Class swarmauri_standard.llms.AI21StudioModel.AI21StudioModel

swarmauri_standard.llms.AI21StudioModel.AI21StudioModel

AI21StudioModel(**data)

Bases: LLMBase

A model class for interacting with the AI21 Studio's language models via HTTP API calls.

This class supports synchronous and asynchronous methods for text generation, message streaming, and batch processing, allowing it to work with conversations and handle different text generation parameters such as temperature, max tokens, and more.

ATTRIBUTE	DESCRIPTION
api_key	API key for authenticating with AI21 Studio's API. TYPE: str
allowed_models	List of model names allowed by the provider. TYPE: List[str]
name	Default model name to use. TYPE: str
type	Specifies the model type, used for internal consistency. TYPE: Literal

Provider resources: https://docs.ai21.com/reference/jamba-15-api-ref

Initializes the GroqToolModel instance, setting up headers for API requests.

PARAMETER	DESCRIPTION
**data	Arbitrary keyword arguments for initialization. TYPE: Dict[str, Any] DEFAULT: {}

Source code in swarmauri_standard/llms/AI21StudioModel.py

def __init__(self, **data: Dict[str, Any]) -> None:
    """
    Initializes the GroqToolModel instance, setting up headers for API requests.

    Parameters:
        **data: Arbitrary keyword arguments for initialization.
    """
    super().__init__(**data)
    self._client = httpx.Client(
        headers={"Authorization": f"Bearer {self.api_key.get_secret_value()}"},
        base_url=self._BASE_URL,
        timeout=self.timeout,
    )
    self._async_client = httpx.AsyncClient(
        headers={"Authorization": f"Bearer {self.api_key.get_secret_value()}"},
        base_url=self._BASE_URL,
        timeout=self.timeout,
    )

api_key instance-attribute

api_key

allowed_models class-attribute instance-attribute

allowed_models = [
    "jamba-large",
    "jamba-mini",
    "jamba-large-1.7",
    "jamba-mini-1.7",
]

name class-attribute instance-attribute

name = 'jamba-large'

type class-attribute instance-attribute

type = 'AI21StudioModel'

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

resource class-attribute instance-attribute

resource = Field(default=LLM.value, frozen=True)

version class-attribute instance-attribute

version = '0.1.0'

timeout class-attribute instance-attribute

timeout = 600.0

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=1.0,
    stop="\n",
    n=1,
)

Synchronously generates a response for a given conversation.

PARAMETER	DESCRIPTION
conversation	The conversation object containing the message history. TYPE: Conversation
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum number of tokens in the response. TYPE: int DEFAULT: 256
top_p	Nucleus sampling parameter. TYPE: float DEFAULT: 1.0
stop	Stop sequence to halt generation. TYPE: str DEFAULT: '\n'
n	Number of completions to generate. TYPE: int DEFAULT: 1

RETURNS	DESCRIPTION
Conversation	Updated conversation with generated message. TYPE: Conversation

Source code in swarmauri_standard/llms/AI21StudioModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def predict(
    self,
    conversation: Conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
    n=1,
) -> Conversation:
    """
    Synchronously generates a response for a given conversation.

    Args:
        conversation (Conversation): The conversation object containing the message history.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum number of tokens in the response.
        top_p (float): Nucleus sampling parameter.
        stop (str): Stop sequence to halt generation.
        n (int): Number of completions to generate.

    Returns:
        Conversation: Updated conversation with generated message.
    """
    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "n": n,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "stop": [stop] if stop else [],
        "response_format": {"type": "text"},
    }

    with DurationManager() as prompt_timer:
        response = self._client.post(self._BASE_URL, json=payload)
        response.raise_for_status()

    response_data = response.json()
    message_content = response_data["choices"][0]["message"]["content"]
    usage_data = response_data.get("usage", {})

    # Prepare usage data if tracking is enabled
    if self.include_usage and usage_data:
        usage = self._prepare_usage_data(usage_data, prompt_timer.duration)
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))
    return conversation

apredict async

apredict(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
    n=1,
)

Asynchronously generates a response for a given conversation.

