184 lines
8.2 KiB
Python
184 lines
8.2 KiB
Python
"""Stand-in for classifyRectificationTurnIntent using an OpenAI-compatible chat API.
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Copies the production instruction strings from turn-intent-classifier.ts.
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Does not change production. Reads DEEPSEEK_API_KEY from the environment only.
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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 time
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import urllib.error
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import urllib.request
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from typing import Any, Mapping, Sequence
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from scripts.research.jev_intent_questions import build_state, enforce_combo
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# Keep these byte-for-byte with frontend/src/lib/rectification-agentic/v9/turn-intent-classifier.ts
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CHOICE_INSTRUCTIONS = """你只做当前生时校正问题的意图分类，不回答用户，也不修改任何状态。
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结合当前问题和动态选项判断用户是在回答当前问题、提供新的带时间经历、要求停止整个校正、询问结果，还是语义不清。
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若是在回答当前问题，answer_class 必须使用某个选项提供的 answer_class；否则 answer_class 必须为 null。
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has_new_dated_event 仅在用户同一句里除了回答当前问题之外，还提供了新的、带大概时间的经历时为 true；单纯的否定或单纯的选项回答必须为 false。
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若同一句话既回答了当前问题又补充了新的带时间经历，intent 仍为 answer_current_focus，has_new_dated_event 为 true。
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“当前方面没有、那段时间没有变化”通常是回答当前问题，不是停止整个流程；只有用户明确要求停止整个校正时才分类为 stop_rectification。
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不要按 A/B/C/D 的位置猜语义，只按选项 label 与 answer_class 判断。"""
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COLLECT_INSTRUCTIONS = """你只做当前生时校正采集题的意图分类，不回答用户，也不修改任何状态。
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当前问题没有点选选项。判断用户是在回答当前采集题、提供新的带时间经历、要求停止整个校正、询问结果，还是语义不清。
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「没有、没发生过、这方面没什么」→ intent 为 answer_current_focus，answer_class 为 no（该方面没有事，本次不再问）。
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「记不清、不记得、忘了、想不起来、以后再说」→ intent 为 answer_current_focus，answer_class 为 unsure（先放着，以后可补）。
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用户用带大概年月的经历直接回答当前采集题 → intent 为 answer_current_focus，answer_class 为 yes（程度较弱时为 weak_yes），has_new_dated_event 为 false。
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不要把「没有」或「记不清」标成 yes。若既没有否定、也没有说记不清、也没有给出带年月经历，intent 为 unclear，answer_class 必须为 null。
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若用户只在补充带时间的经历、并没有回答当前采集题，intent 为 provide_new_evidence，answer_class 必须为 null。
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has_new_dated_event 仅在用户同一句里除了回答当前采集题之外，还提供了新的、带大概时间的经历时为 true；单纯的否定或记不清必须为 false。
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若同一句话既明确否定当前采集题又补充了新的带时间经历，intent 仍为 answer_current_focus 且 answer_class 为 no，不要改成 provide_new_evidence。
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“当前方面没有”通常是回答当前采集题，不是停止整个流程；只有用户明确要求停止整个校正时才分类为 stop_rectification。
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不要按关键词表或正则猜测，只根据当前问题与用户这句话的语义分类。"""
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JSON_SCHEMA_HINT = (
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"只输出一个 JSON 对象，不要解释。字段："
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'{"intent":"answer_current_focus|provide_new_evidence|stop_rectification|ask_about_result|unclear",'
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'"answer_class":"yes"|"weak_yes"|"no"|"unsure"|null,'
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'"has_new_dated_event":true|false}'
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)
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DEFAULT_MODEL = "deepseek-flash"
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DEFAULT_BASE = "https://api.deepseek.com"
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def current_model_id() -> str:
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return os.environ.get("DEEPSEEK_MODEL") or DEFAULT_MODEL
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def _instructions(sample: Mapping[str, Any]) -> str:
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focus = sample.get("focus") if isinstance(sample.get("focus"), dict) else {}
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options = list(focus.get("options") or [])
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return CHOICE_INSTRUCTIONS if options else COLLECT_INSTRUCTIONS
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def _parse_content(text: str) -> dict[str, Any]:
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raw = (text or "").strip()
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if raw.startswith("```"):
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raw = raw.strip("`")
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if raw.lower().startswith("json"):
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raw = raw[4:]
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raw = raw.strip()
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start = raw.find("{")
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end = raw.rfind("}")
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if start < 0 or end <= start:
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raise ValueError("no_json_object")
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payload = json.loads(raw[start : end + 1])
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if not isinstance(payload, dict):
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raise ValueError("json_not_object")
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return payload
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def call_current(sample: Mapping[str, Any], *, timeout: float = 60.0) -> dict[str, Any]:
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api_key = os.environ.get("DEEPSEEK_API_KEY") or ""
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if not api_key:
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raise RuntimeError("DEEPSEEK_API_KEY missing")
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base = (os.environ.get("DEEPSEEK_BASE_URL") or DEFAULT_BASE).rstrip("/")
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model = current_model_id()
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body = {
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"model": model,
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"messages": [
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{"role": "system", "content": _instructions(sample) + "\n" + JSON_SCHEMA_HINT},
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{"role": "user", "content": json.dumps(build_state(sample), ensure_ascii=False)},
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],
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"temperature": 0,
