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module  pixeltable.functions.gemini

Pixeltable UDFs that wrap various endpoints from the Google Gemini API. In order to use them, you must first pip install google-genai and configure your Gemini credentials, as described in the Working with Gemini tutorial.

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func  invoke_tools()

Signature
invoke_tools(
    tools: pixeltable.func.tools.Tools,
    response: pixeltable.exprs.expr.Expr
) -> pixeltable.exprs.inline_expr.InlineDict
Converts an OpenAI response dict to Pixeltable tool invocation format and calls tools._invoke().

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udf  generate_content()

Signature
generate_content(
    contents: pxt.Json,
    *,
    model: pxt.String,
    config: pxt.Json | None = None,
    tools: pxt.Json | None = None
) -> pxt.Json
Generate content from the specified model. Request throttling: Applies the rate limit set in the config (section gemini.rate_limits; use the model id as the key). If no rate limit is configured, uses a default of 600 RPM. Requirements:
  • pip install google-genai
Parameters:
  • contents (pxt.Json): The input content to generate from. Can be a prompt, or a list containing images and text prompts, as described in: https://ai.google.dev/gemini-api/docs/text-generation
  • model (pxt.String): The name of the model to use.
  • config (pxt.Json | None): Configuration for generation, corresponding to keyword arguments of genai.types.GenerateContentConfig. For details on the parameters, see: https://googleapis.github.io/python-genai/genai.html#genai.types.GenerateContentConfig
  • tools (pxt.Json | None): An optional list of Pixeltable tools to use. It is also possible to specify tools manually via the config['tools'] parameter, but at most one of config['tools'] or tools may be used.
Returns:
  • pxt.Json: A dictionary containing the response and other metadata.
Examples: Add a computed column that applies the model gemini-2.5-flash to an existing Pixeltable column tbl.prompt of the table tbl: 
tbl.add_computed_column(
    response=generate_content(tbl.prompt, model='gemini-2.5-flash')
)

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udf  generate_images()

Signature
generate_images(
    prompt: pxt.String,
    *,
    model: pxt.String,
    config: pxt.Json | None = None
) -> pxt.Image
Generates images based on a text description and configuration. For additional details, see: https://ai.google.dev/gemini-api/docs/image-generation Request throttling: Applies the rate limit set in the config (section imagen.rate_limits; use the model id as the key). If no rate limit is configured, uses a default of 600 RPM. Requirements:
  • pip install google-genai
Parameters: Returns:
  • pxt.Image: The generated image.
Examples: Add a computed column that applies the model imagen-4.0-generate-001 to an existing Pixeltable column tbl.prompt of the table tbl: 
tbl.add_computed_column(
    response=generate_images(tbl.prompt, model='imagen-4.0-generate-001')
)

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udf  generate_videos()

Signature
generate_videos(
    prompt: pxt.String | None = None,
    image: pxt.Image | None = None,
    *,
    model: pxt.String,
    config: pxt.Json | None = None
) -> pxt.Video
Generates videos based on a text description and configuration. For additional details, see: https://ai.google.dev/gemini-api/docs/video At least one of prompt or image must be provided. Request throttling: Applies the rate limit set in the config (section veo.rate_limits; use the model id as the key). If no rate limit is configured, uses a default of 600 RPM. Requirements:
  • pip install google-genai
Parameters:
  • prompt (pxt.String | None): A text description of the videos to generate.
  • image (pxt.Image | None): An image to use as the first frame of the video.
  • model (pxt.String): The model to use.
  • config (pxt.Json | None): Configuration for generation, corresponding to keyword arguments of genai.types.GenerateVideosConfig. For details on the parameters, see: https://googleapis.github.io/python-genai/genai.html#genai.types.GenerateVideosConfig
Returns:
  • pxt.Video: The generated video.
Examples: Add a computed column that applies the model veo-3.0-generate-001 to an existing Pixeltable column tbl.prompt of the table tbl: 
tbl.add_computed_column(
    response=generate_videos(tbl.prompt, model='veo-3.0-generate-001')
)