pgvector for AI-powered search in Aiven for PostgreSQL®
In machine learning (ML) models, all data items in a particular data set are mapped into one unified n-dimensional vector space, no matter how big the input data set is.
This optimized way of data representation allows for high performance of AI algorithms. Mapping regular data into a vector space requires so called data vectorizing, which is transforming data items into vectors (data structures with at least two components: magnitude and direction). On the vectorized data, you can perform AI-powered operations using different instruments, one of them being pgvector.
Discover the pgvector extension to Aiven for PostgreSQL® and learn how it works. Check why you might need it and what benefits you get using it.
About pgvector
pgvector is an open-source vector extension for similarity search. It's available as an extension to your Aiven for PostgreSQL® services. pgvector introduces capabilities to store and search over data of the vector type (ML-generated embeddings). Applying a specific index type for querying a table, the extension enables you to search for vector's exact nearest or approximate nearest neighbors (data items).
Vector embeddings
In machine learning, real-world objects and concepts (text, images, video, or audio) are represented as a set of continuous numbers residing in a high-dimensional vector space. These numerical representations are called vector embeddings, and the process of transformation into numerical representations is called vector embedding. Vector embedding allows ML algorithms to identify semantic and syntactic relationships between data, find patterns, and make predictions. Vector representations have different applications, for example, information retrieval, image classification, sentiment analysis, natural language processing, or similarity search.
Vector similarity
Since on vector embeddings you can use AI tools for capturing relationships between objects (vector representations), you are also able to identify similarities between them in a computable and scalable manner.
A vector usually represents a data point, and components of the vector correspond to attributes of the data point. In most cases, vector similarity calculations use distance metrics, for example, by measuring the straight-line distance between two vectors or the cosine of the angle between two vectors. The greater the resulting value of the similarity calculation is, the more similar the vectors are, with 0 as the minimum value and 1 as the maximum value.
How pgvector works
- Enabling pgvector: You enable the extension on your database.
- Vectorizing data: You generate embeddings for your data, for example, for a products catalog using tools such as the OpenAI API client.
- Storing embeddings: You store the embeddings in Aiven for PostgreSQL using the pgvector extension.
- Querying embeddings: You use the embeddings for the vector similarity search on the products catalog.
- Adding indices: By default, pgvector executes the exact nearest neighbor search, which gives the perfect recall. If you add an index to use the approximate nearest neighbor search, you can speed up your search, trading off some recall for performance.
Why use pgvector
With the pgvector extension, you can perform the vector similarity search and use embedding techniques directly in Aiven for PostgreSQL. pgvector allows for efficient handling of high-dimensional vector data within the Aiven for PostgreSQL database for tasks such as similarity search, model training, data augmentation, or machine learning.
pgvector helps you optimize and personalize the similarity search experience by improving searching speed and accuracy (also by adding indices).
Typical use cases
There are multiple industry applications for similarity searches over vector embeddings:
- e-commerce
- recommendation systems
- fraud detection
- AI-powered tools can find similarities between products or transactions, which can be used to produce product recommendations or detect potential scams or frauds.
- Sentiment analysis: words represented with similar vector embeddings have similar sentiment scores.
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