feat(health): implement wildcard resolution and provider grouping for v1.6.2
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vai committed
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+155
-156
+155
-156
@@ -2,10 +2,10 @@
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title: LiteLLM Dashboard
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author: Kagamine Rin & VAI
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author_url: https://rin.captcouch.xyz
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description: High-performance LiteLLM observability — Comprehensive health, cost breakdowns, and request history.
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description: Professional LiteLLM observability — Grouped health, wildcard resolution, and Billion-scale token tracking.
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required_open_webui_version: 0.4.0
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requirements: requests
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version: 1.6.0
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version: 1.6.2
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licence: MIT
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"""
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@@ -21,12 +21,13 @@ class Tools:
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def __init__(self):
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self.valves = self.Valves()
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self._price_cache = {}
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self._model_info_cache = []
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class Valves(BaseModel):
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"""LiteLLM proxy connection settings."""
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litellm_url: str = Field(
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default="http://localhost:4000",
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description="LiteLLM proxy base URL (e.g. http://localhost:4000)",
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description="LiteLLM proxy base URL",
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)
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litellm_master_key: str = Field(
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default="",
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@@ -35,15 +36,15 @@ class Tools:
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)
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default_hide_zero_spend: bool = Field(
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default=True,
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description="Default behavior for zero-spend model filtering. When True, models with sub-$0.001 spend are hidden by default.",
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description="Default behavior for zero-spend model filtering.",
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)
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timezone: str = Field(
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default="UTC",
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description="IANA Timezone name (e.g. America/New_York) for date resolution.",
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description="IANA Timezone name for date resolution.",
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)
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enable_dynamic_pricing: bool = Field(
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default=True,
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description="When True, attempts to fetch live pricing from /v1/model/info for the Audit Engine.",
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description="When True, attempts to fetch live pricing from /v1/model/info.",
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)
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def _headers(self) -> dict:
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@@ -54,16 +55,35 @@ class Tools:
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def _base_url(self) -> str:
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return self.valves.litellm_url.rstrip("/")
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def _fetch_live_prices(self):
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"""Sync internal price cache with LiteLLM's live model registry."""
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def _compact_name(self, name: str, max_len: int = 28) -> str:
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"""Strip redundant provider prefixes and truncate carefully."""
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prefixes = ["gemini/", "openai/", "ollama-cloud/", "anthropic/", "mistral/", "azure/"]
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for p in prefixes:
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if name.startswith(p):
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name = name[len(p):]
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break
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return (name[:max_len-1] + "…") if len(name) > max_len else name
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def _format_tokens(self, n: int) -> str:
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"""Scale tokens to human-readable units (k, M, B)."""
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if n >= 1_000_000_000:
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return f"{n / 1_000_000_000:.2f}B"
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if n >= 1_000_000:
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return f"{n / 1_000_000:.2f}M"
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if n >= 1_000:
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return f"{n / 1_000:.1f}k"
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return str(n)
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def _fetch_live_data(self):
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"""Sync internal price and model cache."""
