ac49bc7f23
- Judge baseline excerpt and library chapter separately; build_memoir_compare_summary for gate, nine-dim and leaf deltas. - Memoir SSE chapter payload: baseline_judge, compare_summary, baseline_judge_error. - MemoirJudgeOutput: loose score coercion and post-validate clamp; memoir judge prompt caps from settings. - app-eval-web: two-column MemoirScoreCard layout, MemoirCompareSummary, chapter blocks and CSS. - Add memoir_compare_summary, log_events, celery_log_context, memoir_pipeline_progress; tests and migration 0014. - Misc: memory/evidence and enrichment paths, task/orchestrator updates, internal-eval docs, env examples.
116 lines
3.4 KiB
Python
116 lines
3.4 KiB
Python
"""LLM JSON 输出校验（memory 富化）。"""
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from __future__ import annotations
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import json
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from typing import Any, TypeVar
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from pydantic import BaseModel, Field, field_validator
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TModel = TypeVar("TModel", bound=BaseModel)
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class ExtractedFactItem(BaseModel):
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fact_type: str = "event"
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subject: str | None = None
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predicate: str | None = None
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object_json: Any = None
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confidence: float = Field(default=0.75, ge=0.0, le=1.0)
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source_chunk_id: str | None = None
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@field_validator("fact_type", mode="before")
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@classmethod
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def _coerce_fact_type(cls, v: object) -> str:
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ft = str(v or "event").strip() or "event"
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if ft not in ("person", "event", "relation", "place", "milestone"):
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return "event"
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return ft
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class FactsExtractionPayload(BaseModel):
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facts: list[ExtractedFactItem] = Field(default_factory=list)
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class EnrichmentPayload(BaseModel):
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"""单轮记忆富化：会话摘要 + 结构化事实（ingest 后一次 LLM 调用）。"""
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summary: str = ""
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facts: list[ExtractedFactItem] = Field(default_factory=list)
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class SessionSummaryPayload(BaseModel):
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summary: str = ""
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class RollingSummaryPayload(BaseModel):
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rolling_summary: str = ""
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class TimelineEventItem(BaseModel):
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event_year: int | None = None
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event_date: str | None = None
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title: str = ""
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description: str | None = None
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source_fact_ids: list[str] = Field(default_factory=list)
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@field_validator("source_fact_ids", mode="before")
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@classmethod
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def _coerce_sf(cls, v: object) -> list[str]:
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if v is None:
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return []
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if isinstance(v, str):
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return [v] if v else []
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if isinstance(v, list):
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return [str(x) for x in v if x]
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return []
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class TimelineEventsPayload(BaseModel):
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events: list[TimelineEventItem] = Field(default_factory=list)
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def parse_json_payload(raw: str, model: type[TModel]) -> TModel | None:
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"""解析 invoke_json_object 返回的 JSON 字符串。"""
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from app.core.json_utils import extract_json_payload
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try:
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cleaned = extract_json_payload(raw)
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data = json.loads(cleaned)
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return model.model_validate(data)
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except (json.JSONDecodeError, ValueError, TypeError):
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return None
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def facts_payload_to_dicts(payload: FactsExtractionPayload) -> list[dict]:
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out: list[dict] = []
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for item in payload.facts:
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d = item.model_dump()
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scid = d.get("source_chunk_id")
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if scid is not None and not isinstance(scid, str):
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d["source_chunk_id"] = str(scid)
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out.append(d)
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return out
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def enrichment_payload_to_fact_dicts(payload: EnrichmentPayload) -> list[dict]:
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"""将 EnrichmentPayload.facts 转为与 extract_facts 一致的字典列表。"""
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return facts_payload_to_dicts(FactsExtractionPayload(facts=list(payload.facts)))
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def timeline_payload_to_dicts(payload: TimelineEventsPayload) -> list[dict]:
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out: list[dict] = []
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for ev in payload.events:
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title = (ev.title or "").strip()
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if not title:
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continue
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out.append(
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{
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"event_year": ev.event_year,
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"event_date": ev.event_date,
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"title": title,
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"description": ev.description,
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"source_fact_ids": ev.source_fact_ids or [],
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}
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)
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return out[:20]
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