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app/models/schemas.py

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+"""
+Pydantic models for API request/response schemas.
+"""
+from pydantic import BaseModel, Field
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+from enum import Enum
+
+
+class DocumentStatus(str, Enum):
+    PENDING = "pending"
+    PROCESSING = "processing"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class ChunkMetadata(BaseModel):
+    """Metadata for a document chunk."""
+    tenant_id: str  # CRITICAL: Multi-tenant isolation
+    kb_id: str
+    user_id: str
+    file_name: str
+    file_type: str
+    chunk_id: str
+    chunk_index: int
+    page_number: Optional[int] = None
+    total_chunks: int
+    document_id: Optional[str] = None  # Track original document
+    created_at: datetime = Field(default_factory=datetime.utcnow)
+
+
+class DocumentChunk(BaseModel):
+    """A chunk of text with metadata."""
+    id: str
+    content: str
+    metadata: ChunkMetadata
+    embedding: Optional[List[float]] = None
+
+
+class UploadRequest(BaseModel):
+    """Request model for file upload."""
+    tenant_id: str  # CRITICAL: Multi-tenant isolation
+    user_id: str
+    kb_id: str
+
+
+class UploadResponse(BaseModel):
+    """Response model for file upload."""
+    success: bool
+    message: str
+    document_id: Optional[str] = None
+    file_name: str
+    chunks_created: int = 0
+    status: DocumentStatus = DocumentStatus.PENDING
+
+
+class Citation(BaseModel):
+    """Citation reference for an answer."""
+    file_name: str
+    chunk_id: str
+    page_number: Optional[int] = None
+    relevance_score: float
+    excerpt: str  # Short excerpt from the chunk
+
+
+class ChatRequest(BaseModel):
+    """Request model for chat endpoint."""
+    tenant_id: str  # CRITICAL: Multi-tenant isolation
+    user_id: str
+    kb_id: str
+    conversation_id: Optional[str] = None
+    question: str
+
+
+class ChatResponse(BaseModel):
+    """Response model for chat endpoint."""
+    success: bool
+    answer: str
+    citations: List[Citation] = []
+    confidence: float  # 0-1 score
+    from_knowledge_base: bool = True
+    escalation_suggested: bool = False
+    conversation_id: str
+    metadata: Dict[str, Any] = {}
+
+
+class RetrievalResult(BaseModel):
+    """Result from vector store retrieval."""
+    chunk_id: str
+    content: str
+    metadata: Dict[str, Any]
+    similarity_score: float
+
+
+class KnowledgeBaseStats(BaseModel):
+    """Statistics for a knowledge base."""
+    tenant_id: str  # CRITICAL: Multi-tenant isolation
+    kb_id: str
+    user_id: str
+    total_documents: int
+    total_chunks: int
+    file_names: List[str]
+    last_updated: Optional[datetime] = None
+
+
+class HealthResponse(BaseModel):
+    """Health check response."""
+    status: str
+    version: str = "1.0.0"
+    vector_db_connected: bool
+    llm_configured: bool
+