End To End Graphrag Implementation: Index text and query graphs

Understand the project

Practical overview

When it fits

Use the guide when you want a terminal-based walkthrough for setting up a GraphRAG project, indexing text into entity and relationship artifacts, and querying that material.

Who it is for

Developers learning to configure and use GraphRAG with Python and Azure OpenAI
Readers who want to index text files into graph artifacts and ask questions against the resulting data

Documented uses

Create a Python 3.10 Conda environment and install GraphRAG.
Configure LLM and embedding settings for Azure OpenAI deployments.
Index a text file into graph artifacts and submit local or global queries.

Before you choose

Requirements & limits

The setup depends on Python, Conda, terminal commands, and manual editing of .env and settings.yaml.
Azure OpenAI LLM and embedding deployments are part of the described workflow; the guide names GPT-4o and text-embedding-small as its recommended and example choices.
The repository provides a guide to the GraphRAG workflow rather than a hosted interface.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and query a GraphRAG project

Configure GraphRAG with Azure OpenAI, index a text file into graph artifacts, and issue a local or global query.

Before you start

Python 3.10 and Conda
VS Code or another terminal
GraphRAG installed in the Conda environment
Azure OpenAI deployments for an LLM and embeddings, plus an API key and endpoint
A .txt file to use as input

Steps

Create and activate a Conda environment with Python 3.10, upgrade pip and setuptools, and install GraphRAG. The guide suggests reinstalling gensim without cache and forcing reinstall if GraphRAG installation raises an error.
Initialize the project with the command below, then configure the .env API key and the LLM and embeddings sections in settings.yaml for Azure OpenAI. The guide recommends GPT-4o for the LLM and names text-embedding-small for embeddings.
Documented command
```
python -m graphrag.index --init --root .
```
Create an input folder, add a text file, and run the command below to generate graph artifacts.
Documented command
```
python -m graphrag.index --root .
```
Ask a question using the command below, choosing local or global.
Documented command
```
python -m graphrag.query --root . --method local/global "Your_Question"
```

Expected outcome

GraphRAG produces Parquet artifacts representing entities and relationships from the input text, and the query command returns an answer using the selected local or global method.

Guide limitations

The guide recommends Python 3.10 because it reports installation errors with the latest Python version.
The author reports GPT-35-turbo-instruct caused issues when creating graphs and recommends GPT-4o; this is the author's experience, not a compatibility guarantee.
The described answer check is a manual search for a word from the response in the source text, not a comprehensive evaluation method.

Documentation 1 ↗

Evidence and transparency

Sources & review

Official repository ↗

Source record details

Source availability: Active
Sources checked: 18 October 2026
Original website title: ApexIQ/End-to-End-Graphrag-implementation · Official homepage title · retained in its source language

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End To End Graphrag Implementation