PARAMETER	DESCRIPTION
conversation	The conversation object containing the message history. TYPE: Conversation
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum number of tokens in the response. TYPE: int DEFAULT: 256
top_p	Nucleus sampling parameter. TYPE: float DEFAULT: 1.0
stop	Stop sequence to halt generation. TYPE: str DEFAULT: '\n'
n	Number of completions to generate. TYPE: int DEFAULT: 1

RETURNS	DESCRIPTION
Conversation	Updated conversation with generated message. TYPE: Conversation

Source code in swarmauri_standard/llms/AI21StudioModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def apredict(
    self,
    conversation: Conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
    n=1,
) -> Conversation:
    """
    Asynchronously generates a response for a given conversation.

    Args:
        conversation (Conversation): The conversation object containing the message history.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum number of tokens in the response.
        top_p (float): Nucleus sampling parameter.
        stop (str): Stop sequence to halt generation.
        n (int): Number of completions to generate.

    Returns:
        Conversation: Updated conversation with generated message.
    """
    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "n": n,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "stop": [stop] if stop else [],
        "response_format": {"type": "text"},
    }

    with DurationManager() as prompt_timer:
        response = await self._async_client.post(self._BASE_URL, json=payload)
        response.raise_for_status()

    response_data = response.json()
    message_content = response_data["choices"][0]["message"]["content"]
    usage_data = response_data.get("usage", {})

    # Prepare usage data if tracking is enabled
    if self.include_usage and usage_data:
        usage = self._prepare_usage_data(usage_data, prompt_timer.duration)
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))
    return conversation

stream

stream(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
)

Synchronously streams responses for a conversation, yielding each chunk.

PARAMETER	DESCRIPTION
conversation	The conversation object containing the message history. TYPE: Conversation
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum number of tokens in the response. TYPE: int DEFAULT: 256
top_p	Nucleus sampling parameter. TYPE: float DEFAULT: 1.0
stop	Stop sequence to halt generation. TYPE: str DEFAULT: '\n'

YIELDS	DESCRIPTION
str	Iterator[str]: Chunks of the response content as they are generated.

Source code in swarmauri_standard/llms/AI21StudioModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def stream(
    self,
    conversation: Conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
) -> Iterator[str]:
    """
    Synchronously streams responses for a conversation, yielding each chunk.

    Args:
        conversation (Conversation): The conversation object containing the message history.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum number of tokens in the response.
        top_p (float): Nucleus sampling parameter.
        stop (str): Stop sequence to halt generation.

    Yields:
        Iterator[str]: Chunks of the response content as they are generated.
    """
    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "n": 1,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "stop": [stop] if stop else [],
        "response_format": {"type": "text"},
        "stream": True,
    }

    with DurationManager() as prompt_timer:
        response = self._client.post(self._BASE_URL, json=payload)
        response.raise_for_status()

        usage_data = {}
        message_content = ""

    with DurationManager() as completion_timer:
        for line in response.iter_lines():
            json_str = line.replace("data: ", "")
            try:
                if json_str:
                    chunk = json.loads(json_str)
                    if (
                        chunk["choices"][0]["delta"]
                        and "content" in chunk["choices"][0]["delta"]
                    ):
                        delta = chunk["choices"][0]["delta"]["content"]
                        message_content += delta
                        yield delta
                    if "usage" in chunk:
                        usage_data = chunk.get("usage", {})
            except json.JSONDecodeError:
                pass

    # Prepare usage data if tracking is enabled
    if self.include_usage and usage_data:
        usage = self._prepare_usage_data(
            usage_data, prompt_timer.duration, completion_timer.duration
        )
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

astream async

astream(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
)

Asynchronously streams responses for a conversation, yielding each chunk.

