Advanced RAG Systems with LangChainProduction-Ready Implementation Guide
Advanced LangChain RAG tutorial for experienced developers. Build production retrieval-augmented generation systems with ChromaDB, Pinecone, Weaviate, and implement advanced optimization techniques.
🎯 Advanced RAG Techniques You'll Master
Vector Embeddings & Similarity Search
Advanced LangChain embeddings tutorial - implement semantic search with Gemini embeddings and cosine similarity.
Topics covered:
- •Understanding vector embeddings
- •Creating embeddings with Gemini
- •Implementing similarity search
- •Cosine similarity and distance metrics
Vector Databases & Storage
LangChain vector database tutorial - master ChromaDB, Pinecone, Weaviate, Qdrant, and FAISS for production RAG.
Topics covered:
- •Overview of vector databases
- •Pinecone vector database with LangChain
- •ChromaDB integration and optimization
- •FAISS for high-performance RAG
Building Your First RAG System
How to build production RAG with LangChain - complete implementation guide with vector stores and retrieval chains.
Topics covered:
- •RAG architecture overview
- •Document ingestion pipeline
- •Retrieval chain implementation
- •Response generation and formatting
Advanced RAG Techniques
Advanced RAG optimization tutorial - implement multi-query retrieval, reranking, and hybrid search with LangChain.
Topics covered:
- •Multi-query retrieval
- •Parent-child chunking
- •Reranking strategies
- •Hybrid search approaches
RAG Evaluation & Optimization
RAG evaluation with RAGAS framework - optimize LangChain RAG systems for production performance and cost efficiency.
Topics covered:
- •RAGAS evaluation framework
- •A/B testing for RAG
- •Performance optimization
- •Cost vs quality tradeoffs
🎉 Congratulations!
After completing this advanced RAG tutorial series, you'll have mastered production-grade retrieval systems with LangChain, vector databases, and optimization techniques. Ready to deploy enterprise AI solutions!
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