Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

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, 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 2. Embeddings Purpose : Convert text to numerical vectors for similarity search Models (2026): Model Dimensions Best For voyage 3 large 1024 Claude apps (Anthropic recommended) voyage code 3 1024 Code search text embedding 3 large 3072 OpenAI apps, high accuracy text embedding 3 small 1536 OpenAI apps, cost effective bge large en v1.5 1024 Open source, local deployment multilingual e5 large 1024 Multi language support 3. Retrieval Strategies Approaches: 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 4. Reranking Purpose : Improve retrieval quality by reordering results Methods: Cross Encoders : BERT based reranking (ms marco MiniLM) Cohere Rerank : API based reranking Maximal Marginal Relevance (MMR) : Diversity + relevance LLM based : Use LLM to score relevance Quick Start with LangGraph Detailed patterns and worked examples Detailed pattern documentation lives in references/details.md . Read that file when the navigation tier above is insufficient.

rag-implementation