moving

benchmarks/calibration/v6-kappa-recal/cold-probes-phase2.py

@@ -0,0 +1,312 @@
+"""
+Manifest v6 Phase 2 pre-flight — cold alias probes
+====================================================
+
+Validates both v6 judge aliases are production-ready with a minimal
+probe sample before any N=400 commit. 6 calls total (3 MiniMax + 3
+Kimi) on the first 3 split instances from §1.3h sample.
+
+Probes use direct HTTP to upstream endpoints (same methodology as
+§1.3g / §1.3h / §1.3h-C / v6 κ re-cal). LiteLLM proxy is NOT in the
+loop — isolates upstream routing/parse behavior from middleware.
+
+Kimi backup has never been exercised in production under v6 authority;
+this is the first production-class test.
+
+Scope: §11-compliant (read-only access to frozen files; new artefact
+under benchmarks/calibration/v6-kappa-recal/).
+"""
+from __future__ import annotations
+
+import json
+import re
+import sys
+import time
+import urllib.error
+import urllib.request
+from datetime import datetime, timezone
+from pathlib import Path
+
+try:
+    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+    sys.stderr.reconfigure(encoding="utf-8", errors="replace")
+except Exception:
+    pass
+
+OUT_PATH = Path("D:/Projects/waggle-os/benchmarks/calibration/v6-kappa-recal/phase2-cold-probes.jsonl")
+# Reuse first 3 split instances from §1.3h — they exercised both providers before
+SAMPLE_PATH = Path("D:/Projects/waggle-os/benchmarks/probes/judge-swap-validation/split-cases-sample.jsonl")
+
+JUDGE_PROMPT_TEMPLATE = "\n".join([
+    "You are evaluating whether an LLM's answer is correct against ground truth.",
+    "",
+    "## Question",
+    "{question}",
+    "",
+    "## Ground-truth answer",
+    "{ground_truth}",
+    "",
+    "## Ground-truth supporting context (excerpt shown to the model)",
+    "{context}",
+    "",
+    "## Model's answer",
+    "{model_answer}",
+    "",
+    "## Your task",
+    "",
+    "Step 1: Determine if the model's answer is correct.",
+    "- \"correct\" means the model's answer contains all required facts from ground truth, with no additional incorrect claims.",
+    "- Minor phrasing differences, synonyms, or alternative but equivalent formulations are acceptable.",
+    "- Extra detail is acceptable ONLY if it is factually correct.",
+    "",
+    "Step 2: If incorrect, assign exactly one failure mode using this decision tree:",
+    "",
+    "1. Does the model explicitly refuse or say it does not know? -> F1 (ABSTAIN)",
+    "2. Does the model answer a DIFFERENT question than was asked (coherent but off-topic)? -> F5 (OFF-TOPIC)",
+    "3. Does the model rely on entities, names, dates, or claims that do NOT appear in the ground-truth context (fabrication)? -> F4 (HALLUCINATED)",
+    "4. Does the model correctly state SOME required facts but miss others, without stating any incorrect facts? -> F2 (PARTIAL)",
+    "5. Otherwise (model states facts derived from the context but gets them wrong): -> F3 (INCORRECT)",
+    "",
+    "Step 3: Return JSON only, no prose, in this exact schema:",
+    "",
+    "{{",
+    "  \"verdict\": \"correct\" | \"incorrect\",",
+    "  \"failure_mode\": null | \"F1\" | \"F2\" | \"F3\" | \"F4\" | \"F5\",",
+    "  \"rationale\": \"one sentence explaining the verdict\"",
+    "}}",
+    "",
+    "If verdict is \"correct\", failure_mode MUST be null.",
+    "If verdict is \"incorrect\", failure_mode MUST be one of F1-F5.",
+])
+
+
+def ts() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def logmsg(msg: str) -> None:
+    print(f"{ts()} {msg}", flush=True)
+
+
+def load_env() -> dict[str, str]:
+    env_path = Path("D:/Projects/waggle-os/.env")
+    out: dict[str, str] = {}
+    for line in env_path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        k, _, v = line.partition("=")
