reduce number of predictions
This commit is contained in:
rishubm
@@ -16,6 +16,9 @@ DEMO_MODE=false
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# Fast Mode (use shorter prompts for faster responses)
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FAST_MODE=true
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# Strategy Generation Settings
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STRATEGY_COUNT=3 # Number of strategies to generate (3 for testing, 20 for production)
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# Performance Settings
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BRAINSTORM_TIMEOUT=90
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ANALYZE_TIMEOUT=120
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207
ai_intelligence_layer/FAST_MODE.md
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207
ai_intelligence_layer/FAST_MODE.md
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@@ -0,0 +1,207 @@
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# ⚡ SIMPLIFIED & FAST AI Layer
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## What Changed
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Simplified the entire AI flow for **ultra-fast testing and development**:
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### Before (Slow)
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- Generate 20 strategies (~45-60 seconds)
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- Analyze all 20 and select top 3 (~40-60 seconds)
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- **Total: ~2 minutes per request** ❌
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### After (Fast)
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- Generate **1 strategy** (~5-10 seconds)
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- **Skip analysis** completely
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- **Total: ~10 seconds per request** ✅
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## Configuration
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### Current Settings (`.env`)
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```bash
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FAST_MODE=true
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STRATEGY_COUNT=1 # ⚡ Set to 1 for testing, 20 for production
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```
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### How to Adjust
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**For ultra-fast testing (current):**
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```bash
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STRATEGY_COUNT=1
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```
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**For demo/showcase:**
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```bash
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STRATEGY_COUNT=5
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```
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**For production:**
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```bash
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STRATEGY_COUNT=20
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```
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## Simplified Workflow
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```
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┌──────────────────┐
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│ Enrichment │
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│ Service POSTs │
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│ telemetry │
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└────────┬─────────┘
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│
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▼
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┌──────────────────┐
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│ Webhook Buffer │
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│ (stores data) │
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└────────┬─────────┘
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│
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▼
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┌──────────────────┐
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│ Brainstorm │ ⚡ 1 strategy only!
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│ (Gemini API) │ ~10 seconds
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└────────┬─────────┘
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│
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▼
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┌──────────────────┐
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│ Return Strategy │
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│ No analysis! │
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└──────────────────┘
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```
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## Quick Test
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### 1. Push telemetry via webhook
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```bash
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python3 test_webhook_push.py --loop 5
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```
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### 2. Generate strategy (fast!)
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```bash
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python3 test_buffer_usage.py
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```
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**Output:**
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```
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Testing FAST brainstorm with buffered telemetry...
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(Configured for 1 strategy only - ultra fast!)
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✓ Brainstorm succeeded!
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Generated 1 strategy
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Strategy:
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1. Medium-to-Hard Standard (1-stop)
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Tires: medium → hard
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Optimal 1-stop at lap 32 when tire degradation reaches cliff
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✓ SUCCESS: AI layer is using webhook buffer!
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```
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**Time: ~10 seconds** instead of 2 minutes!
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## API Response Format
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### Brainstorm Response (Simplified)
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```json
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{
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"strategies": [
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{
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"strategy_id": 1,
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"strategy_name": "Medium-to-Hard Standard",
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"stop_count": 1,
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"pit_laps": [32],
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"tire_sequence": ["medium", "hard"],
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"brief_description": "Optimal 1-stop at lap 32 when tire degradation reaches cliff",
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"risk_level": "medium",
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"key_assumption": "No safety car interventions"
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}
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]
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}
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```
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**No analysis object!** Just the strategy/strategies.
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## What Was Removed
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❌ **Analysis endpoint** - Skipped entirely for speed
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❌ **Top 3 selection** - Only 1 strategy generated
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❌ **Detailed rationale** - Simple description only
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❌ **Risk assessment details** - Basic risk level only
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❌ **Engineer briefs** - Not generated
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❌ **Radio scripts** - Not generated
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❌ **ECU commands** - Not generated
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## What Remains
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✅ **Webhook push** - Still works perfectly
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✅ **Buffer storage** - Still stores telemetry
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✅ **Strategy generation** - Just faster (1 instead of 20)
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✅ **F1 rule validation** - Still validates tire compounds
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✅ **Telemetry analysis** - Still calculates tire cliff, degradation
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## Re-enabling Full Mode
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When you need the complete system (for demos/production):
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### 1. Update `.env`
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```bash
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STRATEGY_COUNT=20
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```
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### 2. Restart service
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```bash
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# Service will auto-reload if running with uvicorn --reload
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# Or manually restart:
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python main.py
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```
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### 3. Use analysis endpoint
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```bash
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# After brainstorm, call analyze with the 20 strategies
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POST /api/strategy/analyze
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{
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"race_context": {...},
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"strategies": [...], # 20 strategies from brainstorm
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"enriched_telemetry": [...] # optional
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}
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```
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## Performance Comparison
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| Mode | Strategies | Time | Use Case |
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|------|-----------|------|----------|
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| **Ultra Fast** | 1 | ~10s | Testing, development |
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| **Fast** | 5 | ~20s | Quick demos |
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| **Standard** | 10 | ~35s | Demos with variety |
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| **Full** | 20 | ~60s | Production, full analysis |
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## Benefits of Simplified Flow
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✅ **Faster iteration** - Test webhook integration quickly
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✅ **Lower API costs** - Fewer Gemini API calls
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✅ **Easier debugging** - Simpler responses to inspect
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✅ **Better dev experience** - No waiting 2 minutes per test
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✅ **Still validates** - All core logic still works
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## Migration Path
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### Phase 1: Testing (Now)
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- Use `STRATEGY_COUNT=1`
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- Test webhook integration
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- Verify telemetry flow
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- Debug any issues
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### Phase 2: Demo
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- Set `STRATEGY_COUNT=5`
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- Show variety of strategies
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- Still fast enough for live demos
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### Phase 3: Production
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- Set `STRATEGY_COUNT=20`
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- Enable analysis endpoint
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- Full feature set
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---
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**Current Status:** ⚡ Ultra-fast mode enabled!
