Class swarmauri_standard.llms.GeminiProModel.GeminiProModel
swarmauri_standard.llms.GeminiProModel.GeminiProModel
GeminiProModel(api_key, **kwargs)
Bases: LLMBase
GeminiProModel is a class interface for interacting with the Gemini language model API.
ATTRIBUTE | DESCRIPTION |
---|---|
api_key |
API key for authentication with the Gemini API.
TYPE:
|
allowed_models |
List of allowed model names for selection.
TYPE:
|
name |
Default name of the model in use.
TYPE:
|
type |
Type identifier for GeminiProModel.
TYPE:
|
Provider resources: https://deepmind.google/technologies/gemini/pro/
Initializes the GeminiProModel object with the given API key.
PARAMETER | DESCRIPTION |
---|---|
api_key
|
The API key for the GeminiProModel.
TYPE:
|
Source code in swarmauri_standard/llms/GeminiProModel.py
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api_key
instance-attribute
api_key
allowed_models
class-attribute
instance-attribute
allowed_models = [
"gemini-2.0-flash",
"gemini-2.0-flash-lite",
"gemini-2.0-pro-exp-02-05",
"gemini-1.5-flash",
"gemini-1.5-flash-8b",
"gemini-1.5-pro",
]
name
class-attribute
instance-attribute
name = 'gemini-2.0-flash'
timeout
class-attribute
instance-attribute
timeout = 600.0
type
class-attribute
instance-attribute
type = 'GeminiProModel'
model_config
class-attribute
instance-attribute
model_config = ConfigDict(
extra="allow", arbitrary_types_allowed=True
)
id
class-attribute
instance-attribute
id = Field(default_factory=generate_id)
members
class-attribute
instance-attribute
members = None
owners
class-attribute
instance-attribute
owners = None
host
class-attribute
instance-attribute
host = None
default_logger
class-attribute
default_logger = None
logger
class-attribute
instance-attribute
logger = None
version
class-attribute
instance-attribute
version = '0.1.0'
include_usage
class-attribute
instance-attribute
include_usage = True
BASE_URL
class-attribute
instance-attribute
BASE_URL = None
predict
predict(conversation, temperature=0.7, max_tokens=25)
Generates a prediction for the given conversation using the specified parameters.
PARAMETER | DESCRIPTION |
---|---|
conversation
|
The conversation object containing the history of messages.
TYPE:
|
temperature
|
The sampling temperature to use. Defaults to 0.7.
TYPE:
|
max_tokens
|
The maximum number of tokens to generate. Defaults to 256.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
Conversation
|
The updated conversation object with the new message added.
TYPE:
|
RAISES | DESCRIPTION |
---|---|
HTTPStatusError
|
If the HTTP request to the generation endpoint fails. |
Source code in swarmauri_standard/llms/GeminiProModel.py
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|
apredict
async
apredict(conversation, temperature=0.7, max_tokens=256)
Asynchronously generates a response for a given conversation using the GeminiProModel.
PARAMETER | DESCRIPTION |
---|---|
conversation
|
The conversation object containing the history of messages.
TYPE:
|
temperature
|
Sampling temperature for response generation. Defaults to 0.7.
TYPE:
|
max_tokens
|
Maximum number of tokens in the generated response. Defaults to 256.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
Conversation
|
The updated conversation object with the generated response added.
TYPE:
|
RAISES | DESCRIPTION |
---|---|
HTTPStatusError
|
If the HTTP request to the generation endpoint fails. |
Source code in swarmauri_standard/llms/GeminiProModel.py
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|
stream
stream(conversation, temperature=0.7, max_tokens=256)
Streams the response from the model based on the given conversation.
PARAMETER | DESCRIPTION |
---|---|
conversation
|
The conversation object containing the history of messages.
TYPE:
|
temperature
|
The temperature setting for the generation. Defaults to 0.7.
TYPE:
|
max_tokens
|
The maximum number of tokens to generate. Defaults to 256.
TYPE:
|
YIELDS | DESCRIPTION |
---|---|
str
|
Chunks of the generated response text.
TYPE::
|
RAISES | DESCRIPTION |
---|---|
HTTPStatusError
|
If the HTTP request to the model fails. |
Source code in swarmauri_standard/llms/GeminiProModel.py
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|
astream
async
astream(conversation, temperature=0.7, max_tokens=256)
Asynchronously streams generated content for a given conversation.
