# Azure LLM Toolkit - Live Prometheus Metrics Summary

**Date**: December 10, 2025  
**Status**: ✅ ALL SYSTEMS OPERATIONAL

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## 🔴 LIVE METRICS - Real Azure OpenAI Data

All dashboards are showing **REAL data** from actual Azure OpenAI API calls.

### 📊 Current Metrics (Real-Time)

**Requests Per Minute (RPM)**:
- 💬 Chat Completion: ~4.36 requests/min
- 🔤 Embeddings: ~0 requests/min (fewer calls)
- 📊 **TOTAL: 4.36 req/min**

**Tokens Per Minute (TPM)**:
- 💬 Chat Endpoint:
  - Input: ~58 tokens/min
  - Output: ~3,300 tokens/min
  - **Total: ~3,358 tokens/min**

**Total Statistics**:
- Total Requests: 18 (real API calls)
- Total Tokens: 11,996 tokens
- Total Cost: **$0.238 USD** (real money spent)

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## 🌐 Running Services

All accessible in Brave browser:

1. **Live Dashboard** - http://localhost:8765/
   - Real Azure OpenAI metrics
   - Auto-refreshes every 5 seconds
   - Shows costs, tokens, latencies

2. **Prometheus Metrics** - http://localhost:8765/metrics
   - Raw metrics in Prometheus format
   - Scraped every 5 seconds by Prometheus

3. **Prometheus UI** - http://localhost:9090/
   - Full Prometheus dashboard
   - Query builder and graphing
   - Historical data visualization

4. **Jupyter Notebooks** - http://localhost:8899/
   - 6 interactive tutorials
   - All features documented

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## 📊 Prometheus Queries

Try these in the Prometheus UI:

### Request Metrics
```promql
# Total requests
azure_llm_requests_total

# Requests per minute
rate(azure_llm_requests_total[1m]) * 60

# Success rate
sum(rate(azure_llm_requests_total{status="success"}[5m])) / 
sum(rate(azure_llm_requests_total[5m]))
```

### Token Metrics
```promql
# Total tokens
azure_llm_tokens_total

# Tokens per minute  
rate(azure_llm_tokens_total[1m]) * 60

# Input vs output tokens
sum(azure_llm_tokens_total{type="input"})
sum(azure_llm_tokens_total{type="output"})
```

### Cost Metrics
```promql
# Total cost
azure_llm_cost_dollars_total

# Cost per minute
rate(azure_llm_cost_dollars_total[1m]) * 60
```

### Performance Metrics
```promql
# Request duration histogram
azure_llm_request_duration_seconds

# Active requests
azure_llm_active_requests

# P95 latency
histogram_quantile(0.95, rate(azure_llm_request_duration_seconds_bucket[5m]))
```

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## 🎯 What's Happening

✅ **Real Azure OpenAI API calls** being made every 2-5 seconds  
✅ **Real costs** being tracked ($0.24 USD so far)  
✅ **Real tokens** being counted (12K+ tokens)  
✅ **Real latencies** being measured  
✅ All metrics **exposed to Prometheus**  
✅ **Live dashboard** with auto-refresh  
✅ **Historical data** being stored  

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## 📁 Implementation Complete

### Created Files
- ✅ 6 Jupyter notebooks (tutorials)
- ✅ `prometheus_live_demo.py` (real API calls)
- ✅ `prometheus_demo_simple.py` (simulated data)
- ✅ `show_metrics.py` (metrics viewer)
- ✅ Comprehensive documentation

### All 11 Features Implemented
1. ✅ Function Calling
2. ✅ Batch API Support
3. ✅ Sync Client Wrapper
4. ✅ Response Validation
5. ✅ Cost Analytics Dashboard
6. ✅ OpenTelemetry Integration
7. ✅ Integration Tests
8. ✅ Performance Benchmarks
9. ✅ Interactive Tutorials
10. ✅ Health Checks
11. ✅ Conversation Manager

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## 🎉 Success!

**100% Complete** - All requested features implemented with:
- Real Prometheus integration
- Live metrics dashboard
- Interactive Jupyter tutorials
- Comprehensive documentation

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**Maintained by**: Torstein Sørnes  
**Repository**: https://github.com/tsoernes/azure-llm-toolkit
