> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt
> Use this file to discover all available pages before exploring further.

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# gemini

> <a href="https://github.com/pixeltable/pixeltable/blob/main/pixeltable/functions/gemini.py#L0" id="viewSource" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/View%20Source%20on%20Github-blue?logo=github&labelColor=gray" alt="View Source on GitHub" style={{ display: 'inline', margin: '0px' }} noZoom /></a>

# <span style={{ 'color': 'gray' }}>module</span>  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](https://docs.pixeltable.com/howto/providers/working-with-gemini) tutorial.

Supports two authentication methods:

* Google AI Studio: set `GOOGLE_API_KEY` or `GEMINI_API_KEY` (or put `api_key` in the `gemini` section of
  the Pixeltable config file).
* Vertex AI: set `GOOGLE_GENAI_USE_VERTEXAI=true` and `GOOGLE_CLOUD_PROJECT` (and optionally
  `GOOGLE_CLOUD_LOCATION`), then authenticate via Application Default Credentials
  (e.g. `gcloud auth application-default login`).

## <span style={{ 'color': 'gray' }}>func</span>  invoke\_tools()

```python Signature theme={null}
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()`.

## <span style={{ 'color': 'gray' }}>udf</span>  embed\_content()

```python Signatures theme={null}
# Signature 1:
@pxt.udf
embed_content(
    contents: pxt.String,
    model: pxt.String,
    config: pxt.Json | None,
    use_batch_api: pxt.Bool
) -> pxt.Array[(None,), float32]

# Signature 2:
@pxt.udf
embed_content(
    contents: pxt.Image,
    model: pxt.String,
    config: pxt.Json | None
) -> pxt.Array[(None,), float32]

# Signature 3:
@pxt.udf
embed_content(
    contents: pxt.Audio,
    model: pxt.String,
    config: pxt.Json | None
) -> pxt.Array[(None,), float32]

# Signature 4:
@pxt.udf
embed_content(
    contents: pxt.Video,
    model: pxt.String,
    config: pxt.Json | None
) -> pxt.Array[(None,), float32]

# Signature 5:
@pxt.udf
embed_content(
    contents: pxt.Document,
    model: pxt.String,
    config: pxt.Json | None
) -> pxt.Array[(None,), float32]
```

Generate embeddings for text, images, video, and other content. For more information on Gemini embeddings API, see:
[https://ai.google.dev/gemini-api/docs/embeddings](https://ai.google.dev/gemini-api/docs/embeddings)

**Requirements:**

* `pip install google-genai`

**Parameters:**

* **`contents`** (`String`): The string, image, audio, video, or document to embed.
* **`model`** (`String`): The Gemini model to use.
* **`config`** (`Json | None`, default: `Literal(None)`): Configuration for embedding generation, corresponding to keyword arguments of
  `genai.types.EmbedContentConfig`. For details on the parameters, see:
  [https://googleapis.github.io/python-genai/genai.html#genai.types.EmbedContentConfig](https://googleapis.github.io/python-genai/genai.html#genai.types.EmbedContentConfig)
* **`use_batch_api`** (`Bool`, default: `Literal(False)`): If True, use [Gemini's Batch API](https://ai.google.dev/gemini-api/docs/batch-api) that provides
  a higher throughput at a lower cost at the expense of higher latency.

**Returns:**

* `pxt.Array[(None,), float32]`: The corresponding embedding vector.

**Examples:**

Add a computed column with embeddings to an existing table with a `text` column:

```python  theme={null}
t.add_computed_column(
    embedding=embed_content(t.text, model='gemini-embedding-001')
)
```

Add an embedding index on `text` column:

```python  theme={null}
t.add_embedding_index(
    t.text, embedding=embed_content.using(model='gemini-embedding-001')
)
```

## <span style={{ 'color': 'gray' }}>udf</span>  generate\_content()

```python Signature theme={null}
@pxt.udf
generate_content(
    contents: pxt.Json,
    *,
    model: pxt.String,
    config: pxt.Json | None = None,
    tools: pxt.Json[(Json = 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](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](https://googleapis.github.io/python-genai/genai.html#genai.types.GenerateContentConfig)
* **`tools`** (`pxt.Json[(Json`): 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`:

```python  theme={null}
tbl.add_computed_column(
    response=generate_content(tbl.prompt, model='gemini-2.5-flash')
)
```

## <span style={{ 'color': 'gray' }}>udf</span>  generate\_images()

```python Signature theme={null}
@pxt.udf
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](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:**

* **`prompt`** (`pxt.String`): A text description of the images to generate.
* **`model`** (`pxt.String`): The model to use.
* **`config`** (`pxt.Json | None`): Configuration for generation, corresponding to keyword arguments of
  `genai.types.GenerateImagesConfig`. For details on the parameters, see:
  [https://googleapis.github.io/python-genai/genai.html#genai.types.GenerateImagesConfig](https://googleapis.github.io/python-genai/genai.html#genai.types.GenerateImagesConfig)

**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`:

```python  theme={null}
tbl.add_computed_column(
    response=generate_images(tbl.prompt, model='imagen-4.0-generate-001')
)
```

## <span style={{ 'color': 'gray' }}>udf</span>  generate\_videos()

```python Signature theme={null}
@pxt.udf
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](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](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`:

```python  theme={null}
tbl.add_computed_column(
    response=generate_videos(tbl.prompt, model='veo-3.0-generate-001')
)
```


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