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JosephRobles23
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langchain-rag

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

Updated Aug 27, 2026
Search Tags:#embeddings#langchain#llm#rag#retrieval#vector store
One-click install
npx skills add https://github.com/JosephRobles23/Vora.IA --skill langchain-rag-josephrobles23
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/JosephRobles23/Vora.IA/tree/main/backend/.windsurf/skills/langchain-rag
Command: npx skills add https://github.com/JosephRobles23/Vora.IA --skill langchain-rag-josephrobles23

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the process of building Retrieval-Augmented Generation (RAG) systems, enabling LLMs to access and utilize external knowledge bases for more informed and context-aware responses.

Core Features & Use Cases

  • Document Loading: Ingest data from various sources like PDFs, web pages, and directories.
  • Text Splitting: Efficiently chunk documents for optimal embedding and retrieval.
  • Embeddings & Vector Stores: Supports multiple embedding models and vector store solutions (Chroma, FAISS, Pinecone) for storing and querying document embeddings.
  • Retrieval Strategies: Implements similarity search and Maximal Marginal Relevance (MMR) for diverse and relevant results.
  • Use Case: Integrate this Skill to allow your AI assistant to answer questions based on your company's internal documentation, ensuring accurate and up-to-date information.

Quick Start

Use the langchain-rag skill to create a basic RAG pipeline by loading documents, splitting them, creating embeddings, storing them in an in-memory vector store, and then retrieving relevant documents to answer a 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 system with LangChain to query my internal PDFs?▼

To build a RAG system with LangChain, load documents from sources like PDFs, split the text into chunks, generate embeddings, and store them in a vector store for retrieval. This enables LLMs to answer questions using your internal documentation.

What text splitting strategies work best for creating document embeddings?▼

Text splitting prepares documents for optimal embedding and retrieval by chunking them into manageable pieces. Efficient text splitting ensures that the generated embeddings accurately capture context for similarity search and MMR retrieval.

Does LangChain RAG support both FAISS and Pinecone vector stores?▼

LangChain RAG supports multiple vector store implementations including FAISS, Pinecone, and Chroma. This allows you to store and query document embeddings using your preferred vector database solution for context retrieval.

What is the difference between similarity search and MMR for context retrieval?▼

Similarity search retrieves documents based on closest vector distance, while Maximal Marginal Relevance (MMR) retrieves diverse and relevant results. MMR reduces redundancy by balancing relevance with diversity among the retrieved document chunks.

Can I ingest web pages and directories as document sources for my LLM?▼

You can ingest data from various document sources including web pages, directories, and PDFs. Loading data from these external sources allows the LLM to access external knowledge bases for more context-aware responses.

What do I need to set up a basic Retrieval-Augmented Generation pipeline?▼

Setting up a basic Retrieval-Augmented Generation pipeline requires loading documents, splitting text, creating embeddings, and storing them in an in-memory vector store. You then retrieve relevant documents to answer queries.

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