Update project and configurations
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Zou-Seay
219
intelligent_cabin/app/services/multi_intent_detector.py
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219
intelligent_cabin/app/services/multi_intent_detector.py
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from __future__ import annotations
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import importlib
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from dataclasses import dataclass, field
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from pathlib import Path
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from time import perf_counter
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from typing import Any, Protocol
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from app.schemas.intent import IntentDefinition
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from app.services.joint_nlu import JointBertNLU
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@dataclass
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class MultiIntentCandidate:
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intent_id: str
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score: float
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label: str | None = None
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@dataclass
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class MultiIntentDetectionResult:
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detected: bool = False
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candidates: list[MultiIntentCandidate] = field(default_factory=list)
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reason: str | None = None
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backend_name: str = "none"
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error_message: str | None = None
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raw_scores: list[dict[str, Any]] = field(default_factory=list)
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class MultiIntentDetector(Protocol):
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def detect(self, text: str, intents: list[IntentDefinition]) -> MultiIntentDetectionResult:
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...
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class JointBertMultiIntentDetector:
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"""
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A multi-intent detector backed by the same Joint BERT runtime as single-intent and slot extraction.
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"""
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def __init__(
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self,
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nlu: JointBertNLU,
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threshold: float | None = None,
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top_k: int = 8,
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max_labels: int = 4,
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) -> None:
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self._nlu = nlu
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self._threshold = threshold
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self._top_k = top_k
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self._max_labels = max_labels
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def detect(self, text: str, intents: list[IntentDefinition]) -> MultiIntentDetectionResult:
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candidates = self._nlu.predict_multi_intents(
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text,
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intents,
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threshold=self._threshold,
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max_labels=self._max_labels,
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top_k=self._top_k,
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)
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return MultiIntentDetectionResult(
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detected=len(candidates) >= 2,
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candidates=[
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MultiIntentCandidate(intent_id=item.intent_id, score=item.score, label=item.intent_id)
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for item in candidates
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],
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reason=f"joint bert multi-label candidates={len(candidates)} threshold={self._threshold}",
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backend_name="joint-bert-multi-label",
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raw_scores=[
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{"intent_id": item.intent_id, "label": item.intent_id, "score": float(item.score)}
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for item in candidates
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],
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)
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def warmup(self, sample_text: str = "打开空调并打开车窗") -> bool:
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return self._nlu.warmup(sample_text)
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class BertMultiIntentDetector:
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"""
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A stage-2 multi-intent detector backed by a dedicated multi-label BERT head.
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"""
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def __init__(
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self,
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model_path: str,
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threshold: float = 0.45,
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top_k: int = 8,
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max_labels: int = 4,
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blocked_labels: set[str] | None = None,
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) -> None:
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self._model_path = model_path
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self._threshold = threshold
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self._top_k = top_k
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self._max_labels = max_labels
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self._blocked_labels = blocked_labels or {"__social__", "__out_of_scope__"}
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self._tokenizer = None
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self._model = None
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self._torch = None
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self._error_message: str | None = None
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self._warmed_up = False
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self._warmup_elapsed_ms: float | None = None
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def detect(self, text: str, intents: list[IntentDefinition]) -> MultiIntentDetectionResult:
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runtime = self._get_runtime()
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if runtime is None:
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return MultiIntentDetectionResult(
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detected=False,
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reason="multi-label detector unavailable",
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backend_name="bert-multi-label",
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error_message=self._error_message,
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)
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torch, tokenizer, model = runtime
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intent_map = {intent.intent_id: intent for intent in intents}
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try:
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encoded = tokenizer(
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text,
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truncation=True,
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padding=False,
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return_tensors="pt",
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)
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model.eval()
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with torch.no_grad():
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logits = model(**encoded).logits.squeeze(0)
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probs = torch.sigmoid(logits).detach().cpu().tolist()
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except Exception as exc:
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return MultiIntentDetectionResult(
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detected=False,
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reason="multi-label forward failed",
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backend_name="bert-multi-label",
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error_message=str(exc),
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)
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id2label = getattr(model.config, "id2label", {}) or {}
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raw_scores: list[dict[str, Any]] = []
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for index, score in enumerate(probs):
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label = str(id2label.get(index, f"LABEL_{index}"))
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raw_scores.append(
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{
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"label": label,
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"intent_id": label,
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"score": float(score),
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}
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)
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raw_scores.sort(key=lambda item: item["score"], reverse=True)
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raw_top = raw_scores[: self._top_k]
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candidates: list[MultiIntentCandidate] = []
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for item in raw_top:
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intent_id = str(item.get("intent_id") or "")
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if intent_id in self._blocked_labels:
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continue
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if intent_id not in intent_map:
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continue
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score = float(item.get("score", 0.0))
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if score < self._threshold:
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continue
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candidates.append(
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MultiIntentCandidate(
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intent_id=intent_id,
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score=score,
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label=str(item.get("label") or intent_id),
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)
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)
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if len(candidates) >= self._max_labels:
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break
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return MultiIntentDetectionResult(
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detected=len(candidates) >= 2,
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candidates=candidates,
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reason=f"bert multi-label candidates={len(candidates)} threshold={self._threshold}",
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backend_name="bert-multi-label",
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raw_scores=raw_top,
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)
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def warmup(self, sample_text: str = "打开空调并打开车窗") -> bool:
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if self._warmed_up:
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return True
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started_at = perf_counter()
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runtime = self._get_runtime()
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if runtime is None:
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self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
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return False
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torch, tokenizer, model = runtime
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try:
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encoded = tokenizer(sample_text, truncation=True, padding=False, return_tensors="pt")
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model.eval()
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with torch.no_grad():
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_ = model(**encoded).logits
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except Exception as exc:
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self._error_message = str(exc)
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self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
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return False
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self._warmed_up = True
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self._warmup_elapsed_ms = round((perf_counter() - started_at) * 1000, 3)
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return True
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def _get_runtime(self):
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if self._tokenizer is not None and self._model is not None and self._torch is not None:
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return self._torch, self._tokenizer, self._model
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if not self._model_path or not Path(self._model_path).exists():
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self._error_message = "multi-intent model path is empty or missing"
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return None
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try:
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transformers = importlib.import_module("transformers")
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torch = importlib.import_module("torch")
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except ImportError as exc:
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self._error_message = str(exc)
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return None
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try:
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self._tokenizer = transformers.AutoTokenizer.from_pretrained(self._model_path)
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self._model = transformers.AutoModelForSequenceClassification.from_pretrained(self._model_path)
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self._torch = torch
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except Exception as exc:
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self._error_message = str(exc)
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return None
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return self._torch, self._tokenizer, self._model
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SigmoidBertMultiIntentDetector = BertMultiIntentDetector
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