Skip to content

bds421/rho-llm-tutorial

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

32 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

rho/llm Tutorial Suite

Welcome to the rho/llm Tutorial Suite! This directory contains a comprehensive guide to mastering the rho/llm library—a production-grade Go wrapper for Large Language Models (LLMs) featuring built-in streaming, multi-key failover, and agentic workflows.

Overview

The rho/llm library provides a unified interface for interacting with various LLM providers, including:

  • Cloud: Anthropic (Claude), Google (Gemini), OpenAI, Groq, Mistral, xAI.
  • Local: Ollama, vLLM, LM Studio.

This suite contains 21 tutorials ranging from basic text completion to advanced stress-testing of concurrent multi-key auth pools.

The library source is at github.com/bds421/rho-llm.

Repository Structure

The tutorials are organized by complexity and feature set:

# Topic Key Concepts
01-02 Core Basics Complete & Stream APIs, Roles, Tokens.
03-04 Agency & Logic Tool Use (Function Calling) & Extended Thinking (Reasoning).
05-07 Production Readiness Error Classification, Backoff, Cost Estimation, Logging Middleware.
08-10 Advanced Flows Multi-key Failover, System Prompts, Multi-turn Chat, Streaming Tools.
11-13 Reliability Abort Control, Request Overrides, Deep Registry Inspection.
14-15 Ecosystem Provider Presets, No-Auth detection, Multi-provider comparisons, Thinking/Reasoning content, live integration tests.
16-18 Internals AuthPool mechanisms, Named Error Constructors, Content Model (Multimodal, Image/Vision), live vision integration tests.
19 Validation Concurrent Stress Tests, Race-condition validation, Performance Benchmarks.
20 Capability Testing Multi-model regression matrix, YAML-driven test cases (L1 factual → L5 epistemic logic/clock trisection), multi-language (EN/DE/ES), -config and -short flags, report generation.
21 Tool Use Benchmark Agentic tool-use loop with mock responses (no external dependencies), YAML-driven multi-model test matrix, parallel-by-provider execution, markdown report generation.

Getting Started

Prerequisites

  • Go 1.26.1+
  • API Keys for Gemini, Anthropic, or OpenAI (optional if using Ollama)

Environment Setup

Create a .env file in the llm directory (or export the variables):

GEMINI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here

Running a Tutorial

Each tutorial is a standalone Go program. Change to the tutorial's directory and run it:

cd 01_basic
go run main.go

Documentation & Reports

  • Consolidated QA Report: Includes the 100% API coverage cross-reference, tutorial execution logs, and tracked bug reports.
  • Stress Test Details: Deep dive into the 49+ tests that ensure library stability.
  • Capability Test Reports: Multi-model regression results across reasoning and formatting tasks (generated locally, not checked in).

Changelog

See CHANGELOG.md for version history.

Stability

All components (especially AuthPool and PooledClient) are verified with Go's -race detector and benchmarked for zero-allocation performance in hot paths.