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.
FunctionCall
FunctionCall
(
*
,
name
:
str
,
args
:
typing
.
Dict
[
str
,
typing
.
Any
])
Function call.
FunctionDeclaration
FunctionDeclaration
(
*
,
name
:
str
,
parameters
:
typing
.
Dict
[
str
,
typing
.
Any
],
description
:
typing
.
Optional
[
str
]
=
None
,
response
:
typing
.
Optional
[
typing
.
Dict
[
str
,
typing
.
Any
]]
=
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"
]
},
# Optional:
response={
"type": "object",
"properties": {
"weather": {
"type": "string",
"description": "The weather in the city"
},
},
},
)
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
,
presence_penalty
:
typing
.
Optional
[
float
]
=
None
,
frequency_penalty
:
typing
.
Optional
[
float
]
=
None
,
response_mime_type
:
typing
.
Optional
[
str
]
=
None
,
response_schema
:
typing
.
Optional
[
typing
.
Dict
[
str
,
typing
.
Any
]]
=
None
,
seed
:
typing
.
Optional
[
int
]
=
None
,
audio_timestamp
:
typing
.
Optional
[
bool
]
=
None
,
routing_config
:
typing
.
Optional
[
RoutingConfig
]
=
None
,
logprobs
:
typing
.
Optional
[
int
]
=
None
,
response_logprobs
:
typing
.
Optional
[
bool
]
=
None
,
response_modalities
:
typing
.
Optional
[
typing
.
List
[
GenerationConfig
.
Modality
]]
=
None
)
Parameters for the generation.
GenerationResponse
GenerationResponse
()
The response from the model.
GenerativeModel
GenerativeModel
(
model_name
:
str
,
*
,
generation_config
:
typing
.
Optional
[
GenerationConfigType
]
=
None
,
safety_settings
:
typing
.
Optional
[
SafetySettingsType
]
=
None
,
tools
:
typing
.
Optional
[
typing
.
List
[
vertexai
.
generative_models
.
_generative_models
.
Tool
]
]
=
None
,
tool_config
:
typing
.
Optional
[
vertexai
.
generative_models
.
_generative_models
.
ToolConfig
]
=
None
,
system_instruction
:
typing
.
Optional
[
PartsType
]
=
None
,
labels
:
typing
.
Optional
[
typing
.
Dict
[
str
,
str
]]
=
None
)
Initializes GenerativeModel.
Usage:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content("Hello"))
```
HarmBlockThreshold
HarmBlockThreshold
(
value
)
Probability based thresholds levels for blocking.
HarmCategory
HarmCategory
(
value
)
Harm categories that will block the 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
],
)
API documentation for ResponseBlockedError
class.
ResponseValidationError
ResponseValidationError
(
message
:
str
,
request_contents
:
typing
.
List
[
vertexai
.
generative_models
.
_generative_models
.
Content
],
responses
:
typing
.
List
[
vertexai
.
generative_models
.
_generative_models
.
GenerationResponse
],
)
API documentation for ResponseValidationError
class.
SafetySetting
SafetySetting
(
*
,
category
:
google
.
cloud
.
aiplatform_v1beta1
.
types
.
content
.
HarmCategory
,
threshold
:
google
.
cloud
.
aiplatform_v1beta1
.
types
.
content
.
SafetySetting
.
HarmBlockThreshold
,
method
:
typing
.
Optional
[
google
.
cloud
.
aiplatform_v1beta1
.
types
.
content
.
SafetySetting
.
HarmBlockMethod
]
=
None
)
Parameters for the generation.
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"},
}
),
))
```
ToolConfig
ToolConfig
(
function_calling_config
:
vertexai
.
generative_models
.
_generative_models
.
ToolConfig
.
FunctionCallingConfig
,
)
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"},
}
),
))
```
