> For the complete documentation index, see [llms.txt](https://karini-ai.gitbook.io/karini-ai-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://karini-ai.gitbook.io/karini-ai-documentation/recipes/graph-rag-recipe.md).

# Graph RAG Recipe

Karini AI’s Graph RAG recipes enhance generative AI applications by incorporating graph-based Retrieval-Augmented Generation (RAG) workflows. Unlike traditional RAG pipelines that rely on unstructured text retrieval, Graph RAG integrates semantic metadata extraction into structured graph databases like Neo4j or Amazon Neptune, creating a knowledge graph that captures relationships between entities and concepts.

By leveraging this structured knowledge graph, Graph RAG enables Large Language Models (LLMs) to retrieve not just relevant information but also semantically linked context, resulting in more accurate and interpretable responses.

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