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langchain-rag

Build retrieval augmented generation systems grounding LLM answers in external documents.

1|Updated Apr 25, 2026
Search Tags:#semantic search#embeddings#langchain#rag#vectorstore#text splitting#document loaders
One-click install
npx skills add https://github.com/collat-labs/collat --skill langchain-rag-collat-labs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/collat-labs/collat/tree/main/.claude/skills/langchain-rag%20copy
Command: npx skills add https://github.com/collat-labs/collat --skill langchain-rag-collat-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the need to turn your own documents into searchable knowledge that a language model can ground its answers on, reducing hallucinations and improving response relevance.

Core Features & Use Cases

  • End-to-end RAG workflow: document loading, text splitting, embedding, vector-store indexing, retrieval, and LLM generation.
  • Document ingestion options: load PDFs, scrape/parse web pages, or load whole directories of text files.
  • Practical vector store choices: InMemory for testing, FAISS and Chroma for local/dev persistence, and Pinecone for production/managed hosting.
  • Use case: Ask questions about a product’s internal docs by indexing those documents once, retrieving the most relevant chunks at query time, and generating an answer constrained to that retrieved context.

Quick Start

Invoke langchain-rag to implement an end-to-end RAG pipeline for your documents by loading them, chunking them with RecursiveCharacterTextSplitter, embedding them with OpenAIEmbeddings, storing them in a vector store, retrieving top-k matches for a question, and prompting the LLM with the retrieved context.

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 pipeline with LangChain to ground LLM answers in my own documents?▼

To build a RAG pipeline with LangChain, you load documents, chunk them using RecursiveCharacterTextSplitter, embed them with OpenAIEmbeddings, store them in a vector store, and retrieve relevant context to feed into the LLM for generation. This grounds answers and reduces hallucinations.

What is the best way to index PDFs and web pages for retrieval augmented generation?▼

The best way to index PDFs and web pages for retrieval augmented generation is to use document loaders to ingest the files, split the text into chunks, and store the embeddings in a vector store like FAISS, Chroma, or Pinecone for query-time retrieval.

Can I use Pinecone or FAISS as a vector store for my LangChain RAG system?▼

Yes, you can use Pinecone or FAISS as a vector store for your LangChain RAG system. The pipeline supports InMemory and Chroma for testing and local persistence, while Pinecone provides a managed hosting solution for production environments.

How does text splitting with RecursiveCharacterTextSplitter work for document ingestion?▼

Text splitting with RecursiveCharacterTextSplitter works by dividing loaded documents into smaller chunks during the ingestion phase. This prepares the text for accurate embedding and indexing, ensuring that the retrieval process returns only the most relevant context.

How do I perform similarity or MMR search with metadata filters in a RAG workflow?▼

To perform similarity or MMR search with metadata filters in a RAG workflow, you query the configured vector store at query time. The system retrieves the top-k matching chunks, optionally applying filters, before feeding the context into the LLM.

Why do I need OpenAIEmbeddings for retrieval augmented generation?▼

You need OpenAIEmbeddings for retrieval augmented generation to convert text chunks into vector representations. These embeddings are stored in the vector store, enabling similarity or MMR search to accurately retrieve relevant context before generating an LLM answer.

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