320 lines
14 KiB
Python
320 lines
14 KiB
Python
"""
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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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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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licence: MIT
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"""
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import requests
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import json
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from datetime import datetime, timedelta
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from zoneinfo import ZoneInfo
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from pydantic import BaseModel, Field
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from typing import Optional, List, Dict, Any
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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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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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)
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litellm_master_key: str = Field(
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default="",
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description="LiteLLM master API key for authentication",
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json_schema_extra={"input": {"type": "password"}},
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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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)
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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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)
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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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)
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def _headers(self) -> dict:
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if not self.valves.litellm_master_key:
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return {}
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return {"Authorization": f"Bearer {self.valves.litellm_master_key}"}
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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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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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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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self._price_cache[name] = {
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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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}
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except Exception: pass
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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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today = datetime.now(ZoneInfo(self.valves.timezone)).date()
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except Exception:
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today = datetime.utcnow().date()
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if date_str == "today": return today.isoformat()
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if date_str == "yesterday": return (today - timedelta(days=1)).isoformat()
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if "days ago" in date_str:
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try:
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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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# Readiness Header
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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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d = r.json()
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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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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 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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except Exception as e:
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return f"❌ Connection 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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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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url = f"{self._base_url()}/user/daily/activity"
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try:
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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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data = resp.json()
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except Exception as e: return f"❌ Connection 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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chars = " ▂▃▄▅▆▇█"
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low, high = min(vals), max(vals)
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rng = (high - low) or 1
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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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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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lines.append(f"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_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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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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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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return "\n".join(lines)
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def litellm_history(self, limit: int = 20, show_failed: bool = True) -> str:
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"""
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Get recent LiteLLM request history with audit data and thought detection.
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Returns a high-density table of recent requests.
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"""
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if not self.valves.litellm_master_key:
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return "❌ Master key required for request history."
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try:
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resp = requests.get(f"{self._base_url()}/spend/logs", headers=self._headers(), timeout=30)
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if resp.status_code != 200: return f"❌ Unexpected status {resp.status_code}"
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logs = sorted(resp.json(), key=lambda x: x.get("startTime", ""), reverse=True)
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except Exception as e: return f"❌ Connection failed: {str(e)}"
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if not show_failed:
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logs = [l for l in logs if not l.get("metadata", {}).get("exception")]
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recent = logs[:max(1, min(50, limit))]
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lines = [f"LiteLLM Request History — {self._base_url()}"]
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lines.append(f"Showing {len(recent)} of {len(logs)} total requests")
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lines.append("=" * 105)
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for i, entry in enumerate(recent, 1):
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start_time = entry.get("startTime", "?")[5:16] # MM-DD HH:MM
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model = entry.get("model", "unknown").split("/")[-1][:28]
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spend = entry.get("spend", 0)
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prompt = entry.get("prompt_tokens", 0) or 0
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completion = entry.get("completion_tokens", 0) or 0
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# Metadata & Status
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meta = entry.get("metadata", {}) or {}
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is_failed = bool(meta.get("exception")) or meta.get("status") == "failure"
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status = "❌ FAIL" if is_failed else "✅ OK"
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# Advanced Tokens
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usage = entry.get("usage", {}) or {}
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thought = usage.get("completion_tokens_details", {}).get("reasoning_tokens", 0)
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thought_str = f" [💭{thought}]" if thought else ""
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cached = entry.get("cache_read_input_tokens", 0) or 0
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cache_str = f" 💾{int(cached/prompt*100)}%" if prompt and cached else ""
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# Format numbers
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p_s = f"{prompt/1000:.1f}k" if prompt >= 1000 else str(prompt)
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c_s = f"{completion/1000:.1f}k" if completion >= 1000 else str(completion)
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audit = self._audit_cost(entry.get("model"), prompt, cached, completion, spend, reasoning=thought)
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line = f" {i:2d}. {start_time} | {model:28s} | {status} | ${spend:.4f}{audit:18s} | {p_s:>6s}→{c_s:<6s}{thought_str}{cache_str}"
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lines.append(line)
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if is_failed:
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err = meta.get("exception", "unknown error")
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lines.append(f" └─ {err[:80]}")
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return "\n".join(lines)
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