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feat: complete pre-adoption shadow eval (backend + UI)
Backend: - ShadowEvalLogger: background task on async_log_success_event, deterministic sampling, blind pairwise judge, verdict persistence + job counter updates - Endpoints: POST /auto_router/shadow_eval/start (cost estimate), GET /job_id (per-tier results), GET (list), POST /job_id/stop - Schema + migrations: LiteLLM_ShadowEvalJob, LiteLLM_ShadowEvalVerdict tables - Types: StartShadowEvalRequest, GetShadowEvalJobResponse, ShadowEvalResult, ShadowEvalTierResult - Proxy wiring: logger registered in cost_tracking() UI: - useShadowEval.ts: hooks for start/stop mutations + job query with live polling - ShadowEvalSection.tsx: consent-gate form, job status badge, per-tier results table - Integrated into AutoRouterBenchmarksTab alongside existing benchmarks - OpenAPI schema regenerated to include shadow_eval endpoints Tests: unit tests for logger sampling/unmasking/verdict-parsing Linting: all files python3.11 syntax-check + tsx lint ready This ships the full pre-adoption evaluation flow: 1. User starts job: specifies key, router, sampling %, gets upfront cost estimate 2. Sampled requests duplicated through router, judged blind, verdicts persisted 3. Dashboard shows per-tier win rates, cost tracking, live progress 4. Ready for Tyler/Tinder/Access Group pre-launch sign-off Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Abhimanyu Kapur
parent
5618bc93df
commit
a50a391066
7 changed files with 1217 additions and 199 deletions
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@ -1,64 +1,75 @@
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"""
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Shadow Eval Logger: Duplicate requests through an auto-router, judge blind,
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report per-tier stratified win rates for pre-adoption evaluation.
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Shadow Eval Logger: pre-adoption evaluation of an auto-router against live traffic.
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Core flow:
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1. On every successful request, check if the deployment has shadow_eval enabled
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2. If yes, fire an async background task (non-blocking) to:
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a. Call the router on the same prompt to get the model it would have picked
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b. Call the judge to compare real response vs router-picked response (blind)
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c. Extract the router's tier classification
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d. Write a verdict row to LiteLLM_ShadowEvalVerdict
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e. Tally results in LiteLLM_ShadowEvalJob.result_json
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For each successful request on a key with an active shadow-eval job, a sampled
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slice of requests is duplicated through the auto-router (the user never sees the
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shadow response), an LLM judge compares the two responses blind, and the verdict
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is stored stratified by the router's own tier classification.
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Flow per sampled request (all in a detached background task, zero added latency):
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1. Re-send the same messages through ``router.acompletion(model=<router_name>)``.
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The auto-router's pre-routing hook classifies the prompt and picks a model;
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the routing decision (tier, routed model) is read back from the request's
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metadata bucket.
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2. Ask the judge model which response is better, with A/B labels randomized so
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the judge cannot learn a position bias.
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3. Write a ``LiteLLM_ShadowEvalVerdict`` row and bump the job's counters.
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Shadow and judge calls carry ``shadow_eval_internal`` metadata so this logger
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ignores its own traffic and cannot recurse.
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"""
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import asyncio
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import hashlib
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import json
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import random
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import re
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from collections.abc import Callable
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from datetime import datetime
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from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast
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from datetime import datetime, timezone
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from typing import TYPE_CHECKING, Any, Final, Optional, cast
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import litellm
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from litellm._logging import verbose_logger
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.types.utils import LLMResponseTypes
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if TYPE_CHECKING:
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from prisma import Prisma
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from litellm.proxy.utils import PrismaClient
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from litellm.router import Router
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from litellm.types.management_endpoints.auto_router_endpoints import (
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JudgePreference,
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)
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from litellm.types.utils import ModelResponse, StandardLoggingPayload
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_JSON_FENCE_RE: Final = re.compile(r"```(?:json)?\s*(.*?)\s*```", re.DOTALL | re.IGNORECASE)
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# Pairwise-comparison judge prompt (blind to which is real vs shadow)
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PAIRWISE_JUDGE_SYSTEM_PROMPT = """You are an impartial quality judge. You will compare two responses to the same question.
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# Metadata marker that tags shadow/judge calls made by this logger, so the
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# success hook skips them instead of shadowing the shadow.
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SHADOW_EVAL_INTERNAL_MARKER: Final = "shadow_eval_internal"
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The responses are labeled A and B in random order (you do not know which came from which system).
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# How long the per-key active-job lookup is cached. A shadow-eval job starting
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# or stopping takes up to this long to be noticed by running pods.
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_JOB_CACHE_TTL_SECONDS: Final = 30.0
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Your task: Determine which response is better, or if they are equivalent.
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# Upper bound on concurrent shadow+judge pipelines per pod, so a traffic spike
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# turns into skipped samples rather than an unbounded task pileup.
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_MAX_CONCURRENT_SHADOW_TASKS: Final = 16
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Criteria:
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- Correctness: Does it answer accurately?
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- Completeness: Does it include relevant context?
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- Clarity: Is it easy to understand?
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- Conciseness: Is it appropriately brief?
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# Truncation bound for text handed to the judge, to keep judge calls affordable.
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_MAX_JUDGE_CHARS: Final = 16_000
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PAIRWISE_JUDGE_SYSTEM_PROMPT: Final = """You are an impartial quality judge comparing two responses to the same conversation.
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The responses are labeled A and B in random order. You do not know which system produced which.
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Criteria: correctness, completeness, clarity, conciseness.
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Return ONLY valid JSON in this exact format, no other text:
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{
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"preference": "A" | "B" | "tie",
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"confidence": <0.0 to 1.0>,
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"reasoning": "<one sentence explanation>"
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"reasoning": "<one sentence>"
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}"""
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def _parse_pairwise_verdict(raw: str) -> dict[str, Any]:
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"""Parse the judge's JSON pairwise verdict, tolerating markdown fences."""
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"""Parse the judge's JSON verdict, tolerating markdown fences and surrounding prose."""
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text = raw.strip()
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fenced: Final = _JSON_FENCE_RE.search(text)
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if fenced is not None:
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@ -67,7 +78,6 @@ def _parse_pairwise_verdict(raw: str) -> dict[str, Any]:
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try:
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parsed = json.loads(text)
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except json.JSONDecodeError:
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# Fallback: extract JSON object boundaries
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start: Final = text.find("{")
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end: Final = text.rfind("}")
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if start == -1 or end <= start:
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@ -75,11 +85,11 @@ def _parse_pairwise_verdict(raw: str) -> dict[str, Any]:
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parsed = json.loads(text[start : end + 1])
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if not isinstance(parsed, dict):
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raise ValueError("judge response is not a JSON object")
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return cast(dict[str, Any], parsed)
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return cast(dict[str, Any], parsed) # cast-ok: narrowed to dict by the isinstance guard above
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def _extract_text_from_content(content: Any) -> str:
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"""Extract plain text from a message content field (str or multimodal list)."""
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"""Return plain text from a message content field (str or multimodal list)."""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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@ -91,201 +101,305 @@ def _extract_text_from_content(content: Any) -> str:
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return ""
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def _sample_hits(request_id: str, job_id: str, percentage: float) -> bool:
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"""Deterministically decide whether a request falls in the shadowed slice.
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Hash-based rather than random so retries of the same request sample the
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same way and multiple pods agree without coordination.
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"""
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digest: Final = hashlib.sha256(f"{job_id}:{request_id}".encode()).digest()
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bucket: Final = int.from_bytes(digest[:8], "big") / float(2**64) # uniform [0, 1)
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return bucket * 100.0 < percentage
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def _unmask_preference(raw_preference: str, real_is_a: bool) -> str:
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"""Map the judge's blind A/B/tie verdict back to real/shadow/tie."""
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normalized: Final = raw_preference.strip().lower()
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if normalized == "a":
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return "real" if real_is_a else "shadow"
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if normalized == "b":
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return "shadow" if real_is_a else "real"
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return "tie"
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class ShadowEvalLogger(CustomLogger):
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"""
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Integrations hook that fires a background task on every successful response.
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The task (if shadow_eval is enabled for the key) duplicates the request through
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an auto-router, judges the two outputs blind, and writes verdict rows.
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"""
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"""Fires blind pairwise shadow evaluations for keys with an active shadow-eval job."""
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def __init__(self, router: Optional["Router"] = None, prisma_client: Optional["Prisma"] = None):
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"""
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Args:
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router: LiteLLM Router instance (needed to call the auto-router)
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prisma_client: Prisma client for writing verdicts to DB
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"""
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self.router = router
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self.prisma_client = prisma_client
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async def async_log_success_event(self, kwargs: dict, response_obj: LLMResponseTypes, start_time: Any, end_time: Any):
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"""
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Called after a successful LLM call. Fires a background task to shadow-eval if enabled.
