kevon217/arealglam-gpt-backend

arealglam python backend for OpenAI gpt integration

README.md

Project Overview

This project involves developing a Python backend application integrated with OpenAI's GPT assistants, primarily for a fashion recommendation platform. The system, built using FastAPI, is designed to interact with a React/Next.js frontend. The core functionality revolves around handling user queries about fashion, with the backend orchestrating responses from multiple GPT assistants.

Project Design/Architecture

• Backend Framework: FastAPI, supporting asynchronous operations with AsyncIO.
• GPT Assistants: Utilizes three custom OpenAI GPT assistants (Orchestrator, Psychologist, and Wardrobe).
• OpenAI Functional Calls: Utilizes a parallel function chat completion endpoint to extract product details from the Orchestrator's suggestion, which are then passed to the Wardrobe Assistant for product search and product ID retrieval.
• WebSocket Communication: Implemented for real-time interaction between backend and frontend.
• Docker Deployment: Containerized backend for deployment, ensuring environment consistency.
• AWS Deployment: Backend application deployed on a Docker container hosted on AWS EC2 instance, ensuring cloud-based WebSocket communication.

What We've Done

1. Developed Core Backend Logic: Services and endpoints created in FastAPI to interact with GPT assistants.
2. AsyncThread Implementation: Built an AsyncThread class for communication with OpenAI assistants, managing threads, messages, and runs.
3. WebSocket Setup: Established WebSocket logic for real-time data transmission to the frontend.
4. Integration Testing: Conducted tests for proper message passing and response retrieval from OpenAI assistants.
5. Environment Setup: Configured environment variables and OpenAI client for API interactions.
6. Dockerization: Containerized the backend application for deployment.
7. Frontend Integration Coordination: Coordinated with the frontend developer for seamless integration, focusing on request handling and WebSocket communication.
8. Testing in Docker Environment: Tested the complete system within a Docker container to ensure stability and performance.
9. AWS Deployment and Testing: Deployed and tested the system in a Docker container on AWS EC2 for cloud websocket communication.

What Remains

10. Performance Optimization: Analyzing response times and optimizing backend efficiency.
11. Error Handling and Edge Case Testing: Implementing robust error handling and testing for edge cases.
12. Production Deployment Readiness: Finalizing the application for production, including security enhancements and scalability considerations.
13. Frontend State Management: Ensuring proper state management on the frontend to sync with backend operations.

Notes for the Frontend Developer

• WebSocket Integration: The backend uses WebSocket for real-time communication. Ensure the frontend can establish and maintain a stable WebSocket connection.
• Handling Backend Responses: Backend sends JSON formatted responses. Parse these responses appropriately in the frontend for display and interaction.
• Environment Consistency: Maintain consistency in environment variables and configurations between frontend and backend.