This GitHub organization collects relevant projects and resources like the pattern catalogue (repository) and more.

Retrieval Augmented Generation (RAG) is a way to ground Large Language Models (LLMs) in factual data to avoid and reduce hallucinations. The user's question will be used to retrieve relevant information from one or more data sources. These facts are then augment together the question as the only ground truth source of information in the context to the LLM to to generate the answer.

GraphRAG are retrieval mechanisms that use graph structures to provide more fine-grained and relevant contextual information than a plan text search (or vector search) would. As it can utlize the rich representation of knowledge about many areas in a knowledge graph.

Contributions are very welcome.

A collection of resources around GraphRAG a set of advanced GenAI RAG (Retrieval Augmented Generation) patterns.