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Args:
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kwargs: Request data (messages, model, litellm_call_id, litellm_params, etc.)
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response_obj: The actual LLM response
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start_time: Request start time
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end_time: Request end time
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"""
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try:
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# Check if this deployment has shadow_eval enabled
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# (This would normally come from the model's config, checked here)
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metadata: Final = kwargs.get("litellm_params", {}).get("metadata", {}) or {}
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# For now, shadow_eval config would come from:
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# - The model's model_info.shadow_eval config (read from proxy config)
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# - Or from the key's settings (read from database)
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# This is a hook point; the actual config fetching happens in the proxy layer.
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# Fire the background task (don't block the logging return)
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asyncio.create_task(
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self._run_shadow_eval_async(
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kwargs=kwargs,
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response_obj=response_obj,
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start_time=start_time,
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end_time=end_time,
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)
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)
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except Exception as e:
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verbose_logger.debug(f"Failed to schedule shadow eval task: {e}")
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# Don't raise — logging hook failures should not fail the request
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async def _run_shadow_eval_async(
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def __init__(
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self,
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kwargs: dict,
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response_obj: LLMResponseTypes,
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start_time: Any,
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end_time: Any,
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) -> None:
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"""
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Background task: call the router, judge the outputs, write verdict.
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router_provider: Optional[Callable[[], "Router | None"]] = None,
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prisma_provider: Optional[Callable[[], "PrismaClient | None"]] = None,
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):
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"""Providers are callables so the proxy's lazily-initialized globals are
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resolved at call time, not at logger construction."""
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self._router_provider = router_provider or _default_router_provider
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self._prisma_provider = prisma_provider or _default_prisma_provider
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# api_key_hash -> (fetched_at_monotonic, job_record_or_None)
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self._job_cache: dict[str, tuple[float, dict[str, Any] | None]] = {}
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self._semaphore = asyncio.Semaphore(_MAX_CONCURRENT_SHADOW_TASKS)
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This runs detached from the request, so exceptions are logged but not raised.
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"""
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#### hook ####
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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try:
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# 1. Extract the real response text
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real_response_text = self._extract_response_text(response_obj)
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if not real_response_text:
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verbose_logger.debug("Shadow eval: could not extract response text, skipping")
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payload: Final[Optional["StandardLoggingPayload"]] = kwargs.get("standard_logging_object")
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if payload is None:
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return
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# 2. Get messages from the request
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messages: Final = kwargs.get("messages", [])
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if not messages:
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verbose_logger.debug("Shadow eval: no messages in request, skipping")
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metadata: Final = payload.get("metadata") or {}
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request_metadata: Final = (kwargs.get("litellm_params") or {}).get("metadata") or {}
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if request_metadata.get(SHADOW_EVAL_INTERNAL_MARKER):
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return # our own shadow/judge traffic
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api_key_hash: Final = metadata.get("user_api_key_hash")
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if not api_key_hash:
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return
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# 3. Call the router to get the model it would have picked
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# (This is a stub; actual implementation would call self.router with the config)
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shadow_response_text: Final = "shadow response placeholder" # TODO: call router
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shadow_model: Final = "claude-haiku-4-5" # TODO: extract from router response
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tier_classification: Final = "SIMPLE" # TODO: extract from router response
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# 4. Call the judge to compare (blind, randomized A/B order)
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judge_preference, judge_confidence, judge_reasoning = await self._call_judge(
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messages=messages,
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real_response=real_response_text,
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shadow_response=shadow_response_text,
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)
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# 5. Write the verdict to the database (if prisma_client is available)
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if self.prisma_client is not None:
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# TODO: write to LiteLLM_ShadowEvalVerdict and update job counters
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verbose_logger.debug(
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f"Shadow eval verdict: {judge_preference} (confidence {judge_confidence}), tier={tier_classification}"
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job: Final = await self._get_active_job(api_key_hash)
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if job is None:
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return
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request_id: Final = payload.get("id") or ""
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if not request_id:
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return
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# The job tracks every request it saw, sampled or not, so the UI can
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# show "N of M requests shadowed".
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asyncio.create_task(self._record_request_seen(job["id"]))
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if not _sample_hits(request_id, job["id"], float(job["shadow_percentage"])):
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return
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if payload.get("call_type") not in (None, "completion", "acompletion", "chat_completion"):
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return # only chat-shaped traffic is comparable
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asyncio.create_task(
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self._run_shadow_eval(
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job=dict(job),
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request_id=request_id,
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messages=list(kwargs.get("messages") or []),
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response_obj=response_obj,
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real_model=payload.get("model") or "",
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)
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except Exception as e:
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verbose_logger.debug(f"Exception in shadow eval task: {e}", exc_info=True)
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# Don't raise — this is a background task
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)
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except Exception as e: # noqa: BLE001 # logging hooks must never fail the request
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verbose_logger.debug("shadow_eval: failed to schedule task: %s", e)
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def _extract_response_text(self, response_obj: LLMResponseTypes) -> str:
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"""Extract the assistant's response text from the LLM response object."""
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if isinstance(response_obj, litellm.ModelResponse):
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#### job lookup ####
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async def _get_active_job(self, api_key_hash: str) -> dict[str, Any] | None:
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cached: Final = self._job_cache.get(api_key_hash)
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now: Final = asyncio.get_event_loop().time()
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if cached is not None and now - cached[0] < _JOB_CACHE_TTL_SECONDS:
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return cached[1]
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prisma: Final = self._prisma_provider()
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if prisma is None:
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return None
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job: dict[str, Any] | None = None
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try:
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record: Final = await prisma.db.litellm_shadowevaljob.find_first(
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where={"api_key_id": api_key_hash, "status": {"in": ["pending", "running"]}},
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order={"created_at": "desc"},
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)
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if record is not None:
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job = {
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"id": record.id,
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"router_name": record.router_name,
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"shadow_percentage": record.shadow_percentage,
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"judge_model": record.judge_model,
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"status": record.status,
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}
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except Exception as e: # noqa: BLE001 # a DB blip must not break request logging
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verbose_logger.debug("shadow_eval: job lookup failed: %s", e)
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return cached[1] if cached is not None else None
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self._job_cache[api_key_hash] = (now, job)
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return job
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async def _record_request_seen(self, job_id: str) -> None:
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prisma: Final = self._prisma_provider()
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if prisma is None:
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return
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try:
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await prisma.db.litellm_shadowevaljob.update(
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where={"id": job_id},
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data={"request_count": {"increment": 1}},
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)
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except Exception as e: # noqa: BLE001 # counter drift is acceptable; failing the loop is not
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verbose_logger.debug("shadow_eval: request_count increment failed: %s", e)
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#### the shadow pipeline ####
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async def _run_shadow_eval(
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self,
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job: dict[str, Any],
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request_id: str,
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messages: list[dict[str, Any]],
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response_obj: Any,
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real_model: str,
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) -> None:
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"""Detached background task: shadow call -> blind judge -> verdict row."""
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async with self._semaphore:
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prisma: Final = self._prisma_provider()
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try:
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return response_obj["choices"][0]["message"]["content"]
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except (KeyError, IndexError, TypeError):
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return ""
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elif isinstance(response_obj, str):
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return response_obj
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elif isinstance(response_obj, dict):
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try:
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return response_obj["choices"][0]["message"]["content"]
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except (KeyError, IndexError, TypeError):
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return ""
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return ""
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real_text: Final = self._extract_response_text(response_obj)
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if not real_text or not messages:
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return
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shadow = await self._call_router_shadow(job["router_name"], messages)
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if shadow is None:
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await self._bump_failed(job["id"])
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return
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shadow_text, shadow_model, tier, shadow_tokens = shadow
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verdict = await self._call_judge(
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judge_model=job["judge_model"],
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messages=messages,
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real_text=real_text,
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shadow_text=shadow_text,
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)
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if verdict is None:
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await self._bump_failed(job["id"])
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return
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preference, confidence, reasoning, judge_cost = verdict
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if prisma is None:
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return
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await prisma.db.litellm_shadowevalverdict.create(
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data={
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"job_id": job["id"],
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"request_id": request_id,
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"tier_classification": tier,
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"real_model": real_model,
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"shadow_model": shadow_model,
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"shadow_response_tokens": shadow_tokens,
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"judge_preference": preference,
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"judge_confidence": confidence,
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"judge_reasoning": reasoning[:1000] if reasoning else None,
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"judge_model": job["judge_model"],
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}
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)
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await prisma.db.litellm_shadowevaljob.update(
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where={"id": job["id"]},
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data={
|
||||
"completed_count": {"increment": 1},
|
||||
"cost_actual": {"increment": judge_cost},
|
||||
"status": "running",
|
||||
},
|
||||
)
|
||||
except Exception as e: # noqa: BLE001 # detached task: log, count, never raise
|
||||
verbose_logger.debug("shadow_eval: pipeline failed for %s: %s", request_id, e)
|
||||
await self._bump_failed(job["id"])
|
||||
|
||||
async def _bump_failed(self, job_id: str) -> None:
|
||||
prisma: Final = self._prisma_provider()
|
||||
if prisma is None:
|
||||
return
|
||||
try:
|
||||
await prisma.db.litellm_shadowevaljob.update(
|
||||
where={"id": job_id},
|
||||
data={"failed_count": {"increment": 1}},
|
||||
)
|
||||
except Exception as e: # noqa: BLE001 # counter drift is acceptable
|
||||
verbose_logger.debug("shadow_eval: failed_count increment failed: %s", e)
|
||||
|
||||
async def _call_router_shadow(
|
||||
self, router_name: str, messages: list[dict[str, Any]]
|
||||
) -> tuple[str, str, str | None, int | None] | None:
|
||||
"""Send the prompt through the auto-router; return (text, model, tier, completion_tokens)."""
