Update project and configurations

intelligent_cabin/app/schemas/__init__.py

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+"""Pydantic schemas for the agent service."""

intelligent_cabin/app/schemas/chat.py

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+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

intelligent_cabin/app/schemas/configuration.py

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+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+from app.schemas.intent import IntentDefinition
+
+
+class ActionDefinition(BaseModel):
+    action_id: str
+    plugin_id: str
+    risk_level: Literal["low", "medium", "high"] = "low"
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class DomainIntentDefinition(BaseModel):
+    intent_id: str
+    domain: str
+    action_id: str | None = None
+    plugin_id: str | None = None
+    risk_level: Literal["low", "medium", "high"] | None = None
+    label: str | None = None
+    required_slots: list[str] = Field(default_factory=list)
+    ask_templates: dict[str, str] = Field(default_factory=dict)
+    keywords: list[str] = Field(default_factory=list)
+    examples: list[str] = Field(default_factory=list)
+
+    def to_intent_definition(self, actions: dict[str, ActionDefinition]) -> IntentDefinition:
+        plugin_id = self.plugin_id
+        risk_level = self.risk_level
+        if self.action_id:
+            action = actions[self.action_id]
+            plugin_id = action.plugin_id
+            if risk_level is None:
+                risk_level = action.risk_level
+        if not plugin_id:
+            raise ValueError(f"intent {self.intent_id} is missing plugin_id/action_id mapping")
+        return IntentDefinition(
+            intent_id=self.intent_id,
+            plugin_id=plugin_id,
+            domain=self.domain,
+            risk_level=risk_level or "low",
+            required_slots=self.required_slots,
+            ask_templates=self.ask_templates,
+            keywords=self.keywords,
+            examples=self.examples,
+        )
+
+
+class DomainConfig(BaseModel):
+    intents: list[DomainIntentDefinition] = Field(default_factory=list)
+
+
+class ActionsConfig(BaseModel):
+    actions: list[ActionDefinition] = Field(default_factory=list)
+
+
+class ResponsesConfig(BaseModel):
+    templates: dict[str, str] = Field(default_factory=dict)
+
+
+class FormDefinition(BaseModel):
+    intent_id: str
+    required_slots: list[str] = Field(default_factory=list)
+    ask_templates: dict[str, str] = Field(default_factory=dict)
+
+
+class FormsConfig(BaseModel):
+    forms: list[FormDefinition] = Field(default_factory=list)
+
+
+class ConfirmationRuleConfig(BaseModel):
+    positive_tokens: list[str] = Field(default_factory=list)
+    negative_tokens: list[str] = Field(default_factory=list)
+    required_intents: list[str] = Field(default_factory=list)
+    required_risk_levels: list[Literal["low", "medium", "high"]] = Field(default_factory=list)
+
+
+class StopRuleConfig(BaseModel):
+    phrases: list[str] = Field(default_factory=list)
+
+
+class DialogRulesConfig(BaseModel):
+    stop: StopRuleConfig = Field(default_factory=StopRuleConfig)
+    confirmation: ConfirmationRuleConfig = Field(default_factory=ConfirmationRuleConfig)
+
+
+class DialogActPatternDefinition(BaseModel):
+    act_id: str
+    phrases: list[str] = Field(default_factory=list)
+    # 正则列表：文本匹配任意一个则命中该 act（用于数字类 inform）
+    numeric_patterns: list[str] = Field(default_factory=list)
+
+
+class DialogActsConfig(BaseModel):
+    acts: list[DialogActPatternDefinition] = Field(default_factory=list)
+
+
+# ── Context Rewrite Config ────────────────────────────────────────────────────
+
+class ParamContextDefinition(BaseModel):
+    """一类可相对调节参数的上下文改写规则。"""
+    intent_ids: list[str]
+    slot_name: str
+    unit: str = ""
+    step: int | float = 1
+    min_value: int | float = 0
+    max_value: int | float = 9999
+    default_value: int | float = 0
+    up_phrases: list[str] = Field(default_factory=list)
+    down_phrases: list[str] = Field(default_factory=list)
+    rewrite_template: str = "{value}"
+
+
+class ContextRewriteConfig(BaseModel):
+    param_contexts: list[ParamContextDefinition] = Field(default_factory=list)
+
+
+class WorkflowTemplateStepOverride(BaseModel):
+    depends_on: list[int] = Field(default_factory=list)
+    condition: dict[str, Any] = Field(default_factory=dict)
+    requires_confirmation: bool = False
+
+
+class WorkflowTemplateDefinition(BaseModel):
+    template_id: str
+    workflow_type: Literal["sequence", "conditional", "parallel"] = "sequence"
+    intent_sequence: list[str] = Field(default_factory=list)
+    step_overrides: list[WorkflowTemplateStepOverride] = Field(default_factory=list)
+    trigger_keywords: list[str] = Field(default_factory=list)
+
+
+class WorkflowTemplatesConfig(BaseModel):
+    templates: list[WorkflowTemplateDefinition] = Field(default_factory=list)

