life-echo/api/app/features/evaluation/schemas.py

"""HTTP / OpenAPI 模型。"""

from __future__ import annotations

from datetime import datetime
from typing import Any, Literal

from pydantic import BaseModel, ConfigDict, Field

EvalJudgeProviderLiteral = Literal["zhipu", "deepseek"]


class SessionDialogueMessageOut(BaseModel):
    model_config = ConfigDict(from_attributes=True)

    role: str
    content: str
    created_at: datetime | None = None


class SessionDialogueOut(BaseModel):
    conversation_id: str
    messages: list[SessionDialogueMessageOut]


class SessionListItem(BaseModel):
    id: str
    user_id: str
    user_phone: str | None = Field(default=None, description="users.phone，列表展示用")
    started_at: datetime | None
    last_message_at: datetime | None = None
    conversation_stage: str | None
    current_topic: str | None
    status: str | None


class SessionListResponse(BaseModel):
    items: list[SessionListItem]
    total: int


class SessionTranscriptOut(BaseModel):
    conversation_id: str
    user_id: str
    user_utterances_from_segments: list[str]
    user_utterances_from_messages: list[str]


class UserExportFixtureTurnOut(BaseModel):
    user: str
    ai: str


class UserExportFixtureListOut(BaseModel):
    items: list[str]


class MemoirSectionBaselineOut(BaseModel):
    title: str
    body: str


class UserExportFixtureDetailOut(BaseModel):
    filename: str
    turns: list[UserExportFixtureTurnOut]
    source_user_id: str | None = None
    memoir_sections: list[MemoirSectionBaselineOut] = Field(default_factory=list)


class ReplayBootstrapBody(BaseModel):
    user_id: str


class ReplayBootstrapOut(BaseModel):
    conversation_id: str


class EvalSandboxOut(BaseModel):
    """内部评测专用：一次性临时账号 + 空白会话，不落真实手机号业务。"""

    user_id: str
    conversation_id: str
    phone: str
    nickname: str


class ReplayConversationBody(BaseModel):
    conversation_id: str
    fixture_filename: str | None = None
    user_utterances: list[str] | None = None
    flush_memoir_after: bool = True
    """为 True 且 skip_memoir 为 False 时，本批结束后 flush 回忆录队列。"""
    skip_memoir: bool = False
    """为 True 时不向回忆录防抖队列入队、不 flush（供 Playground 先只测对话）。"""
    skip_tts: bool = True


class ReplayConversationOut(BaseModel):
    conversation_id: str
    turns_replayed: int
    utterances_echo: list[str] = Field(default_factory=list)
    segment_ids: list[str] = Field(
        default_factory=list,
        description="本批请求创建并已走 orchestrator 的用户 segment id（顺序与落库一致）",
    )
    #: 服务端计量：本 HTTP 请求内回放逻辑耗时（与浏览器轮询间隔无关）
    started_at_utc: datetime | None = None
    finished_at_utc: datetime | None = None
    elapsed_ms: int | None = Field(
        default=None,
        description="服务端 wall 耗时（本请求内 replay_utterances / replay_fixture）",
    )


class MemoirPhase1ReadyOut(BaseModel):
    ready: bool
    checked_segment_ids: list[str] = Field(default_factory=list)
    pending_segment_ids: list[str] = Field(default_factory=list)
    #: 最近一次 Playground memoir-submit 写入 Redis 的提交时间（无记录时为 None）
    job_submitted_at_utc: datetime | None = None
    #: 自 job_submitted_at_utc 至本响应生成时服务端经过的毫秒数
    elapsed_ms_since_submit: int | None = Field(default=None, ge=0)
    #: 可选分步耗时（毫秒），键由服务端定义
    durations_ms: dict[str, int] = Field(default_factory=dict)


class MemoirSubmitOut(BaseModel):
    conversation_id: str
    user_id: str
    segment_ids: list[str] = Field(default_factory=list)
    celery_task_id: str | None = None
    submitted_at_utc: datetime | None = None
    #: 提交接口瞬间耗时，通常为 0；与 Phase1 Celery 执行时间无关
    elapsed_ms: int | None = Field(default=None, ge=0)


class ManualJudgeConversationBody(BaseModel):
    conversation_id: str
    """与当前评测台选中的 MD 一致，供基准 transcript / 整体打分。"""
    fixture_filename: str | None = None
    judge_provider: EvalJudgeProviderLiteral = "zhipu"
    judge_model: str | None = None
    """空则用该供应商默认模型（智谱：eval_judge_model；DeepSeek：eval_judge_deepseek_model）。"""


class ManualJudgeConversationStreamBody(BaseModel):
    conversation_id: str
    fixture_filename: str | None = None
    include_turn_judges: bool = False
    """对当前会话逐轮调用评审 LLM（在整体分之后）。"""
    include_baseline_turn_judges: bool = False
    """对导出基线逐轮调用评审 LLM（需 fixture + 整体基线分成功）。"""
    judge_provider: EvalJudgeProviderLiteral = "zhipu"
    judge_model: str | None = None


class RetryBaselineJudgeBody(BaseModel):
    conversation_id: str
    fixture_filename: str | None = None
    include_baseline_turn_judges: bool = False
    """与流式评分一致：成功重试基准整体分后是否补跑基线逐轮。"""
    judge_provider: EvalJudgeProviderLiteral = "zhipu"
    judge_model: str | None = None


class RetryBaselineJudgeOut(BaseModel):
    ok: bool
    error: str | None = None
    message: str | None = None
    baseline_judge: dict[str, Any] | None = None
    replay_judge: dict[str, Any] | None = None
    compare_summary: dict[str, Any] | None = None
    compare_markdown: str = ""
    baseline_turn_judges: dict[str, Any] = Field(default_factory=dict)
    errors: list[str] = Field(default_factory=list)


class ManualJudgeConversationOut(BaseModel):
    conversation_id: str
    fixture_filename: str | None = None
    baseline_transcript: str = ""
    replay_transcript: str
    baseline_judge: dict[str, Any] | None = None
    replay_judge: dict[str, Any] | None = None
    compare_summary: dict[str, Any] | None = None
    errors: list[str] = Field(default_factory=list)


class PlaygroundConversationJudgeOut(BaseModel):
    """`conversations.playground_conversation_judge_json` 的只读视图。"""

    conversation_id: str
    judge: dict[str, Any] | None = None


class ManualJudgeMemoirBody(BaseModel):
    user_id: str
    baseline_sections: list[MemoirSectionBaselineOut] | None = None
    judge_provider: EvalJudgeProviderLiteral = "zhipu"
    judge_model: str | None = None


class ManualJudgeMemoirOut(BaseModel):
    user_id: str
    chapter_results: list[dict[str, Any]] = Field(default_factory=list)
    story_results: list[dict[str, Any]] = Field(default_factory=list)


class MemoirChapterSnapOut(BaseModel):
    id: str
    title: str
    category: str | None = None
    order_index: int | None = None
    canonical_markdown: str | None = None


class MemoirStorySnapOut(BaseModel):
    id: str
    title: str
    stage: str | None = None
    canonical_markdown: str | None = None


class UserMemoirSnapshotOut(BaseModel):
    user_id: str
    chapters: list[MemoirChapterSnapOut]
    stories: list[MemoirStorySnapOut]