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langchain-rag

Orchestrate end-to-end RAG pipelines with LangChain loaders, splitters, embeddings, and vector stores.

113|9|Updated Jan 24, 2026
Search Tags:#embeddings#langchain#llm#rag#retrieval-augmented-generation#vector-stores#document-loaders
One-click install
npx skills add https://github.com/langchain-ai/skills-benchmarks --skill langchain-rag-langchain-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/main/langchain-rag
Command: npx skills add https://github.com/langchain-ai/skills-benchmarks --skill langchain-rag-langchain-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates end-to-end RAG pipelines by integrating loaders, splitters, embeddings, and vector stores.

Core Features & Use Cases

  • Document loading and parsing from diverse sources
  • Chunking and embedding for scalable retrieval
  • Retrieval-augmented generation across multiple vector stores
  • Use cases include enterprise knowledge bases and code/document search

Quick Start

Provide a minimal end-to-end RAG example using LangChain to load, split, embed, store, retrieve, and 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 an end-to-end RAG pipeline with LangChain?▼

Build an end-to-end RAG pipeline by integrating document loaders, text splitters, embeddings, and vector stores for retrieval-augmented generation. LangChain orchestrates the entire flow from data ingestion to chunking, embedding, and final answer generation across diverse data sources.

What is retrieval-augmented generation and when do I need it for my LLM?▼

Retrieval-augmented generation (RAG) is a mechanism that fetches relevant external documents to provide context for LLM answers. You need RAG when building enterprise knowledge bases or code search applications that require querying private or dynamically updated data.

Can I use LangChain RAG components for enterprise knowledge base search?▼

Yes, LangChain RAG components support enterprise knowledge base search. The modular pipeline applies document loading, chunking, and embedding to scalable retrieval across multiple vector stores, enabling efficient document search and answer generation.

Does the LangChain RAG pipeline support both Python and TypeScript?▼

Yes, the LangChain RAG pipeline supports both Python and TypeScript. It provides cross-language examples within the LangChain ecosystem, allowing developers to implement document loading, embedding, and retrieval-augmented generation in either environment.

What's the best way to chunk and embed documents for scalable retrieval?▼

The best way to chunk and embed documents for scalable retrieval is using LangChain's modular splitters and embeddings. This pipeline processes diverse data sources by segmenting text and generating embeddings for storage across multiple vector stores.

Why use modular components for document ingestion and retrieval?▼

Use modular components for document ingestion and retrieval to maintain flexibility across diverse data sources. LangChain RAG orchestrates interchangeable loaders, splitters, embeddings, and vector stores, allowing you to customize the pipeline for specific document search requirements.

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