Package Classes (1.73.0)

The custom evaluation metric.

A fully-customized CustomMetric that can be used to evaluate a single model by defining a metric function for a computation-based metric. The CustomMetric is computed on the client-side using the user-defined metric function in SDK only, not by the Vertex Gen AI Evaluation Service.

Attributes: name: The name of the metric. metric_function: The user-defined evaluation function to compute a metric score. Must use the dataset row dictionary as the metric function input and return per-instance metric result as a dictionary output. The metric score must mapped to the name of the CustomMetric as key.

Evaluation result.

A class representing an EvalTask.

An Evaluation Tasks is defined to measure the model's ability to perform a certain task in response to specific prompts or inputs. Evaluation tasks must contain an evaluation dataset, and a list of metrics to evaluate. Evaluation tasks help developers compare propmpt templates, track experiments, compare models and their settings, and assess the quality of the model's generated text.

Dataset Details:

Default dataset column names:
    * prompt_column_name: "prompt"
    * reference_column_name: "reference"
    * response_column_name: "response"
    * baseline_model_response_column_name: "baseline_model_response"

Requirement for different use cases:
  * Bring-your-own-response (BYOR): You already have the data that you
      want to evaluate stored in the dataset. Response column name can be
      customized by providing `response_column_name` parameter, or in the
      `metric_column_mapping`. For BYOR pairwise evaluation, the baseline
      model response column name can be customized by providing
      `baseline_model_response_column_name` parameter, or
      in the `metric_column_mapping`. If the `response` column or
      `baseline_model_response` column is present while the
      corresponding model is specified, an error will be raised.

  * Perform model inference without a prompt template: You have a dataset
      containing the input prompts to the model and want to perform model
      inference before evaluation. A column named `prompt` is required
      in the evaluation dataset and is used directly as input to the model.

  * Perform model inference with a prompt template: You have a dataset
      containing the input variables to the prompt template and want to
      assemble the prompts for model inference. Evaluation dataset
      must contain column names corresponding to the variable names in
      the prompt template. For example, if prompt template is
      "Instruction: {instruction}, context: {context}", the dataset must
      contain `instruction` and `context` columns.

Metrics Details:

The supported metrics descriptions, rating rubrics, and the required
input variables can be found on the Vertex AI public documentation page.
[Evaluation methods and metrics](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval).

Usage Examples:

1. To perform bring-your-own-response(BYOR) evaluation, provide the model
responses in the `response` column in the dataset. If a pairwise metric is
used for BYOR evaluation, provide the baseline model responses in the
`baseline_model_response` column.

  ```
  eval_dataset = pd.DataFrame({
          "prompt"  : [...],
          "reference": [...],
          "response" : [...],
          "baseline_model_response": [...],
  })
  eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=[
            "bleu",
            "rouge_l_sum",
            MetricPromptTemplateExamples.Pointwise.FLUENCY,
            MetricPromptTemplateExamples.Pairwise.SAFETY
    ],
    experiment="my-experiment",
  )
  eval_result = eval_task.evaluate(experiment_run_name="eval-experiment-run")
  ```

2. To perform evaluation with Gemini model inference, specify the `model`
parameter with a `GenerativeModel` instance.  The input column name to the
model is `prompt` and must be present in the dataset.

  ```
  eval_dataset = pd.DataFrame({
        "reference": [...],
        "prompt"  : [...],
  })
  result = EvalTask(
      dataset=eval_dataset,
      metrics=["exact_match", "bleu", "rouge_1", "rouge_l_sum"],
      experiment="my-experiment",
  ).evaluate(
      model=GenerativeModel("gemini-1.5-pro"),
      experiment_run_name="gemini-eval-run"
  )
  ```

3. If a `prompt_template` is specified, the `prompt` column is not required.
Prompts can be assembled from the evaluation dataset, and all prompt
template variable names must be present in the dataset columns.
  ```
  eval_dataset = pd.DataFrame({
      "context"    : [...],
      "instruction": [...],
  })
  result = EvalTask(
      dataset=eval_dataset,
      metrics=[MetricPromptTemplateExamples.Pointwise.SUMMARIZATION_QUALITY],
  ).evaluate(
      model=GenerativeModel("gemini-1.5-pro"),
      prompt_template="{instruction}. Article: {context}. Summary:",
  )
  ```

