delete, mock_get): - """Test removing a non-existent API key.""" - mock_get.return_value = None - success, message = remove_api_key("openai") - assert success - assert "No OpenAI key found" in message - mock_delete.assert_not_called() - - -@mock.patch("keyring.get_password") -@mock.patch("keyring.delete_password") -def test_remove_api_key_delete_error(mock_delete, mock_get): - """Test handling error when deleting API key.""" - mock_get.return_value = "some-key" - mock_delete.side_effect = keyring.errors.PasswordDeleteError() - success, message = remove_api_key("openai") - assert success - assert "No OpenAI key found" in message - - -@mock.patch("keyring.get_password") -@mock.patch("keyring.delete_password") -def test_remove_api_key_other_exception(mock_delete, mock_get): - """Test handling other exceptions when removing API key.""" - mock_get.return_value = "some-key" - mock_delete.side_effect = Exception("Generic error") - success, message = remove_api_key("openai") - assert not success - assert "Error removing" in message - - -def test_get_provider_display_name(): - """Test getting display names for providers.""" - assert get_provider_display_name("openai") == "OpenAI" - assert get_provider_display_name("anthropic") == "Anthropic" - assert get_provider_display_name("custom") == "Custom" - - -def test_get_available_providers(): - """Test getting list of available providers.""" - providers = get_available_providers() - assert "openai" in providers - assert "anthropic" in providers - assert isinstance(providers, list) - - -def test_mask_api_key(): - """Test masking API keys for display.""" - masked = mask_api_key("sk-1234567890abcdef") - assert masked.startswith("sk-1") - assert masked.endswith("cdef") - assert "*" in masked - - assert mask_api_key("short") == "****" - assert mask_api_key("") == "****" - assert mask_api_key("12345678").startswith("1234") - assert mask_api_key("12345678").endswith("5678") diff --git a/tests/test_ingestion.py b/tests/test_ingestion.py deleted file mode 100644 index bfa13ab..0000000 --- a/tests/test_ingestion.py +++ /dev/null @@ -1,134 +0,0 @@ -"""Tests for the ingestion module.""" - -import datetime -from pathlib import Path -from unittest import mock - -import numpy as np -import pytest -from docling.chunking import DocChunk -from docling.datamodel.document import DoclingDocument - -from paperchat.core.ingestion import extract_page_numbers, parse_pdf, process_pdf_document - - -@pytest.fixture -def mock_docling_doc(): - """Return a mock DoclingDocument.""" - doc = mock.MagicMock(spec=DoclingDocument) - return doc - - -@pytest.fixture -def mock_doc_converter(): - """Create a mock DocumentConverter.""" - with mock.patch("paperchat.core.ingestion.DocumentConverter") as mock_converter_cls: - mock_converter = mock.MagicMock() - mock_converter_cls.return_value = mock_converter - - mock_result = mock.MagicMock() - mock_result.document = mock.MagicMock(spec=DoclingDocument) - mock_converter.convert.return_value = mock_result - - yield mock_converter - - -def test_parse_pdf(mock_doc_converter): - """Test that parse_pdf calls DocumentConverter correctly.""" - pdf_path = "/path/to/test.pdf" - doc = parse_pdf(pdf_path) - mock_doc_converter.convert.assert_called_once_with(str(Path(pdf_path).resolve())) - - assert doc is mock_doc_converter.convert.return_value.document - - -def test_extract_page_numbers(): - """Test extracting page numbers from a DocChunk.""" - # Create a mock DocChunk with page numbers 1, 2, 1 - chunk = mock.MagicMock(spec=DocChunk) - chunk.meta = mock.MagicMock() - doc_item1 = mock.MagicMock() - doc_item1.prov = [mock.MagicMock(page_no=1)] - doc_item2 = mock.MagicMock() - doc_item2.prov = [mock.MagicMock(page_no=2)] - doc_item3 = mock.MagicMock() - doc_item3.prov = [mock.MagicMock(page_no=1)] - - chunk.meta.doc_items = [doc_item1, doc_item2, doc_item3] - - page_numbers = extract_page_numbers(chunk) - - assert page_numbers == [1, 2] - - -@mock.patch("paperchat.core.ingestion.parse_pdf") -@mock.patch("paperchat.core.ingestion.HybridChunker") -def test_process_pdf_document(mock_chunker_cls, mock_parse_pdf): - """Test processing a PDF document.""" - pdf_path = "/path/to/test.pdf" - mock_doc = mock.MagicMock() - mock_parse_pdf.return_value = mock_doc - - mock_chunker = mock.MagicMock() - mock_chunker_cls.return_value = mock_chunker - - # Create two mock chunks - mock_chunk1 = mock.MagicMock(spec=DocChunk) - mock_chunk1.text = "This is chunk 1" - mock_chunk1.meta = mock.MagicMock() - mock_chunk1.meta.doc_items = [mock.MagicMock()] - mock_chunk1.meta.doc_items[0].prov = [mock.MagicMock(page_no=1)] - mock_chunk1.meta.doc_items[0].label = "label1" - mock_chunk1.meta.headings = ["Heading 1"] - - mock_chunk2 = mock.MagicMock(spec=DocChunk) - mock_chunk2.text = "This is chunk 2" - mock_chunk2.meta = mock.MagicMock() - mock_chunk2.meta.doc_items = [mock.MagicMock()] - mock_chunk2.meta.doc_items[0].prov = [mock.MagicMock(page_no=2)] - mock_chunk2.meta.doc_items[0].label = "label2" - mock_chunk2.meta.headings = ["Heading 2"] - - mock_chunks = [mock_chunk1, mock_chunk2] - mock_chunker.chunk.return_value = mock_chunks - - mock_chunker.contextualize.side_effect = lambda chunk: f"Contextualized {chunk.text}" - - mock_embed_fn = mock.MagicMock() - mock_embed_fn.encode_documents.return_value = [ - np.array([0.1, 0.2, 0.3]), - np.array([0.4, 0.5, 0.6]), - ] - - mock_datetime = datetime.datetime(2023, 1, 1, 12, 0, 0) - with mock.patch("paperchat.core.ingestion.datetime.datetime") as mock_dt: - mock_dt.now.return_value = mock_datetime - - result = process_pdf_document(pdf_path, embed_fn=mock_embed_fn) - - mock_parse_pdf.assert_called_once_with(Path(pdf_path)) - mock_chunker_cls.assert_called_once_with(max_tokens=1024, merge_peers=True) - mock_chunker.chunk.assert_called_once_with(mock_doc) - - assert len(result) == 2 - - assert result[0]["chunk_id"] == "chunk_00" - assert result[0]["text"] == "This is chunk 1" - assert result[0]["label"] == ["label1"] - assert result[0]["page_numbers"] == [1] - assert result[0]["headings"] == "heading 1" - assert result[0]["source"] == Path(pdf_path).stem - assert result[0]["timestamp"] == "2023-01-01T12:00:00" - - assert result[1]["chunk_id"] == "chunk_01" - assert result[1]["text"] == "This is chunk 2" - assert result[1]["label"] == ["label2"] - assert result[1]["page_numbers"] == [2] - assert result[1]["headings"] == "heading 2" - assert result[1]["source"] == Path(pdf_path).stem - assert result[1]["timestamp"] == "2023-01-01T12:00:00" - - # check that embed_fn was called with contextualized chunks - mock_embed_fn.encode_documents.assert_called_once_with( - ["Contextualized This is chunk 1", "Contextualized This is chunk 2"] - ) diff --git a/tests/test_rag_pipeline.py b/tests/test_rag_pipeline.py deleted file mode 100644 index d65b924..0000000 --- a/tests/test_rag_pipeline.py +++ /dev/null @@ -1,162 +0,0 @@ -"""Tests for the llm.py module that contains the RAGPipeline.""" - -from unittest import mock - -import pytest - -from paperchat.core import RAGPipeline - -TEST_MODEL_ID = "test-embedding-model" - - -@pytest.fixture -def mock_llm_manager(): - """Create a mock LLMManager instance.""" - return mock.MagicMock() - - -@pytest.fixture -def mock_vector_store(): - """Create a mock VectorStore instance.""" - with mock.patch("paperchat.core.VectorStore") as mock_vs_cls: - mock_vs = mock.MagicMock() - mock_vs_cls.return_value = mock_vs - yield mock_vs - - -@pytest.fixture -def rag_pipeline(mock_llm_manager, mock_vector_store): - """Fixture