486 lines
18 KiB
Python
486 lines
18 KiB
Python
"""
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§1.3h Judge Swap Stratified Re-Probe — split-case execution
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============================================================
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Executes 28 API calls (7 splits × 4 candidates) on the subset where
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Opus != GPT in the authoritative 100-row κ calibration set
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(`locomo-mini-n20-retry-2026-04-24T00-02-12Z.jsonl`).
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PM-amended (§1.3H-POOL-SHORTAGE OPTION 1):
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- Split pool minimum reduced 12 → 7 (use-all-available)
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- Budget $1.50 cap / $3 halt (reduced from $3/$5)
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- Wall-clock cap 30 min (reduced from 60)
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- Primary operational value focus: MiniMax direct routing E2E
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verification with newly-added MINIMAX_GROUP_ID
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Routing:
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- Kimi: direct api.moonshot.ai, kimi-k2.6, max_tokens=4096
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- DeepSeek: direct api.deepseek.com, deepseek-v4-pro, max_tokens=1024
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- Zhipu: direct api.z.ai, glm-5.1, max_tokens=1024
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- MiniMax: DIRECT FIRST — tries api.minimaxi.com + api.minimax.chat
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with MINIMAX_GROUP_ID. Fallback: OpenRouter
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minimax/minimax-m2.7 if both direct variants fail.
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Scope discipline: manifest v5 anchor fc16925 intact; §11 frozen path
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untouched; no new LiteLLM aliases; no new Opus/GPT calls; additive
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files only under `benchmarks/probes/judge-swap-validation/`.
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Usage:
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python probe-script-split.py
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"""
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from __future__ import annotations
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import json
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import os
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import re
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import sys
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import time
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import traceback
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import urllib.error
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import urllib.request
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from datetime import datetime, timezone
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from pathlib import Path
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try:
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sys.stdout.reconfigure(encoding="utf-8", errors="replace")
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sys.stderr.reconfigure(encoding="utf-8", errors="replace")
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except Exception:
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pass
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# ── Paths + constants ───────────────────────────────────────────────────
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PROBE_DIR = Path("D:/Projects/waggle-os/benchmarks/probes/judge-swap-validation")
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SPLIT_SAMPLE_PATH = PROBE_DIR / "split-cases-sample.jsonl"
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# ── Judge prompt template (verbatim from §1.3g probe-script.py) ────────
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JUDGE_PROMPT_TEMPLATE = "\n".join([
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"You are evaluating whether an LLM's answer is correct against ground truth.",
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"",
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"## Question",
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"{question}",
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"",
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"## Ground-truth answer",
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"{ground_truth}",
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"",
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"## Ground-truth supporting context (excerpt shown to the model)",
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"{context}",
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"",
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"## Model's answer",
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"{model_answer}",
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"",
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"## Your task",
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"",
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"Step 1: Determine if the model's answer is correct.",
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"- \"correct\" means the model's answer contains all required facts from ground truth, with no additional incorrect claims.",
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"- Minor phrasing differences, synonyms, or alternative but equivalent formulations are acceptable.",
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"- Extra detail is acceptable ONLY if it is factually correct.",
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"",
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"Step 2: If incorrect, assign exactly one failure mode using this decision tree:",
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"",
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"1. Does the model explicitly refuse or say it does not know? -> F1 (ABSTAIN)",
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"2. Does the model answer a DIFFERENT question than was asked (coherent but off-topic)? -> F5 (OFF-TOPIC)",
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"3. Does the model rely on entities, names, dates, or claims that do NOT appear in the ground-truth context (fabrication)? -> F4 (HALLUCINATED)",
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"4. Does the model correctly state SOME required facts but miss others, without stating any incorrect facts? -> F2 (PARTIAL)",
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"5. Otherwise (model states facts derived from the context but gets them wrong): -> F3 (INCORRECT)",
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"",
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"Step 3: Return JSON only, no prose, in this exact schema:",
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"",
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"{{",
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" \"verdict\": \"correct\" | \"incorrect\",",
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" \"failure_mode\": null | \"F1\" | \"F2\" | \"F3\" | \"F4\" | \"F5\",",
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" \"rationale\": \"one sentence explaining the verdict\"",
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"}}",
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"",
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"If verdict is \"correct\", failure_mode MUST be null.",
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"If verdict is \"incorrect\", failure_mode MUST be one of F1-F5.",
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])
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def ts() -> str:
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return datetime.now(timezone.utc).isoformat()
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def logmsg(msg: str) -> None:
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print(f"{ts()} {msg}", flush=True)
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# ── .env loader ─────────────────────────────────────────────────────────
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def load_env() -> dict[str, str]:
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env_path = Path("D:/Projects/waggle-os/.env")
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out: dict[str, str] = {}
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if not env_path.exists():
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return out
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for line in env_path.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if not line or line.startswith("#"):
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continue
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if "=" not in line:
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continue
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k, _, v = line.partition("=")
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k, v = k.strip(), v.strip().strip('"').strip("'")
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out[k] = v
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return out
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# ── Verbatim judge-runner JSON extractor ────────────────────────────────
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def extract_json_body(raw: str) -> dict | None:
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if not raw:
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return None
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trimmed = raw.strip()
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if trimmed.startswith("```"):
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m = re.match(r"^```(?:json)?\s*\n?(.*?)```\s*$", trimmed, re.DOTALL)
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if m:
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trimmed = m.group(1).strip()
