rag-implementationlisted

# RAG Implementation Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources. ## When to Use This Skill - 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 ## Core Components ### 1. Vector Databases **Purpose**: Store and retrieve document embeddings efficiently **Options:** - **Pinecone**: Managed, scalable, fast queries - **Weaviate**: Open-source, hybrid search - **Milvus**: High performance, on-premise - **Chroma**: Lightweight, easy to use - **Qdrant**: Fast, filtered search - **FAISS**: Meta's library, local deployment ### 2. Embeddings **Purpose**: Convert text to numerical vectors for similarity search **Models:** - **text-embedding-ada-002** (OpenAI): General purpose, 1536 dims - **all-MiniLM-L6-v2** (Sentence Transformers): Fast, lightweight - **e5-large-v2**: High quality, multilingual - **Instructor**: Task-specific instructions - **bge-large-en-v1.5**: SOTA performance ### 3. Retrieval Strategies **Approaches:** - **Dense Retrieval**: Semantic similarity via embeddings - **Sparse Retrieval**: Keyword matching (BM25, TF-IDF) - **Hybrid Search**: Combine dense + sparse - **Mul