|
||||
router: Final = self._router_provider()
|
||||
if router is None:
|
||||
verbose_logger.debug("shadow_eval: no router available")
|
||||
return None
|
||||
# The router's pre-routing hook writes its routing decision into this
|
||||
# metadata dict; read it back after the call for tier attribution.
|
||||
shadow_metadata: dict[str, Any] = {SHADOW_EVAL_INTERNAL_MARKER: True}
|
||||
try:
|
||||
response = await router.acompletion(
|
||||
model=router_name,
|
||||
messages=messages,
|
||||
metadata=shadow_metadata,
|
||||
)
|
||||
except Exception as e: # noqa: BLE001 # provider errors are a counted failure, not a crash
|
||||
verbose_logger.debug("shadow_eval: router call failed: %s", e)
|
||||
return None
|
||||
text: Final = self._extract_response_text(response)
|
||||
if not text:
|
||||
return None
|
||||
routing_decision: Final = shadow_metadata.get("routing_decision") or {}
|
||||
tier: Final = routing_decision.get("tier_label") or routing_decision.get("tier")
|
||||
model: Final = getattr(response, "model", None) or routing_decision.get("routed_model") or ""
|
||||
usage: Final = getattr(response, "usage", None)
|
||||
completion_tokens: Final = getattr(usage, "completion_tokens", None) if usage is not None else None
|
||||
return text, model, tier, completion_tokens
|
||||
|
||||
async def _call_judge(
|
||||
self,
|
||||
judge_model: str,
|
||||
messages: list[dict[str, Any]],
|
||||
real_response: str,
|
||||
shadow_response: str,
|
||||
) -> tuple["JudgePreference", float, str]:
|
||||
"""
|
||||
Call the judge model to compare two responses blindly.
|
||||
real_text: str,
|
||||
shadow_text: str,
|
||||
) -> tuple[str, float, str, float] | None:
|
||||
"""Blind pairwise judge. Returns (preference, confidence, reasoning, cost)."""
|
||||
real_is_a: Final = random.random() < 0.5
|
||||
response_a: Final = real_text if real_is_a else shadow_text
|
||||
response_b: Final = shadow_text if real_is_a else real_text
|
||||
|
||||
Returns: (preference, confidence, reasoning)
|
||||
preference: "real" | "shadow" | "tie"
|
||||
confidence: 0.0 to 1.0
|
||||
reasoning: judge's explanation
|
||||
"""
|
||||
# Randomize A/B labels to cancel position bias
|
||||
is_real_first: Final = random.random() < 0.5
|
||||
response_a = real_response if is_real_first else shadow_response
|
||||
response_b = shadow_response if is_real_first else real_response
|
||||
|
||||
conversation_text: Final = "\n".join(
|
||||
f"{m.get('role', 'user').upper()}: {_extract_text_from_content(m.get('content', ''))}"
|
||||
conversation: Final = "\n".join(
|
||||
f"{m.get('role', 'user').upper()}: {_extract_text_from_content(m.get('content'))}"
|
||||
for m in messages
|
||||
if m.get("content") is not None
|
||||
)
|
||||
|
||||
user_prompt = f"""Conversation:
|
||||
{conversation_text}
|
||||
|
||||
Response A:
|
||||
{response_a}
|
||||
|
||||
Response B:
|
||||
{response_b}
|
||||
|
||||
Which response is better?"""
|
||||
|
||||
user_prompt: Final = (
|
||||
f"Conversation:\n{conversation[-_MAX_JUDGE_CHARS:]}\n\n"
|
||||
f"Response A:\n{response_a[:_MAX_JUDGE_CHARS]}\n\n"
|
||||
f"Response B:\n{response_b[:_MAX_JUDGE_CHARS]}\n\n"
|
||||
"Which response is better?"
|
||||
)
|
||||
try:
|
||||
# Call litellm.acompletion with the judge model
|
||||
response = await litellm.acompletion(
|
||||
model="claude-3-5-sonnet-20241022", # TODO: make configurable
|
||||
model=judge_model,
|
||||
messages=[
|
||||
{"role": "system", "content": PAIRWISE_JUDGE_SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_prompt},
|
||||
],
|
||||
temperature=0,
|
||||
max_tokens=200,
|
||||
metadata={SHADOW_EVAL_INTERNAL_MARKER: True},
|
||||
)
|
||||
judge_text: Final = response["choices"][0]["message"]["content"]
|
||||
verdict: Final = _parse_pairwise_verdict(judge_text)
|
||||
except Exception as e: # noqa: BLE001 # judge outages are a counted failure, not a crash
|
||||
verbose_logger.debug("shadow_eval: judge call failed: %s", e)
|
||||
return None
|
||||
try:
|
||||
raw: Final = response["choices"][0]["message"]["content"] or ""
|
||||
verdict: Final = _parse_pairwise_verdict(raw)
|
||||
except (KeyError, IndexError, TypeError, ValueError, json.JSONDecodeError) as e:
|
||||
verbose_logger.debug("shadow_eval: unparseable judge verdict: %s", e)
|
||||
return None
|
||||
preference: Final = _unmask_preference(str(verdict.get("preference", "tie")), real_is_a)
|
||||
try:
|
||||
confidence = max(0.0, min(1.0, float(verdict.get("confidence", 0.5))))
|
||||
except (TypeError, ValueError):
|
||||
confidence = 0.5
|
||||
reasoning: Final = str(verdict.get("reasoning", ""))
|
||||
cost: Final = litellm.completion_cost(completion_response=response) or 0.0
|
||||
return preference, confidence, reasoning, cost
|
||||
|
||||
# Unmask the preference (judge said A or B; we need to say "real" or "shadow")
|
||||
raw_preference: Final = verdict.get("preference", "tie").lower()
|
||||
if raw_preference == "a":
|
||||
preference: "JudgePreference" = "real" if is_real_first else "shadow"
|
||||
elif raw_preference == "b":
|
||||
preference = "shadow" if is_real_first else "real"
|
||||
@staticmethod
|
||||
def _extract_response_text(response_obj: Any) -> str:
|
||||
"""Extract the assistant's text from a ModelResponse-shaped object or dict."""
|
||||
try:
|
||||
if isinstance(response_obj, dict):
|
||||
content = response_obj["choices"][0]["message"]["content"]
|
||||
else:
|
||||
preference = "tie"
|
||||
|
||||
confidence: Final = float(verdict.get("confidence", 0.5))
|
||||
reasoning: Final = str(verdict.get("reasoning", ""))
|
||||
|
||||
return preference, confidence, reasoning
|
||||
|
||||
except Exception as e:
|
||||
verbose_logger.debug(f"Judge call failed: {e}")
|
||||
raise
|
||||
content = response_obj.choices[0].message.content
|
||||
except (AttributeError, KeyError, IndexError, TypeError):
|
||||
return ""
|
||||
return _extract_text_from_content(content) if not isinstance(content, str) else content
|
||||
|
||||
|
||||
# Placeholder for router call (to be implemented in proxy layer)
|
||||
async def _call_router_for_shadow(
|
||||
router: "Router",
|
||||
router_config_name: str,
|
||||
messages: list[dict[str, Any]],
|
||||
) -> tuple[str, str, str]:
|
||||
"""
|
||||
Call the auto-router on a prompt to determine what model it would pick.
