Clone the repository: Copygit clone https://github.com/OSOSerious/real-estate-optimizer.git cd real-estate-optimizer
Install dependencies: Copypip install -r requirements.txt
Set up environment variables: Create a .env file in the project root and add: CopyAPI_KEY=your_api_key_here LLAMA_MODEL_PATH=path/to/llama/model.bin
Run the optimizer: Copypython real_estate_optimizer.py
🐝 Swarm Architecture Our platform leverages a swarm-based architecture for efficient and parallel processing:
LLM Swarm: Utilizes LLaMA, GPT-J, and BERT for advanced language understanding and generation Data Analytics Swarm: Processes 400+ data points for comprehensive analysis Real Estate Analytics Swarm: Specialized analysis for property trends and forecasts Optimization Swarm: Implements advanced algorithms for data-driven decision making Data Fetch Swarm: Retrieves real-time data from various sources Visualization Swarm: Creates intuitive and informative data visualizations
📊 Data Insights
AI-powered trend analysis with historical data visualization Property value projections and market comparisons Affordability metrics for low-income families Investment potential assessment for multi-family units Natural language insights generated by multiple LLMs
📚 Documentation For more detailed information on using and contributing to this project, please see our documentation. 🤝 Contributing Contributions are welcome! Check out our Contributing Guide. 📜 License This project is licensed under the MIT License - see the LICENSE file for details. 🔗 Links
GitHub Repository: https://github.com/OSOSerious/real-estate-optimizer Swarms Website: https://swarms.world/ Zillow API: https://www.zillow.com/howto/api/APIOverview.htm Realtor API: https://www.realtor. python real_estate_ai_agent.py
This project is an advanced AI-powered system for analyzing and optimizing real estate investments, with a focus on large multi-family properties (400+ units) suitable for low-income housing. It utilizes multiple AI models, including Claude 3.5 and open-source alternatives, to provide comprehensive analysis and recommendations.
- Multi-model AI analysis using Claude 3.5, LlamaModel, and Dolly-v2-12b
- Specialized agents for market analysis, financial evaluation, property inspection, and community impact assessment
- Concurrent and sequential workflows for comprehensive property evaluation
- Long-term memory storage using ChromaDB
- Customizable for different real estate markets and investment criteria
- Clone the repository:
git clone https://github.com/OSOSerious/Real-Estate-Optimizer-swarm.git cd real-estate-ai-analyzer
Install required packages:
bash Copy code pip install -r requirements.txt Set up environment variables: Create a .env file in the project root and add your Anthropic API key:
plaintext Copy code ANTHROPIC_API_KEY=your_api_key_here Download necessary model weights:
For LlamaModel, download the weights and update the model_path in the code Dolly-v2-12b weights will be downloaded automatically on first run Usage Run the main script:
bash Copy code python real_estate_ai_agent.py The script will analyze real estate opportunities based on the specified location and budget, and provide a comprehensive investment recommendation.
Configuration Adjust the following parameters in the script as needed:
Location Budget Minimum number of units AI model settings (temperature, max tokens, etc.) Contributing Contributions to improve the project are welcome. Please follow these steps:
Fork the repository Create a new branch (git checkout -b feature/AmazingFeature) Make your changes Commit your changes (git commit -m 'Add some AmazingFeature') Push to the branch (git push origin feature/AmazingFeature) Open a Pull Request License Distributed under the MIT License. See LICENSE for more information.
Acknowledgements Anthropic for Claude 3.5 Hugging Face for transformer models Swarms for the multi-agent framework Disclaimer This tool is for informational purposes only. Always consult with qualified real estate professionals before making investment decisions.
perl Copy code
- Ensure your local repository is up-to-date:
git pull origin master
Add the new README file: Save the updated README content to your local repository.
Commit the changes:
bash Copy code git add README.md git commit -m "Updated README with project details and repository link" Push the changes to GitHub:
bash
Copy code
git push origin master
pydantic
transformers
swarm
python-dotenv
numpy
pandas
requests
torch