Unleashing the Power of Vector Search with SQL-Embedding

InterSystems IRIS 2024 recently introduced the vector types.
This addition empowers developers to work with vector search, enabling efficient similarity searches, clustering, and a range of other applications.
In this article, we will delve into the intricacies of vector types, explore their applications, and provide practical examples to guide your implementation.

At its essence, a vector type is a structured collection of numerical values arranged in a predefined order. These values serve to represent different attributes, features, or characteristics of an object.

SQL-Embedding: A Versatile Tool

To streamline the creation and utilization of embeddings for vector searches within SQL queries, we introduces SQL-Embedding tool. This feature enables to leverage a diverse range of embedding models directly within their SQL databases, tailored to their specific requirements.

Practical Example: Similarity Search

Let's consider a scenario where we aim to determine the similarity between two texts using the fastembed model and SQL-Embedding. The following SQL query showcases how this can be accomplished:

SELECT
 VECTOR_DOT_PRODUCT(
 embFastEmbedModel1,
 dc.embedding('my text', 'fastembed/BAAI/bge-small-en-v1.5')
 ) AS "Similarity between 'my text' and itself",
 VECTOR_DOT_PRODUCT(
 embFastEmbedModel1,
 dc.embedding('lorem ipsum', 'fastembed/BAAI/bge-small-en-v1.5')
 ) AS "Similarity between 'my text' and 'lorem ipsum'"
FROM testvector;

Caching

One of the significant benefits of using SQL-Embedding in InterSystems IRIS is its ability to cache repeated embedding requests. This caching mechanism significantly improves performance by reducing the computational overhead associated with generating embeddings for identical or similar inputs.

How Caching Works

When you execute a SQL-Embedding query, InterSystems IRIS checks if the embedding for the given input has already been cached. If it exists, the cached embedding is retrieved and used directly, eliminating the need to regenerate it. This is particularly advantageous in scenarios where the same embeddings are frequently requested, such as in recommendation systems or search applications.

Caching Benefits

• Reduced Latency: By avoiding redundant embedding calculations, caching can significantly reduce query response times.
• Improved Scalability: Caching can handle increased workloads more efficiently, as it reduces the strain on the underlying embedding models.
• Optimized Resource Utilization: Caching helps conserve computational resources by avoiding unnecessary calculations.

In conclusion, the introduction of vector types in InterSystems IRIS presents a robust tool for working with numerical object representations. By harnessing similarity searches, SQL-Embedding, and various applications, developers can unlock new possibilities and enhance their data-driven solutions.

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