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AI-powered assistant for a real estate agency that assists potential buyers and renters in finding their ideal property

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AleksNeStu/ai-real-estate-assistant

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Requirements

Develop an AI-powered assistant for a real estate agency that assists potential buyers and renters in finding their ideal property.

The assistant should engage users in a conversation, asking questions about their preferences such as:

  • location (city, neighborhood)
  • budget range
  • property type (apartment, house, condo)
  • number of bedrooms and bathrooms
  • desired amenities (parking, garden, pool)
  • proximity to schools or public transportation.

Description

POC app will have simple UI and will use local csv with possibility to specify list of external CSV files.

Demos

V1: Used langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent run_v1.sh app_v2.py screen.png

V2: Used different LLM models and RAG with ConversationalRetrievalChain #TODO:

Formatted dataset will contain fake extra fields based on the task requirements, for demo purposes.

DataFrame Columns

This table describes the columns in the DataFrame:

Column Name Description
id Unique identifier for each record.
city Name of the city where the property is located.
type Type of property (e.g., apartment, house).
square_meters Area of the property in square meters.
rooms Number of rooms in the property.
floor Floor number where the property is located.
floor_count Total number of floors in the building.
build_year Year the building was constructed.
latitude Latitude coordinate of the property.
longitude Longitude coordinate of the property.
centre_distance Distance from the property to the city center.
poi_count Number of Points of Interest nearby.
school_distance Distance to the nearest school.
clinic_distance Distance to the nearest clinic.
post_office_distance Distance to the nearest post office.
kindergarten_distance Distance to the nearest kindergarten.
restaurant_distance Distance to the nearest restaurant.
college_distance Distance to the nearest college.
pharmacy_distance Distance to the nearest pharmacy.
ownership Type of ownership (e.g., condominium).
building_material Material used in the construction of the building.
condition Condition of the property (e.g., new, good).
has_parking_space Whether the property has a parking space (True/False).
has_balcony Whether the property has a balcony (True/False).
has_elevator Whether the building has an elevator (True/False).
has_security Whether the property has security (True/False).
has_storage_room Whether the property has a storage room (True/False).
price Price of the property.
price_media Median price of similar properties.
price_delta Difference between the property's price and price_media.
negotiation_rate Possibility of negotiation (e.g., High, Medium, Low).
bathrooms Number of bathrooms in the property.
owner_name Name of the property owner.
owner_phone Contact phone number of the property owner.
has_garden Whether the property has a garden (True/False).
has_pool Whether the property has a pool (True/False).
has_garage Whether the property has a garage (True/False).
has_bike_room Whether the property has a bike room (True/False).

Init project for development

# Install pip and poetry
python -m ensurepip --upgrade
curl -sSL https://install.python-poetry.org | python3 - --version 1.7.0
# Init poetry virtual env
poetry init
poetry env use 3.11
poetry config virtualenvs.in-project true
source .venv/bin/activate
poetry config virtualenvs.prompt 'ai-real-estate-assistant'
poetry config --list
# Add deps
poetry add ...
poetry lock

Run project for development

git clone https://github.com/AleksNeStu/ai-real-estate-assistant.git
poetry install --no-root
source .venv/bin/activate

Run app

Local run

Deploy app

Streamlit Deploy

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AI-powered assistant for a real estate agency that assists potential buyers and renters in finding their ideal property

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