| |
|
|
| from __future__ import annotations |
| import json |
| from typing import Dict, Any |
|
|
| from .models import ( |
| MeaningInferenceResult, |
| EcholaliaDetectionResult, |
| UtteranceAnalysisRequest, |
| UserProfile, |
| DialogueTurn, |
| ) |
| from .llm_utils import load_prompt, call_llm_json |
|
|
|
|
| MEANING_PROMPT_TEMPLATE = load_prompt("meaning") |
|
|
|
|
| def run_meaning_inference( |
| preprocessed: Dict[str, Any], |
| echolalia: EcholaliaDetectionResult, |
| ) -> MeaningInferenceResult: |
| request: UtteranceAnalysisRequest = preprocessed["request"] |
| profile: UserProfile = preprocessed["profile"] |
| normalized_dialogue = preprocessed["normalized_dialogue"] |
|
|
| dialogue_str = "\n".join( |
| f"[{t.turn_index}] {t.speaker}: {t.utterance}" |
| for t in normalized_dialogue |
| ) |
|
|
| schema = MeaningInferenceResult.model_json_schema() |
| schema_json = json.dumps(schema, ensure_ascii=False, indent=2) |
|
|
| prompt = MEANING_PROMPT_TEMPLATE.format( |
| profile=json.dumps(profile.model_dump(), indent=2, ensure_ascii=False), |
| context_description=request.context_description, |
| normalized_dialogue=dialogue_str, |
| echolalia_detection=json.dumps(echolalia.model_dump(), indent=2, ensure_ascii=False), |
| json_schema=schema_json, |
| ) |
|
|
| raw = call_llm_json(prompt, model="gpt-4.1-mini") |
| return MeaningInferenceResult(**raw) |
