Agent Search: Prefix and partial matching for filtering search queries (Preview)
You can configure schema fields to support prefix matching and partial matching in filter expressions:
Prefix matching lets you filter search results based on whether a field value starts with a specific string.
Partial matching lets you filter results based on whether the query contains
some of the words in the field value. Partial matching doesn't require a
perfect match like the ANY operator does.
This feature is in Public Preview. For more information, see Configure field settings.
Agent Search: EXISTS filter for filtering search queries (Preview)
You can use the EXISTS filter to filter search results for documents.
Specifying EXISTS for a field means that a document can only be returned in a
search request if the field has a value and that value is not the default.
This
filter is available for custom search and for media search. Use EXISTS with
other filters such as ANY and IN to create expressions to scope the
documents that can be returned in a search query.
This feature is in Public Preview. For more information, see Filter custom search for structured or unstructured data, Filter website search, and Filter media search.
]]>Agent Search: Table and image annotation in layout parser
The table annotation and image annotation features of the layout parser are generally available (GA).
You can ask the layout parser to annotate images or tables with a descriptive block of text describing the information in the image or table. The annotation can then be used as a source in a generated answer. For more information, see Layout parser.
]]>Agent Search: Agent Search for healthcare is deprecated
Agent Search for healthcare is deprecated.
For a comprehensive, managed solution, consider building custom search apps on Agent Search. Or, if you require fine-grained control over the underlying retrieval mechanisms and are prepared for a more customer-managed integration, use Agent Retrieval (formerly known as Vector Search 2.0).
]]>Agent Search: Weight searchable fields (Preview)
You can specify a weight for searchable fields in your schema to indicate their relative importance in search results.
This feature is in Public Preview. For more information, see Weight searchable fields.
Agent Search: Stream answers using agentic retrieval (GA with allowlist)
You can use agentic retrieval with the streaming answer method.
Agentic retrieval can return better results as compared to the standard streaming answer method. This is because agentic retrieval can do multi-pass searches across multiple data stores. The agent plans and executes searches sequentially, choosing the best tools, such as Google Search and Google Maps, for each step. Agentic retrieval also enables multi-turn search queries (follow-up questions) on blended search apps.
For more information, see Stream answers using agentic retrieval.
This feature is GA with an allowlist, available for select customers.
]]>Agent Search: MCP server (GA)
Agent Search has a Model Context Protocol (MCP) server hosted at the
following endpoint: https://discoveryengine.googleapis.com/mcp
This feature is generally available (GA). For more information, see MCP Reference: discoveryengine.googleapis.com.
Agent Search: Dense reciprocal rank for custom ranking
You can use the dense reciprocal rank transformation function, drr, to
customize search result ranking. It's an improvement on the reciprocal rank
function, rr. Using the dense reciprocal rank function leads to higher
quality ranking when there are duplicate signal values.
Duplicate signal values are more common when the ranking formula contains the following types of signal:
boosting_factor signalgeo_distance() function signalThis feature is generally available (GA). For more information, see Customize search results ranking.
Agent Search: Geodistance function for custom ranking (GA)
The geo_distance function can be used in custom ranking formulas to calculate
the distance between a source location and a destination location. The function
supports query locations extracted from natural language, explicitly provided
coordinates, and addresses.
This feature is generally available (GA). For more information, see Custom ranking: Geodistance—a derived signal.
Agent Search: Filter searches by document-level relevance (GA)
When searching in your Agent Search app, you can specify document-level relevance filters so that only the documents that meet the filter threshold are returned as results.
You can specify either the relevance threshold or semantic-relevance threshold to filter documents by relevance based on keyword and semantic search similarity.
This feature is Generally Available (GA). For more information, see Filter searches by document-level relevance.
Agent Search: Renamed from Vertex AI Search
The Vertex AI Search product has been renamed as Agent Search in the following contexts:
What has not changed:
The user interface in the Google Cloud console is still referred to as Vertex AI Search and AI Applications. See Vertex AI Search.
The APIs still use the Discovery Engine API endpoints. See APIs and reference.
Despite the rebrand, the product functionality remains the same.
]]>Vertex AI Search: Gemini 3.1 Pro and Gemini 3 Flash for answer generation (Preview)
You can generate answers with the Gemini 3.1 Pro (Preview) and Gemini 3 Flash (Preview) models.
For more information, see Answer generation model versions and lifecycle, Gemini 3.1 Pro, and Gemini 3 Flash.
