AI-Powered-User-Query-Backend

A streamlined backend for integrating OpenAI's language models into applications.

Developed with the software and tools below.

📑 Table of Contents

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

📍 Overview

This repository contains the AI Powered User Queries Backend, a Minimum Viable Product (MVP) built with Python and FastAPI for simplifying AI integration. The MVP provides a lightweight and efficient solution to access OpenAI's powerful language models through a user-friendly API.

📦 Features

	Feature	Description
⚙️	Architecture	The MVP utilizes a microservice architecture for modularity and scalability, with independent services for query processing, API integration, and database interaction.
📄	Documentation	This README provides a comprehensive overview of the MVP's functionality, installation instructions, and usage examples.
🔗	Dependencies	The MVP relies on libraries like FastAPI, SQLAlchemy, openai, and requests for API development, database management, and OpenAI integration.
🧩	Modularity	The codebase is organized with separate modules for core services, database interaction, and utility functions.
🧪	Testing	The MVP includes unit tests for core functions and integration tests for verifying end-to-end functionality.
⚡️	Performance	Optimized for efficient query processing and response handling, utilizing caching mechanisms where appropriate.
🔐	Security	Incorporates best practices for secure development, including data validation, input sanitization, and API key management.
🔀	Version Control	Uses Git for version control and includes a CI/CD pipeline for automated testing and deployment.
🔌	Integrations	Seamlessly integrates with OpenAI's API for generating responses and utilizes a PostgreSQL database for storing data.
📶	Scalability	Designed for horizontal scalability by utilizing containerization with Docker and deployment on Kubernetes.

📂 Structure

ai-query-backend
├── api
│   └── main.py
├── config
│   └── settings.py
├── core
│   ├── services
│   │   └── openai_service.py
│   ├── database
│   │   ├── models.py
│   │   └── database.py
│   └── utils
│       ├── utils.py
│       └── logger.py
└── tests
    └── unit
        ├── test_openai_service.py
        └── test_database.py

💻 Installation

🔧 Prerequisites

• Python 3.9+
• Docker
• Kubernetes (kubectl)
• PostgreSQL

🚀 Setup Instructions

1. Clone the repository:

   git clone https://github.com/coslynx/AI-Powered-User-Query-Backend.git
   cd AI-Powered-User-Query-Backend

2. Install dependencies:

   pip install -r requirements.txt

3. Create .env File:

   cp .env.example .env

4. Configure environment variables: Update the .env file with your:

   • DATABASE_URL (PostgreSQL connection string)
   • OPENAI_API_KEY (OpenAI API key)
   • JWT_SECRET (secret key for JWT authentication)

5. Build and Run the Application (Docker):

   docker-compose up -d

6. Deploy to Kubernetes (kubectl):

   kubectl apply -f deployment.yaml

🏗️ Usage

🏃‍♂️ Running the MVP

1. Start the development server:

   uvicorn api.main:app --reload

2. Access the API:

   • http://localhost:8000/docs for API documentation
   • http://localhost:8000/redoc for an alternative API documentation view

⚙️ Configuration

• config/settings.py: Contains configuration settings for database connections, OpenAI API keys, and other essential variables. You can modify these settings based on your specific environment.

📚 Examples

API Endpoints:

• POST /query:

  • Request Body:

    {
      "query": "What is the meaning of life?",
      "model": "text-davinci-003", 
      "temperature": 0.7, 
      "max_length": 256
    }

  • Response Body:

    {
      "query_id": "generated_id"
    }

• GET /response/{query_id}:

  • Response Body:

    {
      "response": "The meaning of life is a question that has been pondered by philosophers and thinkers for centuries..."
    }

🌐 Hosting

🚀 Deployment Instructions

1. Build a Docker image:

   docker build -t ai-query-backend .

2. Push the image to a Docker registry (e.g., Docker Hub):

   docker push coslynx/ai-query-backend:latest

3. Deploy the image to Kubernetes:

   kubectl apply -f deployment.yaml

🔑 Environment Variables

• DATABASE_URL: Connection string for the PostgreSQL database Example: postgresql://user:password@host:port/database
• OPENAI_API_KEY: OpenAI API key Example: sk-your-openai-api-key
• JWT_SECRET: Secret key for JWT authentication Example: your-256-bit-secret

📜 API Documentation

🔍 Endpoints

• POST /query:

  • Description: Processes a user query using OpenAI's API.
  • Request Body:

    {
      "query": "What is the meaning of life?",
      "model": "text-davinci-003", 
      "temperature": 0.7, 
      "max_length": 256
    }

  • Response Body:

    {
      "query_id": "generated_id"
    }

• GET /response/{query_id}:

  • Description: Retrieves the response for a given query ID.
  • Response Body:

    {
      "response": "The meaning of life is a question that has been pondered by philosophers and thinkers for centuries..."
    }

🔒 Authentication

The MVP uses JWT for authentication. To access protected endpoints:

1. Generate a JWT token by registering a new user or logging in.

2. Include the token in the Authorization header of requests:

   Authorization: Bearer YOUR_JWT_TOKEN
   

📜 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-User-Query-Backend

📞 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!