AIknowlEDGE

AIknowlEDGE is a desktop application built with Electron.js and Python FastAPI to showcase Disconnected Containers.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

Azure AI containers gives you the flexibility to run Azure AI services locally in containers. Connected containers run locally in your environment and send usage information to the cloud for billing. Disconnected containers are intended for scenarios where no connectivity with the cloud is needed for the containers to run.

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Built With

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Getting Started

Follow these steps to run the project locally.

Prerequisites

• Python 3.12+
• Docker
• VS Code
• Ollama

Installation

1. Clone the repository:

   git clone https://github.com/Azure-Samples/AI-knowlEDGE.git
   cd AI-knowlEDGE
   

2. Install Docker and start. Then open the cmd and pull the container by running the following command:

   docker pull mcr.microsoft.com/azure-cognitive-services/textanalytics/summarization:cpu

3. Get your Cognitive Services keys and endpoints for the two products (available on Azure) - Azure Document Intelligence and Azure AI Language:

   AZURE_DOCUMENT_ANALYSIS_ENDPOINT
   AZURE_DOCUMENT_ANALYSIS_KEY
   LANGUAGE_ENDPOINT
   LANGUAGE_KEY
   

4. Create a folder on your C:/ drive named ExtractiveModel.

5. Download the SLMs for the Summarization Service. Start Docker and run:

   docker run -v C:\ExtractiveModel:/models mcr.microsoft.com/azure-cognitive-services/textanalytics/summarization:cpu downloadModels=ExtractiveSummarization billing=LANGUAGE_ENDPOINT apikey=LANGUAGE_KEY
   

6. Set up the Python environment and install dependencies:

   cd backend
   python -m venv venv
   venv\Scripts\activate  # On Linux use `source venv/bin/activate`
   pip install -r requirements.txt

7. Create a docker-compose.yml with the following:

   version: "3.9"
   services:
     azure-form-recognizer-read:
       container_name: azure-form-recognizer-read
       image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
       environment:
         - EULA=accept
         - billing=<document-intelligence-endpoint>
         - apiKey=<document-intelligence-key>
       ports:
         - "5000:5000"
       networks:
         - ocrvnet
   
     textanalytics:
       image: mcr.microsoft.com/azure-cognitive-services/textanalytics/summarization:cpu
       environment:
         - eula=accept
         - rai_terms=accept
         - billing=<language-endoint>
         - apikey=<language-key> 
       volumes:
         - "C:\\ExtractiveModel:/models"
       ports:
         - "5001:5000"
   
   networks:
     ocrvnet:
       driver: bridge

8. Create the container by running:

   docker-compose up

9. Make a .env file:

   AZURE_DOCUMENT_ANALYSIS_ENDPOINT=http://localhost:5000
   AZURE_DOCUMENT_ANALYSIS_KEY=<document-intelligence-key>
   LANGUAGE_ENDPOINT=http://localhost:5001
   LANGUAGE_KEY=<language-key>
   

10. Download Ollama and install at least one SLM and one embedding model:

    ollama pull phi3
    ollama pull nomic-embed-text

11. Start the application from VS Code: F5 or Run > Start Debugging

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Debugging

1. Start the FastAPI backend:

   cd backend
   uvicorn main:app --port 8000

2. Start the Streamlit app:

   cd frontend
   streamlit run app.py --server.port=8501

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Roadmap

• Other Containers Integration
  • Speech service
  • Translation service
• Other Use Cases Integration
• Packaging
  • Single-click installation
  • Cross-platform installation

See the open issues for proposed features and known issues.

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Contributing

Contributions make open source great. We appreciate all contributions.

1. Fork this repo
2. Create a Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more info.

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Contact

• Raoui Lassoued LinkedIn
• Serge Retkowsky LinkedIn
• Farid El Attaoui LinkedIn
• Alibek Jakupov @ajakupov1 LinkedIn

Project Link: AIKnowlEDGE

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Acknowledgments

• Microsoft France

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