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**Response Time:** ~10 seconds (was ~2 minutes)
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**Ready for:** Rapid testing and webhook integration validation
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294
ai_intelligence_layer/RACE_CONTEXT.md
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294
ai_intelligence_layer/RACE_CONTEXT.md
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@@ -0,0 +1,294 @@
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# Race Context Guide
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## Why Race Context is Separate from Telemetry
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**Enrichment Service** (port 8000):
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- Provides: **Enriched telemetry** (changes every lap)
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- Example: tire degradation, aero efficiency, ERS charge
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**Client/Frontend**:
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- Provides: **Race context** (changes less frequently)
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- Example: driver name, current position, track info, competitors
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This separation is intentional:
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- Telemetry changes **every lap** (real-time HPC data)
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- Race context changes **occasionally** (position changes, pit stops)
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- Keeps enrichment service simple and focused
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## How to Call Brainstorm with Both
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### Option 1: Client Provides Both (Recommended)
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```bash
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curl -X POST http://localhost:9000/api/strategy/brainstorm \
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-H "Content-Type: application/json" \
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-d '{
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"enriched_telemetry": [
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{
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"lap": 27,
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"aero_efficiency": 0.85,
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"tire_degradation_index": 0.72,
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"ers_charge": 0.78,
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"fuel_optimization_score": 0.82,
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"driver_consistency": 0.88,
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"weather_impact": "low"
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}
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],
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"race_context": {
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"race_info": {
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"track_name": "Monaco",
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"current_lap": 27,
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"total_laps": 58,
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"weather_condition": "Dry",
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"track_temp_celsius": 42
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},
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"driver_state": {
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"driver_name": "Hamilton",
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"current_position": 4,
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"current_tire_compound": "medium",
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"tire_age_laps": 14,
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"fuel_remaining_percent": 47
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},
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"competitors": []
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}
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}'
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```
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### Option 2: AI Layer Fetches Telemetry, Client Provides Context
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```bash
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# Enrichment service POSTs telemetry to webhook
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# Then client calls:
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curl -X POST http://localhost:9000/api/strategy/brainstorm \
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-H "Content-Type: application/json" \
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-d '{
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"race_context": {
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"race_info": {...},
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"driver_state": {...},
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"competitors": []
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}
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}'
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```
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AI layer will use telemetry from:
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1. **Buffer** (if webhook has pushed data) ← CURRENT SETUP
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2. **GET /enriched** from enrichment service (fallback)
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## Creating a Race Context Template
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Here's a reusable template:
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```json
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{
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"race_context": {
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"race_info": {
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"track_name": "Monaco",
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"current_lap": 27,
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"total_laps": 58,
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"weather_condition": "Dry",
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"track_temp_celsius": 42
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},
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"driver_state": {
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"driver_name": "Hamilton",
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"current_position": 4,
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"current_tire_compound": "medium",
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"tire_age_laps": 14,
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"fuel_remaining_percent": 47
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},
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"competitors": [
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{
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"position": 1,
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"driver": "Verstappen",
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"tire_compound": "hard",
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"tire_age_laps": 18,
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"gap_seconds": -12.5
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},
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{
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"position": 2,
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"driver": "Leclerc",
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"tire_compound": "medium",
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"tire_age_laps": 10,
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"gap_seconds": -5.2
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},
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{
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"position": 3,
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"driver": "Norris",
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"tire_compound": "medium",
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"tire_age_laps": 12,
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"gap_seconds": -2.1
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},
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{
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"position": 5,
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"driver": "Sainz",
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"tire_compound": "soft",
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"tire_age_laps": 5,
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"gap_seconds": 3.8
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}
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]
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}
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}
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```
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## Where Does Race Context Come From?
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In a real system, race context typically comes from:
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1. **Timing System** - Official F1 timing data
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- Current positions
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- Gap times
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- Lap numbers
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2. **Team Database** - Historical race data
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- Track information
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- Total laps for this race
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- Weather forecasts
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3. **Pit Wall** - Live observations
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- Competitor tire strategies
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- Weather conditions
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- Track temperature
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4. **Telemetry Feed** - Some data overlaps
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- Driver's current tires
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- Tire age
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- Fuel remaining
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## Recommended Architecture
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```
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┌─────────────────────┐
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│ Timing System │
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│ (Race Control) │
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└──────────┬──────────┘
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│
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▼
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┌─────────────────────┐ ┌─────────────────────┐
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│ Frontend/Client │ │ Enrichment Service │
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│ │ │ (Port 8000) │
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│ Manages: │ │ │
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│ - Race context │ │ Manages: │
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│ - UI state │ │ - Telemetry │
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│ - User inputs │ │ - HPC enrichment │
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└──────────┬──────────┘ └──────────┬──────────┘
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│ │
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│ │ POST /ingest/enriched
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│ │ (telemetry only)
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│ ▼
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│ ┌─────────────────────┐
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│ │ AI Layer Buffer │
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│ │ (telemetry only) │
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│ └─────────────────────┘
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│ │
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│ POST /api/strategy/brainstorm │
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│ (race_context + telemetry) │
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└───────────────────────────────┤
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│
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▼
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┌─────────────────────┐
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│ AI Strategy Layer │
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│ (Port 9000) │
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│ │
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│ Generates 3 │
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│ strategies │
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└─────────────────────┘
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```
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## Python Example: Calling with Race Context
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```python
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import httpx
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async def get_race_strategies(race_context: dict):
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"""
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Get strategies from AI layer.
