LangChain RAG Pipeline

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.