Skip to content

get_started.md

Latest commit

 

History

History
131 lines (85 loc) · 4.37 KB

get_started.md

File metadata and controls

131 lines (85 loc) · 4.37 KB

Getting Started

⚠️ GraphRAG can consume a lot of LLM resources! We strongly recommend starting with the tutorial dataset here until you understand how the system works, and consider experimenting with fast/inexpensive models first before committing to a big indexing job.

Requirements

Python 3.10-3.12

The following is a simple end-to-end example for using GraphRAG on the command line after installing from pypi.

It shows how to use the system to index some text, and then use the indexed data to answer questions about the documents.

Install GraphRAG

To get started, create a project space and python virtual environment to install graphrag.

Create Project Space

mkdir graphrag_quickstart
cd graphrag_quickstart
python -m venv .venv

Activate Python Virtual Environment - Unix/MacOS

source .venv/bin/activate

Activate Python Virtual Environment - Windows

.venv\Scripts\activate

Install GraphRAG

python -m pip install graphrag

Initialize GraphRAG

To initialize your workspace, first run the graphrag init command.

graphrag init

This will create two files, .env and settings.yaml, and a directory input, in the current directory.

  • input Location of text files to process with graphrag.
  • .env contains the environment variables required to run the GraphRAG pipeline. If you inspect the file, you'll see a single environment variable defined, GRAPHRAG_API_KEY=<API_KEY>. Replace <API_KEY> with your own OpenAI or Azure API key.
  • settings.yaml contains the settings for the pipeline. You can modify this file to change the settings for the pipeline.

Download Sample Text

Get a copy of A Christmas Carol by Charles Dickens from a trusted source:

curl https://www.gutenberg.org/cache/epub/24022/pg24022.txt -o ./input/book.txt

Set Up Workspace Variables

Using OpenAI

If running in OpenAI mode, you only need to update the value of GRAPHRAG_API_KEY in the .env file with your OpenAI API key.

Using Azure OpenAI

In addition to setting your API key, Azure OpenAI users should set the variables below in the settings.yaml file. To find the appropriate sections, just search for the models: root configuration; you should see two sections, one for the default chat endpoint and one for the default embeddings endpoint. Here is an example of what to add to the chat model config:

type: chat
model_provider: azure
model: gpt-4.1
deployment_name: <AZURE_DEPLOYMENT_NAME>
api_base: https://<instance>.openai.azure.com
api_version: 2024-02-15-preview # You can customize this for other versions

Using Managed Auth on Azure

To use managed auth, edit the auth_type in your model config and remove the api_key line:

auth_type: azure_managed_identity # Default auth_type is is api_key

You will also need to login with az login and select the subscription with your endpoint.

Index

Now we're ready to index!

graphrag index

pipeline executing from the CLI

This process will usually take a few minutes to run. Once the pipeline is complete, you should see a new folder called ./output with a series of parquet files.

Query

Now let's ask some questions using this dataset.

Here is an example using Global search to ask a high-level question:

graphrag query "What are the top themes in this story?"

Here is an example using Local search to ask a more specific question about a particular character:

graphrag query \
"Who is Scrooge and what are his main relationships?" \
--method local

Please refer to Query Engine docs for detailed information about how to leverage our Local and Global search mechanisms for extracting meaningful insights from data after the Indexer has wrapped up execution.

Going Deeper