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For Windows user on x86_64, please go and download WSL2 and use ./setup.sh

If you are currently using a arm64 or x86_32 only cpu other than Apple Silicon on MacOS, please manage your own way to do this development.

The content below is not recommended and is no longer being maintained.

WatAIOliver Project: Manual Setup Guide (Windows, VS Code)

This guide outlines the steps to set up and run the WatAIOliver project on Windows (VS Code environment).

Please note:

  • Lines surrounded by "<>" should be replaced with relevant lines specific to your computer
  • Please ensure that you have included a .env file in the backend folder and in the machine_learning folder with the right setup info. If you have not or are not sure if you have written your .env file correctly, please ask for help

1. Environment Setup

a. Create & Activate Virtual Environment

cd <Path to project route>
python3.12 -m venv venv
.\venv\Scripts\activate

b. Upgrade pip

python -m pip install --upgrade pip

2. Install Python Dependencies

a. Install backend dependencies

pip install -r backend/requirements.txt

b. Install PDF processor dependencies

pip install -r machine_learning/pdf_processor/requirements.txt

c. Install RAG system dependencies

pip install -r machine_learning/rag_system/requirements.txt -c constraints.txt --upgrade --upgrade-strategy eager

d. Install Agent system dependencies

pip install -r machine_learning/ai_agents/requirements.txt

Note: If there are package conflicts, resolve them manually by installing the correct versions as shown by pip error messages.

3. Install Frontend Dependencies

cd frontend
npm install

Ensure the marked package is installed:

npm list marked

If not installed:

npm install marked

4. Run Each Service (Use Separate Terminal Tabs)

a. Tab 1: Frontend (Vite Server)

cd <Path to project root>\frontend
npm run dev

b. Tab 2: Backend API

cd <Path to project root>\backend
..\venv\Scripts\python.exe -m uvicorn src.main:app --reload --host 0.0.0.0 --port 8000

c. Tab 3: PDF Processor

cd <Path to project root>\machine_learning\pdf_processor
& "<Path to project root>\venv\Scripts\python.exe" -m uvicorn main:app --reload --host 0.0.0.0 --port 8001

d. Tab 4: RAG System (Make sure you have .env loaded)

cd <Path to project root>\machine_learning\rag_system
& "<Path to project root>\venv\Scripts\python.exe" -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8002

e. Tab 5: Agent System

cd <Path to project root>\machine_learning\ai_agents
& "<Path to project root>\venv\Scripts\python.exe" -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8003

Tips:

  • Always cd into the correct directory for backend, PDF processor, and RAG system before running Uvicorn.
  • Always use the absolute path to Python executable inside your venv to avoid path issues.
  • If you see import errors, double-check your working directory.

Final Notes

  • Run each service in its own VS Code terminal tab.
  • Ensure your .env is correctly placed and loaded for services requiring credentials.
  • Always activate the virtual environment before running any commands:
.\venv\Scripts\activate