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langchain-rag

Builds RAG pipelines with LangChain.jsUTE, including data ingestion, vectorization and semantic search retrieval.

Updated Feb 13, 2026
Search Tags:#embeddings#langchain#llm#rag#knowledge base#document retrieval
One-click install
npx skills add https://github.com/evanfang0054/x-codegen-agent --skill langchain-rag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/evanfang0054/x-codegen-agent/tree/main/.claude/skills/langchain-rag
Command: npx skills add https://github.com/evanfang0054/x-codegen-agent --skill langchain-rag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables the creation of powerful Retrieval Augmented Generation (RAG) systems, allowing Large Language Models (LLMs) to access and utilize external knowledge sources for more informed and context-aware responses.

Core Features & Use Cases

  • End-to-End RAG Pipeline: Manages the entire process from document loading and splitting to embedding, storage, and retrieval.
  • Flexible Component Selection: Supports various document loaders, text splitters, embedding models, and vector stores (e.g., Chroma, Pinecone).
  • Use Case: Enhance a customer support chatbot by allowing it to retrieve information from your company's knowledge base (FAQs, product manuals) to answer user queries accurately.

Quick Start

Use the langchain-rag skill to set up a basic RAG pipeline with in-memory storage and OpenAI embeddings.

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 RAG system with LangChain to query my own documents?▼

LangChain builds a RAG pipeline by loading and splitting documents, generating embeddings, storing them in a vector database, and retrieving relevant text to inject into LLM prompts for context-aware responses.

Can I use Pinecone or Chroma as a vector store for my LangChain RAG pipeline?▼

Yes, this LangChain RAG pipeline supports flexible component selection, allowing you to use vector stores like Pinecone or Chroma for embedding storage and document retrieval.

What is the process for splitting documents and generating embeddings for a knowledge base?▼

The process involves using text splitters to chunk loaded documents, then applying embedding models like OpenAI's text-embedding-3-small to convert text chunks into vectors for storage.

How does retrieval augmented generation inject context into LLM prompts?▼

Retrieval augmented generation dynamically fetches relevant document chunks from a vector store based on a user query and injects this external context into the LLM prompt to enhance response accuracy.

Do I need OpenAI's text-embedding-3-small model to set up a basic RAG pipeline?▼

You can set up a basic RAG pipeline using OpenAI's text-embedding-3-small model for generating vector representations, which facilitates in-memory storage and subsequent document retrieval.

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