Neon Image Search: Efficient Embedding Tool for Image Similarity

Project Overview

Introduction

The Neon Image Search project is an innovative image search application built using NeonDB and Vertex AI. This app converts data images into high-dimensional vectors, known as embeddings, using Google Cloud’s Vertex AI. These embeddings are then stored in NeonDB, a database that supports vector search through the PGVector extension.

When a user uploads an image and performs a similarity search, the app converts the request image into an embedding and uses NeonDB to find similar images based on this vector representation. This process enables efficient and accurate image matching, making it a valuable tool for various applications. Exploring this project can provide insights into advanced image processing and database technologies.

Key Features

Overview: The Neon Image Search project is an image search application built using NeonDB and Google Cloud’s Vertex AI. It converts images into high-dimensional vectors (embeddings) and stores them in NeonDB for efficient similarity searches.

Key Features:

• Image Conversion: Uses Vertex AI to convert images into embeddings.
• Storage: Stores the embeddings in NeonDB, which supports vector search using the PGVector extension.
• Similarity Search: When a user uploads an image, the app converts it into an embedding and performs a similarity search using NeonDB’s PGVector extension.
• Results: Returns a list of similar images ranked by their similarity scores, calculated using cosine distance.
• Installation: Involves cloning the repository, installing dependencies, generating embeddings for data images, and starting the application.

This project enables users to find similar images efficiently by leveraging advanced vector search capabilities.

Real-World Applications

Image Similarity Search in E-commerce

• Users can upload an image of a product, and the app will suggest similar products from the database, enhancing the shopping experience.

Content Moderation

• By converting images into embeddings, the app can help identify and filter out inappropriate or duplicate content on platforms.

Medical Imaging

• Healthcare professionals can upload medical images to find similar cases or diagnoses, aiding in research and patient care.

Art and Design

• Artists and designers can upload their work to find similar styles or inspirations from a database of images.

Exploring the Repository

Installation and Setup

• Clone the repository and install dependencies using yarn or npm.
• Configure the environment variables in .env.local.
• Generate embeddings for the data images using yarn run generate-embeddings or npm run generate-embeddings.
• Start the app with yarn run dev or npm run dev.

Benefits

• Utilize Vertex AI for image embedding and NeonDB for efficient vector search.
• Perform similarity searches based on cosine distance to find highly similar images.
• Integrate the app into various applications requiring image comparison and retrieval.

Conclusion

Key Points:

• Image Search Capability: Converts images into embeddings using Vertex AI and stores them in NeonDB for efficient similarity searches.
• Technology Integration: Utilizes Google Cloud’s Vertex AI and NeonDB with PGVector extension for vector search.
• User Experience: Enables users to upload images and receive ranked results of similar images based on cosine distance.
• Future Potential: Can be expanded to various domains such as e-commerce, healthcare, and security by enhancing the accuracy and speed of image searches.
• Scalability: Can handle large datasets and scale with the use of cloud services.

Summary

Neon-Image-Search is an image search app leveraging Vertex AI and NeonDB to perform similarity searches. It converts images into embeddings, stores them in NeonDB, and uses PGVector for efficient searches. The project has significant potential for expansion into various industries, offering scalable and accurate image search capabilities.

For further insights and to explore the project further, check out the original ItzCrazyKns/Neon-Image-Search repository.

Attributions

Content derived from the ItzCrazyKns/Neon-Image-Search repository on GitHub. Original materials are licensed under their respective terms.