3b09df6e07
* Migrate towards using static output directories
- Fixes load_config eagering resolving directories.
Directories are only resolved when the output
directories are local.
- Add support for `--output` and `--reporting` flags
for index CLI. To achieve previous output structure
`index --output run1/artifacts --reports run1/reports`.
- Use static output directories when initializing
a new project.
- Maintains backward compatibility for those using
timestamp outputs locally.
* fix smoke tests
* update query cli to work with static directories
* remove eager path resolution from load_config. Support CLI overrides that can be resolved.
* add docs and output logs/artifacts to same directory
* use match statement
* switch back to if statement
---------
Co-authored-by: Alonso Guevara <alonsog@microsoft.com>
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| .github | ||
| .semversioner | ||
| .vscode | ||
| docsite | ||
| examples | ||
| examples_notebooks | ||
| graphrag | ||
| scripts | ||
| tests | ||
| .gitignore | ||
| .vsts-ci.yml | ||
| CHANGELOG.md | ||
| CODE_OF_CONDUCT.md | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| cspell.config.yaml | ||
| DEVELOPING.md | ||
| dictionary.txt | ||
| LICENSE | ||
| poetry.lock | ||
| pyproject.toml | ||
| RAI_TRANSPARENCY.md | ||
| README.md | ||
| SECURITY.md | ||
| SUPPORT.md | ||
| v1-breaking-changes.md | ||
GraphRAG
👉 Use the GraphRAG Accelerator solution
👉 Microsoft Research Blog Post
👉 Read the docs
👉 GraphRAG Arxiv
Overview
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.
Quickstart
To get started with the GraphRAG system we recommend trying the Solution Accelerator package. This provides a user-friendly end-to-end experience with Azure resources.
Repository Guidance
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.
Diving Deeper
- To learn about our contribution guidelines, see CONTRIBUTING.md
- To start developing GraphRAG, see DEVELOPING.md
- Join the conversation and provide feedback in the GitHub Discussions tab!
Prompt Tuning
Using GraphRAG with your data out of the box may not yield the best possible results. We strongly recommend to fine-tune your prompts following the Prompt Tuning Guide in our documentation.
Responsible AI FAQ
- What is GraphRAG?
- What can GraphRAG do?
- What are GraphRAG’s intended use(s)?
- How was GraphRAG evaluated? What metrics are used to measure performance?
- What are the limitations of GraphRAG? How can users minimize the impact of GraphRAG’s limitations when using the system?
- What operational factors and settings allow for effective and responsible use of GraphRAG?
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.