309a051038
数据库与模型：新增多版迁移（章节证据快照、对话血缘、记忆事实/时间线 lineage 等），把「成稿 ↔ 对话/记忆」的溯源信息落到表结构里。 业务链路：会话与 WS、回忆录/故事流水线、记忆写入与 enrichment 等跟着接上线索与快照；新增章节证据快照与评测侧 EvalTraceService 等模块，方便组评审用的证据包。 内部评测：自动化 run 与手工 memoir 评审共用可追溯证据；rubric/ judge 相关脚本与文档有配套调整。 app-eval-web：Memoir/实验详情里能展开看证据摘要与 evidence_trace（含对话轮次 id）；Vite 代理与 development.sh 注入的 API 端口与当前默认内部评测端口一致，避免改端口后页面连错服务。 工程杂项：GitHub Actions / 仓库说明有更新；各适配器与支付/配额/plan 等多处为小改动或跟随主改动的收尾；新增/扩充了?
110 lines
3.6 KiB
Python
110 lines
3.6 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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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.memoir.schemas import BatchPhase1LLMOutput
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from app.agents.stage_constants import STAGE_SLOT_KEYS
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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.llm_call import LLMCallError, llm_json_call
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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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k: frozenset(v) for k, v in STAGE_SLOT_KEYS.items()
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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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try:
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parsed = llm_json_call(
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llm,
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prompt,
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BatchPhase1LLMOutput,
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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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except LLMCallError as e:
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logger.warning("batch phase1 LLM 解析失败: {}", e)
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raise ValueError("batch phase1: llm parse failed") from e
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rows = parsed.segments
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if not rows:
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raise ValueError("batch phase1: segments must be a non-empty list")
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by_id: Dict[str, BatchPhase1SegmentRow] = {}
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for row in rows:
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sid = str(row.id).strip()
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if not sid:
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continue
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ds = str(row.detected_stage or "").strip().lower()
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slots_raw = row.slots or {}
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slots = {
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k: v if isinstance(v, str) else str(v)
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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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}
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cat_raw = str(row.chapter_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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