How to Build a RAG Chatbot with Python

Short summary

Build a RAG chatbot that answers questions from your own documents using Python, ChromaDB, and Claude. The tutorial covers indexing (load → chunk → embed → store), querying (embed question → retrieve chunks → generate answer), and includes production-ready code for PDFs and text files. Features best practices: chunk size tuning (300–600 words), metadata filtering, cross-encoder reranking, embedding caching, and handling complex questions with higher retrieval results.

• •Complete RAG pipeline: load documents → split chunks → embed → store in ChromaDB → query with Claude
• •Production-ready Python code with PDF/text handling, metadata filtering, and full working examples
• •Optimization techniques: chunk sizing (300–600 words), caching, reranking, and scaling retrieval results

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