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langchain-rag

Build RAG pipelines with document loading, text splitting, embeddings, and vector stores.

Updated Mar 7, 2026
Search Tags:#embeddings#langchain#llm#rag#retrieval#vector store
One-click install
npx skills add https://github.com/tivon-x/deep-research --skill langchain-rag-tivon-x
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/tivon-x/deep-research/tree/main/.agents/skills/langchain-rag
Command: npx skills add https://github.com/tivon-x/deep-research --skill langchain-rag-tivon-x

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides the essential building blocks and examples for creating Retrieval-Augmented Generation (RAG) systems, enabling LLMs to access and utilize external knowledge.

Core Features & Use Cases

  • Document Loading: Ingest data from various sources like PDFs, web pages, and directories.
  • Text Splitting: Efficiently chunk documents for optimal processing.
  • Embeddings & Vector Stores: Convert text to vectors and store them for efficient retrieval using options like Chroma, FAISS, and Pinecone.
  • Retrieval Strategies: Implement similarity search and MMR for diverse and relevant results.
  • Use Case: Integrate external documentation into a chatbot to provide accurate, context-aware answers.

Quick Start

Use the langchain-rag skill to set up a basic RAG pipeline by loading documents, splitting them, creating embeddings, storing them in a vector store, and retrieving relevant context for an LLM 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 RAG pipeline to let an LLM access external documents?▼

To build a RAG pipeline, load documents from sources like PDFs or web pages, split the text into chunks, generate embeddings, and store them in a vector database for retrieval to provide context to an LLM.

What is the best way to chunk documents for retrieval-augmented generation?▼

The best way to chunk documents for retrieval-augmented generation is using text splitting tools to divide the source material into smaller, manageable segments. This ensures optimal processing and more accurate retrieval from the vector store.

Does this RAG implementation support vector stores like FAISS and Pinecone?▼

Yes, this RAG implementation supports various vector stores including FAISS, Pinecone, and Chroma. This allows you to store generated text embeddings efficiently and retrieve relevant context for LLM queries.

Can I use MMR retrieval strategy instead of similarity search with LangChain?▼

Yes, you can use the MMR retrieval strategy alongside standard similarity search with LangChain. This ensures you retrieve diverse and relevant document chunks from your vector store rather than redundant context.

Why do I need embeddings for my LLM chatbot?▼

You need embeddings to convert text documents into numerical vectors, enabling the LLM chatbot to efficiently search and retrieve relevant external knowledge. This allows the model to generate accurate, context-aware answers based on your specific data.

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