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
250 lines
7.8 KiB
Python
250 lines
7.8 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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权威层级（可靠性 hardening）：
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- **Chunk 原文**（未 excluded）为首要证据；rolling 摘要/故事摘录为便利视图，不得压过冲突的 chunk。
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- **MemoryFact**：`confirmed` 为检索默认集；`candidate` 可被上游提升；`stale` 由 compaction 等标出，检索时应排除。
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- 事实 FTS 无命中时是否退回「最近事实」由 `memory_fact_search_use_recent_fallback` 控制（默认可避免串台）。
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Celery 使用 sync；`HybridRetriever` 使用 async + RRF chunk 合并。
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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 sqlalchemy.orm import Session
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from app.core.config import settings
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from app.features.memory.repo import (
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list_summaries_for_evidence_async,
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list_summaries_for_evidence_sync,
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search_chunks_fts,
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search_chunks_fts_sync,
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search_facts_for_user_async,
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search_facts_for_user_sync,
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search_timeline_events_for_user_async,
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search_timeline_events_for_user_sync,
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)
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from app.features.story.repo import (
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list_recent_stories_for_evidence,
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list_recent_stories_for_evidence_sync,
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)
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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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def fetch_evidence_metadata_sync(
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session: Session, user_id: str, q: str, top_k: int
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) -> dict:
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"""非 chunk 证据：摘要、事实、时间线、故事（sync）。"""
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facts = search_facts_for_user_sync(session, user_id, q, top_k)
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events = search_timeline_events_for_user_sync(session, user_id, q, top_k)
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relevant_summaries = list_summaries_for_evidence_sync(
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session, user_id=user_id, q=q, limit=top_k
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)
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story_rows = list_recent_stories_for_evidence_sync(
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session, 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 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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def _empty_query_bundle_sync(session: Session, user_id: str, top_k: int) -> dict:
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"""无 FTS query 时的「浏览」降级：rolling 摘要 + 事实/时间线 fallback。"""
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from app.features.memory.models import MemorySummary
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from sqlalchemy import select
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from app.features.memory.repo import (
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get_facts_for_user_sync,
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get_timeline_events_for_user_sync,
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)
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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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summaries = []
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if rolling:
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summaries = [
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{
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"id": rolling.id,
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"summary_type": rolling.summary_type,
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"content": rolling.content,
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"source_chunk_ids": rolling.source_chunk_ids,
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}
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]
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facts = get_facts_for_user_sync(session, user_id, top_k)
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events = get_timeline_events_for_user_sync(session, user_id, top_k)
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return {
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"relevant_chunks": [],
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"relevant_summaries": summaries,
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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_stories": [],
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}
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async def _empty_query_bundle_async(db: AsyncSession, user_id: str, top_k: int) -> dict:
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from sqlalchemy import select
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from app.features.memory.models import MemorySummary
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from app.features.memory.repo import (
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get_facts_for_user,
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get_timeline_events_for_user,
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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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rolling = r_result.unique().scalar_one_or_none()
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summaries = []
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if rolling:
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summaries = [
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{
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"id": rolling.id,
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"summary_type": rolling.summary_type,
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"content": rolling.content,
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"source_chunk_ids": rolling.source_chunk_ids,
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}
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]
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facts = await get_facts_for_user(db, user_id=user_id, limit=top_k)
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events = await get_timeline_events_for_user(db, user_id=user_id, limit=top_k)
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return {
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"relevant_chunks": [],
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"relevant_summaries": summaries,
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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_stories": [],
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}
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def retrieve_evidence_bundle_sync(
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session: Session, user_id: str, query: str, *, top_k: int = 10
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) -> dict:
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"""Celery / 叙事流水线：FTS-only chunks + 元数据。"""
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if not query or not query.strip():
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if settings.memory_evidence_empty_query_include_rolling:
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return _empty_query_bundle_sync(session, user_id, top_k)
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return dict(EMPTY_EVIDENCE_BUNDLE)
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q = query.strip()
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chunk_rows = search_chunks_fts_sync(session, user_id, q, top_k)
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relevant_chunks = [
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{"id": r["id"], "content": r["content"], "chunk_index": r["chunk_index"]}
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for r in chunk_rows
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]
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meta = fetch_evidence_metadata_sync(session, user_id, q, top_k)
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return {
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"relevant_chunks": relevant_chunks,
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**meta,
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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 RRF 合并；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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if settings.memory_evidence_empty_query_include_rolling:
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return await _empty_query_bundle_async(db, user_id, top_k)
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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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