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LangChain RAG Pipeline

Orchestrate document loading, splitting, embedding, and vector store management for RAG pipelines.

1.1k|86|Updated Jan 22, 2026
Search Tags:#embeddings#langchain#llm#rag#vector store#document retrieval
One-click install
npx skills add https://github.com/langchain-ai/langchain-skills --skill langchain-rag-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: LangChain RAG Pipeline
Source: https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langchain-rag
Command: npx skills add https://github.com/langchain-ai/langchain-skills --skill langchain-rag-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for building Retrieval Augmented Generation (RAG) systems, addressing common challenges in document loading, splitting, embedding, and vector storage.

Core Features & Use Cases

  • End-to-End RAG: Covers the full pipeline from document ingestion to LLM generation.
  • Flexible Components: Supports various document loaders (PDF, web, directory), text splitters, embedding models, and vector stores (Chroma, FAISS, Pinecone).
  • Problem Solving: Offers solutions for chunk size/overlap issues, embedding dimension mismatches, and FAISS deserialization.
  • Use Case: Quickly set up a RAG system to answer questions based on your company's internal documentation.

Quick Start

Use the LangChain RAG Pipeline skill to create a basic RAG setup by loading documents, splitting them, embedding, storing, retrieving, and generating a response.

Frequently Asked Questions about LangChain RAG Pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG pipeline with LangChain for internal documents?▼

To build a RAG pipeline with LangChain, you load documents from sources like PDFs or directories, split the text into chunks, generate embeddings, store them in a vector database, and retrieve them to augment LLM generation. This skill orchestrates that full workflow in Python or TypeScript.

What is the best way to fix embedding dimension mismatch in a RAG system?▼

Fixing embedding dimension mismatch in a RAG system requires ensuring the embedding model's output dimensions align exactly with your vector store configuration. This skill addresses common pipeline issues including chunking strategies, embedding dimension consistency, and vector store persistence.

Can I use FAISS and Pinecone as vector stores for document retrieval?▼

Yes, you can use FAISS and Pinecone as vector stores for document retrieval. This skill supports flexible vector store components including Chroma, FAISS, and Pinecone, allowing you to manage vector storage and handle issues like FAISS deserialization.

How do I handle chunk size and overlap when splitting text for embeddings?▼

Handling chunk size and overlap when splitting text for embeddings involves configuring text splitters to divide documents into manageable segments. This skill provides solutions for common chunking strategies to optimize retrieval accuracy and ensure proper document processing.

Does LangChain RAG support TypeScript implementations for loading PDFs?▼

Yes, LangChain RAG supports TypeScript implementations for loading PDFs and other document sources. This skill provides comprehensive pipeline orchestration for both Python and TypeScript environments, covering document loading, splitting, embedding, and vector store management.

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