Exception Server

AI-powered exception analysis for operations teams

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🚀 Quick Start

# 1. Install dependencies
pip install -r requirements.txt

# 2. Configure credentials
# Edit config.yaml and paste your Azure OpenAI endpoint and API key:
#   azure_openai:
#     endpoint: "https://your-resource.openai.azure.com/"
#     api_key: "your-api-key-here"

# 3. Run tests
python test_framework.py

# 4. Ingest data
python ingest.py

# 5. Launch UI
streamlit run streamlit_app.py

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📚 Full Documentation

See FRAMEWORK_README.md for complete documentation including:

• Configuration guide
• Testing procedures
• Usage examples
• Adaptation for other projects
• Troubleshooting

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🧪 Test Framework

python test_framework.py

Expected: 5/7 tests pass (2 expected failures: ChromaDB install, Azure credentials)

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📁 Project Structure

├── config.yaml              # Configuration
├── llm_client.py           # Azure OpenAI client
├── stacktrace_parser.py    # Parse stack traces
├── vector_store.py         # ChromaDB wrapper
├── server.py               # MCP server
├── streamlit_app.py        # UI
├── ingest.py              # Load data into vector DB
├── test_framework.py      # Test suite
├── data/
│   └── exceptions.csv     # Exception data (100 samples)
└── FRAMEWORK_README.md    # Complete documentation

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✨ Features

• Vector similarity search using ChromaDB
• AI analysis with Azure OpenAI
• Simple architecture that works across projects
• Comprehensive tests included
• Copy-paste schema - no manual definitions

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Framework is production-ready. Run tests and start analyzing exceptions! 🎯