4. To perform evaluation with custom model inference, specify the `model`
parameter with a custom inference function. The input column name to the
custom inference function is `prompt` and must be present in the dataset.

  ```
  from openai import OpenAI
  client = OpenAI()
  def custom_model_fn(input: str) -> str:
    response = client.chat.completions.create(
      model="gpt-3.5-turbo",
      messages=[
        {"role": "user", "content": input}
      ]
    )
    return response.choices[0].message.content

  eval_dataset = pd.DataFrame({
        "prompt"  : [...],
        "reference": [...],
  })
  result = EvalTask(
      dataset=eval_dataset,
      metrics=[MetricPromptTemplateExamples.Pointwise.SAFETY],
      experiment="my-experiment",
  ).evaluate(
      model=custom_model_fn,
      experiment_run_name="gpt-eval-run"
  )
  ```

5. To perform pairwise metric evaluation with model inference step, specify
the `baseline_model` input to a `PairwiseMetric` instance and the candidate
`model` input to the `EvalTask.evaluate()` function. The input column name
to both models is `prompt` and must be present in the dataset.

  ```
  baseline_model = GenerativeModel("gemini-1.0-pro")
  candidate_model = GenerativeModel("gemini-1.5-pro")

  pairwise_groundedness = PairwiseMetric(
      metric_prompt_template=MetricPromptTemplateExamples.get_prompt_template(
          "pairwise_groundedness"
      ),
      baseline_model=baseline_model,
  )
  eval_dataset = pd.DataFrame({
        "prompt"  : [...],
  })
  result = EvalTask(
      dataset=eval_dataset,
      metrics=[pairwise_groundedness],
      experiment="my-pairwise-experiment",
  ).evaluate(
      model=candidate_model,
      experiment_run_name="gemini-pairwise-eval-run",
  )
  ```

Examples of metric prompt templates for model-based evaluation.

Example PairwiseMetric instances.

Example PointwiseMetric instances.

A Model-based Pairwise Metric.

A model-based evaluation metric that compares two generative models' responses side-by-side, and allows users to A/B test their generative models to determine which model is performing better.

For more details on when to use pairwise metrics, see Evaluation methods and metrics.

Result Details:

* In `EvalResult.summary_metrics`, win rates for both the baseline and
candidate model are computed. The win rate is computed as proportion of
wins of one model's responses to total attempts as a decimal value
between 0 and 1.

* In `EvalResult.metrics_table`, a pairwise metric produces two
evaluation results per dataset row:
    * `pairwise_choice`: The choice shows whether the candidate model or
      the baseline model performs better, or if they are equally good.
    * `explanation`: The rationale behind each verdict using
      chain-of-thought reasoning. The explanation helps users scrutinize
      the judgment and builds appropriate trust in the decisions.

See [documentation
page](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval#understand-results)
for more details on understanding the metric results.

Usage Examples:

```
baseline_model = GenerativeModel("gemini-1.0-pro")
candidate_model = GenerativeModel("gemini-1.5-pro")

pairwise_groundedness = PairwiseMetric(
    metric_prompt_template=MetricPromptTemplateExamples.get_prompt_template(
        "pairwise_groundedness"
    ),
    baseline_model=baseline_model,
)
eval_dataset = pd.DataFrame({
      "prompt"  : [...],
})
pairwise_task = EvalTask(
    dataset=eval_dataset,
    metrics=[pairwise_groundedness],
    experiment="my-pairwise-experiment",
)
pairwise_result = pairwise_task.evaluate(
    model=candidate_model,
    experiment_run_name="gemini-pairwise-eval-run",
)
```

Pairwise metric prompt template for pairwise model-based metrics.

A Model-based Pointwise Metric.

A model-based evaluation metric that evaluate a single generative model's response.