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if not self.valves.enable_dynamic_pricing or self._price_cache:
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return
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try:
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url = f"{self._base_url()}/v1/model/info"
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resp = requests.get(url, headers=self._headers(), timeout=10)
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if resp.status_code == 200:
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data = resp.json().get("data", [])
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for m in data:
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self._model_info_cache = resp.json().get("data", [])
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for m in self._model_info_cache:
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name = m.get("model_name")
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info = m.get("model_info", {})
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if name and info:
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@@ -71,10 +91,31 @@ class Tools:
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"input": info.get("input_cost_per_token", 0),
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"input_cached": info.get("cache_read_input_token_cost", 0),
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"output": info.get("output_cost_per_token", 0),
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"reasoning": info.get("output_cost_per_reasoning_token", info.get("output_cost_per_token", 0))
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"reasoning": info.get("output_cost_per_reasoning_token", info.get("output_cost_per_token", 0)),
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"search_fee": info.get("search_context_cost_per_query", {}).get("search_context_size_low", 0)
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}
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except Exception: pass
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def _audit_cost(self, model: str, prompt: int, cached: int, completion: int, reported_cost: float, reasoning: int = 0, has_search: bool = False) -> str:
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self._fetch_live_data()
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pricing = self._price_cache.get(model)
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if not pricing: return ""
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uncached = max(0, prompt - cached)
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actual_output = max(0, completion - reasoning)
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calc = (cached * (pricing.get("input_cached") or 0)) + \
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(uncached * (pricing.get("input") or 0)) + \
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(actual_output * (pricing.get("output") or 0)) + \
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(reasoning * (pricing.get("reasoning") or pricing.get("output") or 0))
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if has_search:
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calc += pricing.get("search_fee", 0)
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delta = reported_cost - calc
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symbol = "⚠️" if abs(delta) > 0.005 else "✓"
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return f" [calc: ${calc:.4f} {symbol}]"
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def _resolve_date(self, date_str: str) -> str:
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date_str = date_str.strip().lower()
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try:
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@@ -88,47 +129,37 @@ class Tools:
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n = int(date_str.split()[0])
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return (today - timedelta(days=n)).isoformat()
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except Exception: pass
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# Day names
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days = ["monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday"]
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if date_str in days:
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target_day = days.index(date_str)
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days_back = (today.weekday() - target_day) % 7
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return (today - timedelta(days=days_back)).isoformat()
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try:
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datetime.strptime(date_str, "%Y-%m-%d")
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return date_str
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except ValueError: pass
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return date_str
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def _audit_cost(self, model: str, prompt: int, cached: int, completion: int, reported_cost: float, reasoning: int = 0) -> str:
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"""Audit reported cost against dynamic pricing."""
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self._fetch_live_prices()
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pricing = self._price_cache.get(model)
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if not pricing: return ""
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uncached = max(0, prompt - cached)
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actual_output = max(0, completion - reasoning)
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calc = (cached * (pricing.get("input_cached") or 0)) + \
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(uncached * (pricing.get("input") or 0)) + \
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(actual_output * (pricing.get("output") or 0)) + \
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(reasoning * (pricing.get("reasoning") or pricing.get("output") or 0))
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delta = reported_cost - calc
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symbol = "⚠️" if abs(delta) > 0.005 else "✓"
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return f" [calc: ${calc:.4f} {symbol}]"
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def litellm_health(self) -> str:
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"""
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Check LiteLLM proxy health and authentication status.
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Returns detailed model health, rate limits, and infrastructure readiness.
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"""
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lines = [f"LiteLLM Health Check — {self._base_url()}"]
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lines.append("=" * 60)
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"""Check LiteLLM proxy health, proactive budget, and grouped model readiness."""
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self._fetch_live_data()
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lines = [f"LiteLLM Health & Budget — {self._base_url()}"]
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lines.append("=" * 70)
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# Readiness Header
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# 1. Budget Sentinel
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if self.valves.litellm_master_key:
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try:
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r = requests.get(f"{self._base_url()}/spend/keys", headers=self._headers(), timeout=10)
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if r.status_code == 200:
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for k in r.json():
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spend, limit = k.get("spend", 0), k.get("max_budget")
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if limit:
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pct = (spend / limit) * 100
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alert = "🚨 [CRITICAL]" if pct > 90 else "💳"
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lines.append(f"{alert} Key: {k.get('key_name')} | Spend: ${spend:.3f} / ${limit:.2f} ({pct:.1f}%)")
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except Exception: pass
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# 2. Infrastructure Readiness
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try:
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r = requests.get(f"{self._base_url()}/health/readiness", timeout=5)
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if r.status_code == 200:
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@@ -136,73 +167,61 @@ class Tools:
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lines.append(f"✅ Proxy: {d.get('status')} | DB: {d.get('db')} | v{d.get('litellm_version')}")
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except Exception: pass
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if not self.valves.litellm_master_key:
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lines.append("⚠️ No master key configured — showing public health only")
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else:
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lines.append("✅ Authenticated with master key")
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# 3. Model Fleet (Grouped by Provider + Wildcard Linkage)
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try:
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url = f"{self._base_url()}/health"
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resp = requests.get(url, headers=self._headers(), timeout=15)
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if resp.status_code != 200:
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return f"❌ Unexpected status {resp.status_code}: {resp.text[:500]}"
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data = resp.json()
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healthy = data.get("healthy_endpoints", [])
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unhealthy = data.get("unhealthy_endpoints", [])
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lines.append(f"\nHealthy: {len(healthy)} | Unhealthy: {len(unhealthy)}")
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if healthy:
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lines.append(f"\n--- Healthy Models ({len(healthy)}) ---")
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for ep in healthy:
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model = ep.get("model", "unknown")
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display = model.replace("gemini/", "").replace("openai/", "")
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rpm = ep.get("rpm", "?")
