exemplar-prompt-hub 0.1.0

A modern REST API service for managing and serving AI prompts

🚀 Exemplar Prompt Hub

📑 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

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.

✨ 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)
• Docker and Docker Compose (for containerized setup)

Installation

Using pip

You can install the package directly from PyPI:

pip install exemplar-prompt-hub

Or install from the source:

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

# Install the package
pip install -e .

After installation, you can use the following commands:

• prompt-hub - Start the FastAPI server
• prompt-hub-ui - Start the Streamlit UI

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
   • Streamlit UI at http://localhost:8501

3. Access the services:

   • API Documentation: http://localhost:8000/docs
   • Streamlit UI: http://localhost:8501

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/1"

Update a Prompt

# Replace {prompt_id} with actual ID
curl -X PUT "http://localhost:8000/api/v1/prompts/1" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "updated-example-prompt",
    "text": "This is the updated prompt text",
    "description": "Updated description",
    "version": 2,
    "meta": {
      "author": "test-user",
      "category": "example",
      "updated": true
    },
    "tags": ["example", "test", "updated"]
  }'

Delete a Prompt

# Replace {prompt_id} with actual ID
curl -X DELETE "http://localhost:8000/api/v1/prompts/1"

Complete Flow Example

Here's a complete flow example using a single prompt:

# 1. Create a new prompt
CREATE_RESPONSE=$(curl -s -X POST "http://localhost:8000/api/v1/prompts/" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "flow-example",
    "text": "Initial prompt text",
    "description": "Example for complete flow",
    "version": 1,
    "meta": {"author": "test-user"},
    "tags": ["flow", "example"]
  }')

# Extract prompt ID from response
PROMPT_ID=$(echo $CREATE_RESPONSE | jq -r '.id')

# 2. Get the created prompt
curl "http://localhost:8000/api/v1/prompts/$PROMPT_ID"

# 3. Update the 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",
    "version": 2,
    "meta": {"author": "test-user", "updated": true},
    "tags": ["flow", "example", "updated"]
  }'

# 4. Get the updated prompt
curl "http://localhost:8000/api/v1/prompts/$PROMPT_ID"

# 5. Delete the prompt
curl -X DELETE "http://localhost:8000/api/v1/prompts/$PROMPT_ID"

# 6. Verify deletion
curl "http://localhost:8000/api/v1/prompts/$PROMPT_ID"

Note: The complete flow example requires jq to be installed for JSON parsing. You can install it using:

• Ubuntu/Debian: sudo apt-get install jq
• macOS: brew install jq
• Windows: Download from https://stedolan.github.io/jq/download/

📁 Project Structure

exemplar-prompt-hub/
├── app/
│   ├── api/             # API endpoints
│   ├── core/            # Core functionality
│   ├── db/              # Database models and session
│   ├── schemas/         # Pydantic models
│   └── main.py          # Application entry point
├── tests/               # Test files
├── .env                 # Environment variables
├── .env.example         # Example environment variables
├── requirements.txt     # Project dependencies
└── README.md           # Project documentation

📊 Database Table Structure

The application uses the following database tables:

Prompts Table

• id: Integer (Primary Key)
• name: String (Unique)
• text: String
• description: String
• version: Integer
• meta: JSON
• created_at: DateTime
• updated_at: DateTime

Tags Table

• id: Integer (Primary Key)
• name: String (Unique)

PromptVersions Table

• id: Integer (Primary Key)
• prompt_id: Integer (Foreign Key to Prompts)
• version: Integer
• text: String
• meta: JSON
• created_at: DateTime

🔄 Updating Prompts with Versioning

To update a prompt with versioning, follow these steps:

1. Retrieve the Prompt: Use the GET /api/v1/prompts/{prompt_id} endpoint to retrieve the prompt you want to update.

2. Update the Prompt: Use the PUT /api/v1/prompts/{prompt_id} endpoint to update the prompt. You can include the following fields:

   • name: (Optional) The new name of the prompt.
   • text: (Optional) The new text of the prompt.
   • description: (Optional) The new description of the prompt.
   • version: (Optional) The new version number.
   • meta: (Optional) Any additional metadata.

3. Versioning Logic:

   • If you provide a new version number, the system will create a new entry in the PromptVersions