skills.rest
brivaro
brivaroCOMMUNITY
Explore all skills by this author →
GitHub

langchain-rag

Build RAG pipelines with document loaders, splitters, and vector stores.

Updated Mar 8, 2026
Search Tags:#embeddings#langchain#llm#rag#retrieval#vector store
One-click install
npx skills add https://github.com/brivaro/brivaro-ai-wizard --skill langchain-rag-brivaro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/brivaro/brivaro-ai-wizard/tree/main/skills/langchain-rag
Command: npx skills add https://github.com/brivaro/brivaro-ai-wizard --skill langchain-rag-brivaro

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 AI agents to access and utilize external knowledge bases for more informed responses.

Core Features & Use Cases

  • End-to-End RAG Pipeline: Covers document loading, splitting, embedding, vector storage, retrieval, and generation.
  • Vector Store Integration: Supports various vector stores like Chroma, FAISS, and Pinecone for efficient data management.
  • Use Case: Integrate this Skill into your chatbot to allow it to answer questions based on your company's internal documentation, providing accurate and context-aware responses.

Quick Start

Use the langchain-rag skill to set up a basic RAG pipeline by loading documents, splitting them, embedding, storing, retrieving, and generating a response.

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 with LangChain for document retrieval?▼

A RAG pipeline enables an LLM to access external knowledge bases for more informed responses. It works by loading documents, splitting text, generating embeddings, storing vectors, retrieving relevant chunks, and feeding them to the LLM.

Can I use FAISS or Pinecone as a vector store for my RAG system?▼

Yes, you can use FAISS or Pinecone as a vector store for your RAG system. This Skill supports various vector stores including Chroma, FAISS, and Pinecone for efficient embedding management and retrieval.

What is the best way to make a chatbot answer questions from internal company documentation?▼

The best way is to integrate a RAG pipeline that ingests your internal documentation, stores it in a vector database, and retrieves relevant context to generate accurate, context-aware chatbot responses.

Do I need text splitters and embedding models to start a RAG pipeline?▼

Yes, text splitters and embedding models are required to process documents into chunks and convert them into vectors for storage and retrieval within the RAG pipeline.

How does document ingestion work when setting up retrieval-augmented generation?▼

Document ingestion for retrieval-augmented generation involves loading files, splitting them into text chunks, and embedding those chunks into vectors. These vectors are then stored in a vector database for later retrieval.

Related Skills

Explore More Software Engineering Skills→
patornpatorn

langchain-rag

Build end-to-end RAG pipelines with LangChain document loaders, embeddings, and vector stores.

Community
Advanced
Godhand-ArnavGodhand-Arnav

rag-architect

Design and implement end-to-end RAG pipelines for context-aware LLM responses.

Community
Advanced
hochoa13hochoa13

qdrant-vector-search

Provides scalable vector similarity search and hybrid search with metadata filtering for RAG systems.

Community
Advanced
truongnattruongnat

fullstack-rag-pro

Construct a RAG pipeline with document ingestion, embeddings, vector querying, and LLM synthesis.

Community
Advanced