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timeout = ep.get("timeout", "?")
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lines.append(f" ✅ {display:30s} — rpm: {rpm:>3}, timeout: {timeout}s")
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if resp.status_code == 200:
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hd = resp.json()
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healthy = hd.get("healthy_endpoints", [])
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unhealthy = hd.get("unhealthy_endpoints", [])
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groups = {}
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for ep in healthy + unhealthy:
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m_raw = ep.get("model", "unknown")
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# Wildcard Linkage: Cross-reference with model_info_cache
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m_display = self._compact_name(m_raw, 32)
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for info in self._model_info_cache:
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# If this model name is the 'health_check_model' for a wildcard
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if info.get("model_info", {}).get("health_check_model") == m_raw:
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m_display = f"{self._compact_name(info.get('model_name'), 25)} (via {m_display.split('/')[-1]})"
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break
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if unhealthy:
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lines.append(f"\n--- Unhealthy Models ({len(unhealthy)}) ---")
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for ep in unhealthy:
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lines.append(f" ❌ {ep.get('model', 'unknown')}")
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provider = m_raw.split("/")[0] if "/" in m_raw else "other"
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if provider not in groups: groups[provider] = {"ok": [], "fail": []}
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icon = "✅" if ep in healthy else "❌"
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info_line = f" {icon} {m_display:35s} | rpm: {ep.get('rpm','?'):>3}"
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if ep in healthy: groups[provider]["ok"].append(info_line)
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else: groups[provider]["fail"].append(info_line)
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except Exception as e:
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return f"❌ Connection failed: {str(e)}"
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lines.append(f"\nFleet Status: {len(healthy)} healthy, {len(unhealthy)} unhealthy")
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for prov, stats in groups.items():
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lines.append(f"\n--- {prov.upper()} ---")
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lines.extend(stats["ok"] + stats["fail"])
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else: lines.append(f"\n❌ Fleet Check Failed: Status {resp.status_code}")
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except Exception as e: lines.append(f"\n❌ Fleet Check Failed: {str(e)}")
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return "\n".join(lines)
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def litellm_cost(self, start_date: str, end_date: str, hide_zero_spend: bool = False) -> str:
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"""
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Get LiteLLM cost breakdown for a date range.
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Returns per-model and per-day spend, token counts, cache hit rate, and trend sparklines.
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"""
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if not self.valves.litellm_master_key:
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return "❌ Master key required for cost data."
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"""Comprehensive cost report with Billion-scale token tracking and sparklines."""
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if not self.valves.litellm_master_key: return "❌ Master key required."
