Real Estate Agent

Overview

The Real Estate Agent example demonstrates how to use the Memories-Dev framework to create an AI agent that analyzes real estate properties using comprehensive earth memory data. This agent provides deep insights into property characteristics, environmental factors, and future risks.

Key Features

  • Property Analysis: Comprehensive analysis of property characteristics and value

  • Environmental Assessment: Detailed evaluation of environmental conditions and risks

  • Historical Trends: Analysis of historical changes in the property area

  • Future Predictions: AI-powered predictions of future trends and risks

  • Recommendation Engine: Personalized property recommendations based on preferences

System Architecture

+---------------------+      +----------------------+     +--------------------+
|                     |      |                      |     |                    |
| Property Data       |----->| Earth Memory System  |---->| Analysis Engine    |
| (Location, Details) |      | (Processing & Storage)|    | (AI-powered)       |
|                     |      |                      |     |                    |
+---------------------+      +----------------------+     +--------------------+
                                      |
                                      v
                             +--------------------+
                             |                    |
                             | Recommendation     |
                             | Engine             |
                             |                    |
                             +--------------------+

Implementation

The Real Estate Agent is implemented as a Python class that integrates with the Memories-Dev framework:

from memories import MemoryStore, Config
from memories.utils.earth_memory import (
    OvertureClient,
    SentinelClient,
    TerrainAnalyzer,
    ClimateDataFetcher,
    EnvironmentalImpactAnalyzer
)

class RealEstateAgent:
    def __init__(
        self,
        memory_store: MemoryStore,
        embedding_model: str = "all-MiniLM-L6-v2",
        embedding_dimension: int = 384,
        similarity_threshold: float = 0.75,
        analysis_radius_meters: int = 2000,
        temporal_analysis_years: int = 10
    ):
        # Initialization code...

    async def add_property(self, property_data: Dict[str, Any]) -> Dict[str, Any]:
        # Add property to the memory store
        # Fetch and analyze earth data
        # Return property ID and basic analysis

    async def analyze_property_environment(self, property_id: str) -> Dict[str, Any]:
        # Retrieve property from memory store
        # Perform comprehensive environmental analysis
        # Return detailed analysis results

    async def get_property_recommendations(self, preferences: Dict[str, Any]) -> List[Dict[str, Any]]:
        # Find properties matching user preferences
        # Rank properties based on analysis results
        # Return recommended properties

Usage Example

Here’s how to use the Real Estate Agent in your application:

from examples.real_estate_agent import RealEstateAgent
from memories import MemoryStore, Config
import asyncio

async def main():
    # Initialize memory store
    config = Config(
        storage_path="./real_estate_data",
        hot_memory_size=50,
        warm_memory_size=200,
        cold_memory_size=1000
    )
    memory_store = MemoryStore(config)

    # Initialize agent
    agent = RealEstateAgent(memory_store, enable_earth_memory=True)

    # Add a property
    property_data = {
        "location": "San Francisco, CA",
        "coordinates": {"lat": 37.7749, "lon": -122.4194},
        "price": 1250000,
        "bedrooms": 2,
        "bathrooms": 2,
        "square_feet": 1200,
        "property_type": "Condo",
        "year_built": 2015
    }

    # Add property and analyze
    result = await agent.add_property(property_data)
    analysis = await agent.analyze_property_environment(result["property_id"])

    print(f"Property added: {result['property_id']}")
    print(f"Environmental analysis: {analysis}")

    # Get property recommendations
    preferences = {
        "location": "San Francisco Bay Area",
        "price_range": (1000000, 1500000),
        "bedrooms": 2,
        "property_type": "Condo",
        "priorities": ["low_climate_risk", "good_air_quality", "walkability"]
    }

    recommendations = await agent.get_property_recommendations(preferences)
    print(f"Recommended properties: {recommendations}")

if __name__ == "__main__":
    asyncio.run(main())

Advanced Features

Earth Memory Integration

The Real Estate Agent leverages multiple earth memory components:

  1. Terrain Analysis: Evaluates elevation, slope, and landforms

  2. Climate Data: Analyzes temperature, precipitation, and extreme weather risks

  3. Environmental Impact: Assesses air quality, noise levels, and pollution risks

  4. Land Use Classification: Identifies surrounding land use patterns

  5. Water Resource Analysis: Evaluates water availability and flood risks

  6. Geological Data: Analyzes soil composition and geological hazards

  7. Urban Development: Tracks urban growth patterns and development trends

  8. Biodiversity Analysis: Assesses local ecosystem health and biodiversity

  9. Solar Potential: Calculates solar energy potential for the property

  10. Walkability Analysis: Evaluates pedestrian-friendliness of the area

Future Enhancements

Planned enhancements for future versions:

  1. Real-time Market Integration: Connect to real estate market APIs for live data

  2. 3D Visualization: Generate 3D models of properties and surroundings

  3. AR/VR Support: Enable virtual property tours with environmental overlays

  4. Smart Home Integration: Connect with IoT devices for real-time property monitoring

  5. Blockchain Integration: Enable secure property transactions and verification