278 lines
8.6 KiB
Python
278 lines
8.6 KiB
Python
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"""
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Transcript ingest 之后的记忆富化：摘要、事实、时间线。
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由 Celery（sync）与 MemoryService.ingest（async）调用；失败仅打日志，不阻断主流程。
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"""
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from __future__ import annotations
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.orm import Session
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from app.core.logging import get_logger
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from app.features.memory.extractor import (
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extract_facts_from_transcript_async,
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extract_facts_from_transcript_sync,
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)
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from app.features.memory.models import MemoryChunk, MemorySummary
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from app.features.memory.repo import (
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create_memory_fact,
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create_memory_fact_sync,
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create_memory_summary,
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create_memory_summary_sync,
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create_timeline_event,
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create_timeline_event_sync,
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delete_timeline_events_by_memory_source,
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delete_timeline_events_by_memory_source_sync,
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list_chunks_for_source_sync,
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upsert_rolling_summary_sync,
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)
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from app.features.memory.summarizer import (
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generate_rolling_summary_async,
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generate_rolling_summary_sync,
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generate_session_summary_async,
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generate_session_summary_sync,
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)
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from app.features.memory.enrichment_pipeline import dedupe_key, normalize_object_json
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from app.features.memory.timeline import (
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build_timeline_events_from_facts_async,
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build_timeline_events_from_facts_sync,
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)
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logger = get_logger(__name__)
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def _resolve_llm_sync() -> Any | None:
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try:
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from app.core.dependencies import get_llm_provider
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return get_llm_provider().langchain_llm
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except Exception as e:
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logger.warning("memory enrichment 无法获取 LLM: {}", e)
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return None
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def enrich_memory_after_ingest_sync(
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session: Session,
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user_id: str,
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source_id: str,
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llm: Any | None = None,
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) -> None:
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from app.core.config import settings
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if not settings.memory_enrichment_enabled:
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return
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if llm is None:
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llm = _resolve_llm_sync()
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if not llm:
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return
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chunks = list_chunks_for_source_sync(session, source_id)
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if not chunks:
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return
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chunk_texts = [c.content for c in chunks]
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chunk_ids = [c.id for c in chunks]
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numbered = "\n\n".join(
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f"[chunk_id={cid}]\n{txt}" for cid, txt in zip(chunk_ids, chunk_texts)
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)
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session_summary_text = generate_session_summary_sync(llm, chunk_texts)
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if session_summary_text:
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create_memory_summary_sync(
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session,
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user_id=user_id,
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summary_type="session",
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content=session_summary_text,
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source_chunk_ids=chunk_ids,
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)
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existing_rolling = (
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session.execute(
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select(MemorySummary)
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.where(
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MemorySummary.user_id == user_id,
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MemorySummary.summary_type == "rolling",
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)
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.order_by(MemorySummary.updated_at.desc())
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.limit(1)
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)
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.unique()
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.scalar_one_or_none()
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)
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existing_text = existing_rolling.content if existing_rolling else None
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rolling_text = generate_rolling_summary_sync(llm, existing_text, chunk_texts)
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if rolling_text:
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upsert_rolling_summary_sync(
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session,
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user_id=user_id,
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content=rolling_text,
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source_chunk_ids=chunk_ids,
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)
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raw_facts = extract_facts_from_transcript_sync(llm, numbered)
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seen: set[tuple] = set()
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inserted: list[dict] = []
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for f in raw_facts:
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key = dedupe_key(f)
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if key in seen:
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continue
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seen.add(key)
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scid = f.get("source_chunk_id")
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if scid and scid not in chunk_ids:
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scid = chunk_ids[0] if chunk_ids else None
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row = create_memory_fact_sync(
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session,
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user_id=user_id,
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fact_type=f.get("fact_type") or "event",
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subject=f.get("subject"),
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predicate=f.get("predicate"),
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object_json=normalize_object_json(f.get("object_json")),
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confidence=float(f.get("confidence") or 0.75),
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source_chunk_id=scid,
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status="confirmed",
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)
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inserted.append(
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{
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"id": row.id,
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"fact_type": row.fact_type,
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"subject": row.subject,
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"predicate": row.predicate,
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"object_json": row.object_json,
