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langchain-rag

Assemble end-to-end RAG pipelines with LangChain loaders, splitters, embeddings, and vector stores.

7|1|Updated Mar 15, 2026
Search Tags:#embeddings#langchain#rag#retrieval-augmented-generation#vector-stores#text-splitting#document-loaders
One-click install
npx skills add https://github.com/Harmeet10000/skills --skill langchain-rag-harmeet10000
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/Harmeet10000/skills/tree/main/skills/ai-ml/langchain-rag
Command: npx skills add https://github.com/Harmeet10000/skills --skill langchain-rag-harmeet10000

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables building robust retrieval-augmented generation pipelines by knitting together doc loaders, text splitting, embeddings, and vector stores for LLM grounding.

Core Features & Use Cases

  • Document loaders ingest data from files, web pages, and directories to supply grounding material.
  • Text splitters break documents into chunks suitable for embedding and efficient search.
  • Embeddings and vector stores enable scalable indexing, retrieval, and ranking across InMemory, FAISS, Chroma, and Pinecone.
  • Use Case: Ground an assistant with a knowledge base built from internal docs to answer questions with sources.

Quick Start

Build a minimal end-to-end RAG pipeline using LangChain to load documents, split text, embed, store, and retrieve.

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 pipeline with LangChain?▼

To build a retrieval-augmented generation pipeline with LangChain, you assemble modular components like document loaders, text splitters, embeddings, and vector stores to ingest, chunk, index, and retrieve external data for grounding LLM answers.

What is the best way to split text into chunks for embeddings?▼

The best way to split text for embeddings is using text splitters to break documents into chunks suitable for efficient search and scalable indexing across vector stores like FAISS, Chroma, or Pinecone.

Can I use FAISS, Chroma, and Pinecone as vector stores for RAG?▼

Yes, you can use FAISS, Chroma, and Pinecone as vector stores for retrieval-augmented generation. These integrations enable scalable indexing, retrieval, and ranking of embedded document chunks.

Do I need document loaders to ingest files and web pages for LLM grounding?▼

Yes, you need document loaders to ingest files, web pages, and directories for LLM grounding. They supply the external grounding material required for retrieval-augmented generation pipelines.

When should I use retrieval-augmented generation for a knowledge base?▼

You should use retrieval-augmented generation for a knowledge base when you need to ground LLM answers with external documents. It ensures responses are sourced from internal docs, web pages, or specific files rather than relying solely on model training data.

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