This page describes Gemini Enterprise apps and data stores. With Gemini Enterprise, you create an app and connect it to a data store. A given Google Cloud project can contain multiple apps. Key terms: - **App** : A Gemini Enterprise app provides search results, actions, and agents to your end users. The term *app* can be used interchangeably with the term *engine* in the context of APIs. - **Data store** : A data store is an entity that contains the data ingested from a first-party data source such as Cloud Storage or third-party applications such as Jira or Salesforce. Data stores that contain data from third-party applications are also called *data connectors*. ## Relationship between apps and data stores An app must be connected to a data store in order to use the data from it to serve search results, answers, or actions. Apps have a many-to-many relationship with data stores. When multiple data stores are connected to a single app, this is referred to as *blended search* . For information about limitations of connecting a search app to more than one data store, see [About blended search](https://docs.cloud.google.com/gemini/enterprise/docs/apps-data-stores.md.txt#blended). > [!NOTE] > **Note:** You can't connect website data stores to your Gemini Enterprise search and assistant apps. ## Method of app creation and data ingestion How you create an app and ingest data depends on the type of data you have: - For third-party data, you use the Google Cloud console, not the API, to create your app and ingest data. - For other data, you can use either the Google Cloud console or the API. ## Documents Each data store has one or more data records, called *documents*. What a document represents varies depending on the type of data in the data store: - **Data for third-party data sources**. A document is an entity specific to the third-party data source, such as a Jira issue or Confluence space. - **Structured data**. A document is a row in a table or a JSON record that follows a particular schema. You can provide this schema yourself or you can let Gemini Enterprise derive the schema from the ingested data. - **Unstructured data**. A document is a file in HTML, PDF with embedded text, or TXT format. PPTX and DOCX formats are available in Preview. ## Data stores and apps In Gemini Enterprise, there are various kinds of data stores. A data store can contain only one type of data. - [Third-party data](https://docs.cloud.google.com/gemini/enterprise/docs/apps-data-stores.md.txt#structured-third-party) - [Structured data](https://docs.cloud.google.com/gemini/enterprise/docs/apps-data-stores.md.txt#structured-data) - [Unstructured data](https://docs.cloud.google.com/gemini/enterprise/docs/apps-data-stores.md.txt#unstructured-data) ### Structured data for third-party data stores The following third-party data source connectors are available in Preview with allowlist: - Confluence - Jira - Salesforce - Sharepoint Online - Slack Data from these third parties is considered structured data. When you set up a new connector, you select a synchronization frequency. You also select which entities to sync. Entities vary depending on the source, such as issues for Jira and content and spaces for Confluence. A unique data store is created for each entity. Entity data stores are grouped by connector instance. #### What's next - [Connect a third-party data source](https://docs.cloud.google.com/gemini/enterprise/docs/connect-third-party-data-source). - [Create an app](https://docs.cloud.google.com/gemini/enterprise/docs/create-app.md.txt). ### Structured data A data store with structured data enables semantic search or recommendations over structured data. You can import data from BigQuery or Cloud Storage. You can also manually upload structured JSON data through the API. For example, you can enable search or recommendations over a product catalog for your ecommerce experience or a directory of doctors for provider search or recommendations. Gemini Enterprise auto-detects the schema from the data that you import. Optionally, you can provide a schema for your data. Providing a schema for your data typically improves the quality of results. #### What's next - [Prepare structured data for ingestion](https://docs.cloud.google.com/gemini/enterprise/docs/prepare-data#structured). - Create a search data store using one of these methods: - [Import from BigQuery](https://docs.cloud.google.com/gemini/enterprise/docs/connectors/connect-bigquery.md.txt). - [Import from Cloud Storage](https://docs.cloud.google.com/gemini/enterprise/docs/connectors/connect-cloud-storage.md.txt). - [Create