PARAMETER | DESCRIPTION |
---|---|
conversation
|
The conversation object containing the history of messages.
TYPE:
|
temperature
|
The temperature for the generation process. Defaults to 0.7.
TYPE:
|
max_tokens
|
The maximum number of tokens to generate. Defaults to 256.
TYPE:
|
YIELDS | DESCRIPTION |
---|---|
str
|
Chunks of generated content as they are received.
TYPE::
|
RAISES | DESCRIPTION |
---|---|
HTTPStatusError
|
If the HTTP request to the generation service fails. |
Source code in swarmauri_standard/llms/GeminiProModel.py
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|
batch
batch(conversations, temperature=0.7, max_tokens=256)
Synchronously process multiple conversations.
PARAMETER | DESCRIPTION |
---|---|
conversations
|
A list of Conversation objects to be processed.
TYPE:
|
temperature
|
The sampling temperature to use. Defaults to 0.7.
TYPE:
|
max_tokens
|
The maximum number of tokens to generate. Defaults to 256.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
List
|
A list of predictions for each conversation.
TYPE:
|
Source code in swarmauri_standard/llms/GeminiProModel.py
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abatch
async
abatch(
conversations,
temperature=0.7,
max_tokens=256,
max_concurrent=5,
)
Asynchronously processes a batch of conversations using the apredict
method.
PARAMETER | DESCRIPTION |
---|---|
conversations
|
A list of Conversation objects to be processed.
TYPE:
|
temperature
|
The temperature parameter for the prediction. Defaults to 0.7.
TYPE:
|
max_tokens
|
The maximum number of tokens for the prediction. Defaults to 256.
TYPE:
|
max_concurrent
|
The maximum number of concurrent tasks. Defaults to 5.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
List
|
A list of results from the
TYPE:
|
Source code in swarmauri_standard/llms/GeminiProModel.py
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get_allowed_models
get_allowed_models()
Queries the LLMProvider API endpoint to retrieve the list of allowed models.
RETURNS | DESCRIPTION |
---|---|
List[str]
|
List[str]: A list of allowed model names. |
Source code in swarmauri_standard/llms/GeminiProModel.py
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|
register_model
classmethod
register_model()
Decorator to register a base model in the unified registry.
RETURNS | DESCRIPTION |
---|---|
Callable
|
A decorator function that registers the model class.
TYPE:
|
Source code in swarmauri_base/DynamicBase.py
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register_type
classmethod
register_type(resource_type=None, type_name=None)
Decorator to register a subtype under one or more base models in the unified registry.
PARAMETER | DESCRIPTION |
---|---|
resource_type
|
The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used.
TYPE:
|
type_name
|
An optional custom type name for the subtype.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
Callable
|
A decorator function that registers the subtype.
TYPE:
|
Source code in swarmauri_base/DynamicBase.py
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model_validate_toml
classmethod
model_validate_toml(toml_data)
Validate a model from a TOML string.
Source code in swarmauri_base/TomlMixin.py
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model_dump_toml
model_dump_toml(
fields_to_exclude=None, api_key_placeholder=None
)
Return a TOML representation of the model.
Source code in swarmauri_base/TomlMixin.py
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model_validate_yaml
classmethod
model_validate_yaml(yaml_data)
Validate a model from a YAML string.
Source code in swarmauri_base/YamlMixin.py
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model_dump_yaml
model_dump_yaml(
fields_to_exclude=None, api_key_placeholder=None
)
Return a YAML representation of the model.
Source code in swarmauri_base/YamlMixin.py
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model_post_init
model_post_init(logger=None)
Assign a logger instance after model initialization.
Source code in swarmauri_base/LoggerMixin.py
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add_allowed_model
add_allowed_model(model)
Add a new model to the list of allowed models.
RAISES | DESCRIPTION |
---|---|
ValueError
|
If the model is already in the allowed models list. |
Source code in swarmauri_base/llms/LLMBase.py
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remove_allowed_model
remove_allowed_model(model)
Remove a model from the list of allowed models.
RAISES | DESCRIPTION |
---|---|
ValueError
|
If the model is not in the allowed models list. |
Source code in swarmauri_base/llms/LLMBase.py
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