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try:
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return json.loads(trimmed)
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except Exception:
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pass
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first = trimmed.find("{")
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last = trimmed.rfind("}")
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if first != -1 and last != -1 and last > first:
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try:
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return json.loads(trimmed[first:last+1])
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except Exception:
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return None
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return None
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def parse_verdict(raw_text: str) -> tuple[str | None, str | None, str | None]:
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body = extract_json_body(raw_text or "")
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if not isinstance(body, dict):
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return (None, None, None)
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v = body.get("verdict")
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fm = body.get("failure_mode")
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rat = body.get("rationale")
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if v not in ("correct", "incorrect"):
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return (None, None, None)
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if fm is not None and fm not in ("F1", "F2", "F3", "F4", "F5"):
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fm = None
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return (v, fm, rat if isinstance(rat, str) else None)
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# ── HTTP helper ─────────────────────────────────────────────────────────
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def http_post_json(url: str, headers: dict, body: dict, timeout_s: int = 60) -> tuple[int, dict | str]:
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req = urllib.request.Request(
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url,
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data=json.dumps(body).encode("utf-8"),
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method="POST",
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headers={"Content-Type": "application/json", **headers},
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)
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try:
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with urllib.request.urlopen(req, timeout=timeout_s) as resp:
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raw = resp.read().decode("utf-8", errors="replace")
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try:
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return resp.status, json.loads(raw)
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except Exception:
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return resp.status, raw
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except urllib.error.HTTPError as e:
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try:
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body_err = e.read().decode("utf-8", errors="replace")
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except Exception:
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body_err = ""
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return e.code, body_err[:2000]
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except Exception as e:
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return 0, f"{type(e).__name__}: {e}"
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# ── Provider callers ────────────────────────────────────────────────────
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def _retry_call(
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url: str,
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headers: dict,
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body: dict,
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model_id: str,
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routing: str,
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max_attempts: int = 3,
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) -> dict:
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started = time.time()
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last_err = None
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retries = 0
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for attempt in range(max_attempts):
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status, resp = http_post_json(url, headers, body, timeout_s=60)
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if status == 200 and isinstance(resp, dict):
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choices = resp.get("choices") or []
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if choices:
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msg = choices[0].get("message") or {}
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content = msg.get("content") or msg.get("reasoning_content") or ""
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usage = resp.get("usage", {})
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return {
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"raw_text": content,
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"status": 200,
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"error": None,
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"retries": retries,
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"model_id": model_id,
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"routing": routing,
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"latency_ms": int((time.time() - started) * 1000),
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"prompt_tokens": usage.get("prompt_tokens"),
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"completion_tokens": usage.get("completion_tokens"),
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}
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last_err = f"status={status} resp={str(resp)[:400]}"
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retries += 1
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if attempt < max_attempts - 1:
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time.sleep(2 ** attempt)
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return {
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"raw_text": "",
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"status": 0,
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"error": last_err,
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"retries": retries,
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"model_id": model_id,
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"routing": routing,
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"latency_ms": int((time.time() - started) * 1000),
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"prompt_tokens": None,
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"completion_tokens": None,
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}
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def call_kimi(prompt: str, env: dict) -> dict:
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url = "https://api.moonshot.ai/v1/chat/completions"
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headers = {"Authorization": f"Bearer {env['MOONSHOT_API_KEY']}"}
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body = {
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"model": "kimi-k2.6",
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": 4096,
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}
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return _retry_call(url, headers, body, "kimi-k2.6", routing="direct")
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def call_deepseek(prompt: str, env: dict) -> dict:
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url = "https://api.deepseek.com/v1/chat/completions"
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headers = {"Authorization": f"Bearer {env['DEEPSEEK_API_KEY']}"}
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body = {
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"model": "deepseek-v4-pro",
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.0,
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"max_tokens": 1024,
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}
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return _retry_call(url, headers, body, "deepseek-v4-pro", routing="direct")
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def call_zhipu(prompt: str, env: dict) -> dict:
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url = "https://api.z.ai/api/paas/v4/chat/completions"
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headers = {"Authorization": f"Bearer {env['ZHIPU_API_KEY']}"}
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body = {
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"model": "glm-5.1",
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.0,
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"max_tokens": 1024,
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}
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return _retry_call(url, headers, body, "glm-5.1", routing="direct")
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# ── MiniMax direct routing with endpoint fallback ──────────────────────
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#
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# MiniMax direct chat completion has two known v2 endpoints:
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# 1. https://api.minimaxi.com/v1/text/chatcompletion_v2 (international)
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# 2. https://api.minimax.chat/v1/text/chatcompletion_v2 (legacy/CN)
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# v2 is OpenAI-compatible format. GroupId is passed as query param.
