AI Job Search Agent
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$10,000.00 / 1,000 full processes
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$10,000.00 / 1,000 full processes
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mohamed el hadi msaid
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a year ago
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# First, specify the base Docker image.# You can see the Docker images from Apify at https://hub.docker.com/r/apify/.# You can also use any other image from Docker Hub.FROM apify/actor-python:3.13
# Second, copy just requirements.txt into the Actor image,# since it should be the only file that affects the dependency installation in the next step,# in order to speed up the build.COPY requirements.txt ./
# Install the packages specified in requirements.txt,# print the installed Python version, pip version,# and all installed packages with their versions for debugging.RUN echo "Python version:" \ && python --version \ && echo "Pip version:" \ && pip --version \ && echo "Installing dependencies:" \ && pip install -r requirements.txt \ && echo "All installed Python packages:" \ && pip freeze
# Next, copy the remaining files and directories with the source code.# Since we do this after installing the dependencies, quick builds will be really fast# for most source file changes.COPY . ./
# Use compileall to ensure the runnability of the Actor Python code.RUN python3 -m compileall -q .
# Create and run as a non-root user.RUN useradd -m apify && \ chown -R apify:apify ./USER apify
# Specify how to launch the source code of your Actor.# By default, the "python3 -m ." command is run.CMD ["python3", "-m", "src"]{ "actorSpecification": 1, "name": "AI-Job-Search-Agent", "title": "Python LangGraph Agent", "description": "LangGraph agent in python", "version": "0.0", "buildTag": "latest", "input": "./input_schema.json", "storages": { "dataset": "./dataset_schema.json" }, "meta": { "templateId": "python-langgraph" }, "dockerfile": "./Dockerfile"}{ "actorSpecification": 1, "views": { "overview": { "title": "Overview", "transformation": { "fields": ["response", "structured_response"] }, "display": { "component": "table", "properties": { "response": { "label": "Response", "format": "text" }, "structured_response": { "label": "Structured Response", "format": "object" } } } } }}{ "title": "AI Job Search Agent", "type": "object", "schemaVersion": 1, "properties": { "resume": { "title": "Resume", "type": "string", "description": "The resume text to analyze for job matching", "editor": "textarea", "prefill": "Enter your resume text here..." }, "location": { "title": "Location", "type": "string", "description": "Preferred job location (city, state, or 'Remote')", "editor": "textfield", "default": "Remote" }, "jobType": { "title": "Job Type", "type": "string", "description": "Type of employment desired", "enum": [ "full-time", "part-time", "contract", "internship", "remote" ], "default": "full-time" }, "keywords": { "title": "Keywords", "type": "string", "description": "Additional search keywords (comma-separated)", "editor": "textfield", "example": "python, machine learning, data science" }, "modelName": { "title": "AI Model", "type": "string", "description": "The OpenAI model to use for analysis", "enum": [ "gpt-4o-mini", "gpt-4-turbo", "gpt-3.5-turbo" ], "default": "gpt-4o-mini" } }, "required": ["resume"]}{ "actor-start-gb": { "eventTitle": "Actor start per 1 GB", "eventDescription": "Flat fee for starting an Actor run for each 1 GB of memory.", "eventPriceUsd": 0.005 }, "openai-100-tokens-gpt-4o": { "eventTitle": "Price per 100 OpenAI tokens for gpt-4o", "eventDescription": "Flat fee for each 100 gpt-4o tokens used.", "eventPriceUsd": 0.001 }, "openai-100-tokens-gpt-4o-mini": { "eventTitle": "Price per 100 OpenAI tokens for gpt-4o-mini", "eventDescription": "Flat fee for each 100 gpt-4o-mini tokens used.", "eventPriceUsd": 0.00006 }, "openai-100-tokens-gpt-o1": { "eventTitle": "Price per 100 OpenAI tokens for o1", "eventDescription": "Flat fee for each 100 o1tokens used.", "eventPriceUsd": 0.006 }, "openai-100-tokens-gpt-o3-mini": { "eventTitle": "Price per 100 OpenAI tokens for o3-mini", "eventDescription": "Flat fee for each 100 o3-mini tokens used.", "eventPriceUsd": 0.00044 }}1
1import asyncio2
3from .main import main4
5# Execute the Actor entry point.6asyncio.run(main())1"""This module defines the main entry point for the Apify Actor.2
3Feel free to modify this file to suit your specific needs.4
5To build Apify Actors, utilize the Apify SDK toolkit, read more at the official documentation:6https://docs.apify.com/sdk/python7"""8
