07c6478742
- 新增 utterance_substance：短时/应答/元话语可跳过记忆检索、阶段 LLM 与资料抽取 LLM；可配置 - 输入归一化：LLM 模式默认仅语音/ASR；配置项写入 .env.example - Memoir Phase1：可选 batch LLM 一次性抽取+分类（失败回退逐段）；Extraction 空槽位时阶段与 current_stage 对齐，prompt 约束收紧 - 叙事与忠实度：narrative_safety、证据重叠/场合锚点、标题 slots 与履历短语 grounded；fidelity 解析失败 fail-open 可配置 - 章节管线：锁 TTL 上调、锁竞争 Celery 重试、Phase2 immediate singleflight 等；story_pipeline_sync / chapter_compose / memoir_tasks 联动 - Memory：compaction / repo / summarizer / evidence 小修；事实 FTS 未命中是否回退最近事实可配置 - 新增 memoir_pipeline_trace；补充 memoir_reliability 文档与多项回归/门控测试
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
"""
|
||
ExtractionAgent：从用户消息中提取 5-stage 状态与 slots。
|
||
对应现有逻辑：get_state_extraction_prompt + JSON 解析
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import json
|
||
from dataclasses import dataclass
|
||
from typing import Any, Dict
|
||
|
||
from app.agents.memoir.prompts import get_state_extraction_prompt
|
||
from app.agents.stage_constants import normalize_chat_stage
|
||
from app.core.langchain_llm import invoke_json_object
|
||
from app.core.logging import get_logger
|
||
from app.core.json_utils import extract_json_payload
|
||
|
||
logger = get_logger(__name__)
|
||
|
||
|
||
@dataclass
|
||
class ExtractionResult:
|
||
"""状态提取结果"""
|
||
|
||
detected_stage: str
|
||
slots: Dict[str, str]
|
||
|
||
|
||
class ExtractionAgent:
|
||
"""从用户消息中提取 detected_stage 和 slots"""
|
||
|
||
def extract(
|
||
self,
|
||
user_message: str,
|
||
current_stage: str,
|
||
stage_slots: Dict[str, Any],
|
||
llm: Any,
|
||
) -> ExtractionResult:
|
||
"""
|
||
提取结构化信息并判断阶段。
|
||
llm 需支持 .invoke(prompt) 同步调用（Celery 任务内使用）。
|
||
"""
|
||
detected_stage = current_stage
|
||
extracted_slots: Dict[str, str] = {}
|
||
|
||
if not llm:
|
||
return ExtractionResult(
|
||
detected_stage=detected_stage, slots=extracted_slots
|
||
)
|
||
|
||
try:
|
||
prompt = get_state_extraction_prompt(
|
||
user_message=user_message,
|
||
current_stage=current_stage,
|
||
stage_slots={
|
||
k: v.model_dump() if hasattr(v, "model_dump") else v
|
||
for k, v in (stage_slots or {}).items()
|
||
},
|
||
)
|
||
raw = invoke_json_object(
|
||
llm,
|
||
prompt,
|
||
max_tokens=1024,
|
||
agent="ExtractionAgent.extract",
|
||
)
|
||
parsed = json.loads(extract_json_payload(raw))
|
||
raw_slots = parsed.get("slots", {}) or {}
|
||
extracted_slots = {
|
||
k: v if isinstance(v, str) else str(v) for k, v in raw_slots.items()
|
||
}
|
||
if not extracted_slots:
|
||
# 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）
|
||
detected_stage = normalize_chat_stage(
|
||
current_stage, fallback=current_stage
|
||
)
|
||
else:
|
||
raw_detected = parsed.get("detected_stage", current_stage)
|
||
detected_stage = normalize_chat_stage(
|
||
str(raw_detected) if raw_detected is not None else None,
|
||
fallback=current_stage,
|
||
)
|
||
except (json.JSONDecodeError, Exception) as e:
|
||
logger.warning("ExtractionAgent LLM 解析失败: {}", e)
|
||
|
||
return ExtractionResult(detected_stage=detected_stage, slots=extracted_slots)
|