125 lines
4 KiB
Python
125 lines
4 KiB
Python
"""
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Provider Health Dashboard + Credit Burn Tracking
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=================================================
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Live status of all 10 embedding providers + enterprise credit burn rate.
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"""
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import logging
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import time
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from app.rate_limiter import get_dispatcher
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logger = logging.getLogger("health.dashboard")
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async def provider_health() -> dict:
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"""Live health status of all embedding providers."""
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d = await get_dispatcher()
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rl = await d.tracker.stats()
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providers = []
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for p in rl["providers"]:
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# Determine health status
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day_pct = p["day_pct"]
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if not p["available"]:
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status = "exhausted" if day_pct >= 100 else "rate_limited"
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elif day_pct > 80:
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status = "warning"
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elif day_pct > 50:
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status = "active_warm"
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else:
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status = "healthy"
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providers.append(
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{
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"id": p["id"],
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"name": p["name"],
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"status": status,
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"day_used": p["day_used"],
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"day_limit": p["day_limit"],
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"day_pct": p["day_pct"],
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"minute_used": p["minute_used"],
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"minute_limit": p["minute_limit"],
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"total_calls": p["total"],
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"free": p["free"],
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}
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)
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healthy = sum(1 for p in providers if p["status"] == "healthy")
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warning = sum(1 for p in providers if p["status"] == "warning")
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degraded = sum(1 for p in providers if p["status"] in ("active_warm", "rate_limited"))
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exhausted = sum(1 for p in providers if p["status"] == "exhausted")
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return {
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"summary": {
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"total": len(providers),
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"healthy": healthy,
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"warning": warning,
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"degraded": degraded,
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"exhausted": exhausted,
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},
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"providers": providers,
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"cache": d.cache.stats(),
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"timestamp": time.time(),
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}
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async def credit_burn() -> dict:
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"""Enterprise credit burn tracking — daily rate, projected exhaustion."""
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d = await get_dispatcher()
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rl = await d.tracker.stats()
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# Calculate cost estimates
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# Vertex AI: $0.000025 per 1K chars (~$0.000025 per embedding call)
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# Gemini AI Studio: free up to 1,500/day per key
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# OpenRouter: free with credits
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# Mistral: free (1B tokens/month)
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total_calls_today = 0
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total_free_limit = 0
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provider_usage = []
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for p in rl["providers"]:
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total_calls_today += p["day_used"]
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total_free_limit += p["day_limit"] if p["free"] else 0
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# Estimate cost (only Vertex AI burns credits from enterprise trial)
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cost_est = 0
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if p["id"] == "vertex_ai":
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# $0.000025 per embedding call (approximate)
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cost_est = round(p["day_used"] * 0.000025, 6)
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if p["day_used"] > 0:
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provider_usage.append(
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{
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"name": p["name"],
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"calls_today": p["day_used"],
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"estimated_cost_usd": cost_est,
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"free": p["free"],
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}
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)
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# $300 trial, assume 90 days started ~June 1, 2026
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trial_start = time.mktime(time.strptime("2026-06-01", "%Y-%m-%d"))
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trial_end = trial_start + 90 * 86400
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days_remaining = max(0, (trial_end - time.time()) / 86400)
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# Daily burn rate estimate
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daily_cost = sum(p["estimated_cost_usd"] for p in provider_usage)
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return {
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"trial": {
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"credits": 300,
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"days_remaining": round(days_remaining, 0),
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"exhaustion_date": "2026-08-30" if days_remaining > 0 else "now",
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},
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"today": {
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"total_calls": total_calls_today,
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"free_limit": total_free_limit,
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"utilization_pct": round(total_calls_today / max(total_free_limit, 1) * 100, 2),
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"estimated_cost_usd": daily_cost,
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"daily_burn_rate": f"${daily_cost:.6f}",
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},
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"provider_usage": provider_usage,
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"note": "Gemini, Mistral, NVIDIA, and OpenRouter are FREE. Only Vertex AI burns enterprise credits.",
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}
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