9from __future__ import annotations10
11import logging12import os13from typing import Dict, Any, List14import json15import re16
17from apify import Actor18from langchain_openai import ChatOpenAI19from langchain.agents import AgentExecutor, create_react_agent as create_base_agent, initialize_agent20from langchain.prompts import PromptTemplate21from langchain.agents import AgentType22from pydantic import BaseModel23
24from src.models import AgentStructuredOutput25from src.ppe_utils import charge_for_actor_Run26from src.tools import tool_linkedin_search, tool_indeed_search, tool_dice_search, analyze_resume27from src.utils import log_state28
29os.environ["OPENAI_API_KEY"] = "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"30
31# fallback input is provided only for testing, you need to delete this line32fallback_input = {33 'query': 'This is fallback test query, do not nothing and ignore it.',34 'modelName': 'gpt-4o-mini',35}36
37def setup_react_agent(llm: ChatOpenAI, tools: list, response_format: Any) -> AgentExecutor:38 """Create a ReAct agent with the given LLM and tools."""39 40 prompt = PromptTemplate.from_template("""Answer the following questions as best you can. You have access to the following tools:41
42{tools}43
44Use the following format:45
46Question: the input question you must answer47Thought: you should always think about what to do48Action: the action to take, should be one of {tool_names}49Action Input: the input to the action50Observation: the result of the action51... (this Thought/Action/Action Input/Observation can repeat N times)52Thought: I now know the final answer53Final Answer: the final answer to the original input question54
55Begin! Remember to ALWAYS follow the format above - start with Thought, then Action, then Action Input.56
57Question: {input}58
59{agent_scratchpad}""")60 61 # Create the agent using LangChain's create_react_agent62 agent = create_base_agent(llm, tools, prompt)63 64 return AgentExecutor(65 agent=agent,66 tools=tools,67 verbose=True,68 handle_parsing_errors=True,69 max_iterations=6 # Limit the number of iterations to prevent infinite loops70 )71
72def format_job_results(jobs: List[Dict[str, Any]]) -> str:73 """Format job results into a readable report"""74 if not jobs:75 return "No jobs found matching your criteria."76 77 report = "# Available Job Opportunities\n\n"78 79 for i, job in enumerate(jobs, 1):80 report += f"## {i}. {job['title']}\n"81 report += f"**Company:** {job['company']}\n"82 report += f"**Location:** {job['location']}\n"83 report += f"**Type:** {job['employment_type']}\n"84 report += f"**Salary:** {job['salary']}\n"85 report += f"**Posted:** {job['posting_date']}\n"86 report += f"**Description:** {job['description']}\n"87 report += f"**Apply here:** {job['url']}\n\n"88 report += "---\n\n"89 90 return report91
92# Update the agent's system message to enforce strict JSON output93system_message = """You are a job search assistant. When searching for jobs, you MUST ONLY return a JSON response wrapped in code block markers, with NO OTHER TEXT before or after. Format exactly like this:94
95```json96{97 "summary": {98 "total_jobs_found": <number>,99 "skills_matched": ["skill1", "skill2", ...],100 "experience_years": <number>,101 "previous_position": "position title"102 },103 "jobs": [104 {105 "title": "Job Title",106 "company": "Company Name",107 "location": "Location",108 "posting_date": "YYYY-MM-DD",109 "employment_type": "Full-time/Contract/etc",110 "salary": "Salary Range",111 "description": "Brief job description",112 "url": "Application URL",113 "is_remote": true/false,114 "skills_match": ["matched_skill1", "matched_skill2", ...],115 "match_percentage": 85116 }117 ]118}119```120
121CRITICAL RULES:1221. Return ONLY the JSON code block above - no other text1232. Always start with ```json and end with ```1243. Ensure the JSON is valid and properly formatted1254. Do not include any explanations or thoughts in the output1265. Fill in all fields, using "Not specified" for missing values127"""128
129async def charge_for_actor_start() -> None:130 # Implement charging logic here131 pass132
133async def main() -> None:134 """Main entry point for the Apify Actor."""135 async with Actor:136 # Charge for the start event137 await charge_for_actor_Run()138 # Get input139 actor_input = await Actor.get_input() or fallback_input140 resume = actor_input.get('resume', '')141 location = actor_input.get('location', 'Remote')142 job_type = actor_input.get('jobType', 'full-time')143 keywords = actor_input.get('keywords', '')144 model_name=actor_input.get('model_name', '')145