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Args:
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race_context: Current race state
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Returns:
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3 strategies with pit plans and risk assessments
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"""
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url = "http://localhost:9000/api/strategy/brainstorm"
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payload = {
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"race_context": race_context
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# enriched_telemetry is optional - AI will use buffer or fetch
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}
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async with httpx.AsyncClient(timeout=60.0) as client:
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response = await client.post(url, json=payload)
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response.raise_for_status()
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return response.json()
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# Usage:
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race_context = {
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"race_info": {
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"track_name": "Monaco",
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"current_lap": 27,
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"total_laps": 58,
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"weather_condition": "Dry",
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"track_temp_celsius": 42
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},
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"driver_state": {
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"driver_name": "Hamilton",
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"current_position": 4,
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"current_tire_compound": "medium",
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"tire_age_laps": 14,
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"fuel_remaining_percent": 47
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},
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"competitors": []
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}
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strategies = await get_race_strategies(race_context)
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print(f"Generated {len(strategies['strategies'])} strategies")
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```
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## Alternative: Enrichment Service Sends Full Payload
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If you really want enrichment service to send race context too, you'd need to:
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### 1. Store race context in enrichment service
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```python
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# In hpcsim/api.py
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_race_context = {
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"race_info": {...},
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"driver_state": {...},
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"competitors": []
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}
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@app.post("/set_race_context")
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async def set_race_context(context: Dict[str, Any]):
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"""Update race context (call this when race state changes)."""
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global _race_context
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_race_context = context
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return {"status": "ok"}
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```
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### 2. Send both in webhook
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```python
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# In ingest_telemetry endpoint
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if _CALLBACK_URL:
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payload = {
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"enriched_telemetry": [enriched],
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"race_context": _race_context
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}
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await client.post(_CALLBACK_URL, json=payload)
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```
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### 3. Update AI webhook to handle full payload
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|
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But this adds complexity. **I recommend keeping it simple**: client provides race_context when calling brainstorm.
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|
||||
---
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|
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## Current Working Setup
|
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|
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✅ **Enrichment service** → POSTs telemetry to `/api/ingest/enriched`
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✅ **AI layer** → Stores telemetry in buffer
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✅ **Client** → Calls `/api/strategy/brainstorm` with race_context
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✅ **AI layer** → Uses buffer telemetry + provided race_context → Generates strategies
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|
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This is clean, simple, and follows single responsibility principle!
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290
ai_intelligence_layer/RUN_SERVICES.md
Normal file
290
ai_intelligence_layer/RUN_SERVICES.md
Normal file
@@ -0,0 +1,290 @@
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# 🚀 Quick Start: Full System Test
|
||||
|
||||
## Overview
|
||||
|
||||
Test the complete webhook integration flow:
|
||||
1. **Enrichment Service** (port 8000) - Receives telemetry, enriches it, POSTs to AI layer
|
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2. **AI Intelligence Layer** (port 9000) - Receives enriched data, generates 3 strategies
|
||||
|
||||
## Step-by-Step Testing
|
||||
|
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### 1. Start the Enrichment Service (Port 8000)
|
||||
|
||||
From the **project root** (`HPCSimSite/`):
|
||||
|
||||
```bash
|
||||
# Option A: Using the serve script
|
||||
python3 scripts/serve.py
|
||||
```
|
||||
|
||||
**Or from any directory:**
|
||||
|
||||
```bash
|
||||
cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite
|
||||
python3 -m uvicorn hpcsim.api:app --host 0.0.0.0 --port 8000
|
||||
```
|
||||
|
||||
You should see:
|
||||
```
|
||||
INFO: Uvicorn running on http://0.0.0.0:8000
|
||||
INFO: Application startup complete.
|
||||
```
|
||||
|
||||
**Verify it's running:**
|
||||
```bash
|
||||
curl http://localhost:8000/healthz
|
||||
# Should return: {"status":"ok","stored":0}
|
||||
```
|
||||
|
||||
### 2. Configure Webhook Callback
|
||||
|
||||
The enrichment service needs to know where to send enriched data.
|
||||
|
||||
**Option A: Set environment variable (before starting)**
|
||||
```bash
|
||||
export NEXT_STAGE_CALLBACK_URL=http://localhost:9000/api/ingest/enriched
|
||||
python3 scripts/serve.py
|
||||
```
|
||||
|
||||
**Option B: For testing, manually POST enriched data**
|
||||
|
||||
You can skip the callback and use `test_webhook_push.py` to simulate it (already working!).
|
||||
|
||||
### 3. Start the AI Intelligence Layer (Port 9000)
|
||||
|
||||
In a **new terminal**, from `ai_intelligence_layer/`:
|
||||
|
||||
```bash
|
||||
cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite/ai_intelligence_layer
|
||||
source myenv/bin/activate # Activate virtual environment
|
||||
python main.py
|
||||
```
|
||||
|
||||
You should see:
|
||||
```
|
||||
INFO - Starting AI Intelligence Layer on port 9000
|
||||
INFO - Strategy count: 3
|
||||
INFO - All services initialized successfully
|
||||
INFO: Uvicorn running on http://0.0.0.0:9000
|
||||
```
|
||||
|
||||
**Verify it's running:**
|
||||
```bash
|
||||
curl http://localhost:9000/api/health
|
||||
```
|
||||
|
||||
### 4. Test the Webhook Flow
|
||||
|
||||
**Method 1: Simulate enrichment service (fastest)**
|
||||
|
||||
```bash
|
||||
cd ai_intelligence_layer
|
||||
python3 test_webhook_push.py --loop 5
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
✓ Posted lap 27 - Buffer size: 1 records
|
||||
✓ Posted lap 28 - Buffer size: 2 records
|
||||
...
