rag-systems

What problem does it solve?

This Skill addresses the challenge of building robust and efficient Retrieval-Augmented Generation (RAG) systems, enabling AI to leverage custom knowledge bases for more accurate and context-aware responses.

Core Features & Use Cases

• RAG Architecture: Understands and implements the core RAG flow from query to response.
• Document Processing: Offers various strategies for chunking text (fixed-size, semantic, markdown-aware).
• Embedding & Vector Stores: Integrates with popular embedding models (OpenAI, Sentence Transformers) and vector databases (Pinecone, ChromaDB, pgvector).
• Retrieval & Reranking: Implements advanced retrieval techniques like hybrid search and multi-query, along with reranking for relevance.
• Evaluation & Production: Provides methods for evaluating retrieval quality and implementing production patterns like caching and async pipelines.
• Use Case: Develop a customer support chatbot that can accurately answer questions based on a company's extensive product documentation by implementing a RAG system.

Quick Start

Use the rag-systems skill to implement a RAG pipeline for a given set of documents and a user query.