ai-modules 0.1.0

A flexible and extensible framework for managing and processing various types of data objects using AI techniques.

Object Data AI System

Overview

The Object Data AI System is a flexible and extensible framework for managing, processing, and transforming various types of data objects using AI techniques. It provides a RESTful API built with FastAPI for interacting with the system.

Project Structure

/
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── api.py
│   ├── app.py
│   └── models/
│       ├── __init__.py
│       └── models.py
├── Dockerfile
└── docker-compose.yml

• /app/: Contains the main application code
  • __init__.py: Initializes the app package
  • main.py: Entry point of the application
  • api.py: Defines the API routes
  • app.py: Creates and configures the FastAPI application
  • models/: Contains the data models
    • __init__.py: Initializes the models package
    • models.py: Defines the data object models and system components
• Dockerfile: Defines the Docker image for the application
• docker-compose.yml: Defines the services for running the application

Key Components

1. Data Objects: The system supports various types of data objects, including:

   • TextData: For textual content
   • MediaData: For images, audio, and video content
   • CodeData: For source code content

2. ObjectDataAISystem: The core system that manages data objects, transformers, and storage.

3. DataObjectFactory: Responsible for creating appropriate data objects based on the input type.

4. TransformerRegistry: Manages a collection of transformers that can be applied to data objects.

5. DataLake: Provides storage and retrieval capabilities for data objects.

6. API: A RESTful API for interacting with the system, including endpoints for creating, retrieving, processing, and transforming data objects.

Setup and Running

Prerequisites

• Docker
• Docker Compose

Running the Application

1. Clone the repository to your local machine.

2. Navigate to the root directory of the project.

3. Build and run the Docker containers:

   docker-compose up --build
   

4. The API will be available at http://localhost:8000.

API Usage

The API provides the following main endpoints:

• POST /objects/: Create a new data object
• GET /objects/{obj_id}: Retrieve a data object
• POST /objects/{obj_id}/process: Process a data object
• POST /objects/{obj_id}/transform: Apply a transformer to a data object
• GET /transformers/: List available transformers
• POST /transformers/: Register a new transformer

For detailed API documentation, visit http://localhost:8000/docs when the application is running.

Extending the System

To extend the system:

1. Add new data object types in app/models/models.py.
2. Implement new transformers and register them with the TransformerRegistry.
3. Extend the ObjectDataAISystem with new processing capabilities.
4. Add new API endpoints in app/api.py as needed.

Proto Buffer (gRPC)

To use this proto file:

1. Save it as object_data_ai.proto in your project directory.
2. Use the protobuf compiler (protoc) to generate the necessary code for your chosen programming language.
3. Implement the service defined in the proto file in your backend.
4. Use the generated client code to interact with your service from other parts of your application or from separate client applications.

For Python, you might generate the code like this:

protoc -I=. --python_out=. --grpc_python_out=. object_data_ai.proto

This will generate object_data_ai_pb2.py (containing message classes) and object_data_ai_pb2_grpc.py (containing service classes).

Remember to install the necessary gRPC and protobuf libraries in your Python environment:

pip install grpcio grpcio-tools

Contributing

Contributions to the Object Data AI System are welcome. Please ensure that your code adheres to the project's coding standards and include appropriate tests for new features.

License