Elastic AI Infused Property Search

An intelligent property search system that combines Elasticsearch's powerful search capabilities with Azure OpenAI's natural language processing to create a conversational property search experience.

Demo

Watch a demo of the property search system in action:

<iframe width="560" height="315" src="https://videos.elastic.co/embed/1qQtsuYSdeXGpvdhfYosnn" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

This demo showcases:

• Natural language property search
• Location-based search capabilities
• Real-time property recommendations
• Interactive chat interface

Features

• Interactive chat interface powered by Chainlit
• Azure OpenAI API integration for natural language understanding
• Elasticsearch for powerful property search
• Google Maps API integration for location services
• Real-time streaming responses
• Dynamic query generation
• Location-based search capabilities

Architecture

The system consists of four main components:

1. Chainlit UI Layer

   • Interactive chat interface
   • Real-time message streaming
   • Property result display
   • Image visualization

2. Azure OpenAI Integration

   • Natural language processing
   • Entity detection
   • Context management
   • Response generation

3. Elasticsearch Backend

   • Property data storage
   • Search templates
   • Geo-spatial search
   • Result ranking

4. MCP Server

   • Tool orchestration
   • API integration
   • Response processing
   • Query generation

Prerequisites

• Python 3.8 or higher
• Azure OpenAI API access
• Elasticsearch Serverless instance
• Google Maps API key

Installation

1. Clone the repository:

git clone https://github.com/yourusername/elastic-ai-property-search.git
cd elastic-ai-property-search

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

   Option 1: Using setenv.sh (Recommended)

   # Copy the template file
   cp setenv.sh.template setenv.sh
   
   # Edit setenv.sh with your credentials
   # IMPORTANT: Never commit setenv.sh to version control!
   nano setenv.sh  # or use your preferred editor
   
   # Source the environment variables
   source setenv.sh

   Option 2: Manual Setup Create an azure.env file with the following content:

   AZURE_OPENAI_MODEL=your-model-name
   AZURE_OPENAI_ENDPOINT=your-endpoint
   AZURE_OPENAI_API_KEY=your-api-key
   OPENAI_API_VERSION=2023-05-15

Usage

1. Start the application:

chainlit run src/app.py 

2. Open your browser and navigate to http://localhost:8000

3. Start searching for properties using natural language queries like:

   • "Find me a 3-bedroom house near downtown with a pool"
   • "Show me apartments under $2000 with parking"
   • "I need a pet-friendly condo within 5 miles of the beach"

Project Structure

.
├── src/               # Source code
│   ├── app.py        # Main application file
│   └── __init__.py   # Package initialization
├── tests/            # Test directory
├── docs/             # Documentation
│   └── ai_property_search_article.md
├── public/           # Public assets
├── .chainlit/        # Chainlit configuration
├── requirements.txt  # Project dependencies
├── setenv.sh.template # Template for environment variables
└── .gitignore       # Git ignore file

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.