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.
267 lines
9.4 KiB
Python
267 lines
9.4 KiB
Python
"""
|
||
Transcript ingest 之后的记忆富化：单次 LLM 调用产出会话摘要 + 结构化事实。
|
||
|
||
由 Celery（sync）与 MemoryService.ingest（async）调用；失败仅打日志，不阻断主流程。
|
||
不再维护 ingest 路径上的 rolling 摘要与 timeline 物化。
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
from typing import Any
|
||
|
||
from sqlalchemy import select
|
||
from sqlalchemy.ext.asyncio import AsyncSession
|
||
from sqlalchemy.orm import Session
|
||
|
||
from app.core.langchain_llm import ainvoke_json_object, invoke_json_object
|
||
from app.core.logging import get_logger
|
||
from app.features.memory.enrichment_pipeline import (
|
||
dedupe_key,
|
||
normalize_object_json,
|
||
normalize_subject,
|
||
)
|
||
from app.features.memory.llm_schemas import (
|
||
EnrichmentPayload,
|
||
enrichment_payload_to_fact_dicts,
|
||
parse_json_payload,
|
||
)
|
||
from app.features.memory.models import MemoryChunk, MemorySource
|
||
from app.features.memory.repo import (
|
||
create_memory_fact,
|
||
create_memory_fact_sync,
|
||
create_memory_summary,
|
||
create_memory_summary_sync,
|
||
list_chunks_for_source_sync,
|
||
)
|
||
from app.features.user.models import User
|
||
|
||
logger = get_logger(__name__)
|
||
|
||
|
||
def _lineage_snapshot_from_source(source: MemorySource | None) -> dict | None:
|
||
raw = getattr(source, "lineage_json", None) if source else None
|
||
return raw if isinstance(raw, dict) and raw else None
|
||
|
||
|
||
def _resolve_llm_sync() -> Any | None:
|
||
try:
|
||
from app.core.dependencies import get_llm_provider_fast
|
||
|
||
return get_llm_provider_fast().langchain_llm
|
||
except Exception as e:
|
||
logger.warning("memory enrichment 无法获取 LLM: {}", e)
|
||
return None
|
||
|
||
|
||
def _max_enrichment_chars() -> int:
|
||
from app.core.config import settings
|
||
|
||
return settings.memory_enrichment_max_chars
|
||
|
||
|
||
def _enrichment_prompt(numbered_blocks: str, narrator_label: str) -> str:
|
||
"""合并会话摘要说明与事实抽取规则，供单次 JSON 输出。"""
|
||
text = numbered_blocks.strip()[: _max_enrichment_chars()]
|
||
return (
|
||
"你是回忆录记忆分析助手。用户正在口述人生回忆，所有内容默认是**过去发生的事**，"
|
||
"而非当前或未来计划（除非原文明确说「现在」「打算」「准备将要」等）。\n\n"
|
||
"请从下列口述内容中完成两件事：\n"
|
||
"1) 用 2～8 句中文概括要点（不编造、不评价）\n"
|
||
"2) 抽取结构化事实列表（见下方规则）\n\n"
|
||
"## 事实抽取规则\n"
|
||
"1. subject 必须用明确的人名或固定称谓：\n"
|
||
f" - 叙述者本人统一用「{narrator_label}」\n"
|
||
" - 其他人用全名或稳定专名（如「王伟」），禁止用「他」「她」「我」「我们大伙」等代词作 subject；"
|
||
"若代词在上下文中可唯一解析为某人，则 subject 写该人姓名/专名\n"
|
||
"2. 事件、职务变动、地点迁移等一律按**过去回忆**理解；travel/调动/命令类表述勿写成「即将要做」"
|
||
"除非原文明确为未来时态\n"
|
||
"3. 若可推断大约年代或人生阶段，将 approximate_era 写入 object_json（与 value 等字段并存），"
|
||
'例如 "1990年代"、"2001年"、"退休后"、"30岁前后"\n'
|
||
"4. fact_type: person|event|relation|place|milestone\n"
|
||
"5. predicate：简短中文谓语（如「出生地」「担任职务」「调往」）\n"
|
||
"6. object_json：字符串或对象；可含 value、approximate_era 等\n"
|
||
