rag-implementation

Build knowledge-grounded LLM applications with vector databases, semantic search, and retrieval strategies. Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources. Purpose : Store and retrieve document embeddings efficiently Options: Purpose : Convert text to numerical vectors for similarity search Models (2026): Approaches: Purpose : Improve retrieval quality by reordering results Methods: - Supports six vector database options (Pinecone, Weaviate, Milvus, Chroma, Qdrant, pgvector) and six embedding models optimized for different use cases and providers - Covers five advanced retrieval patterns: hybrid search combining dense and sparse retrieval, multi-query generation, contextual compression, parent document retrieval, and HyDE (hypothetical document embeddings) - Includes four document chunking strategies (recursive character, token-based, semantic, markdown header) and metadata filtering, MMR diversity balancing, and cross-encoder reranking for optimization - Provides complete LangGraph implementation examples with async retrieval and generation nodes, plus evaluation metrics for measuring retrieval precision, recall, answer relevance, and faithfulness - Building Q&A systems over proprietary documents - Creating chatbots with current, factual information - Implementing semantic search...