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langchain-rag

Build Retrieval Augmented Generation systems integrating external knowledge sources with LLMs.

Updated Mar 20, 2026
Search Tags:#langchain#RAG#AI generation#vector store#document retrieval
One-click install
npx skills add https://github.com/orezek/paradegolf --skill langchain-rag-orezek
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/orezek/paradegolf/tree/main/.agents/skills/langchain-rag
Command: npx skills add https://github.com/orezek/paradegolf --skill langchain-rag-orezek

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the creation of Retrieval Augmented Generation (RAG) systems, enabling users to efficiently fetch and integrate external knowledge sources into their LLM responses.

Core Features & Use Cases

  • Document Loading: Supports loading documents from various sources including PDFs, web pages, and directories.
  • Text Splitting: Offers advanced text splitting capabilities to create manageable document chunks.
  • Embeddings and Vector Stores: Provides options for embeddings and vector stores like Chroma, FAISS, and Pinecone for efficient document storage and retrieval.
  • Retrieval and Generation: Facilitates retrieval of relevant documents and generation of responses based on combined information.

Quick Start

Use the langchain-rag skill to build a RAG system for your AI agent with the following command: langchain-rag --load documents --split --embed --store --retrieve --generate "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 Retrieval Augmented Generation system to connect external documents to an LLM?▼

To build a Retrieval Augmented Generation system, you need to load external documents, split them into manageable chunks, embed them into vectors, store them in a vector database, and retrieve relevant context to generate LLM responses. This Skill automates that entire workflow.

Can I use LangChain to load PDFs and web pages for contextually rich AI responses?▼

Yes, LangChain supports loading documents from various sources including PDFs, web pages, and directories. These documents are then processed through text splitters and embeddings to provide contextually rich AI responses for professional applications.

What is the best way to split large documents for vector store retrieval?▼

The best way to split large documents for vector store retrieval is using advanced text splitting capabilities to create manageable document chunks. This ensures efficient document storage and accurate retrieval when generating AI responses.

Does this RAG implementation support FAISS and Pinecone for vector storage?▼

Yes, this RAG implementation supports multiple vector stores including Chroma, FAISS, and Pinecone. This provides flexible options for efficient document storage and retrieval when integrating external knowledge sources with LLMs.

How do I retrieve relevant documents and generate responses based on combined information?▼

You can retrieve relevant documents and generate responses by querying the stored vector embeddings. The system facilitates retrieval of relevant context and generates responses based on combined information from external knowledge sources and the LLM.

Do I need langchain_openai and langchain_core dependencies to use this Skill?▼

Yes, you need langchain_openai, langchain_community, and langchain_core dependencies to use this Skill. These libraries provide the foundational framework required to build and run the Retrieval Augmented Generation systems.

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