a3f61fcc0f
- DB: segments 用户输入文本（Alembic 0002） - Chat: 阶段检测/阶段提示/回复限制，编排与访谈/画像 prompts 调整 - Memoir: 忠实度检查 agent，叙事与分类等链路更新 - Core: agent 日志、Alembic 启动、LangChain/日志/配置等 - Story: time_hints；Memory 检索与相关测试 - Expo: 助手头像、会话页与消息拆分、实时会话与文案/i18n - Docs/scripts/tests: 迁移脚本、LLM JSON/记忆检索文档、新增单测
75 lines
2.3 KiB
Python
75 lines
2.3 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 invoke_json_object
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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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raw = invoke_json_object(
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llm,
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prompt,
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max_tokens=1024,
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agent="ExtractionAgent.extract",
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)
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parsed = json.loads(extract_json_payload(raw))
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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: {}", e)
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return ExtractionResult(detected_stage=detected_stage, slots=extracted_slots)
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