style(core): restore high-density professional tables and dense metrics for v1.5.1

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vai committed 2026-06-27 09:51:18 +00:00
1 parent 6add0680ac
commit 6359a2b2a5
1 file changed
+53 -48
+53 -48
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@@ -2,10 +2,10 @@
title: LiteLLM Dashboard
author: Kagamine Rin & VAI
author_url: https://rin.captcouch.xyz
description: Consolidated Hardcore LiteLLM observability — status, usage, and history.
description: Professional LiteLLM observability — High-density health, cost breakdown, and request history.
required_open_webui_version: 0.4.0
requirements: requests
version: 1.5.0
version: 1.5.1
licence: MIT
"""
@@ -26,7 +26,7 @@ class Tools:
"""LiteLLM proxy connection settings."""
litellm_url: str = Field(
default="http://localhost:4000",
description="LiteLLM proxy base URL",
description="LiteLLM proxy base URL (e.g. http://localhost:4000)",
)
litellm_master_key: str = Field(
default="",
@@ -55,7 +55,6 @@ class Tools:
return self.valves.litellm_url.rstrip("/")
def _fetch_live_prices(self):
"""Sync internal price cache with LiteLLM's live model registry."""
if not self.valves.enable_dynamic_pricing or self._price_cache:
return
try:
@@ -95,7 +94,6 @@ class Tools:
return date_str
def _audit_cost(self, model: str, prompt: int, cached: int, completion: int, reported_cost: float, reasoning: int = 0) -> str:
"""Audit reported cost with dynamic pricing."""
self._fetch_live_prices()
pricing = self._price_cache.get(model)
if not pricing: return ""
@@ -114,40 +112,38 @@ class Tools:
Comprehensive system check: Proxy readiness, DB connectivity, and Model health.
Use this to verify the infrastructure is up and which models are available.
"""
lines = [f"LiteLLM Infrastructure Status — {self._base_url()}", "=" * 50]
lines = [f"LiteLLM Infrastructure Status — {self._base_url()}"]
lines.append("=" * 60)
# 1. Readiness
try:
r = requests.get(f"{self._base_url()}/health/readiness", timeout=5)
if r.status_code == 200:
d = r.json()
lines.append(f"Proxy: ✅ {d.get('status')} | DB: {d.get('db')} | v{d.get('litellm_version')}")
else: lines.append(f"Proxy Readiness: ⚠️ Status {r.status_code}")
except Exception: lines.append("Proxy Readiness: ❌ Connection Error")
lines.append(f"✅ Proxy: {d.get('status')} | DB: {d.get('db')} | v{d.get('litellm_version')}")
else: lines.append(f"⚠️ Proxy Readiness: Status {r.status_code}")
except Exception: lines.append("❌ Proxy Readiness: Connection Error")
# 2. Model Health
try:
h = requests.get(f"{self._base_url()}/health", headers=self._headers(), timeout=10)
if h.status_code == 200:
hd = h.json()
healthy = hd.get("healthy_endpoints", [])
unhealthy = hd.get("unhealthy_endpoints", [])
lines.append(f"Models: {len(healthy)} healthy, {len(unhealthy)} unhealthy")
for ep in healthy[:15]: # Cap display to save tokens
lines.append(f"\nHealthy Models: {len(healthy)} | Unhealthy: {len(unhealthy)}")
lines.append("-" * 60)
for ep in healthy:
m = ep.get("model", "unknown")
lines.append(f" ✅ {m:25s} | rpm: {ep.get('rpm','?'):>3}")
if len(healthy) > 15: lines.append(f" ... and {len(healthy)-15} more healthy models.")
display = m.replace("gemini/", "").replace("openai/", "")
lines.append(f" ✅ {display:30s} — rpm: {ep.get('rpm','?'):>3}, timeout: {ep.get('timeout','?')}s")
for ep in unhealthy:
lines.append(f" ❌ {ep.get('model', 'unknown')}")
except Exception: lines.append("Model Health: ❌ Failed to fetch")
except Exception as e: lines.append(f"\n❌ Health Check Failed: {str(e)}")
return "\n".join(lines)
def litellm_usage(self, start_date: str = "today", end_date: str = "today", detail_level: Literal["summary", "full"] = "summary") -> str:
"""
Get fiscal usage and spend data.
:param start_date: YYYY-MM-DD or shortcut (today, yesterday, 7 days ago)
:param end_date: YYYY-MM-DD or shortcut
:param detail_level: 'summary' for quick spend snapshot, 'full' for daily breakdown and sparklines.
"""
if not self.valves.litellm_master_key: return "❌ Master key required."
