exemplar-prompt-hub 0.1.10

A modern REST API service for managing and serving AI prompts

🚀 Exemplar Prompt Hub

A modern REST API service for managing and serving AI prompts. This service provides a centralized repository for storing, versioning, and retrieving prompts for various AI applications. It uses PostgreSQL as the database for robust and scalable data management.

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📑 Table of Contents

• Features
• Getting Started
  • Prerequisites
  • Installation
  • Quick Start with Docker
  • Manual Installation
• Running Tests
• Contributing
• License
• API Documentation
• API Usage Examples
• Project Structure
• Database Table Structure
• Updating Prompts with Versioning

✨ Features

For a detailed checklist of implemented and planned features, see FEATURES.md.

• RESTful API for prompt management
• Version control for prompts
• Tag-based prompt organization
• Metadata support for prompts
• Authentication and authorization
• Search and filtering capabilities

🛠️ Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• Git
• PostgreSQL (for database) (by default it uses sqlite as per .env.example)
• Docker and Docker Compose (for containerized setup)

Installation

Using pip

You can install the package directly from PyPI:

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install the package
pip install exemplar-prompt-hub

# Copy .env.example from the installed package
cp $(python -c "import site; print(site.getsitepackages()[0])")/exemplar_prompt_hub/.env.example .env

# Edit the .env file to configure your database and other settings

Or install from the source:

# Clone the repository
git clone https://github.com/yourusername/exemplar-prompt-hub.git
cd exemplar-prompt-hub

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install the package
pip install -e .

# Copy .env.example to .env
cp .env.example .env

# Edit .env to configure your database and other settings

After installation, you can use the following command:

• prompt-hub - Start the FastAPI server

Using Docker

The easiest way to get started is using Docker Compose:

1. Clone the repository:

   git clone https://github.com/yourusername/exemplar-prompt-hub.git
   cd exemplar-prompt-hub
   

2. Start the services:

   docker-compose up -d
   

   This will start:

   • FastAPI backend at http://localhost:8000
   • PostgreSQL database at localhost:5432

3. Access the services:

   • API Documentation: http://localhost:8000/docs

4. Stop the services:

   docker-compose down
   

Manual Installation

If you prefer to run the services manually:

1. Clone the repository:

   git clone https://github.com/yourusername/exemplar-prompt-hub.git
   cd exemplar-prompt-hub
   

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\\Scripts\\activate`
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Set up environment variables:

   • Copy .env.example to .env:

     cp .env.example .env
     

   • Edit .env to configure your database and other settings.

5. Start the application:

   uvicorn app.main:app --reload
   

Running Tests

To run the tests, use:

pytest

For detailed test coverage, use:

pytest --cov=app --cov-report=term-missing

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For detailed contribution guidelines, please refer to the CONTRIBUTING.md file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

📚 API Documentation

Once the server is running, you can access the interactive API documentation at:

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

🔄 API Usage Examples

Here are some example curl commands to interact with the API:

Create a Prompt

curl -X POST "http://localhost:8000/api/v1/prompts/" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "example-prompt",
    "text": "This is an example prompt text",
    "description": "A sample prompt for demonstration",
    "version": 1,
    "meta": {
      "author": "test-user",
      "category": "example"
    },
    "tags": ["example", "test"]
  }'

Get All Prompts

# Get all prompts
curl "http://localhost:8000/api/v1/prompts/"

# Get prompts with search
curl "http://localhost:8000/api/v1/prompts/?search=example"

# Get prompts with tag filter
curl "http://localhost:8000/api/v1/prompts/?tag=test"

# Get prompts with pagination
curl "http://localhost:8000/api/v1/prompts/?skip=0&limit=10"

Get a Specific Prompt

# Replace {prompt_id} with actual ID
curl "http://localhost:8000/api/v1/prompts/{prompt_id}"

Update a Prompt

curl -X PUT "http://localhost:8000/api/v1/prompts/{prompt_id}" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Updated prompt text",
    "description": "Updated description",
    "meta": {
      "author": "test-user",
      "category": "updated"
    },
    "tags": ["updated", "test"]
  }'

Delete a Prompt

curl -X DELETE "http://localhost:8000/api/v1/prompts/{prompt_id}"

📁 Project Structure

exemplar-prompt-hub/
├── app/
│   ├── api/
│   │   └── endpoints/
│   │       └── prompts.py
│   ├── core/
│   │   └── config.py
│   ├── db/
│   │   ├── base_class.py
│   │   ├── models.py
│   │   └── session.py
│   ├── schemas/
│   │   └── prompt.py
│   └── main.py
├── tests/
│   └── test_prompts.py
├── alembic/
│   └── versions/
├── .env.example
├── .gitignore
├── docker-compose.yml
├── Dockerfile
├── LICENSE
├── MANIFEST.in
├── pyproject.toml
├── README.md
├── requirements.txt
└── setup.py

📊 Database Table Structure

Prompts Table

CREATE TABLE prompts (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    text TEXT NOT NULL,
    description TEXT,
    version INTEGER NOT NULL,
    meta JSONB,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP WITH TIME ZONE
);

Tags Table

CREATE TABLE tags (
    id SERIAL PRIMARY KEY,
    name VARCHAR(50) NOT NULL UNIQUE
);

Prompt Tags Table (Many-to-Many Relationship)

CREATE TABLE prompt_tags (
    prompt_id INTEGER REFERENCES prompts(id) ON DELETE CASCADE,
    tag_id INTEGER REFERENCES tags(id) ON DELETE CASCADE,
    PRIMARY KEY (prompt_id, tag_id)
);

🔄 Updating Prompts with Versioning

The API supports versioning of prompts. When updating a prompt:

1. The current version is incremented
2. A new record is created with the updated content
3. The old version is preserved for reference

To update a prompt, use the PUT endpoint with the prompt ID:

curl -X PUT "http://localhost:8000/api/v1/prompts/{prompt_id}" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Updated prompt text",
    "description": "Updated description",
    "meta": {
      "author": "test-user",
      "category": "updated"
    },
    "tags": ["updated", "test"]
  }'

The API will automatically handle versioning and maintain the history of changes.