> ## Documentation Index > Fetch the complete documentation index at: https://docs.maia.ai/llms.txt > Use this file to discover all available pages before exploring further. # Embed Production use of this feature is only available on specific Maia plans. [Contact our Sales team](https://www.maia.ai/contact-us) for more information. \ \ \ \ \ \ \ \
Project Availability
Component TypeTransformation
Connection InputsOne
Connection OutputsUnlimited
The **Embed** transformation component uses the Google BigQuery [AI.EMBED](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-embed) function to convert text into a vector embedding, using a Google embedding model. The component takes text from a single input column and creates a vector embedding for each row. The output of this component is a column containing the vector embedding as an `ARRAY` value. This column is named by **Result column name**, which is `embedding` by default.### Use caseThis component lets you create embeddings directly in your transformation pipeline, which can then be used for semantic searching, clustering, and identifying similar content. For example, you can use this component to:* Generate embeddings for video transcripts, which you can then search to find relevant content. * Convert product descriptions into "Similar products" embeddings, which you can use to suggest products to your customers. * Categorize support tickets by matching them to the appropriate support team.> [!NOTE] > > The component calls the embedding model once for each input row, so running it on large tables can incur significant inference costs. Usage is also subject to Google BigQuery's [generative AI quotas](https://docs.cloud.google.com/bigquery/quotas#generative_ai_functions).***## PrerequisitesThe Google Cloud service account that calls the embedding model must have the [Agent Platform User](https://docs.cloud.google.com/iam/docs/roles-permissions/aiplatform#aiplatform.user) role (`roles/aiplatform.user`). Which service account this is depends on the **Agent platform connection** property:- **Agent platform connection left blank:** Google BigQuery calls the model as the Google Cloud service account that your Maia environment uses to access Google BigQuery. This is the service account in the [cloud provider credentials](https://docs.maia.ai/docs/guides/cloud-credentials.md) associated with the environment or, for a [Hybrid SaaS](https://docs.maia.ai/docs/guides/hybrid-saas-bigquery.md) environment with no associated credentials, the Maia runner's own service account. - **Agent platform connection set:** Google BigQuery calls the model as the service account of the Google BigQuery connection. To create a connection and grant its service account the required role, read [Run generative AI queries with a Google BigQuery Connection](https://docs.cloud.google.com/bigquery/docs/permissions-for-ai-functions#run-connect).***## Properties**Name** `string` *required*A human-readable name for the component.**Text column** `drop-down` *required*Select the input column to use to create embeddings. Only text (`STRING`) columns are listed.**Model** `drop-down` *required*The embedding model to use to generate the embedding. The following options are currently available:- `text-embedding-005`: This is the default setting. - `gemini-embedding-001` - `text-multilingual-embedding-002` - `[Custom]`: Use an embedding model that isn't listed here, which you name in **Custom model**.`text-embedding-005` supports English text only. For text in other languages, select `gemini-embedding-001` or `text-multilingual-embedding-002`.To set the number of dimensions of the embedding, use **Model parameters**.**Custom model** `string` *required*The name of the embedding model to use—for example, `gemini-embedding-2`. Read the [AI.EMBED](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-embed#choose_a_model) documentation for the embedding models that Google BigQuery supports.This property is only available when **Model** is set to **\[Custom]**.**Task type** `drop-down`The intended use of the embedding, which helps the model produce better quality embeddings. If you leave this blank, the model uses `RETRIEVAL_QUERY`. The following options are available:- `RETRIEVAL_QUERY` - `RETRIEVAL_DOCUMENT` - `SEMANTIC_SIMILARITY` - `CLASSIFICATION` - `CLUSTERING` - `QUESTION_ANSWERING` - `FACT_VERIFICATION` - `CODE_RETRIEVAL_QUERY`For semantic search, use `RETRIEVAL_DOCUMENT` to embed the texts that you search over, and `RETRIEVAL_QUERY` to embed the search queries. Read the [AI.EMBED](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-embed#arguments) documentation to learn what each task type means.The `gemini-embedding-2` model doesn't accept a task type or a title.**Title** `string`A document title, which the model uses to improve the quality of the embedding.This property is only available, and only used, when **Task type** is set to **RETRIEVAL\_DOCUMENT**.**Agent platform connection** `string`The Google BigQuery connection to use to call the embedding model, in the format `..`—for example, `myproject.us.myconnection`. Leave this blank to call the model as the Google Cloud service account that your environment uses to access Google BigQuery.**Model parameters** `JSON`A JSON object of additional parameters to send to the model. You can set any field of the text embeddings [parameters object](https://docs.cloud.google.com/gemini-enterprise-agent-platform/reference/models/text-embeddings-api#parameter-list). The JSON must be well-formed.For example, to return a 256-dimensional embedding for each row:```json theme={null} {"outputDimensionality": 256} ```**Include input columns** `boolean` *required*- **Yes:** Outputs both your source input columns *and* the new embedding column. This also includes input columns *not* selected in **Text column**. This is the default setting. - **No:** Only outputs the new embedding column, and the status column if **Include status** is set to **Yes**.**Result column name** `string` *required*The name of the column that holds the embedding. The default is `embedding`.When **Include status** is set to **Yes**, the name must be different from **Status column name**. When **Include input columns** is set to **Yes**, it must also be different from the names of the input columns.**Include status** `boolean` *required*- **Yes:** Adds a column that reports the outcome of the embedding call for each row. Read [Explanation of output](#explanation-of-output) for details. - **No:** Doesn't add a status column. This is the default setting.**Status column name** `string` *required*The name of the status column. The default is `status`.The name must be different from **Result column name**. When **Include input columns** is set to **Yes**, it must also be different from the names of the input columns.This property is only available when **Include status** is set to **Yes**.## Explanation of outputThe output contains the input columns (if **Include input columns** is set to **Yes**), followed by the embedding column, named by **Result column name**, which holds each row's embedding as an `ARRAY` value. If **Include status** is set to **Yes**, the status column comes last.The status is empty when the embedding call succeeds. Otherwise, it contains the API response status for that row\.A row whose embedding call fails doesn't stop the run. Its embedding is empty: NULL, which becomes an empty array when written to a table. To find which rows failed and why, set **Include status** to **Yes**. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.