For more details on when to use model-based pointwise metrics, see Evaluation methods and metrics.

Usage Examples:

```
candidate_model = GenerativeModel("gemini-1.5-pro")
eval_dataset = pd.DataFrame({
    "prompt"  : [...],
})
fluency_metric = PointwiseMetric(
    metric="fluency",
    metric_prompt_template=MetricPromptTemplateExamples.get_prompt_template('fluency'),
)
pointwise_eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=[
        fluency_metric,
        MetricPromptTemplateExamples.Pointwise.GROUNDEDNESS,
    ],
)
pointwise_result = pointwise_eval_task.evaluate(
    model=candidate_model,
)
```

Pointwise metric prompt template for pointwise model-based metrics.

A prompt template for creating prompts with variables.

The PromptTemplate class allows users to define a template string with variables represented in curly braces {variable}. The variable names cannot contain spaces. These variables can be replaced with specific values using the assemble method, providing flexibility in generating dynamic prompts.

Usage:

```
template_str = "Hello, {name}! Today is {day}. How are you?"
prompt_template = PromptTemplate(template_str)
completed_prompt = prompt_template.assemble(name="John", day="Monday")
print(completed_prompt)
```

The ROUGE Metric.

Calculates the recall of n-grams in prediction as compared to reference and returns a score ranging between 0 and 1. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.

A response candidate generated by the model.

Chat session holds the chat history.

The multi-part content of a message.

Usage:

response = model.generate_content(contents=[
    Content(role="user", parts=[Part.from_text("Why is sky blue?")])
])
```

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

Function call.

A representation of a function declaration.

Usage: Create function declaration and tool:

get_current_weather_func = generative_models.FunctionDeclaration(
    name="get_current_weather",
    description="Get the current weather in a given location",
    parameters={
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {
                "type": "string",
                "enum": [
                    "celsius",
                    "fahrenheit",
                ]
            }
        },
        "required": [
            "location"
        ]
    },
)
weather_tool = generative_models.Tool(
    function_declarations=[get_current_weather_func],
)
```
Use tool in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
))
```
Use tool in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

Parameters for the generation.

The configuration for model router requests.

The routing config is either one of the two nested classes:

• AutoRoutingMode: Automated routing.
• ManualRoutingMode: Manual routing.

Usage:

• AutoRoutingMode:

  routing_config=generative_models.RoutingConfig(
      routing_config=generative_models.RoutingConfig.AutoRoutingMode(
          model_routing_preference=generative_models.RoutingConfig.AutoRoutingMode.ModelRoutingPreference.BALANCED,
      ),
  )
  

• ManualRoutingMode:

  routing_config=generative_models.RoutingConfig(
      routing_config=generative_models.RoutingConfig.ManutalRoutingMode(
          model_name="gemini-1.5-pro-001",
      ),
  )
  

When automated routing is specified, the routing will be determined by the routing model predicted quality and customer provided model routing preference.

The model routing preference.

When manual routing is set, the specified model will be used directly.

The response from the model.

Initializes GenerativeModel.

Usage:

model = GenerativeModel("gemini-pro")
print(model.generate_content("Hello"))
```

Probability based thresholds levels for blocking.

Harm categories that will block the content.

The image that can be sent to a generative model.

A part of a multi-part Content message.

Usage:

text_part = Part.from_text("Why is sky blue?")
image_part = Part.from_image(Image.load_from_file("image.jpg"))
video_part = Part.from_uri(uri="gs://.../video.mp4", mime_type="video/mp4")
function_response_part = Part.from_function_response(
    name="get_current_weather",
    response={
        "content": {"weather_there": "super nice"},
    }
)

