41518bda11
Embedding 端口增加 is_available()，聊天和回忆录日志用统一方式表示向量是否真能调用。 记忆整理（compaction）支持 Beat 定期扫用户； 事实抽取提示与 subject 归一化，减少同一人多种称呼；
125 lines
4.8 KiB
Python
125 lines
4.8 KiB
Python
"""从 transcript 块中抽取结构化事实（LLM + JSON）。"""
|
||
|
||
from __future__ import annotations
|
||
|
||
from typing import Any
|
||
|
||
from app.core.langchain_llm import ainvoke_json_object, invoke_json_object
|
||
from app.core.logging import get_logger
|
||
from app.features.memory.llm_schemas import (
|
||
FactsExtractionPayload,
|
||
facts_payload_to_dicts,
|
||
parse_json_payload,
|
||
)
|
||
|
||
logger = get_logger(__name__)
|
||
|
||
|
||
def _max_transcript_chars() -> int:
|
||
from app.core.config import settings
|
||
|
||
return settings.memory_enrichment_max_chars
|
||
|
||
|
||
def _facts_extraction_instructions(narrator_label: str) -> str:
|
||
return (
|
||
"你是回忆录事实抽取助手。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"
|
||
"## 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：{"facts":[...]}，无事实则 {"facts":[]}。\n\n'
|
||
)
|
||
|
||
|
||
def extract_facts_from_transcript_sync(
|
||
llm: Any,
|
||
numbered_blocks: str,
|
||
*,
|
||
narrator_name: str | None = None,
|
||
) -> list[dict]:
|
||
"""同步：带 chunk_id 标记的文本 → 事实列表。"""
|
||
if not llm or not (numbered_blocks or "").strip():
|
||
return []
|
||
text = numbered_blocks.strip()[: _max_transcript_chars()]
|
||
narrator_label = (narrator_name or "").strip() or "叙述者"
|
||
prompt = _facts_extraction_instructions(narrator_label) + text
|
||
try:
|
||
raw = invoke_json_object(
|
||
llm,
|
||
prompt,
|
||
max_tokens=4096,
|
||
agent="memory.extract_facts_sync",
|
||
)
|
||
parsed = parse_json_payload(raw, FactsExtractionPayload)
|
||
if parsed is None:
|
||
return []
|
||
return facts_payload_to_dicts(parsed)
|
||
except (TypeError, ValueError) as e:
|
||
logger.warning("extract_facts_from_transcript_sync 解析失败: {}", e)
|
||
return []
|
||
|
||
|
||
async def extract_facts_from_transcript_async(
|
||
llm: Any,
|
||
numbered_blocks: str,
|
||
*,
|
||
narrator_name: str | None = None,
|
||
) -> list[dict]:
|
||
"""异步版。"""
|
||
if not llm or not (numbered_blocks or "").strip():
|
||
return []
|
||
text = numbered_blocks.strip()[: _max_transcript_chars()]
|
||
narrator_label = (narrator_name or "").strip() or "叙述者"
|
||
prompt = _facts_extraction_instructions(narrator_label) + text
|
||
try:
|
||
raw = await ainvoke_json_object(
|
||
llm,
|
||
prompt,
|
||
max_tokens=4096,
|
||
agent="memory.extract_facts_async",
|
||
)
|
||
parsed = parse_json_payload(raw, FactsExtractionPayload)
|
||
if parsed is None:
|
||
return []
|
||
return facts_payload_to_dicts(parsed)
|
||
except (TypeError, ValueError) as e:
|
||
logger.warning("extract_facts_from_transcript_async 解析失败: {}", e)
|
||
return []
|
||
|
||
|
||
async def extract_facts(chunk_text: str, *, user_id: str) -> list[dict]:
|
||
"""兼容旧接口：单块文本（无 chunk id 时传空 source_chunk_id）。"""
|
||
from app.core.db import AsyncSessionLocal
|
||
from app.core.dependencies import get_llm_provider_fast
|
||
from app.features.user.models import User
|
||
|
||
llm = get_llm_provider_fast().langchain_llm
|
||
narrator_name: str | None = None
|
||
try:
|
||
async with AsyncSessionLocal() as db:
|
||
u = await db.get(User, user_id)
|
||
if u and (u.nickname or "").strip():
|
||
narrator_name = (u.nickname or "").strip()
|
||
except Exception:
|
||
pass
|
||
|
||
blocks = f"[chunk_id=null]\n{chunk_text}"
|
||
facts = await extract_facts_from_transcript_async(
|
||
llm, blocks, narrator_name=narrator_name
|
||
)
|
||
for f in facts:
|
||
if f.get("source_chunk_id") in (None, "null", ""):
|
||
f["source_chunk_id"] = None
|
||
return facts
|