53e0065e3e
配置 SSOT（TOML + .env） 统一错误契约 Auth 与事务边界 Redis / Celery 可靠性:业务 Redis（DB/0）与 Celery broker/backend（DB/1）显式拆分；连接池、sync client 可观测性（OpenTelemetry + LGTM）
90 lines
2.9 KiB
Python
90 lines
2.9 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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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.agents.memoir.schemas import StateExtractionOutput
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from app.agents.stage_constants import normalize_chat_stage
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from app.core.config import settings
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from app.core.llm_call import LLMCallError, llm_json_call
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from app.core.logging import get_logger
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from app.features.memoir.constants import memoir
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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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*,
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language: str = "zh",
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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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language=language,
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)
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parsed = llm_json_call(
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llm,
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prompt,
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StateExtractionOutput,
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max_tokens=memoir.extraction_max_tokens,
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agent="ExtractionAgent.extract",
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)
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raw_slots = parsed.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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if not extracted_slots:
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# 无实质 slot 时不推断阶段，避免元话语被标成任意 childhood 等（与服务端护栏一致）
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detected_stage = normalize_chat_stage(
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current_stage, fallback=current_stage
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)
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else:
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raw_detected = parsed.detected_stage or current_stage
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detected_stage = normalize_chat_stage(
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str(raw_detected) if raw_detected is not None else None,
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fallback=current_stage,
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)
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except LLMCallError as e:
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logger.warning("ExtractionAgent LLM 解析失败: {}", e)
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return ExtractionResult(detected_stage=detected_stage, slots=extracted_slots)
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