rag-implementation

Build knowledge-grounded LLM applications with vector databases, semantic search, and retrieval strategies.

- 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

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