When I first came across GraphRAG – all the documentation pointed to this solution accelerator. While that’s great, to give it a go I wanted something simpler. After a bit of digging I finally came across this article.
There’s lots of setup options but I’m running with Azure Open AI
Prerequisites
- At the time of writing needed Python 3.11. See here for easy way to switch versions
- Install the Python package
- Azure Open AI key
- Text Embedding Deployment
- Model Deployment
Settings
This one got a bit tricky but here’s what worked for me.
model_supports_json: false – the default file generates true and for me would fail on creating community reports step until i tried this as false
api_version: this doesn’t seem to the be what you get from your deployment – I changed this back to the default and it worked
api_base: get this from the endpoint setting in azure portal
model and deployment_name:get these from model name and name under deployments in azure ai studio
Running the index pipeline
python -m graphrag.index --root ./ragtestOnce you run successfully it will look like below. An output folder gets created with artifacts and reports. Under reports there is a log file which is where you need to go looking if you encounter any errors.
Queries
When I first tried this it would not work at all. I upgraded to v0.2.2 and suddenly queries started working.