"7. confidence 0..1；source_chunk_id 必须等于某段 [chunk_id=...] 中的 id\n\n"
|
||
'只输出 JSON：{"summary":"...","facts":[...]}，无事实则 "facts":[]。\n\n'
|
||
f"{text}"
|
||
)
|
||
|
||
|
||
def _run_enrichment_llm_sync(
|
||
llm: Any, numbered: str, narrator_label: str
|
||
) -> EnrichmentPayload | None:
|
||
if not llm or not (numbered or "").strip():
|
||
return None
|
||
prompt = _enrichment_prompt(numbered, narrator_label)
|
||
try:
|
||
raw = invoke_json_object(
|
||
llm,
|
||
prompt,
|
||
max_tokens=8192,
|
||
agent="memory.enrichment_sync",
|
||
)
|
||
return parse_json_payload(raw, EnrichmentPayload)
|
||
except (TypeError, ValueError) as e:
|
||
logger.warning("enrichment LLM sync 解析失败: {}", e)
|
||
return None
|
||
|
||
|
||
async def _run_enrichment_llm_async(
|
||
llm: Any, numbered: str, narrator_label: str
|
||
) -> EnrichmentPayload | None:
|
||
if not llm or not (numbered or "").strip():
|
||
return None
|
||
prompt = _enrichment_prompt(numbered, narrator_label)
|
||
try:
|
||
raw = await ainvoke_json_object(
|
||
llm,
|
||
prompt,
|
||
max_tokens=8192,
|
||
agent="memory.enrichment_async",
|
||
)
|
||
return parse_json_payload(raw, EnrichmentPayload)
|
||
except (TypeError, ValueError) as e:
|
||
logger.warning("enrichment LLM async 解析失败: {}", e)
|
||
return None
|
||
|
||
|
||
def enrich_memory_after_ingest_sync(
|
||
session: Session,
|
||
user_id: str,
|
||
source_id: str,
|
||
llm: Any | None = None,
|
||
) -> None:
|
||
from app.core.config import settings
|
||
|
||
if not settings.memory_enrichment_enabled:
|
||
return
|
||
if llm is None:
|
||
llm = _resolve_llm_sync()
|
||
if not llm:
|
||
return
|
||
narrator_name: str | None = None
|
||
u_row = session.get(User, user_id)
|
||
if u_row and (u_row.nickname or "").strip():
|
||
narrator_name = (u_row.nickname or "").strip()
|
||
chunks = list_chunks_for_source_sync(session, source_id)
|
||
if not chunks:
|
||
return
|
||
src_row = session.get(MemorySource, source_id)
|
||
lineage_snapshot = _lineage_snapshot_from_source(src_row)
|
||
chunk_ids = [c.id for c in chunks]
|
||
chunk_texts = [c.content for c in chunks]
|
||
numbered = "\n\n".join(
|
||
f"[chunk_id={cid}]\n{txt}" for cid, txt in zip(chunk_ids, chunk_texts)
|
||
)
|
||
narrator_label = (narrator_name or "").strip() or "叙述者"
|
||
|
||
payload = _run_enrichment_llm_sync(llm, numbered, narrator_label)
|
||
if payload is None:
|
||
return
|
||
|
||
session_summary_text = str(payload.summary or "").strip()
|
||
if session_summary_text:
|
||
create_memory_summary_sync(
|
||
session,
|
||
user_id=user_id,
|
||
summary_type="session",
|
||
content=session_summary_text,
|
||
source_chunk_ids=chunk_ids,
|
||
)
|
||
|
||
raw_facts = enrichment_payload_to_fact_dicts(payload)
|
||
seen: set[tuple] = set()
|
||
for f in raw_facts:
|
||
key = dedupe_key(f, narrator_name=narrator_name)
|
||
if key in seen:
|
||
continue
|
||
seen.add(key)
