The LLM Real Estate Agent project leverages large language models and vector databases to generate personalized real estate narratives based on buyer preferences. This application aims to enhance the home-buying experience by providing tailored property descriptions that align with individual buyer needs.
- Generate real estate listings based on buyer preferences.
- Utilize large language models to create engaging narratives.
- Store and retrieve property embeddings using a vector database.
- Augment property descriptions based on specific buyer criteria.
- Clone the repository:
git clone https://github.com/yourusername/llm-real-estate-agent.git - Navigate to the project directory:
cd llm-real-estate-agent
Install pyenv and pyenv-virtualenv:
brew update
brew install pyenv
brew install pyenv-virtualenv
Add the following lines to your shell profile (e.g., ~/.bash_profile, ~/.zshrc, ~/.profile, or ~/.bashrc):
export PATH="$HOME/.pyenv/bin:$PATH"
eval "$(pyenv init --path)"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"
Restart your shell:
source ~/.zshrc # For zsh
Create and Activate Virtual Environment:
pyenv install 3.12.0 # Install the desired Python version if not already installed
pyenv virtualenv 3.12.0 myproject-env
pyenv activate myproject-env
-
Install the required dependencies:
pip3 install -r requirements.txt -
Set environment variables:
export OPENAI_API_KEY="your_api_key_here"
To run the application, execute the following command:
streamlit run src/HomeMatch.py
Follow the prompts to input buyer preferences and generate personalized real estate narratives.
The real estate listings can be found in the data/Listings.txt file.
The following is a demo video of the application:
Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.
This project is licensed under the MIT License. See the LICENSE file for more details.