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docs: Add release documentation for v0.1.0
Complete release documentation including changelog, release notes, and license: CHANGELOG.md (195 lines): - Semantic versioning format following Keep a Changelog - Comprehensive feature list for v0.1.0 - Organized by feature category: * Core Infrastructure (registry, client, adapters, streaming, tracing) * Document Processing & RAG (10+ loaders, 5 vector stores, embeddings) * Advanced LLM (agents, tools, memory, chains) * Graph & Multi-Agent Systems (StateGraph, collaboration, supervisors) * Multimodal AI (vision, audio, web search, ML integration) * Production Features (cost estimation, evaluation, fine-tuning, error handling) * Developer Experience (CLI, documentation, examples, tests) * CI/CD & Infrastructure (GitHub Actions, security scanning, deployment) - Dependencies and system requirements - Planned features for future releases RELEASE_NOTES.md (243 lines): - Executive summary of v0.1.0 - Key highlights with emoji icons for visual appeal - Getting started guide with installation instructions - Quick start code examples - Complete feature overview (14 core modules) - Supported models across 4 providers (50+ models) - System requirements and performance characteristics - Documentation overview (900+ lines theory, 600+ tutorials) - Contributing guidelines - Known issues and roadmap (v0.2.0, v0.3.0, future) - Support channels and acknowledgments LICENSE (21 lines): - MIT License for open-source distribution - Copyright 2024 llmkit contributors - Standard MIT license terms Release highlights: - Unified multi-provider interface (OpenAI, Anthropic, Google, Ollama) - Production-ready RAG with one-line setup - Advanced agent systems with multi-agent collaboration - Multimodal AI (vision, audio, web search) - Cost optimization with token counting and model recommendations - Comprehensive 16-week learning curriculum - 50+ code examples and best practices - Full CI/CD pipeline with automated testing and deployment Ready for PyPI publication and GitHub release.
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‎CHANGELOG.md‎

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# Changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [0.1.0] - 2024-12-19
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### Added
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#### Core Infrastructure
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- Model registry with automatic provider detection
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- Unified client interface supporting OpenAI, Anthropic, Google, and Ollama
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- Intelligent adapters for seamless provider switching
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- Response streaming with callback support
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- Distributed tracing integration (OpenTelemetry)
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- Configuration management with environment variable support
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#### Document Processing & RAG
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- 10+ document loaders (PDF, Word, Markdown, CSV, JSON, HTML, etc.)
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- Intelligent text splitters (recursive, semantic, token-based)
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- Complete RAG pipeline with vector store integration
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- Support for 5 vector stores (Chroma, FAISS, Pinecone, Weaviate, Qdrant)
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- Embeddings support (OpenAI, Sentence Transformers, custom)
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- RAG debugging and evaluation tools
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- Document chunking with overlap and metadata preservation
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#### Advanced LLM Features
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- Agent framework with ReAct and function calling
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- Tool integration system with built-in and custom tools
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- Conversation memory (buffer, summary, vector-based)
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- Chain of Thought prompting
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- Sequential and parallel chains
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- Router chains for dynamic routing
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- MapReduce chains for document processing
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#### Graph & Multi-Agent Systems
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- StateGraph for complex workflows
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- Conditional branching and routing
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- Multi-agent collaboration framework
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- Supervisor agents for coordination
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- Hierarchical agent structures
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- Graph persistence and checkpointing
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#### Multimodal AI
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- Vision API integration (GPT-4V, Claude 3, Gemini)
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- Image analysis and description
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- OCR and document understanding
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- Vision-Language Model (VLM) support
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- ML model integration (scikit-learn, PyTorch, TensorFlow)
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- Model deployment and serving utilities
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#### Web & Audio Processing
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- Web search integration (Tavily, SerpAPI, DuckDuckGo)
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- Web scraping with BeautifulSoup and Playwright
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- Audio transcription (Whisper API)
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- Text-to-speech generation
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- Audio file processing
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- Web content extraction and parsing
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#### Production Features
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- Token counting with tiktoken
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- Cost estimation for 50+ models
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- Cost optimization recommendations
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- Prompt templates (few-shot, chat, chain-of-thought)
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- Evaluation metrics (BLEU, ROUGE, semantic similarity)
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- LLM-as-Judge evaluation framework
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- Fine-tuning data preparation and API integration
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- Error handling (retry, circuit breaker, rate limiting)
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- Production monitoring and logging
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#### Developer Experience
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- Rich CLI interface with interactive commands
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- Comprehensive documentation (900+ lines theory, 600+ lines tutorials)
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- 16-week learning curriculum
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- 50+ code examples
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- Type hints throughout codebase
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- Async/await support
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- Extensive test coverage
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#### CI/CD & Infrastructure
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- GitHub Actions workflows for testing (multi-OS, multi-Python)
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- Automated PyPI publishing
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- CodeQL security scanning
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- Dependabot for dependency updates
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- Documentation deployment to GitHub Pages
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- Issue and PR templates
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- Contributing guidelines
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### Documentation
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- Complete API reference
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- 9 theory documents covering graduate-level concepts
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- 9 hands-on tutorials with real-world examples
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- Learning path from basics to advanced topics
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- 50+ practical examples
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- Migration guides and best practices
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### Dependencies
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- Core: `httpx`, `python-dotenv`, `openai`, `anthropic`, `rich`
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- Optional: `google-generativeai` (Gemini), `ollama` (local models)
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- Development: `pytest`, `black`, `ruff`, `mypy`, `pytest-cov`
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### Notes
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- Python 3.11+ required
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- Supports macOS, Linux, and Windows
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- Modular design allows installing only needed providers
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- Comprehensive test coverage with pytest
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- Production-ready with error handling and monitoring
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## [Unreleased]
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### Planned
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- Additional vector store integrations
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- More evaluation metrics
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- Enhanced multi-agent collaboration patterns
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- Streaming support for all providers
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- Plugin system for extensions
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- GUI dashboard for monitoring
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---
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For detailed information about each feature, see the [documentation](docs/).
