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
115 lines
4.0 KiB
Python
115 lines
4.0 KiB
Python
"""
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Phase1 批处理：一次 LLM 调用完成多段的抽取 + 章节分类（与逐段循环语义对齐）。
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from typing import Any, Dict, List
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from app.agents.memoir.prompts import get_batch_memoir_phase1_prep_prompt
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from app.agents.state_schema import MemoirStateSchema
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from app.core.config import settings
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from app.core.json_utils import extract_json_payload
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from app.core.langchain_llm import invoke_json_object
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from app.core.logging import get_logger
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from app.features.conversation.models import Segment
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logger = get_logger(__name__)
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STAGE_ALLOWED_SLOTS: Dict[str, frozenset[str]] = {
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"childhood": frozenset(
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{"place", "people", "daily_life", "emotion", "turning_event"}
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),
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"education": frozenset({"school", "city", "motivation", "challenge", "change"}),
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"career": frozenset({"job", "environment", "decision", "pressure", "growth"}),
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"family": frozenset(
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{"relationship", "conflict", "support", "responsibility", "change"}
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),
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"belief": frozenset({"value", "regret", "pride", "lesson"}),
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}
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def _slots_snapshot(state: MemoirStateSchema) -> dict:
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snap: dict = {}
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for stage, buckets in (state.slots or {}).items():
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snap[stage] = {}
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for k, v in (buckets or {}).items():
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if hasattr(v, "snippet"):
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sn = getattr(v, "snippet", None) or ""
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elif isinstance(v, dict):
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sn = (
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(v.get("snippet") or "")
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if isinstance(v.get("snippet"), str)
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else ""
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)
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else:
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sn = ""
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snap[stage][k] = (sn or "")[:120]
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return snap
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@dataclass(frozen=True)
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class BatchPhase1SegmentRow:
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detected_stage: str
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slots: Dict[str, str]
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chapter_category_raw: str
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def run_batch_phase1_prep(
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segments: List[Segment],
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state: MemoirStateSchema,
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llm: Any,
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) -> Dict[str, BatchPhase1SegmentRow]:
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"""对 segments 顺序批量调用 LLM；返回 id → 行。id 集合必须与入参完全一致。"""
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if not llm:
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raise ValueError("batch phase1 requires llm")
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if not segments:
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return {}
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items = [(str(s.id), (s.user_input_text or "").strip()) for s in segments]
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prompt = get_batch_memoir_phase1_prep_prompt(
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system_current_stage=state.current_stage or "childhood",
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slots_snapshot=_slots_snapshot(state),
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segment_items=items,
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)
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raw = invoke_json_object(
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llm,
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prompt,
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max_tokens=int(settings.memoir_phase1_batch_llm_max_tokens),
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agent="BatchPhase1Prep.run",
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)
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parsed = json.loads(extract_json_payload(raw))
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rows = parsed.get("segments") or []
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if not isinstance(rows, list):
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raise ValueError("batch phase1: segments must be a list")
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by_id: Dict[str, BatchPhase1SegmentRow] = {}
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for row in rows:
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if not isinstance(row, dict):
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continue
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sid = str(row.get("id", "")).strip()
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if not sid:
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continue
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ds = str(row.get("detected_stage", "") or "").strip().lower()
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slots_raw = row.get("slots") or {}
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slots: Dict[str, str] = {}
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if isinstance(slots_raw, dict):
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for k, v in slots_raw.items():
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if k and isinstance(k, str):
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slots[k] = v if isinstance(v, str) else str(v)
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cat_raw = str(row.get("chapter_category", row.get("category", "")) or "")
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by_id[sid] = BatchPhase1SegmentRow(
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detected_stage=ds or (state.current_stage or "childhood"),
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slots=slots,
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chapter_category_raw=cat_raw,
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)
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expected = {str(s.id) for s in segments}
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if by_id.keys() != expected:
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missing = expected - by_id.keys()
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extra = by_id.keys() - expected
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logger.warning("batch phase1 id mismatch missing={} extra={}", missing, extra)
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raise ValueError("batch phase1 response segment ids do not match input")
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return by_id
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