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"max_tokens": 256,
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"thinking": {"type": "disabled"},
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"response_format": {"type": "json_object"},
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"stream": False,
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}
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request = urllib.request.Request(
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f"{base}/chat/completions",
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data=json.dumps(body).encode("utf-8"),
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}",
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},
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method="POST",
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)
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started = time.perf_counter()
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with urllib.request.urlopen(request, timeout=timeout) as response:
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payload = json.loads(response.read().decode("utf-8"))
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elapsed_ms = int((time.perf_counter() - started) * 1000)
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content = (((payload.get("choices") or [{}])[0].get("message") or {}).get("content")) or ""
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parsed = _parse_content(content)
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combo = enforce_combo(
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parsed.get("intent"),
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parsed.get("answer_class"),
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parsed.get("has_new_dated_event"),
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)
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usage = payload.get("usage") or {}
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return {
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"ok": True,
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"unavailable": False,
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"model": payload.get("model") or model,
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"intent": combo["intent"],
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"answer_class": combo["answer_class"],
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"has_new_dated_event": combo["has_new_dated_event"],
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"confidence": None,
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"raw": parsed,
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"input_tokens": usage.get("prompt_tokens") or usage.get("input_tokens"),
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"output_tokens": usage.get("completion_tokens") or usage.get("output_tokens"),
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"elapsed_ms": elapsed_ms,
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}
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def call_current_retry(sample: Mapping[str, Any], *, retries: int = 4) -> dict[str, Any]:
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last_error = ""
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delay = 1.0
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for _attempt in range(retries):
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try:
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return call_current(sample)
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except urllib.error.HTTPError as exc:
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last_error = f"HTTP{exc.code}"
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if exc.code in {429, 500, 502, 503, 529}:
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time.sleep(delay)
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delay = min(delay * 2, 16)
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continue
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break
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except Exception as exc: # noqa: BLE001
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last_error = type(exc).__name__
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time.sleep(delay)
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delay = min(delay * 2, 16)
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return {
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"ok": False,
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"unavailable": True,
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"model": current_model_id(),
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"intent": None,
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"answer_class": None,
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"has_new_dated_event": None,
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"confidence": None,
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"raw": None,
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"input_tokens": None,
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"output_tokens": None,
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"elapsed_ms": None,
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"error": last_error,
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}
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def stratified_sample(
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rows: Sequence[Mapping[str, Any]],
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*,
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fraction: float,
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seed: int,
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) -> list[dict[str, Any]]:
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import random
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rng = random.Random(seed)
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picked: list[dict[str, Any]] = []
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by_layer: dict[str, list[Mapping[str, Any]]] = {"choice": [], "collect": [], "none": []}
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for row in rows:
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layer = str(row.get("layer") or "none")
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by_layer.setdefault(layer, []).append(row)
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for layer, group in by_layer.items():
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items = list(group)
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rng.shuffle(items)
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n = max(1, int(round(len(items) * fraction))) if items else 0
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picked.extend(dict(item) for item in items[:n])
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return picked
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