Overview

Caution

DEPRECATED NEW FULL REVAMP COMING SOON. GraphFleet is an advanced implementation of GraphRAG from Microsoft, an important update is expected by the end of december

streamlit-github.mp4

GraphFleet

GraphFleet uses knowledge graphs to provide substantial improvements in question-and-answer performance when reasoning about complex information. It addresses limitations of traditional RAG approaches:

Roadmap

• Provide a FleetUI Design Kit and a quicker way of starting GraphFleet locally.
• Provide a Toddle interface ready to use for GraphFleet
• Add integrations of Composio
• Add integrations of LangSmith
• Add few selfhosting one click deploy solutions.
• Access GraphFleet through a secure and enterprise-ready Azure Cloud hosting version.
• And way more... 👀

Key Features

• Structured, hierarchical approach to Retrieval Augmented Generation.
• Knowledge graph extraction from raw text.
• Community hierarchy building.
• Hierarchical summarization.
• Enhanced reasoning capabilities for LLMs on private datasets.

Our current API Endpoints : https://agenticfleet.apidocumentation.com/reference#tag/search/POST/search/global

Contribute

• Leave us a star ♥
• Fork and contribute to the project
• 

Getting Started

Prerequisites

• Python 3.11

• Poetry

• Make sure to have a virtual environment manager such as virtualenv installed

Installation

1. Clone the repository:

   git clone https://github.com/Qredence/GraphFleet.git
   cd GraphFleet

2. Install the dependencies:

   poetry shell
   poetry install

Usage

1. Configuration: Environment Variables: Set up your environment variables in a .env file (refer to the .env.example file for available options). Key variables include:

Fill in the .env file in the root folder and the one in the graphfleet folder.

export GRAPHRAG_API_KEY="your_api_key_here"
export GRAPHRAG_API_BASE="<https://your-azure-openai-resource.openai.azure.com/>"
export GRAPHRAG_API_VERSION=""
export GRAPHRAG_DEPLOYMENT_NAME="your_deployment_name"
export GRAPHRAG_API_TYPE="azure_openai"
export GRAPHRAG_EMBEDDING_MODEL="text-embedding-ada-002"
export GRAPHRAG_LLM_MODEL="gpt-4"
export GRAPHRAG_DATA_PATH="./your_data_directory"
export GRAPHRAG_EMBEDDING_TYPE="azure_openai_embedding"
export GRAPHRAG_EMBEDDING_KEY="your_embedding_key_here"
export GRAPHRAG_EMBEDDING_ENDPOINT="<https://your-azure-openai-embedding-resource.openai.azure.com/>"
export GRAPHRAG_EMBEDDING_DEPLOYMENT_NAME="your_embedding_deployment_name"

settings.yaml: Customize GraphFleet's behavior further by modifying the settings.yaml file within the graphfleet directory.

1. Interacting with GraphFleet: settings.yaml: Customize GraphFleet's behavior further by modifying the settings.yaml file within the graphfleet directory. Jupyter Notebooks: Explore GraphFleet's capabilities with the provided notebooks: get-started-graphfleet.ipynb: A comprehensive guide to indexing your data and running basic queries. Local Search Notebook.ipynb: Demonstrates local search techniques. app.py (FastAPI Application): Run a Streamlit-powered web interface to interact with GraphFleet using a user-friendly chat-like interface.

Add your text files in ./graphfleet/input/ and run the auto_prompt function

!python -m graphrag.prompt_tune \
    --config ./graphfleet/settings.yaml \
    --root ./graphfleet \
    --no-entity-types \
    --output ./graphfleet/prompts

Data Indexing

Jupyter Notebook Guide: Follow the instructions provided in the get-started-graphfleet.ipynb notebook to learn how to index your data with GraphFleet. This notebook provides a hands-on experience for setting up your knowledge base.

! python -m graphrag.index \
    --verbose \
    --root ./graphfleet \
    --config ./graphfleet/settings.yaml

Recommended Run these notebook to get started with GraphFleet

Jupyter Notebooks: Explore GraphFleet's capabilities with the provided notebooks:

(Get Started Quickly.ipynb): A comprehensive guide to indexing your data and running basic queries. (Local Search Notebook.ipynb:) Demonstrates local search techniques.

(Global Search Notebook)

Running the API only (or run CLI commands for local search or global search)

To run the API, save the code in a file named api.py and execute the following command in your terminal:

uvicorn app:main --reload --port 8001 

Running Streamlit

To run the API, save the code in a file named api.py and execute the following command in your terminal:

streamlit run app/streamlit_app.py

Security

For details about our security policy, please see Security

License

This project is licensed under the Apache License 2.0. For the full license text, please see License

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