238 lines
11 KiB
Python
238 lines
11 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: Consolidated Hardcore LiteLLM observability — status, usage, and history.
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required_open_webui_version: 0.4.0
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requirements: requests
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version: 1.5.0
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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, Literal
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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",
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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.",
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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).",
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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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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 with 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", 0)) + \
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(uncached * pricing.get("input", 0)) + \
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(actual_output * pricing.get("output", 0)) + \
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(reasoning * pricing.get("reasoning", pricing.get("output", 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_status(self) -> str:
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"""
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Comprehensive system check: Proxy readiness, DB connectivity, and Model health.
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Use this to verify the infrastructure is up and which models are available.
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"""
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lines = [f"LiteLLM Infrastructure Status — {self._base_url()}", "=" * 50]
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# 1. 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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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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else: lines.append(f"Proxy Readiness: ⚠️ Status {r.status_code}")
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except Exception: lines.append("Proxy Readiness: ❌ Connection Error")
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# 2. Model Health
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try:
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h = requests.get(f"{self._base_url()}/health", headers=self._headers(), timeout=10)
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if h.status_code == 200:
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hd = h.json()
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healthy = hd.get("healthy_endpoints", [])
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unhealthy = hd.get("unhealthy_endpoints", [])
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lines.append(f"Models: {len(healthy)} healthy, {len(unhealthy)} unhealthy")
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for ep in healthy[:15]: # Cap display to save tokens
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m = ep.get("model", "unknown")
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lines.append(f" ✅ {m:25s} | rpm: {ep.get('rpm','?'):>3}")
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if len(healthy) > 15: lines.append(f" ... and {len(healthy)-15} more healthy models.")
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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: lines.append("Model Health: ❌ Failed to fetch")
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return "\n".join(lines)
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def litellm_usage(self, start_date: str = "today", end_date: str = "today", detail_level: Literal["summary", "full"] = "summary") -> str:
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"""
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Get fiscal usage and spend data.
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:param start_date: YYYY-MM-DD or shortcut (today, yesterday, 7 days ago)
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:param end_date: YYYY-MM-DD or shortcut
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:param detail_level: 'summary' for quick spend snapshot, 'full' for daily breakdown and sparklines.
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"""
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if not self.valves.litellm_master_key: return "❌ Master key required."
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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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if detail_level == "summary":
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try:
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s = requests.get(f"{self._base_url()}/global/spend/models", headers=self._headers(), timeout=10)
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if s.status_code == 200:
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models = sorted(s.json(), key=lambda x: x.get("total_spend", 0), reverse=True)
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total = sum(m.get("total_spend", 0) for m in models)
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lines = [f"LiteLLM Spend Summary (Lifetime)", "=" * 40, f"Total Lifetime Spend: ${total:.2f}"]
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for m in models[:5]:
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lines.append(f" - {m['model']}: ${m['total_spend']:.2f}")
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return "\n".join(lines)
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except Exception: pass
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# Full Report
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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"❌ Error {resp.status_code}"
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data = resp.json()
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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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if not results: return f"No usage found for {r_start} to {r_end}."
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spends = [d.get("metrics", {}).get("spend", 0) for d 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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meta = data.get("metadata", {})
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lines = [f"LiteLLM Usage Report: {r_start} to {r_end}", "=" * 60]
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lines.append(f"Spend: ${meta.get('total_spend', 0):.2f}{get_spark(spends)}")
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lines.append(f"Requests: {meta.get('total_successful_requests', 0)} ok, {meta.get('total_failed_requests', 0)} fail")
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lines.append("\nBreakdown by Endpoint:")
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ends = results[0].get("breakdown", {}).get("endpoints", {}) if results else {}
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for path, edata in ends.items():
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m = edata.get("metrics", {})
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lines.append(f" {path:20s} | ${m.get('spend',0):.4f} | {m.get('successful_requests',0)} reqs")
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return "\n".join(lines)
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def litellm_history(self, limit: int = 20, filter_model: Optional[str] = None) -> str:
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"""
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Get recent request logs with audit data and thought detection.
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:param limit: Number of requests (1-50)
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:param filter_model: Optional string to filter by model name (e.g. 'gpt-4')
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"""
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if not self.valves.litellm_master_key: return "❌ Master key required."
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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"❌ Error {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"❌ Failed: {str(e)}"
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if filter_model:
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logs = [l for l in logs if filter_model.lower() in l.get("model", "").lower()]
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recent = logs[:max(1, min(50, limit))]
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lines = [f"LiteLLM History (Most Recent {len(recent)})", "=" * 95]
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for i, entry in enumerate(recent, 1):
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ts = entry.get("startTime", "?")[5:16]
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model = entry.get("model", "unknown")[:25]
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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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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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audit_display = self._audit_cost(entry.get("model"), prompt, cached, completion, spend, reasoning=thought)
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status = "❌" if entry.get("metadata", {}).get("exception") else "✅"
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lines.append(f"{i:2d}. {ts} {status} | {model:25s} | ${spend:.4f}{audit_display} | {prompt}→{completion}{thought_str}{cache_str}")
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return "\n".join(lines)
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