Vertex AI Search
Build Google-quality search for your own data in hours, not months
Vertex AI Search helps developers build Google-quality search experiences for websites, structured and unstructured data. It also provides an out-of-the-box grounding system and DIY grounding APIs for building generative AI agents and apps. Vertex AI Search is now part of Vertex AI Agent Builder.
Overview
What is Vertex AI Search?
Vertex AI Search is a Google Search quality information retrieval and answer generation system that can be a component of any generative AI application that uses your enterprise data. It’s a fully managed platform that can help you create both new RAG-powered gen apps or improve the performance of your existing applications with generative AI.
What can I use Vertex AI Search for?
There are two key opportunities for developers to use Vertex AI Search:
The first is to improve the quality of search experiences across internal and customer facing applications with generative AI. With Vertex AI Search, you can go from frustrating keyword matching to modern conversational search experiences similar to Google’s new generative search experience.
The second opportunity is to improve the quality of your generative AI applications by grounding them in your enterprise data using Vertex AI Search. Here Vertex AI serves as an out-of-the-box system for retrieval augmented generation or RAG.
Why should I choose Vertex AI Search?
Vertex AI Search makes it significantly easier for you to build high-quality, AI-powered search experiences into your applications. It is built on Google’s deep expertise and decades of experience in semantic search and so provides more relevant search results. This improves the quality of information retrieval for apps that use your enterprise data. Customization options let you tailor the search experience to your specific needs, while robust enterprise-grade features take care of scalability, privacy, and governance. For more specialized use cases, Vertex AI Search offers vertical specific offerings for retail, media, healthcare, and DIY vector search capabilities.
Can I use Vertex AI Search as a RAG system?
Yes, you can. Today, there is a lot of excitement about RAG, an architecture that combines LLMs with a data retrieval system, or in other words, a search engine. By grounding LLM responses in your company's own data, it ensures improved accuracy, reliability, and relevance, something that's critical for real-world business applications. You could build your own retrieval augmented generation-based Search but this can be a highly complex process. Vertex AI Search functions as an out-of-the-box RAG system for information retrieval. Under the hood with Vertex AI Search, we’ve simplified the end-to-end search and discovery process of managing ETL, OCR, chunking, embedding, indexing, storing, input cleaning, schema adjustments, information retrieval, and summarization to just a few clicks. This makes it super easy for you to build RAG-powered apps using Vertex AI Search as your retrieval engine.
Can I use Vertex AI Search to build my own RAG system?
Developing a well-functioning Retrieval Augmented Generation (RAG) system for DIY grounding can be complex. To address this, Vertex AI offers a comprehensive set of APIs that help developers create bespoke DIY solutions and maintain them. These APIs expose the underlying components of Vertex AI Search's out-of-the-box RAG system, empowering developers to address custom use cases or serve customers who want granular control. These include the Document AI Layout Parser API, Ranking API, Grounded generation API, and Check Grounding API.
How does Vertex AI Search offer Google-quality results?
Vertex AI Search is underpinned by a variety of Google Search technologies, including semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user’s query input. Vertex AI Search also benefits from Google’s expertise in understanding how users search and factors in content relevance to order displayed results.
How can I get access to Vertex AI Search?
Vertex AI Search is now generally available. You can access via the Google Cloud Console. Please don't hesitate to contact your Google Cloud sales team for assistance or access to preview features.
What generative AI features does Vertex AI Search offer?
Vertex AI Search is powered by foundation models. This means you can offer your customers multi-turn (the ability to easily ask follow-up questions), multimodal (search using images in addition to text), immersive search experiences that are similar to Google's search generative experience. Your customers or employees can view crisp summaries on top of search results with citations and links to data sources that help in knowledge discovery.
Is my data privacy protected while using Vertex AI Search?
Yes, when you use Vertex AI Search from Google Cloud, your data is secure in your cloud instance. Google does not access or use your data to train models or for any other purpose you have not explicitly authorized. Vertex AI Search also meets specific industry compliance standards like HIPAA, ISO 27000-series, and SOC -1/2/3. We’re expanding support for access transparency to provide customers with awareness of Googler administrative access to their data. Virtual Private Cloud Service Controls prevent customers or employees from infiltrating or exfiltrating data. We are also offering Customer-managed Encryption Keys (CMEK) in preview, allowing customers to encrypt their core content with their own encryption keys.
How does Vertex AI Search ensure results are relevant?
All search results from Vertex AI Search are grounded to your enterprise data or applications you have provided access to. Google Cloud offers grounding out of the box for search results in applications built using Vertex AI Search. Further, Vertex AI Search offers citations and links for summaries generated, which means information presented can be verified by users. You have full control in determining what data sources are used and you can even program responses for off-topic questions.
How does Vertex AI Search maintain freshness of results?
Vertex AI Search can connect to your first-party, Google, and third-party applications through Vertex AI extensions and data connectors. Vertex AI extensions help in ingesting data and drive transactions on the users' behalf while data connectors ingest data with read-only access to key applications like Jira, Confluence, and Salesforce. Together, Vertex AI extensions and data connectors ensure your data is fresh across your search engines.