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effective_hide = hide_zero_spend or self.valves.default_hide_zero_spend
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r_start = self._resolve_date(start_date)
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r_end = self._resolve_date(end_date)
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r_start, r_end = self._resolve_date(start_date), self._resolve_date(end_date)
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url = f"{self._base_url()}/user/daily/activity"
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try:
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url = f"{self._base_url()}/user/daily/activity"
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resp = requests.get(url, headers=self._headers(), params={"start_date": r_start, "end_date": r_end}, timeout=30)
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if resp.status_code != 200: return f"❌ Unexpected status {resp.status_code}"
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if resp.status_code != 200: return f"❌ Error {resp.status_code}"
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data = resp.json()
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except Exception as e: return f"❌ Connection failed: {str(e)}"
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except Exception as e: return f"❌ Failed: {str(e)}"
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results = data.get("results", [])
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metadata = data.get("metadata", {})
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if not results: return f"No cost data found for {r_start} to {r_end}."
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# Totals
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total_spend = metadata.get("total_spend", 0)
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total_prompt = metadata.get("total_prompt_tokens", 0)
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total_completion = metadata.get("total_completion_tokens", 0)
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total_tokens = metadata.get("total_tokens", 0)
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cache_read = metadata.get("total_cache_read_input_tokens", 0)
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# Sparkline
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spends = [day.get("metrics", {}).get("spend", 0) for day in results]
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def get_spark(vals):
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if len(vals) < 2: return ""
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@@ -212,108 +231,88 @@ class Tools:
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return " [" + "".join(chars[int((v - low) / rng * 7)] for v in vals) + "]"
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lines = [f"LiteLLM Cost Report: {r_start} to {r_end}"]
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lines.append("=" * 75)
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lines.append(f"Total Spend: ${total_spend:.2f}{get_spark(spends)}")
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lines.append(f"Total Tokens: {total_tokens:,} ({total_prompt:,} prompt + {total_completion:,} completion)")
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lines.append("=" * 85)
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lines.append(f"Total Spend: ${metadata.get('total_spend', 0):.2f}{get_spark(spends)}")
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if total_prompt > 0 and cache_read > 0:
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hit_rate = (cache_read / total_prompt) * 100
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lines.append(f"Cache Hit Rate: {hit_rate:.1f}% ({cache_read:,} / {total_prompt:,} input tokens)")
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t_in, t_out = metadata.get('total_prompt_tokens',0), metadata.get('total_completion_tokens',0)
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lines.append(f"Total Tokens: {self._format_tokens(metadata.get('total_tokens', 0))} ({self._format_tokens(t_in)} in + {self._format_tokens(t_out)} out)")
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lines.append(f"Requests: {metadata.get('total_successful_requests', 0)} ok, {metadata.get('total_failed_requests', 0)} fail")
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c_read = metadata.get("total_cache_read_input_tokens", 0)
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if t_in > 0 and c_read > 0:
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lines.append(f"Global Cache: {(c_read/t_in*100):.1f}% ({self._format_tokens(c_read)} saved)")
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lines.append(f"Total Requests: {metadata.get('total_successful_requests', 0)} ok, {metadata.get('total_failed_requests', 0)} fail")
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# Model Aggregation
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# Model Table
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model_totals = {}
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for day in results:
