Source URL: https://cloud.google.com/blog/products/databases/cloud-sql-for-mysql-vector-storage-and-similarity-search-is-ga/
Source: Cloud Blog
Title: Vector similarity search for Cloud SQL for MySQL is now GA
Feedly Summary: If you used the internet today, you’ve probably already benefited from generative AI. Whether it helped you get your work done faster, research home repairs, or find the perfect gift, gen AI is transforming how we get things done. These generative AI experiences use searches against vector embeddings — multi-dimensional representations of data’s meaning — to match your intent with the best answer.
But integrating vector technology into existing applications can be challenging. Many databases have historically not supported vector search, so developers have had to integrate specialized vector databases side-by-side with their existing databases.
Enter MySQL similarity search
Cloud SQL for MySQL now supports vector storage and similarity search, which means you can transform your MySQL databases in place to integrate gen AI capabilities without a specialized vector database. Now generally available, it’s as simple as adding a new column to your existing table and loading in your vector embeddings, which you can generate using your favorite models; for example, you can use Vertex AI’s pre-trained text embeddings models. Once you’ve imported your dataset, you can perform both k-nearest neighbors (kNN) and approximate nearest neighbors (ANN) searches by adding the right index for your use case; these search indexes were developed using Google’s open-source ScaNN libraries. Our GA offering includes the same ACID support and crash recovery for vectors that you expect from a relational database.
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To think about this in action, imagine you’re the developer for a hardware store’s online shopping experience. By integrating ANN similarity search into your catalog, when a shopper asks “what do I need to fix a crack in my dining table?” you can convert this question into a vector embedding and match against all products in your catalog to find items that can be used to fix dining table cracks.
We’ve collaborated closely with companies that rely on MySQL to help them integrate generative AI into their existing applications. For instance, supply chain solution provider Manhattan Associates is exploring similarity search in MySQL to improve search results for customers using its applications.
“Similarity search in MySQL enables us to easily integrate gen AI capabilities into the fleet of applications we’ve built on Cloud SQL for MySQL. For example, we’re exploring how we can use similarity search against product information to render better search results. This can be expanded to various searches across the application solutions we provide.” – Sanjeev Siotia, Executive Vice President & Chief Technology Officer, Manhattan Associates
Get started building
Ready to build generative AI apps on top of your MySQL databases? We have a few solutions to help you get started:
Sample app: Lets you customize the datastore for a bot-based app, with Cloud SQL for MySQL as an option. This app uses kNN search as the search type.
Code lab: Walks you through the basics of deploying a gen AI app with Cloud SQL and LangChain, a popular gen AI app development framework.
We can’t wait to see what you create!
AI Summary and Description: Yes
Summary: The text discusses the integration of generative AI capabilities into MySQL databases through vector storage and similarity search. It highlights how developers can leverage this technology to enhance search functionalities in existing applications without the need for specialized databases, making it relevant for professionals in the fields of AI, cloud computing, and database management.
Detailed Description:
The text outlines significant advancements in the integration of generative AI with traditional databases, specifically focusing on Cloud SQL for MySQL. This development represents a critical evolution in how databases can facilitate AI applications, reflecting the growing demand for immediate and contextually relevant search results.
Key points include:
– **Generative AI Integration**: Generative AI is increasingly becoming a pivotal resource in day-to-day internet activities, aiding various tasks by delivering comprehensive answers tailored to user intent.
– **Vector Embeddings**: The use of vector embeddings allows for multi-dimensional representations of data, which optimizes the matching process between user inquiries and relevant database entries.
– **Enhanced MySQL Capabilities**:
– Cloud SQL for MySQL now supports vector storage and similarity search.
– This enables developers to integrate generative AI functionalities into existing MySQL databases without requiring a separate vector database.
– Developers can enhance their databases simply by adding a column for vector embeddings, which can be generated using models like Vertex AI’s pre-trained text embedding models.
– **Search Functionality**:
– Introduction of k-nearest neighbors (kNN) and approximate nearest neighbors (ANN) searches.
– Utilization of Google’s open-source ScaNN libraries to develop search indexes tailored to specific use cases.
– Retention of conventional database features such as ACID support and crash recovery.
– **Practical Applications**: An example is provided where a developer can utilize ANN similarity search in an online hardware store’s catalog to assist customers in finding appropriate products based on queries related to specific needs (e.g., fixing cracks in furniture).
– **Collaborative Efforts**: The text mentions partnerships with companies like Manhattan Associates, who are exploring these new capabilities to enhance search results within their applications, showcasing real-world applications of the technology.
– **Resources for Developers**:
– Sample applications and Code Labs are available to help developers transition to utilizing generative AI in their MySQL databases.
Overall, this development signifies a strategic shift in database management and AI capabilities, potentially enabling businesses to provide more intelligent, context-aware search functionalities in their applications. Security professionals, particularly those focusing on database security and AI integration, may find these advancements critical for ensuring data integrity and compliance while enhancing user experience.