Skill tracking almost working
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7586725f11
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170
src/backend/agents/job_requirements.py
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170
src/backend/agents/job_requirements.py
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from __future__ import annotations
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from pydantic import model_validator, Field # type: ignore
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from typing import (
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Dict,
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Literal,
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ClassVar,
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Any,
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AsyncGenerator,
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List,
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Optional
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# override
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) # NOTE: You must import Optional for late binding to work
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import inspect
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import re
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import json
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import traceback
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import asyncio
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import time
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import asyncio
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import numpy as np # type: ignore
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from .base import Agent, agent_registry, LLMMessage
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from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType, JobRequirements
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import model_cast
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from logger import logger
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import defines
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class JobRequirementsAgent(Agent):
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agent_type: Literal["job_requirements"] = "job_requirements" # type: ignore
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_agent_type: ClassVar[str] = agent_type # Add this for registration
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# Stage 1A: Job Analysis Implementation
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def create_job_analysis_prompt(self, job_description: str) -> tuple[str, str]:
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"""Create the prompt for job requirements analysis."""
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logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
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system_prompt = """
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You are an objective job requirements analyzer. Your task is to extract and categorize the specific skills,
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experiences, and qualifications required in a job description WITHOUT any reference to any candidate.
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## INSTRUCTIONS:
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1. Analyze ONLY the job description provided.
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2. Extract and categorize all requirements and preferences.
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3. DO NOT consider any candidate information - this is a pure job analysis task.
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## OUTPUT FORMAT:
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```json
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{
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"job_requirements": {
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"technical_skills": {
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"required": ["skill1", "skill2"],
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"preferred": ["skill1", "skill2"]
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},
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"experience_requirements": {
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"required": ["exp1", "exp2"],
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"preferred": ["exp1", "exp2"]
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},
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"education_requirements": ["req1", "req2"],
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"soft_skills": ["skill1", "skill2"],
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"industry_knowledge": ["knowledge1", "knowledge2"],
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"responsibilities": ["resp1", "resp2"],
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"company_values": ["value1", "value2"]
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}
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}
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```
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Be specific and detailed in your extraction. Break down compound requirements into individual components.
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For example, "5+ years experience with React, Node.js and MongoDB" should be separated into:
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- Experience: "5+ years software development"
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- Technical skills: "React", "Node.js", "MongoDB"
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Avoid vague categorizations and be precise about whether skills are explicitly required or just preferred.
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"""
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prompt = f"Job Description:\n{job_description}"
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return system_prompt, prompt
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async def analyze_job_requirements(
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self, llm: Any, model: str, user_message: ChatMessage
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) -> AsyncGenerator[ChatMessage, None]:
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"""Analyze job requirements from job description."""
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system_prompt, prompt = self.create_job_analysis_prompt(user_message.content)
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analyze_message = user_message.model_copy()
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analyze_message.content = prompt
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generated_message = None
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async for generated_message in self.llm_one_shot(llm, model, system_prompt=system_prompt, user_message=analyze_message):
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if generated_message.status == ChatStatusType.ERROR:
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generated_message.content = "Error analyzing job requirements."
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yield generated_message
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return
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if not generated_message:
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status_message = ChatMessage(
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session_id=user_message.session_id,
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sender=ChatSenderType.AGENT,
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status = ChatStatusType.ERROR,
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type = ChatMessageType.ERROR,
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content = "Job requirements analysis failed to generate a response.")
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yield status_message
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return
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generated_message.status = ChatStatusType.DONE
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generated_message.type = ChatMessageType.RESPONSE
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yield generated_message
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return
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async def generate(
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self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate | None, temperature=0.7
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) -> AsyncGenerator[ChatMessage, None]:
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# Stage 1A: Analyze job requirements
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status_message = ChatMessage(
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session_id=user_message.session_id,
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sender=ChatSenderType.AGENT,
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status=ChatStatusType.STATUS,
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type=ChatMessageType.THINKING,
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content = f"Analyzing job requirements")
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yield status_message
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generated_message = None
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async for generated_message in self.analyze_job_requirements(llm, model, user_message):
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if generated_message.status == ChatStatusType.ERROR:
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status_message.status = ChatStatusType.ERROR
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status_message.content = generated_message.content
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yield status_message
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return
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if not generated_message:
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status_message.status = ChatStatusType.ERROR
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status_message.content = "Job requirements analysis failed."
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yield status_message
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return
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json_str = self.extract_json_from_text(generated_message.content)
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job_requirements : JobRequirements | None = None
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job_requirements_data = ""
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try:
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job_requirements_data = json.loads(json_str)
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job_requirements_data = job_requirements_data.get("job_requirements", None)
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job_requirements = JobRequirements.model_validate(job_requirements_data)
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if not job_requirements:
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raise ValueError("Job requirements data is empty or invalid.")