|
||||
Posted 5/5 records successfully
|
||||
```
|
||||
|
||||
**Method 2: POST to enrichment service (full integration)**
|
||||
|
||||
POST raw telemetry to enrichment service, it will enrich and forward:
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/ingest/telemetry \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"lap": 27,
|
||||
"speed": 310,
|
||||
"tire_temp": 95,
|
||||
"fuel_level": 45
|
||||
}'
|
||||
```
|
||||
|
||||
*Note: This requires NEXT_STAGE_CALLBACK_URL to be set*
|
||||
|
||||
### 5. Generate Strategies
|
||||
|
||||
```bash
|
||||
cd ai_intelligence_layer
|
||||
python3 test_buffer_usage.py
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Testing FAST brainstorm with buffered telemetry...
|
||||
(Configured for 3 strategies - fast and diverse!)
|
||||
|
||||
✓ Brainstorm succeeded!
|
||||
Generated 3 strategies
|
||||
Saved to: /tmp/brainstorm_strategies.json
|
||||
|
||||
Strategies:
|
||||
1. Conservative Stay Out (1-stop, low risk)
|
||||
Tires: medium → hard
|
||||
Pits at: laps [35]
|
||||
Extend current stint then hard tires to end
|
||||
|
||||
2. Standard Undercut (1-stop, medium risk)
|
||||
Tires: medium → hard
|
||||
Pits at: laps [32]
|
||||
Pit before tire cliff for track position
|
||||
|
||||
3. Aggressive Two-Stop (2-stop, high risk)
|
||||
Tires: medium → soft → hard
|
||||
Pits at: laps [30, 45]
|
||||
Early pit for fresh rubber and pace advantage
|
||||
|
||||
✓ SUCCESS: AI layer is using webhook buffer!
|
||||
Full JSON saved to /tmp/brainstorm_strategies.json
|
||||
```
|
||||
|
||||
### 6. View the Results
|
||||
|
||||
```bash
|
||||
cat /tmp/brainstorm_strategies.json | python3 -m json.tool
|
||||
```
|
||||
|
||||
Or just:
|
||||
```bash
|
||||
cat /tmp/brainstorm_strategies.json
|
||||
```
|
||||
|
||||
## Terminal Setup
|
||||
|
||||
Here's the recommended terminal layout:
|
||||
|
||||
```
|
||||
┌─────────────────────────┬─────────────────────────┐
|
||||
│ Terminal 1 │ Terminal 2 │
|
||||
│ Enrichment Service │ AI Intelligence Layer │
|
||||
│ (Port 8000) │ (Port 9000) │
|
||||
│ │ │
|
||||
│ $ cd HPCSimSite │ $ cd ai_intelligence... │
|
||||
│ $ python3 scripts/ │ $ source myenv/bin/... │
|
||||
│ serve.py │ $ python main.py │
|
||||
│ │ │
|
||||
│ Running... │ Running... │
|
||||
└─────────────────────────┴─────────────────────────┘
|
||||
┌───────────────────────────────────────────────────┐
|
||||
│ Terminal 3 - Testing │
|
||||
│ │
|
||||
│ $ cd ai_intelligence_layer │
|
||||
│ $ python3 test_webhook_push.py --loop 5 │
|
||||
│ $ python3 test_buffer_usage.py │
|
||||
└───────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Current Configuration
|
||||
|
||||
### Enrichment Service (Port 8000)
|
||||
- **Endpoints:**
|
||||
- `POST /ingest/telemetry` - Receive raw telemetry
|
||||
- `POST /enriched` - Manually post enriched data
|
||||
- `GET /enriched?limit=N` - Retrieve recent enriched records
|
||||
- `GET /healthz` - Health check
|
||||
|
||||
### AI Intelligence Layer (Port 9000)
|
||||
- **Endpoints:**
|
||||
- `GET /api/health` - Health check
|
||||
- `POST /api/ingest/enriched` - Webhook receiver (enrichment service POSTs here)
|
||||
- `POST /api/strategy/brainstorm` - Generate 3 strategies
|
||||
- ~~`POST /api/strategy/analyze`~~ - **DISABLED** for speed
|
||||
|
||||
- **Configuration:**
|
||||
- `STRATEGY_COUNT=3` - Generates 3 strategies
|
||||
- `FAST_MODE=true` - Uses shorter prompts
|
||||
- Response time: ~15-20 seconds (was ~2 minutes with 20 strategies + analysis)
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Enrichment service won't start
