Kye Gomez
3 days ago
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# Realtor Agent Example
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This example demonstrates how to create an AI-powered rental property specialist using the Swarms framework and the Realtor API.
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## Quick Start
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1. Install dependencies:
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```bash
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pip install swarms
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```
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2. Get your Realtor API key:
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- Visit [Realtor Search API](https://rapidapi.com/ntd119/api/realtor-search/)
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- Sign up for RapidAPI
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- Subscribe to the API
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- Copy your API key
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3. Update the API key in `realtor_agent.py`:
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```python
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headers = {
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"x-rapidapi-key": "YOUR_API_KEY_HERE",
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"x-rapidapi-host": "realtor-search.p.rapidapi.com",
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}
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```
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4. Run the example:
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```python
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from realtor_agent import agent
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# Search single location
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response = agent.run(
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"What are the best properties in Menlo Park for rent under $3,000?"
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f"Data: {get_realtor_data_from_one_source('Menlo Park, CA')}"
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)
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print(response)
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```
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## Features
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- Property search across multiple locations
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- Detailed property analysis
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- Location assessment
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- Financial analysis
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- Tenant matching recommendations
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For full documentation, see [docs/examples/realtor_agent.md](../docs/examples/realtor_agent.md).
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"""
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Mortgage and Tax Panel Discussion Example
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This example demonstrates a panel of mortgage and tax specialists discussing complex
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financial situations using InteractiveGroupChat with different speaker functions.
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The panel includes specialists from different financial fields who can collaborate
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on complex mortgage and tax planning cases.
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"""
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from swarms import Agent
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from swarms.structs.interactive_groupchat import (
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InteractiveGroupChat,
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)
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def create_mortgage_tax_panel():
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"""Create a panel of mortgage and tax specialists for discussion."""
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# Tax Attorney - Specializes in tax law and complex tax situations
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tax_attorney = Agent(
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agent_name="tax_attorney",
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system_prompt="""You are Sarah Mitchell, J.D., a tax attorney with 15 years of experience.
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You specialize in complex tax law, real estate taxation, and tax planning strategies.
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You have expertise in:
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- Federal and state tax regulations
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- Real estate tax law and property taxation
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- Tax implications of mortgage transactions
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- Tax planning for real estate investments
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- IRS dispute resolution and tax litigation
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- Estate tax planning and trusts
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When discussing cases, provide legally sound tax advice, consider recent tax law changes,
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and collaborate with other specialists to ensure comprehensive financial planning.""",
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model_name="claude-3-5-sonnet-20240620",
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streaming_on=True,
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print_on=True,
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)
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# Mortgage Broker - Lending and mortgage specialist
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mortgage_broker = Agent(
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agent_name="mortgage_broker",
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system_prompt="""You are Michael Chen, a senior mortgage broker with 12 years of experience.
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You specialize in residential and commercial mortgage lending.
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You have expertise in:
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- Conventional, FHA, VA, and jumbo loans
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- Commercial mortgage financing
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- Mortgage refinancing strategies
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- Interest rate analysis and trends
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- Loan qualification requirements
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- Mortgage insurance considerations
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When discussing cases, analyze lending options, consider credit profiles,
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and evaluate debt-to-income ratios for optimal mortgage solutions.""",
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model_name="claude-3-5-sonnet-20240620",
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streaming_on=True,
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print_on=True,
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)
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# Real Estate CPA - Accounting specialist
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real_estate_cpa = Agent(
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agent_name="real_estate_cpa",
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system_prompt="""You are Emily Rodriguez, CPA, a certified public accountant with 10 years of experience.
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You specialize in real estate accounting and tax preparation.
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You have expertise in:
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- Real estate tax accounting
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- Property depreciation strategies
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- Mortgage interest deductions
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- Real estate investment taxation
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- Financial statement analysis
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- Tax credit optimization
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When discussing cases, focus on accounting implications, tax efficiency,
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and financial reporting requirements for real estate transactions.""",
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model_name="claude-3-5-sonnet-20240620",
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streaming_on=True,
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print_on=True,
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)
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# Financial Advisor - Investment and planning specialist
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financial_advisor = Agent(
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agent_name="financial_advisor",
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system_prompt="""You are James Thompson, CFP®, a financial advisor with 8 years of experience.
