Ireneusz Bachanowicz
2763b40b60
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- Simplified the FastAPI application structure and improved error handling with middleware. - Introduced a retry decorator for asynchronous functions to enhance reliability. - Modularized the LLM initialization and prompt loading into separate functions for better maintainability. - Updated Pydantic models for Jira webhook payload and analysis flags to ensure proper validation and structure. - Implemented a structured logging configuration for better traceability and debugging. - Added comprehensive unit tests for prompt loading, response validation, and webhook handling. - Established a CI/CD pipeline with GitHub Actions for automated testing and coverage reporting. - Enhanced the prompt template for LLM analysis to include specific instructions for handling escalations.
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
from langchain_ollama import OllamaLLM
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import PromptTemplate
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from langchain_core.output_parsers import JsonOutputParser
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from loguru import logger
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from config import settings
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from .models import AnalysisFlags
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# Initialize LLM
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llm = None
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if settings.llm.mode == 'openai':
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logger.info(f"Initializing ChatOpenAI with model: {settings.openai_model}")
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llm = ChatOpenAI(
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model=settings.openai_model,
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temperature=0.7,
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max_tokens=2000,
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api_key=settings.openai_api_key,
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base_url=settings.openai_api_base_url
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)
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elif settings.llm.mode == 'ollama':
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logger.info(f"Initializing OllamaLLM with model: {settings.llm.ollama_model} at {settings.llm.ollama_base_url}")
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llm = OllamaLLM(
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model=settings.llm.ollama_model,
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base_url=settings.llm.ollama_base_url,
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streaming=False
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)
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if llm is None:
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logger.error("LLM could not be initialized. Exiting.")
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sys.exit(1)
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# Set up Output Parser for structured JSON
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parser = JsonOutputParser(pydantic_object=AnalysisFlags)
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# Load prompt template from file
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def load_prompt_template(version="v1.0.0"):
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try:
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with open(f"llm/prompts/jira_analysis_{version}.txt", "r") as f:
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template = f.read()
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return PromptTemplate(
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template=template,
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input_variables=[
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"issueKey", "summary", "description", "status", "labels",
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"assignee", "updated", "comment"
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],
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partial_variables={"format_instructions": parser.get_format_instructions()},
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)
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except Exception as e:
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logger.error(f"Failed to load prompt template: {str(e)}")
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raise
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# Fallback prompt template
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FALLBACK_PROMPT = PromptTemplate(
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template="Please analyze this Jira ticket and provide a basic summary.",
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input_variables=["issueKey", "summary"]
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)
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# Create chain with fallback mechanism
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def create_analysis_chain():
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try:
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prompt_template = load_prompt_template()
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return prompt_template | llm | parser
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except Exception as e:
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logger.warning(f"Using fallback prompt due to error: {str(e)}")
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return FALLBACK_PROMPT | llm | parser
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# Initialize analysis chain
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analysis_chain = create_analysis_chain()
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# Response validation function
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def validate_response(response: dict) -> bool:
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required_fields = ["hasMultipleEscalations", "customerSentiment"]
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return all(field in response for field in required_fields) |