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langchain-rag

Build RAG pipelines that retrieve document context for grounded LLM answers.

1|1|Updated Apr 20, 2026
Search Tags:#langchain#rag#retrieval#vector store#question answering#document loaders#openai embeddings
One-click install
npx skills add https://github.com/svssdeva/agentic-skills --skill langchain-rag-svssdeva
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/svssdeva/agentic-skills/tree/main/ai/langchain-rag
Command: npx skills add https://github.com/svssdeva/agentic-skills --skill langchain-rag-svssdeva

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the problem of building reliable retrieval-augmented generation (RAG) systems by providing an end-to-end blueprint that turns your documents into searchable knowledge for LLM answers.

Core Features & Use Cases

  • Indexing pipeline: Load documents, split them into context-preserving chunks, embed them, and store them in a vector database.
  • Retrieval pipeline: Embed the user query and fetch the most relevant chunks using similarity or MMR, optionally with metadata filters.
  • Generation integration: Combine retrieved context with the user query to produce grounded responses, including an agent/tool pattern for iterative QA.

Use case example: You have a folder of mixed documents (PDFs, web pages, and text files) and want a chatbot that answers questions using only your internal content; use this Skill to implement document loading, chunking, embeddings, vector store persistence (or in-memory for tests), retrieval, and context-grounded generation.

Quick Start

Use this Skill to implement a complete RAG pipeline by choosing a document loader, splitting strategy, embedding model, and vector store, then wiring retrieval results into your LLM prompt for grounded answers.

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 LangChain RAG pipeline for document question answering?▼

To build a LangChain RAG pipeline, load documents, split them into chunks using RecursiveCharacterTextSplitter, embed them with OpenAIEmbeddings, and store them in a vector database to fetch relevant context for LLM generation.

What is the best way to retrieve relevant documents using LangChain?▼

The best way to retrieve documents using LangChain is by applying similarity search or MMR retrieval. You can embed your user query and fetch the most relevant chunks from your vector store, optionally applying metadata filters to refine the results.

Can I use FAISS and Chroma vector stores with LangChain retrieval pipelines?▼

Yes, LangChain retrieval pipelines support FAISS and Chroma vector stores, alongside Pinecone and InMemory options. You can configure retrieval parameters and use metadata filtering to fetch context-preserving chunks across any of these supported vector databases.

How does context-injected generation work for RAG applications?▼

Context-injected generation for RAG applications works by combining retrieved document chunks with the user query to produce grounded LLM responses. This ensures the model answers questions using external documents rather than relying solely on its internal knowledge.

Do I need OpenAIEmbeddings to implement document chunking and indexing?▼

You need OpenAIEmbeddings to convert document chunks into vector representations for indexing in this LangChain pipeline. The process involves loading documents, splitting them contextually, and embedding them before storing the vectors in your chosen database.

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