chore: capture semantic pipeline baseline
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"""Real LLM NCM evaluation over known-answer cases (mission REAL LLM EVALUATION V1).
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Phases: gold first (65 cases, gold completely hidden from the prompt); silver and noisy
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run ONLY after the gold quality gate passes (selection accuracy >= 0.95 among valid
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answers AND high-confidence wrong rate <= 0.02). Batching verified progressively
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(5/10/20) with 1:1 id matching; results cached permanently per
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(input_fingerprint, candidate_set_hash, prompt_version, model, dataset_versions) so an
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identical re-execution makes ZERO new calls. AI_WRITE_ALLOWED=false; nothing here can
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produce a Firebird write.
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If no AI credential exists the run refuses to call anything and exits with
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BLOCKED_EXTERNAL_AI_CREDENTIAL (no key is ever invented).
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"""
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import json
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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from sqlalchemy import select
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from central.app.ai.eval_metrics import attribution, confidence_buckets, metrics
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from central.app.ai.llm_provider import (PROMPT_VERSION_LLM, LlmAnswerError, LlmEvalItem, OpenAICompatibleProvider,
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candidate_set_hash, input_fingerprint, validate_answer)
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from central.app.ai.retrieval import EnrichedNcmCorpus, KbNeighborIndex, derive_abbreviation_lexicon, hybrid_candidates
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from central.app.config import settings
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from central.app.db import SessionLocal
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from central.app.models import AiEvalCache, Dataset, NcmEntry
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GATE_SELECTION_ACCURACY = 0.95
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GATE_HIGH_CONF_WRONG = 0.02
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BATCH_PROBE_SIZES = (20, 10, 5)
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def _load_samples(session, per_segment=40):
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from central.scripts.run_ncm_retrieval_eval_v2 import _samples
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return _samples(session, per_segment)
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def _build_items(samples, corpus, index, lexicon, top_k=20):
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items = []
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meta = []
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for sample in samples:
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ranked = hybrid_candidates(corpus, index, sample["description"], k=top_k, lexicon=lexicon, exclude_product_id=sample["product_id"])
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item = LlmEvalItem(
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work_item_id=f"{sample['segment']}:{sample['product_id']}",
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description=sample["description"],
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normalized_description=sample["description"],
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brand=None, unit=None, segment=sample["segment"],
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candidates=ranked,
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)
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items.append(item)
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meta.append({**sample, "gold_in_candidates": sample["true_ncm"] in {code for code, *_ in ranked}})
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return items, meta
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def _cached_answers(session, items, model, ncm_sha, phase):
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hits = {}
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for item in items:
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row = session.scalar(select(AiEvalCache).where(
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AiEvalCache.input_fingerprint == input_fingerprint(item),
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AiEvalCache.candidate_set_hash == candidate_set_hash(item),
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AiEvalCache.prompt_version == PROMPT_VERSION_LLM,
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AiEvalCache.model == model,
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AiEvalCache.dataset_version_ncm == ncm_sha,
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AiEvalCache.phase == phase,
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))
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if row:
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hits[item.work_item_id] = row.answer
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return hits
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def _diagnose_http(status_code: int) -> str:
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return {401: "credential invalid (AI_API_KEY)", 403: "permission/plan does not allow this endpoint or model", 404: "endpoint or model not found (AI_BASE_URL/AI_MODEL)", 429: "quota or rate limit"}.get(status_code, f"http {status_code}")
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def _select_model(available: list, requested: str) -> tuple:
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"""Prefer the operator-requested model; else a current GLM suitable for classification.
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Never invents a name and never switches vendor — returns (model, source)."""
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if requested in available:
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return requested, "requested"
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glm = [model for model in available if model.lower().startswith("glm")]
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for candidate in glm:
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if "flash" in candidate.lower():
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return candidate, "fallback_glm_flash"
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if glm:
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return sorted(glm)[-1], "fallback_glm_latest"
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return None, "no_glm_available"
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def _preflight(provider: OpenAICompatibleProvider, requested_model: str) -> dict:
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"""Cheap connectivity check (GET /models — no completion tokens). Validates auth,
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base URL and model availability; diagnoses 401/403/404/429 autonomously."""