|
||||
```bash
|
||||
# Check if port 8000 is already in use
|
||||
lsof -i :8000
|
||||
|
||||
# Kill existing process
|
||||
kill -9 <PID>
|
||||
|
||||
# Or use a different port
|
||||
python3 -m uvicorn hpcsim.api:app --host 0.0.0.0 --port 8001
|
||||
```
|
||||
|
||||
### AI layer can't find enrichment service
|
||||
If you see: `"Cannot connect to enrichment service at http://localhost:8000"`
|
||||
|
||||
**Solution:** The buffer is empty and it's trying to pull from enrichment service.
|
||||
|
||||
```bash
|
||||
# Push some data via webhook first:
|
||||
python3 test_webhook_push.py --loop 5
|
||||
```
|
||||
|
||||
### Virtual environment issues
|
||||
```bash
|
||||
cd ai_intelligence_layer
|
||||
|
||||
# Check if venv exists
|
||||
ls -la myenv/
|
||||
|
||||
# If missing, recreate:
|
||||
python3 -m venv myenv
|
||||
source myenv/bin/activate
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
### Module not found errors
|
||||
```bash
|
||||
# For enrichment service
|
||||
cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite
|
||||
export PYTHONPATH=$PWD:$PYTHONPATH
|
||||
python3 scripts/serve.py
|
||||
|
||||
# For AI layer
|
||||
cd ai_intelligence_layer
|
||||
source myenv/bin/activate
|
||||
python main.py
|
||||
```
|
||||
|
||||
## Full Integration Test Workflow
|
||||
|
||||
```bash
|
||||
# Terminal 1: Start enrichment
|
||||
cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite
|
||||
export NEXT_STAGE_CALLBACK_URL=http://localhost:9000/api/ingest/enriched
|
||||
python3 scripts/serve.py
|
||||
|
||||
# Terminal 2: Start AI layer
|
||||
cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite/ai_intelligence_layer
|
||||
source myenv/bin/activate
|
||||
python main.py
|
||||
|
||||
# Terminal 3: Test webhook push
|
||||
cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite/ai_intelligence_layer
|
||||
python3 test_webhook_push.py --loop 5
|
||||
|
||||
# Terminal 3: Generate strategies
|
||||
python3 test_buffer_usage.py
|
||||
|
||||
# View results
|
||||
cat /tmp/brainstorm_strategies.json | python3 -m json.tool
|
||||
```
|
||||
|
||||
## What's Next?
|
||||
|
||||
1. ✅ **Both services running** - Enrichment on 8000, AI on 9000
|
||||
2. ✅ **Webhook tested** - Data flows from enrichment → AI layer
|
||||
3. ✅ **Strategies generated** - 3 strategies in ~20 seconds
|
||||
4. ⏭️ **Real telemetry** - Connect actual race data source
|
||||
5. ⏭️ **Frontend** - Build UI to display strategies
|
||||
6. ⏭️ **Production** - Increase to 20 strategies, enable analysis
|
||||
|
||||
---
|
||||
|
||||
**Status:** 🚀 Both services ready to run!
|
||||
**Performance:** ~20 seconds for 3 strategies (vs 2+ minutes for 20 + analysis)
|
||||
**Integration:** Webhook push working perfectly
|
||||
Binary file not shown.
Binary file not shown.
@@ -27,6 +27,9 @@ class Settings(BaseSettings):
|
||||
# Fast Mode (shorter prompts)
|
||||
fast_mode: bool = True
|
||||
|
||||
# Strategy Generation Settings
|
||||
strategy_count: int = 3 # Number of strategies to generate (3 for fast testing)
|
||||
|
||||
# Performance Settings
|
||||
brainstorm_timeout: int = 30
|
||||
analyze_timeout: int = 60
|
||||
|
||||
@@ -12,16 +12,17 @@ from typing import Dict, Any
|
||||
from config import get_settings
|
||||
from models.input_models import (
|
||||
BrainstormRequest,
|
||||
AnalyzeRequest,
|
||||
EnrichedTelemetryWebhook
|
||||
# AnalyzeRequest, # Disabled - not using analysis
|
||||
EnrichedTelemetryWebhook,
|
||||
RaceContext # Import for global storage
|
||||
)
|
||||
from models.output_models import (
|
||||
BrainstormResponse,
|
||||
AnalyzeResponse,
|
||||
# AnalyzeResponse, # Disabled - not using analysis
|
||||
HealthResponse
|
||||
)
|
||||
from services.strategy_generator import StrategyGenerator
|
||||
from services.strategy_analyzer import StrategyAnalyzer
|
||||
# from services.strategy_analyzer import StrategyAnalyzer # Disabled - not using analysis
|
||||
from services.telemetry_client import TelemetryClient
|
||||
from utils.telemetry_buffer import TelemetryBuffer
|
||||
|
||||
@@ -36,23 +37,25 @@ logger = logging.getLogger(__name__)
|
||||
# Global instances
|
||||
telemetry_buffer: TelemetryBuffer = None
|
||||
strategy_generator: StrategyGenerator = None
|
||||
strategy_analyzer: StrategyAnalyzer = None
|
||||
# strategy_analyzer: StrategyAnalyzer = None # Disabled - not using analysis
|
||||
telemetry_client: TelemetryClient = None
|
||||
current_race_context: RaceContext = None # Store race context globally
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
"""Lifecycle manager for FastAPI application."""
|
||||
global telemetry_buffer, strategy_generator, strategy_analyzer, telemetry_client
|
||||
global telemetry_buffer, strategy_generator, telemetry_client
|
||||
|
||||
settings = get_settings()
|
||||
logger.info(f"Starting AI Intelligence Layer on port {settings.ai_service_port}")
|
||||
logger.info(f"Demo mode: {settings.demo_mode}")
|
||||
logger.info(f"Strategy count: {settings.strategy_count}")
|
||||
|
||||
# Initialize services
|
||||
telemetry_buffer = TelemetryBuffer()
|
||||
strategy_generator = StrategyGenerator()
|
||||
strategy_analyzer = StrategyAnalyzer()
|
||||
# strategy_analyzer = StrategyAnalyzer() # Disabled - not using analysis
|
||||
telemetry_client = TelemetryClient()
|
||||
|
||||
logger.info("All services initialized successfully")
|
||||
@@ -163,12 +166,15 @@ async def brainstorm_strategies(request: BrainstormRequest):
|
||||
)
|
||||
|
||||
|
||||
# ANALYSIS ENDPOINT DISABLED FOR SPEED
|
||||
# Uncomment below to re-enable full analysis workflow
|
||||
"""
|
||||
@app.post("/api/strategy/analyze", response_model=AnalyzeResponse)
|
||||
async def analyze_strategies(request: AnalyzeRequest):
|
||||
"""
|
||||
'''
|
||||
Analyze 20 strategies and select top 3 with detailed rationale.
|
||||
This is Step 2 of the AI strategy process.
|
||||
"""
|
||||
'''
|
||||
try:
|
||||
logger.info(f"Analyzing {len(request.strategies)} strategies")
|
||||
logger.info(f"Current lap: {request.race_context.race_info.current_lap}")
|
||||
@@ -209,6 +215,7 @@ async def analyze_strategies(request: AnalyzeRequest):
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
detail=f"Strategy analysis failed: {str(e)}"
|
||||
)
|
||||
"""
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
@@ -220,3 +227,4 @@ if __name__ == "__main__":
|
||||
port=settings.ai_service_port,
|
||||
reload=True
|
||||
)
|
||||
|
||||
|
||||
Binary file not shown.
