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

AI-Powered Real Estate Investment Analyzer

Description

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.

Features

• 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

Installation

1. 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

Instructions to Update the Repository

1. 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