|
||
scid = f.get("source_chunk_id")
|
||
if scid and scid not in chunk_ids:
|
||
scid = chunk_ids[0] if chunk_ids else None
|
||
create_memory_fact_sync(
|
||
session,
|
||
user_id=user_id,
|
||
fact_type=f.get("fact_type") or "event",
|
||
subject=normalize_subject(f.get("subject"), narrator_name),
|
||
predicate=f.get("predicate"),
|
||
object_json=normalize_object_json(f.get("object_json")),
|
||
confidence=float(f.get("confidence") or 0.75),
|
||
source_chunk_id=scid,
|
||
status="confirmed",
|
||
lineage_json=lineage_snapshot,
|
||
)
|
||
|
||
|
||
async def enrich_memory_after_ingest_async(
|
||
db: AsyncSession,
|
||
user_id: str,
|
||
source_id: str,
|
||
llm: Any | None = None,
|
||
) -> None:
|
||
from app.core.config import settings
|
||
|
||
if not settings.memory_enrichment_enabled:
|
||
return
|
||
if llm is None:
|
||
llm = _resolve_llm_sync()
|
||
if not llm:
|
||
return
|
||
narrator_name: str | None = None
|
||
u_row = await db.get(User, user_id)
|
||
if u_row and (u_row.nickname or "").strip():
|
||
narrator_name = (u_row.nickname or "").strip()
|
||
stmt = (
|
||
select(MemoryChunk)
|
||
.where(MemoryChunk.source_id == source_id)
|
||
.order_by(MemoryChunk.chunk_index.asc())
|
||
)
|
||
result = await db.execute(stmt)
|
||
chunks = list(result.unique().scalars().all())
|
||
if not chunks:
|
||
return
|
||
src_row = await db.get(MemorySource, source_id)
|
||
lineage_snapshot = _lineage_snapshot_from_source(src_row)
|
||
chunk_ids = [c.id for c in chunks]
|
||
chunk_texts = [c.content for c in chunks]
|
||
numbered = "\n\n".join(
|
||
f"[chunk_id={cid}]\n{txt}" for cid, txt in zip(chunk_ids, chunk_texts)
|
||
)
|
||
narrator_label = (narrator_name or "").strip() or "叙述者"
|
||
|
||
payload = await _run_enrichment_llm_async(llm, numbered, narrator_label)
|
||
if payload is None:
|
||
return
|
||
|
||
session_summary_text = str(payload.summary or "").strip()
|
||
if session_summary_text:
|
||
await create_memory_summary(
|
||
db,
|
||
user_id=user_id,
|
||
summary_type="session",
|
||
content=session_summary_text,
|
||
source_chunk_ids=chunk_ids,
|
||
)
|
||
|
||
raw_facts = enrichment_payload_to_fact_dicts(payload)
|
||
seen: set[tuple] = set()
|
||
for f in raw_facts:
|
||
key = dedupe_key(f, narrator_name=narrator_name)
|
||
if key in seen:
|
||
continue
|
||
seen.add(key)
|
||
scid = f.get("source_chunk_id")
|
||
if scid and scid not in chunk_ids:
|
||
scid = chunk_ids[0] if chunk_ids else None
|
||
await create_memory_fact(
|
||
db,
|
||
user_id=user_id,
|
||
fact_type=f.get("fact_type") or "event",
|
||
subject=normalize_subject(f.get("subject"), narrator_name),
|
||
predicate=f.get("predicate"),
|
||
object_json=normalize_object_json(f.get("object_json")),
|
||
confidence=float(f.get("confidence") or 0.75),
|
||
source_chunk_id=scid,
|
||
status="confirmed",
|
||
lineage_json=lineage_snapshot,
|
||
)
|