Vertex AI Search: Gemini 3 Pro (Preview) for answer generation discontinued
The Gemini 3 Pro (Preview) model has been discontinued and is no longer available for answer generation. If you have been using that model, upgrade to the Gemini 3.1 Pro (Preview) model.
For information about available models, see Answer generation model versions and lifecycle.
]]>Vertex AI Search: Change the pricing model for a project
There are two pricing models for apps and data stores: the general model (pay-as-you-go consumption-based) and the configurable subscription model (a monthly subscription).
You can switch from configurable pricing to general pricing for a project if all its data stores and apps use the general model. For more information, see Disable configurable pricing for project.
]]>Vertex AI Search: Gemini 3 Pro for answer generation (Preview)
You can generate answers with the Gemini 3 Pro model.
For more information, see Answer generation model versions and lifecycle and Gemini 3 Pro.
]]>Vertex AI Search: MCP server (Preview)
Vertex AI Search has a Model Context Protocol (MCP) server hosted at the
following endpoint: https://discoveryengine.googleapis.com/mcp
This feature is in Public Preview. For more information, see MCP Reference: discoveryengine.googleapis.com.
]]>Vertex AI Search: Change the pricing model for apps and data stores
There are two pricing models for apps and data stores: the general model (pay-as-you-go consumption-based) and the configurable subscription model (a monthly subscription).
If you have enabled configurable pricing for your project, you can change the pricing model for apps and data stores from configurable to general, as well as from general to configurable. See Disable configurable pricing.
]]>Vertex AI Search: Filter searches by document-level relevance (Preview)
When searching in your Vertex AI Search app, you can specify document-level relevance filters so that only the documents that meet the filter threshold are returned as results.
You can specify either the relevance threshold or semantic-relevance threshold to filter documents by relevance based on keyword and semantic search similarity.
This feature is in Public Preview. For more information, see Filter searches by document-level relevance.
]]>Vertex AI Search: Configurable pricing for custom search (GA)
Configurable pricing is available for custom search apps and data stores.
Configurable pricing offers a flexible alternative to the default pay-as-you-go model. Configurable pricing lets you to choose a subscription that fits your needs. There are two subscriptions, one for storage and one for search queries, plus add-ons that let you add more features according to your needs.
For storage, the minimum subscription is 50 GiB/month and the available add-on is semantic embedding. For search queries, the minimum subscription is 1000 queries per minute per project, and the available add-ons are semantic query, KPI & personalization, and AI overview.
After you set up configurable pricing for a project, apply configurable pricing to apps and data stores as needed. In a project, you can have some apps and data store using the general pricing and others using configurable pricing.
Configurable pricing is generally available (GA). For more information, see Enable configurable pricing for custom search and Vertex AI Search pricing.
]]>Vertex AI Search: Natural language query filters (GA)
For queries on structured data stores, the natural language queries can be
reformulated as filters and a residual query. For example, "Find a coffee shop
serving banana bread" becomes "query": "banana bread", "filter": "type":
ANY("cafe").
The natural-language query understanding feature only applies to custom search apps attached to a single, structured, data store.
By default, a hard restriction filter is applied, but a softer, boost-like filter can be used instead.
This feature is generally available (GA). For more information, see Filter with natural language understanding.
]]>Vertex AI Search: Allowlist fields for natural language query understanding
You can specify an allowlist of fields to be used for filter extraction. If you have some fields, perhaps for internal use, that you don't want to be used in filters, then specify an allowlist to restrict the fields that can be used.
For more information, see Specify fields for natural-language queries.
]]>Vertex AI Search: Updated file size restrictions for unstructured data
The file size restrictions for unstructured data import have been unified. For unstructured data, you can import files up to 200 MB regardless of the parser type.
For more information, see Unstructured data.
]]>Vertex AI Search: Gemini layout parser (Preview)
For data stores with unstructured documents, you can use Gemini to get layout analysis and content extraction on PDF files. Layout parsing with Gemini provides high quality table recognition, improved reading order and more accurate text recognition.
This feature is in Public Preview. For more information, see Parse and chunk documents.
]]>Vertex AI Search: Layout parser support for DOCX, PPTX, and XLSX (GA)
With the layout parser, support for parsing DOCX, PPTX, and XLSX file formats is Generally Available (GA). Both the layout and digital parsers can parse PDF, HTML, DOCX, PPTX, and XLSX files. For more information about the parsers, see Parse and chunk documents.
