langchain-rag
Complete RAG pipeline for document ingestion, embedding, retrieval, and LLM-powered response generation.
- Supports multiple document loaders (PDF, web pages, directories) and persistent vector stores (Chroma, FAISS, Pinecone) with configurable chunk size and overlap for optimal context preservation
- Includes similarity search, MMR (Maximal Marginal Relevance) retrieval, and metadata filtering to balance relevance and diversity in results
- Works with OpenAI embeddings and integrates seamlessly with LangChain agents and chat models for end-to-end RAG workflows
- Provides Python and TypeScript implementations with best-practice guidance on chunk sizing (500–1500 characters), consistent embedding models, and persistent storage to avoid data loss
Pipeline:
- Index: Load → Split → Embed → Store
- Retrieve: Query → Embed → Search → Return docs
- Generate: Docs + Query → LLM → Response
Key Components:
- Document Loaders: Ingest data from files, web, databases
- Text Splitters: Break documents into chunks
- Embeddings: Convert text to vectors
- Vector Stores: Store and search embeddings
| Vector Store | Use Case | Persistence |
|---|
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