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langchain-rag

Orchestrate RAG pipelines with document loading, splitting, embedding, and retrieval.

2|Updated Mar 7, 2026
Search Tags:#embeddings#langchain#llm#rag#retrieval#vector store
One-click install
npx skills add https://github.com/Lincyaw/AgentM --skill langchain-rag-lincyaw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/Lincyaw/AgentM/tree/main/.claude/skills/langchain-rag
Command: npx skills add https://github.com/Lincyaw/AgentM --skill langchain-rag-lincyaw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for building Retrieval-Augmented Generation (RAG) systems, enabling LLMs to access and utilize external knowledge sources for more informed and accurate responses.

Core Features & Use Cases

  • End-to-End RAG Pipeline: Covers document loading, splitting, embedding, storage, retrieval, and generation.
  • Vector Store Integration: Supports various vector stores like Chroma, FAISS, and Pinecone for efficient data indexing and retrieval.
  • Use Case: Integrate this Skill into your chatbot to allow it to answer questions based on your company's internal documentation, providing contextually relevant and up-to-date information.

Quick Start

Use the langchain-rag skill to set up a basic RAG pipeline by loading documents, splitting them into chunks, embedding them, storing them in a vector store, and then retrieving relevant documents to answer a query.

Frequently Asked Questions about langchain-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a retrieval-augmented generation pipeline with LangChain?▼

To build a retrieval-augmented generation pipeline, you load documents, split them into chunks, generate embeddings, store them in a vector database, and retrieve relevant context to answer queries.

How do I split documents and generate embeddings for a RAG system?▼

Splitting documents and generating embeddings involves processing text into manageable chunks and mapping them into vector space, allowing the RAG system to retrieve semantically relevant information.

Does this RAG skill work with Chroma and FAISS vector stores?▼

Yes, this RAG skill integrates with vector stores like Chroma and FAISS to manage efficient data indexing and retrieval for your language model applications.

Can I implement a RAG pipeline using Python and TypeScript?▼

Yes, you can implement RAG pipelines using both Python and TypeScript, enabling you to integrate document loaders and vector databases within your preferred development environment.

What is the best way to let an LLM access internal company documentation?▼

The best way to let an LLM access internal documentation is by building a RAG system that retrieves relevant text chunks from a vector store, providing contextually accurate and informed responses.

What are the steps to load and store documents in a vector database for retrieval?▼

The steps involve loading your source files, splitting the text into chunks, generating embeddings, and storing them in a vector database like Chroma or FAISS to enable fast retrieval.

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