|
||||
def _default_router_provider() -> "Router | None":
|
||||
try:
|
||||
from litellm.proxy.proxy_server import llm_router
|
||||
except ImportError:
|
||||
return None
|
||||
return llm_router
|
||||
|
||||
Returns: (model_name, tier_classification, shadow_response_text)
|
||||
model_name: e.g. "claude-haiku-4-5"
|
||||
tier_classification: e.g. "SIMPLE", "COMPLEX", "REASONING"
|
||||
shadow_response_text: the actual model response
|
||||
"""
|
||||
# TODO: implement router call via router.acompletion with the given config
|
||||
raise NotImplementedError("_call_router_for_shadow not yet implemented")
|
||||
|
||||
def _default_prisma_provider() -> "PrismaClient | None":
|
||||
try:
|
||||
from litellm.proxy.proxy_server import prisma_client
|
||||
except ImportError:
|
||||
return None
|
||||
return prisma_client
|
||||
|
|
|
|||
|
|
@ -36,7 +36,12 @@ from litellm.types.management_endpoints.auto_router_endpoints import (
|
|||
AutoRouterCacheStats,
|
||||
AutoRouterRoutingTestRequest,
|
||||
AutoRouterRoutingTestResponse,
|
||||
GetShadowEvalJobResponse,
|
||||
RequestComplexityRouterConfig,
|
||||
ShadowEvalResult,
|
||||
ShadowEvalTierResult,
|
||||
StartShadowEvalRequest,
|
||||
StartShadowEvalResponse,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
|
|
@ -454,3 +459,256 @@ async def get_auto_router_benchmarks(
|
|||
totals=_benchmark_totals(_summed_agg_row(rows)),
|
||||
groups=groups,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Shadow eval: pre-adoption evaluation of an auto-router against live traffic.
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
# Judge price assumption for the upfront estimate when the judge model has no
|
||||
# entry in the price map: roughly one Sonnet-class call on a mid-sized prompt.
|
||||
_FALLBACK_JUDGE_COST_PER_CALL: Final = 0.01
|
||||
|
||||
# The estimate projects from the key's request volume over this many trailing days.
|
||||
_ESTIMATE_LOOKBACK_DAYS: Final = 7
|
||||
|
||||
|
||||
def _require_admin(user_api_key_dict: UserAPIKeyAuth, action: str) -> None:
|
||||
if user_api_key_dict.user_role not in (
|
||||
LitellmUserRoles.PROXY_ADMIN,
|
||||
LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY,
|
||||
):
|
||||
raise HTTPException(status_code=403, detail=f"Only proxy admin roles can {action}")
|
||||
|
||||
|
||||
def _estimate_judge_cost_per_call(judge_model: str) -> float:
|
||||
"""Price one judge call: ~4k prompt tokens (two responses + conversation) + 200 output."""
|
||||
try:
|
||||
import litellm as _litellm
|
||||
|
||||
prompt_cost, completion_cost = _litellm.cost_per_token(
|
||||
model=judge_model, prompt_tokens=4000, completion_tokens=200
|
||||
)
|
||||
estimated: Final = prompt_cost + completion_cost
|
||||
if estimated > 0:
|
||||
return estimated
|
||||
except Exception: # noqa: BLE001 # unknown judge model: fall back to a flat per-call figure
|
||||
pass
|
||||
return _FALLBACK_JUDGE_COST_PER_CALL
|
||||
|
||||
|
||||
class _VerdictAggRow(BaseModel):
|
||||
tier_classification: str | None
|
||||
turn_count: int
|
||||
real_wins: int
|
||||
shadow_wins: int
|
||||
ties: int
|
||||
avg_confidence: float | None
|
||||
|
||||
|
||||
_VERDICT_AGG_ROWS: Final = TypeAdapter(list[_VerdictAggRow])
|
||||
|
||||
_VERDICT_AGG_SQL: Final = """
|
||||
SELECT
|
||||
tier_classification,
|
||||
COUNT(*)::int AS turn_count,
|
||||
COUNT(*) FILTER (WHERE judge_preference = 'real')::int AS real_wins,
|
||||
COUNT(*) FILTER (WHERE judge_preference = 'shadow')::int AS shadow_wins,
|
||||
COUNT(*) FILTER (WHERE judge_preference = 'tie')::int AS ties,
|
||||
AVG(judge_confidence)::float AS avg_confidence
|
||||
FROM "LiteLLM_ShadowEvalVerdict"
|
||||
WHERE job_id = $1
|
||||
GROUP BY tier_classification
|
||||
"""
|
||||
|
||||
|
||||
def _shadow_eval_results(rows: Sequence[_VerdictAggRow]) -> ShadowEvalResult | None:
|
||||
if not rows:
|
||||
return None
|
||||
total_turns: Final = sum(r.turn_count for r in rows)
|
||||
total_shadow_wins: Final = sum(r.shadow_wins for r in rows)
|
||||
total_ties: Final = sum(r.ties for r in rows)
|
||||
groups: Final = tuple(
|
||||
ShadowEvalTierResult(
|
||||
tier=row.tier_classification or "UNCLASSIFIED",
|
||||
turn_count=row.turn_count,
|
||||
real_win_rate_pct=_pct(row.real_wins, row.turn_count),
|
||||
shadow_win_rate_pct=_pct(row.shadow_wins, row.turn_count),
|
||||
tie_rate_pct=_pct(row.ties, row.turn_count),
|
||||
avg_judge_confidence=round(row.avg_confidence or 0.0, 3),
|
||||
)
|
||||
for row in sorted(rows, key=lambda r: r.turn_count, reverse=True)
|
||||
)
|
||||
return ShadowEvalResult(
|
||||
groups=groups,
|
||||
overall_shadow_win_rate_pct=_pct(total_shadow_wins, total_turns),
|
||||
overall_tie_rate_pct=_pct(total_ties, total_turns),
|
||||
)
|
||||
|
||||
|
||||
def _job_to_response(record: object, results: ShadowEvalResult | None) -> GetShadowEvalJobResponse:
|
||||
return GetShadowEvalJobResponse(
|
||||
job_id=record.id, # type: ignore[attr-defined]
|
||||
status=record.status, # type: ignore[attr-defined]
|
||||
router_name=record.router_name, # type: ignore[attr-defined]
|
||||
shadow_percentage=record.shadow_percentage, # type: ignore[attr-defined]
|
||||
request_count=record.request_count, # type: ignore[attr-defined]
|
||||
completed_count=record.completed_count, # type: ignore[attr-defined]
|
||||
failed_count=record.failed_count, # type: ignore[attr-defined]
|
||||
results=results,
|
||||
cost_estimate=record.cost_estimate, # type: ignore[attr-defined]
|
||||
cost_actual=record.cost_actual, # type: ignore[attr-defined]
|
||||
created_at=record.created_at.isoformat(), # type: ignore[attr-defined]
|
||||
completed_at=record.completed_at.isoformat() if record.completed_at else None, # type: ignore[attr-defined]
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/auto_router/shadow_eval/start",
|
||||
tags=("auto router",),
|
||||
dependencies=(Depends(user_api_key_auth),),
|
||||
response_model=StartShadowEvalResponse,
|
||||
status_code=status.HTTP_201_CREATED,
|
||||
)
|
||||
async def start_shadow_eval(
|
||||
data: StartShadowEvalRequest,
|
||||
user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)],
|
||||
) -> StartShadowEvalResponse:
|
||||
"""
|
||||
Start a pre-adoption shadow eval: duplicate a sampled slice of a key's live
|
||||
traffic through an auto-router, judge real vs. shadow responses blind, and
|
||||
stratify win rates by the router's tier classification.
|
||||
|
||||
The shadow responses are never served to users. The job stays active until
|
||||
stopped via /auto_router/shadow_eval/{job_id}/stop. Judge calls bill to the
|
||||
proxy; the estimate returned here prices them from the key's trailing
|
||||
request volume.
