AI Connector for User Requests

A Python backend that simplifies user access to OpenAI's powerful language models.

Developed with the software and tools below.

📑 Table of Contents

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

📍 Overview

This repository contains the backend for the AI Connector for User Requests MVP. It provides a simple and user-friendly interface for interacting with OpenAI's powerful language models, enabling users to leverage AI capabilities without needing technical expertise.

📦 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 MVP, its dependencies, and usage instructions.
🔗	Dependencies	The codebase relies on various external libraries and packages such as FastAPI, Uvicorn, Pydantic, SQLAlchemy, psycopg2-binary, OpenAI, python-multipart, dotenv, pytest, pytest-cov, and flake8, which are essential for building and managing the backend, interacting with the database and the OpenAI API, and handling file uploads.
🧩	Modularity	The modular structure allows for easier maintenance and reusability of the code, with separate directories and files for different functionalities such as the API, models, services, utils, and tests.
🧪	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 database and hardware being used. Consider implementing caching strategies and asynchronous operations for better efficiency.
🔐	Security	Enhance security by implementing measures such as input validation, API key management, data encryption, and secure communication protocols.
🔀	Version Control	Utilizes Git for version control with appropriate workflow files for automated build and release processes.
🔌	Integrations	Interacts with external services through HTTP requests, including integrations with OpenAI's API.
📶	Scalability	Design the system to handle increased user load and data volume, utilizing caching strategies and cloud-based solutions for better scalability.

📂 Structure

ai-connector-mvp/
├── src/
│   ├── api/
│   │   └── main.py
│   ├── models/
│   │   └── models.py
│   ├── utils/
│   │   └── logger.py
│   ├── services/
│   │   └── openai_service.py
│   ├── tests/
│   │   ├── test_openai_service.py
│   │   └── test_api.py
│   └── config/
│       └── settings.py
├── .env
├── startup.sh
├── requirements.txt
├── .flake8
└── Dockerfile

💻 Installation

🔧 Prerequisites

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

🚀 Setup Instructions

1. Clone the repository:

   git clone https://github.com/coslynx/ai-connector-mvp.git
   cd ai-connector-mvp

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   • Create a .env file in the root directory and populate it with your OpenAI API key:

   OPENAI_API_KEY=your_api_key
   

5. (Optional) Set up PostgreSQL:

   • Install PostgreSQL if you haven't already.
   • Create a database for the application.
   • Update the DATABASE_URL in your .env file with the correct connection string.

🏗️ Usage

🏃‍♂️ Running the MVP

1. Start the application:

   uvicorn src.api.main:app --reload

2. Access the API:

   • Open your browser to http://127.0.0.1:8000/docs to view the API documentation.

🌐 Hosting

🚀 Deployment Instructions

Deploying to Heroku:

1. Create a Heroku app.
2. Push your application code to Heroku using the Heroku CLI.
3. Configure environment variables on Heroku (OPENAI_API_KEY, DATABASE_URL, etc.).
4. Run migrations if you are using a database.

🔑 Environment Variables

• OPENAI_API_KEY: Your OpenAI API key.
• DATABASE_URL: The connection string for your PostgreSQL database (if using a database).
• LOG_LEVEL: The logging level for the application.

📜 API Documentation

🔍 Endpoints

• POST /api/v1/request:
  • Description: Sends a user request to OpenAI's API.
  • Request Body:

    {
      "text": "Summarize this text: ...", // User request
      "model": "text-davinci-003", // OpenAI model to use (optional, defaults to 'text-davinci-003')
      "max_tokens": 1024, // Maximum number of tokens in the response (optional)
      "temperature": 0.7 // Controls the creativity of the response (optional)
    }

  • Response Body:

    {
      "message": "Success", // Success message
      "response": "...", // AI-generated response
      "error": null // Error message (if any)
    }

📜 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-connector-mvp

📞 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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