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except json.JSONDecodeError as e:
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status_message.status = ChatStatusType.ERROR
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status_message.content = f"Failed to parse job requirements JSON: {str(e)}\n\n{job_requirements_data}"
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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except ValueError as e:
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status_message.status = ChatStatusType.ERROR
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status_message.content = f"Job requirements validation error: {str(e)}\n\n{job_requirements_data}"
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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except Exception as e:
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status_message.status = ChatStatusType.ERROR
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status_message.content = f"Unexpected error processing job requirements: {str(e)}\n\n{job_requirements_data}"
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logger.error(traceback.format_exc())
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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status_message.status = ChatStatusType.DONE
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status_message.type = ChatMessageType.RESPONSE
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status_message.content = json.dumps(job_requirements.model_dump(mode="json", exclude_unset=True))
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yield status_message
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logger.info(f"✅ Job requirements analysis completed successfully.")
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return
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# Register the base agent
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agent_registry.register(JobRequirementsAgent._agent_type, JobRequirementsAgent)
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233
src/backend/agents/skill_match.py
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233
src/backend/agents/skill_match.py
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from __future__ import annotations
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from pydantic import model_validator, Field # type: ignore
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from typing import (
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Dict,
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Literal,
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ClassVar,
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Any,
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AsyncGenerator,
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List,
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Optional
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# override
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) # NOTE: You must import Optional for late binding to work
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import inspect
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import re
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import json
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import traceback
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import asyncio
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import time
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import asyncio
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import numpy as np # type: ignore
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from .base import Agent, agent_registry, LLMMessage
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from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType, SkillMatch
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import model_cast
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from logger import logger
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import defines
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class SkillMatchAgent(Agent):
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agent_type: Literal["skill_match"] = "skill_match" # type: ignore
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_agent_type: ClassVar[str] = agent_type # Add this for registration
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def generate_skill_assessment_prompt(self, skill, rag_content):
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"""
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Generate a system prompt to query the LLM for evidence of a specific skill
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Parameters:
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- skill (str): The specific skill to assess from job requirements
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- rag_content (str): Additional RAG content queried from candidate documents
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Returns:
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- str: A system prompt tailored to assess the specific skill
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"""
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system_prompt = f"""You are an objective skill assessor. Your task is to determine if a candidate possesses
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a SPECIFIC skill based solely on their resume and supporting evidence.
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## SKILL TO ASSESS:
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"{skill}"
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## INSTRUCTIONS:
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1. Focus exclusively on assessing the candidate's proficiency with the skill: "{skill}".
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2. Examine the resume and supporting documents for both explicit mentions and clearly demonstrated applications of the skill.
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3. Do NOT infer the skill unless it is either:
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- Directly mentioned in association with experience, or
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- Clearly evidenced through relevant tools, technologies, responsibilities, or outcomes.
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- Referenced as being used or listed as part of a broader initiative.
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4. Evaluate each piece of evidence and assign a confidence rating:
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- STRONG: Explicit use with clear context or repeated/prolonged experience
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- MODERATE: Inferred through tools, environments, or outcomes (e.g., Python used in a listed project/tool)
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- WEAK: Mentioned in a list or indirectly implied without context
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- NONE: No relevant evidence
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5. Provide up to 10 evidence_details. Each should include:
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- source: where the evidence appears
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- quote: an exact snippet from the document(s)
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- context: a short rationale explaining how this supports the skill claim
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6. When no supporting evidence exists, output a "NONE" rating with an empty evidence details list.
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7. IMPORTANT: Even if the skill is only listed in a technologies or languages section, consider this valid evidence.
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## OUTPUT FORMAT:
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```json
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{{
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"skill_assessment": {{
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"skill": "{skill}",
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"evidence_found": true/false,
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"evidence_strength": "STRONG/MODERATE/WEAK/NONE",
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"description": "short (two to three sentence) description of what {skill} means with a concise example of what you're looking for",
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"evidence_details": [
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{{
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"source": "resume section/position/project",
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"quote": "exact text from resume showing evidence",
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"context": "brief explanation of how this demonstrates the skill"
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}}
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]
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}}
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}}
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```
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IMPORTANT: Be factual and precise. If you cannot find strong evidence for this specific skill, it's better to indicate "evidence_strength": WEAK than to stretch for connections.
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Focus only on "{skill}" and not similar skills unless they directly demonstrate the required skill.
