project

Project Orion: AI Agent Integration Framework

Vision: “เราจะสร้างมาตรฐานกลางสำหรับ AI Agent Integration ที่ทำให้ทุกองค์กรสามารถใช้ประโยชน์จาก AI ได้อย่างปลอดภัย มีประสิทธิภาพ และยั่งยืน” “To establish the central standard for AI Agent Integration, enabling all organizations to leverage AI safely, efficiently, and sustainably.”

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This repository contains the source code for Project Orion, an integrated platform designed to be a comprehensive AI Agent Framework and Business OS. It is built with a modular, API-centric, and cloud-ready architecture to support robust workflow automation and a thriving developer ecosystem.

🏗️ Architecture: A Microservices Ecosystem

The project is architected as a collection of containerized microservices, orchestrated by Docker. This design ensures scalability, maintainability, and independent deployment of each component.

/
├── 📄 docker-compose.yml       # Main orchestrator for all services
├── 📂 api_gateway/              # (FastAPI) The single entry point for all incoming traffic
├── 📂 backend/                  # (Flask) Core business logic, database, and legacy integrations
├── 📂 frontend/                 # (React) The main user interface
├── 📂 image-generation-service/ # (FastAPI) Dedicated service for image generation
├── 📂 rag-model-service/        # (FastAPI) Handles RAG and agentic workflows
├── 📂 ai-assistant-extension/   # (Chrome Extension) Browser-based UI component
└── 📂 docs/                     # High-level documentation

🛠️ Technology Stack

• Orchestration: Docker, Docker Compose
• API Gateway: FastAPI, Uvicorn
• Core Services: Python, FastAPI, Flask, SQLAlchemy
• Frontend: React.js, Node.js, Material-UI, Recharts, React Flow
• AI & Machine Learning: LangChain, Google Vertex AI (Imagen 2), Pinecone, Sentence Transformers
• Database: PostgreSQL (Production), SQLite (Development)
• Workflow Automation: n8n (planned)

🚀 Getting Started: Development Environment

The entire development environment is managed by Docker. The single command below is all you need to get started.

Prerequisites

• Docker
• Docker Compose

Running the Platform

1. Prepare Environment Files: Each service that requires credentials or specific configurations has an .env.example file. Copy it to a .env file in the same directory and fill in the required values.
   • cp backend/.env.example backend/.env
   • cp image-generation-service/.env.example image-generation-service/.env
   • cp rag-model-service/.env.example rag-model-service/.env
2. Build and Run All Services: From the project root directory, run:

   docker-compose up --build
   

   This command will:

   • Build the Docker image for each service.
   • Start all containers.
   • Connect them to a shared network.

   Services will be available at:

   • Frontend: http://localhost:3000
   • API Gateway: http://localhost:8080
   • Backend (Direct): http://localhost:5001
   • Image Generation (Direct): http://localhost:8000
   • RAG Service (Direct): http://localhost:8081

📈 Project Status & Progress

This project follows the phased development plan outlined in the “Business OS Integration” proposal.

• Phase 0: Automation Foundation - ✅ Completed
  • Established baseline automation and logging.
• Phase 1: Core Infrastructure & API Gateway - ✅ Completed
  • Google Cloud Workstation environment is prepared.
  • Docker configurations for all services are complete.
  • API Gateway v0.1 (api_gateway) has been developed and deployed.
• Phase 2: Microservices & Automation - 🚧 In Progress
  • Core microservices (rag-model-service, image-generation-service) have been scaffolded.
  • Next steps: Implement core logic within these services and set up the n8n automation hub.
• Phase 3: Business OS Integration - ⏳ Pending
• Phase 4: Marketplace & Ecosystem - ⏳ Pending

📚 Documentation

• Project Overview (TH/EN): A high-level summary of the project’s features and goals.
• AI Agent Instructions: Essential guidelines for AI agents contributing to this codebase.
• Service-Specific README.md: Each service directory contains its own README.md with detailed information.

🤝 Contributing

1. Fork the repository.
2. Create a new feature branch (git checkout -b feature/YourAmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/YourAmazingFeature).
5. Open a Pull Request.

📄 License

This project is licensed under the MIT License.