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langchain-rag

Construct end-to-end RAG pipelines with LangChain for document question-answering.

36|27|Updated Jan 28, 2026
Search Tags:#embeddings#langchain#rag#vector-stores#text-splitting#document-loaders
One-click install
npx skills add https://github.com/caio-moliveira/ai-engineer-roadmap --skill langchain-rag-caio-moliveira
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/caio-moliveira/ai-engineer-roadmap/tree/main/.agents/skills/langchain-rag
Command: npx skills add https://github.com/caio-moliveira/ai-engineer-roadmap --skill langchain-rag-caio-moliveira

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guides developers to implement retrieval-augmented generation (RAG) systems by providing a cohesive LangChain-based workflow that coordinates data ingestion, semantic search, and answer generation.

Core Features & Use Cases

  • Document loaders, text splitters, embeddings, and vector stores to build scalable RAG pipelines.
  • End-to-end workflows for QA, summarization, and knowledge-grounded reasoning across multiple sources such as documents and web content.
  • Easy swapping of vector stores (Chroma, FAISS, Pinecone) and embedding models to match cost and latency goals.

Quick Start

Build a basic RAG pipeline that loads documents, splits content, creates embeddings, stores them in a vector store, and uses an LLM to 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 an end-to-end RAG pipeline with LangChain?▼

Build a RAG pipeline by using LangChain document loaders to ingest files, splitting content with RecursiveCharacterTextSplitter, generating OpenAI embeddings, and storing them in a vector store for LLM query answering.

How do I compare and switch vector stores like Chroma, FAISS, and Pinecone for retrieval?▼

Compare vector stores like Chroma, FAISS, and Pinecone to match specific cost and latency goals, enabling easy swapping of storage targets and embedding models within your LangChain RAG pipeline.

What do I need to know before implementing retrieval-augmented generation for documents?▼

Implementing retrieval-augmented generation requires prerequisite knowledge of document loaders, RecursiveCharacterTextSplitter, OpenAI embeddings, and vector stores to successfully coordinate data ingestion and semantic search.

Can I use LangChain for question answering on web content and multiple sources?▼

Yes, LangChain supports question answering and knowledge-grounded reasoning across multiple sources like web content and documents, applying embeddings for scalable semantic retrieval.

What is the best way to split text for semantic search in a RAG system?▼

The best way to split text for semantic search in a RAG system is using LangChain's RecursiveCharacterTextSplitter, which prepares document content for accurate embedding generation and scalable vector storage.

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