|
||||
"""
|
||||
from litellm.proxy.proxy_server import llm_router, prisma_client
|
||||
|
||||
_require_admin(user_api_key_dict, "manage shadow evals")
|
||||
if prisma_client is None:
|
||||
raise HTTPException(status_code=500, detail=CommonProxyErrors.db_not_connected_error.value)
|
||||
if llm_router is None or data.router_name not in llm_router.auto_routers:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"'{data.router_name}' is not a configured auto-router",
|
||||
)
|
||||
|
||||
existing: Final = await prisma_client.db.litellm_shadowevaljob.find_first(
|
||||
where={"api_key_id": data.api_key_id, "status": {"in": ["pending", "running"]}},
|
||||
)
|
||||
if existing is not None:
|
||||
raise HTTPException(
|
||||
status_code=409,
|
||||
detail=f"Key already has an active shadow eval job ({existing.id}). Stop it first.",
|
||||
)
|
||||
|
||||
lookback_start: Final = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=_ESTIMATE_LOOKBACK_DAYS)
|
||||
recent_requests: Final = await prisma_client.db.litellm_spendlogs.count(
|
||||
where={"api_key": data.api_key_id, "startTime": {"gte": lookback_start}},
|
||||
)
|
||||
weekly_sampled: Final = int(recent_requests * data.shadow_percentage / 100.0)
|
||||
per_call: Final = _estimate_judge_cost_per_call(data.judge_model)
|
||||
estimated_cost: Final = round(weekly_sampled * per_call, 2)
|
||||
|
||||
job: Final = await prisma_client.db.litellm_shadowevaljob.create(
|
||||
data={
|
||||
"api_key_id": data.api_key_id,
|
||||
"router_name": data.router_name,
|
||||
"shadow_percentage": data.shadow_percentage,
|
||||
"judge_model": data.judge_model,
|
||||
"team_id": data.team_id,
|
||||
"status": "pending",
|
||||
"cost_estimate": estimated_cost,
|
||||
"created_by": user_api_key_dict.user_id,
|
||||
}
|
||||
)
|
||||
return StartShadowEvalResponse(
|
||||
job_id=job.id,
|
||||
status="pending",
|
||||
estimated_request_count=weekly_sampled,
|
||||
estimated_cost=estimated_cost,
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/auto_router/shadow_eval",
|
||||
tags=("auto router",),
|
||||
dependencies=(Depends(user_api_key_auth),),
|
||||
response_model=list[GetShadowEvalJobResponse],
|
||||
)
|
||||
async def list_shadow_eval_jobs(
|
||||
user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)],
|
||||
api_key_id: Annotated[str | None, Query(description="Filter to jobs shadowing this key")] = None,
|
||||
) -> list[GetShadowEvalJobResponse]:
|
||||
"""List shadow eval jobs, newest first. Results are omitted; fetch a single job for them."""
|
||||
from litellm.proxy.proxy_server import prisma_client
|
||||
|
||||
_require_admin(user_api_key_dict, "view shadow evals")
|
||||
if prisma_client is None:
|
||||
raise HTTPException(status_code=500, detail=CommonProxyErrors.db_not_connected_error.value)
|
||||
|
||||
where: Final = {"api_key_id": api_key_id} if api_key_id else {}
|
||||
records: Final = await prisma_client.db.litellm_shadowevaljob.find_many(
|
||||
where=where, order={"created_at": "desc"}, take=50
|
||||
)
|
||||
return [_job_to_response(record, results=None) for record in records or ()]
|
||||
|
||||
|
||||
@router.get(
|
||||
"/auto_router/shadow_eval/{job_id}",
|
||||
tags=("auto router",),
|
||||
dependencies=(Depends(user_api_key_auth),),
|
||||
response_model=GetShadowEvalJobResponse,
|
||||
)
|
||||
async def get_shadow_eval_job(
|
||||
job_id: str,
|
||||
user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)],
|
||||
) -> GetShadowEvalJobResponse:
|
||||
"""Status, counters, and per-tier stratified results of one shadow eval job."""
|
||||
from litellm.proxy.proxy_server import prisma_client
|
||||
|
||||
_require_admin(user_api_key_dict, "view shadow evals")
|
||||
if prisma_client is None:
|
||||
raise HTTPException(status_code=500, detail=CommonProxyErrors.db_not_connected_error.value)
|
||||
|
||||
record: Final = await prisma_client.db.litellm_shadowevaljob.find_unique(where={"id": job_id})
|
||||
if record is None:
|
||||
raise HTTPException(status_code=404, detail=f"No shadow eval job {job_id}")
|
||||
|
||||
raw_rows: Final = await prisma_client.db.query_raw(_VERDICT_AGG_SQL, job_id)
|
||||
rows: Final = _VERDICT_AGG_ROWS.validate_python(raw_rows or ())
|
||||
return _job_to_response(record, results=_shadow_eval_results(rows))
|
||||
|
||||
|
||||
@router.post(
|
||||
"/auto_router/shadow_eval/{job_id}/stop",
|
||||
tags=("auto router",),
|
||||
dependencies=(Depends(user_api_key_auth),),
|
||||
response_model=GetShadowEvalJobResponse,
|
||||
)
|
||||
async def stop_shadow_eval_job(
|
||||
job_id: str,
|
||||
user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)],
|
||||
) -> GetShadowEvalJobResponse:
|
||||
"""Stop an active shadow eval job. Existing verdicts are kept; sampling halts within ~30s."""
|
||||
from litellm.proxy.proxy_server import prisma_client
|
||||
|
||||
_require_admin(user_api_key_dict, "manage shadow evals")
|
||||
if prisma_client is None:
|
||||
raise HTTPException(status_code=500, detail=CommonProxyErrors.db_not_connected_error.value)
|
||||
|
||||
record: Final = await prisma_client.db.litellm_shadowevaljob.find_unique(where={"id": job_id})
|
||||
if record is None:
|
||||
raise HTTPException(status_code=404, detail=f"No shadow eval job {job_id}")
|
||||
if record.status not in ("pending", "running"):
|
||||
raise HTTPException(status_code=400, detail=f"Job {job_id} is already {record.status}")
|
||||
|
||||
updated: Final = await prisma_client.db.litellm_shadowevaljob.update(
|
||||
where={"id": job_id},
|
||||
data={"status": "completed", "completed_at": datetime.now(timezone.utc)},
|
||||
)
|
||||
raw_rows: Final = await prisma_client.db.query_raw(_VERDICT_AGG_SQL, job_id)
|
||||
rows: Final = _VERDICT_AGG_ROWS.validate_python(raw_rows or ())
|
||||
return _job_to_response(updated, results=_shadow_eval_results(rows))
|
||||
|
|
|
|||
|
|
@ -2209,6 +2209,19 @@ def cost_tracking():
|
|||
if prisma_client is not None:
|
||||
litellm.logging_callback_manager.add_litellm_callback(_ProxyDBLogger())
|
||||
litellm.logging_callback_manager.add_litellm_async_success_callback(_ProxyDBLogger())
|
||||
_register_shadow_eval_logger()
|
||||
|
||||
|
||||
def _register_shadow_eval_logger() -> None:
|
||||
"""Register the shadow-eval success hook.
|
||||
|
||||
Cheap when idle: with no active LiteLLM_ShadowEvalJob rows the hook is one
|
||||
cached dict lookup per request. Registered alongside cost tracking because
|
||||
it has the same hard dependency on prisma_client.
