backend.im/airesumejobmatchingapi-5g56kf
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Update code via agent code generation

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Automated Action 2025-06-27 17:33:33 +00:00
parent 2735438f01
commit 33579e7e0b
3 changed files with 191 additions and 154 deletions
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4
app/core/config.py
View File

@ -9,8 +9,8 @@ class Settings(BaseSettings):
ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
ALGORITHM: str = "HS256"
# OpenAI Configuration
OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
# Cohere Configuration
COHERE_API_KEY: str = os.getenv("COHERE_API_KEY", "")
# CORS settings
CORS_ORIGINS: list = ["*"]

243
app/services/ai_service.py
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@ -1,6 +1,6 @@
import asyncio
import hashlib
from openai import AsyncOpenAI
import cohere
from typing import Dict, List, Any
from app.core.config import settings
from app.core.cache import ai_cache, cache_response
@ -9,7 +9,7 @@ import json
class AIService:
def __init__(self):
self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
self.client = cohere.AsyncClient(api_key=settings.COHERE_API_KEY)
self._semaphore = asyncio.Semaphore(5) # Limit concurrent AI calls
def _create_cache_key(self, text: str, operation: str) -> str:
@ -18,7 +18,7 @@ class AIService:
return f"{operation}:{text_hash}"
async def analyze_resume(self, resume_text: str) -> Dict[str, Any]:
"""Extract structured data from resume text using AI with caching"""
"""Extract structured data from resume text using Cohere AI with caching"""
# Check cache first
cache_key = self._create_cache_key(resume_text, "analyze_resume")
cached_result = ai_cache.get(cache_key)
@ -27,54 +27,63 @@ class AIService:
# Rate limiting with semaphore
async with self._semaphore:
prompt = f"""
Analyze the following resume text and extract structured information:
prompt = f"""Analyze this resume and extract structured information. Return only valid JSON.
{resume_text[:4000]} # Limit text length for faster processing
Resume text:
{resume_text[:4000]}
Please return a JSON object with the following structure:
Extract the following information in JSON format:
{{
"skills": ["skill1", "skill2", ...],
"experience_years": number,
"education_level": "string",
"education_level": "Bachelor's/Master's/PhD/High School/etc",
"work_experience": [
{{
"company": "string",
"position": "string",
"duration": "string",
"description": "string"
"company": "company name",
"position": "job title",
"duration": "time period",
"description": "brief description"
}}
],
"education": [
{{
"institution": "string",
"degree": "string",
"field": "string",
"year": "string"
"institution": "school name",
"degree": "degree type",
"field": "field of study",
"year": "graduation year"
}}
],
"contact_info": {{
"email": "string",
"phone": "string",
"location": "string"
"email": "email address",
"phone": "phone number",
"location": "location"
}}
}}
"""
JSON:"""
try:
response = await self.client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an expert resume analyzer. Return only valid JSON."},
{"role": "user", "content": prompt}
],
response = await self.client.chat(
model="command-r",
message=prompt,
temperature=0.1,
max_tokens=1500, # Limit response length
timeout=30 # 30 second timeout
max_tokens=1500,
connectors=[]
)
result = response.choices[0].message.content
result = response.text.strip()
# Try to extract JSON from the response
if result.startswith('{') and result.endswith('}'):
parsed_result = json.loads(result)
else:
# Try to find JSON in the response
import re
json_match = re.search(r'\{.*\}', result, re.DOTALL)
if json_match:
parsed_result = json.loads(json_match.group())
else:
raise ValueError("No valid JSON found in response")
# Cache the result for 1 hour
ai_cache.set(cache_key, parsed_result, ttl=3600)
@ -88,7 +97,7 @@ class AIService:
return empty_result
async def analyze_job_description(self, job_description: str) -> Dict[str, Any]:
"""Extract structured data from job description using AI with caching"""
"""Extract structured data from job description using Cohere AI with caching"""
# Check cache first
cache_key = self._create_cache_key(job_description, "analyze_job")
cached_result = ai_cache.get(cache_key)
@ -96,38 +105,47 @@ class AIService:
return cached_result
async with self._semaphore:
prompt = f"""
Analyze the following job description and extract structured information:
