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langchain-rag

Construct RAG systems with document loaders, text splitters, embeddings, and vector stores.

1|Updated Apr 30, 2026
Search Tags:#langchain#llm#rag#knowledge base#retrieval-augmented generation
One-click install
npx skills add https://github.com/wpsadi/stock-agent --skill langchain-rag-wpsadi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/wpsadi/stock-agent/tree/main/.agents/skills/langchain-rag
Command: npx skills add https://github.com/wpsadi/stock-agent --skill langchain-rag-wpsadi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill Unit provides the tools and framework to build retrieval-augmented generation (RAG) systems, enhancing the responses of LLMs by integrating external knowledge sources.

Core Features & Use Cases

  • Document Loaders: Support for loading documents from various sources such as files, web, and databases.
  • Text Splitters: Advanced text splitting capabilities to ensure efficient embedding and retrieval.
  • Embeddings: Integration with OpenAI embeddings for vector representation of text.
  • Vector Stores: Utilization of vector stores like Chroma, FAISS, and Pinecone for efficient retrieval.
  • Use Case: Build a RAG system to provide context- enriched responses to user queries based on a comprehensive knowledge base.

Quick Start

To get started, invoke the langchain-rag skill with the query 'What is RAG?'.

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 system to connect an LLM to my external knowledge base?▼

To build a RAG system, you load documents from sources like files or databases, split the text into chunks, generate embeddings, and store them in a vector store like Chroma or FAISS for LLM retrieval. This framework provides the necessary document loaders and text splitters to complete the process.

What is retrieval-augmented generation and when do I need it for my LLM application?▼

Retrieval-augmented generation (RAG) enhances LLM responses by integrating external knowledge sources. You need RAG when your application requires the LLM to reference external information, ensuring context-enriched and accurate answers based on a comprehensive knowledge base.

Can I use OpenAI embeddings with FAISS vector stores for document retrieval?▼

Yes, you can use OpenAI embeddings to create vector representations of your text and store them in FAISS vector stores. This integration enables efficient retrieval of document chunks to provide context for LLM queries.

What is the best way to split long documents for embedding and retrieval?▼

The best way to split long documents is using advanced text splitters, which divide the text into manageable chunks to ensure efficient embedding and retrieval. This prevents information loss and optimizes the accuracy of the retrieved context.

Does LangChain support loading documents from databases and web sources for RAG?▼

Yes, LangChain provides document loaders that support loading documents from various sources including files, web pages, and databases. This allows you to aggregate diverse external knowledge into your RAG pipeline for comprehensive LLM context.

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