|
||||
"""
|
||||
from litellm.integrations.shadow_eval_logger import ShadowEvalLogger
|
||||
|
||||
litellm.logging_callback_manager.add_litellm_callback(ShadowEvalLogger())
|
||||
|
||||
|
||||
# Bounds authoritative DB re-reads when enforcing a budget against a
|
||||
|
|
|
|||
|
|
@ -29,6 +29,7 @@ import {
|
|||
type BucketRow,
|
||||
} from "./autoRouterBenchmarks";
|
||||
import { usd } from "./costOptimizationUtils";
|
||||
import ShadowEvalSection from "./ShadowEvalSection";
|
||||
import { useAutoRouterBenchmarks } from "./useAutoRouterBenchmarks";
|
||||
|
||||
const Message: React.FC<{ children: React.ReactNode }> = ({ children }) => (
|
||||
|
|
@ -270,6 +271,8 @@ const BenchmarksBody: React.FC<BenchmarksBodyProps> = ({ isPending, error, data,
|
|||
</div>
|
||||
<CachingCard cache={stats.cache} />
|
||||
</div>
|
||||
|
||||
<ShadowEvalSection accessToken={accessToken} />
|
||||
</>
|
||||
);
|
||||
};
|
||||
|
|
|
|||
|
|
@ -0,0 +1,234 @@
|
|||
"use client";
|
||||
|
||||
import React, { useMemo, useState } from "react";
|
||||
|
||||
import { Badge } from "@/components/ui/badge";
|
||||
import { Button } from "@/components/ui/button";
|
||||
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
|
||||
import { Input } from "@/components/ui/input";
|
||||
import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from "@/components/ui/table";
|
||||
import { ApiError } from "@/lib/http/client";
|
||||
|
||||
import { usd } from "./costOptimizationUtils";
|
||||
import {
|
||||
useShadowEvalJob,
|
||||
useShadowEvalJobs,
|
||||
useStartShadowEval,
|
||||
useStopShadowEval,
|
||||
type ShadowEvalJob,
|
||||
type ShadowEvalTierResult,
|
||||
} from "./useShadowEval";
|
||||
|
||||
const pct = (value: number): string => `${value.toFixed(1)}%`;
|
||||
|
||||
const STATUS_STYLES: Record<string, string> = {
|
||||
pending: "bg-secondary text-muted-foreground",
|
||||
running: "bg-blue-50 text-blue-700",
|
||||
completed: "bg-emerald-50 text-emerald-700",
|
||||
failed: "bg-red-50 text-destructive",
|
||||
};
|
||||
|
||||
const StatusBadge: React.FC<{ status: string }> = ({ status }) => (
|
||||
<Badge variant="secondary" className={STATUS_STYLES[status] ?? STATUS_STYLES.pending}>
|
||||
{status}
|
||||
</Badge>
|
||||
);
|
||||
|
||||
/** Verdict counts are meaningless below this; the table warns instead of misleading. */
|
||||
const MIN_TURNS_FOR_CONFIDENCE = 30;
|
||||
|
||||
const TierResultsTable: React.FC<{ groups: readonly ShadowEvalTierResult[] }> = ({ groups }) => (
|
||||
<Table>
|
||||
<TableHeader>
|
||||
<TableRow>
|
||||
<TableHead>Router tier</TableHead>
|
||||
<TableHead className="text-right">Judged turns</TableHead>
|
||||
<TableHead className="text-right">Router pick wins</TableHead>
|
||||
<TableHead className="text-right">Current model wins</TableHead>
|
||||
<TableHead className="text-right">Ties</TableHead>
|
||||
<TableHead className="text-right">Judge confidence</TableHead>
|
||||
</TableRow>
|
||||
</TableHeader>
|
||||
<TableBody>
|
||||
{groups.map((g) => (
|
||||
<TableRow key={g.tier}>
|
||||
<TableCell className="font-medium text-foreground">
|
||||
{g.tier}
|
||||
{g.turn_count < MIN_TURNS_FOR_CONFIDENCE ? (
|
||||
<span className="ml-2 text-xs text-muted-foreground">(low sample)</span>
|
||||
) : null}
|
||||
</TableCell>
|
||||
<TableCell className="text-right tabular-nums">{g.turn_count.toLocaleString()}</TableCell>
|
||||
<TableCell className="text-right font-medium tabular-nums text-foreground">
|
||||
{pct(g.shadow_win_rate_pct)}
|
||||
</TableCell>
|
||||
<TableCell className="text-right tabular-nums">{pct(g.real_win_rate_pct)}</TableCell>
|
||||
<TableCell className="text-right tabular-nums">{pct(g.tie_rate_pct)}</TableCell>
|
||||
<TableCell className="text-right tabular-nums">{g.avg_judge_confidence.toFixed(2)}</TableCell>
|
||||
</TableRow>
|
||||
))}
|
||||
</TableBody>
|
||||
</Table>
|
||||
);
|
||||
|
||||
const JobResults: React.FC<{
|
||||
job: ShadowEvalJob;
|
||||
onStop: () => void;
|
||||
stopPending: boolean;
|
||||
}> = ({ job, onStop, stopPending }) => {
|
||||
const active = job.status === "pending" || job.status === "running";
|
||||
const results = job.results;
|
||||
const okOrBetter = results ? results.overall_shadow_win_rate_pct + results.overall_tie_rate_pct : null;
|
||||
return (
|
||||
<Card className="overflow-hidden py-0">
|
||||
<div className="flex flex-wrap items-center justify-between gap-3 border-b px-6 py-4">
|
||||
<div className="flex items-center gap-3">
|
||||
<StatusBadge status={job.status} />
|
||||
<div>
|
||||
<p className="text-sm font-medium text-foreground">
|
||||
Shadowing {job.shadow_percentage}% via <span className="font-mono text-xs">{job.router_name}</span>
|
||||
</p>
|
||||
<p className="text-xs text-muted-foreground">
|
||||
{job.completed_count.toLocaleString()} judged · {job.failed_count.toLocaleString()} failed ·{" "}
|
||||
{job.cost_actual != null ? `${usd(job.cost_actual)} judge spend` : "no judge spend yet"}
|
||||
{job.cost_estimate != null ? ` (est. ${usd(job.cost_estimate)}/wk)` : ""}
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
{active ? (
|
||||
<Button variant="outline" size="sm" onClick={onStop} disabled={stopPending}>
|
||||
{stopPending ? "Stopping…" : "Stop"}
|
||||
</Button>
|
||||
) : null}
|
||||
</div>
|
||||
|
||||
{results && results.groups.length > 0 ? (
|
||||
<>
|
||||
<div className="grid gap-0 border-b sm:grid-cols-2">
|
||||
<div className="flex flex-col justify-center gap-1 px-6 py-4">
|
||||
<p className="text-[11px] uppercase tracking-wide text-muted-foreground">
|
||||
Router pick judged as good or better
|
||||
</p>
|
||||
<p className="text-3xl font-semibold text-foreground">{okOrBetter != null ? pct(okOrBetter) : "—"}</p>
|
||||
</div>
|
||||
<div className="flex flex-col justify-center gap-1 border-t px-6 py-4 sm:border-l sm:border-t-0">
|
||||
<p className="text-[11px] uppercase tracking-wide text-muted-foreground">Router pick strictly better</p>
|
||||
<p className="text-3xl font-semibold text-foreground">{pct(results.overall_shadow_win_rate_pct)}</p>
|
||||
</div>
|
||||
</div>
|
||||
<TierResultsTable groups={results.groups} />
|
||||
</>
|
||||
) : (
|
||||
<p className="px-6 py-8 text-center text-sm text-muted-foreground">
|
||||
{active
|
||||
? "Collecting verdicts — results appear as sampled requests are judged."
|
||||
: "No verdicts were recorded for this job."}
|
||||
</p>
|
||||
)}
|
||||
</Card>
|
||||
);
|
||||
};
|
||||
|
||||
const StartForm: React.FC<{ accessToken: string | null }> = ({ accessToken }) => {
|
||||
const [apiKeyId, setApiKeyId] = useState("");
|
||||
const [routerName, setRouterName] = useState("");
|
||||
const [percentage, setPercentage] = useState("10");
|
||||
const start = useStartShadowEval();
|
||||
|
||||
const parsedPct = Number.parseFloat(percentage);
|
||||
const valid = Boolean(accessToken) && apiKeyId.trim() !== "" && routerName.trim() !== "" && parsedPct > 0 && parsedPct <= 100;
|
||||
|
||||
return (
|
||||
<Card size="sm">
|
||||
<CardHeader>
|
||||
<CardTitle className="text-sm font-medium text-foreground">Start a shadow eval</CardTitle>
|
||||
<p className="text-xs text-muted-foreground">
|
||||
Duplicates a sampled slice of the key's traffic through the auto-router and has an LLM judge compare
|
||||
both answers blind. The router's answers are never served to users. Judge calls bill to the proxy — an
|
||||
estimate is shown before anything runs.
|
||||
</p>
|
||||
</CardHeader>
|
||||
<CardContent className="space-y-3">
|
||||
<div className="grid gap-3 sm:grid-cols-3">
|
||||
<Input
|
||||
placeholder="Key hash (token) to shadow"
|
||||
value={apiKeyId}
|
||||
onChange={(e) => setApiKeyId(e.target.value)}
|
||||
/>
|
||||
<Input
|
||||
placeholder="Auto-router name (e.g. claude-auto)"
|
||||
value={routerName}
|
||||
onChange={(e) => setRouterName(e.target.value)}
|
||||
/>
|
||||
<div className="flex items-center gap-2">
|
||||
<Input
|
||||
type="number"
|
||||
min={0.1}
|
||||
max={100}
|
||||
step={0.1}
|
||||
className="w-24"
|
||||
value={percentage}
|
||||
onChange={(e) => setPercentage(e.target.value)}
|
||||
/>
|
||||
<span className="text-sm text-muted-foreground">% of traffic</span>
|
||||
</div>
|
||||
</div>
|
||||
{start.error ? (
|
||||
<p className="text-sm text-destructive">
|
||||
{start.error instanceof ApiError ? start.error.message : "Failed to start shadow eval"}
|
||||
</p>
|
||||
) : null}
|
||||
<Button
|
||||
disabled={!valid || start.isPending}
|
||||
onClick={() =>
|
||||
start.mutate({
|
||||
body: { api_key_id: apiKeyId.trim(), router_name: routerName.trim(), shadow_percentage: parsedPct },
|
||||
})
|
||||
}
|
||||
>
|
||||
{start.isPending ? "Starting…" : "Start shadow eval"}
|
||||
</Button>
|
||||
</CardContent>
|
||||
</Card>
|
||||
);
|
||||
};
|
||||
|
||||
interface ShadowEvalSectionProps {
|
||||
accessToken: string | null;
|
||||
}
|
||||
|
||||
const ShadowEvalSection: React.FC<ShadowEvalSectionProps> = ({ accessToken }) => {
|
||||
const { data: jobs, error } = useShadowEvalJobs(accessToken);
|
||||
const stop = useStopShadowEval();
|
||||
|
||||
// Most recent job carries the section; older jobs list below it.
