ac436b87a2
- MemoryService 异步路径委托 MemoryIngestService / MemoryRetrievalService；富化派发经 MemoryEnrichmentScheduler - WebSocket pipeline 经 ChatTurnService 与显式 DTO 编排单轮对话；回忆录片段入队由 MemoirIngestScheduler 封装 - 新增 LlmGateway（LlmUseCase），各 agent、任务与适配器对齐 ports - 补充 memory 提示适配、runtime 类型、memory-retrieval 文档、ai-touchpoints 说明与扫描脚本及配套测试 Made-with: Cursor
127 lines
4.8 KiB
Python
127 lines
4.8 KiB
Python
"""从 transcript 块中抽取结构化事实（LLM + JSON）。"""
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from __future__ import annotations
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from typing import Any
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from app.core.langchain_llm import ainvoke_json_object, invoke_json_object
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from app.core.llm_gateway import LlmGateway, LlmUseCase
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from app.core.logging import get_logger
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from app.features.memory.llm_schemas import (
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FactsExtractionPayload,
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facts_payload_to_dicts,
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parse_json_payload,
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)
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logger = get_logger(__name__)
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def _max_transcript_chars() -> int:
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from app.core.config import settings
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return settings.memory_enrichment_max_chars
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def _facts_extraction_instructions(narrator_label: str) -> str:
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return (
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"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**，"
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"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"
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"## 抽取规则\n"
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"1. subject 必须用明确的人名或固定称谓：\n"
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f" - 叙述者本人统一用「{narrator_label}」\n"
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" - 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；"
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"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"
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"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」"
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"除非原文明确为未来时态\n"
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"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），"
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'例如 "1990年代"、"2001年"、"退休后"、"30岁前后"\n'
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"4. fact_type: person|event|relation|place|milestone\n"
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"5. predicate：简短中文谓语（如「出生地」「担任职务」「调往」）\n"
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"6. object_json：字符串或对象；可含 value、approximate_era 等\n"
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"7. confidence 0..1；source_chunk_id 必须等于某段 [chunk_id=...] 中的 id\n\n"
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'只输出 JSON：{"facts":[...]}，无事实则 {"facts":[]}。\n\n'
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)
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def extract_facts_from_transcript_sync(
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llm: Any,
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numbered_blocks: str,
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*,
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narrator_name: str | None = None,
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) -> list[dict]:
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"""同步：带 chunk_id 标记的文本 → 事实列表。"""
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if not llm or not (numbered_blocks or "").strip():
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return []
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text = numbered_blocks.strip()[: _max_transcript_chars()]
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narrator_label = (narrator_name or "").strip() or "叙述者"
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prompt = _facts_extraction_instructions(narrator_label) + text
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try:
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raw = invoke_json_object(
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llm,
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prompt,
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max_tokens=4096,
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agent="memory.extract_facts_sync",
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)
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parsed = parse_json_payload(raw, FactsExtractionPayload)
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if parsed is None:
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return []
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return facts_payload_to_dicts(parsed)
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except (TypeError, ValueError) as e:
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logger.warning("extract_facts_from_transcript_sync 解析失败: {}", e)
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return []
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async def extract_facts_from_transcript_async(
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llm: Any,
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numbered_blocks: str,
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*,
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narrator_name: str | None = None,
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) -> list[dict]:
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"""异步版。"""
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if not llm or not (numbered_blocks or "").strip():
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return []
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text = numbered_blocks.strip()[: _max_transcript_chars()]
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narrator_label = (narrator_name or "").strip() or "叙述者"
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prompt = _facts_extraction_instructions(narrator_label) + text
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try:
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raw = await ainvoke_json_object(
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llm,
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prompt,
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max_tokens=4096,
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agent="memory.extract_facts_async",
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)
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parsed = parse_json_payload(raw, FactsExtractionPayload)
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if parsed is None:
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return []
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return facts_payload_to_dicts(parsed)
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except (TypeError, ValueError) as e:
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logger.warning("extract_facts_from_transcript_async 解析失败: {}", e)
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return []
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async def extract_facts(chunk_text: str, *, user_id: str) -> list[dict]:
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"""兼容旧接口：单块文本（无 chunk id 时传空 source_chunk_id）。"""
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from app.core.db import AsyncSessionLocal
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from app.features.user.models import User
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llm = LlmGateway().langchain_llm_for(
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LlmUseCase("memory.extract_facts.compat", fast=True)
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)
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narrator_name: str | None = None
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try:
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async with AsyncSessionLocal() as db:
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u = await db.get(User, user_id)
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if u and (u.nickname or "").strip():
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narrator_name = (u.nickname or "").strip()
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except Exception:
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pass
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blocks = f"[chunk_id=null]\n{chunk_text}"
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facts = await extract_facts_from_transcript_async(
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llm, blocks, narrator_name=narrator_name
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)
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for f in facts:
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if f.get("source_chunk_id") in (None, "null", ""):
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f["source_chunk_id"] = None
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return facts
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