GraphRAG

GraphRAG: Enhancing LLMs with Knowledge Graphs

GraphRAG is an open-source project by Microsoft Research, designed to enhance Large Language Model (LLM) outputs using knowledge graph memory structures. It's a modular data pipeline and transformation suite that extracts meaningful, structured data from unstructured text using the power of LLMs.

What is GraphRAG?

GraphRAG (Graph-based Retrieval-Augmented Generation) is a system that uses knowledge graphs to improve the reasoning capabilities of LLMs. By structuring information into a graph format, GraphRAG enables LLMs to access and process data more efficiently, leading to better and more accurate outputs.

How does GraphRAG work?

GraphRAG works by:

1. Extracting Data: It extracts data from unstructured text using LLMs.
2. Structuring Data: It transforms the extracted data into a knowledge graph.
3. Enhancing LLM Outputs: It uses the knowledge graph to enhance the outputs of LLMs.

Key Features:

• Modular Design: GraphRAG is designed with a modular architecture, allowing for easy customization and extension.
• Data Pipeline: It provides a complete data pipeline for extracting, transforming, and loading data into a knowledge graph.
• LLM Integration: It seamlessly integrates with LLMs to enhance their reasoning abilities.

How to use GraphRAG?

To get started with GraphRAG, follow these steps:

1. Installation: Clone the repository and install the necessary dependencies.
2. Initialization: Run graphrag init --root [path] --force to initialize the system.
3. Configuration: Configure the system to connect to your data sources and LLMs.
4. Indexing: Index your data to create the knowledge graph. Be aware that GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.
5. Prompt Tuning: Fine-tune your prompts to achieve the best possible results.

Why choose GraphRAG?

• Improved Reasoning: GraphRAG enhances the reasoning capabilities of LLMs, leading to more accurate and reliable outputs.
• Structured Data: It transforms unstructured text into structured knowledge graphs, making it easier to access and process data.
• Open-Source: GraphRAG is an open-source project, allowing for community contributions and customization.

Who is GraphRAG for?

GraphRAG is suitable for:

• Researchers: Who are exploring the use of knowledge graphs to enhance LLMs.
• Developers: Who are building applications that require advanced reasoning capabilities.
• Organizations: Who want to improve the accuracy and reliability of their LLM outputs.

Example Use Cases:

• Question Answering: Improve the accuracy of question answering systems by leveraging knowledge graphs.
• Data Integration: Integrate data from multiple sources into a unified knowledge graph.
• Knowledge Discovery: Discover new insights and relationships within your data.

Versioning: Always run graphrag init --root [path] --force between minor version bumps to ensure you have the latest config format. Run the provided migration notebook between major version bumps if you want to avoid re-indexing prior datasets. Note that this will overwrite your configuration and prompts, so backup if necessary.

Responsible AI: Please see RAI_TRANSPARENCY.md for responsible AI considerations.

By using knowledge graphs, GraphRAG enables LLMs to access and process data more efficiently, leading to better and more accurate results. This makes it a valuable tool for anyone looking to enhance the reasoning capabilities of their LLMs.