to create a RAGPipeline instance with mocked dependencies.""" - with ( - mock.patch( - "paperchat.core.rag_pipeline.get_active_embedding_model", - return_value=TEST_MODEL_ID, - ), - mock.patch("paperchat.core.rag_pipeline.VectorStore", return_value=mock_vector_store), - ): - pipeline = RAGPipeline(llm_manager=mock_llm_manager) - return pipeline - - -def test_init(mock_llm_manager): - """Test initialization of RAGPipeline.""" - with ( - mock.patch( - "paperchat.core.rag_pipeline.get_active_embedding_model", - return_value=TEST_MODEL_ID, - ) as mock_get_active_model, - mock.patch("paperchat.core.rag_pipeline.VectorStore") as mock_vs_cls, - ): - mock_vs_instance = mock.MagicMock() - mock_vs_cls.return_value = mock_vs_instance - - pipeline = RAGPipeline(llm_manager=mock_llm_manager) - - assert pipeline.llm_manager is mock_llm_manager - mock_get_active_model.assert_called_once() - mock_vs_cls.assert_called_once_with(model_identifier=TEST_MODEL_ID) - assert pipeline.vector_store is mock_vs_instance - - -def test_retrieve(rag_pipeline, mock_vector_store): - """Test the retrieve method.""" - query = "test query" - top_k = 3 - threshold = 0.8 - filter_expression = "source == 'test.pdf'" - - mock_inner_results = [{"text": "test result"}] - mock_vector_store.search.return_value = mock_inner_results - - results = rag_pipeline.retrieve(query, top_k, threshold, filter_expression) - - assert results == mock_inner_results - mock_vector_store.search.assert_called_once_with( - query_text=query, - top_k=top_k, - threshold=threshold, - filter_expression=filter_expression, - ) - - -def test_generate_non_streaming(rag_pipeline, mock_llm_manager): - """Test the generate method in non-streaming mode.""" - query = "test query" - retrieved_results = [ - {"text": "context 1", "source": "doc1"}, - {"text": "context 2", "source": "doc2"}, - ] - expected_response = "This is the answer." - - with mock.patch("paperchat.core.rag_pipeline.format_context") as mock_format_context: - mock_format_context.return_value = "formatted context" - mock_llm_manager.generate_response.return_value = expected_response - - response = rag_pipeline.generate(query, retrieved_results, stream=False) - - assert response == expected_response - mock_format_context.assert_called_once_with(retrieved_results, include_metadata=True) - mock_llm_manager.generate_response.assert_called_once_with(query, "formatted context") - mock_llm_manager.generate_streaming_response.assert_not_called() - - -def test_generate_streaming(rag_pipeline, mock_llm_manager): - """Test the generate method in streaming mode.""" - query = "test query" - retrieved_results = [ - {"text": "context 1", "source": "doc1"}, - {"text": "context 2", "source": "doc2"}, - ] - - mock_generator = mock.MagicMock() - - with mock.patch("paperchat.core.rag_pipeline.format_context") as mock_format_context: - mock_format_context.return_value = "formatted context" - mock_llm_manager.generate_streaming_response.return_value = mock_generator - - response = rag_pipeline.generate(query, retrieved_results, stream=True) - - assert response == mock_generator - mock_format_context.assert_called_once_with(retrieved_results, include_metadata=True) - mock_llm_manager.generate_streaming_response.assert_called_once_with( - query, "formatted context" - ) - mock_llm_manager.generate_response.assert_not_called() - - -def test_run_non_streaming(rag_pipeline): - """Test the run method in non-streaming mode.""" - query = "test query" - top_k = 3 - - retrieved_chunks = [{"text": "chunk 1"}] - expected_response = "This is the answer." - - rag_pipeline.retrieve = mock.MagicMock(return_value=retrieved_chunks) - rag_pipeline.generate = mock.MagicMock(return_value=expected_response) - - response, chunks = rag_pipeline.run(query, top_k, stream=False) - - assert response == expected_response - assert chunks == retrieved_chunks - rag_pipeline.retrieve.assert_called_once_with(query, top_k) - rag_pipeline.generate.assert_called_once_with(query, retrieved_chunks, False) - - -def test_run_streaming(rag_pipeline): - """Test the run method in streaming mode.""" - query = "test query" - top_k = 3 - - retrieved_chunks = [{"text": "chunk 1"}] - mock_generator = mock.MagicMock() - - rag_pipeline.retrieve = mock.MagicMock(return_value=retrieved_chunks) - rag_pipeline.generate = mock.MagicMock(return_value=mock_generator) - - response, chunks = rag_pipeline.run(query, top_k, stream=True) - - assert response == mock_generator - assert chunks == retrieved_chunks - rag_pipeline.retrieve.assert_called_once_with(query, top_k) - rag_pipeline.generate.assert_called_once_with(query, retrieved_chunks, True) diff --git a/tests/test_vector_store.py b/tests/test_vector_store.py deleted file mode 100644 index 8604abd..0000000 --- a/tests/test_vector_store.py +++ /dev/null @@ -1,523 +0,0 @@ -"""Tests for the vector store module.""" - -import tempfile -from datetime import datetime -from pathlib import Path -from unittest import mock - -import numpy as np -import pytest - -from paperchat.core.vector_store import ( - DEFAULT_SIMILARITY_THRESHOLD, - DEFAULT_SIMILARITY_TOP_K, - VectorStore, - sanitize_model_identifier, -) - -MAIN_FIELD_NAMES = { - "pk", - "chunk_id", - "embedding", - "text", - "label", - "page_numbers", - "headings", - "source", -} -METADATA_FIELD_NAMES = {"pk", "source", "original_filename", "date_added"} - -DEFAULT_MODEL_ID = "pymilvus/default" -DEFAULT_MODEL_NAME = "GPTCache/paraphrase-albert-onnx" -DEFAULT_MODEL_DIM = 768 -EXPLICIT_MODEL_ID = "openai/text-embedding-3-small" -EXPLICIT_MODEL_DIM = 1536 - - -@pytest.fixture -def temp_db_path(): - """Create a temporary directory for the test database.""" - with tempfile.TemporaryDirectory() as tmpdir: - yield str(Path(tmpdir) / "paperchat.db") - - -@pytest.fixture -def mock_milvus_client(): - """Create a mock MilvusClient.""" - with mock.patch("paperchat.core.vector_store.MilvusClient") as mock_client_class: - mock_client = mock.MagicMock() - mock_client.query.return_value = [] - mock_client.insert.return_value = {"insert_count": 1} - mock_delete_result = mock.MagicMock() - mock_delete_result.delete_count = 1 - mock_client.delete.return_value = mock_delete_result - mock_client_class.return_value = mock_client - yield mock_client - - -@pytest.fixture -def vector_store_init_mocks(): - """Mocks dependencies for VectorStore initialization testing.""" - with ( - mock.patch("paperchat.core.vector_store.MilvusClient") as mock_client_class, - mock.patch( - "paperchat.core.vector_store.VectorStore._get_or_create_collection" - ) as mock_get_create, - mock.patch("paperchat.core.vector_store.VectorStore._build_indices") as mock_build_indices, - mock.patch("paperchat.core.vector_store.get_component_logger") as mock_get_logger, - mock.patch("paperchat.core.vector_store.get_embedding_model") as mock_get_model, - ): - mock_client = mock.MagicMock() - mock_client_class.return_value = mock_client - mock_get_logger.return_value = mock.MagicMock() - - def mock_model_loader(model_id): - mock_embed_fn = mock.MagicMock() - if model_id == DEFAULT_MODEL_ID: - mock_embed_fn.dim = DEFAULT_MODEL_DIM - mock_embed_fn.model_name = DEFAULT_MODEL_NAME - elif model_id == EXPLICIT_MODEL_ID: - mock_embed_fn.dim = EXPLICIT_MODEL_DIM - else: - raise ValueError(f"Unexpected model_id in mock: {model_id}") - return mock_embed_fn - - mock_get_model.side_effect = mock_model_loader - - yield { - "mock_client_class": mock_client_class, - "mock_get_create": mock_get_create, - "mock_build_indices": mock_build_indices, - "mock_get_model": mock_get_model, - } - - -@pytest.fixture -def vector_store(mock_milvus_client, temp_db_path): - """Create a VectorStore instance using the default model.""" - with ( - mock.patch("paperchat.core.vector_store.VectorStore._get_or_create_collection"), - mock.patch("paperchat.core.vector_store.VectorStore._build_indices"), - mock.patch("paperchat.core.vector_store.get_component_logger") as mock_get_logger, - mock.patch("paperchat.core.vector_store.get_embedding_model") as mock_get_model, - ): - mock_get_logger.return_value = mock.MagicMock() - mock_embed_fn = mock.MagicMock() - mock_embed_fn.dim = DEFAULT_MODEL_DIM - mock_embed_fn.model_name = DEFAULT_MODEL_NAME - mock_embed_fn.encode_queries.return_value = [np.array([0.1] * DEFAULT_MODEL_DIM)] - mock_get_model.return_value = mock_embed_fn - - store = VectorStore(db_path=temp_db_path, model_identifier=DEFAULT_MODEL_ID) - store.client = mock_milvus_client - store._embed_fn = mock_embed_fn - store.main_schema = store._define_main_schema() - store.metadata_schema = store._define_metadata_schema() - yield store - - -def test_init_default_model(vector_store_init_mocks, temp_db_path): - """Test initialization with the default model identifier.""" - mocks = vector_store_init_mocks - mock_get_model = mocks["mock_get_model"] - mock_get_create = mocks["mock_get_create"] - mock_build_indices = mocks["mock_build_indices"] - - store = VectorStore(db_path=temp_db_path, model_identifier=DEFAULT_MODEL_ID) - - assert store.model_identifier == DEFAULT_MODEL_ID - mock_get_model.assert_called_once_with(DEFAULT_MODEL_ID) - assert store.embedding_dim == DEFAULT_MODEL_DIM - - expected_default_suffix = sanitize_model_identifier(DEFAULT_MODEL_NAME) - expected_collection_name = f"paperchat_docs_{expected_default_suffix}" - assert store.collection_name == expected_collection_name - assert store.metadata_collection_name == VectorStore.METADATA_COLLECTION_NAME - - mock_get_create.assert_has_calls( - [ - mock.call(expected_collection_name, mock.ANY), - mock.call(store.metadata_collection_name, mock.ANY), - ], - any_order=True, - ) - mock_build_indices.assert_called_once() - - -def test_init_explicit_model(vector_store_init_mocks, temp_db_path): - """Test initialization with an explicit model identifier.""" - mocks = vector_store_init_mocks - mock_get_model = mocks["mock_get_model"] - mock_get_create = mocks["mock_get_create"] - mock_build_indices = mocks["mock_build_indices"] - - store = VectorStore(db_path=temp_db_path, model_identifier=EXPLICIT_MODEL_ID) - - assert store.model_identifier == EXPLICIT_MODEL_ID - mock_get_model.assert_called_once_with(EXPLICIT_MODEL_ID) - assert store.embedding_dim == EXPLICIT_MODEL_DIM - - expected_explicit_suffix = sanitize_model_identifier(EXPLICIT_MODEL_ID) - expected_collection_name = f"paperchat_docs_{expected_explicit_suffix}" - assert store.collection_name == expected_collection_name - assert store.metadata_collection_name == VectorStore.METADATA_COLLECTION_NAME - - mock_get_create.assert_has_calls( - [ - mock.call(expected_collection_name, mock.ANY), - mock.call(store.metadata_collection_name, mock.ANY), - ], - any_order=True, - ) - mock_build_indices.assert_called_once() - - -@mock.patch("paperchat.core.vector_store.datetime") -def test_add_document_success(mock_dt, vector_store): - """Test successfully adding a new document inserts chunks and metadata.""" - pdf_path = Path("/fake/path/to/document.pdf") - source_id = pdf_path.stem - mock_now = datetime(2023, 1, 1, 12, 0, 0) - mock_dt.now.return_value = mock_now - iso_timestamp = mock_now.isoformat() - - mock_chunk_data = [{"chunk_id": "chunk1", "embedding": [0.1], "text": "text1"}] - expected_insert_data = [ - {"chunk_id": "chunk1", "embedding": [0.1], "text": "text1", "source": source_id} - ] - expected_metadata = [ - { - "source": source_id, - "original_filename": pdf_path.name, - "date_added": iso_timestamp, - } - ] - - with mock.patch( - "paperchat.core.vector_store.process_pdf_document", return_value=mock_chunk_data - ) as mock_process: - vector_store.client.reset_mock() - vector_store.client.query.return_value = [] - vector_store.client.insert.side_effect = [ - {"insert_count": len(expected_insert_data)}, - {"insert_count": 1}, - ] - - result = vector_store.add_document(str(pdf_path), source_id=source_id) - - assert result is True - vector_store.client.query.assert_called_once_with( - collection_name=vector_store.metadata_collection_name, - filter=f'source == "{source_id}"', - output_fields=["source"], - limit=1, - ) - mock_process.assert_called_once() - assert vector_store.client.insert.call_count == 2 - vector_store.client.insert.assert_has_calls( - [ - mock.call(collection_name=vector_store.collection_name, data=expected_insert_data), - mock.call( - collection_name=vector_store.metadata_collection_name, data=expected_metadata - ), - ] - ) - - -def test_add_document_already_exists(vector_store): - """Test adding a document that already exists skips processing and insertion.""" - pdf_path = Path("/path/to/existing.pdf") - source_id = pdf_path.stem - vector_store.client.reset_mock() - vector_store.client.query.return_value = [{"source": source_id}] - - with mock.patch("paperchat.core.vector_store.process_pdf_document") as mock_process: - result = vector_store.add_document(str(pdf_path), source_id=source_id) - - assert result is True - vector_store.client.query.assert_called_once_with( - collection_name=vector_store.metadata_collection_name, - filter=f'source == "{source_id}"', - output_fields=["source"], - limit=1, - ) - mock_process.assert_not_called() - vector_store.client.insert.assert_not_called() - vector_store.logger.info.assert_any_call( - f"Document ID '{source_id}' already exists in metadata. Skipping." - ) - - -@mock.patch("paperchat.core.vector_store.process_pdf_document", return_value=[{"text": "chunk"}]) -def test_add_document_chunk_insert_failure(mock_process, vector_store): - """Test failure during chunk insertion logs error and returns False.""" - pdf_path = Path("/path/to/fail_chunk.pdf") - source_id = pdf_path.stem - vector_store.client.reset_mock() - vector_store.logger.reset_mock() - vector_store.client.query.return_value = [] - - vector_store.client.insert.side_effect = [ - {"insert_count": 0, "primary_keys": []}, - {"insert_count": 1}, - ] - - result = vector_store.add_document(str(pdf_path), source_id=source_id) - - assert result is False - vector_store.client.query.assert_called_once() - mock_process.assert_called_once() - assert vector_store.client.insert.call_count == 1 - vector_store.client.insert.assert_any_call( - collection_name=vector_store.collection_name, data=mock.ANY - ) - vector_store.logger.error.assert_called_once() - assert ( - f"Mismatch in chunk insertion count for {source_id}" - in vector_store.logger.error.call_args[0][0] - ) - - -@mock.patch("paperchat.core.vector_store.datetime") -@mock.patch("paperchat.core.vector_store.process_pdf_document", return_value=[{"text": "chunk"}]) -def test_add_document_metadata_insert_failure(mock_process, mock_dt, vector_store): - """Test failure during metadata insertion logs error and returns False.""" - pdf_path = Path("/path/to/fail_meta.pdf") - source_id = pdf_path.stem - mock_now = datetime(2023, 1, 1, 12, 0, 0) - mock_dt.now.return_value = mock_now - - vector_store.client.reset_mock() - vector_store.logger.reset_mock() - vector_store.client.query.return_value = [] - - vector_store.client.insert.side_effect = [ - {"insert_count": 1}, - {"insert_count": 