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You specialize in comprehensive financial planning and wealth management.
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You have expertise in:
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- Investment portfolio management
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- Retirement planning
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- Real estate investment strategy
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- Cash flow analysis
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- Risk management
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- Estate planning coordination
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When discussing cases, consider overall financial goals, investment strategy,
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and how mortgage decisions impact long-term financial planning.""",
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model_name="claude-3-5-sonnet-20240620",
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streaming_on=True,
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print_on=True,
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)
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# Real Estate Attorney - Property law specialist
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real_estate_attorney = Agent(
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agent_name="real_estate_attorney",
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system_prompt="""You are Lisa Park, J.D., a real estate attorney with 11 years of experience.
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You specialize in real estate law and property transactions.
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You have expertise in:
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- Real estate contract law
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- Property title analysis
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- Mortgage document review
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- Real estate closing procedures
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- Property rights and zoning
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- Real estate litigation
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When discussing cases, evaluate legal implications, ensure compliance,
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and address potential legal issues in real estate transactions.""",
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model_name="claude-3-5-sonnet-20240620",
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streaming_on=True,
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print_on=True,
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)
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return [
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tax_attorney,
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mortgage_broker,
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real_estate_cpa,
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financial_advisor,
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real_estate_attorney,
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]
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def example_mortgage_tax_panel():
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"""Example with random dynamic speaking order."""
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print("=== MORTGAGE AND TAX SPECIALIST PANEL ===\n")
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agents = create_mortgage_tax_panel()
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group_chat = InteractiveGroupChat(
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name="Mortgage and Tax Panel Discussion",
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description="A collaborative panel of mortgage and tax specialists discussing complex cases",
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agents=agents,
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interactive=False,
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speaker_function="random-speaker",
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)
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# Case 1: Complex mortgage refinancing with tax implications
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case1 = """CASE PRESENTATION:
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@tax_attorney, @real_estate_cpa, and @real_estate_attorney, please discuss the possible legal and accounting strategies
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for minimizing or potentially eliminating property taxes in Los Altos, California. Consider legal exemptions,
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special assessments, and any relevant California property tax laws that could help achieve this goal.
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"""
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group_chat.run(case1)
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if __name__ == "__main__":
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example_mortgage_tax_panel()
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@ -0,0 +1,154 @@
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from typing import List
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import http.client
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import json
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from swarms import Agent
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from dotenv import load_dotenv
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load_dotenv()
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import os
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def get_realtor_data_from_one_source(location: str):
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"""
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Fetch rental property data from the Realtor API for a specified location.
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Args:
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location (str): The location to search for rental properties (e.g., "Menlo Park, CA")
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Returns:
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str: JSON-formatted string containing rental property data
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Raises:
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http.client.HTTPException: If the API request fails
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json.JSONDecodeError: If the response cannot be parsed as JSON
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"""
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conn = http.client.HTTPSConnection(
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"realtor-search.p.rapidapi.com"
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)
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headers = {
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"x-rapidapi-key": os.getenv("RAPIDAPI_KEY"),
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"x-rapidapi-host": "realtor-search.p.rapidapi.com",
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}
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# URL encode the location parameter
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encoded_location = location.replace(" ", "%20").replace(
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",", "%2C"
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)
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endpoint = f"/properties/search-rent?location=city%3A{encoded_location}&sortBy=best_match"
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conn.request(
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"GET",
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endpoint,
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headers=headers,
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)
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res = conn.getresponse()
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data = res.read()
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return "chicken data"
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# # Parse and format the response
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# try:
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# json_data = json.loads(data.decode("utf-8"))
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# # Return formatted string instead of raw JSON
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# return json.dumps(json_data, indent=2)
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# except json.JSONDecodeError:
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# return "Error: Could not parse API response"
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def get_realtor_data_from_multiple_sources(
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locations: List[str],
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) -> List[str]:
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"""
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Fetch rental property data from multiple sources for a specified location.