response1 = model.generate_content([text_part, image_part])
response2 = model.generate_content(video_part)
response3 = chat.send_message(function_response_part)
```

API documentation for ResponseValidationError class.

Parameters for the generation.

Probability vs severity.

Probability based thresholds levels for blocking.

Harm categories that will block the content.

A collection of functions that the model may use to generate response.

Usage: Create tool from function declarations:

get_current_weather_func = generative_models.FunctionDeclaration(...)
weather_tool = generative_models.Tool(
    function_declarations=[get_current_weather_func],
)
```
Use tool in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
))
```
Use tool in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

Config shared for all tools provided in the request.

Usage: Create ToolConfig

tool_config = ToolConfig(
    function_calling_config=ToolConfig.FunctionCallingConfig(
        mode=ToolConfig.FunctionCallingConfig.Mode.ANY,
        allowed_function_names=["get_current_weather_func"],
))
```
Use ToolConfig in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
    tool_config=tool_config,
))
```
Use ToolConfig in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
    tool_config=tool_config,
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

Grounding namespace.

Config for dynamic retrieval.

The mode of the predictor to be used in dynamic retrieval.

Tool to retrieve public web data for grounding, powered by Google Search.

A chat message.

ChatModel represents a language model that is capable of chat.

Examples::

chat_model = ChatModel.from_pretrained("chat-bison@001")

chat = chat_model.start_chat(
    context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",
    examples=[
        InputOutputTextPair(
            input_text="Who do you work for?",
            output_text="I work for Ned.",
        ),
        InputOutputTextPair(
            input_text="What do I like?",
            output_text="Ned likes watching movies.",
        ),
    ],
    temperature=0.3,
)

chat.send_message("Do you know any cool events this weekend?")

ChatSession represents a chat session with a language model.

Within a chat session, the model keeps context and remembers the previous conversation.

CodeChatModel represents a model that is capable of completing code.

.. rubric:: Examples

code_chat_model = CodeChatModel.from_pretrained("codechat-bison@001")

code_chat = code_chat_model.start_chat( context="I'm writing a large-scale enterprise application.", max_output_tokens=128, temperature=0.2, )

code_chat.send_message("Please help write a function to calculate the min of two numbers")

CodeChatSession represents a chat session with code chat language model.

Within a code chat session, the model keeps context and remembers the previous converstion.

Creates a LanguageModel.

This constructor should not be called directly. Use LanguageModel.from_pretrained(model_name=...) instead.

API documentation for GroundingSource class.

InlineContext represents a grounding source using provided inline context. .. attribute:: inline_context

The content used as inline context.

:type: str

VertexAISearchDatastore represents a grounding source using Vertex AI Search datastore .. attribute:: data_store_id

Data store ID of the Vertex AI Search datastore.

:type: str

WebSearch represents a grounding source using public web search. .. attribute:: disable_attribution

If set to True, skip finding claim attributions (i.e not generate grounding citation). Default: False.

:type: bool

InputOutputTextPair represents a pair of input and output texts.

Text embedding vector and statistics.

Structural text embedding input.

Creates a LanguageModel.

This constructor should not be called directly. Use LanguageModel.from_pretrained(model_name=...) instead.

Creates a LanguageModel.

This constructor should not be called directly. Use LanguageModel.from_pretrained(model_name=...) instead.

TextGenerationResponse represents a response of a language model. .. attribute:: text

The generated text

:type: str

Model that can be tuned with supervised fine tuning (SFT).

Responder that automatically responds to model's function calls.

A function declaration plus a function.

A response candidate generated by the model.

Chat session holds the chat history.

The multi-part content of a message.

Usage:

response = model.generate_content(contents=[
    Content(role="user", parts=[Part.from_text("Why is sky blue?")])
])
```

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

Function call.

A representation of a function declaration.

Usage: Create function declaration and tool:

get_current_weather_func = generative_models.FunctionDeclaration(
    name="get_current_weather",
    description="Get the current weather in a given location",
    parameters={
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {
                "type": "string",
                "enum": [
                    "celsius",
                    "fahrenheit",
                ]
            }
        },
        "required": [
            "location"
        ]
    },
)
weather_tool = generative_models.Tool(
    function_declarations=[get_current_weather_func],
)
```
Use tool in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
))
```
Use tool in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

Parameters for the generation.

The configuration for model router requests.

The routing config is either one of the two nested classes:

• AutoRoutingMode: Automated routing.
• ManualRoutingMode: Manual routing.

