skills.rest
HyunjunJeon
HyunjunJeonCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

langchain-rag

Build retrieval-augmented generation pipelines with LangChain components.

Updated Aug 27, 2026
Search Tags:#embeddings#langchain#rag#vector-stores#text-splitting#document-loaders
One-click install
npx skills add https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1 --skill langchain-rag-hyunjunjeon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1/tree/main/Day-01/.agents/skills/langchain-rag
Command: npx skills add https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1 --skill langchain-rag-hyunjunjeon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a guided, end-to-end blueprint for building retrieval-augmented generation (RAG) pipelines using LangChain components, covering document loaders, text splitting, embeddings, and vector stores.

Core Features & Use Cases

  • Document Loaders: ingest data from files, web, and databases
  • Text Splitters: partition documents into chunks for efficient retrieval
  • Embeddings: convert text to vectors (OpenAI embeddings)
  • Vector Stores: store and search embeddings across FAISS, Chroma, Pinecone, and in-memory options
  • Use Case: Build a Q&A assistant that retrieves relevant docs and answers questions from a knowledge base.

Quick Start

Provide an end-to-end RAG setup by loading documents, splitting text, embedding, storing vectors, retrieving relevant docs, 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 an end-to-end RAG pipeline with LangChain?▼

To build an end-to-end RAG pipeline with LangChain, orchestrate document loaders, text splitters, embeddings, and vector stores to ingest, partition, and retrieve relevant documents before generating responses.

What is the best way to split documents for retrieval-augmented generation?▼

Splitting documents for retrieval-augmented generation involves using text splitters to partition large texts into smaller chunks, enabling efficient embedding generation and precise vector retrieval from knowledge bases.

Does LangChain RAG work with both local and hosted vector stores?▼

LangChain RAG works with both local and hosted vector stores, supporting deployment-ready integrations across FAISS, Chroma, Pinecone, and InMemory options for storing and searching embeddings.

Can I ingest data from webpages and databases for my RAG knowledge base?▼

You can ingest data from webpages and databases for your RAG knowledge base using LangChain document loaders, which prepare files, web content, and database records for text splitting and embedding.

How do OpenAI embeddings integrate with LangChain vector stores?▼

OpenAI embeddings integrate with LangChain vector stores by converting chunked text into vectors, which are then stored and searched within FAISS, Chroma, Pinecone, or InMemory databases for retrieval.

Related Skills

Explore More Software Engineering Skills→
bethanychamberlainbethanychamberlain
2

rag

Configure LibreChat's RAG pipeline for document ingestion and retrieval.

Community
Advanced
floflo777floflo777
33

rag-eval

Evaluate RAG systems using retrieval, generation, and latency metrics.

Community
Intermediate
wpsadiwpsadi

langchain-short-memory

Manage thread-level conversation history for LangChain/LangGraph chat agents.

Community
Intermediate
CodeAtCodeCodeAtCode
14

llama-index

Manage data ingestion, indexing, and retrieval for LLM applications with LlamaIndex.

Official
Advanced