reduce number of predictions
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rishubm
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ai_intelligence_layer/RUN_SERVICES.md
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290
ai_intelligence_layer/RUN_SERVICES.md
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# 🚀 Quick Start: Full System Test
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## Overview
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Test the complete webhook integration flow:
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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
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## Step-by-Step Testing
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### 1. Start the Enrichment Service (Port 8000)
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From the **project root** (`HPCSimSite/`):
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```bash
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# Option A: Using the serve script
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python3 scripts/serve.py
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```
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**Or from any directory:**
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```bash
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cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite
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python3 -m uvicorn hpcsim.api:app --host 0.0.0.0 --port 8000
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```
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You should see:
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```
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INFO: Uvicorn running on http://0.0.0.0:8000
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INFO: Application startup complete.
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```
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**Verify it's running:**
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```bash
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curl http://localhost:8000/healthz
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# Should return: {"status":"ok","stored":0}
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```
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### 2. Configure Webhook Callback
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The enrichment service needs to know where to send enriched data.
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**Option A: Set environment variable (before starting)**
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```bash
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export NEXT_STAGE_CALLBACK_URL=http://localhost:9000/api/ingest/enriched
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python3 scripts/serve.py
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```
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**Option B: For testing, manually POST enriched data**
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You can skip the callback and use `test_webhook_push.py` to simulate it (already working!).
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### 3. Start the AI Intelligence Layer (Port 9000)
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In a **new terminal**, from `ai_intelligence_layer/`:
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```bash
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cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite/ai_intelligence_layer
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source myenv/bin/activate # Activate virtual environment
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python main.py
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```
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You should see:
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```
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INFO - Starting AI Intelligence Layer on port 9000
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INFO - Strategy count: 3
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INFO - All services initialized successfully
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INFO: Uvicorn running on http://0.0.0.0:9000
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```
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**Verify it's running:**
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```bash
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curl http://localhost:9000/api/health
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```
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### 4. Test the Webhook Flow
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**Method 1: Simulate enrichment service (fastest)**
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```bash
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cd ai_intelligence_layer
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python3 test_webhook_push.py --loop 5
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```
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Output:
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```
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✓ Posted lap 27 - Buffer size: 1 records
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✓ Posted lap 28 - Buffer size: 2 records
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...
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Posted 5/5 records successfully
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```
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**Method 2: POST to enrichment service (full integration)**
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POST raw telemetry to enrichment service, it will enrich and forward:
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```bash
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curl -X POST http://localhost:8000/ingest/telemetry \
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-H "Content-Type: application/json" \
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-d '{
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"lap": 27,
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"speed": 310,
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"tire_temp": 95,
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"fuel_level": 45
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}'
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```
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*Note: This requires NEXT_STAGE_CALLBACK_URL to be set*
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### 5. Generate Strategies
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```bash
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cd ai_intelligence_layer
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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 3 strategies - fast and diverse!)
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✓ Brainstorm succeeded!
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Generated 3 strategies
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Saved to: /tmp/brainstorm_strategies.json
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Strategies:
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1. Conservative Stay Out (1-stop, low risk)
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Tires: medium → hard
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Pits at: laps [35]
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Extend current stint then hard tires to end
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2. Standard Undercut (1-stop, medium risk)
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Tires: medium → hard
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Pits at: laps [32]
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Pit before tire cliff for track position
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3. Aggressive Two-Stop (2-stop, high risk)
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Tires: medium → soft → hard
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Pits at: laps [30, 45]
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Early pit for fresh rubber and pace advantage
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✓ SUCCESS: AI layer is using webhook buffer!
