OpenAI-API-Wrapper-Service

A Python backend service that simplifies interaction with OpenAI's API for AI developers

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) for a Python backend service that acts as a user-friendly wrapper for OpenAI's API. It simplifies the process of interacting with OpenAI's powerful language models, allowing developers to easily integrate AI capabilities into their projects.

📦 Features

	Feature	Description
⚙️	Architecture	The codebase follows a modular architectural pattern with separate directories for different functionalities, ensuring easier maintenance and scalability.
📄	Documentation	This README file provides a detailed overview of the MVP, its dependencies, and usage instructions.
🔗	Dependencies	The codebase relies on various external libraries and packages such as FastAPI, openai, sqlalchemy, and uvicorn, which are essential for building the API, interacting with OpenAI, and handling database interactions.
🧩	Modularity	The modular structure allows for easier maintenance and reusability of the code, with separate directories and files for different functionalities such as API routes, dependencies, and models.
🧪	Testing	Implement unit tests using frameworks like pytest to ensure the reliability and robustness of the codebase.
⚡️	Performance	The performance of the system can be optimized based on factors such as the browser and hardware being used. Consider implementing performance optimizations for better efficiency.
🔐	Security	Enhance security by implementing measures such as input validation, data encryption, and secure communication protocols.
🔀	Version Control	Utilizes Git for version control with GitHub Actions workflow files for automated build and release processes.
🔌	Integrations	Integrates with OpenAI's API through the openai-python library.
📶	Scalability	Designed to handle increased user load and data volume, utilizing caching strategies and cloud-based solutions for better scalability.

📂 Structure

├── api
│   ├── routes
│   │   ├── openai_routes.py
│   ├── dependencies
│   │   ├── openai_service.py
│   ├── models
│   │   ├── openai_models.py
├── config
│   ├── settings.py
├── utils
│   ├── logger.py
│   └── exceptions.py
├── tests
│   └── unit
│       └── test_openai_service.py
├── main.py
├── requirements.txt
├── .gitignore
├── startup.sh
├── commands.json
├── .env.example
├── .env
└── Dockerfile

💻 Installation

🔧 Prerequisites

• Python 3.9+
• pip
• PostgreSQL (optional)

🚀 Setup Instructions

1. Clone the repository:

   git clone https://github.com/coslynx/OpenAI-API-Wrapper-Service.git
   cd OpenAI-API-Wrapper-Service

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the database:

   # If using a database:
   # - Create a PostgreSQL database.
   # - Configure database connection details in the .env file.

4. Configure environment variables:

   cp .env.example .env
   # Fill in the OPENAI_API_KEY with your OpenAI API key.
   # Fill in any database connection details if using a database.

🏗️ Usage

🏃‍♂️ Running the MVP

1. Start the server:

   uvicorn main:app --host 0.0.0.0 --port 5000

2. Access the API:

   • API endpoint: http://localhost:5000/openai/generate_response

⚙️ Configuration

• config/settings.py: Defines global settings for the application, including API keys, database connections, and debugging options.
• .env: Stores environment-specific variables that should not be committed to the repository (e.g., API keys, database credentials).

📚 Examples

1. Generating Text:

Request:

{
  "model": "text-davinci-003",
  "prompt": "Write a short story about a cat who goes on an adventure."
}

Response:

{
  "response": "Once upon a time, in a quaint little cottage nestled amidst a lush garden, there lived a mischievous tabby cat named Whiskers. Whiskers, with his emerald eyes and a coat as sleek as midnight, had an insatiable curiosity for the unknown. One sunny afternoon, as Whiskers was lounging on the windowsill, his gaze fell upon a peculiar object in the garden - a tiny, silver key. Intrigued, he leaped down from his perch and snatched the key in his paws. With a mischievous glint in his eyes, Whiskers knew he had stumbled upon an adventure. "
}

2. Translating Text:

Request:

{
  "model": "gpt-3.5-turbo",
  "prompt": "Translate 'Hello, world!' into Spanish."
}

Response:

{
  "response": "¡Hola, mundo!"
}

3. Summarizing Text:

Request:

{
  "model": "text-davinci-003",
  "prompt": "Summarize the following text: 'The quick brown fox jumps over the lazy dog.'"
}

Response:

{
  "response": "A quick brown fox jumps over a lazy dog."
}

🌐 Hosting

🚀 Deployment Instructions

1. Create a virtual environment:

   python3 -m venv env
   source env/bin/activate

2. Install dependencies:

   pip install -r requirements.txt

3. Set environment variables:

   cp .env.example .env
   # Fill in the required environment variables.

4. Start the server:

   uvicorn main:app --host 0.0.0.0 --port 5000

Note: For production deployment, consider using a web server like Gunicorn or Uvicorn with appropriate configurations. You can also use cloud services like Heroku or AWS for easier deployment.

📜 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-API-Wrapper-Service

📞 Contact

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

• Website: CosLynx.com
• Twitter: @CosLynxAI

🌐 CosLynx.com

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