@@ -160,13 +156,15 @@ class Tools:
if s.status_code == 200:
models = sorted(s.json(), key=lambda x: x.get("total_spend", 0), reverse=True)
total = sum(m.get("total_spend", 0) for m in models)
lines = [f"LiteLLM Spend Summary (Lifetime)", "=" * 40, f"Total Lifetime Spend: ${total:.2f}"]
for m in models[:5]:
lines.append(f" - {m['model']}: ${m['total_spend']:.2f}")
lines = [f"LiteLLM Spend Snapshot (Lifetime)", "=" * 50, f"Total Lifetime Spend: ${total:.2f}\n"]
lines.append(f"{'Model':30s} | {'Spend':>10s}")
lines.append("-" * 45)
for m in models[:8]:
display = m['model'].split("/")[-1]
lines.append(f" {display:28s} | ${m['total_spend']:9.2f}")
return "\n".join(lines)
except Exception: pass
# Full Report
try:
url = f"{self._base_url()}/user/daily/activity"
resp = requests.get(url, headers=self._headers(), params={"start_date": r_start, "end_date": r_end}, timeout=30)
@@ -186,43 +184,47 @@ class Tools:
return " [" + "".join(chars[int((v - low) / rng * 7)] for v in vals) + "]"
meta = data.get("metadata", {})
lines = [f"LiteLLM Usage Report: {r_start} to {r_end}", "=" * 60]
lines.append(f"Spend: ${meta.get('total_spend', 0):.2f}{get_spark(spends)}")
lines.append(f"Requests: {meta.get('total_successful_requests', 0)} ok, {meta.get('total_failed_requests', 0)} fail")
lines = [f"LiteLLM Usage Report: {r_start} to {r_end}"]
lines.append("=" * 75)
lines.append(f"Total Spend: ${meta.get('total_spend', 0):.2f}{get_spark(spends)}")
lines.append(f"Total Tokens: {meta.get('total_tokens', 0):,} ({meta.get('total_prompt_tokens',0):,} prompt + {meta.get('total_completion_tokens',0):,} completion)")
lines.append(f"Requests: {meta.get('total_successful_requests', 0)} successful, {meta.get('total_failed_requests', 0)} failed")
# Aggregated Model Breakdown
# Model Breakdown
model_totals = {}
for day in results:
models = day.get("breakdown", {}).get("models", {})
for m_name, m_data in models.items():
metrics = m_data.get("metrics", {})
if m_name not in model_totals:
model_totals[m_name] = {"spend": 0, "reqs": 0}
model_totals[m_name] = {"spend": 0, "reqs": 0, "tokens": 0}
model_totals[m_name]["spend"] += metrics.get("spend", 0)
model_totals[m_name]["reqs"] += metrics.get("successful_requests", 0)
model_totals[m_name]["tokens"] += metrics.get("total_tokens", 0)
if model_totals:
lines.append("\nBy Model (Spend > $0.001):")
sorted_models = sorted(model_totals.items(), key=lambda x: x[1]["spend"], reverse=True)
for m_name, m_stats in sorted_models:
if m_stats["spend"] >= 0.001 or not self.valves.default_hide_zero_spend:
# Strip common prefixes for readability
display = m_name.split("/")[-1]
lines.append(f" {display:25s} | ${m_stats['spend']:7.2f} | {m_stats['reqs']:4d} ok")
lines.append(f"\n{'Model':30s} | {'Spend':>10s} | {'Requests':>12s} | {'Tokens':>12s}")
lines.append("-" * 75)
sorted_models = sorted(model_totals.items(), key=lambda x: x[1]["spend"], reverse=True)
hidden = 0
for m_name, m_stats in sorted_models:
if m_stats["spend"] < 0.001 and self.valves.default_hide_zero_spend:
hidden += 1
continue
display = m_name.split("/")[-1][:30]
lines.append(f" {display:28s} | ${m_stats['spend']:9.2f} | {m_stats['reqs']:10d} ok | {m_stats['tokens']:10,d}")
if hidden: lines.append(f" ... ({hidden} models with sub-$0.001 spend hidden)")
lines.append("\nBy Endpoint:")
lines.append(f"\n{'Endpoint':30s} | {'Spend':>10s} | {'Requests':>12s}")
lines.append("-" * 60)
ends = results[0].get("breakdown", {}).get("endpoints", {}) if results else {}
for path, edata in ends.items():
for path, edata in sorted(ends.items(), key=lambda x: x[1].get("metrics",{}).get("spend",0), reverse=True):
m = edata.get("metrics", {})
lines.append(f" {path:20s} | ${m.get('spend',0):.4f} | {m.get('successful_requests',0)} reqs")
lines.append(f" {path:28s} | ${m.get('spend',0):9.4f} | {m.get('successful_requests',0):10d} ok")
return "\n".join(lines)
def litellm_history(self, limit: int = 20, filter_model: Optional[str] = None) -> str:
"""
Get recent request logs with audit data and thought detection.
:param limit: Number of requests (1-50)
:param filter_model: Optional string to filter by model name (e.g. 'gpt-4')
"""
"""Get recent request logs with audit data and thought detection."""
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)
@@ -234,11 +236,11 @@ class Tools:
logs = [l for l in logs if filter_model.lower() in l.get("model", "").lower()]
recent = logs[:max(1, min(50, limit))]
lines = [f"LiteLLM History (Most Recent {len(recent)})", "=" * 95]
lines = [f"LiteLLM History — {self._base_url()}", "=" * 105]
for i, entry in enumerate(recent, 1):
ts = entry.get("startTime", "?")[5:16]
model = entry.get("model", "unknown")[:25]
model = entry.get("model", "unknown").split("/")[-1][:25]
spend = entry.get("spend", 0)
prompt = entry.get("prompt_tokens", 0) or 0
completion = entry.get("completion_tokens", 0) or 0
@@ -249,9 +251,12 @@ class Tools:
cached = entry.get("cache_read_input_tokens", 0) or 0
cache_str = f" 💾{int(cached/prompt*100)}%" if prompt and cached else ""
audit_display = self._audit_cost(entry.get("model"), prompt, cached, completion, spend, reasoning=thought)
status = "❌" if entry.get("metadata", {}).get("exception") else "✅"
audit = self._audit_cost(entry.get("model"), prompt, cached, completion, spend, reasoning=thought)
status = "✅" if not entry.get("metadata", {}).get("exception") else "❌"
lines.append(f"{i:2d}. {ts} {status} | {model:25s} | ${spend:.4f}{audit_display} | {prompt}→{completion}{thought_str}{cache_str}")
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)
lines.append(f"{i:2d}. {ts} {status} | {model:25s} | ${spend:.4f}{audit:18s} | {p_s:>6s}→{c_s:<6s}{thought_str}{cache_str}")
return "\n".join(lines)