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[0.1.0]: https://github.com/leebeanbin/llmkit/releases/tag/v0.1.0

‎RELEASE_NOTES.md‎

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# llmkit v0.1.0 Release Notes
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**Release Date:** December 19, 2024
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We're excited to announce the first release of **llmkit** - a unified, production-ready toolkit for managing and using multiple LLM providers with advanced features for RAG, agents, multi-modal AI, and production deployment.
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## 🎯 Overview
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llmkit v0.1.0 is a comprehensive LLM toolkit that brings together the best features from multiple providers (OpenAI, Anthropic, Google, Ollama) with a unified interface. This release includes everything needed to build production-grade AI applications, from basic completions to complex multi-agent systems.
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## ✨ Highlights
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### 🤖 Unified Multi-Provider Interface
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- **Single API** for OpenAI, Anthropic, Google Gemini, and Ollama
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- **Automatic provider detection** from model names
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- **Seamless switching** between providers without code changes
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- **Streaming support** with real-time callbacks
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### 📚 Production-Ready RAG
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- **One-line RAG**: `RAGChain.from_documents("docs/")`
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- **10+ document loaders** (PDF, DOCX, CSV, JSON, HTML, etc.)
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- **5 vector stores** (Chroma, FAISS, Pinecone, Weaviate, Qdrant)
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- **Intelligent text splitting** with semantic and token-based strategies
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- **RAG debugging tools** for retrieval analysis
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### 🧠 Advanced Agent Systems
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- **ReAct agents** with function calling
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- **Tool integration** with 20+ built-in tools
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- **Multi-agent collaboration** with supervisor patterns
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- **Graph workflows** for complex decision trees
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- **Memory systems** (buffer, summary, vector-based)
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### 🎨 Multimodal AI
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- **Vision APIs** (GPT-4V, Claude 3, Gemini Vision)
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- **Image analysis** and OCR
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- **Audio processing** (Whisper transcription, TTS)
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- **Web search** integration (Tavily, SerpAPI, DuckDuckGo)
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- **ML model** integration (scikit-learn, PyTorch, TensorFlow)
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### 💰 Cost Optimization
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- **Token counting** with tiktoken for accurate estimates
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- **Cost calculation** for 50+ models
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- **Model recommendations** based on cost and performance
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- **Usage tracking** and budget monitoring
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### 🎓 Comprehensive Documentation
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- **900+ lines** of graduate-level theory
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- **600+ lines** of hands-on tutorials
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- **16-week curriculum** from basics to advanced
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- **50+ code examples** for common use cases
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- **Best practices** for production deployment
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## 🚀 Getting Started
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### Installation
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```bash
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# Basic installation (OpenAI + Anthropic)
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pip install llmkit
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# With all providers
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pip install llmkit[all]
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# Development installation
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pip install llmkit[dev]
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```
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### Quick Start
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```python
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from llmkit import Client
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# Basic usage
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client = Client(model="gpt-4o")
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response = client.chat("Explain quantum computing")
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print(response.content)
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# RAG in one line
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from llmkit import RAGChain
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rag = RAGChain.from_documents("docs/")
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answer = rag.query("What is the main topic?")