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models = day.get("breakdown", {}).get("models", {})
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for m_name, m_data in models.items():
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m_metrics = m_data.get("metrics", {})
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if m_name not in model_totals:
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model_totals[m_name] = {"spend": 0, "reqs": 0, "tokens": 0, "cache": 0}
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model_totals[m_name]["spend"] += m_metrics.get("spend", 0)
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model_totals[m_name]["reqs"] += m_metrics.get("successful_requests", 0)
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model_totals[m_name]["tokens"] += m_metrics.get("total_tokens", 0)
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model_totals[m_name]["cache"] += m_metrics.get("cache_read_input_tokens", 0)
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for m_name, m_data in day.get("breakdown", {}).get("models", {}).items():
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m = m_data.get("metrics", {})
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if m_name not in model_totals: model_totals[m_name] = {"spend": 0, "reqs": 0, "tokens": 0, "cache": 0}
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model_totals[m_name]["spend"] += m.get("spend", 0)
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model_totals[m_name]["reqs"] += m.get("successful_requests", 0)
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model_totals[m_name]["tokens"] += m.get("total_tokens", 0)
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model_totals[m_name]["cache"] += m.get("cache_read_input_tokens", 0)
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lines.append(f"\n{'Model':30s} | {'Spend':>10s} | {'Requests':>14s} | {'Tokens':>12s}")
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lines.append("-" * 75)
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hidden_count = 0
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sorted_models = sorted(model_totals.items(), key=lambda x: x[1]["spend"], reverse=True)
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for name, mt in sorted_models:
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lines.append(f"\n{'Model':32s} | {'Spend':>10s} | {'Requests':>12s} | {'Tokens':>12s}")
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lines.append("-" * 85)
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hidden = 0
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for name, mt in sorted(model_totals.items(), key=lambda x: x[1]["spend"], reverse=True):
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if effective_hide and mt["spend"] < 0.001:
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hidden_count += 1
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continue
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display = name.replace("gemini/", "").replace("openai/", "")[:30]
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cache_info = ""
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if mt["cache"] > 0:
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hit = (mt["cache"] / (mt["tokens"] or 1) * 100)
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cache_info = f" 💾{hit:.0f}%"
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lines.append(f" {display:28s} | ${mt['spend']:9.2f} | {mt['reqs']:6d} ok | {mt['tokens']:10,d}{cache_info}")
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if hidden_count: lines.append(f" ... ({hidden_count} model(s) with sub-$0.001 spend hidden)")
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# Date breakdown
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lines.append("\nBy Date:")
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for day in results:
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m = day.get("metrics", {})
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lines.append(f" {day.get('date')}: ${m.get('spend',0):.2f} | {m.get('successful_requests',0)} ok | {m.get('total_tokens',0):,} tokens")
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hidden += 1; continue
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c_inf = f" 💾{int(mt['cache']/(mt['tokens'] or 1)*100)}%" if mt["cache"] else ""
|
||||
lines.append(f" {self._compact_name(name, 30):30s} | ${mt['spend']:9.2f} | {mt['reqs']:8d} ok | {self._format_tokens(mt['tokens']):>10s}{c_inf}")
|
||||
if hidden: lines.append(f" ... ({hidden} models with sub-$0.001 spend hidden)")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
def litellm_history(self, limit: int = 20, show_failed: bool = True) -> str:
|
||||
"""
|
||||
Get recent LiteLLM request history with audit data and thought detection.
|
||||
Returns a high-density table of recent requests.
|
||||
"""
|
||||
if not self.valves.litellm_master_key:
|
||||
return "❌ Master key required for request history."
|
||||
|
||||
"""Deep forensic history including Thought detection, Latency, and Cost Audit."""
|
||||
if not self.valves.litellm_master_key: return "❌ Master key required."