146 # Initialize the LLM147 llm = ChatOpenAI(148 model_name="gpt-3.5-turbo",149 temperature=0.7,150 max_tokens=2000151 )152
153 # Create the tools list154 tools = [tool_linkedin_search, tool_indeed_search, tool_dice_search, analyze_resume]155
156 # Get tool names for the prompt157 tool_names = [tool.name for tool in tools]158
159 # Create the agent160 agent = setup_react_agent(llm, tools, None)161
162 # Process the query163 result = await agent.ainvoke(164 {165 "input": f"""Find relevant job opportunities based on this resume and preferences:166Resume:167{resume}168
169Job Preferences:170- Location: {location}171- Job Type: {job_type}172- Keywords: {keywords}173
174Analyze the resume and search for matching jobs. Return a JSON response with:1751. A brief summary of the search results1762. An array of relevant jobs found (limit to top 5 most relevant)1773. Recommended next steps for the job seeker178
179Format the response as a JSON object with these exact fields:180{{181 "summary": "Brief overview of search results",182 "jobs": [183 {{184 "title": "Job title",185 "company": "Company name",186 "location": "Job location",187 "salary": "Salary if available",188 "match_score": "Relevance score 0-1",189 "url": "Job posting URL"190 }}191 ],192 "recommendations": ["List of recommended next steps"]193}}""",194 "tools": tools,195 "tool_names": tool_names196 }197 )198
199 # Process and push final results only once200 try:201 if isinstance(result, dict) and 'output' in result:202 output = result['output']203 204 # Try to extract JSON from various formats205 json_data = None206 207 # Try direct JSON parsing first208 if isinstance(output, str):209 try:210 json_data = json.loads(output)211 except json.JSONDecodeError:212 # Try extracting from markdown block213 json_match = re.search(r'```(?:json)?\s*({\s*".*?})\s*```', output, re.DOTALL)214 if json_match:215 try:216 json_data = json.loads(json_match.group(1).strip())217 except json.JSONDecodeError:218 pass219
220 if json_data:221 # Validate and clean the data222 cleaned_data = {223 "summary": json_data.get("summary", "No summary provided"),224 "jobs": json_data.get("jobs", [])[:5], # Limit to top 5 jobs225 "recommendations": json_data.get("recommendations", [])226 }227 await Actor.push_data(cleaned_data)228 else:229 await Actor.push_data({230 "error": "Could not parse JSON output",231 "raw_output": output232 })233 else:234 await Actor.push_data({235 "error": "Unexpected output format",236 "raw_output": str(result)237 })238 239 except Exception as e:240 Actor.log.error(f"Failed to process results: {str(e)}")241 await Actor.push_data({242 "error": f"Failed to process results: {str(e)}",243 "raw_output": str(result)244 })245
246if __name__ == "__main__":247 Actor.main(main)1"""This module defines Pydantic models for this project.2
3These models are used mainly for the structured tool and LLM outputs.4Resources:5- https://docs.pydantic.dev/latest/concepts/models/6"""7
8from __future__ import annotations9
10from pydantic import BaseModel, Field11from typing import List, Optional, Dict12
13
14class JobPreferences(BaseModel):15 location: str = Field(..., description="Preferred job location")16 job_types: List[str] = Field(default=["full-time"], description="Types of employment")17 salary_range: Optional[Dict[str, float]] = Field(None, description="Desired salary range")18 remote_preference: str = Field(default="hybrid", description="Remote work preference: 'remote', 'hybrid', 'onsite'")19 industries: Optional[List[str]] = Field(None, description="Preferred industries")20 experience_level: str = Field(default="mid-level", description="Experience level: 'entry', 'mid-level', 'senior'")21
22
23class JobMatch(BaseModel):24 title: str = Field(..., description="Job title")25 company: str = Field(..., description="Company name")26 location: str = Field(..., description="Job location")27 url: str = Field(..., description="Job posting URL")28 match_score: float = Field(..., description="Match score between 0 and 1")29 salary_range: Optional[str] = Field(None, description="Salary range if available")30 key_requirements: List[str] = Field(default_factory=list, description="Key job requirements")31 skill_matches: List[str] = Field(default_factory=list, description="Matching skills from resume")32 missing_skills: List[str] = Field(default_factory=list, description="Required skills not found in resume")33 job_description: str = Field(..., description="Brief job description")34 posting_date: Optional[str] = Field(None, description="When the job was posted")35
36