an app](https://docs.cloud.google.com/gemini/enterprise/docs/create-app.md.txt). ### Unstructured data An unstructured data store enables semantic search or recommendations over data such as documents and images. Unstructured data stores support documents in HTML, PDF with embedded text, and TXT format. PPTX and DOCX formats are available in Preview. Search provides results in the form of 10 URLs and summarized answers for natural language queries. Documents must be uploaded to a Cloud Storage bucket with appropriate access permissions. For example, a financial institution can enable search or recommendations over their private corpus of financial research publications, or a biotech company can enable search or recommendations over their private repository of medical research. #### What's next - [Prepare unstructured data for ingestion](https://docs.cloud.google.com/gemini/enterprise/docs/prepare-data#unstructured). - Create a search data store using one of these methods: - [Import from BigQuery](https://docs.cloud.google.com/gemini/enterprise/docs/connectors/connect-bigquery.md.txt). - [Import from Cloud Storage](https://docs.cloud.google.com/gemini/enterprise/docs/connectors/connect-cloud-storage.md.txt). - [Sync from Google Drive](https://docs.cloud.google.com/gemini/enterprise/docs/connectors/gdrive.md.txt). - [Create a first-party datastore](https://docs.cloud.google.com/gemini/enterprise/docs/create-data-store) for your unstructured data. - [Create an app](https://docs.cloud.google.com/gemini/enterprise/docs/create-app.md.txt). ## About blended search With blended search, multiple data stores can be connected to a single app. This lets the app search across multiple sources and types of data. > [!NOTE] > **Note:** Blended search is rate-limited in the Gemini Enterprise Standard and Gemini Enterprise Frontline editions. Unlimited and enhanced blended search requires the Gemini Enterprise Plus edition. To make a blended search app, you connect one or more data stores to an app after it has been created. You can change which data stores are connected to an app after it has been created, with the following restrictions when you use the `engines.patch` method: - An app that is connected to a single data store can't add or remove data stores, and can't replace its data store, through the API. The request fails with the error `Engines with a single data store cannot add or remove data stores`. - An app that is connected to multiple data stores must keep at least two data stores connected. A request that leaves fewer than two data stores fails with the error `Engines with multiple data stores can not remove data stores such that <2 data stores remain linked`. The same error is returned if you try to connect the first data store to an app that has none. - To make these changes, use the Google Cloud console, which can convert an app between single-data-store and multi-data-store configurations. When getting search results, you can either search across all data stores, or filter for results from a single data store. Blended search has the following limitations: - Data stores that contain unstructured data imported using BigQuery are not supported. - Blended search allows the following fields in [search requests](https://docs.cloud.google.com/gemini/enterprise/docs/reference/rest/v1/projects.locations.collections.engines.servingConfigs/search.md.txt): - `boostSpec` - `contentSearchSpec` - `dataStoreSpecs` - `facetSpecs` - `filter` - `languageCode` - `offset` - `oneBoxPageSize` - `orderBy` - `query` - `pageSize` - `pageToken` - `relevanceScoreSpec` - `relevanceThreshold` - `session` - `sessionSpec` - `spellCorrectionSpec` - `userInfo` - `userPseudoId` - Blended search allows the following fields in `dataStoreSpecs`: - `dataStore` - `boostSpec`: If there are boost specs specified for both `SearchRequest` and `dataStoreSpecs`, both boost specs are applied to search results - `filter`: If there are filters specified for both `SearchRequest` and `dataStoreSpecs`, both filters are applied to search results - Create, Read, Update, and Delete (CRUD) operations on serving configs are supported for blended apps. Only the following fields can be added or updated in a serving config: - `boostControlIds` - `displayName` - `filterControlIds` - `genericConfig`: - `contentSearchSpec` - `name` - `solutionType` - `synonymsControlIds` - CRUD operations on the following controls are supported for blended search apps: - `boostAction` - `synonymAction` - `filterAction` - If one data store uses a CMEK configuration, all other data stores must also use the same CMEK configuration.