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# If both v2 endpoints fail, fall back to OpenRouter (§1.3g routing).
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_MINIMAX_ROUTE_CACHE: dict[str, str] = {"chosen": None, "first_tried": []}
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def call_minimax_direct_with_fallback(prompt: str, env: dict) -> dict:
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"""Tries MiniMax direct (international then legacy), falls back to
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OpenRouter on failure. Caches first-successful route to avoid re-
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discovering per call."""
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group_id = env.get("MINIMAX_GROUP_ID", "").strip()
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mm_key = env.get("MINIMAX_API_KEY", "").strip()
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or_key = env.get("OPENROUTER_API_KEY", "").strip()
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# If a working route is already cached, use it
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if _MINIMAX_ROUTE_CACHE["chosen"] == "direct_international":
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return _call_minimax_direct(prompt, mm_key, group_id, "international")
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if _MINIMAX_ROUTE_CACHE["chosen"] == "direct_legacy":
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return _call_minimax_direct(prompt, mm_key, group_id, "legacy")
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if _MINIMAX_ROUTE_CACHE["chosen"] == "openrouter":
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return _call_minimax_openrouter(prompt, or_key)
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# First call: try international, then legacy, then OR
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r = _call_minimax_direct(prompt, mm_key, group_id, "international")
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_MINIMAX_ROUTE_CACHE["first_tried"].append("international")
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if r["status"] == 200 and r["raw_text"]:
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_MINIMAX_ROUTE_CACHE["chosen"] = "direct_international"
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logmsg(f"[minimax] DIRECT international SUCCESSFUL — caching route")
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return r
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logmsg(f"[minimax] direct international FAILED: status={r['status']} err={(r.get('error') or '')[:200]}")
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r = _call_minimax_direct(prompt, mm_key, group_id, "legacy")
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_MINIMAX_ROUTE_CACHE["first_tried"].append("legacy")
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if r["status"] == 200 and r["raw_text"]:
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_MINIMAX_ROUTE_CACHE["chosen"] = "direct_legacy"
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logmsg(f"[minimax] DIRECT legacy SUCCESSFUL — caching route")
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return r
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logmsg(f"[minimax] direct legacy FAILED: status={r['status']} err={(r.get('error') or '')[:200]}")
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_MINIMAX_ROUTE_CACHE["chosen"] = "openrouter"
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_MINIMAX_ROUTE_CACHE["first_tried"].append("openrouter")
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logmsg(f"[minimax] falling back to OpenRouter for all subsequent calls")
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return _call_minimax_openrouter(prompt, or_key)
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def _call_minimax_direct(prompt: str, api_key: str, group_id: str, variant: str) -> dict:
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if variant == "international":
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base = "https://api.minimaxi.com/v1/text/chatcompletion_v2"
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elif variant == "legacy":
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base = "https://api.minimax.chat/v1/text/chatcompletion_v2"
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else:
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return {"raw_text": "", "status": 0, "error": f"unknown variant {variant}",
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"retries": 0, "model_id": "unknown", "routing": f"direct_{variant}",
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"latency_ms": 0, "prompt_tokens": None, "completion_tokens": None}
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# GroupId as query param
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url = f"{base}?GroupId={group_id}" if group_id else base
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headers = {"Authorization": f"Bearer {api_key}"}
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body = {
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"model": "MiniMax-M2",
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.0,
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"max_tokens": 4096,
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}
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return _retry_call(url, headers, body, "MiniMax-M2", routing=f"direct_{variant}", max_attempts=2)
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def _call_minimax_openrouter(prompt: str, or_key: str) -> dict:
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url = "https://openrouter.ai/api/v1/chat/completions"
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headers = {"Authorization": f"Bearer {or_key}"}
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body = {
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"model": "minimax/minimax-m2.7",
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.0,
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"max_tokens": 4096,
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}
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return _retry_call(url, headers, body, "minimax/minimax-m2.7", routing="openrouter")
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# ── Sample loader ──────────────────────────────────────────────────────
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def load_split_sample() -> list[dict]:
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rows = []
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with SPLIT_SAMPLE_PATH.open("r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if line:
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rows.append(json.loads(line))
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return rows
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# ── Runner ─────────────────────────────────────────────────────────────