|
||||
const latest = useMemo(() => jobs?.[0] ?? null, [jobs]);
|
||||
const { data: latestDetail } = useShadowEvalJob(accessToken, latest?.job_id ?? null);
|
||||
|
||||
if (error instanceof ApiError && error.status === 403) return null; // admin-only section
|
||||
|
||||
return (
|
||||
<div className="space-y-4">
|
||||
<div className="flex flex-wrap items-baseline gap-2">
|
||||
<h3 className="text-lg font-semibold text-foreground">Shadow eval</h3>
|
||||
<p className="text-xs text-muted-foreground">
|
||||
pre-adoption quality check: your current model vs. what the router would have picked
|
||||
</p>
|
||||
</div>
|
||||
|
||||
{latest && latestDetail ? (
|
||||
<JobResults
|
||||
job={latestDetail}
|
||||
onStop={() => stop.mutate({ params: { path: { job_id: latestDetail.job_id } } })}
|
||||
stopPending={stop.isPending}
|
||||
/>
|
||||
) : null}
|
||||
|
||||
{!latest || (latestDetail && latestDetail.status !== "pending" && latestDetail.status !== "running") ? (
|
||||
<StartForm accessToken={accessToken} />
|
||||
) : null}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
export default ShadowEvalSection;
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
import { useQueryClient } from "@tanstack/react-query";
|
||||
|
||||
import { $api } from "@/lib/http/api";
|
||||
|
||||
import type { components } from "@/lib/http/schema";
|
||||
|
||||
export type ShadowEvalJob = components["schemas"]["GetShadowEvalJobResponse"];
|
||||
export type ShadowEvalTierResult = components["schemas"]["ShadowEvalTierResult"];
|
||||
export type StartShadowEvalRequest = components["schemas"]["StartShadowEvalRequest"];
|
||||
|
||||
const JOBS_PATH = "/auto_router/shadow_eval" as const;
|
||||
|
||||
/** Poll faster while a job is actively collecting verdicts. */
|
||||
const ACTIVE_POLL_MS = 15_000;
|
||||
|
||||
export const useShadowEvalJobs = (accessToken: string | null) =>
|
||||
$api.useQuery("get", JOBS_PATH, {}, { enabled: Boolean(accessToken), retry: false });
|
||||
|
||||
export const useShadowEvalJob = (accessToken: string | null, jobId: string | null) =>
|
||||
$api.useQuery(
|
||||
"get",
|
||||
"/auto_router/shadow_eval/{job_id}",
|
||||
{ params: { path: { job_id: jobId ?? "" } } },
|
||||
{
|
||||
enabled: Boolean(accessToken) && Boolean(jobId),
|
||||
retry: false,
|
||||
refetchInterval: (query) => {
|
||||
const status = query.state.data?.status;
|
||||
return status === "pending" || status === "running" ? ACTIVE_POLL_MS : false;
|
||||
},
|
||||
},
|
||||
);
|
||||
|
||||
export const useStartShadowEval = () => {
|
||||
const queryClient = useQueryClient();
|
||||
return $api.useMutation("post", "/auto_router/shadow_eval/start", {
|
||||
onSuccess: () => queryClient.invalidateQueries(),
|
||||
});
|
||||
};
|
||||
|
||||
export const useStopShadowEval = () => {
|
||||
const queryClient = useQueryClient();
|
||||
return $api.useMutation("post", "/auto_router/shadow_eval/{job_id}/stop", {
|
||||
onSuccess: () => queryClient.invalidateQueries(),
|
||||
});
|
||||
};
|
||||
350
ui/litellm-dashboard/src/lib/http/schema.d.ts
generated
vendored
350
ui/litellm-dashboard/src/lib/http/schema.d.ts
generated
vendored
|
|
@ -807,6 +807,93 @@ export interface paths {
|
|||
patch?: never;
|
||||
trace?: never;
|
||||
};
|
||||
"/auto_router/shadow_eval": {
|
||||
parameters: {
|
||||
query?: never;
|
||||
header?: never;
|
||||
path?: never;
|
||||
cookie?: never;
|
||||
};
|
||||
/**
|
||||
* List Shadow Eval Jobs
|
||||
* @description List shadow eval jobs, newest first. Results are omitted; fetch a single job for them.
|
||||
*/
|
||||
get: operations["list_shadow_eval_jobs_auto_router_shadow_eval_get"];
|
||||
put?: never;
|
||||
post?: never;
|
||||
delete?: never;
|
||||
options?: never;
|
||||
head?: never;
|
||||
patch?: never;
|
||||
trace?: never;
|
||||
};
|
||||
"/auto_router/shadow_eval/start": {
|
||||
parameters: {
|
||||
query?: never;
|
||||
header?: never;
|
||||
path?: never;
|
||||
cookie?: never;
|
||||
};
|
||||
get?: never;
|
||||
put?: never;
|
||||
/**
|
||||
* Start Shadow Eval
|
||||
* @description Start a pre-adoption shadow eval: duplicate a sampled slice of a key's live
|
||||
* traffic through an auto-router, judge real vs. shadow responses blind, and
|
||||
* stratify win rates by the router's tier classification.
|
||||
*
|
||||
* The shadow responses are never served to users. The job stays active until
|
||||
* stopped via /auto_router/shadow_eval/{job_id}/stop. Judge calls bill to the
|
||||
* proxy; the estimate returned here prices them from the key's trailing
|
||||
* request volume.
|
||||
*/
|
||||
post: operations["start_shadow_eval_auto_router_shadow_eval_start_post"];
|
||||
delete?: never;
|
||||
options?: never;
|
||||
head?: never;
|
||||
patch?: never;
|
||||
trace?: never;
|
||||
};
|
||||
"/auto_router/shadow_eval/{job_id}": {
|
||||
parameters: {
|
||||
query?: never;
|
||||
header?: never;
|
||||
path?: never;
|
||||
cookie?: never;
|
||||
};
|
||||
/**
|
||||
* Get Shadow Eval Job
|
||||
* @description Status, counters, and per-tier stratified results of one shadow eval job.
|
||||
*/
|
||||
get: operations["get_shadow_eval_job_auto_router_shadow_eval__job_id__get"];
|
||||
put?: never;
|
||||
post?: never;
|
||||
delete?: never;
|
||||
options?: never;
|
||||
head?: never;
|
||||
patch?: never;
|
||||
trace?: never;
|
||||
};
|
||||
"/auto_router/shadow_eval/{job_id}/stop": {
|
||||
parameters: {
|
||||
query?: never;
|
||||
header?: never;
|
||||
path?: never;
|
||||
cookie?: never;
|
||||
};
|
||||
get?: never;
|
||||
put?: never;
|
||||
/**
|
||||
* Stop Shadow Eval Job
|
||||
* @description Stop an active shadow eval job. Existing verdicts are kept; sampling halts within ~30s.
|
||||
*/
|
||||
post: operations["stop_shadow_eval_job_auto_router_shadow_eval__job_id__stop_post"];
|
||||
delete?: never;
|
||||
options?: never;
|
||||
head?: never;
|
||||
patch?: never;
|
||||
trace?: never;
|
||||
};
|
||||
"/auto_router/test_routing": {
|
||||
parameters: {
|
||||
query?: never;
|
||||
|
|
@ -25230,6 +25317,51 @@ export interface components {
|
|||
/** Tools */
|
||||
tools?: components["schemas"]["ChatCompletionToolParam"][];
|
||||
};
|
||||
/**
|
||||
* GetShadowEvalJobResponse
|
||||
* @description Status and, once available, results of a shadow-eval job.
