from __future__ import annotations

from typing import Any, Literal

from pydantic import BaseModel, Field

from app.schemas.debug import RoutingDebug
from app.schemas.workflow import Workflow


class ChatRequest(BaseModel):
    session_id: str
    user_id: str
    channel: str = "app"
    input_text: str
    input_type: Literal["text", "voice"] = "text"
    metadata: dict[str, Any] = Field(default_factory=dict)


class FillSlotsRequest(BaseModel):
    session_id: str
    user_id: str
    input_text: str


class ChatResponse(BaseModel):
    session_id: str
    reply_type: Literal["text", "ask_slot", "ask_confirmation", "workflow_result", "fallback", "clarify", "reject"] = "text"
    reply_text: str
    intent: str | None = None
    domain: str | None = None
    decision: str | None = None
    decision_reason: str | None = None
    status: str
    pending_slots: list[str] = Field(default_factory=list)
    filled_slots: dict[str, Any] = Field(default_factory=dict)
    workflow: Workflow | None = None
    routing_debug: RoutingDebug | None = None
    first_response_latency_ms: float | None = None
    total_latency_ms: float | None = None
    processing_breakdown: dict[str, float] = Field(default_factory=dict)
    trace_id: str
    # ── 知识库查询结果（LLM function call 命中时填充）──────────────────────────
    knowledge_doc_id: str | None = None     # 知识文档 ID（MD 文件名）
    knowledge_doc_title: str | None = None  # 知识文档标题
    knowledge_content: str | None = None    # 完整 MD 正文，供前端渲染知识卡片