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langchain-rag

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INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).

config/skills/langchain-rag/SKILL.md

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Repository last updated
Nov 10, 2026
License
MIT

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<overview>
Retrieval Augmented Generation (RAG) enhances LLM responses by fetching relevant context from external knowledge sources.

**Pipeline:**
1. **Index**: Load → Split → Embed → Store
2. **Retrieve**: Query → Embed → Search → Return docs
3. **Generate**: Docs + Query → LLM → Response

**Key Components:**
- **Document Loaders**: Ingest data from files, web, databases
- **Text Splitters**: Break documents into chunks
- **Embeddings**: Convert text to vectors
- **Vector Stores**: Store and search embeddings
</overview>
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