+        out[k.strip()] = v.strip().strip('"').strip("'")
+    return out
+
+
+def extract_json_body(raw: str) -> dict | None:
+    if not raw:
+        return None
+    trimmed = raw.strip()
+    if trimmed.startswith("```"):
+        m = re.match(r"^```(?:json)?\s*\n?(.*?)```\s*$", trimmed, re.DOTALL)
+        if m:
+            trimmed = m.group(1).strip()
+    try:
+        return json.loads(trimmed)
+    except Exception:
+        pass
+    first = trimmed.find("{")
+    last = trimmed.rfind("}")
+    if first != -1 and last != -1 and last > first:
+        try:
+            return json.loads(trimmed[first:last + 1])
+        except Exception:
+            return None
+    return None
+
+
+def parse_verdict(raw: str) -> tuple[str | None, str | None, str | None]:
+    body = extract_json_body(raw)
+    if not isinstance(body, dict):
+        return (None, None, None)
+    v = body.get("verdict")
+    fm = body.get("failure_mode")
+    rat = body.get("rationale")
+    if v not in ("correct", "incorrect"):
+        return (None, None, None)
+    if fm is not None and fm not in ("F1", "F2", "F3", "F4", "F5"):
+        fm = None
+    return (v, fm, rat if isinstance(rat, str) else None)
+
+
+def http_post_json(url: str, headers: dict, body: dict, timeout_s: int = 60) -> tuple[int, dict | str]:
+    req = urllib.request.Request(
+        url, data=json.dumps(body).encode("utf-8"), method="POST",
+        headers={"Content-Type": "application/json", **headers},
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=timeout_s) as resp:
+            raw = resp.read().decode("utf-8", errors="replace")
+            try:
+                return resp.status, json.loads(raw)
+            except Exception:
+                return resp.status, raw
+    except urllib.error.HTTPError as e:
+        try:
+            return e.code, e.read().decode("utf-8", errors="replace")[:2000]
+        except Exception:
+            return e.code, ""
+    except Exception as e:
+        return 0, f"{type(e).__name__}: {e}"
+
+
+def call_minimax(prompt: str, or_key: str) -> dict:
+    url = "https://openrouter.ai/api/v1/chat/completions"
+    headers = {"Authorization": f"Bearer {or_key}"}
+    body = {
+        "model": "minimax/minimax-m2.7",
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.0,
+        "max_tokens": 4096,
+    }
+    started = time.time()
+    status, resp = http_post_json(url, headers, body)
+    latency = int((time.time() - started) * 1000)
+    if status == 200 and isinstance(resp, dict):
+        choices = resp.get("choices") or []
+        if choices:
+            msg = choices[0].get("message") or {}
+            content = msg.get("content") or msg.get("reasoning_content") or ""
+            usage = resp.get("usage", {})
+            return {
+                "raw_text": content,
+                "status": 200,
+                "latency_ms": latency,
+                "prompt_tokens": usage.get("prompt_tokens"),
+                "completion_tokens": usage.get("completion_tokens"),
+                "provider": "minimax",
+                "alias": "minimax-m27-via-openrouter",
+                "routing": "openrouter_direct_http",
+                "error": None,
+            }
+    return {
+        "raw_text": "",
+        "status": status,
+        "latency_ms": latency,
+        "error": str(resp)[:300],
+        "provider": "minimax",
+        "alias": "minimax-m27-via-openrouter",
+        "routing": "openrouter_direct_http",
+        "prompt_tokens": None,
+        "completion_tokens": None,
+    }
+
+
+def call_kimi(prompt: str, moonshot_key: str) -> dict:
+    """Kimi K2.6 via Moonshot direct intl endpoint. First production-class
+    run of the v6 kimi-k26-direct alias equivalent (LiteLLM proxy not in
+    loop; routes directly to upstream)."""
