GraphRAG: a blueprint for knowledge-graph question answering over your documents

[u/Sea_Anteater6139]

Hi everyone,

I've recently finished the first version of Agentic GraphRAG Blueprint, a reference architecture for question answering over large document collections.

Instead of plain chunk retrieval, it builds a knowledge graph combined with vector search, so answers can connect facts across documents.

Key features:

• Incremental ingestion - unchanged files are skipped via content hashing, and community reports regenerate only for affected communities, keeping token costs low as the corpus grows.

• Hybrid search - local mode for fact-level answers, global mode for cross-document synthesis.

• Domain-agnostic LLM prompts - easily swapped via PROMPTS_PATH, with Leiden-based community detection.

• Deployment - run it locally with Docker or provision everything in the cloud with Terraform and CI/CD.

Link: https://github.com/sebastianbrzustowicz/Agentic-GraphRAG-Blueprint

I'm looking for any feedback.

[u/samurai618]

How is this reinforcementlearning related?

[u/Pretend-Paper4137]

Congratulations.  You need a new name because GraphRAG is already taken by a project that does exactly what you describe yours to do, but it came out years earlier.

[u/Sea_Anteater6139 → u/Pretend-Paper4137]

Oh, I didn't know that. I should have looked into similar solutions before implementing it. Thanks buddy

[u/Alarming-Rate9655]

Interesting take on GraphRAG, the incremental hashing to skip unchanged files is a smart touch for keeping costs in check.