Langchain Rag
langchain-ai/langchain-skillsOfficial
A comprehensive LangChain RAG implementation guide that covers the complete pipeline from document ingestion to response generation. Shows you how to load documents from PDFs, web pages, and directories, split them with RecursiveCharacterTextSplitter, embed with OpenAI, and store in Chroma, FAISS, or Pinecone. Includes practical examples for similarity search, retrieval configuration, and connecting everything into a working RAG system. The vector store comparison table is helpful for choosing between local development (Chroma), high performance (FAISS), and production deployment (Pinecone). Code examples are thorough and show both the basic patterns and real implementation details you'll need.
- skills.sh installs
- 18.4K
- GitHub stars
- 1.3K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add langchain-ai/langchain-skills --skill langchain-rag --agent claude-codeInstalls into .claude/skills of the current project.
Files
Retrieval Augmented Generation (RAG) enhances LLM responses by fetching relevant context from external knowledge sources.
Pipeline:
- Index: Load → Split → Embed → Store
- Retrieve: Query → Embed → Search → Return docs
- 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
| Vector Store | Use Case | Persistence |
|---|---|---|
| InMemory | Testing | Memory only |
| FAISS | Local, high performance | Disk |
| Chroma | Development | Disk |
| Pinecone | Production, managed | Cloud |
Complete RAG Pipeline
End-to-end RAG pipeline: load documents, split into chunks, embed, store, retrieve, and generate a response.Sponsors
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- Repository
- github.com/langchain-ai/langchain-skills
- Category
- AI & Agent Building
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