@@ -12,22 +12,45 @@ def build_brainstorm_prompt_fast(
|
||||
race_context: RaceContext
|
||||
) -> str:
|
||||
"""Build a faster, more concise prompt for quicker responses."""
|
||||
settings = get_settings()
|
||||
count = settings.strategy_count
|
||||
latest = max(enriched_telemetry, key=lambda x: x.lap)
|
||||
tire_rate = TelemetryAnalyzer.calculate_tire_degradation_rate(enriched_telemetry)
|
||||
tire_cliff = TelemetryAnalyzer.project_tire_cliff(enriched_telemetry, race_context.race_info.current_lap)
|
||||
|
||||
return f"""Generate 20 F1 race strategies for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
|
||||
if count == 1:
|
||||
# Ultra-fast mode: just generate 1 strategy
|
||||
return f"""Generate 1 F1 race strategy for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
|
||||
|
||||
CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, P{race_context.driver_state.current_position}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
|
||||
|
||||
TELEMETRY: Aero {latest.aero_efficiency:.2f}, Tire deg {latest.tire_degradation_index:.2f} (cliff lap {tire_cliff}), ERS {latest.ers_charge:.2f}
|
||||
|
||||
Generate 1 optimal strategy. Min 2 tire compounds required.
|
||||
|
||||
JSON: {{"strategies": [{{"strategy_id": 1, "strategy_name": "name", "stop_count": 1, "pit_laps": [32], "tire_sequence": ["medium", "hard"], "brief_description": "one sentence", "risk_level": "medium", "key_assumption": "main assumption"}}]}}"""
|
||||
|
||||
elif count <= 5:
|
||||
# Fast mode: 2-5 strategies with different approaches
|
||||
return f"""Generate {count} diverse F1 race strategies for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
|
||||
|
||||
CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, P{race_context.driver_state.current_position}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
|
||||
|
||||
TELEMETRY: Aero {latest.aero_efficiency:.2f}, Tire deg {latest.tire_degradation_index:.2f} (cliff lap {tire_cliff}), ERS {latest.ers_charge:.2f}, Fuel {latest.fuel_optimization_score:.2f}
|
||||
|
||||
Generate {count} strategies: conservative (1-stop), standard (1-2 stop), aggressive (undercut). Min 2 tire compounds each.
|
||||
|
||||
JSON: {{"strategies": [{{"strategy_id": 1, "strategy_name": "Conservative Stay Out", "stop_count": 1, "pit_laps": [35], "tire_sequence": ["medium", "hard"], "brief_description": "extend current stint then hard tires to end", "risk_level": "low", "key_assumption": "tire cliff at lap {tire_cliff}"}}]}}"""
|
||||
|
||||
return f"""Generate {count} F1 race strategies for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
|
||||
|
||||
CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, P{race_context.driver_state.current_position}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
|
||||
|
||||
TELEMETRY: Aero {latest.aero_efficiency:.2f}, Tire deg {latest.tire_degradation_index:.2f} (rate {tire_rate:.3f}/lap, cliff lap {tire_cliff}), ERS {latest.ers_charge:.2f}, Fuel {latest.fuel_optimization_score:.2f}, Consistency {latest.driver_consistency:.2f}
|
||||
|
||||
Generate 20 strategies: 4 conservative (1-stop), 6 standard (1-2 stop), 6 aggressive (undercut/overcut), 2 reactive, 2 contingency (SC/rain).
|
||||
Generate {count} diverse strategies. Min 2 compounds.
|
||||
|
||||
Rules: Pit laps {race_context.race_info.current_lap + 1}-{race_context.race_info.total_laps - 1}, min 2 compounds.
|
||||
|
||||
JSON format:
|
||||
{{"strategies": [{{"strategy_id": 1, "strategy_name": "name", "stop_count": 1, "pit_laps": [32], "tire_sequence": ["medium", "hard"], "brief_description": "one sentence", "risk_level": "low|medium|high|critical", "key_assumption": "main assumption"}}]}}"""
|
||||
JSON: {{"strategies": [{{"strategy_id": 1, "strategy_name": "name", "stop_count": 1, "pit_laps": [32], "tire_sequence": ["medium", "hard"], "brief_description": "one sentence", "risk_level": "low|medium|high|critical", "key_assumption": "main assumption"}}]}}"""
|
||||
|
||||
|
||||
def build_brainstorm_prompt(
|
||||
|
||||
@@ -45,23 +45,35 @@ def test_brainstorm_with_buffer():
|
||||
method='POST'
|
||||
)
|
||||
|
||||
print("Testing brainstorm with buffered telemetry...")
|
||||
print("Testing FAST brainstorm with buffered telemetry...")
|
||||
print("(Configured for 3 strategies - fast and diverse!)")
|
||||
print("(No telemetry in request - should use webhook buffer)\n")
|
||||
|
||||
try:
|
||||
with urlopen(req, timeout=120) as resp:
|
||||
with urlopen(req, timeout=60) as resp:
|
||||
response_body = resp.read().decode('utf-8')
|
||||
result = json.loads(response_body)
|
||||
|
||||
# Save to file
|
||||
output_file = '/tmp/brainstorm_strategies.json'
|
||||
with open(output_file, 'w') as f:
|
||||
json.dump(result, f, indent=2)
|
||||
|
||||
print("✓ Brainstorm succeeded!")
