Author(s): Vatsal Saglani

Originally published on Towards AI.

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Disclaimer: This implementation of GraphRAG is inspired by the paper From Local to Global: A Graph RAG Approach to Query-Focused Summarization by Darren Edge et. al. The code is not entirely similar to the paper’s codebase, though the prompts for certain tasks are taken from the paper’s codebase.

This is the second blog in a multi-part blog series series about GraphRAG. In this blog series, our goal is to achieve the following,

Understand the fundamentals of GraphRAGThe need for GraphRAG: GraphRAG vs. Semantic/Keyword-based RAGImplement GraphRAG components from scratch in PythonApply GraphRAG for Content-Based Movie Recommendation: “GraphRAG4Reccomendation”Use GPT-4o-Mini for creating the graph and providing recommendations

We will achieve the following output by the end of this multi-part blog series.

Implementation Output by AuthorVideo by Author

The following is the GitHub repository for the GraphRAG4Rec codebase.

A naive implementation of GraphRAG for Movie Recommendation on IMDB Top 1000 movies dataset. …

github.com

Part 1: Introduction to GraphRAGPart 3: Extract — entities, relations, and claims to build the graph (coming soon)Part 4: Batch communities and prepare summarization reports (coming soon)Part 5: Query processing and recommendation generation via map-reduce prompting (coming soon)

We’ll quickly understand the need for a graph-based retrieval augmented generation (GraphRAG) approach. We’ll compare this approach with… Read the full blog for free on Medium.

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Published via Towards AI