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from typing import List, Optional, Dict, Union
from pydantic import BaseModel, Field
from enum import Enum
class SentimentEnum(str, Enum):
FRUSTRATED = "Frustrated"
CURIOUS = "Curious"
ANGRY = "Angry"
NEUTRAL = "Neutral"
class PriorityEnum(str, Enum):
P0 = "P0 (High)"
P1 = "P1 (Medium)"
P2 = "P2 (Low)"
class TopicTagEnum(str, Enum):
HOW_TO = "How-to"
PRODUCT = "Product"
CONNECTOR = "Connector"
LINEAGE = "Lineage"
API_SDK = "API/SDK"
SSO = "SSO"
GLOSSARY = "Glossary"
BEST_PRACTICES = "Best practices"
SENSITIVE_DATA = "Sensitive data"
SECURITY = "Security"
RBAC = "RBAC"
AUTOMATION = "Automation"
TROUBLESHOOTING = "Troubleshooting"
INTEGRATION = "Integration"
class Ticket(BaseModel):
id: str = Field(..., description="Unique ticket identifier")
subject: str = Field(..., description="Ticket subject line")
body: str = Field(..., description="Ticket body content")
class TicketClassification(BaseModel):
topic_tags: List[TopicTagEnum] = Field(..., description="Relevant topic tags for the ticket")
sentiment: SentimentEnum = Field(..., description="Customer sentiment")
priority: PriorityEnum = Field(..., description="Ticket priority level")
reasoning: Optional[str] = Field(None, description="AI reasoning for the classification")
class ClassifiedTicket(BaseModel):
ticket: Ticket
classification: TicketClassification
class SingleTicketRequest(BaseModel):
ticket: Ticket
class BulkTicketRequest(BaseModel):
tickets: List[Ticket]
class ClassificationResponse(BaseModel):
success: bool
data: Optional[List[ClassifiedTicket]] = None
error: Optional[str] = None
total_processed: int = 0
class InteractiveAnalysis(BaseModel):
topic_tags: List[str]
sentiment: str
priority: str
reasoning: str
class DirectAnswerResponse(BaseModel):
type: str = "direct_answer"
answer: str
sources: List[str] = []
class RoutingResponse(BaseModel):
type: str = "routing"
message: str
class InteractiveAgentResponse(BaseModel):
internal_analysis: InteractiveAnalysis
final_response: Dict
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