78 lines
3.0 KiB
Python
78 lines
3.0 KiB
Python
from typing import Optional, List, Union
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from enum import Enum
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from loguru import logger
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from pydantic import BaseModel, ConfigDict, field_validator, Field
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from datetime import datetime
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from config import settings
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class LLMResponse(BaseModel):
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status: str
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message: str
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class CustomerSentiment(str, Enum):
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NEUTRAL = "neutral"
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FRUSTRATED = "frustrated"
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CALM = "calm"
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# Add other sentiments as needed
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class JiraWebhookPayload(BaseModel):
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model_config = ConfigDict(alias_generator=lambda x: ''.join(word.capitalize() if i > 0 else word for i, word in enumerate(x.split('_'))), populate_by_name=True)
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issueKey: str
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summary: str
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description: Optional[str] = None
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comment: Optional[str] = None
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labels: Optional[Union[List[str], str]] = []
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@field_validator('labels', mode='before')
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@classmethod
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def convert_labels_to_list(cls, v):
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if isinstance(v, str):
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return [v]
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return v or []
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status: Optional[str] = None
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assignee: Optional[str] = None
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updated: Optional[str] = None
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class AnalysisFlags(BaseModel):
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hasMultipleEscalations: bool = Field(alias="Hasmultipleescalations", description="Is there evidence of multiple escalation attempts?")
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customerSentiment: Optional[CustomerSentiment] = Field(alias="CustomerSentiment", description="Overall customer sentiment (e.g., 'neutral', 'frustrated', 'calm').")
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model: Optional[str] = Field(None, alias="Model", description="The LLM model used for analysis.")
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def __init__(self, **data):
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super().__init__(**data)
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# Track model usage if Langfuse is enabled and client is available
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if settings.langfuse.enabled and hasattr(settings, 'langfuse_client'):
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try:
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if settings.langfuse_client is None:
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logger.warning("Langfuse client is None despite being enabled")
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return
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settings.langfuse_client.start_span( # Use start_span
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name="LLM Model Usage",
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input=data,
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metadata={
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"model": self.model, # Use the new model attribute
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"analysis_flags": {
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"hasMultipleEscalations": self.hasMultipleEscalations,
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"customerSentiment": self.customerSentiment.value if self.customerSentiment else None
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}
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}
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).end() # End the trace immediately as it's just for tracking model usage
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except Exception as e:
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logger.error(f"Failed to track model usage: {e}")
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class JiraAnalysisResponse(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: int
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issue_key: str
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status: str
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issue_summary: str
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request_payload: dict
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analysis_result: Optional[dict] = None
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created_at: datetime
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updated_at: datetime
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error_message: Optional[str] = None
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raw_response: Optional[dict] = None |