|
||||
try:
|
||||
resp = requests.get(f"{self._base_url()}/spend/logs", headers=self._headers(), timeout=30)
|
||||
if resp.status_code != 200: return f"❌ Unexpected status {resp.status_code}"
|
||||
if resp.status_code != 200: return f"❌ Error {resp.status_code}"
|
||||
logs = sorted(resp.json(), key=lambda x: x.get("startTime", ""), reverse=True)
|
||||
except Exception as e: return f"❌ Connection failed: {str(e)}"
|
||||
except Exception as e: return f"❌ Failed: {str(e)}"
|
||||
|
||||
if not show_failed:
|
||||
logs = [l for l in logs if not l.get("metadata", {}).get("exception")]
|
||||
|
||||
recent = logs[:max(1, min(50, limit))]
|
||||
lines = [f"LiteLLM Request History — {self._base_url()}"]
|
||||
lines = [f"LiteLLM Forensic History — {self._base_url()}"]
|
||||
lines.append(f"Showing {len(recent)} of {len(logs)} total requests")
|
||||
lines.append("=" * 105)
|
||||
lines.append("=" * 115)
|
||||
lines.append(f" # {'Time':8s} | {'Model':28s} | {'Status':7s} | {'Dur':>6s} | {'Spend':>10s} | {'Tokens':>12s}")
|
||||
lines.append("-" * 115)
|
||||
|
||||
for i, entry in enumerate(recent, 1):
|
||||
start_time = entry.get("startTime", "?")[5:16] # MM-DD HH:MM
|
||||
model = entry.get("model", "unknown").split("/")[-1][:28]
|
||||
ts = entry.get("startTime", "?")[5:16]
|
||||
model = self._compact_name(entry.get("model", "unknown"), 28)
|
||||
spend = entry.get("spend", 0)
|
||||
prompt = entry.get("prompt_tokens", 0) or 0
|
||||
completion = entry.get("completion_tokens", 0) or 0
|
||||
p_tok = entry.get("prompt_tokens", 0) or 0
|
||||
c_tok = entry.get("completion_tokens", 0) or 0
|
||||
|
||||
# Metadata & Status
|
||||
meta = entry.get("metadata", {}) or {}
|
||||
is_failed = bool(meta.get("exception")) or meta.get("status") == "failure"
|
||||
status = "❌ FAIL" if is_failed else "✅ OK"
|
||||
|
||||
# Advanced Tokens
|
||||
dur = entry.get("request_duration_ms", 0) or 0
|
||||
dur_s = f"{(dur/1000):.1f}s" if dur > 1000 else f"{dur}ms"
|
||||
slow = "⚠️" if dur > 15000 else ""
|
||||
|
||||
usage = entry.get("usage", {}) or {}
|
||||
thought = usage.get("completion_tokens_details", {}).get("reasoning_tokens", 0)
|
||||
thought_str = f" [💭{thought}]" if thought else ""
|
||||
thought_s = f" [💭{self._format_tokens(thought)}]" if thought else ""
|
||||
has_search = bool(meta.get("web_search_options")) or "search" in entry.get("call_type", "")
|
||||
search_s = " 🔍" if has_search else ""
|
||||
|
||||
cached = entry.get("cache_read_input_tokens", 0) or 0
|
||||
cache_str = f" 💾{int(cached/prompt*100)}%" if prompt and cached else ""
|
||||
cache_s = f" 💾{int(cached/p_tok*100)}%" if p_tok and cached else ""
|
||||
|
||||
# Format numbers
|
||||
p_s = f"{prompt/1000:.1f}k" if prompt >= 1000 else str(prompt)
|
||||
c_s = f"{completion/1000:.1f}k" if completion >= 1000 else str(completion)
|
||||
audit = self._audit_cost(entry.get("model"), p_tok, cached, c_tok, spend, reasoning=thought, has_search=has_search)
|
||||
|
||||
audit = self._audit_cost(entry.get("model"), prompt, cached, completion, spend, reasoning=thought)
|
||||
|
||||
line = f" {i:2d}. {start_time} | {model:28s} | {status} | ${spend:.4f}{audit:18s} | {p_s:>6s}→{c_s:<6s}{thought_str}{cache_str}"
|
||||
line = f" {i:2d}. {ts} | {model:28s} | {status} | {dur_s:>6s}{slow} | ${spend:.4f}{audit:18s} | {self._format_tokens(p_tok):>6s}→{self._format_tokens(c_tok):<6s}{thought_s}{cache_s}{search_s}"
|
||||
lines.append(line)
|
||||
|
||||
if is_failed:
|
||||
err = meta.get("exception", "unknown error")
|
||||
lines.append(f" └─ {err[:80]}")
|
||||
if is_failed: lines.append(f" └─ Error: {meta.get('exception', 'unknown')[:85]}")
|
||||
|
||||
return "\n".join(lines)
|
||||
Reference in new issue
Block a user