121 lines
4.9 KiB
Python
121 lines
4.9 KiB
Python
from fastapi import HTTPException
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from loguru import logger
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import json
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from typing import Optional, List, Union
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from pydantic import BaseModel, ConfigDict, field_validator
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from datetime import datetime
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from config import settings
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from langfuse import Langfuse
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from llm.models import JiraWebhookPayload, AnalysisFlags
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from llm.chains import analysis_chain, validate_response
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class BadRequestError(HTTPException):
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def __init__(self, detail: str):
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super().__init__(status_code=400, detail=detail)
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class RateLimitError(HTTPException):
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def __init__(self, detail: str):
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super().__init__(status_code=429, detail=detail)
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class ValidationError(HTTPException):
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def __init__(self, detail: str):
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super().__init__(status_code=422, detail=detail)
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class JiraWebhookHandler:
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def __init__(self):
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self.analysis_chain = analysis_chain
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async def handle_webhook(self, payload: JiraWebhookPayload):
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try:
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if not payload.issueKey:
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raise BadRequestError("Missing required field: issueKey")
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if not payload.summary:
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raise BadRequestError("Missing required field: summary")
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logger.bind(
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issue_key=payload.issueKey,
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timestamp=datetime.utcnow().isoformat()
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).info("Received webhook")
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# Create Langfuse trace if enabled
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trace = None
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if settings.langfuse.enabled:
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trace = settings.langfuse_client.start_span(
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name="Jira Webhook",
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input=payload.dict(),
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metadata={
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"trace_id": f"webhook-{payload.issueKey}",
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"issue_key": payload.issueKey,
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"timestamp": datetime.utcnow().isoformat()
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}
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)
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llm_input = {
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"issueKey": payload.issueKey,
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"summary": payload.summary,
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"description": payload.description if payload.description else "No description provided.",
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"status": payload.status if payload.status else "Unknown",
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"labels": ", ".join(payload.labels) if payload.labels else "None",
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"assignee": payload.assignee if payload.assignee else "Unassigned",
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"updated": payload.updated if payload.updated else "Unknown",
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"comment": payload.comment if payload.comment else "No new comment provided."
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}
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# Create Langfuse span for LLM processing if enabled
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llm_span = None
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if settings.langfuse.enabled and trace:
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llm_span = trace.start_span(
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name="LLM Processing",
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input=llm_input,
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metadata={
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"model": settings.llm.model if settings.llm.mode == 'openai' else settings.llm.ollama_model
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}
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)
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try:
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analysis_result = await self.analysis_chain.ainvoke(llm_input)
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# Update Langfuse span with output if enabled
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if settings.langfuse.enabled and llm_span:
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llm_span.update(output=analysis_result)
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llm_span.end()
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# Validate LLM response
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if not validate_response(analysis_result):
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logger.warning(f"Invalid LLM response format for {payload.issueKey}")
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analysis_result = {
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"hasMultipleEscalations": False,
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"customerSentiment": "neutral"
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}
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logger.debug(f"LLM Analysis Result for {payload.issueKey}: {json.dumps(analysis_result, indent=2)}")
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return {"status": "success", "analysis_flags": analysis_result}
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except Exception as e:
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logger.error(f"LLM processing failed for {payload.issueKey}: {str(e)}")
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# Log error to Langfuse if enabled
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if settings.langfuse.enabled and llm_span:
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llm_span.error(e)
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llm_span.end()
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return {
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"status": "error",
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"analysis_flags": {
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"hasMultipleEscalations": False,
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"customerSentiment": "neutral"
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},
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"error": str(e)
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}
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except Exception as e:
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logger.error(f"Error processing webhook: {str(e)}")
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import traceback
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logger.error(f"Stack trace: {traceback.format_exc()}")
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# Log error to Langfuse if enabled
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if settings.langfuse.enabled and trace:
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trace.end(error=e)
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raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}") |