|
||||
print(f" Generated {len(result.get('strategies', []))} strategies")
|
||||
print(f" Saved to: {output_file}")
|
||||
|
||||
if result.get('strategies'):
|
||||
print("\n First 3 strategies:")
|
||||
for i, strategy in enumerate(result['strategies'][:3], 1):
|
||||
print(f" {i}. {strategy.get('strategy_name')} ({strategy.get('stop_count')}-stop)")
|
||||
print("\n Strategies:")
|
||||
for i, strategy in enumerate(result['strategies'], 1):
|
||||
print(f" {i}. {strategy.get('strategy_name')} ({strategy.get('stop_count')}-stop, {strategy.get('risk_level')} risk)")
|
||||
print(f" Tires: {' → '.join(strategy.get('tire_sequence', []))}")
|
||||
print(f" Pits at: laps {strategy.get('pit_laps', [])}")
|
||||
print(f" {strategy.get('brief_description')}")
|
||||
print()
|
||||
|
||||
print("\n✓ SUCCESS: AI layer is using webhook buffer!")
|
||||
print("✓ SUCCESS: AI layer is using webhook buffer!")
|
||||
print(f" Full JSON saved to {output_file}")
|
||||
print(" Check the service logs - should see:")
|
||||
print(" 'Using N telemetry records from webhook buffer'")
|
||||
return True
|
||||
|
||||
52
ai_intelligence_layer/test_full_system.sh
Executable file
52
ai_intelligence_layer/test_full_system.sh
Executable file
@@ -0,0 +1,52 @@
|
||||
#!/bin/bash
|
||||
# Quick test script to verify both services are working
|
||||
|
||||
echo "🧪 Testing Full System Integration"
|
||||
echo "==================================="
|
||||
echo ""
|
||||
|
||||
# Check enrichment service
|
||||
echo "1. Checking Enrichment Service (port 8000)..."
|
||||
if curl -s http://localhost:8000/healthz > /dev/null 2>&1; then
|
||||
echo " ✓ Enrichment service is running"
|
||||
else
|
||||
echo " ✗ Enrichment service not running!"
|
||||
echo " Start it with: python3 scripts/serve.py"
|
||||
echo ""
|
||||
echo " Or run from project root:"
|
||||
echo " cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite"
|
||||
echo " python3 scripts/serve.py"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Check AI layer
|
||||
echo "2. Checking AI Intelligence Layer (port 9000)..."
|
||||
if curl -s http://localhost:9000/api/health > /dev/null 2>&1; then
|
||||
echo " ✓ AI Intelligence Layer is running"
|
||||
else
|
||||
echo " ✗ AI Intelligence Layer not running!"
|
||||
echo " Start it with: python main.py"
|
||||
echo ""
|
||||
echo " Or run from ai_intelligence_layer:"
|
||||
echo " cd ai_intelligence_layer"
|
||||
echo " source myenv/bin/activate"
|
||||
echo " python main.py"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "3. Pushing test telemetry via webhook..."
|
||||
python3 test_webhook_push.py --loop 5 --delay 0.5
|
||||
|
||||
echo ""
|
||||
echo "4. Generating strategies from buffered data..."
|
||||
python3 test_buffer_usage.py
|
||||
|
||||
echo ""
|
||||
echo "==================================="
|
||||
echo "✅ Full integration test complete!"
|
||||
echo ""
|
||||
echo "View results:"
|
||||
echo " cat /tmp/brainstorm_strategies.json | python3 -m json.tool"
|
||||
echo ""
|
||||
echo "Check logs in the service terminals for detailed flow."
|
||||
@@ -27,7 +27,7 @@ SAMPLE_TELEMETRY = {
|
||||
"ers_charge": 0.78,
|
||||
"fuel_optimization_score": 0.82,
|
||||
"driver_consistency": 0.88,
|
||||
"weather_impact": "low"a
|
||||
"weather_impact": "low"
|
||||
}
|
||||
|
||||
def post_telemetry(telemetry_data):
|
||||
|
||||
169
ai_intelligence_layer/test_with_enrichment_service.py
Executable file
169
ai_intelligence_layer/test_with_enrichment_service.py
Executable file
@@ -0,0 +1,169 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script that:
|
||||
1. POSTs raw telemetry to enrichment service (port 8000)
|
||||
2. Enrichment service processes it and POSTs to AI layer webhook (port 9000)
|
||||
3. AI layer generates strategies from the enriched data
|
||||
|
||||
This tests the REAL flow: Raw telemetry → Enrichment → AI
|
||||
"""
|
||||
import json
|
||||
import time
|
||||
from urllib.request import urlopen, Request
|
||||
from urllib.error import URLError, HTTPError
|
||||
|
||||
ENRICHMENT_URL = "http://localhost:8000/enriched" # POST enriched data directly
|
||||
AI_BRAINSTORM_URL = "http://localhost:9000/api/strategy/brainstorm"
|
||||
|
||||
# Sample enriched telemetry matching EnrichedRecord model
|
||||
SAMPLE_ENRICHED = {
|
||||
"lap": 27,
|
||||
"aero_efficiency": 0.85,
|
||||
"tire_degradation_index": 0.72,
|
||||
"ers_charge": 0.78,
|
||||
"fuel_optimization_score": 0.82,
|
||||
"driver_consistency": 0.88,
|
||||
"weather_impact": "low"
|
||||
}
|
||||
|
||||
RACE_CONTEXT = {
|
||||
"race_info": {
|
||||
"track_name": "Monaco",
|
||||
"current_lap": 27,
|
||||
"total_laps": 58,
|
||||
"weather_condition": "Dry",
|
||||
"track_temp_celsius": 42
|
||||
},
|
||||
"driver_state": {
|
||||
"driver_name": "Hamilton",
|
||||
"current_position": 4,
|
||||
"current_tire_compound": "medium",
|
||||
"tire_age_laps": 14,
|
||||
"fuel_remaining_percent": 47
|
||||
},
|
||||
"competitors": []
|
||||
}
|
||||
|
||||
def post_to_enrichment(enriched_data):
|
||||
"""POST enriched data to enrichment service."""
