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life-echo/api/app/features/memory/llm_schemas.py

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"""LLM JSON 输出校验memory 富化)。"""
from __future__ import annotations
import json
from typing import Any, TypeVar
from pydantic import BaseModel, Field, field_validator
TModel = TypeVar("TModel", bound=BaseModel)
class ExtractedFactItem(BaseModel):
fact_type: str = "event"
subject: str | None = None
predicate: str | None = None
object_json: Any = None
confidence: float = Field(default=0.75, ge=0.0, le=1.0)
source_chunk_id: str | None = None
@field_validator("fact_type", mode="before")
@classmethod
def _coerce_fact_type(cls, v: object) -> str:
ft = str(v or "event").strip() or "event"
if ft not in ("person", "event", "relation", "place", "milestone"):
return "event"
return ft
class FactsExtractionPayload(BaseModel):
facts: list[ExtractedFactItem] = Field(default_factory=list)
class SessionSummaryPayload(BaseModel):
summary: str = ""
class RollingSummaryPayload(BaseModel):
rolling_summary: str = ""
class TimelineEventItem(BaseModel):
event_year: int | None = None
event_date: str | None = None
title: str = ""
description: str | None = None
source_fact_ids: list[str] = Field(default_factory=list)
@field_validator("source_fact_ids", mode="before")
@classmethod
def _coerce_sf(cls, v: object) -> list[str]:
if v is None:
return []
if isinstance(v, str):
return [v] if v else []
if isinstance(v, list):
return [str(x) for x in v if x]
return []
class TimelineEventsPayload(BaseModel):
events: list[TimelineEventItem] = Field(default_factory=list)
def parse_json_payload(raw: str, model: type[TModel]) -> TModel | None:
"""解析 invoke_json_object 返回的 JSON 字符串。"""
refactor(agents): 抽取阶段常量与对话上下文;快档 LLM;图片 prompt 可禁止回退 访谈与阶段 - 新增 app/agents/stage_constants.py:集中 CHAT_STAGES、章节分类/顺序、阶段到默认 memoir 类别等,与 MemoirState 默认槽位顺序对齐;减少散落在 prompts 内的重复常量。 - 新增 app/agents/chat/prompt_context.py:以 ChatPromptContext 汇总 guided 系统提示所需字段(阶段、槽位、轮次、人设、记忆证据、回复长度模式、背景声线、职业等),统一走 get_guided_conversation_prompt。 - 大幅收敛 app/agents/chat/prompts_conversation.py;调整 prompts.py、stage_prompts.py、stage_detection.py;同步 interview_agent、profile_agent、helpers 与 state_schema,使对话侧构造提示的方式一致、可测。 回忆录流水线 - memoir/prompts.py 删除已迁至 stage_constants / 独立模板的大段常量与图片占位相关逻辑;classification / extraction / fidelity / narrative agents 与 orchest(全量历史仍可用于计数,注入模型时按轮次与字符上限截断)、image_prompt_fallback_disabled。 - dependencies 增加 get_llm_provider_fast(LRU 缓存,可与默认共用密钥与 base_url)。 任务与编排 - memoir_tasks:prepare_batches 注入 llm_fast;开启独立快档模型时打结构化日志。 - chapter_cover_tasks、story_image_tasks:与图片 prompt / JSON 工具路径或策略变更对齐(import 与行为一致)。 - story_pipeline_sync 等小处同步。 其它核心 - langchain_llm、text_normalize 随上述调用链微调。 开发者体验 - .cursor/settings.json:启用 redis-development、postman 插件。 测试 - 新增 test_image_prompt_policy:覆盖「禁止回退」等图片 prompt 策略。 - 更新 test_interview_prompts、test_interview_reply_length、test_experience_regressions、test_json_and_memory_utils,匹配新常量位置、json_utils 与对话/长度行为。
2026-04-02 12:00:00 +08:00
from app.core.json_utils import extract_json_payload
try:
cleaned = extract_json_payload(raw)
data = json.loads(cleaned)
return model.model_validate(data)
except (json.JSONDecodeError, ValueError, TypeError):
return None
def facts_payload_to_dicts(payload: FactsExtractionPayload) -> list[dict]:
out: list[dict] = []
for item in payload.facts:
d = item.model_dump()
scid = d.get("source_chunk_id")
if scid is not None and not isinstance(scid, str):
d["source_chunk_id"] = str(scid)
out.append(d)
return out
def timeline_payload_to_dicts(payload: TimelineEventsPayload) -> list[dict]:
out: list[dict] = []
for ev in payload.events:
title = (ev.title or "").strip()
if not title:
continue
out.append(
{
"event_year": ev.event_year,
"event_date": ev.event_date,
"title": title,
"description": ev.description,
"source_fact_ids": ev.source_fact_ids or [],
}
)
return out[:20]