yussufbiyik/langchain-chromadb-rag-example
📦 Open Source Projectyussufbiyik
A clean, beginner-friendly implementation of RAG using LangChain, ChromaDB, and Ollama for local document chat.
This project serves as a practical blueprint for developers looking to integrate RAG into their workflows without the overhead of complex enterprise frameworks. By utilizing LangChain's modular architecture, the repository demonstrates how to ingest documents, chunk text, generate embeddings, and store them in a ChromaDB vector database. The implementation is specifically optimized for Ollama, enabling users to run private, local LLM inference while maintaining high retrieval accuracy. The codebase is structured to be highly readable, making it an excellent starting point for those new to vector databases or those who want to understand the underlying mechanics of document retrieval and context injection. It covers the full lifecycle of a RAG pipeline, from initial document loading to the final response generation, providing a robust foundation for building custom AI-powered knowledge bases.
💡Highlights
- ├─Clean, modular Python RAG pipeline
- ├─Optimized for Ollama local LLMs
- └─Easy-to-follow ChromaDB integration
🎯For
- ├─AI Developers
- ├─Python Beginners
- └─RAG Enthusiasts