🚀 AI Leadership Insight Agent - Setup Guide

This application consists of a Flask-based Python backend using Langchain and Google Gemini, and a React-Vite frontend with a premium, minimalist design.

📁 Project Structure

• backend/: Python Flask application, AI logic, and documents.
• frontend/: React + Vite application with custom vanilla CSS.

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🛠️ Step 1: Backend Setup

1. Navigate to the backend directory.
2. Install dependencies:

   pip install -r requirements.txt

3. Configure your API Key:
   • Open backend/.env.
   • Replace your_gemini_api_key_here with your actual Google Gemini API Key.
4. Run the backend server:

   python app.py

   The server will run on http://localhost:5000.

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💻 Step 2: Frontend Setup

1. Navigate to the frontend directory.
2. Install dependencies:

   npm install

3. Run the development server:

   npm run dev

   The app will be available at http://localhost:5173.

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📄 Managing Documents

The AI agent bases its answers on the files inside backend/documents/.

• To add more knowledge, simply drop .txt files into that folder.
• Restart the backend to re-index the new documents into the vector store.

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✅ Key Features Implemented

• RAG Architecture: Uses FAISS for local vector storage and Google Gemini for grounding answers in provided text.
• Premium UI: Built with vanilla CSS for a high-end, responsive feel without external overhead.
• Factual Grounding: The system specifically looks for information in provided reports (Annual, Strategic, Departmental) before generating answers.
• Micro-animations: Included loading states and fade-in transitions for a smooth executive experience.