初始化

app/schemas/__init__.py

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+from .doc import (
+    DocumentUploadResponse,
+    DocumentInfo,
+    DocumentListResponse,
+    ChunkInfo,
+    ParseResponse,
+    EmbedResponse,
+)
+from .rag import (
+    RagChatRequest,
+    RetrievedDoc,
+    SourceInfo,
+    QuickQuestion,
+    QuickQuestionsResponse,
+)
+from .compliance import (
+    RiskLevel,
+    ComplianceStatus,
+    Regulation,
+    ComplianceSegment,
+    RiskDashboard,
+    PriorityAction,
+    ComplianceResult,
+    ComplianceChatRequest,
+    AnalyzeResponse,
+)
+
+__all__ = [
+    "DocumentUploadResponse",
+    "DocumentInfo",
+    "DocumentListResponse",
+    "ChunkInfo",
+    "ParseResponse",
+    "EmbedResponse",
+    "RagChatRequest",
+    "RetrievedDoc",
+    "SourceInfo",
+    "QuickQuestion",
+    "QuickQuestionsResponse",
+    "RiskLevel",
+    "ComplianceStatus",
+    "Regulation",
+    "ComplianceSegment",
+    "RiskDashboard",
+    "PriorityAction",
+    "ComplianceResult",
+    "ComplianceChatRequest",
+    "AnalyzeResponse",
+]

app/schemas/compliance.py

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+from pydantic import BaseModel
+from typing import Optional
+from enum import Enum
+
+
+class RiskLevel(str, Enum):
+    high = "high"
+    medium = "medium"
+    low = "low"
+
+
+class ComplianceStatus(str, Enum):
+    pass_status = "pass"
+    warning = "warning"
+    fail = "fail"
+
+
+class Regulation(BaseModel):
+    id: int
+    name: str
+    clause: Optional[str] = None
+    score: float
+    match_keyword: str
+    category: RiskLevel
+    full_content: str
+
+
+class ComplianceSegment(BaseModel):
+    id: int
+    index: int
+    intent: str
+    start_pos: int
+    end_pos: int
+    content: str
+    risk_level: RiskLevel
+    regulations: list[Regulation]
+
+
+class RiskDashboard(BaseModel):
+    score: float
+    high_risk_count: int
+    medium_risk_count: int
+    low_risk_count: int
+    need_fix_segments: int
+    status: ComplianceStatus
+    status_label: str
+
+
+class PriorityAction(BaseModel):
+    regulation: str
+    issue: str
+    suggestion: str
+    severity: RiskLevel
+
+
+class ComplianceResult(BaseModel):
+    task_id: str
+    dashboard: RiskDashboard
+    segments: list[ComplianceSegment]
+    priority_actions: list[PriorityAction]
+
+
+class ComplianceChatRequest(BaseModel):
+    query: str
+
+
+class AnalyzeResponse(BaseModel):
+    task_id: str
+    status: str = "processing"

app/schemas/doc.py

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+from pydantic import BaseModel
+from typing import Optional
+from datetime import datetime
+
+
+class DocumentUploadResponse(BaseModel):
+    doc_id: str
+    filename: str
+    size: int
+    status: str = "uploaded"
+
+
+class DocumentInfo(BaseModel):
+    id: str
+    name: str
+    chunks: int
+    status: str
+    created_at: Optional[datetime] = None
+
+
+class DocumentListResponse(BaseModel):
+    docs: list[DocumentInfo]
+
+
+class ChunkInfo(BaseModel):
+    chunk_id: str
+    doc_name: str
+    clause_id: Optional[str] = None
+    chapter: Optional[str] = None
+    content: str
+    token_count: int
+    chunk_index: int
+
+
+class ParseResponse(BaseModel):
+    doc_id: str
+    chunks: int
+    status: str = "parsed"
+
+
+class EmbedResponse(BaseModel):
+    doc_id: str
+    vectors: int
+    status: str = "embedded"

app/schemas/rag.py

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+from pydantic import BaseModel
+from typing import Optional
+
+
+class RagChatRequest(BaseModel):
+    query: str
+    top_k: int = 5
+
+
+class RetrievedDoc(BaseModel):
+    id: str
+    doc_name: str
+    clause_id: Optional[str] = None
+    score: float
+    content: str
+    preview: str
+
+
+class SourceInfo(BaseModel):
+    name: str
+    clause: Optional[str] = None
+
+
+class QuickQuestion(BaseModel):
+    id: str
+    question: str
+    category: str
+
+
+class QuickQuestionsResponse(BaseModel):
+    questions: list[QuickQuestion]