AI-Powered-Request-Handler-Service

A Python backend service that simplifies interacting with OpenAI's language models.

Developed with the software and tools below.

📑 Table of Contents

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

📍 Overview

This repository contains an AI Powered Request Handler Service, a Python backend service designed to simplify the interaction with OpenAI's language models. This service empowers developers to effortlessly integrate AI capabilities into their applications without directly handling OpenAI's APIs.

📦 Features

	Feature	Description
⚙️	Architecture	The codebase follows a modular architectural pattern with separate directories for different functionalities, ensuring easier maintenance and scalability.
📄	Documentation	The repository includes a README file that provides a detailed overview of the service, its dependencies, and usage instructions.
🔗	Dependencies	The codebase relies on various external libraries and packages such as FastAPI, uvicorn, openai, sqlalchemy, psycopg2-binary, python-dotenv, and PyJWT, which are essential for building the API, interacting with the database, and handling authentication.
🧩	Modularity	The modular structure allows for easier maintenance and reusability of the code, with separate directories and files for different functionalities such as routes, services, and models.
🧪	Testing	Includes unit tests using pytest to ensure the reliability and robustness of the codebase.
⚡️	Performance	The performance of the service can be optimized by using caching mechanisms and asynchronous processing.
🔐	Security	Enhances security by implementing measures such as input validation, API key management, and JWT authentication.
🔀	Version Control	Utilizes Git for version control with a startup.sh script for automated deployment processes.
🔌	Integrations	Interacts with OpenAI's API for text generation and includes a PostgreSQL database (optional) for data storage.
📶	Scalability	The service is designed to handle increased user load and data volume, utilizing asynchronous processing and database optimization for better scalability.

📂 Structure

AI-Powered-Request-Handler-Service/
├── src/
│   ├── main.py
│   ├── routes/
│   │   └── request_routes.py
│   ├── services/
│   │   └── request_service.py
│   └── models/
│       └── request.py
├── .env
├── tests/
│   └── test_request_service.py
├── startup.sh
└── requirements.txt

💻 Installation

🔧 Prerequisites

• Python 3.9+
• PostgreSQL 15+ (optional)
• Docker (optional)

🚀 Setup Instructions

1. Clone the repository:

   git clone https://github.com/coslynx/AI-Powered-Request-Handler-Service.git
   cd AI-Powered-Request-Handler-Service

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the database:

   # Create a database (optional):
   createdb your_database_name
   # Set the database URL in the .env file:
   DATABASE_URL=postgresql://user:password@host:port/your_database_name

4. Configure environment variables:

   cp .env.example .env
   # Fill in the OPENAI_API_KEY and DATABASE_URL (if using a database)

5. (Optional) Build the Docker image:

   docker build -t ai-request-handler .

6. (Optional) Run the application using Docker:

   docker run -p 8000:8000 ai-request-handler

🏗️ Usage

🏃‍♂️ Running the MVP

1. Start the API server:

   uvicorn main:app --host 0.0.0.0 --port 8000

2. Access the API endpoint:
   • Send a POST request to http://localhost:8000/request with the following JSON body:

     {
       "prompt": "This is a text prompt for the OpenAI model."
     }

   • The response will contain the AI-generated text.

🌐 Hosting

🚀 Deployment Instructions

Deploying to Heroku (optional)

1. Install the Heroku CLI:

   npm install -g heroku

2. Login to Heroku:

   heroku login

3. Create a new Heroku app:

   heroku create ai-request-handler-production

4. Set up environment variables:

   heroku config:set OPENAI_API_KEY=your_api_key
   heroku config:set DATABASE_URL=your_database_url_here

5. Deploy the code:

   git push heroku main

Deploying using Docker (optional)

1. Build the Docker image:

   docker build -t ai-request-handler .

2. Push the image to a Docker registry:

   docker push your_docker_registry/ai-request-handler:latest

3. Deploy the image to your chosen cloud platform (e.g., AWS ECS, Google Kubernetes Engine):
   • Follow the deployment instructions for your chosen platform, referencing the Docker image name and tag.

🔑 Environment Variables

• OPENAI_API_KEY: Your OpenAI API key (required).
• DATABASE_URL: Connection string for the PostgreSQL database (optional, if using a database).

📜 API Documentation

🔍 Endpoints

• POST /request
  • Description: Handles user requests and sends them to the OpenAI API.
  • Request Body:

    {
      "prompt": "Your text prompt here."
    }

  • Response Body:

    {
      "text": "AI-generated text based on the prompt."
    }

📜 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: AI-Powered-Request-Handler-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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