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import httpx
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try:
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available = provider.list_models()
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except httpx.HTTPStatusError as error:
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return {"zai_connection": "FAIL", "diagnosis": _diagnose_http(error.response.status_code), "model_available": False}
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except httpx.HTTPError as error:
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return {"zai_connection": "FAIL", "diagnosis": f"transport: {type(error).__name__}", "model_available": False}
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model, source = _select_model(available, requested_model)
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return {"zai_connection": "PASS", "model_available": model is not None, "model": model, "model_source": source,
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"available_models": available if model is None else None, "base_url": provider.base_url}
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def _probe_batch_size(provider, items):
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"""Try the largest batch that keeps valid JSON with 1:1 ids on a small probe slice."""
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for size in BATCH_PROBE_SIZES:
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probe = items[:size]
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try:
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provider.select_batch(probe)
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return size
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except (LlmAnswerError, json.JSONDecodeError):
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continue
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return 1
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def run_phase(provider_factory, phase, samples, corpus, index, lexicon, session, ncm_sha, valid_ncm):
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items, meta = _build_items(samples, corpus, index, lexicon)
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cached = _cached_answers(session, items, provider_factory.model if hasattr(provider_factory, "model") else settings.ai_model, ncm_sha, phase)
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todo = [item for item in items if item.work_item_id not in cached]
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answers = dict(cached)
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provider = None
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batch_size = 0
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if todo:
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provider = provider_factory()
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batch_size = _probe_batch_size(provider, todo)
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for start in range(0, len(todo), batch_size):
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for answer in provider.select_batch(todo[start:start + batch_size]):
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answers[answer["work_item_id"]] = answer
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rows, errors = [], []
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for item, sample in zip(items, meta):
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answer = answers.get(item.work_item_id)
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invalid = False
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if answer is None:
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invalid = True
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answer = {"selected_ncm": None, "confidence": 0.0, "abstain": True, "reason_short": "missing"}
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else:
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try:
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validate_answer(answer, item)
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if answer["selected_ncm"] is not None and answer["selected_ncm"] not in valid_ncm:
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invalid = True
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except LlmAnswerError:
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invalid = True
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selected = None if (invalid or answer["abstain"] or answer["selected_ncm"] is None) else answer["selected_ncm"]
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rows.append({"gold_in_candidates": sample["gold_in_candidates"], "selected": selected, "gold": sample["true_ncm"], "confidence": answer["confidence"], "invalid": invalid})
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if selected is not None and selected != sample["true_ncm"]:
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errors.append({"work_item_id": item.work_item_id, "gold": sample["true_ncm"], "selected": selected, "confidence": answer["confidence"], "gold_in_candidates": sample["gold_in_candidates"], "reason_short": answer.get("reason_short", "")})
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if invalid or answer.get("abstain") or selected is not None:
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if item.work_item_id not in cached:
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session.add(AiEvalCache(input_fingerprint=input_fingerprint(item), candidate_set_hash=candidate_set_hash(item), prompt_version=PROMPT_VERSION_LLM,
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model=provider.model if provider else settings.ai_model, dataset_version_ncm=ncm_sha, phase=phase,
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answer=answer, gold_ncm=sample["true_ncm"]))
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session.commit()
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return rows, errors, provider, batch_size, len(cached)
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def main(output_dir="/tmp", per_segment=40) -> dict:
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if not settings.ai_api_key:
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print(json.dumps({"status": "BLOCKED_EXTERNAL_AI_CREDENTIAL",
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"operator_action": "set AI_API_KEY (and optionally AI_BASE_URL/AI_MODEL) in the Hub environment (.env); never commit or log it"}, indent=2))
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sys.exit(2)
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session = SessionLocal()
