liurenchaxin/.kiro/steering/tech.md

Technology Stack

Core Technologies

• Python 3.x: Primary programming language
• Streamlit: Web interface framework for the main application
• FastAPI: API framework for backend services (if applicable)

AI & ML Stack

• OpenRouter: Multi-model AI API gateway
• Anthropic Claude: Primary AI model for debates
• OpenAI GPT: Alternative AI model support
• Vector Databases: Zilliz for semantic search and embeddings

Database Technologies

• PostgreSQL: Primary relational database
• MongoDB: Document database for flexible data storage
• Zilliz: Vector database for AI embeddings and semantic search

Configuration & Security

• Doppler: Centralized secrets and configuration management
• Environment Variables: Local configuration override
• GitGuardian: Automated secret scanning and security

Development Tools

• Git: Version control
• Pre-commit Hooks: Automated code quality and security checks
• Virtual Environment: Python dependency isolation

Common Commands

Environment Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

Application Commands

# Start Streamlit application
streamlit run app/streamlit_app.py

# Run with specific port
streamlit run app/streamlit_app.py --server.port 8501

Development Commands

# Run tests
python -m pytest tests/

# Code quality checks
pre-commit run --all-files

# Security scan
doppler secrets download --no-file --format env > .env.local

Database Commands

# PostgreSQL connection test
python -c "from src.database import test_postgres_connection; test_postgres_connection()"

# MongoDB connection test  
python -c "from src.database import test_mongo_connection; test_mongo_connection()"

Security Requirements

• Zero Hardcoded Secrets: All secrets must come from Doppler or environment variables
• Environment Isolation: Development, staging, and production environments must be separate
• Automated Scanning: All commits must pass GitGuardian security checks
• Access Control: Database and API access through proper authentication only

Performance Considerations

• Async Operations: Use async/await for AI API calls to prevent blocking
• Connection Pooling: Implement database connection pooling for better performance
• Caching: Cache frequently accessed data and AI responses when appropriate
• Resource Limits: Set appropriate timeouts and rate limits for external API calls