I want to create my own vector database using embeddings from scratch. Does Vertex AI support this?
Vertex AI Search lets organizations and developers set up search engines out of the box. These search engines offer adequate customization for most enterprise needs and even offer automatic fine-tuning for embeddings. In some cases, you may have custom embeddings, and Vertex AI Search works fine with your own embeddings. However, more advanced developers who need direct control of a highly performant vector database to power niche use cases like recommendations and ad serving can use vector search (formerly known as Vertex matching engine), the vector database used by Vertex AI Search as a component for their use cases. We’ve recently updated vector search’s user experience so developers can create and deploy indexes without coding. We’ve also significantly reduced indexing latency from hours to minutes for smaller data sets.
Does Vertex AI Search have industry specific offerings?
Yes, Vertex AI Search has specialized offerings tuned for unique requirements like searching product catalogs, media libraries, and clinical data repositories. Vertex AI Search for retail is generally available and offers retailers the ability to improve the search, product recommendations, and browsing experience on their channels. Vertex AI Search for media, which is now in preview, offers media and entertainment companies the ability to provide more personalized content recommendations powered by generative AI, increasing consumer time spent on their platforms, which can lead to higher engagement, revenue, and retention. Vertex AI Search for healthcare and life sciences, also in preview, is a medically-tuned search that improves patient and provider experience.
How It Works
Your organization may have terabytes of data; and organizing it to be easily found can be one of the most challenging problems to solve. You could also have a public-facing website and need high-quality search for your customers. For both cases, you can use Vertex AI Search to create search engines. Watch this video and discover how to make an internal search app with minimal coding and minimal setup.
Common Uses
Delivering generative AI-powered search
Get started with Vertex AI Search
Vertex AI Search brings together the power of deep information retrieval, state-of-the-art natural language processing, and the latest in large language processing to understand user intent and return the most relevant results for the user.
This tutorial explains how to create three search apps, one for each type of data: website, structured data, and unstructured data.
Watch the video on the right for an overview and head over to the product documentation link below for detailed instructions.
How-tos
Get started with Vertex AI Search
Vertex AI Search brings together the power of deep information retrieval, state-of-the-art natural language processing, and the latest in large language processing to understand user intent and return the most relevant results for the user.
This tutorial explains how to create three search apps, one for each type of data: website, structured data, and unstructured data.
Watch the video on the right for an overview and head over to the product documentation link below for detailed instructions.
DIY with Vector Search and embeddings
Build a recommendation engine with Vector Search
Find similar things in seconds, even with billions of items. Vector Search unlocks powerful semantic matching for recommendations, chatbots, and more. Let's see how to build a recommendation engine with Vector Search:
- Generate embeddings: Create a numerical representation (embedding) of your items to capture their semantic relationships. You can do this externally or use Vertex AI's generative AI.
- Upload to Cloud Storage: Store your embeddings in Cloud Storage for Vector Search to access.
- Connect to Vector Search: Link your embeddings to Vector Search to perform nearest neighbor search.
- Create and deploy index: Build an index from your embeddings and deploy it to an endpoint for querying.
- Query for recommendations: Use the index endpoint to query for approximate nearest neighbors, finding items semantically similar to your query.
- Evaluate and adjust: Assess the results and refine the algorithm's parameters or scaling as needed to ensure accuracy and performance.
How-tos
Build a recommendation engine with Vector Search
Find similar things in seconds, even with billions of items. Vector Search unlocks powerful semantic matching for recommendations, chatbots, and more. Let's see how to build a recommendation engine with Vector Search:
- Generate embeddings: Create a numerical representation (embedding) of your items to capture their semantic relationships. You can do this externally or use Vertex AI's generative AI.
- Upload to Cloud Storage: Store your embeddings in Cloud Storage for Vector Search to access.
- Connect to Vector Search: Link your embeddings to Vector Search to perform nearest neighbor search.
- Create and deploy index: Build an index from your embeddings and deploy it to an endpoint for querying.
- Query for recommendations: Use the index endpoint to query for approximate nearest neighbors, finding items semantically similar to your query.
- Evaluate and adjust: Assess the results and refine the algorithm's parameters or scaling as needed to ensure accuracy and performance.
Vertex AI Search for healthcare
A medically-tuned Google search experience on healthcare data
Searching data in healthcare can be a difficult task due to the complexities of medical terminology and data standardization.
Vertex AI Search uses its medical tuning to find relevant information from structured and unstructured patient records. It understands medical abbreviations like "abx" and can answer questions with MedLM to provide generative AI answers grounded on patient data. The product integrates with Healthcare Data Engine for a seamless experience.
How-tos
A medically-tuned Google search experience on healthcare data
Searching data in healthcare can be a difficult task due to the complexities of medical terminology and data standardization.
Vertex AI Search uses its medical tuning to find relevant information from structured and unstructured patient records. It understands medical abbreviations like "abx" and can answer questions with MedLM to provide generative AI answers grounded on patient data. The product integrates with Healthcare Data Engine for a seamless experience.
Take the next step with Vertex AI Search
Contact your Google Cloud sales team
Get access to features in preview
Learn more about Vertex AI Search
Find out about Vertex AI Agent Builder
