langchain-rag

What problem does it solve?

This Skill simplifies the process of building Retrieval-Augmented Generation (RAG) systems, enabling LLMs to access and utilize external knowledge bases for more informed and context-aware responses.

Core Features & Use Cases

• Document Loading: Ingest data from various sources like PDFs, web pages, and directories.
• Text Splitting: Efficiently chunk documents for optimal embedding and retrieval.
• Embeddings & Vector Stores: Supports multiple embedding models and vector store solutions (Chroma, FAISS, Pinecone) for storing and querying document embeddings.
• Retrieval Strategies: Implements similarity search and Maximal Marginal Relevance (MMR) for diverse and relevant results.
• Use Case: Integrate this Skill to allow your AI assistant to answer questions based on your company's internal documentation, ensuring accurate and up-to-date information.

Quick Start

Use the langchain-rag skill to create a basic RAG pipeline by loading documents, splitting them, creating embeddings, storing them in an in-memory vector store, and then retrieving relevant documents to answer a query.