prompt = f"""Analyze this job description and extract structured information. Return only valid JSON.
{job_description[:3000]} # Limit text length
Job description:
{job_description[:3000]}
Please return a JSON object with the following structure:
Extract the following information in JSON format:
{{
"required_skills": ["skill1", "skill2", ...],
"preferred_skills": ["skill1", "skill2", ...],
"experience_level": "entry/mid/senior",
"education_requirement": "string",
"key_responsibilities": ["resp1", "resp2", ...],
"education_requirement": "minimum education required",
"key_responsibilities": ["responsibility1", "responsibility2", ...],
"company_benefits": ["benefit1", "benefit2", ...],
"job_type": "full-time/part-time/contract",
"remote_option": "yes/no/hybrid"
}}
"""
JSON:"""
try:
response = await self.client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an expert job description analyzer. Return only valid JSON."},
{"role": "user", "content": prompt}
],
response = await self.client.chat(
model="command-r",
message=prompt,
temperature=0.1,
max_tokens=1000,
timeout=30
connectors=[]
)
result = response.choices[0].message.content
result = response.text.strip()
# Try to extract JSON from the response
if result.startswith('{') and result.endswith('}'):
parsed_result = json.loads(result)
else:
# Try to find JSON in the response
import re
json_match = re.search(r'\{.*\}', result, re.DOTALL)
if json_match:
parsed_result = json.loads(json_match.group())
else:
raise ValueError("No valid JSON found in response")
# Cache for 1 hour
ai_cache.set(cache_key, parsed_result, ttl=3600)
@ -142,7 +160,7 @@ class AIService:
async def calculate_match_score(
self, resume_data: Dict[str, Any], job_data: Dict[str, Any]
) -> Dict[str, Any]:
"""Calculate match score between resume and job description with caching"""
"""Calculate match score between resume and job description using Cohere AI with caching"""
# Create cache key from both resume and job data
combined_data = f"{json.dumps(resume_data, sort_keys=True)}{json.dumps(job_data, sort_keys=True)}"
cache_key = self._create_cache_key(combined_data, "match_score")
@ -155,41 +173,52 @@ class AIService:
limited_resume = {k: v for k, v in resume_data.items() if k in ["skills", "experience_years", "education_level"]}
limited_job = {k: v for k, v in job_data.items() if k in ["required_skills", "preferred_skills", "experience_level", "education_requirement"]}
prompt = f"""
Calculate a match score between this resume and job description:
prompt = f"""Calculate a match score between this resume and job requirements. Return only valid JSON.
RESUME: {json.dumps(limited_resume)}
JOB: {json.dumps(limited_job)}
RESUME DATA:
{json.dumps(limited_resume)}
Return JSON:
JOB REQUIREMENTS:
{json.dumps(limited_job)}
Analyze and return a match score in this JSON format:
{{
"overall_score": number (0-100),
"skill_match_score": number (0-100),
"experience_match_score": number (0-100),
"education_match_score": number (0-100),
"overall_score": number_0_to_100,
"skill_match_score": number_0_to_100,
"experience_match_score": number_0_to_100,
"education_match_score": number_0_to_100,
"missing_skills": [
{{"skill": "string", "importance": "required/preferred", "suggestion": "string"}}
{{"skill": "skill_name", "importance": "required/preferred", "suggestion": "how_to_acquire"}}
],
"strengths": ["strength1", "strength2"],
"weaknesses": ["weakness1", "weakness2"],
"overall_feedback": "brief feedback"
"overall_feedback": "brief_summary"
}}
"""
JSON:"""
try:
response = await self.client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an expert HR analyst. Provide accurate match scoring. Be concise."},
{"role": "user", "content": prompt}
],
response = await self.client.chat(
model="command-r",
message=prompt,
temperature=0.2,
max_tokens=1500,
timeout=30
connectors=[]
)
result = response.choices[0].message.content
result = response.text.strip()
# Try to extract JSON from the response
if result.startswith('{') and result.endswith('}'):
parsed_result = json.loads(result)
else:
# Try to find JSON in the response
import re
json_match = re.search(r'\{.*\}', result, re.DOTALL)
if json_match:
parsed_result = json.loads(json_match.group())
else:
raise ValueError("No valid JSON found in response")
# Cache for 30 minutes
ai_cache.set(cache_key, parsed_result, ttl=1800)
@ -204,7 +233,7 @@ class AIService:
async def generate_resume_suggestions(
self, resume_data: Dict[str, Any], job_data: Dict[str, Any], match_analysis: Dict[str, Any]