+    url = "https://api.moonshot.ai/v1/chat/completions"
+    headers = {"Authorization": f"Bearer {moonshot_key}"}
+    body = {
+        "model": "kimi-k2.6",
+        "messages": [{"role": "user", "content": prompt}],
+        "max_tokens": 4096,
+    }
+    started = time.time()
+    status, resp = http_post_json(url, headers, body)
+    latency = int((time.time() - started) * 1000)
+    if status == 200 and isinstance(resp, dict):
+        choices = resp.get("choices") or []
+        if choices:
+            msg = choices[0].get("message") or {}
+            content = msg.get("content") or msg.get("reasoning_content") or ""
+            usage = resp.get("usage", {})
+            return {
+                "raw_text": content,
+                "status": 200,
+                "latency_ms": latency,
+                "prompt_tokens": usage.get("prompt_tokens"),
+                "completion_tokens": usage.get("completion_tokens"),
+                "provider": "kimi",
+                "alias": "kimi-k26-direct",
+                "routing": "moonshot_direct_http",
+                "error": None,
+            }
+    return {
+        "raw_text": "",
+        "status": status,
+        "latency_ms": latency,
+        "error": str(resp)[:300],
+        "provider": "kimi",
+        "alias": "kimi-k26-direct",
+        "routing": "moonshot_direct_http",
+        "prompt_tokens": None,
+        "completion_tokens": None,
+    }
+
+
+def main() -> int:
+    logmsg("[cold-probes] Phase 2 pre-flight START")
+    env = load_env()
+    or_key = env.get("OPENROUTER_API_KEY", "").strip()
+    moonshot_key = env.get("MOONSHOT_API_KEY", "").strip()
+    if not or_key or not moonshot_key:
+        logmsg("[cold-probes] FATAL missing keys (OR or MOONSHOT)")
+        return 2
+
+    sample = []
+    with SAMPLE_PATH.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                sample.append(json.loads(line))
+    sample = sample[:3]  # first 3 instances
+    logmsg(f"[cold-probes] loaded {len(sample)} probe instances from §1.3h split sample")
+
+    rows = []
+    for i, s in enumerate(sample):
+        prompt = JUDGE_PROMPT_TEMPLATE.format(
+            question=s["question"], ground_truth=s["ground_truth"],
+            context=s["context"], model_answer=s["model_answer"],
+        )
+        for fn, label in ((call_minimax, "minimax"), (call_kimi, "kimi")):
+            resp = fn(prompt, or_key if label == "minimax" else moonshot_key)
+            verdict, fm, rat = parse_verdict(resp["raw_text"])
+            rows.append({
+                "instance_id": s["instance_id"],
+                "cell": s["cell"],
+                "provider": resp["provider"],
+                "alias": resp["alias"],
+                "routing": resp["routing"],
+                "http_status": resp["status"],
+                "error": resp.get("error"),
+                "latency_ms": resp["latency_ms"],
+                "prompt_tokens": resp.get("prompt_tokens"),
+                "completion_tokens": resp.get("completion_tokens"),
+                "parsed_verdict": verdict,
+                "parsed_failure_mode": fm,
+                "parsed_rationale": rat,
+                "raw_text": resp["raw_text"],
+                "opus_verdict_ref": s.get("opus_verdict"),
+                "gpt_verdict_ref": s.get("gpt_verdict"),
+            })
+            logmsg(
+                f"[cold-probes] {label:7} {i+1}/3 {s['instance_id']} status={resp['status']} "
+                f"verdict={verdict} lat={resp['latency_ms']}ms"
+            )
+
+    OUT_PATH.parent.mkdir(parents=True, exist_ok=True)
+    with OUT_PATH.open("w", encoding="utf-8") as f:
+        for r in rows:
+            f.write(json.dumps(r, ensure_ascii=False) + "\n")
+
+    # Summary
+    mm_rows = [r for r in rows if r["provider"] == "minimax"]
+    km_rows = [r for r in rows if r["provider"] == "kimi"]
+    mm_parsed = sum(1 for r in mm_rows if r["parsed_verdict"] is not None)
+    km_parsed = sum(1 for r in km_rows if r["parsed_verdict"] is not None)
+    logmsg(f"[cold-probes] SUMMARY MiniMax: {mm_parsed}/3 parsed  Kimi: {km_parsed}/3 parsed")
+    logmsg("[cold-probes] END")
+    return 0 if (mm_parsed == 3 and km_parsed == 3) else 3
+
+
+if __name__ == "__main__":
+    sys.exit(main())