langchain-rag

What problem does it solve?

This Skill enables the creation of powerful Retrieval Augmented Generation (RAG) systems, allowing Large Language Models (LLMs) to access and utilize external knowledge sources for more informed and context-aware responses.

Core Features & Use Cases

• End-to-End RAG Pipeline: Manages the entire process from document loading and splitting to embedding, storage, and retrieval.
• Flexible Component Selection: Supports various document loaders, text splitters, embedding models, and vector stores (e.g., Chroma, Pinecone).
• Use Case: Enhance a customer support chatbot by allowing it to retrieve information from your company's knowledge base (FAQs, product manuals) to answer user queries accurately.

Quick Start

Use the langchain-rag skill to set up a basic RAG pipeline with in-memory storage and OpenAI embeddings.