71fbd39e32
Route all memory ingest/retrieve/enrichment/compaction through async MemoryService. Remove legacy sync memory implementations (ingest/retrieve/compaction); Celery and memoir Phase2 call asyncio.run into MemoryService-backed helpers. Memoir Phase1 batch ingest uses MemoryService.ingest_transcripts_batch; drop chapters. evidence_bundle_json mirror (Alembic 0015). Evaluation uses snapshot/link-only bundles; raise EvidenceClosureMissing instead of partial/fallback lineage tiers. Split memoir state into NarrativeCoverageState and InterviewControlState; delete the _interview_meta_store adapter layer. Remove rolling-query and recent-fact fallback settings from config and evidence assembly. Update judges, docs, tests, and PlaygroundPage alignment. Made-with: Cursor
108 lines
2.9 KiB
Python
108 lines
2.9 KiB
Python
"""
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证据包组装：跨 memory + story 的检索结果合并（业务层，非纯 repo）。
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Memory evidence 只保留 async 单链路：chunk 原文为首要证据，结构化事实/时间线/
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摘要/故事均按本次 query 命中进入 evidence，不再做 rolling/recent 历史降级。
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"""
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from __future__ import annotations
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.features.memory.repo import (
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list_summaries_for_evidence_async,
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search_facts_for_user_async,
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search_timeline_events_for_user_async,
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)
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from app.features.story.repo import list_recent_stories_for_evidence
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EMPTY_EVIDENCE_BUNDLE: dict = {
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"relevant_chunks": [],
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"relevant_summaries": [],
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"relevant_facts": [],
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"timeline_hints": [],
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"relevant_stories": [],
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}
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def _facts_to_dicts(facts) -> list[dict]:
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return [
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{
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"id": f.id,
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"fact_type": f.fact_type,
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"subject": f.subject,
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"predicate": f.predicate,
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"object_json": f.object_json,
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}
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for f in facts
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]
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def _timeline_to_dicts(events) -> list[dict]:
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return [
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{
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"id": e.id,
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"event_year": e.event_year,
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"event_date": e.event_date,
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"title": e.title,
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"description": e.description,
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}
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for e in events
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]
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def _stories_to_dicts(story_rows) -> list[dict]:
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return [
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{
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"id": s.id,
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"title": s.title,
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"summary": s.summary,
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"stage": s.stage,
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"story_type": s.story_type,
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}
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for s in story_rows
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]
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async def fetch_evidence_metadata_async(
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db: AsyncSession, user_id: str, q: str, top_k: int
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) -> dict:
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"""非 chunk 证据（async）。"""
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facts = await search_facts_for_user_async(db, user_id, q, top_k)
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events = await search_timeline_events_for_user_async(db, user_id, q, top_k)
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relevant_summaries = await list_summaries_for_evidence_async(
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db, user_id=user_id, q=q, limit=top_k
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)
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story_rows = await list_recent_stories_for_evidence(
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db, user_id=user_id, query=q, limit=top_k
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)
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return {
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"relevant_facts": _facts_to_dicts(facts),
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"timeline_hints": _timeline_to_dicts(events),
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"relevant_summaries": relevant_summaries,
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"relevant_stories": _stories_to_dicts(story_rows),
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}
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async def retrieve_evidence_bundle_async(
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db: AsyncSession,
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user_id: str,
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query: str,
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*,
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top_k: int = 10,
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merged_chunk_dicts: list[dict],
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) -> dict:
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"""
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异步路径：chunk 已由调用方（如 HybridRetriever）向量检索填入；此处只拼元数据。
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merged_chunk_dicts: [{"id","content","chunk_index"}, ...]
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"""
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if not query or not query.strip():
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return dict(EMPTY_EVIDENCE_BUNDLE)
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q = query.strip()
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meta = await fetch_evidence_metadata_async(db, user_id, q, top_k)
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return {
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"relevant_chunks": merged_chunk_dicts,
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**meta,
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}
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