RAG - Retrieval Augmented Generation with LangChain and Advanced Techniques

Learn Retrieval Augmented Generation (RAG) techniques through comprehensive examples using LangChain and various tools in this nearly 3-hour tutorial series. Master fundamental RAG implementations starting with retrieval QA over multiple files using ChromaDB, then progress to working with instructor embeddings for PDF processing. Explore local LLM implementations that eliminate dependency on OpenAI, including RetrievalQA with LLaMA 2 70b and Chroma DB integration. Discover how to implement BGE embeddings and create custom prompts for LLaMA-2 7B models. Advance to sophisticated RAG techniques including self-querying retrieval, parent document retrieval, and hybrid search combining BM25 with ensemble methods. Implement contextual compressors and filters, explore HyDE (Hypothetical Document Embeddings), and understand RAG Fusion methodologies. Conclude with specialized applications including EmbeddingGemma for mobile device micro embeddings and Gemini RAG file search tools, providing a complete foundation for building robust retrieval-augmented generation systems.