) -> List[Dict[str, str]]:
"""Generate suggestions for improving resume based on job requirements with caching"""
"""Generate suggestions for improving resume using Cohere AI with caching"""
# Create cache key from all input data
combined_data = f"{json.dumps(resume_data, sort_keys=True)}{json.dumps(job_data, sort_keys=True)}{json.dumps(match_analysis, sort_keys=True)}"
cache_key = self._create_cache_key(combined_data, "resume_suggestions")
@ -215,41 +244,50 @@ class AIService:
async with self._semaphore:
# Use only essential data for faster processing
limited_data = {
"skills": resume_data.get("skills", []),
"current_skills": resume_data.get("skills", []),
"missing_skills": match_analysis.get("missing_skills", []),
"weaknesses": match_analysis.get("weaknesses", [])
}
prompt = f"""
Provide 3-5 specific resume improvement suggestions based on this analysis:
prompt = f"""Provide 3-5 specific resume improvement suggestions. Return only valid JSON.
DATA: {json.dumps(limited_data)}
Analysis data:
{json.dumps(limited_data)}
Return JSON array:
Return suggestions in this JSON array format:
[
{{
"section": "skills/experience/education/summary",
"suggestion": "specific actionable suggestion",
"suggestion": "specific_actionable_suggestion",
"priority": "high/medium/low",
"impact": "brief explanation"
"impact": "brief_explanation"
}}
]
"""
JSON:"""
try:
response = await self.client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an expert resume coach. Be concise and actionable."},
{"role": "user", "content": prompt}
],
response = await self.client.chat(
model="command-r",
message=prompt,
temperature=0.3,
max_tokens=800,
timeout=30
connectors=[]
)
result = response.choices[0].message.content
result = response.text.strip()
# Try to extract JSON from the response
if result.startswith('[') and result.endswith(']'):
parsed_result = json.loads(result)
else:
# Try to find JSON array in the response
import re
json_match = re.search(r'\[.*\]', result, re.DOTALL)
if json_match:
parsed_result = json.loads(json_match.group())
else:
raise ValueError("No valid JSON array found in response")
# Cache for 1 hour
ai_cache.set(cache_key, parsed_result, ttl=3600)
@ -264,7 +302,7 @@ class AIService:
async def generate_cover_letter(
self, resume_data: Dict[str, Any], job_data: Dict[str, Any], user_name: str
) -> str:
"""Generate a personalized cover letter with caching"""
"""Generate a personalized cover letter using Cohere AI with caching"""
# Create cache key from resume, job, and user name
combined_data = f"{json.dumps(resume_data, sort_keys=True)}{json.dumps(job_data, sort_keys=True)}{user_name}"
cache_key = self._create_cache_key(combined_data, "cover_letter")
@ -275,7 +313,7 @@ class AIService:
async with self._semaphore:
# Use essential data only
essential_resume = {
"skills": resume_data.get("skills", []),
"skills": resume_data.get("skills", [])[:8], # Top 8 skills
"work_experience": resume_data.get("work_experience", [])[:2] # Only first 2 jobs
}
essential_job = {
@ -284,32 +322,31 @@ class AIService:
"required_skills": job_data.get("required_skills", [])[:5] # Top 5 skills
}
prompt = f"""
Write a professional cover letter for {user_name}:
prompt = f"""Write a professional cover letter for {user_name} applying to this job.
RESUME: {json.dumps(essential_resume)}
JOB: {json.dumps(essential_job)}
APPLICANT BACKGROUND:
{json.dumps(essential_resume)}
Requirements:
- 3 paragraphs
- Professional tone
- Highlight relevant skills
- Show enthusiasm
"""
JOB DETAILS:
{json.dumps(essential_job)}
Write a compelling 3-paragraph cover letter that:
- Opens with enthusiasm for the specific role
- Highlights relevant skills and experience
- Closes with a call to action
Keep it professional, concise, and engaging. Do not include placeholders or brackets."""
try:
response = await self.client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an expert cover letter writer. Write compelling, concise cover letters."},
{"role": "user", "content": prompt}
],
response = await self.client.chat(
model="command-r",
message=prompt,
temperature=0.4,
max_tokens=600,
timeout=30
connectors=[]
)
result = response.choices[0].message.content
result = response.text.strip()
# Cache for 30 minutes
ai_cache.set(cache_key, result, ttl=1800)

2
requirements.txt
View File

@ -9,7 +9,7 @@ python-multipart==0.0.6
python-jose[cryptography]==3.3.0
passlib[bcrypt]==1.7.4
httpx==0.25.2
openai>=1.6.1
cohere==4.47
PyPDF2==3.0.1
python-docx==1.1.0
cachetools==5.3.2