183 lines
8.5 KiB
Python
183 lines
8.5 KiB
Python
"""
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Prompt template for strategy brainstorming.
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"""
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from typing import List
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from models.input_models import EnrichedTelemetryWebhook, RaceContext
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from utils.validators import TelemetryAnalyzer
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from config import get_settings
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def build_brainstorm_prompt_fast(
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enriched_telemetry: List[EnrichedTelemetryWebhook],
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race_context: RaceContext
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) -> str:
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"""Build a faster, more concise prompt for quicker responses (lap-level data)."""
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settings = get_settings()
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count = settings.strategy_count
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latest = max(enriched_telemetry, key=lambda x: x.lap)
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pit_window = latest.optimal_pit_window
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# Format position and competitive info
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position = race_context.driver_state.current_position
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gap_to_leader = race_context.driver_state.gap_to_leader
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gap_to_ahead = race_context.driver_state.gap_to_ahead
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comp_info = f"P{position}"
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if gap_to_ahead > 0:
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comp_info += f", {gap_to_ahead:.1f}s behind P{position-1}"
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if gap_to_leader > 0 and position > 1:
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comp_info += f", {gap_to_leader:.1f}s from leader"
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if count == 1:
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# Ultra-fast mode: just generate 1 strategy
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return f"""Generate 1 F1 race strategy for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
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CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, {comp_info}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
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TELEMETRY: Tire deg {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Position trend {latest.position_trend}, Competitive pressure {latest.competitive_pressure:.2f}
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Generate 1 optimal strategy considering competitive position. Min 2 tire compounds required.
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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"}}]}}"""
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elif count <= 5:
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# Fast mode: 2-5 strategies with different approaches
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return f"""Generate {count} diverse F1 race strategies for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
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CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, {comp_info}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
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TELEMETRY: Tire deg {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Delta {latest.performance_delta:+.2f}s, Position trend {latest.position_trend}, Competitive pressure {latest.competitive_pressure:.2f}
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COMPETITIVE SITUATION: Gap to ahead {gap_to_ahead:.1f}s ({"DRS RANGE - attack opportunity!" if gap_to_ahead < 1.0 else "close battle" if gap_to_ahead < 3.0 else "need to push"})
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Generate {count} strategies balancing tire management with competitive pressure. Consider if aggressive undercut makes sense given gaps. Min 2 tire compounds each.
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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 risk stays below 0.7"}}]}}"""
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return f"""Generate {count} F1 race strategies for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
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CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, {comp_info}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
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TELEMETRY: Tire deg {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Delta {latest.performance_delta:+.2f}s, Position trend {latest.position_trend}, Competitive pressure {latest.competitive_pressure:.2f}
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COMPETITIVE: Gap ahead {gap_to_ahead:.1f}s, Position trending {latest.position_trend}
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Generate {count} diverse strategies considering both tire management AND competitive positioning. Min 2 compounds.
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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"}}]}}"""
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def build_brainstorm_prompt(
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enriched_telemetry: List[EnrichedTelemetryWebhook],
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race_context: RaceContext
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) -> str:
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"""
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Build the brainstorm prompt for Gemini.
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Args:
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enriched_telemetry: Recent enriched telemetry data
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race_context: Current race context
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Returns:
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Formatted prompt string
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"""
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# Get latest telemetry
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latest = max(enriched_telemetry, key=lambda x: x.lap)
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# Format telemetry data (lap-level)
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telemetry_data = []
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for t in sorted(enriched_telemetry, key=lambda x: x.lap, reverse=True)[:10]:
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telemetry_data.append({
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"lap": t.lap,
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"tire_degradation_rate": round(t.tire_degradation_rate, 3),
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"pace_trend": t.pace_trend,
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"tire_cliff_risk": round(t.tire_cliff_risk, 3),
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"optimal_pit_window": t.optimal_pit_window,
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"performance_delta": round(t.performance_delta, 2),
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"competitive_pressure": round(t.competitive_pressure, 3),
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"position_trend": t.position_trend
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})
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# Format competitors
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competitors_data = []
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for c in race_context.competitors:
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competitors_data.append({
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"position": c.position,
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"driver": c.driver,
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"tire_compound": c.tire_compound,
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"tire_age_laps": c.tire_age_laps,
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"gap_seconds": round(c.gap_seconds, 1)
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})
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prompt = f"""You are an expert F1 strategist. Generate 20 diverse race strategies based on lap-level telemetry AND competitive positioning.
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LAP-LEVEL TELEMETRY METRICS:
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- tire_degradation_rate: 0-1 (higher = worse tire wear)
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- tire_cliff_risk: 0-1 (probability of hitting tire cliff)
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- pace_trend: "improving", "stable", or "declining"
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- position_trend: "gaining", "stable", or "losing" positions
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- competitive_pressure: 0-1 (combined metric from position and gaps)
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- optimal_pit_window: [start_lap, end_lap] recommended pit range
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- performance_delta: seconds vs baseline (negative = slower)
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RACE STATE:
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Track: {race_context.race_info.track_name}
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Current Lap: {race_context.race_info.current_lap} / {race_context.race_info.total_laps}
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Weather: {race_context.race_info.weather_condition}
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Track Temperature: {race_context.race_info.track_temp_celsius}°C
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DRIVER STATE:
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Driver: {race_context.driver_state.driver_name}
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Position: P{race_context.driver_state.current_position}
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Current Tires: {race_context.driver_state.current_tire_compound} ({race_context.driver_state.tire_age_laps} laps old)
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Fuel Remaining: {race_context.driver_state.fuel_remaining_percent}%
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COMPETITORS:
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{competitors_data}
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ENRICHED TELEMETRY (Last {len(telemetry_data)} laps, newest first):
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{telemetry_data}
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KEY INSIGHTS:
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- Latest tire degradation rate: {latest.tire_degradation_rate:.3f}
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- Latest tire cliff risk: {latest.tire_cliff_risk:.3f}
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- Latest pace trend: {latest.pace_trend}
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- Optimal pit window: Laps {latest.optimal_pit_window[0]}-{latest.optimal_pit_window[1]}
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- Laps remaining: {race_context.race_info.total_laps - race_context.race_info.current_lap}
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TASK: Generate exactly 20 diverse strategies.
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DIVERSITY: Conservative (1-stop), Standard (balanced), Aggressive (undercut), Reactive (competitor), Contingency (safety car)
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RULES:
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- Pit laps: {race_context.race_info.current_lap + 1} to {race_context.race_info.total_laps - 1}
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- Min 2 tire compounds (F1 rule)
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- Consider optimal pit window and tire cliff risk
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For each strategy provide:
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- strategy_id: 1-20
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- strategy_name: Short descriptive name
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- stop_count: 1, 2, or 3
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- pit_laps: [array of lap numbers]
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- tire_sequence: [array of compounds: "soft", "medium", "hard"]
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- brief_description: One sentence rationale
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- risk_level: "low", "medium", "high", or "critical"
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- key_assumption: Main assumption this strategy relies on
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OUTPUT FORMAT (JSON only, no markdown):
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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": "Conservative 1-Stop",
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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": "Extend mediums to lap 32, safe finish on hards",
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"risk_level": "low",
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"key_assumption": "Tire degradation stays below 0.85 until lap 32"
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}}
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]
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}}"""
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return prompt
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