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Classes for working with the Gemini models.
Classes
AutomaticFunctionCallingResponder
AutomaticFunctionCallingResponder(max_automatic_function_calls: int = 1)
Responder that automatically responds to model's function calls.
CallableFunctionDeclaration
CallableFunctionDeclaration(
name: str,
function: typing.Callable[[...], typing.Any],
parameters: typing.Dict[str, typing.Any],
description: typing.Optional[str] = None,
)
A function declaration plus a function.
Candidate
Candidate()
A response candidate generated by the model.
ChatSession
ChatSession(
model: vertexai.generative_models._generative_models._GenerativeModel,
*,
history: typing.Optional[
typing.List[vertexai.generative_models._generative_models.Content]
] = None,
response_validation: bool = True,
responder: typing.Optional[
vertexai.generative_models._generative_models.AutomaticFunctionCallingResponder
] = None,
raise_on_blocked: typing.Optional[bool] = None
)
Chat session holds the chat history.
Content
Content(
*,
parts: typing.Optional[
typing.List[vertexai.generative_models._generative_models.Part]
] = None,
role: typing.Optional[str] = None
)
The multi-part content of a message.
Usage:
response = model.generate_content(contents=[
Content(role="user", parts=[Part.from_text("Why is sky blue?")])
])
```
FinishReason
FinishReason(value)
The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.
Values: FINISH_REASON_UNSPECIFIED (0): The finish reason is unspecified. STOP (1): Natural stop point of the model or provided stop sequence. MAX_TOKENS (2): The maximum number of tokens as specified in the request was reached. SAFETY (3): The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output. RECITATION (4): The token generation was stopped as the response was flagged for unauthorized citations. OTHER (5): All other reasons that stopped the token generation BLOCKLIST (6): The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist. PROHIBITED_CONTENT (7): The token generation was stopped as the response was flagged for the prohibited contents. SPII (8): The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents.
FunctionDeclaration
FunctionDeclaration(
*,
name: str,
parameters: typing.Dict[str, typing.Any],
description: typing.Optional[str] = None
)
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"},
}
),
))
```
GenerationConfig
GenerationConfig(
*,
temperature: typing.Optional[float] = None,
top_p: typing.Optional[float] = None,
top_k: typing.Optional[int] = None,
candidate_count: typing.Optional[int] = None,
max_output_tokens: typing.Optional[int] = None,
stop_sequences: typing.Optional[typing.List[str]] = None
)
Parameters for the generation.
GenerationResponse
GenerationResponse()
The response from the model.
GenerativeModel
GenerativeModel(
model_name: str,
*,
generation_config: typing.Optional[
typing.Union[
vertexai.generative_models._generative_models.GenerationConfig,
typing.Dict[str, typing.Any],
]
] = None,
safety_settings: typing.Optional[
typing.Union[
typing.List[google.cloud.aiplatform_v1beta1.types.content.SafetySetting],
typing.Dict[
google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
],
]
] = None,
tools: typing.Optional[
typing.List[vertexai.generative_models._generative_models.Tool]
] = None
)
Initializes GenerativeModel.
Usage:
model = GenerativeModel("gemini-pro")
print(model.generate_content("Hello"))
```
Parameter | |
---|---|
Name | Description |
model_name |
str
Model Garden model resource name. |
HarmBlockThreshold
HarmBlockThreshold(value)
Probability based thresholds levels for blocking.
Values: HARM_BLOCK_THRESHOLD_UNSPECIFIED (0): Unspecified harm block threshold. BLOCK_LOW_AND_ABOVE (1): Block low threshold and above (i.e. block more). BLOCK_MEDIUM_AND_ABOVE (2): Block medium threshold and above. BLOCK_ONLY_HIGH (3): Block only high threshold (i.e. block less). BLOCK_NONE (4): Block none.
HarmCategory
HarmCategory(value)
Harm categories that will block the content.
Values: HARM_CATEGORY_UNSPECIFIED (0): The harm category is unspecified. HARM_CATEGORY_HATE_SPEECH (1): The harm category is hate speech. HARM_CATEGORY_DANGEROUS_CONTENT (2): The harm category is dangerous content. HARM_CATEGORY_HARASSMENT (3): The harm category is harassment. HARM_CATEGORY_SEXUALLY_EXPLICIT (4): The harm category is sexually explicit content.
Image
Image()
The image that can be sent to a generative model.
Part
Part()
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)
```
ResponseBlockedError
ResponseBlockedError(
message: str,
request_contents: typing.List[
vertexai.generative_models._generative_models.Content
],
responses: typing.List[
vertexai.generative_models._generative_models.GenerationResponse
],
)
Common base class for all non-exit exceptions.
ResponseValidationError
ResponseValidationError(
message: str,
request_contents: typing.List[
vertexai.generative_models._generative_models.Content
],
responses: typing.List[
vertexai.generative_models._generative_models.GenerationResponse
],
)
Common base class for all non-exit exceptions.
Tool
Tool(
function_declarations: typing.List[
vertexai.generative_models._generative_models.FunctionDeclaration
],
)
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"},
}
),
))
```
grounding
grounding()
Grounding namespace.