rag-retrieval

What problem does it solve?

This Skill addresses the challenge of LLM hallucinations and lack of factual grounding by implementing advanced Retrieval-Augmented Generation (RAG) techniques. It ensures LLM responses are accurate, verifiable, and based on provided context.

Core Features & Use Cases

• Core RAG: Basic retrieval, context assembly, and citation generation.
• Hybrid Search: Combines semantic (vector) and keyword (BM25) search for comprehensive coverage.
• Advanced RAG Patterns: Implements HyDE, Agentic RAG, Multimodal RAG, Query Decomposition, and Reranking.
• Vector Database Integration: Supports PGVector for efficient hybrid search.
• Use Case: Building a customer support chatbot that answers questions based on product documentation, ensuring all answers are cited and factually accurate.

Quick Start

Use the rag-retrieval skill to implement a RAG pipeline that answers questions with inline citations.