Have you ever found yourself in a situation where you remember what happened in a movie but struggle to recall its name and can’t come up with the right words to search for it? Or, maybe you’ve had that moment when you have a picture of a product you wish to purchase but aren't sure how to find it online.
Unlike traditional search systems that rely on mentions of keywords, tags, or other metadata, vector search leverages machine learning to capture the meaning of data, allowing you to search by what you mean. Consider the second scenario from the paragraph above as an example. In a vector search system, you can simply use the image of a product to find similar ones instead of struggling to come up with the proper search words. This method is also known as "reverse image search." Vector search works by transforming data, such as text, images, videos, and audio, into a numerical representation that is called vector embedding and applying nearest neighbor algorithms to find similar data.
I am a senior Electrical and Computer Engineering student at the Aristotle University of Thessaloniki (Greece) interested in wireless communications, IoT, AI, cloud technologies, and biomedical engineering. Always passionate about teaching and learning new things, I love helping people expand their technical skills through sharing articles on my blog.
A few days ago, I had a wonderful time delivering a session about building a vector search system using Azure Cosmos DB for PostgreSQL and Azure AI Vision at the monthly Azure Cosmos DB Usergroup’s virtual show hosted by Jay Gordon and Microsoft Reactor. In this article, I will document the main points of my session and provide you with learning resources and code examples.
Understand the concept of vector embeddings and how a vector search similarity system works.
Use the Azure AI Vision Image Retrieval APIs for converting images and text into a vector representation.
Build a vector search system with Azure Cosmos DB for PostgreSQL and Azure AI Vision.
Before you start, follow these steps to set up your workspace:
In simple terms, vector embeddings are numerical representations of data, such as images, text, videos, and audio. These vectors are high-dimensional dense vectors, with each dimension containing information about the original content. By translating data into vectors, computers can capture the meaning of the data and understand the semantic similarity between two objects. We can quantify semantic similarity of two objects by their proximity in a vector space.
Vector embeddings and vector similarity.
We can measure the semantic similarity by using a distance metric such as Euclidean distance, inner product, or cosine distance. In the following examples, we will use the cosine similarity, which is defined as the cosine of the angle between the two vectors.
There are numerous embedding models available, including OpenAI, Hugging Face, and Azure AI Vision. Azure AI Vision (formerly known as Azure Computer Vision) provides two Image Retrieval APIs for vectorizing image and text queries: the Vectorize Image API and the Vectorize Text API. This vectorization converts images and text into coordinates in a 1024-dimensional vector space, enabling users to search a collection of images using text and/or images without the need for metadata, such as image tags, labels, or captions.
How vector similarity search works?
A vector search system works by comparing the vector embedding of a user’s query with a set of pre-stored vector embeddings to find a list of vectors that are the most similar to the query vector. The diagram below illustrates the workflow.
Overview of vector similarity search flow.
Vector embeddings can be stored in a vector database, which is a specialized type of database optimized for storing and querying vectors with a large number of dimensions.
In the following video, you can learn more about vector embeddings and vector similarity search with Azure Cosmos DB for PostgreSQL and Azure AI Vision and walk through the Jupyter Notebooks that are available on my GitHub repository.
Python code samples
In my GitHub repository, you can find some Jupyter Notebooks to help you gain hands-on experience with the concepts introduced in the above video. You may want to refer to the README of the project for instructions on how to set up your Azure resources.
In the quickstart, you will explore the Image Retrieval APIs of Azure AI Vision and the basics of the pgvector extension. You will build a simple app to search a collection of images from a wide range of natural scenes. The images were taken from Kaggle.
Example scenario: Vector search with Azure Cosmos DB for PostgreSQL, Azure Blob Storage and Azure AI Vision