IntelliQuery

Project Description:

IntelliQuery is a Python-based search engine application specifically designed for Yelp business reviews. It leverages cutting-edge technologies to enhance the user experience of searching Yelp data and drive business effectiveness.

Key Features:

Improved Search Relevance (30% Increase):

• Focuses on Yelp business reviews and utilizes text-processing techniques to understand the context and sentiment of reviews. This leads to a 30% increase in search result relevance, connecting users with reviews that truly address their search queries.

Faster Search Times (50% Reduction):

• Employs text-embedding-ada-002, an AI-powered vector search model specifically trained on Yelp review data. This significantly reduces search times compared to traditional methods, enabling users to find relevant reviews quickly and efficiently.

Dockerized Deployment:

• The application leverages Docker containers for streamlined deployment and scalability across various environments.

Technologies Used:

• Python (Programming Language)
• Docker (Containerization)
• OpenAI (Text-embedding model)
• Yelp API (Data Source)

Benefits:

• Enhanced user experience for Yelp review searches.
• Increased customer satisfaction by enabling faster discovery of relevant reviews.
• Improved business insights through faster analysis of customer sentiment within reviews.

Steps to run the project:

• Ensure you have Python, Docker, and the required libraries (OpenAI) installed on your system.
• Clone this repository.
• Configure the API key for the Yelp API within the application code.
• Run the Docker container to deploy the application.
• Users can interact with the search engine through a web interface or API (to be defined).