RAG Patterns

What problem does it solve?

This Skill provides expert patterns to build robust and high-performing Retrieval Augmented Generation (RAG) systems, ensuring accurate and relevant information retrieval for your LLM applications.

Core Features & Use Cases

• Document Ingestion: Load, parse, and chunk various document types (PDF, TXT) while preserving metadata.
• Embedding & Indexing: Store document embeddings efficiently using PostgreSQL with pgvector.
• Hybrid Search: Combine keyword (BM25) and semantic (dense vector) search for superior retrieval accuracy.
• Re-ranking: Enhance precision by re-ranking candidate documents using cross-encoders or APIs like Cohere.
• Evaluation: Measure RAG system performance with RAGAS metrics for faithfulness, relevancy, and recall.
• Use Case: Building a legal AI assistant that can accurately answer questions about complex contracts by retrieving and synthesizing information from a large corpus of legal documents.

Quick Start

Ingest documents from a list of file paths using the ingest_documents function.