71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
"""
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ExtractionAgent：从用户消息中提取 5-stage 状态与 slots。
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对应现有逻辑：get_state_extraction_prompt + JSON 解析
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from typing import Any, Dict
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from app.agents.memoir.prompts import get_state_extraction_prompt
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from app.core.langchain_llm import bind_json_object_mode
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from app.core.logging import get_logger
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from app.features.memoir.memoir_images.json_payload import extract_json_payload
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logger = get_logger(__name__)
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@dataclass
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class ExtractionResult:
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"""状态提取结果"""
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detected_stage: str
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slots: Dict[str, str]
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class ExtractionAgent:
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"""从用户消息中提取 detected_stage 和 slots"""
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def extract(
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self,
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user_message: str,
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current_stage: str,
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stage_slots: Dict[str, Any],
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llm: Any,
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) -> ExtractionResult:
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"""
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提取结构化信息并判断阶段。
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llm 需支持 .invoke(prompt) 同步调用（Celery 任务内使用）。
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"""
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detected_stage = current_stage
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extracted_slots: Dict[str, str] = {}
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if not llm:
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return ExtractionResult(
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detected_stage=detected_stage, slots=extracted_slots
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)
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try:
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prompt = get_state_extraction_prompt(
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user_message=user_message,
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current_stage=current_stage,
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stage_slots={
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k: v.model_dump() if hasattr(v, "model_dump") else v
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for k, v in (stage_slots or {}).items()
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},
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)
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json_llm = bind_json_object_mode(llm, max_tokens=1024)
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response = json_llm.invoke(prompt)
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parsed = json.loads(extract_json_payload(response.content))
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detected_stage = parsed.get("detected_stage", detected_stage)
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raw_slots = parsed.get("slots", {}) or {}
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extracted_slots = {
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k: v if isinstance(v, str) else str(v) for k, v in raw_slots.items()
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}
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except (json.JSONDecodeError, Exception) as e:
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logger.warning("ExtractionAgent LLM 解析失败: %s", e)
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return ExtractionResult(detected_stage=detected_stage, slots=extracted_slots)
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