File size: 2,691 Bytes
4655858 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | from typing import Any, Dict
from llm_client import llm_client
from prompt_loader import prompt_loader
from metrics import log_metric
class DraftMicroFunction:
def run(self, data: Dict[str, Any]) -> Dict[str, Any]:
enriched_data = data.get("enriched", {})
scraped_text = data.get("scraped_text", "")
if not enriched_data or enriched_data.get("error"):
return {**data, "draft": "Unable to draft content due to enrichment errors."}
try:
# Prepare context for drafting
context = {
"role": enriched_data.get("role", "Unknown Role"),
"company": enriched_data.get("company", "Unknown Company"),
"level": enriched_data.get("level", "Unknown Level"),
"requirements": enriched_data.get("requirements", []),
"responsibilities": enriched_data.get("responsibilities", []),
"tech_stack": enriched_data.get("tech_stack", []),
"salary_range": enriched_data.get("salary_range", "Not specified"),
"work_mode": enriched_data.get("work_mode", "Not specified")
}
# Use LLM to draft comprehensive content
prompt = prompt_loader.get_prompt("draft_prompt", job_data=str(context))
detailed_prompt = prompt + f"""
Based on this job data: {context}
Create a comprehensive role preview and interview preparation kit with:
## 🎯 Role Overview
- Role summary and key focus areas
- Company context and culture fit
## 📋 Key Responsibilities & Requirements
- Core responsibilities breakdown
- Must-have vs nice-to-have skills
- Technical requirements analysis
## 💰 Compensation & Benefits
- Salary analysis and market context
- Benefits and perquisites
## 🎯 Interview Preparation
- Likely interview questions based on the role
- Technical topics to review
- Company-specific research areas
- Questions to ask the interviewer
## 🚀 Next Steps
- Application strategy
- Timeline expectations
- Follow-up recommendations
Format as clear, actionable markdown suitable for job seekers.
"""
draft_content = llm_client.call_llm(detailed_prompt)
log_metric("draft_success", {
"role": context["role"],
"company": context["company"],
"content_length": len(draft_content)
})
return {**data, "draft": draft_content}
except Exception as e:
log_metric("draft_error", {"error": str(e)})
return {**data, "draft": f"Draft generation failed: {e}"} |