Address Collector

Collect Addresses for each given country

first agent will get country name as an input and use SerpAPI to find some real estate websites in the given country name and get 5 real addresses

second agent will classify 3 address patterns from the first agent's output. Also, it will use Faker or use OpenAI to generate 50 fake addresses on each address pattern.

Date: 20241121

Creating a multi-agent system using the LangChain framework involves integrating agents that communicate with each other to accomplish specific tasks. Here's a structured approach for implementing your two agents:

---

Agent 1: Fetch Real Estate Websites and Addresses

This agent uses SerpAPI to search for real estate websites in a given country and extract 5 real addresses.

Steps:

1. Input: Country name.

2. Task: Use SerpAPI to search for real estate websites and scrape addresses.

3. Output: A list of 5 real addresses.

Implementation:

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.tools.serpapi import SerpAPITool
from bs4 import BeautifulSoup
import requests

def fetch_addresses(country_name):
    # Using SerpAPI to search for real estate websites
    serp_tool = SerpAPITool.from_api_key("your_serpapi_key")
    search_query = f"real estate websites in {country_name}"
    serp_results = serp_tool.run(search_query)

    # Extract 5 real estate website links
    websites = []
    for result in serp_results.get("organic_results", [])[:5]:
        websites.append(result.get("link"))

    # Fetch and parse addresses from these websites
    addresses = []
    for site in websites:
        try:
            response = requests.get(site)
            soup = BeautifulSoup(response.text, "html.parser")
            # Assume addresses are in a specific tag for simplicity
            addresses.extend(soup.find_all("address")[:5])
        except Exception as e:
            continue

    return [addr.get_text() for addr in addresses[:5]]

# Create Agent 1
llm = OpenAI(temperature=0)
agent_1_tool = Tool(
    name="FetchRealEstateAddresses",
    func=fetch_addresses,
    description="Fetches real addresses from real estate websites."
)
agent_1 = initialize_agent([agent_1_tool], llm, agent="zero-shot-react-description")

---

Agent 2: Classify Address Patterns and Generate Fake Addresses

This agent classifies addresses into 3 patterns and generates 50 fake addresses for each pattern using Faker or OpenAI.

Steps:

1. Input: Addresses from Agent 1.

2. Task: Classify addresses into patterns and generate fake addresses.

3. Output: A dictionary with 3 address patterns and 50 fake addresses for each pattern.

Implementation:

from faker import Faker
import random

faker = Faker()

def classify_addresses_and_generate_fakes(addresses):
    # Classification logic (simple example)
    patterns = {
        "Pattern1": [],
        "Pattern2": [],
        "Pattern3": []
    }

    for addr in addresses:
        if "Street" in addr:
            patterns["Pattern1"].append(addr)
        elif "Avenue" in addr:
            patterns["Pattern2"].append(addr)
        else:
            patterns["Pattern3"].append(addr)

    # Generate fake addresses
    fake_addresses = {}
    for pattern, real_addresses in patterns.items():
        fake_addresses[pattern] = [faker.address() for _ in range(50)]

    return fake_addresses

# Create Agent 2
agent_2_tool = Tool(
    name="ClassifyAndGenerateFakeAddresses",
    func=classify_addresses_and_generate_fakes,
    description="Classifies real addresses into patterns and generates fake addresses."
)
agent_2 = initialize_agent([agent_2_tool], llm, agent="zero-shot-react-description")

---

Workflow

1. User provides a country name.

2. Agent 1 fetches 5 real addresses.

3. Output from Agent 1 is fed into Agent 2.

4. Agent 2 classifies addresses and generates fake ones.

Workflow Code:

# Step 1: Agent 1 gets real addresses
country_name = "USA"
real_addresses = agent_1.run(country_name)

# Step 2: Agent 2 classifies and generates fake addresses
fake_addresses = agent_2.run(real_addresses)

# Output the results
print("Real Addresses:", real_addresses)
print("Fake Addresses:", fake_addresses)

---

Improvements

• Use a better parsing strategy for fetching addresses from websites (e.g., scraping APIs or structured HTML).

• Enhance address classification with a more robust NLP model.

• Use OpenAI for address classification if Faker doesn’t meet specific requirements.

Would you like help setting up a live demo or additional refinements?