Personalized Real Estate Agent (HomeMatch) Notebook

Overview

This notebook demonstrates the HomeMatch application, which matches users with suitable homes based on their preferences. It covers synthetic data generation, semantic search, and augmented response generation.

Functionality

Synthetic Data Generation

• Generating Real Estate Listings with an LLM: Uses a Large Language Model (LLM) to generate at least 10 diverse and realistic real estate listings.

Semantic Search

• Creating a Vector Database and Storing Listings: Shows the creation of a vector database and storing real estate listing embeddings.
• Semantic Search of Listings Based on Buyer Preferences: Searches listings based on buyer preferences, returning matches that closely align with input preferences.

Augmented Response Generation

• Logic for Searching and Augmenting Listing Descriptions: Uses buyer preferences to search and personalize real estate listing descriptions.
• Use of LLM for Generating Personalized Descriptions: Generates unique, appealing descriptions tailored to buyer preferences.

Running the Code

1. Create and activate the conda environment:

   conda create --name agent python=3.10.11
   conda activate agent

2. Install the required packages:

   pip install -r requirements.txt

3. Launch the notebook:

   jupyter notebook HomeMatch.ipynb

API Key Replacement

Replace the placeholder API key with your actual key in the notebook:

API_KEY = 'your_actual_api_key_here'