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}
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)
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if inserted:
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delete_timeline_events_by_memory_source_sync(
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session, user_id=user_id, memory_source_id=source_id
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)
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events = build_timeline_events_from_facts_sync(llm, inserted)
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for ev in events:
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create_timeline_event_sync(
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session,
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user_id=user_id,
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event_year=ev.get("event_year"),
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event_date=ev.get("event_date"),
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title=ev["title"],
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description=ev.get("description"),
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source_fact_ids=ev.get("source_fact_ids") or None,
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memory_source_id=source_id,
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)
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async def enrich_memory_after_ingest_async(
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db: AsyncSession,
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user_id: str,
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source_id: str,
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llm: Any | None = None,
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) -> None:
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from app.core.config import settings
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if not settings.memory_enrichment_enabled:
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return
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if llm is None:
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llm = _resolve_llm_sync()
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if not llm:
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return
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stmt = (
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select(MemoryChunk)
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.where(MemoryChunk.source_id == source_id)
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.order_by(MemoryChunk.chunk_index.asc())
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)
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result = await db.execute(stmt)
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chunks = list(result.unique().scalars().all())
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if not chunks:
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return
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chunk_texts = [c.content for c in chunks]
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chunk_ids = [c.id for c in chunks]
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numbered = "\n\n".join(
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f"[chunk_id={cid}]\n{txt}" for cid, txt in zip(chunk_ids, chunk_texts)
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)
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session_summary_text = await generate_session_summary_async(llm, chunk_texts)
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if session_summary_text:
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await create_memory_summary(
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db,
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user_id=user_id,
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summary_type="session",
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content=session_summary_text,
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source_chunk_ids=chunk_ids,
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)
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roll_stmt = (
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select(MemorySummary)
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.where(
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MemorySummary.user_id == user_id,
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MemorySummary.summary_type == "rolling",
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)
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.order_by(MemorySummary.updated_at.desc())
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.limit(1)
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)
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r_result = await db.execute(roll_stmt)
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existing_row = r_result.unique().scalar_one_or_none()
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existing_text = existing_row.content if existing_row else None
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rolling_text = await generate_rolling_summary_async(llm, existing_text, chunk_texts)
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if rolling_text:
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if existing_row:
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existing_row.content = rolling_text
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existing_row.source_chunk_ids = chunk_ids
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else:
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await create_memory_summary(
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db,
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user_id=user_id,
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summary_type="rolling",
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content=rolling_text,
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source_chunk_ids=chunk_ids,
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)
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raw_facts = await extract_facts_from_transcript_async(llm, numbered)
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seen: set[tuple] = set()
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inserted: list[dict] = []
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for f in raw_facts:
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key = dedupe_key(f)
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if key in seen:
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continue
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seen.add(key)
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scid = f.get("source_chunk_id")
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if scid and scid not in chunk_ids:
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scid = chunk_ids[0] if chunk_ids else None
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row = await create_memory_fact(
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db,
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user_id=user_id,
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fact_type=f.get("fact_type") or "event",
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subject=f.get("subject"),
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predicate=f.get("predicate"),
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object_json=normalize_object_json(f.get("object_json")),
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confidence=float(f.get("confidence") or 0.75),
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source_chunk_id=scid,
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status="confirmed",
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)
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inserted.append(
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{
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"id": row.id,
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"fact_type": row.fact_type,
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"subject": row.subject,
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"predicate": row.predicate,
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"object_json": row.object_json,
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}
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)
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if inserted:
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await delete_timeline_events_by_memory_source(
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db, user_id=user_id, memory_source_id=source_id
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)
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events = await build_timeline_events_from_facts_async(llm, inserted)
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for ev in events:
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await create_timeline_event(
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db,
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user_id=user_id,
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event_year=ev.get("event_year"),
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event_date=ev.get("event_date"),
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title=ev["title"],
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description=ev.get("description"),
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source_fact_ids=ev.get("source_fact_ids") or None,
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memory_source_id=source_id,
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)
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