37class AgentStructuredOutput(BaseModel):38 """Structured output for the ReAct agent."""39 matches: List[JobMatch] = Field(..., description="List of matching jobs")40 summary: str = Field(..., description="Summary of job search results")41 recommended_actions: List[str] = Field(..., description="Recommended next steps")42 total_matches: int = Field(..., description="Total number of matches found")43 average_match_score: float = Field(..., description="Average match score across all jobs")1from decimal import ROUND_CEILING, Decimal2
3from apify import Actor4from langchain_core.messages import AIMessage, BaseMessage5
6
7
8
9async def charge_for_actor_Run() -> None:10 """Charges for the Actor start event.11
12 This function calculates the memory usage in gigabytes and charges for the Actor start event accordingly.13 """14 await Actor.charge(event_name='actor-run')1"""This module defines the tools used by the agent.2
3Feel free to modify or add new tools to suit your specific needs.4
5To learn how to create a new tool, see:6- https://python.langchain.com/docs/concepts/tools/7- https://python.langchain.com/docs/how_to/#tools8
9Tools for job searching and resume analysis using various job board scrapers.10"""11
12from __future__ import annotations13
14from typing import List, Dict, Any, Optional, TypedDict15from pydantic import BaseModel, Field16
17from apify import Actor18from langchain_core.tools import Tool19from langchain_openai import ChatOpenAI20from langchain.prompts import ChatPromptTemplate21from langchain_core.output_parsers import JsonOutputParser22
23from src.models import JobMatch, JobPreferences24
25
26class JobSearchInput(BaseModel):27 """Input schema for job search tools."""28 query: str = Field(..., description="Job title or keywords")29 location: str = Field(default="Remote", description="Job location")30
31
32class JobSearchResult(TypedDict):33 """Standardized job search result format."""34 title: str35 company: str36 location: str37 posting_date: str38 employment_type: str39 salary: str40 description: str41 url: str42 is_remote: bool43
44
45class ResumeInput(BaseModel):46 resume_text: str47
48
49async def base_job_search(50 query: str,51 actor_id: str,52 location: str = "Remote"53) -> List[JobSearchResult]:54 """Base function for job searching across different platforms."""55 try:56 run_input = {57 "query": query.split(',')[0].strip(),58 "location": location if ',' not in query else query.split(',')[1].strip(),59 "limit": 1060 }61 62 run = await Actor.apify_client.actor(actor_id).call(run_input=run_input)63 if not run:64 return []65 66 dataset_items = (await Actor.apify_client.dataset(run["defaultDatasetId"]).list_items()).items67 return format_job_results(dataset_items)68 except Exception as e:69 Actor.log.error(f"Job search failed for {actor_id}: {str(e)}")70 return []71
72
73def format_job_results(items: List[Dict[str, Any]]) -> List[JobSearchResult]:74 """Format raw job listings into standardized format."""75 formatted_jobs = []76 for job in items:77 try:78 formatted_job = JobSearchResult(79 title=job.get('title', '').strip(),80 company=job.get('companyName', '').strip(),81 location=job.get('jobLocation', {}).get('displayName', '').strip(),82 posting_date=job.get('postedDate', ''),83 employment_type=job.get('employmentType', ''),84 salary=job.get('salary', 'Not specified'),85 description=job.get('summary', '')[:300] + '...' if job.get('summary') else '', # Limit description length86 url=job.get('detailsPageUrl', ''),87 is_remote=job.get('isRemote', False)88 )89 formatted_jobs.append(formatted_job)90 except Exception as e:91 Actor.log.error(f"Failed to format job listing: {str(e)}")92 continue93 94 return formatted_jobs[:5] # Limit to top 5 results95
96
97async def _linkedin_search(query: str) -> List[JobSearchResult]:98 """Search for jobs on LinkedIn."""99 return await base_job_search(query, "bebity/linkedin-jobs-scraper")100
101
102# Create LinkedIn search tool103tool_linkedin_search = Tool(104 name="search_linkedin_jobs",105 description="Search for jobs on LinkedIn. Input format: 'job title, location'",106 func=_linkedin_search,107 coroutine=_linkedin_search108)109
110
111async def _indeed_search(query: str) -> List[JobSearchResult]:112 """Search for jobs on Indeed."""113 return await base_job_search(query, "curious_coder/indeed-scraper")114
115
116# Create Indeed search tool117tool_indeed_search = Tool(118 name="search_indeed_jobs",119 description="Search for jobs on Indeed. Input format: 'job title, location'",120 func=_indeed_search,121 coroutine=_indeed_search122)123
124
