47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
from __future__ import annotations
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from typing import Any, Literal
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from pydantic import BaseModel, Field
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from app.schemas.debug import RoutingDebug
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from app.schemas.workflow import Workflow
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class ChatRequest(BaseModel):
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session_id: str
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user_id: str
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channel: str = "app"
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input_text: str
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input_type: Literal["text", "voice"] = "text"
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metadata: dict[str, Any] = Field(default_factory=dict)
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class FillSlotsRequest(BaseModel):
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session_id: str
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user_id: str
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input_text: str
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class ChatResponse(BaseModel):
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session_id: str
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reply_type: Literal["text", "ask_slot", "ask_confirmation", "workflow_result", "fallback", "clarify", "reject"] = "text"
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reply_text: str
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intent: str | None = None
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domain: str | None = None
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decision: str | None = None
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decision_reason: str | None = None
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status: str
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pending_slots: list[str] = Field(default_factory=list)
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filled_slots: dict[str, Any] = Field(default_factory=dict)
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workflow: Workflow | None = None
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routing_debug: RoutingDebug | None = None
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first_response_latency_ms: float | None = None
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total_latency_ms: float | None = None
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processing_breakdown: dict[str, float] = Field(default_factory=dict)
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trace_id: str
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# ── 知识库查询结果（LLM function call 命中时填充）──────────────────────────
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knowledge_doc_id: str | None = None # 知识文档 ID（MD 文件名）
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knowledge_doc_title: str | None = None # 知识文档标题
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knowledge_content: str | None = None # 完整 MD 正文，供前端渲染知识卡片
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