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dataset = session.scalar(select(Dataset).where(Dataset.kind == "NCM", Dataset.status == "ACTIVE"))
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valid_ncm = set(session.scalars(select(NcmEntry.code).where(NcmEntry.dataset_id == dataset.id)).all())
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corpus = EnrichedNcmCorpus.build(session)
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index = KbNeighborIndex.build(session)
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lexicon = derive_abbreviation_lexicon(index.corpus(), min_support=3)
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samples = _load_samples(session, per_segment)
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# ---- preflight: cheap /models call, never spends completion tokens ----
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pre_provider = OpenAICompatibleProvider(api_key=settings.ai_api_key, model=settings.ai_model, base_url=settings.ai_base_url)
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preflight = _preflight(pre_provider, settings.ai_model)
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if preflight["zai_connection"] != "PASS" or not preflight["model_available"]:
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print(json.dumps({"status": "ZAI_PREFLIGHT_FAIL", "preflight": preflight}, indent=2))
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sys.exit(3)
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model_name = preflight["model"]
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factory = lambda: OpenAICompatibleProvider(api_key=settings.ai_api_key, model=model_name, base_url=settings.ai_base_url)
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gold = [sample for sample in samples if sample["quality"] == "GOLD"]
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provider = factory()
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rows, errors, provider_used, batch_size, cache_hits = run_phase(lambda: provider, "gold", gold, corpus, index, lexicon, session, dataset.sha256, valid_ncm)
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m = metrics(rows)
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prices_configured = settings.ai_price_in_per_m is not None and settings.ai_price_out_per_m is not None
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usage = provider.usage if provider is not None else {"input_tokens": 0, "output_tokens": 0, "cached_tokens": 0}
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cost_total = round(usage["input_tokens"] / 1e6 * settings.ai_price_in_per_m + usage["output_tokens"] / 1e6 * settings.ai_price_out_per_m, 6) if prices_configured else "UNKNOWN"
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report = {
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"artifact": "REAL_LLM_NCM_EVALUATION_V1", "phase": "gold",
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"provider": "z.ai", "base_url": settings.ai_base_url, "model": model_name, "prompt_version": PROMPT_VERSION_LLM,
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"preflight": preflight,
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"metrics": m, "attribution": attribution(rows), "confidence_buckets": confidence_buckets(rows),
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"batch_size_selected": batch_size,
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"gate": {"selection_accuracy_min": GATE_SELECTION_ACCURACY, "high_conf_wrong_max": GATE_HIGH_CONF_WRONG,
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"passed": (m["SELECTION_ACCURACY_WHEN_GOLD_AVAILABLE"] or 0) >= GATE_SELECTION_ACCURACY and (m["HIGH_CONFIDENCE_WRONG_RATE"] or 0) <= GATE_HIGH_CONF_WRONG},
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"cache_hits_first_run": cache_hits, "ai_write_allowed": False,
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"usage": {**usage, "http_calls": provider.http_calls, "retries": provider.retries,
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"average_items_per_call": round(len(rows) / max(provider.http_calls, 1), 2),
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"tokens_per_item": round((usage["input_tokens"] + usage["output_tokens"]) / max(len(rows), 1), 1),
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"cost_total": cost_total, "cost_per_item": (round(cost_total / max(len(rows), 1), 6) if prices_configured else "UNKNOWN"),
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"prices_configured": prices_configured},
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}
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Path(f"{output_dir}/REAL_LLM_NCM_EVALUATION_V1.json").write_text(json.dumps(report, indent=2, ensure_ascii=False), encoding="utf-8")
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Path(f"{output_dir}/REAL_LLM_NCM_ERRORS_V1.json").write_text(json.dumps(errors, indent=2, ensure_ascii=False), encoding="utf-8")
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# cache proof: identical re-execution must make zero new calls
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provider2 = factory()
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rows2, _, _, _, cache_hits2 = run_phase(lambda: provider2, "gold", gold, corpus, index, lexicon, session, dataset.sha256, valid_ncm)
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second_calls = provider2.http_calls
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report["cache_proof"] = {"second_run_calls": second_calls, "second_run_cache_hits": cache_hits2,
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"cache_hit_reexecution": second_calls == 0, "identical_metrics": metrics(rows2)["CORRECT"] == m["CORRECT"]}
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Path(f"{output_dir}/REAL_LLM_NCM_EVALUATION_V1.json").write_text(json.dumps(report, indent=2, ensure_ascii=False), encoding="utf-8")
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Path(f"{output_dir}/REAL_LLM_COST_V1.json").write_text(json.dumps({"artifact": "REAL_LLM_COST_V1", "usage": report["usage"], "previous_estimate_usd": 0.44, "cache_proof": report["cache_proof"]}, indent=2), encoding="utf-8")
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session.close()
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return report
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--output-dir", default="/tmp")
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parser.add_argument("--per-segment", type=int, default=40)
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args = parser.parse_args()
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print(json.dumps(main(args.output_dir, args.per_segment), indent=2))
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