Class swarmauri_standard.llms.PerplexityModel.PerplexityModel
swarmauri_standard.llms.PerplexityModel.PerplexityModel
PerplexityModel(**data)
Bases: LLMBase
Represents a language model interface for Perplexity API.
Provides methods for synchronous and asynchronous predictions, streaming, and batch processing of conversations using the Perplexity language models.
ATTRIBUTE | DESCRIPTION |
---|---|
api_key |
API key for authenticating requests to the Perplexity API.
TYPE:
|
allowed_models |
List of allowed model names that can be used.
TYPE:
|
name |
The default model name to use for predictions.
TYPE:
|
type |
The type identifier for this class.
TYPE:
|
timeout |
Timeout for API requests in seconds.
TYPE:
|
Provider resources: https://docs.perplexity.ai/guides/model-cards Link to deprecated models: https://docs.perplexity.ai/changelog/changelog#model-deprecation-notice
Initialize the GroqAIAudio class with the provided data.
PARAMETER | DESCRIPTION |
---|---|
**data
|
Arbitrary keyword arguments containing initialization data.
TYPE:
|
Source code in swarmauri_standard/llms/PerplexityModel.py
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|
api_key
instance-attribute
api_key
allowed_models
class-attribute
instance-attribute
allowed_models = [
"sonar-reasoning-pro",
"sonar-reasoning",
"sonar-pro",
"sonar",
]
name
class-attribute
instance-attribute
name = 'sonar'
type
class-attribute
instance-attribute
type = 'PerplexityModel'
timeout
class-attribute
instance-attribute
timeout = 600.0
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=256,
top_p=None,
top_k=None,
return_citations=False,
presence_penalty=None,
frequency_penalty=None,
)
Makes a synchronous prediction request.
PARAMETER | DESCRIPTION |
---|---|
conversation
|
The conversation object containing the history.
TYPE:
|
temperature
|
Sampling temperature for response generation. Defaults to 0.7.
TYPE:
|
max_tokens
|
Maximum number of tokens for the response. Defaults to 256.
TYPE:
|
top_p
|
Nucleus sampling parameter. If specified,
TYPE:
|
top_k
|
Top-k sampling parameter. If specified,
TYPE:
|
return_citations
|
Whether to return citations in the response. Defaults to False.
TYPE:
|
presence_penalty
|
Penalty for new tokens based on presence.
TYPE:
|
frequency_penalty
|
Penalty for new tokens based on frequency.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
Conversation
|
An updated Conversation object with the model's response.
TYPE:
|
Source code in swarmauri_standard/llms/PerplexityModel.py
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|
apredict
async
apredict(
conversation,
temperature=0.7,
max_tokens=256,
top_p=None,
top_k=None,
return_citations=False,
presence_penalty=None,
frequency_penalty=None,
)
Makes an asynchronous prediction request.
PARAMETER | DESCRIPTION |
---|---|
conversation
|
The conversation object containing the history.
TYPE:
|
temperature
|
Sampling temperature for response generation. Defaults to 0.7.
TYPE:
|
max_tokens
|
Maximum number of tokens for the response. Defaults to 256.
TYPE:
|
top_p
|
Nucleus sampling parameter. If specified,
TYPE:
|
top_k
|
Top-k sampling parameter. If specified,
TYPE:
|
return_citations
|
Whether to return citations in the response. Defaults to False.
TYPE:
|
presence_penalty
|
Penalty for new tokens based on presence.
TYPE:
|
frequency_penalty
|
Penalty for new tokens based on frequency.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
Conversation
|
An updated Conversation object with the model's response.
TYPE:
|
Source code in swarmauri_standard/llms/PerplexityModel.py
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|
stream
stream(
conversation,
temperature=0.7,
max_tokens=256,
top_p=None,
top_k=None,
return_citations=False,
presence_penalty=None,
frequency_penalty=None,
)
Synchronously streams the response for a given conversation.
PARAMETER | DESCRIPTION |
---|---|
conversation
|
The conversation object containing message history.
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:
|
top_p
|
Nucleus sampling parameter. If specified,
TYPE:
|
top_k
|
Top-k sampling parameter. If specified,
TYPE:
|
return_citations
|
Whether to return citations in the response. Defaults to False.
TYPE:
|
presence_penalty
|
Penalty for introducing new topics. Defaults to None.
TYPE:
|
frequency_penalty
|
Penalty for repeating existing tokens. Defaults to None.
TYPE:
|
YIELDS | DESCRIPTION |
---|---|
str
|
Chunks of response content as the data is streamed.
TYPE::
|
Source code in swarmauri_standard/llms/PerplexityModel.py
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|
astream
async
astream(
conversation,
temperature=0.7,
max_tokens=256,
top_p=None,
top_k=None,
return_citations=False,
presence_penalty=None,
frequency_penalty=None,
)
Asynchronously streams the response for a given conversation.
PARAMETER | DESCRIPTION |
---|---|
conversation
|
The conversation object containing message history.
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:
|
top_p
|
Nucleus sampling parameter. If specified,
TYPE:
|
top_k
|
Top-k sampling parameter. If specified,
TYPE:
|
return_citations
|
Whether to return citations in the response. Defaults to False.
TYPE:
|
presence_penalty
|
Penalty for introducing new topics. Defaults to None.
TYPE:
|
frequency_penalty
|
Penalty for repeating existing tokens. Defaults to None.
TYPE:
|
YIELDS | DESCRIPTION |
---|---|
str
|
Chunks of response content as the data is streamed asynchronously.
TYPE::
|
Source code in swarmauri_standard/llms/PerplexityModel.py
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|
batch
batch(
conversations,
temperature=0.7,
max_tokens=256,
top_p=None,
top_k=None,
return_citations=False,
presence_penalty=None,
frequency_penalty=None,
)
Processes a batch of conversations synchronously.
PARAMETER | DESCRIPTION |
---|---|
conversations
|
List of conversation objects.
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:
|
top_p
|
Nucleus sampling parameter. If specified,
TYPE:
|
top_k
|
Top-k sampling parameter. If specified,
TYPE:
|
return_citations
|
Whether to return citations in the response. Defaults to False.
TYPE:
|
presence_penalty
|
Penalty for introducing new topics. Defaults to None.
TYPE:
|
frequency_penalty
|
Penalty for repeating existing tokens. Defaults to None.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
List[Conversation]
|
List[Conversation]: List of updated conversation objects after processing. |
Source code in swarmauri_standard/llms/PerplexityModel.py
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|
abatch
async
abatch(
conversations,
temperature=0.7,
max_tokens=256,
top_p=None,
top_k=None,
return_citations=False,
presence_penalty=None,
frequency_penalty=None,
max_concurrent=5,
)
Asynchronously processes a batch of conversations with a limit on concurrent tasks.
PARAMETER | DESCRIPTION |
---|---|
conversations
|
List of conversation objects.
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:
|
top_p
|
Nucleus sampling parameter. If specified,
TYPE:
|
top_k
|
Top-k sampling parameter. If specified,
TYPE:
|
return_citations
|
Whether to return citations in the response. Defaults to False.
TYPE:
|
presence_penalty
|
Penalty for introducing new topics. Defaults to None.
TYPE:
|
frequency_penalty
|
Penalty for repeating existing tokens. Defaults to None.
TYPE:
|
max_concurrent
|
Maximum number of concurrent tasks. Defaults to 5.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
List[Conversation]
|
List[Conversation]: List of updated conversation objects after processing asynchronously. |
Source code in swarmauri_standard/llms/PerplexityModel.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]: List of allowed model names. |
Source code in swarmauri_standard/llms/PerplexityModel.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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|