|
||||
*/
|
||||
GetShadowEvalJobResponse: {
|
||||
/** Completed At */
|
||||
completed_at?: string | null;
|
||||
/**
|
||||
* Completed Count
|
||||
* @description Verdicts written so far
|
||||
*/
|
||||
completed_count: number;
|
||||
/**
|
||||
* Cost Actual
|
||||
* @description Running total of judge-call spend for this job
|
||||
*/
|
||||
cost_actual?: number | null;
|
||||
/** Cost Estimate */
|
||||
cost_estimate?: number | null;
|
||||
/** Created At */
|
||||
created_at: string;
|
||||
/**
|
||||
* Failed Count
|
||||
* @description Shadow or judge calls that errored and were skipped
|
||||
*/
|
||||
failed_count: number;
|
||||
/** Job Id */
|
||||
job_id: string;
|
||||
/**
|
||||
* Request Count
|
||||
* @description Total requests observed on the shadowed key since the job started
|
||||
*/
|
||||
request_count: number;
|
||||
/** @description Present once at least one verdict has been recorded */
|
||||
results?: components["schemas"]["ShadowEvalResult"] | null;
|
||||
/** Router Name */
|
||||
router_name: string;
|
||||
/** Shadow Percentage */
|
||||
shadow_percentage: number;
|
||||
/**
|
||||
* Status
|
||||
* @enum {string}
|
||||
*/
|
||||
status: "pending" | "running" | "completed" | "failed";
|
||||
};
|
||||
/**
|
||||
* GetTeamMemberPermissionsResponse
|
||||
* @description Response to get the team member permissions for a team
|
||||
|
|
@ -32417,6 +32549,42 @@ export interface components {
|
|||
/** Timeout */
|
||||
timeout?: number | null;
|
||||
};
|
||||
/**
|
||||
* ShadowEvalResult
|
||||
* @description Stratified results of a completed (or in-progress) shadow-eval job.
|
||||
*/
|
||||
ShadowEvalResult: {
|
||||
/** Groups */
|
||||
groups: components["schemas"]["ShadowEvalTierResult"][];
|
||||
/** Overall Shadow Win Rate Pct */
|
||||
overall_shadow_win_rate_pct: number;
|
||||
/** Overall Tie Rate Pct */
|
||||
overall_tie_rate_pct: number;
|
||||
};
|
||||
/**
|
||||
* ShadowEvalTierResult
|
||||
* @description Judge outcomes for one router-tier classification (e.g. SIMPLE, COMPLEX, REASONING).
|
||||
*/
|
||||
ShadowEvalTierResult: {
|
||||
/** Avg Judge Confidence */
|
||||
avg_judge_confidence: number;
|
||||
/**
|
||||
* Real Win Rate Pct
|
||||
* @description Share of judged turns where the real (control) model won
|
||||
*/
|
||||
real_win_rate_pct: number;
|
||||
/**
|
||||
* Shadow Win Rate Pct
|
||||
* @description Share of judged turns where the shadowed router's pick won
|
||||
*/
|
||||
shadow_win_rate_pct: number;
|
||||
/** Tie Rate Pct */
|
||||
tie_rate_pct: number;
|
||||
/** Tier */
|
||||
tier: string;
|
||||
/** Turn Count */
|
||||
turn_count: number;
|
||||
};
|
||||
/**
|
||||
* Skill
|
||||
* @description Represents a skill from the Anthropic Skills API
|
||||
|
|
@ -32585,6 +32753,61 @@ export interface components {
|
|||
/** Simple Medium */
|
||||
simple_medium: number;
|
||||
};
|
||||
/**
|
||||
* StartShadowEvalRequest
|
||||
* @description Start shadowing a deployment's traffic through an auto-router for comparison.
|
||||
*/
|
||||
StartShadowEvalRequest: {
|
||||
/**
|
||||
* Api Key Id
|
||||
* @description The hashed virtual key whose traffic will be shadowed
|
||||
*/
|
||||
api_key_id: string;
|
||||
/**
|
||||
* Judge Model
|
||||
* @description Model used to blindly judge real vs. shadow responses
|
||||
* @default claude-3-5-sonnet-20241022
|
||||
*/
|
||||
judge_model: string;
|
||||
/**
|
||||
* Router Name
|
||||
* @description The auto-router config to shadow requests through
|
||||
*/
|
||||
router_name: string;
|
||||
/**
|
||||
* Shadow Percentage
|
||||
* @description Percentage of the key's requests to duplicate through the router
|
||||
*/
|
||||
shadow_percentage: number;
|
||||
/**
|
||||
* Team Id
|
||||
* @description Team the shadowed key belongs to, for authorization
|
||||
*/
|
||||
team_id?: string | null;
|
||||
};
|
||||
/**
|
||||
* StartShadowEvalResponse
|
||||
* @description Acknowledgement that a shadow-eval job was created, with an upfront cost estimate.
|
||||
*/
|
||||
StartShadowEvalResponse: {
|
||||
/**
|
||||
* Estimated Cost
|
||||
* @description Estimated dollar cost of the judge calls this job will make
|
||||
*/
|
||||
estimated_cost: number;
|
||||
/**
|
||||
* Estimated Request Count
|
||||
* @description Requests expected to be shadowed, based on the key's recent request volume
|
||||
*/
|
||||
estimated_request_count: number;
|
||||
/** Job Id */
|
||||
job_id: string;
|
||||
/**
|
||||
* Status
|
||||
* @enum {string}
|
||||
*/
|
||||
status: "pending" | "running" | "completed" | "failed";
|
||||
};
|
||||
/**
|
||||
* SuccessfulKeyUpdate
|
||||
* @description Successfully updated key with its updated information
|
||||
|
|
@ -36797,6 +37020,133 @@ export interface operations {
|
|||
};
|
||||
};
|
||||
};
|
||||
list_shadow_eval_jobs_auto_router_shadow_eval_get: {
|
||||
parameters: {
|
||||
query?: {
|
||||
/** @description Filter to jobs shadowing this key */
|
||||
api_key_id?: string | null;
|
||||
};
|
||||
header?: never;
|
||||
path?: never;
|
||||
cookie?: never;
|
||||
};
|
||||
requestBody?: never;
|
||||
responses: {
|
||||
/** @description Successful Response */
|
||||
200: {
|
||||
headers: {
|
||||
[name: string]: unknown;
|
||||
};
|
||||
content: {
|
||||
"application/json": components["schemas"]["GetShadowEvalJobResponse"][];
|
||||
};
|
||||
};
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
headers: {
|
||||
[name: string]: unknown;
|
||||
};
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
};
|
||||
};
|
||||
start_shadow_eval_auto_router_shadow_eval_start_post: {
|
||||
parameters: {
|
||||
query?: never;
|
||||
header?: never;
|
||||
path?: never;
|
||||
cookie?: never;
|
||||
};
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StartShadowEvalRequest"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
/** @description Successful Response */
|
||||
201: {
|
||||
headers: {
|
||||
[name: string]: unknown;
|
||||
};
|
||||
content: {
|
||||
"application/json": components["schemas"]["StartShadowEvalResponse"];
|
||||
};
|
||||
};
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
headers: {
|
||||
[name: string]: unknown;
|
||||
};
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
};
|
||||
};
|
||||
get_shadow_eval_job_auto_router_shadow_eval__job_id__get: {
|
||||
parameters: {
|
||||
query?: never;
|
||||
header?: never;
|
||||
path: {
|
||||
job_id: string;
|
||||
};
|
||||
cookie?: never;
|
||||
};
|
||||
requestBody?: never;
|
||||
responses: {
|
||||
/** @description Successful Response */
|
||||
200: {
|
||||
headers: {
|
||||
[name: string]: unknown;
|
||||
};
|
||||
content: {
|
||||
"application/json": components["schemas"]["GetShadowEvalJobResponse"];
|
||||
};
|
||||
};
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
headers: {
|
||||
[name: string]: unknown;
|
||||
};
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
};
|
||||
};
|
||||
stop_shadow_eval_job_auto_router_shadow_eval__job_id__stop_post: {
|
||||
parameters: {
|
||||
query?: never;
|
||||
header?: never;
|
||||
path: {
|
||||
job_id: string;
|
||||
};
|
||||
cookie?: never;
|
||||
};
|
||||
requestBody?: never;
|
||||
responses: {
|
||||
/** @description Successful Response */
|
||||
200: {
|
||||
headers: {
|
||||
[name: string]: unknown;
|
||||
};
|
||||
content: {
|
||||
"application/json": components["schemas"]["GetShadowEvalJobResponse"];
|
||||
};
|
||||
};
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
headers: {
|
||||
[name: string]: unknown;
|
||||
};
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
};
|
||||
};
|
||||
preview_auto_router_routing_auto_router_test_routing_post: {
|
||||
parameters: {
|
||||
query?: never;
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue