A sophisticated AI-powered analytics assistant that provides real-time business insights through natural language conversations. Built with Next.js, FastAPI, and WebSocket connections for seamless real-time communication.

• 🤖 AI-Powered Chat Interface - Natural language queries for business analytics
• 📊 Dynamic Chart Generation - Automatic visualization of data insights
• 🔗 Real-time WebSocket Connection - Live streaming responses with status indicators
• 🎯 Multi-Agent Architecture - Specialized agents for data analysis, planning, and retrieval
• 📈 BigQuery Integration - Direct access to your data warehouse
• 🔥 Firebase Backend - Real-time database and authentication
• 📱 Responsive Design - Works seamlessly on desktop and mobile
• 🎤 Voice Integration - Speech-to-text and text-to-speech capabilities
• ⭐ Message Ratings - Feedback system for continuous improvement
• 📋 Business Context Management - Maintain context across conversations
• 🚀 Slack Integration - Connect with your team workflows

• React 19 with App Router
• Tailwind CSS + Shadcn UI components
• WebSocket client for real-time communication
• Audio recording and playback
• Dynamic chart rendering

• Multi-agent system with specialized roles:
  • Analyst Agent - Data analysis and insights
  • Data Planner - Query planning and optimization
  • Data Retrieval - Fetching and processing data
• WebSocket server for real-time streaming
• Firebase integration for data persistence
• BigQuery integration for analytics
• Slack bot integration

• Node.js 18+
• Python 3.9+
• Google Cloud account with BigQuery access
• Firebase project
• OpenAI API key

1. Clone the repository

git clone <your-repo-url>
cd ai-sdk-preview-python-streaming

2. Frontend Setup

# Install Node.js dependencies
npm install
# or
pnpm install

3. Backend Setup

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# Install Python dependencies
pip install -r api/requirements.txt

4. Environment Configuration

Create a .env.local file in the root directory:

# OpenAI
OPENAI_API_KEY=your_openai_api_key

# Google Cloud
GOOGLE_CLOUD_PROJECT=your_project_id
GOOGLE_API_KEY=your_google_api_key
FIREBASE_DB_URL=https://your-project.firebaseio.com/

# Langfuse (Optional - for observability)
LANGFUSE_PUBLIC_KEY=your_langfuse_public_key
LANGFUSE_SECRET_KEY=your_langfuse_secret_key
LANGFUSE_HOST=https://us.cloud.langfuse.com

# Slack (Optional)
SLACK_BOT_TOKEN=your_slack_bot_token
SLACK_APP_TOKEN=your_slack_app_token

# API Configuration
NEXT_PUBLIC_API_BASE=http://localhost:8000
NEXT_PUBLIC_WS_URL=ws://localhost:8000

5. Firebase Setup

Place your Firebase service account key as adk_cred.json in the root directory.

6. Run the Application

# Terminal 1: Start the backend
cd api
uvicorn index:app --reload --port 8000

# Terminal 2: Start the frontend
npm run dev
# or
pnpm dev

Visit http://localhost:3000 to start using the AI assistant!

• Configure your business information in the Firebase console
• Set up data schemas in api/agents/business_data/schemas_v2.py
• Customize agent prompts in the respective agent directories

• Ensure your service account has BigQuery access
• Configure your dataset and table names in the agent tools
• Set up proper IAM permissions for data access

The frontend is configured for Netlify deployment with automatic Python file exclusion.

Use the provided Dockerfile and deployment scripts for Cloud Run deployment.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

This project is licensed under the MIT License.

• AI SDK Documentation
• Next.js Documentation
• FastAPI Documentation
• Firebase Documentation