rag-implementation

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: - Building Q&A systems over proprietary documents - Creating chatbots with current, factual information - Implementing semantic search with natural language queries - Reducing hallucinations with grounded responses - Enabling LLMs to access domain-specific knowledge - Building documentation assistants - Creating research tools with source citation - Pinecone : Managed, scalable, serverless - Weaviate : Open-source, hybrid search, GraphQL - Milvus : High performance, on-premise - Chroma : Lightweight, easy to use, local development - Qdrant : Fast, filtered search, Rust-based - pgvector : PostgreSQL extension, SQL integration - Dense Retrieval : Semantic similarity via embeddings - Sparse Retrieval : Keyword matching (BM25, TF-IDF) - Hybrid Search : Combine dense + sparse with weighted fusion - Multi-Query : Generate multiple query variations - HyDE : Generate hypothetical documents for better retrieval - Cross-Encoders : BERT-based reranking (ms-marco-MiniLM) - Cohere Rerank : API-based reranking - Maximal...