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Args:
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location (List[str]): List of locations to search for rental properties (e.g., ["Menlo Park, CA", "Palo Alto, CA"])
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"""
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output = []
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for location in locations:
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data = get_realtor_data_from_one_source(location)
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output.append(data)
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return output
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agent = Agent(
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agent_name="Rental-Property-Specialist",
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system_prompt="""
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You are an expert rental property specialist with deep expertise in real estate analysis and tenant matching. Your core responsibilities include:
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1. Property Analysis & Evaluation
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- Analyze rental property features and amenities
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- Evaluate location benefits and drawbacks
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- Assess property condition and maintenance needs
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- Compare rental rates with market standards
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- Review lease terms and conditions
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- Identify potential red flags or issues
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2. Location Assessment
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- Analyze neighborhood safety and demographics
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- Evaluate proximity to amenities (schools, shopping, transit)
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- Research local market trends and development plans
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- Consider noise levels and traffic patterns
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- Assess parking availability and restrictions
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- Review zoning regulations and restrictions
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3. Financial Analysis
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- Calculate price-to-rent ratios
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- Analyze utility costs and included services
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- Evaluate security deposit requirements
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- Consider additional fees (pet rent, parking, etc.)
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- Compare with similar properties in the area
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- Assess potential for rent increases
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4. Tenant Matching
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- Match properties to tenant requirements
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- Consider commute distances
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- Evaluate pet policies and restrictions
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- Assess lease term flexibility
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- Review application requirements
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- Consider special accommodations needed
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5. Documentation & Compliance
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- Review lease agreement terms
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- Verify property certifications
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- Check compliance with local regulations
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- Assess insurance requirements
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- Review maintenance responsibilities
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- Document property condition
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When analyzing properties, always consider:
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- Value for money
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- Location quality
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- Property condition
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- Lease terms fairness
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- Safety and security
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- Maintenance and management quality
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- Future market potential
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- Tenant satisfaction factors
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When you receive property data:
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1. Parse and analyze the JSON data
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2. Format the output in a clear, readable way
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3. Focus on properties under $3,000
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4. Include key details like:
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- Property name/address
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- Price
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- Number of beds/baths
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- Square footage
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- Key amenities
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- Links to listings
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5. Sort properties by price (lowest to highest)
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Provide clear, objective analysis while maintaining professional standards and ethical considerations.""",
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model_name="claude-3-sonnet-20240229",
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max_loops=1,
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tools=[get_realtor_data_from_one_source],
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print_on=True,
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)
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agent.run(
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"What are the best properties in Menlo Park, CA for rent under 3,000$?"
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)
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@ -0,0 +1,66 @@
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import time
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from typing import Any, Callable, Type, Union, Tuple
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from loguru import logger
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def retry_function(
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func: Callable,
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*args: Any,
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max_retries: int = 3,
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delay: float = 1.0,
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backoff_factor: float = 2.0,
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exceptions: Union[
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Type[Exception], Tuple[Type[Exception], ...]
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] = Exception,
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**kwargs: Any,
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) -> Any:
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"""
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A function that retries another function if it raises specified exceptions.
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Args:
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func (Callable): The function to retry
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*args: Positional arguments to pass to the function
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max_retries (int): Maximum number of retries before giving up. Defaults to 3.
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delay (float): Initial delay between retries in seconds. Defaults to 1.0.
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backoff_factor (float): Multiplier applied to delay between retries. Defaults to 2.0.
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exceptions (Exception or tuple): Exception(s) that trigger a retry. Defaults to Exception.
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**kwargs: Keyword arguments to pass to the function
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Returns:
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Any: The return value of the function if successful
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Example:
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def fetch_data(url: str) -> dict:
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return requests.get(url).json()
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# Retry the fetch_data function
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result = retry_function(
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fetch_data,
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"https://api.example.com",
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max_retries=3,
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exceptions=(ConnectionError, TimeoutError)
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)
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"""
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retries = 0
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current_delay = delay
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while True:
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try:
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return func(*args, **kwargs)
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except exceptions as e:
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retries += 1
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if retries > max_retries:
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logger.error(
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f"Function {func.__name__} failed after {max_retries} retries. "
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f"Final error: {str(e)}"
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)
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raise
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logger.warning(
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f"Retry {retries}/{max_retries} for function {func.__name__} "
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f"after error: {str(e)}. "
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f"Waiting {current_delay} seconds..."
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)
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time.sleep(current_delay)
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current_delay *= backoff_factor
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Loading…
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