uvicorn app.main:app --reload

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

API Documentation

Once the server is running, you can access:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

API Tags

The API endpoints are organized into the following tags for better navigation and documentation:

• users: User authentication and management endpoints
• items: General item management endpoints
• tasks: Task management and CRUD operations
• llm: LLM integration and AI-powered features

Key Endpoints

Authentication (users tag)

• POST /users/register - Register a new user
• POST /users/login - User login and JWT token generation

Tasks (tasks tag)

• POST /tasks/ - Create a new task
• GET /tasks/ - Get all tasks with pagination
• GET /tasks/{task_id} - Get a specific task
• PUT /tasks/{task_id} - Update a task
• DELETE /tasks/{task_id} - Delete a task
• GET /tasks/user/{user_id} - Get tasks by user
• GET /tasks/status/{status} - Get tasks by status
• GET /tasks/search/ - Vector search tasks by title or description

Items (items tag)

• POST /items/ - Create a new item
• GET /items/ - Get all items
• GET /items/{item_id} - Get a specific item
• PUT /items/{item_id} - Update an item
• DELETE /items/{item_id} - Delete an item

LLM Integration (llm tag)

• POST /llm/chat - Chat completion with Ollama
• POST /llm/completion - Text completion with Ollama
• GET /llm/models - List available Ollama models
• POST /llm/models/pull - Pull a new model
• DELETE /llm/models/{model_name} - Delete a model

Vector Search

The application now includes vector search capabilities for tasks using:

• ChromaDB: Vector database for storing and searching task embeddings
• sentence-transformers: Pre-trained models for generating text embeddings
• all-MiniLM-L6-v2: Default model for encoding task descriptions and queries

How Vector Search Works

1. When tasks are created, their titles and descriptions are encoded into vector embeddings
2. These embeddings are stored in ChromaDB for efficient similarity search
3. When searching, the query is also encoded and compared against stored embeddings
4. Results are ranked by semantic similarity, providing more relevant matches than traditional text search

Search Endpoint

GET /tasks/search/?query=your_search_query

The search endpoint accepts a query parameter and returns tasks ranked by semantic similarity to the search query.

Project Structure

task-manager-backend/
├── alembic/
│   ├── versions/         # Database migration files
│   ├── env.py            # Alembic environment configuration
│   ├── script.py.mako    # Migration script template
│   └── README
├── app/
│   ├── routers/          # API route modules
│   │   ├── items.py      # Item-related endpoints
│   │   ├── llm.py        # LLM integration endpoints
│   │   ├── tasks.py      # Task management endpoints
│   │   └── users.py      # User authentication endpoints
│   ├── auth.py           # Authentication utilities
│   ├── config.py         # Application configuration
│   ├── crud.py           # Database CRUD operations
│   ├── database.py       # Database connection and vector DB setup
│   ├── main.py           # FastAPI application entry point
│   ├── models.py         # SQLAlchemy database models
│   └── schemas.py        # Pydantic schemas for request/response
├── alembic.ini           # Alembic configuration
├── requirements.txt      # Python dependencies
└── README.md             # This file

Configuration

The application can be configured through environment variables. Key configuration options:

• DATABASE_URL: Database connection string (default: sqlite:///./test.db)
• SECRET_KEY: JWT secret key (default: CHANGE_THIS - change in production!)
• OLLAMA_HOST: Ollama server host (default: http://localhost:11434)
• OLLAMA_MODEL: Default Ollama model (default: llama3.2)
• OLLAMA_TIMEOUT: Ollama request timeout in seconds (default: 30)

Database Migrations

To create new database migrations:

1. Make changes to your models in app/models.py
2. Generate a migration:

alembic revision --autogenerate -m "Description of changes"

3. Apply the migration:

alembic upgrade head

To rollback migrations:

alembic downgrade -1

Development

Running Tests

pytest

Code Style

The project follows PEP 8 coding standards. You can check code style with:

flake8 app/

Adding New Features

1. Add new models in app/models.py
2. Create corresponding schemas in app/schemas.py
3. Add CRUD operations in app/crud.py
4. Create API endpoints in appropriate router files under app/routers/
5. Generate and apply database migrations

Security Notes

• Change the default SECRET_KEY in production
• Use HTTPS in production environments
• Implement rate limiting for API endpoints
• Validate all user inputs
• Keep dependencies updated

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License.