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# Cost optimization
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from llmkit import estimate_cost, get_cheapest_model
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cost = estimate_cost(
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input_text="Your prompt",
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output_text="Expected response",
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model="gpt-4o"
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)
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```
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## 📦 What's Included
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### Core Modules (14 total)
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1. **llmkit.client** - Unified LLM interface
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2. **llmkit.registry** - Model and provider management
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3. **llmkit.adapters** - Provider-specific implementations
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4. **llmkit.document_loaders** - Document ingestion
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5. **llmkit.text_splitters** - Intelligent chunking
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6. **llmkit.embeddings** - Vector embedding generation
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7. **llmkit.vector_stores** - Vector database integration
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8. **llmkit.rag** - Complete RAG pipeline
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9. **llmkit.agents** - Agent framework
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10. **llmkit.tools** - Tool integration system
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11. **llmkit.memory** - Conversation memory
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12. **llmkit.chains** - Chain of thought and workflows
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13. **llmkit.graphs** - Graph-based workflows
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14. **llmkit.multi_agent** - Multi-agent systems
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### Production Features
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- **Token counting** (`llmkit.token_counter`)
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- **Cost estimation** (`llmkit.cost_estimator`)
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- **Prompt templates** (`llmkit.prompts`)
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- **Evaluation metrics** (`llmkit.evaluation`)
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- **Error handling** (`llmkit.error_handling`)
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- **Fine-tuning** (`llmkit.finetuning`)
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### Developer Tools
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- **CLI interface** with rich formatting
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- **Streaming utilities** for real-time processing
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- **Tracing integration** with OpenTelemetry
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- **Debugging tools** for RAG and agents
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- **Testing utilities** with pytest integration
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## 🔧 Technical Details
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### Supported Models
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**OpenAI:**
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- GPT-4 Turbo, GPT-4o, GPT-4o-mini
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- GPT-3.5 Turbo variants
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- Embedding models (text-embedding-3-small/large)
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**Anthropic:**
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- Claude 3.5 Sonnet, Claude 3 Opus
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- Claude 3 Sonnet, Claude 3 Haiku
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**Google:**
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- Gemini 1.5 Pro, Gemini 1.5 Flash
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- Gemini 1.0 Pro
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**Ollama:**
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- Llama 3/3.1, Mistral, Mixtral
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- CodeLlama, Phi-3, and more
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### System Requirements
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- **Python:** 3.11 or higher
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- **OS:** macOS, Linux, Windows
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- **Memory:** 4GB minimum (8GB+ recommended for vector stores)
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- **Storage:** 500MB for package + models (varies by provider)
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### Performance
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- **Streaming:** Real-time token streaming for all providers
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- **Async support:** Full async/await compatibility
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- **Batch processing:** Efficient batch operations
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- **Caching:** Built-in response caching
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- **Rate limiting:** Automatic rate limit handling
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## 📖 Documentation
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- **Theory Docs:** 9 comprehensive guides with mathematical foundations
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- **Tutorials:** 9 hands-on tutorials with real code
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- **Learning Path:** 16-week curriculum (3 hours/week)
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- **Examples:** 50+ code examples for common tasks
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- **API Reference:** Complete API documentation
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Access docs at: [docs/](docs/)
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## 🤝 Contributing
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We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
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Key areas for contribution:
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- New provider integrations
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- Additional vector store support
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- More evaluation metrics
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- Enhanced multi-agent patterns
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- Documentation improvements
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## 🐛 Known Issues
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- Some vector stores require additional system dependencies
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- Async support varies by provider
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- Fine-tuning only supports OpenAI API currently
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See [GitHub Issues](https://github.com/leebeanbin/llmkit/issues) for full list.
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## 🗺️ Roadmap
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### v0.2.0 (Q1 2025)
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- Additional vector store integrations
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- Enhanced streaming for all providers
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- GUI dashboard for monitoring
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- More evaluation metrics
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### v0.3.0 (Q2 2025)
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- Plugin system for extensions
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- Advanced multi-agent patterns
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- Model fine-tuning enhancements
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- Performance optimizations
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### Future
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- Cloud deployment templates
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- Kubernetes operators
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- Enterprise features
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- Advanced security features
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## 📄 License
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MIT License - See [LICENSE](LICENSE) for details
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## 🙏 Acknowledgments
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Built with support from:
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- OpenAI for GPT models and API
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- Anthropic for Claude models
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- Google for Gemini models
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- Ollama for local model support
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- The open-source community
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## 📞 Support
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- **Documentation:** [GitHub README](README.md)
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- **Issues:** [GitHub Issues](https://github.com/leebeanbin/llmkit/issues)
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- **Discussions:** [GitHub Discussions](https://github.com/leebeanbin/llmkit/discussions)
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## 🎉 Get Started Today
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```bash
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pip install llmkit
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```
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Start building production-grade AI applications with llmkit!
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---
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**Full Changelog:** https://github.com/leebeanbin/llmkit/blob/main/CHANGELOG.md

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