When discussing applications and systems using generative AI and the new opportunities they present, one component of the ecosystem is irreplaceable - data. Specifically, the data that companies gather, hold, and use daily. This data serves as the backbone for applications, analytics, knowledge bases, and much more. We use databases to store and work with this data, and most, if not all, AI-driven initiatives and new applications are going to use that data layer.
But how can we start to use the data in our AI systems? Let me introduce you to some of the labs showing how to prepare and use the data with AI models in Google databases.
Our journey starts by preparing our data for semantic search and running first tests to augment the Gen AI model's response by grounding it with your semantic search results. The grounding data is the basis for RAG (Retrieval Augmented Generation). Then, you can improve the performance of your search by indexing your embeddings using the latest indexing techniques.
One of the options is the Google AlloyDB database, which has direct integration with AI models and supports the most demanding workloads. The following lab guides us through all the steps, starting from creating an AlloyDB cluster, loading sample data, and generating embeddings, to using those embeddings to generate an augmented response from the Gen AI model.
Objective: Create an AlloyDB cluster, generate vector embeddings for sample data, and perform semantic search to ground a Gen AI model's response.
AI integration is not limited to AlloyDB. All Google Cloud databases have AI integration and are capable of generating and using embeddings for semantic search. For example, if you are using Cloud SQL, you can also generate and use embeddings for semantic search directly within your existing PostgreSQL or MySQL instances.
The next two labs are very similar to the previous one, but instead of Google AlloyDB for PostgreSQL, we are using Cloud SQL for PostgreSQL and Cloud SQL for MySQL to use semantic search as the grounding engine for the model's response. Some steps are of course different due to variations in SQL language and different database engines, but the main idea stays the same: use our data to ground the model response and improve output.
Objective: Create Cloud SQL for PostgreSQL instance with AI integration, generate vector embeddings for sample data, and perform semantic search to ground a Gen AI model's response.
Objective: Create Cloud SQL for MySQL instance with AI integration, generate vector embeddings for sample data, and perform semantic search to ground a Gen AI model's response.
Semantic search using text data is one of the cornerstones and important features making responses much more reliable and useful, but Google Gen AI models can offer much more. Let's talk about multimodal search.
In real life, of course, we use all our senses, including vision, to evaluate the world around us. The Google multimodal embedding models bring an additional layer of understanding, improving search by using embeddings not only for text but also for images.
In the following lab, we use a catalog of products placed in AlloyDB and supplemented by images in Google Cloud Storage. In the lab, we show how we can use both text descriptions and images for semantic search, supplementing and replacing each other, naturally incorporating search based on image input into our response.
Objective: Deploy an AlloyDB cluster, import data and use text and image semantic search using multimodal embeddings, try hybrid search approach.
Preparing the data and making first steps are important for a general understanding of RAG and tools available for the search, but Google has other cases when direct AI integration can help with your data analysis without any data preparations.
Google AlloyDB database comes with additional AI integrations that help you use some AI capabilities without data preparation. For example, the AI.IF function can perform semantic search on the fly, evaluating sentiment or comparing data in columns with a natural language query, returning results filtered by the query condition. Also, you can apply a ranking function to the search output, improving the final result. You can try some of the new functionality using the following lab and let us know if it can help in your use case.
Objective: Deploy AlloyDB cluster and enable AI query functions, use AI query functions to filter data and ranking function to improve the search results.
But what if somebody is not particularly savvy with SQL or not familiar with the data structure in your database? The QueryData for AlloyDB can help you with that.
QueryData lets you interact with the data in your database using conversational language and build data agents. You provide database-specific information in the form of context sets to improve generator accuracy.