intelligent_cabin/app/schemas/debug.py

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+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class IntentCandidate(BaseModel):
+    intent_id: str
+    score: float = 0.0
+    reason: str | None = None
+    model_name: str | None = None
+    raw_label: str | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class MatcherStageDebug(BaseModel):
+    stage: str
+    accepted: bool = False
+    selected_intent: str | None = None
+    score: float = 0.0
+    elapsed_ms: float | None = None
+    reason: str | None = None
+    model_name: str | None = None
+    backend: str | None = None
+    fallback_used: bool = False
+    raw_label: str | None = None
+    error_message: str | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+    candidates: list[IntentCandidate] = Field(default_factory=list)
+
+
+class RoutingDebug(BaseModel):
+    selected_intent: str | None = None
+    matched_stage: str | None = None
+    decision: str = "reject"
+    decision_reason: str | None = None
+    confidence_grade: str | None = None
+    total_match_latency_ms: float | None = None
+    unknown_detected: bool = False
+    extracted_slots: dict[str, Any] = Field(default_factory=dict)
+    stages: list[MatcherStageDebug] = Field(default_factory=list)

intelligent_cabin/app/schemas/demo.py

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+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class DemoRuntimeConfig(BaseModel):
+    matcher_pipeline: Literal["classifier"]
+    classifier_backend: Literal["mock", "bert", "remote", "joint_bert"]
+    session_backend: Literal["memory", "redis"]
+    slot_extractor_backend: str
+    planner_backend: str
+    planner_model_name: str
+
+
+class DemoRuntimeUpdateRequest(BaseModel):
+    matcher_pipeline: Literal["classifier"] = Field(default="classifier")
+    classifier_backend: Literal["mock", "bert", "remote", "joint_bert"]
+    session_backend: Literal["memory", "redis"]

intelligent_cabin/app/schemas/intent.py

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+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class IntentDefinition(BaseModel):
+    intent_id: str
+    plugin_id: str
+    domain: str
+    risk_level: Literal["low", "medium", "high"] = "low"
+    required_slots: list[str] = Field(default_factory=list)
+    ask_templates: dict[str, str] = Field(default_factory=dict)
+    keywords: list[str] = Field(default_factory=list)
+    examples: list[str] = Field(default_factory=list)

intelligent_cabin/app/schemas/workflow.py

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+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+
+class WorkflowStep(BaseModel):
+    step: int
+    step_id: str
+    intent_id: str
+    plugin_id: str
+    action: str
+    status: Literal["pending", "running", "completed", "failed", "skipped", "waiting_confirmation"] = "pending"
+    depends_on: list[str] = Field(default_factory=list)
+    slots: dict[str, Any] = Field(default_factory=dict)
+    condition: dict[str, Any] = Field(default_factory=dict)
+    requires_confirmation: bool = False
+    timeout_ms: int = 1500
+
+
+class MissingSlot(BaseModel):
+    slot_name: str
+    ask_template: str
+    priority: int = 1
+
+
+class Workflow(BaseModel):
+    workflow_id: str
+    workflow_type: Literal["single", "sequence", "conditional", "parallel"] = "single"
+    domain: str
+    intent_id: str
+    status: Literal["ready", "waiting_slot", "waiting_confirmation", "running", "completed", "failed"] = "ready"
+    risk_level: Literal["low", "medium", "high"] = "low"
+    slots: dict[str, Any] = Field(default_factory=dict)
+    missing_slots: list[MissingSlot] = Field(default_factory=list)
+    steps: list[WorkflowStep] = Field(default_factory=list)
+    meta: dict[str, Any] = Field(default_factory=dict)