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PROVIDER_FNS = [
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("kimi", call_kimi),
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("minimax", call_minimax_direct_with_fallback),
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("deepseek", call_deepseek),
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("zhipu", call_zhipu),
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]
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def run_provider(name: str, call_fn, sample: list[dict], env: dict) -> list[dict]:
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logmsg(f"[{name}] start — {len(sample)} split instances")
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out: list[dict] = []
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for i, s in enumerate(sample):
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prompt = JUDGE_PROMPT_TEMPLATE.format(
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question=s.get("question") or "",
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ground_truth=s.get("ground_truth") or "",
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context=s.get("context") or "",
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model_answer=s.get("model_answer") or "",
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)
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resp = call_fn(prompt, env)
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verdict, fm, rat = parse_verdict(resp["raw_text"])
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row = {
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"instance_id": s["instance_id"],
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"cell": s["cell"],
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"provider": name,
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"model_id": resp["model_id"],
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"routing": resp["routing"],
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"http_status": resp["status"],
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"error": resp.get("error"),
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"retries": resp["retries"],
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"latency_ms": resp["latency_ms"],
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"prompt_tokens": resp.get("prompt_tokens"),
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"completion_tokens": resp.get("completion_tokens"),
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"raw_text": resp["raw_text"],
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"parsed_verdict": verdict,
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"parsed_failure_mode": fm,
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"parsed_rationale": rat,
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"opus_verdict_ref": s.get("opus_verdict"),
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"gpt_verdict_ref": s.get("gpt_verdict"),
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}
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out.append(row)
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logmsg(
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f"[{name}] {i+1:>2}/{len(sample)} {s['instance_id']:30} cell={s['cell']:14} "
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f"status={resp['status']} verdict={verdict} retries={resp['retries']} "
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f"routing={resp['routing']}"
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)
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logmsg(f"[{name}] done")
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return out
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def write_jsonl(path: Path, rows: list[dict]) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("w", encoding="utf-8") as f:
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for r in rows:
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f.write(json.dumps(r, ensure_ascii=False) + "\n")
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def main() -> int:
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logmsg("[probe] §1.3h judge swap stratified re-probe START")
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env = load_env()
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required = [
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"MOONSHOT_API_KEY",
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"MINIMAX_API_KEY",
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"MINIMAX_GROUP_ID",
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"DEEPSEEK_API_KEY",
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"ZHIPU_API_KEY",
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"OPENROUTER_API_KEY",
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]
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missing = [k for k in required if not env.get(k)]
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if missing:
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logmsg(f"[probe] FATAL missing env keys: {missing}")
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return 2
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sample = load_split_sample()
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logmsg(f"[probe] loaded {len(sample)} split instances from {SPLIT_SAMPLE_PATH.name}")
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if len(sample) < 1:
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logmsg("[probe] FATAL empty sample")
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return 3
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logmsg("[probe] executing 4 providers in parallel (4 threads)")
|
||
provider_rows: dict[str, list[dict]] = {}
|
||
with ThreadPoolExecutor(max_workers=4) as pool:
|
||
futures = {
|
||
pool.submit(run_provider, name, fn, sample, env): name
|
||
for (name, fn) in PROVIDER_FNS
|
||
}
|
||
for fut in as_completed(futures):
|
||
name = futures[fut]
|
||
try:
|
||
rows = fut.result()
|
||
provider_rows[name] = rows
|
||
except Exception as e:
|
||
logmsg(f"[probe] provider {name} FAILED: {type(e).__name__}: {e}")
|
||
provider_rows[name] = []
|
||
|
||
logmsg("[probe] writing per-provider JSONL artefacts")
|
||
for name, rows in provider_rows.items():
|
||
write_jsonl(PROBE_DIR / f"{name}-split-responses.jsonl", rows)
|
||
parsed = sum(1 for r in rows if r.get("parsed_verdict") is not None)
|
||
logmsg(f"[probe] {name}: wrote {len(rows)} rows, parsed_ok={parsed}")
|
||
|
||
logmsg(f"[probe] minimax_routing_resolution: {_MINIMAX_ROUTE_CACHE}")
|
||
logmsg("[probe] END")
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
try:
|
||
sys.exit(main())
|
||
except Exception as e:
|
||
logmsg(f"[FATAL] {type(e).__name__}: {e}")
|
||
logmsg(traceback.format_exc()[:2000])
|
||
sys.exit(99)
|