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langchain-rag

Build end-to-end RAG pipelines with LangChain document loaders, embeddings, and vector stores.

Updated May 29, 2026
Search Tags:#embeddings#langchain#llm#rag#retrieval-augmented-generation#vector-stores#document-loaders
One-click install
npx skills add https://github.com/patorn/langgraph-template --skill langchain-rag-patorn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/patorn/langgraph-template/tree/main/.agents/skills/langchain-rag
Command: npx skills add https://github.com/patorn/langgraph-template --skill langchain-rag-patorn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Retrieval augmented generation (RAG) helps systems fetch relevant information from external sources to augment LLM responses, reducing hallucinations and increasing accuracy.

Core Features & Use Cases

  • Document loaders, text splitters, embeddings, and vector stores to build end-to-end RAG pipelines.
  • Supports multiple vector stores (InMemory, FAISS, Chroma, Pinecone) for local development and scalable production.
  • Real-world use: build a Q&A assistant over a document corpus, or a tech docs search tool that answers from internal docs.

Quick Start

Run a minimal RAG pipeline to load documents, split them into chunks, generate embeddings, store and retrieve relevant context, and answer a user 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 answer questions over my documents?▼

To build a retrieval-augmented generation pipeline, use document loaders, text splitters, embeddings, and vector stores to ingest, chunk, and retrieve relevant context for an LLM to generate accurate answers.

What vector stores can I use with LangChain for local development and scalable production RAG?▼

LangChain supports multiple vector stores including InMemory, FAISS, Chroma, and Pinecone, allowing you to scale retrieval-augmented generation from local development to production environments.

When do I need retrieval-augmented generation for my LLM application?▼

You need retrieval-augmented generation when your LLM must fetch relevant information from external sources to augment responses, which reduces hallucinations and increases accuracy over specific document corpora.

Can I use LangChain to chunk and embed web content for a tech docs search tool?▼

Yes, LangChain can ingest web content using document loaders, split text into chunks, generate embeddings, and store them in vector stores to build a tech docs search tool that answers from internal data.

What is the best way to orchestrate data ingestion and answer generation for a Q&A assistant over enterprise data?▼

The best way to orchestrate a Q&A assistant is to use LangChain to manage data ingestion, embedding, storage, retrieval, and answer generation across your enterprise data using scalable vector stores.

Does retrieval-augmented generation reduce LLM hallucinations when summarizing documents?▼

Retrieval-augmented generation reduces hallucinations by fetching relevant information from external sources to augment LLM responses, ensuring the summarization or Q&A is grounded in your actual documents.

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