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
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