0}, - ] - - result = vector_store.add_document(str(pdf_path), source_id=source_id) - - assert result is False - vector_store.client.query.assert_called_once() - mock_process.assert_called_once() - assert vector_store.client.insert.call_count == 2 - vector_store.client.insert.assert_has_calls( - [ - mock.call(collection_name=vector_store.collection_name, data=mock.ANY), - mock.call(collection_name=vector_store.metadata_collection_name, data=mock.ANY), - ] - ) - vector_store.logger.error.assert_called_once() - assert f"Failed to insert metadata for {source_id}" in vector_store.logger.error.call_args[0][0] - - -def test_retrieve_success(vector_store): - """Test retrieving documents successfully.""" - query_text = "test query" - expected_results = [{"entity": {"text": "result", "pk": 1, "source": "s1"}, "distance": 0.9}] - vector_store.client.reset_mock() - mock_client_output = [expected_results] - vector_store.client.search.return_value = mock_client_output - - output_fields = [f.name for f in vector_store.main_schema.fields if f.name != "embedding"] - - results = vector_store.search(query_text) - - assert results == expected_results - vector_store.client.search.assert_called_once() - call_args, call_kwargs = vector_store.client.search.call_args - assert call_kwargs["collection_name"] == vector_store.collection_name - assert len(call_kwargs["data"]) == 1 - assert call_kwargs["limit"] == DEFAULT_SIMILARITY_TOP_K - assert call_kwargs["output_fields"] == output_fields - assert call_kwargs["filter"] is None - assert call_kwargs["search_params"]["radius"] == DEFAULT_SIMILARITY_THRESHOLD - - -def test_retrieve_with_filter_and_custom_params(vector_store): - """Test retrieve with a filter expression and custom top_k/threshold.""" - query_text = "filtered query" - filter_expr = "source == 'doc1'" - custom_k = 3 - custom_threshold = 0.5 - custom_output_fields = ["text", "source"] - expected_results = [{"entity": {"text": "filtered result", "source": "doc1"}, "distance": 0.6}] - vector_store.client.reset_mock() - mock_client_output = [expected_results] - vector_store.client.search.return_value = mock_client_output - - results = vector_store.search( - query_text, - top_k=custom_k, - threshold=custom_threshold, - output_fields=custom_output_fields, - filter_expression=filter_expr, - ) - - assert results == expected_results - vector_store.client.search.assert_called_once() - call_args, call_kwargs = vector_store.client.search.call_args - assert call_kwargs["collection_name"] == vector_store.collection_name - assert len(call_kwargs["data"]) == 1 - assert call_kwargs["limit"] == custom_k - assert call_kwargs["output_fields"] == custom_output_fields - assert call_kwargs["filter"] == filter_expr - assert call_kwargs["search_params"]["radius"] == custom_threshold - - -def test_retrieve_embedding_failure(vector_store): - """Test retrieve returns empty list if embedding fails.""" - vector_store._embed_fn.encode_queries.side_effect = Exception("Embedding error") - vector_store.client.reset_mock() - vector_store.logger.reset_mock() - - results = vector_store.search("query") - - assert results == [] - vector_store.client.search.assert_not_called() - vector_store.logger.exception.assert_called_once() - - -def test_retrieve_search_failure(vector_store): - """Test retrieve returns empty list if search fails.""" - vector_store.client.search.side_effect = Exception("Search error") - vector_store.client.reset_mock() - vector_store.logger.reset_mock() - - results = vector_store.search("query") - - assert results == [] - vector_store.client.search.assert_called_once() - vector_store.logger.exception.assert_called_once() - - -def test_list_documents_success(vector_store): - """Test listing documents successfully.""" - expected_metadata = [ - {"source": "doc1", "original_filename": "doc1.pdf", "date_added": "t1"}, - {"source": "doc2", "original_filename": "doc2.pdf", "date_added": "t2"}, - ] - vector_store.client.reset_mock() - vector_store.client.query.return_value = expected_metadata - - results = vector_store.list_documents() - - assert results == expected_metadata - vector_store.client.query.assert_called_once_with( - collection_name=vector_store.metadata_collection_name, - output_fields=["source", "original_filename", "date_added"], - limit=100, - ) - - -def test_list_documents_failure(vector_store): - """Test listing documents returns empty list on failure.""" - vector_store.client.reset_mock() - vector_store.logger.reset_mock() - vector_store.client.query.side_effect = Exception("Query failed") - - results = vector_store.list_documents() - - assert results == [] - vector_store.client.query.assert_called_once() - vector_store.logger.exception.assert_called_once() - - -def test_remove_document_success(vector_store): - """Test removing a document successfully deletes from both collections.""" - source_id = "doc_to_remove" - vector_store.client.reset_mock() - - mock_meta_delete = mock.MagicMock() - mock_meta_delete.delete_count = 1 - mock_chunk_delete = mock.MagicMock() - mock_chunk_delete.delete_count = 5 - vector_store.client.delete.side_effect = [mock_meta_delete, mock_chunk_delete] - - meta_deleted, chunks_deleted = vector_store.remove_document(source_id) - - assert meta_deleted == 1 - assert chunks_deleted == 5 - assert vector_store.client.delete.call_count == 2 - expected_filter = f'source == "{source_id}"' - vector_store.client.delete.assert_has_calls( - [ - mock.call( - collection_name=vector_store.metadata_collection_name, filter=expected_filter - ), - mock.call(collection_name=vector_store.collection_name, filter=expected_filter), - ] - ) - - -def test_remove_document_not_found(vector_store): - """Test removing a non-existent document returns (0, 0).""" - source_id = "doc_not_found" - vector_store.client.reset_mock() - - mock_zero_delete = mock.MagicMock() - mock_zero_delete.delete_count = 0 - vector_store.client.delete.return_value = mock_zero_delete - - meta_deleted, chunks_deleted = vector_store.remove_document(source_id) - - assert meta_deleted == 0 - assert chunks_deleted == 0 - assert vector_store.client.delete.call_count == 2 - - -def test_remove_document_metadata_delete_failure(vector_store): - """Test failure during metadata deletion logs error, attempts chunk deletion, returns 0 meta count.""" - source_id = "fail_meta_delete" - vector_store.client.reset_mock() - vector_store.logger.reset_mock() - - mock_chunk_delete = mock.MagicMock() - mock_chunk_delete.delete_count = 5 - vector_store.client.delete.side_effect = [ - Exception("Metadata delete failed"), - mock_chunk_delete, - ] - - meta_deleted, chunks_deleted = vector_store.remove_document(source_id) - - assert meta_deleted == 0 - assert chunks_deleted == 5 - assert vector_store.client.delete.call_count == 2 - vector_store.logger.exception.assert_called_once() - assert ( - f"Failed to remove from metadata collection for '{source_id}'" - in vector_store.logger.exception.call_args[0][0] - ) - - -def test_remove_document_chunk_delete_failure(vector_store): - """Test failure during chunk deletion logs error and returns 0 chunk count.""" - source_id = "fail_chunk_delete" - vector_store.client.reset_mock() - vector_store.logger.reset_mock() - - mock_meta_delete = mock.MagicMock() - mock_meta_delete.delete_count = 1 - vector_store.client.delete.side_effect = [mock_meta_delete, Exception("Chunk delete failed")] - - meta_deleted, chunks_deleted = vector_store.remove_document(source_id) - 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