Deep Research Application

A comprehensive full-stack application for AI-powered research using Azure AI Foundry Agent Service. This application orchestrates multiple large language models to conduct deep research, provides real-time progress tracking, and exports findings in multiple formats.

🚀 Features

Core Capabilities

• Multi-LLM Orchestration: Coordinate GPT-4, GPT-3.5-turbo, Deepseek, Grok, and Mistral for specialized research tasks
• Azure AI Foundry Integration: Leverage Azure's Agent Service for sophisticated AI workflows
• Real-time Web Grounding: Integrate live web search via Bing API for current information
• Multiple Export Formats: Generate reports in Markdown, PDF, and PowerPoint presentations
• Real-time Progress Tracking: WebSocket-based live updates during research execution
• Enterprise Security: Azure AD B2C authentication with role-based access control

Advanced Features

• Intelligent Task Decomposition: Break complex research topics into manageable subtasks
• Source Citation and Verification: Automatic source tracking and credibility assessment
• Template-based PowerPoint Generation: Match corporate presentation templates
• Collaborative Research: Multi-user research sessions with shared progress tracking
• Research History and Analytics: Track research patterns and optimize workflows

🏗️ Architecture

deep_research/
├── backend/                 # Python FastAPI backend
│   ├── app/
│   │   ├── api/            # REST API endpoints
│   │   ├── core/           # Configuration and Azure services
│   │   ├── models/         # Data models and schemas
│   │   ├── services/       # Business logic services
│   │   └── main.py         # FastAPI application
│   ├── tests/              # Backend test suite
│   ├── Dockerfile          # Container configuration
│   └── requirements.txt    # Python dependencies
├── frontend/               # React.js frontend (coming next)
│   ├── src/
│   │   ├── components/     # React components
│   │   ├── pages/          # Page components
│   │   ├── services/       # API client services
│   │   └── utils/          # Utility functions
│   └── package.json        # Node.js dependencies
├── infrastructure/         # Azure Infrastructure as Code
│   ├── bicep/             # Bicep templates
│   └── terraform/         # Terraform alternatives
└── .github/               # CI/CD workflows
    └── workflows/

🎯 Technology Stack

Backend (Python)

• FastAPI: High-performance async API framework
• Azure AI Foundry: Agent Service for LLM orchestration
• Azure SDK: Comprehensive Azure service integration
• Pydantic: Data validation and settings management
• WebSockets: Real-time communication
• WeasyPrint & python-pptx: Document generation

Frontend (React.js)

• React 18: Modern React with hooks and concurrent features
• TypeScript: Type-safe development
• Material-UI/Chakra UI: Modern component library
• React Query: State management and caching
• WebSocket Client: Real-time updates
• Chart.js: Progress visualization

Azure Services

• Azure AI Foundry Hub & Project: AI orchestration platform
• Azure Container Apps: Scalable container hosting
• Azure Cosmos DB: Document database for session management
• Azure Blob Storage: File storage for exports
• Azure Key Vault: Secure secret management
• Azure Monitor: Logging and telemetry
• Azure AD B2C: Authentication and authorization

🚀 Quick Start

Prerequisites

• Python 3.11+ for backend development
• Node.js 18+ for frontend development (coming next)
• Azure Subscription with required services
• Azure CLI installed and configured
• Docker (optional, for containerization)

Backend Setup

1. Clone and navigate to backend:

   git clone <repository-url>
   cd deep_research/backend

2. Run the setup script:

   # Windows PowerShell
   .\setup.ps1
   
   # Or manually:
   python -m venv venv
   venv\Scripts\activate
   pip install -r requirements.txt

3. Configure Azure services:

   # Copy environment template
   cp .env.example .env
   
   # Update .env with your Azure configuration
   # See backend/README.md for detailed configuration

4. Start the development server:

   python run.py serve

   The API will be available at:

   • API Documentation: http://localhost:8010/docs
   • Health Check: http://localhost:8010/api/v1/health/

Frontend Setup

The React.js frontend will be implemented in the next phase, featuring:

• Modern research interface inspired by Microsoft's Copilot Studio
• Real-time progress visualization
• Interactive model selection and configuration
• Export management and download interface
• Collaborative research session management

🔧 Configuration

Required Azure Services

1. Azure AI Foundry Hub and Project
2. Azure Cosmos DB (for research session management)
3. Azure Blob Storage (for export file storage)
4. Azure Key Vault (for secure secret management)
5. Bing Search API (for web grounding capabilities)

Environment Configuration

Key environment variables (see backend/.env.example):

# Azure Core
AZURE_SUBSCRIPTION_ID=your-subscription-id
AZURE_TENANT_ID=your-tenant-id
AZURE_RESOURCE_GROUP=deep-research-rg

# Azure AI Foundry
AZURE_AI_PROJECT_NAME=deep-research-project
AZURE_AI_ENDPOINT=https://your-endpoint.cognitiveservices.azure.com

# Azure Services
KEY_VAULT_URL=https://your-keyvault.vault.azure.net/
COSMOS_DB_ENDPOINT=https://your-cosmosdb.documents.azure.com:443/
STORAGE_ACCOUNT_URL=https://yourstorageaccount.blob.core.windows.net