FinDocs - Multi-Agent Financial Due-Diligence System

A comprehensive skeleton setup for a multi-agent financial due-diligence system, featuring a FastAPI Python backend and a React + Vite + TypeScript + Tailwind CSS frontend.

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🛠️ Project Structure

• /backend: FastAPI backend app.
  • app/main.py: Main application setup.
  • app/routes/health.py: Health check route (GET /api/health).
  • app/utils/llm_client.py: Shared Google Gen AI SDK client.
  • app/agents/, app/models/, app/utils/: Structuring placeholders for agents and data layers.
• /frontend: React client application.
  • src/App.tsx: Main dashboard showcasing connection status, server health payload, and system stats.

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🚀 Setup & Execution Instructions

1. Backend Server Setup

Navigate into the backend directory:

cd backend

Step A: Configure Environment Variables

Copy the template .env.example file to .env:

cp .env.example .env

Open the .env file and fill in your placeholders:

• GEMINI_API_KEY: Your Gemini API Key from Google AI Studio.
• SCREENER_SESSION_TOKEN: Session token for scraping Screener financial reports.
• NEWS_API_KEY: API Key for accessing finance/market news providers.

Step B: Virtual Environment Setup & Installation

Create a Python virtual environment and activate it:

• Windows (PowerShell):

  python -m venv .venv
  .\.venv\Scripts\Activate.ps1

• macOS / Linux:

  python -m venv .venv
  source .venv/bin/activate

Install requirements:

pip install -r requirements.txt

Step C: Start the FastAPI Server

Run the local uvicorn server in watch mode:

uvicorn app.main:app --reload --port 8000

Verify the server is running by visiting: http://localhost:8000/api/health.

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2. Frontend Client Setup

Navigate into the frontend directory:

cd frontend

Step A: Install Dependencies

npm install

Step B: Run Frontend Development Server

npm run dev

Open your browser and navigate to: http://localhost:5173/ to verify the application skeleton and health status displays.