Usage:

• AutoRoutingMode:

  routing_config=generative_models.RoutingConfig(
      routing_config=generative_models.RoutingConfig.AutoRoutingMode(
          model_routing_preference=generative_models.RoutingConfig.AutoRoutingMode.ModelRoutingPreference.BALANCED,
      ),
  )
  

• ManualRoutingMode:

  routing_config=generative_models.RoutingConfig(
      routing_config=generative_models.RoutingConfig.ManutalRoutingMode(
          model_name="gemini-1.5-pro-001",
      ),
  )
  

When automated routing is specified, the routing will be determined by the routing model predicted quality and customer provided model routing preference.

The model routing preference.

When manual routing is set, the specified model will be used directly.

The response from the model.

Initializes GenerativeModel.

Usage:

model = GenerativeModel("gemini-pro")
print(model.generate_content("Hello"))
```

Probability based thresholds levels for blocking.

Harm categories that will block the content.

The image that can be sent to a generative model.

A part of a multi-part Content message.

Usage:

text_part = Part.from_text("Why is sky blue?")
image_part = Part.from_image(Image.load_from_file("image.jpg"))
video_part = Part.from_uri(uri="gs://.../video.mp4", mime_type="video/mp4")
function_response_part = Part.from_function_response(
    name="get_current_weather",
    response={
        "content": {"weather_there": "super nice"},
    }
)

response1 = model.generate_content([text_part, image_part])
response2 = model.generate_content(video_part)
response3 = chat.send_message(function_response_part)
```

API documentation for ResponseBlockedError class.

API documentation for ResponseValidationError class.

Parameters for the generation.

Probability vs severity.

Probability based thresholds levels for blocking.

Harm categories that will block the content.

A collection of functions that the model may use to generate response.

Usage: Create tool from function declarations:

get_current_weather_func = generative_models.FunctionDeclaration(...)
weather_tool = generative_models.Tool(
    function_declarations=[get_current_weather_func],
)
```
Use tool in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
))
```
Use tool in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

Config shared for all tools provided in the request.

Usage: Create ToolConfig

tool_config = ToolConfig(
    function_calling_config=ToolConfig.FunctionCallingConfig(
        mode=ToolConfig.FunctionCallingConfig.Mode.ANY,
        allowed_function_names=["get_current_weather_func"],
))
```
Use ToolConfig in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
    tool_config=tool_config,
))
```
Use ToolConfig in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
    tool_config=tool_config,
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

A chat message.

The response from a count_tokens request. .. attribute:: total_tokens

The total number of tokens counted across all instances passed to the request.

:type: int

The evaluation metric response for classification metrics.

The evaluation metric response.

Spec for question answering model evaluation tasks.

Spec for text classification model evaluation tasks.

Spec for text generation model evaluation tasks.

Spec for text summarization model evaluation tasks.

InputOutputTextPair represents a pair of input and output texts.

Text embedding vector and statistics.

Structural text embedding input.

TextGenerationResponse represents a response of a language model. .. attribute:: text

The generated text

:type: str

Specification for model evaluation to perform during tuning.

A Langchain Agent.

See https://cloud.google.com/vertex-ai/generative-ai/docs/reasoning-engine/develop for details.

Protocol for Reasoning Engine applications that can be queried.

Represents a Vertex AI Reasoning Engine resource.

Represents a TuningJob that runs with Google owned models.

Initializes class with project, location, and api_client.

Control image config.

Control reference image.

This encapsulates the control reference image type.

Entity label holding a text label and any associated confidence score.

Generated image.

Generated image mask.

Image.

Generates captions from image.

Examples::

model = ImageCaptioningModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

Generates images from text prompt.

Examples::

model = ImageGenerationModel.from_pretrained("imagegeneration@002")
response = model.generate_images(
    prompt="Astronaut riding a horse",
    # Optional:
    number_of_images=1,
    seed=0,
)
response[0].show()
response[0].save("image1.png")

Image generation response.

Answers questions about an image.

Examples::

model = ImageQnAModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

Segments an image.