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Full JSON saved to /tmp/brainstorm_strategies.json
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```
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### 6. View the Results
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```bash
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cat /tmp/brainstorm_strategies.json | python3 -m json.tool
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```
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Or just:
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```bash
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cat /tmp/brainstorm_strategies.json
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```
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## Terminal Setup
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Here's the recommended terminal layout:
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```
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┌─────────────────────────┬─────────────────────────┐
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│ Terminal 1 │ Terminal 2 │
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│ Enrichment Service │ AI Intelligence Layer │
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│ (Port 8000) │ (Port 9000) │
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│ │ │
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│ $ cd HPCSimSite │ $ cd ai_intelligence... │
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│ $ python3 scripts/ │ $ source myenv/bin/... │
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│ serve.py │ $ python main.py │
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│ │ │
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│ Running... │ Running... │
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└─────────────────────────┴─────────────────────────┘
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┌───────────────────────────────────────────────────┐
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│ Terminal 3 - Testing │
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│ │
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│ $ cd ai_intelligence_layer │
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│ $ python3 test_webhook_push.py --loop 5 │
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│ $ python3 test_buffer_usage.py │
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└───────────────────────────────────────────────────┘
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```
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## Current Configuration
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### Enrichment Service (Port 8000)
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- **Endpoints:**
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- `POST /ingest/telemetry` - Receive raw telemetry
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- `POST /enriched` - Manually post enriched data
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- `GET /enriched?limit=N` - Retrieve recent enriched records
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- `GET /healthz` - Health check
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### AI Intelligence Layer (Port 9000)
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- **Endpoints:**
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- `GET /api/health` - Health check
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- `POST /api/ingest/enriched` - Webhook receiver (enrichment service POSTs here)
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- `POST /api/strategy/brainstorm` - Generate 3 strategies
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- ~~`POST /api/strategy/analyze`~~ - **DISABLED** for speed
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- **Configuration:**
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- `STRATEGY_COUNT=3` - Generates 3 strategies
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- `FAST_MODE=true` - Uses shorter prompts
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- Response time: ~15-20 seconds (was ~2 minutes with 20 strategies + analysis)
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## Troubleshooting
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### Enrichment service won't start
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```bash
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# Check if port 8000 is already in use
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lsof -i :8000
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# Kill existing process
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kill -9 <PID>
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# Or use a different port
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python3 -m uvicorn hpcsim.api:app --host 0.0.0.0 --port 8001
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```
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### AI layer can't find enrichment service
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If you see: `"Cannot connect to enrichment service at http://localhost:8000"`
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**Solution:** The buffer is empty and it's trying to pull from enrichment service.
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```bash
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# Push some data via webhook first:
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python3 test_webhook_push.py --loop 5
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```
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### Virtual environment issues
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```bash
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cd ai_intelligence_layer
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# Check if venv exists
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ls -la myenv/
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# If missing, recreate:
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python3 -m venv myenv
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source myenv/bin/activate
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pip install -r requirements.txt
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```
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### Module not found errors
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```bash
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# For enrichment service
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cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite
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export PYTHONPATH=$PWD:$PYTHONPATH
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python3 scripts/serve.py
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# For AI layer
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cd ai_intelligence_layer
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source myenv/bin/activate
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python main.py
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```
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## Full Integration Test Workflow
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```bash
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# Terminal 1: Start enrichment
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cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite
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export NEXT_STAGE_CALLBACK_URL=http://localhost:9000/api/ingest/enriched
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python3 scripts/serve.py
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# Terminal 2: Start AI layer
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cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite/ai_intelligence_layer
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source myenv/bin/activate
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python main.py
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# Terminal 3: Test webhook push
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cd /Users/rishubmadhav/Documents/GitHub/HPCSimSite/ai_intelligence_layer
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python3 test_webhook_push.py --loop 5
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# Terminal 3: Generate strategies
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python3 test_buffer_usage.py
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# View results
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cat /tmp/brainstorm_strategies.json | python3 -m json.tool
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```
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## What's Next?
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1. ✅ **Both services running** - Enrichment on 8000, AI on 9000
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2. ✅ **Webhook tested** - Data flows from enrichment → AI layer
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3. ✅ **Strategies generated** - 3 strategies in ~20 seconds
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4. ⏭️ **Real telemetry** - Connect actual race data source
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5. ⏭️ **Frontend** - Build UI to display strategies
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6. ⏭️ **Production** - Increase to 20 strategies, enable analysis
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---
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**Status:** 🚀 Both services ready to run!
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**Performance:** ~20 seconds for 3 strategies (vs 2+ minutes for 20 + analysis)
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**Integration:** Webhook push working perfectly
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