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Remember that a skill listed in a "Languages" or "Technologies" section should be considered valid evidence.
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Adhere strictly to the JSON output format requested. Do not include any additional text or commentary outside the JSON structure.
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"""
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prompt = f"""Analyze the candidate information below for evidence of "{skill}".
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RESPOND WITH ONLY VALID JSON USING THE EXACT FORMAT SPECIFIED.
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<candidate_info>
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{rag_content}
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</candidate_info>
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JSON RESPONSE:"""
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return system_prompt, prompt
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async def analyze_job_requirements(
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self, llm: Any, model: str, user_message: ChatMessage
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) -> AsyncGenerator[ChatMessage, None]:
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"""Analyze job requirements from job description."""
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system_prompt, prompt = self.create_job_analysis_prompt(user_message.content)
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analyze_message = user_message.model_copy()
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analyze_message.content = prompt
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generated_message = None
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async for generated_message in self.llm_one_shot(llm, model, system_prompt=system_prompt, user_message=analyze_message):
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if generated_message.status == ChatStatusType.ERROR:
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generated_message.content = "Error analyzing job requirements."
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yield generated_message
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return
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if not generated_message:
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status_message = ChatMessage(
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session_id=user_message.session_id,
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sender=ChatSenderType.AGENT,
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status = ChatStatusType.ERROR,
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type = ChatMessageType.ERROR,
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content = "Job requirements analysis failed to generate a response.")
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yield status_message
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return
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generated_message.status = ChatStatusType.DONE
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generated_message.type = ChatMessageType.RESPONSE
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yield generated_message
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return
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async def generate(
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self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate | None, temperature=0.7
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) -> AsyncGenerator[ChatMessage, None]:
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# Stage 1A: Analyze job requirements
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status_message = ChatMessage(
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session_id=user_message.session_id,
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sender=ChatSenderType.AGENT,
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status=ChatStatusType.STATUS,
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type=ChatMessageType.THINKING,
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content = f"Analyzing job requirements")
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yield status_message
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rag_message = None
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async for rag_message in self.generate_rag_results(chat_message=user_message):
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if rag_message.status == ChatStatusType.ERROR:
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status_message.status = ChatStatusType.ERROR
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status_message.content = rag_message.content
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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if rag_message is None:
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status_message.status = ChatStatusType.ERROR
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status_message.content = "Failed to retrieve RAG context."
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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logger.info(f"🔍 RAG content retrieved: {len(rag_message.content)} bytes")
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system_prompt, prompt = self.generate_skill_assessment_prompt(skill=user_message.content, rag_content=rag_message.content)
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user_message.content = prompt
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skill_assessment = None
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async for skill_assessment in self.llm_one_shot(llm=llm, model=model, user_message=user_message, system_prompt=system_prompt, temperature=0.1):
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if skill_assessment.status == ChatStatusType.ERROR:
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status_message.status = ChatStatusType.ERROR
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status_message.content = skill_assessment.content
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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if skill_assessment is None:
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status_message.status = ChatStatusType.ERROR
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status_message.content = "Failed to generate skill assessment."
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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json_str = self.extract_json_from_text(skill_assessment.content)
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skill_match = json_str#: SkillMatch | None = None
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skill_assessment_data = ""
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try:
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skill_assessment_data = json.loads(json_str)
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match_level = (
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skill_assessment_data
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.get("skill_assessment", {})
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.get("evidence_strength", "UNKNOWN")
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)
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skill_description = (
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skill_assessment_data
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.get("skill_assessment", {})
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.get("description", "")
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)
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except json.JSONDecodeError as e:
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status_message.status = ChatStatusType.ERROR
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status_message.content = f"Failed to parse Skill assessment JSON: {str(e)}\n\n{skill_assessment_data}"
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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except ValueError as e:
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status_message.status = ChatStatusType.ERROR
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status_message.content = f"Skill assessment validation error: {str(e)}\n\n{skill_assessment_data}"
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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except Exception as e:
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status_message.status = ChatStatusType.ERROR
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status_message.content = f"Unexpected error processing Skill assessment: {str(e)}\n\n{skill_assessment_data}"
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logger.error(traceback.format_exc())
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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if skill_match is None:
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status_message.status = ChatStatusType.ERROR
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status_message.content = "Skill assessment analysis failed to produce valid data."
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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status_message.status = ChatStatusType.DONE
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status_message.type = ChatMessageType.RESPONSE
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status_message.content = skill_match
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yield status_message
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logger.info(f"✅ Skill assessment completed successfully.")
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return
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# Register the base agent
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agent_registry.register(SkillMatchAgent._agent_type, SkillMatchAgent)
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