]]>Vertex AI Search: Renamed from AI Applications
The AI Applications product has been renamed as Vertex AI Search in the following contexts:
What has not changed:
Despite the rebrand, the product functionality remains the same.
]]>Vertex AI Search: Custom ranking of search results (GA)
You can modify the ranking behavior of your search app using custom ranking. Custom ranking lets you provide a mathematical expression that relies on a set of model-computed signals (such as semantic relevance score and keyword similarity score) and document-based signals (such as a custom field like distance or document age). The resulting ranking of the search results is more considered and likely matches a user's needs better than a purely embedding-based ranking.
For more information, see Customize search results ranking.
]]>Vertex AI Search: Advanced autocomplete (GA)
Use advanced autocomplete to enable autocomplete on blended search apps.
For more information, see Configure advanced autocomplete. This feature is Generally Available (GA).
]]>Vertex AI Search: gemini-2.5-flash/answer_gen/v1 model is the default model
Model version gemini-2.5-flash/answer_gen/v1 is the default model for generating answers in Vertex AI Search.
For more information, see Answer generation model versions and lifecycle.
]]>AI Applications: Quotas
AI Applications offers the following allocation quotas in the global region
(global), the US multi-region (us), and the EU multi-region (eu) under the
Discovery Engine API:
The number of data stores, documents, user events, and engines across all locations can't exceed the total per-project quota for that resource.
For more information, see Quotas.
]]>Vertex AI Search: Search for an exact match (GA)
To search for an exact match, you can enclose your search query in double quotes
("). For example, when you search for "Mary had a little lamb", Vertex AI
Search looks for the phrase exactly as it is. It doesn't return search results
that contain Mary had lamb, which has missing words; or a little lamb had
Mary, which has the words in a different order.
This feature is Generally Available when you use the
engines.servingConfigs.search
method to get search results for custom
data, media
data, and healthcare
data.
Vertex AI Search: gemini-2.5-flash/answer_gen/v1 model
You can generate answers with the Gemini 2.5 Flash (gemini-2.5-flash) model. This model is tuned to address context-based question and answering tasks.
For more information, see Answer generation model versions and lifecycle.
]]>AI Applications: Custom search and recommendations
The vertical-agnostic apps, formerly known as generic search and recommendations, are renamed to custom search and recommendations. You'll see this new name in the product console and the documentation set. The functionality and the endpoints remain the same.
]]>Vertex AI Search: Skip layout parsing for types of HTML content (GA)
The layout parser can skip parsing specific types of HTML content. By excluding less relevant content such as boilerplate, you can improve data quality. The layout parser can exclude based on HTML tags and IDs and on CSS classes.
This feature is Generally Available (GA) and accessible only through the API. For more information, see Exclude HTML content.
]]>Vertex AI Search: Adjust autocomplete settings to reduce risk of PII leaks (Public Preview)
If you use either the search history or user events model for autocomplete suggestions and you have concerns about your users entering their personally identifiable information (PII) as search queries, then see Reduce the risk of returning suggestions that contain PII.
This feature is in Public Preview.
]]>Vertex AI Search: Media search automatically chooses the results mode
Media search can respond to queries as full queries or as partial (search-as-you-type) queries. You can specify which query mode to use or you can specify the auto mode.
With auto mode, Media search makes the choice for you by assessing and weighting various signals such as query length and content. Use the auto mode if you have one search box that some users expect to respond with search-as-you-type suggestions and others expect to respond as a full query search.
Auto mode is the default mode. For more information, see Get search results for media.
]]>Vertex AI Search: Obtain claim-level grounding scores (GA)
Claim-level scores from the check grounding API is Generally Available (GA). In addition to the answer-level support score, you can obtain a support score for each claim in an answer candidate.
For more information, see Obtain claim-level scores for an answer candidate.
]]>Vertex AI Search: Stream Google Cloud Storage buckets to data stores
In addition to one time and periodic imports from Cloud Storage, you can stream unstructured data from Cloud Storage into a data store. This lets you serve results from the bucket to your users in near real time.
Streaming must be set up at the bucket-level (not at the folder- or file-level), and the bucket may only contain unstructured data.
For general information about creating data stores, see Create a search data store.
Vertex AI Search: Grounded generation with the generateGroundedContent API
The generateGroundedContent API to that grounds your answers with your inline text, Vertex AI Search data store, and Google Search is no longer available.
Instead, to generate grounded answers, Google recommends that you use the Generally Available groundContent API. You can either ground your answers with Google Search or with your own data. For more information, see Overview.