Image Segmentation response.

Generates text from images.

Examples::

model = ImageTextModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")

captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

Mask image config.

Mask reference image. This encapsulates the mask reference image type.

Generates embedding vectors from images and videos.

Examples::

model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")
image = Image.load_from_file("image.png")
video = Video.load_from_file("video.mp4")

embeddings = model.get_embeddings(
    image=image,
    video=video,
    contextual_text="Hello world",
)
image_embedding = embeddings.image_embedding
video_embeddings = embeddings.video_embeddings
text_embedding = embeddings.text_embedding

The multimodal embedding response.

Raw reference image.

This encapsulates the raw reference image type.

Reference image.

This is a new base API object for Imagen 3.0 Capabilities.

Input scribble for image segmentation.

Style image config.

Style reference image. This encapsulates the style reference image type.

Subject image config.

Subject reference image.

This encapsulates the subject reference image type.

Video.

Embeddings generated from video with offset times.

The specific video segments (in seconds) the embeddings are generated for.

Verifies if an image has a watermark.

WatermarkVerificationResponse(_prediction_response: Any, watermark_verification_result: Optional[str] = None)

Initializer for ModelMonitor.

Initializer for ModelMonitoringJob.

Example Usage:

 my_monitoring_job = aiplatform.ModelMonitoringJob(
     model_monitoring_job_name='projects/123/locations/us-central1/modelMonitors/\
     my_model_monitor_id/modelMonitoringJobs/my_monitoring_job_id'
 )
 or
 my_monitoring_job = aiplatform.aiplatform.ModelMonitoringJob(
     model_monitoring_job_name='my_monitoring_job_id',
     model_monitor_id='my_model_monitor_id',
 )

Data drift monitoring spec.

Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period.

.. rubric:: Example

feature_drift_spec=DataDriftSpec( features=["feature1"] categorical_metric_type="l_infinity", numeric_metric_type="jensen_shannon_divergence", default_categorical_alert_threshold=0.01, default_numeric_alert_threshold=0.02, feature_alert_thresholds={"feature1":0.02, "feature2":0.01}, )

Feature attribution spec.

.. rubric:: Example

feature_attribution_spec=FeatureAttributionSpec( features=["feature1"] default_alert_threshold=0.01, feature_alert_thresholds={"feature1":0.02, "feature2":0.01}, batch_dedicated_resources=BatchDedicatedResources( starting_replica_count=1, max_replica_count=2, machine_spec=my_machine_spec, ), )

Field Schema.

The class identifies the data type of a single feature, which combines together to form the Schema for different fields in ModelMonitoringSchema.

Initializer for ModelMonitoringSchema.

Model monitoring data input spec.

Initializer for NotificationSpec.

Initializer for ObjectiveSpec.

Initializer for OutputSpec.

Initializer for TabularObjective.

Generated image.

Image.

Generates captions from image.

Examples::

model = ImageCaptioningModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

Generates images from text prompt.

Examples::

model = ImageGenerationModel.from_pretrained("imagegeneration@002")
response = model.generate_images(
    prompt="Astronaut riding a horse",
    # Optional:
    number_of_images=1,
    seed=0,
)
response[0].show()
response[0].save("image1.png")

Image generation response.

Answers questions about an image.

Examples::

model = ImageQnAModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

Generates text from images.

Examples::

model = ImageTextModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")

captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

Generates embedding vectors from images and videos.

Examples::

model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")
image = Image.load_from_file("image.png")
video = Video.load_from_file("video.mp4")

embeddings = model.get_embeddings(
    image=image,
    video=video,
    contextual_text="Hello world",
)
image_embedding = embeddings.image_embedding
video_embeddings = embeddings.video_embeddings
text_embedding = embeddings.text_embedding

The multimodal embedding response.

Video.

Embeddings generated from video with offset times.

The specific video segments (in seconds) the embeddings are generated for.

Classes for working with language models.

Classes for working with the Gemini models.

Classes for working with language models.

Classes for supervised tuning.

Classes for working with vision models.