125async def _dice_search(query: str) -> List[JobSearchResult]:126 """Search for jobs on Dice."""127 return await base_job_search(query, "mohamedgb00714/dicecom-job-scraper")128
129
130# Create Dice search tool131tool_dice_search = Tool(132 name="search_dice_jobs",133 description="Search for jobs on Dice. Input format: 'job title, location'",134 func=_dice_search,135 coroutine=_dice_search136)137
138
139async def _analyze_resume(resume_text: str) -> Dict[str, Any]:140 """Analyze a resume to extract key information."""141 if not resume_text.strip():142 return {143 "error": "Empty resume text provided",144 "skills": [], "experience": [], "education": [],145 "summary": "No resume to analyze", "years_experience": 0146 }147
148 try:149 llm = ChatOpenAI(temperature=0)150 output_parser = JsonOutputParser()151 152 prompt = ChatPromptTemplate.from_template(153 """Analyze this resume and extract key information. Return ONLY a JSON object:154 155 Resume: {resume_text}156 157 Format: {format_instructions}158 """159 )160 161 chain = prompt | llm | output_parser162 163 analysis = await chain.ainvoke({164 "resume_text": resume_text,165 "format_instructions": output_parser.get_format_instructions()166 })167 168 return {**analysis, "raw_text": resume_text}169 170 except Exception as e:171 Actor.log.error(f"Resume analysis failed: {str(e)}")172 return {173 "error": str(e),174 "skills": [], "experience": [], "education": [],175 "summary": "Analysis failed", "years_experience": 0,176 "raw_text": resume_text177 }178
179
180# Create analyze_resume tool181analyze_resume = Tool(182 name="analyze_resume",183 description="Analyze a resume to extract skills, experience, and other key information.",184 func=_analyze_resume,185 coroutine=_analyze_resume186)1from apify import Actor2from langchain_core.messages import ToolMessage3
4
5def log_state(state: dict) -> None:6 """Logs the state of the graph.7
8 Uses the `Actor.log.debug` method to log the state of the graph.9
10 Args:11 state (dict): The state of the graph.12 """13 message = state['messages'][-1]14 # Traverse all tool messages and print them15 # if multiple tools are called in parallel16 if isinstance(message, ToolMessage):17 # Until the analyst message with tool_calls18 for _message in state['messages'][::-1]:19 if hasattr(_message, 'tool_calls'):20 break21 Actor.log.debug('-------- Tool Result --------')22 Actor.log.debug('Tool: %s', _message.name)23 Actor.log.debug('Result: %s', _message.content)24
25 Actor.log.debug('-------- Message --------')26 Actor.log.debug('Message: %s', message)27
28 # Print all tool calls29 if hasattr(message, 'tool_calls'):30 for tool_call in getattr(message, 'tool_calls', []):31 Actor.log.debug('-------- Tool Call --------')32 Actor.log.debug('Tool: %s', tool_call['name'])33 Actor.log.debug('Args: %s', tool_call['args']).git.mise.toml.nvim.luastorage
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# Cython debug symbolscython_debug/
# PyCharm# JetBrains specific template is maintained in a separate JetBrains.gitignore that can# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore# and can be added to the global gitignore or merged into this file. For a more nuclear# option (not recommended) you can uncomment the following to ignore the entire idea folder..idea/
# Added by Apify CLInode_modules{ "query": "please find me a job related to my resume", "resume": "I am a software engineer with 5 years of experience in Python and web development. Skills include: Python, JavaScript, React, Node.js, AWS, Docker, and CI/CD. Previously worked at Tech Corp as Senior Developer leading a team of 5 engineers...", "preferences": { "location": "new york", "job_types": ["full-time"], "remote_preference": "remote", "industries": ["technology", "software"], "experience_level": "mid-level", "salary_range": { "min": 100000, "max": 150000 } }, "modelName": "gpt-4o-mini"}{ "query": "find me a data science position in San Francisco", "resume": "I am a data scientist with 3 years of experience specializing in machine learning and AI. Proficient in Python, TensorFlow, PyTorch, and scikit-learn. Experience with big data technologies like Spark and Hadoop. Previously worked at AI Solutions Inc as ML Engineer developing predictive models...", "preferences": { "location": "san francisco", "job_types": ["full-time"], "remote_preference": "hybrid", "industries": ["technology", "artificial intelligence"], "experience_level": "mid-level", "salary_range": { "min": 120000, "max": 180000 } }, "modelName": "gpt-4o-mini"}1apify<3.0.02langchain-openai==0.3.63langgraph==0.2.734aiohttp>=3.8.05beautifulsoup4>=4.12.06langchain>=0.1.07pydantic>=2.0.08langchain-core>=0.1.09langchain-openai>=0.0.5