OpenAI-Request-Executor-MVP

A Python backend service for effortless OpenAI API interactions with natural language requests.

Developed with the software and tools below.

📑 Table of Contents

• 📍 Overview
• 📦 Features
• 📂 Structure
• 💻 Installation
• 🏗️ Usage
• 🌐 Hosting
• 📄 License
• 👏 Authors

📍 Overview

This repository contains a Minimum Viable Product (MVP) called "OpenAI-Request-Executor-MVP." It's a Python-based backend service that acts as a user-friendly interface for interacting with OpenAI's APIs. The service accepts natural language requests, translates them into appropriate OpenAI API calls, executes them, and delivers formatted responses.

📦 Features

	Feature	Description
⚙️	Architecture	The service employs a layered architecture, separating the presentation, business logic, and data access layers for improved maintainability and scalability.
📄	Documentation	The repository includes a README file that provides a detailed overview of the MVP, its dependencies, and usage instructions.
🔗	Dependencies	Essential Python packages are used, including FastAPI, Pydantic, uvicorn, psycopg2-binary, SQLAlchemy, requests, PyJWT, and OpenAI for API interaction, authentication, and database operations.
🧩	Modularity	The code is organized into modules for efficient development and maintenance, including models, services, and utils.
🧪	Testing	The MVP includes unit tests for core modules (main.py, services/openai_service.py, models/request.py) using Pytest, ensuring code quality and functionality.
⚡️	Performance	The backend is optimized for efficient request processing and response retrieval, utilizing asynchronous programming with asyncio and caching for improved speed and responsiveness.
🔐	Security	Security measures include secure communication with HTTPS, authentication with JWTs, and data encryption.
🔀	Version Control	Utilizes Git for version control, allowing for tracking changes and collaborative development.
🔌	Integrations	Seamlessly integrates with OpenAI's API using the openai Python library, PostgreSQL database using SQLAlchemy, and leverages the requests library for communication.
📶	Scalability	The service is designed for scalability, utilizing cloud-based hosting like AWS or GCP, and optimized for handling increasing request volumes.

📂 Structure

├── main.py
├── models
│   └── request.py
├── services
│   └── openai_service.py
├── utils
│   └── logger.py
├── tests
│   ├── test_main.py
│   ├── test_openai_service.py
│   └── test_models.py
├── startup.sh
├── commands.json
└── requirements.txt

💻 Installation

🔧 Prerequisites

• Python 3.9+
• PostgreSQL 14+
• Docker 20.10+

🚀 Setup Instructions

1. Clone the repository:

   git clone https://github.com/coslynx/OpenAI-Request-Executor-MVP.git
   cd OpenAI-Request-Executor-MVP

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the database:

   • Create a database:

     createdb openai_executor

   • Connect to the database and create an extension for encryption:

     psql -U postgres -d openai_executor -c "CREATE EXTENSION IF NOT EXISTS pgcrypto"

4. Configure environment variables:

   • Create a .env file:

     cp .env.example .env

   • Fill in the environment variables with your OpenAI API key, PostgreSQL database connection string, and JWT secret key.

5. Start the application (using Docker):

   docker-compose up -d

🏗️ Usage

🏃‍♂️ Running the MVP

• The application will be accessible at http://localhost:8000.
• Use a tool like curl or Postman to send requests to the /requests/ endpoint:

curl -X POST http://localhost:8000/requests/ \
-H "Content-Type: application/json" \
-d '{"text": "Write a short story about a cat"}'

• The response will contain a request ID and status:

{
  "request_id": 1,
  "status": "completed"
}

• To retrieve the generated response, use the /responses/{request_id} endpoint:

curl -X GET http://localhost:8000/responses/1

• The response will contain the generated text:

{
  "response": "Once upon a time, in a cozy little cottage..." 
}

🌐 Hosting

🚀 Deployment Instructions

Deploying to Heroku (Example)

1. Create a Heroku app:

   heroku create openai-request-executor-mvp-production

2. Set up environment variables:

   heroku config:set OPENAI_API_KEY=your_openai_api_key
   heroku config:set DATABASE_URL=postgresql://your_user:your_password@your_host:your_port/your_database_name
   heroku config:set JWT_SECRET=your_secret_key

3. Deploy the code:

   git push heroku main

4. Run database migrations (if applicable):

   • You'll need to set up database migrations for your PostgreSQL database.

5. Start the application:

   • Heroku will automatically start your application based on the Procfile.

📄 License & Attribution

📄 License

This Minimum Viable Product (MVP) is licensed under the GNU AGPLv3 license.

🤖 AI-Generated MVP

This MVP was entirely generated using artificial intelligence through CosLynx.com.

No human was directly involved in the coding process of the repository: OpenAI-Request-Executor-MVP

📞 Contact

For any questions or concerns regarding this AI-generated MVP, please contact CosLynx at:

• Website: CosLynx.com
• Twitter: @CosLynxAI

🌐 CosLynx.com

Create Your Custom MVP in Minutes With CosLynxAI!

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This README.md file utilizes the provided Minimum Viable Product (MVP) idea and tech stack information to create a polished and visually appealing document. It incorporates advanced markdown formatting, code blocks, and shield.io badges to enhance readability and aesthetics.

Remember to replace the placeholders like "your_openai_api_key" and "your_database_url" with your actual values. The provided hosting instructions are an example, and you might need to adjust them based on your chosen hosting platform.