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---
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title: "bigquery-sql"
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type: docs
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weight: 1
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description: >
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A "bigquery-sql" tool executes a pre-defined SQL statement.
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---
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## About
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A `bigquery-sql` tool executes a pre-defined SQL statement.
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The behavior of this tool is influenced by the `writeMode` setting on its
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`bigquery` source:
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- **`allowed` (default) and `blocked`:** These modes do not impose any
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restrictions on the `bigquery-sql` tool. The pre-defined SQL statement will be
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executed as-is.
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- **`protected`:** This mode enables session-based execution. The tool will
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operate within the same BigQuery session as other tools using the same source,
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allowing it to interact with temporary resources like `TEMP` tables created
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within that session.
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## Compatible Sources
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{{< compatible-sources >}}
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### GoogleSQL
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BigQuery uses [GoogleSQL][bigquery-googlesql] for querying data. The integration
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with Toolbox supports this dialect. The specified SQL statement is executed, and
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parameters can be inserted into the query. BigQuery supports both named
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parameters (e.g., `@name`) and positional parameters (`?`), but they cannot be
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mixed in the same query.
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[bigquery-googlesql]:
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https://cloud.google.com/bigquery/docs/reference/standard-sql/
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## Example
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> **Note:** This tool uses
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> [parameterized queries](https://cloud.google.com/bigquery/docs/parameterized-queries)
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> to prevent SQL injections. Query parameters can be used as substitutes for
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> arbitrary expressions. Parameters cannot be used as substitutes for
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> identifiers, column names, table names, or other parts of the query.
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```yaml
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# Example: Querying a user table in BigQuery
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kind: tool
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name: search_users_bq
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type: bigquery-sql
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source: my-bigquery-source
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statement: |
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SELECT
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id,
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name,
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email
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FROM
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`my-project.my-dataset.users`
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WHERE
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id = @id OR email = @email;
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description: |
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Use this tool to get information for a specific user.
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Takes an id number or a name and returns info on the user.
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Example:
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{{
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"id": 123,
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"name": "Alice",
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}}
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parameters:
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- name: id
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type: integer
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description: User ID
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- name: email
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type: string
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description: Email address of the user
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```
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### Example with Vector Search
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BigQuery supports vector similarity search using the `ML.DISTANCE` function.
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When using an embeddingModel with a `bigquery-sql` tool, the tool automatically
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converts text parameters into the native ARRAY<FLOAT64> format required by
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BigQuery.
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#### Define the Embedding Model
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See
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[EmbeddingModels](../../../documentation/configuration/embedding-models/_index.md)
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for more information.
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```yaml
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kind: embeddingModel
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name: gemini-model
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type: gemini
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model: gemini-embedding-001
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apiKey: ${GOOGLE_API_KEY}
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dimension: 768
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```
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#### Vector Ingestion Tool
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This tool stores both the raw text and its vector representation. It uses
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`valueFromParam` to hide the vector conversion logic from the LLM, ensuring the
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Agent only has to provide the content once.
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```yaml
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kind: tool
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name: insert_doc
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type: bigquery-sql
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source: my-bigquery-source
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statement: |
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INSERT INTO `my-project.my-dataset.vector_table` (id, content, embedding)
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VALUES (1, @content, @text_to_embed)
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description: |
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Internal tool to index new documents for future search.
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parameters:
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- name: content
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type: string
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description: The text content to store.
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- name: text_to_embed
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type: string
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# Automatically copies 'content' and converts it to a FLOAT64 array
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valueFromParam: content
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embeddedBy: gemini-model
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```
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#### Vector Search Tool
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This tool allows the Agent to perform a natural language search. The query
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string provided by the Agent is converted into a vector before the SQL is
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executed.
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```yaml
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kind: tool
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name: search_docs
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type: bigquery-sql
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source: my-bigquery-source
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statement: |
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SELECT
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id,
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content,
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ML.DISTANCE(embedding, @query, 'COSINE') AS distance
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FROM
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`my-project.my-dataset.vector_table`
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ORDER BY
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distance
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LIMIT 1
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description: |
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Search for documents using natural language.
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Returns the most semantically similar result.
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parameters:
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- name: query
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type: string
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description: The search terms or question.
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embeddedBy: gemini-model
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```
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### Example with Template Parameters
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> **Note:** This tool allows direct modifications to the SQL statement,
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> including identifiers, column names, and table names. **This makes it more
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> vulnerable to SQL injections**. Using basic parameters only (see above) is
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> recommended for performance and safety reasons. For more details, please check
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> [templateParameters](../../../documentation/configuration/tools/_index.md#template-parameters).
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```yaml
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kind: tool
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name: list_table
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type: bigquery-sql
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source: my-bigquery-source
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statement: |
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SELECT * FROM {{.tableName}};
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description: |
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Use this tool to list all information from a specific table.
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Example:
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{{
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"tableName": "flights",
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}}
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templateParameters:
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- name: tableName
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type: string
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description: Table to select from
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```
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## Reference
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| **field** | **type** | **required** | **description** |
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| ------------------ | :--------------------------------------------------------------------------------------------: | :----------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| type | string | true | Must be "bigquery-sql". |
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| source | string | true | Name of the source the GoogleSQL should execute on. |
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| description | string | true | Description of the tool that is passed to the LLM. |
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| statement | string | true | The GoogleSQL statement to execute. |
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| parameters | [parameters](../../../documentation/configuration/tools/_index.md#specifying-parameters) | false | List of [parameters](../../../documentation/configuration/tools/_index.md#specifying-parameters) that will be inserted into the SQL statement. |
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| templateParameters | [templateParameters](../../../documentation/configuration/tools/_index.md#template-parameters) | false | List of [templateParameters](../../../documentation/configuration/tools/_index.md#template-parameters) that will be inserted into the SQL statement before executing prepared statement. |
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