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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
3 releases in the last 90 days, the latest 29 days ago, with 51 contributors on GitHub.
Use Cases & Benefits
- Implements a modular graph-based Retrieval-Augmented Generation system to extract structured data from unstructured text using large language models.
- Enhances LLM reasoning by integrating knowledge graph memory structures for more meaningful and context-aware outputs.
- Use for building advanced data pipelines that transform narrative private data into structured knowledge graphs for improved LLM querying.
- Use for fine-tuning prompt strategies to optimize retrieval and generation performance on domain-specific datasets.
- Use for research and experimentation with retrieval-augmented generation techniques leveraging graph-based memory in Python.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.
To learn more about GraphRAG and how it can be used to enhance your LLM's ability to reason about your private data, please visit the Microsoft Research Blog Post.
To get started with the GraphRAG system we recommend trying the command line quickstart.
This repository presents a methodology for using knowledge graph memory structures to enhance LLM outputs. Please note that the provided code serves as a demonstration and is not an officially supported Microsoft offering.
⚠️ Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
3.9K+156
Open issues
61-4
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| D4Vinci/Scrapling | 101.1K | +6.3K | 75 | 6 days ago |
| thedotmack/claude-mem | 115.8K | +4.7K | 75 | today |
| heygen-com/hyperframes | 82.3K | +4.0K | 75 | today |
| harry0703/MoneyPrinterTurbo | 142.0K | +3.8K | 75 | 3 days ago |
| github/spec-kit | 149.2K | +2.6K | 75 | today |
| microsoft/graphrag | 36.6K | — | 63 | 29 days ago |
| Category median | 115.8K | +4.0K | 75 | — |
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