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abdullahhqaiser
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langchain-rag

Build RAG pipelines that load, split, embed, store, and retrieve documents.

Updated Apr 7, 2026
Search Tags:#RAG#embedding#vector store#text splitting#document loading
One-click install
npx skills add https://github.com/abdullahhqaiser/langgraph_diligence --skill langchain-rag-abdullahhqaiser
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/abdullahhqaiser/langgraph_diligence/tree/main/.agents/skills/langchain-rag
Command: npx skills add https://github.com/abdullahhqaiser/langgraph_diligence --skill langchain-rag-abdullahhqaiser

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain_openai, langchain_community, langchain_core, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a framework for constructing Retrieval Augmented Generation (RAG) systems, enabling LLMs to fetch relevant context from external knowledge sources and improve their responses.

Core Features & Use Cases

  • RAG Pipeline: Offers a complete RAG pipeline for loading, splitting, embedding, storing, retrieving, and generating responses.
  • Document Loaders: Ingests data from files, web, and databases.
  • Text Splitters: Utilizes RecursiveCharacterTextSplitter for efficient document splitting.
  • Embeddings: Converts text to vectors using OpenAI embeddings.
  • Vector Stores: Supports vector stores like InMemory, FAISS, Chroma, and Pinecone for efficient searching.
  • Use Case: Build a RAG system that fetches relevant information from a database and provides context- enriched responses to user queries.

Quick Start

Load documents, split them, create embeddings, store them, retrieve relevant documents for a query, and generate a response using the langchain-rag skill.

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 fetch external context for LLM responses?▼

To build a RAG pipeline, load documents from files or web sources, split the text, generate embeddings, store them in a vector store, and retrieve relevant context to generate enhanced LLM responses. This Skill provides the framework for that entire process.

What document loaders and text splitters work with LangChain for RAG?▼

LangChain RAG supports document loaders for files, web, and databases, and uses RecursiveCharacterTextSplitter for efficient document splitting. These components ingest and partition external data before embedding.

Can I use FAISS or Chroma as a vector store for LangChain RAG?▼

Yes, you can use FAISS, Chroma, Pinecone, or InMemory vector stores with LangChain RAG. These vector stores manage your embeddings and enable efficient similarity searching to retrieve relevant documents for your queries.

Do I need OpenAI embeddings to implement retrieval augmented generation?▼

LangChain RAG utilizes OpenAI embeddings to convert text into vectors for retrieval. You need the langchain_openai dependency to generate these embeddings and store them in your chosen vector store.

What is the best way to split documents for RAG systems?▼

The best way to split documents for RAG systems is using RecursiveCharacterTextSplitter. It efficiently partitions loaded text into manageable chunks, ensuring accurate embedding generation and retrieval from your vector store.

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