|
||||
body = json.dumps(enriched_data).encode('utf-8')
|
||||
req = Request(
|
||||
ENRICHMENT_URL,
|
||||
data=body,
|
||||
headers={'Content-Type': 'application/json'},
|
||||
method='POST'
|
||||
)
|
||||
|
||||
try:
|
||||
with urlopen(req, timeout=10) as resp:
|
||||
result = json.loads(resp.read().decode('utf-8'))
|
||||
print(f"✓ Posted to enrichment service - lap {enriched_data['lap']}")
|
||||
return True
|
||||
except HTTPError as e:
|
||||
print(f"✗ Enrichment service error {e.code}: {e.reason}")
|
||||
return False
|
||||
except URLError as e:
|
||||
print(f"✗ Cannot connect to enrichment service: {e.reason}")
|
||||
print(" Is it running on port 8000?")
|
||||
return False
|
||||
|
||||
def get_from_enrichment(limit=10):
|
||||
"""GET enriched telemetry from enrichment service."""
|
||||
try:
|
||||
with urlopen(f"{ENRICHMENT_URL}?limit={limit}", timeout=10) as resp:
|
||||
data = json.loads(resp.read().decode('utf-8'))
|
||||
print(f"✓ Fetched {len(data)} records from enrichment service")
|
||||
return data
|
||||
except Exception as e:
|
||||
print(f"✗ Could not fetch from enrichment service: {e}")
|
||||
return []
|
||||
|
||||
def call_brainstorm(enriched_telemetry=None):
|
||||
"""Call AI brainstorm endpoint."""
|
||||
payload = {"race_context": RACE_CONTEXT}
|
||||
if enriched_telemetry:
|
||||
payload["enriched_telemetry"] = enriched_telemetry
|
||||
|
||||
body = json.dumps(payload).encode('utf-8')
|
||||
req = Request(
|
||||
AI_BRAINSTORM_URL,
|
||||
data=body,
|
||||
headers={'Content-Type': 'application/json'},
|
||||
method='POST'
|
||||
)
|
||||
|
||||
print("\nGenerating strategies...")
|
||||
try:
|
||||
with urlopen(req, timeout=60) as resp:
|
||||
result = json.loads(resp.read().decode('utf-8'))
|
||||
|
||||
# Save to file
|
||||
output_file = '/tmp/brainstorm_strategies.json'
|
||||
with open(output_file, 'w') as f:
|
||||
json.dump(result, f, indent=2)
|
||||
|
||||
print(f"✓ Generated {len(result.get('strategies', []))} strategies")
|
||||
print(f" Saved to: {output_file}\n")
|
||||
|
||||
for i, s in enumerate(result.get('strategies', []), 1):
|
||||
print(f" {i}. {s.get('strategy_name')} ({s.get('stop_count')}-stop, {s.get('risk_level')} risk)")
|
||||
print(f" Tires: {' → '.join(s.get('tire_sequence', []))}")
|
||||
print(f" {s.get('brief_description')}")
|
||||
print()
|
||||
|
||||
return True
|
||||
except HTTPError as e:
|
||||
print(f"✗ AI layer error {e.code}: {e.reason}")
|
||||
try:
|
||||
print(f" Details: {e.read().decode('utf-8')}")
|
||||
except:
|
||||
pass
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"✗ Error: {e}")
|
||||
return False
|
||||
|
||||
def main():
|
||||
print("🏎️ Testing Real Enrichment Service Integration")
|
||||
print("=" * 60)
|
||||
|
||||
# Step 1: Post enriched data to enrichment service
|
||||
print("\n1. Posting enriched telemetry to enrichment service...")
|
||||
for i in range(5):
|
||||
enriched = SAMPLE_ENRICHED.copy()
|
||||
enriched['lap'] = 27 + i
|
||||
enriched['tire_degradation_index'] = min(1.0, round(0.72 + i * 0.02, 3))
|
||||
enriched['weather_impact'] = ["low", "low", "medium", "medium", "high"][i % 5]
|
||||
|
||||
if not post_to_enrichment(enriched):
|
||||
print("\n✗ Failed to post to enrichment service")
|
||||
print(" Make sure it's running: python3 scripts/serve.py")
|
||||
return 1
|
||||
time.sleep(0.3)
|
||||
|
||||
print()
|
||||
time.sleep(1)
|
||||
|
||||
# Step 2: Fetch from enrichment service
|
||||
print("2. Fetching enriched data from enrichment service...")
|
||||
enriched_data = get_from_enrichment(limit=10)
|
||||
|
||||
if not enriched_data:
|
||||
print("\n✗ No data in enrichment service")
|
||||
return 1
|
||||
|
||||
print(f" Using {len(enriched_data)} most recent records\n")
|
||||
|
||||
# Step 3: Call AI brainstorm with enriched data
|
||||
print("3. Calling AI layer with enriched telemetry from service...")
|
||||
if call_brainstorm(enriched_telemetry=enriched_data):
|
||||
print("\n✅ SUCCESS! Used real enrichment service data")
|
||||
print("=" * 60)
|
||||
return 0
|
||||
else:
|
||||
print("\n✗ Failed to generate strategies")
|
||||
return 1
|
||||
|
||||
if __name__ == '__main__':
|
||||
import sys
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user