PARAMETER	DESCRIPTION
conversation	The conversation object containing the message history. TYPE: Conversation
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum number of tokens in the response. TYPE: int DEFAULT: 256
top_p	Nucleus sampling parameter. TYPE: float DEFAULT: 1.0
stop	Stop sequence to halt generation. TYPE: str DEFAULT: '\n'

YIELDS	DESCRIPTION
AsyncIterator[str]	AsyncIterator[str]: Chunks of the response content as they are generated.

Source code in swarmauri_standard/llms/AI21StudioModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def astream(
    self,
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
) -> AsyncIterator[str]:
    """
    Asynchronously streams responses for a conversation, yielding each chunk.

    Args:
        conversation (Conversation): The conversation object containing the message history.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum number of tokens in the response.
        top_p (float): Nucleus sampling parameter.
        stop (str): Stop sequence to halt generation.

    Yields:
        AsyncIterator[str]: Chunks of the response content as they are generated.
    """
    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "n": 1,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "stop": [stop] if stop else [],
        "response_format": {"type": "text"},
        "stream": True,
    }

    with DurationManager() as prompt_timer:
        response = await self._async_client.post(self._BASE_URL, json=payload)
        response.raise_for_status()

    usage_data = {}
    message_content = ""

    with DurationManager() as completion_timer:
        async for line in response.aiter_lines():
            json_str = line.replace("data: ", "")
            try:
                if json_str:
                    chunk = json.loads(json_str)
                    if (
                        chunk["choices"][0]["delta"]
                        and "content" in chunk["choices"][0]["delta"]
                    ):
                        delta = chunk["choices"][0]["delta"]["content"]
                        message_content += delta
                        yield delta
                    if "usage" in chunk:
                        usage_data = chunk.get("usage", {})
            except json.JSONDecodeError:
                pass

    # Prepare usage data if tracking is enabled
    if self.include_usage and usage_data:
        usage = self._prepare_usage_data(
            usage_data, prompt_timer.duration, completion_timer.duration
        )
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

batch

batch(
    conversations,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
    n=1,
)

Processes a batch of conversations synchronously, generating responses for each.

PARAMETER	DESCRIPTION
conversations	List of conversation objects. TYPE: List[Conversation]
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum number of tokens in the response. TYPE: int DEFAULT: 256
top_p	Nucleus sampling parameter. TYPE: float DEFAULT: 1.0
stop	Stop sequence to halt generation. TYPE: str DEFAULT: '\n'
n	Number of completions to generate per conversation. TYPE: int DEFAULT: 1

RETURNS	DESCRIPTION
List[Conversation]	List[Conversation]: List of updated conversations.

Source code in swarmauri_standard/llms/AI21StudioModel.py

def batch(
    self,
    conversations: List[Conversation],
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
    n=1,
) -> List[Conversation]:
    """
    Processes a batch of conversations synchronously, generating responses for each.

    Args:
        conversations (List[Conversation]): List of conversation objects.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum number of tokens in the response.
        top_p (float): Nucleus sampling parameter.
        stop (str): Stop sequence to halt generation.
        n (int): Number of completions to generate per conversation.

    Returns:
        List[Conversation]: List of updated conversations.
    """
    return [
        self.predict(
            conv,
            temperature=temperature,
            max_tokens=max_tokens,
            top_p=top_p,
            stop=stop,
            n=n,
        )
        for conv in conversations
    ]

abatch async

abatch(
    conversations,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
    n=1,
    max_concurrent=5,
)

Processes a batch of conversations asynchronously, generating responses for each.

PARAMETER	DESCRIPTION
conversations	List of conversation objects. TYPE: List
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum number of tokens in the response. TYPE: int DEFAULT: 256
top_p	Nucleus sampling parameter. TYPE: float DEFAULT: 1.0
stop	Stop sequence to halt generation. TYPE: str DEFAULT: '\n'
n	Number of completions to generate per conversation. TYPE: int DEFAULT: 1
max_concurrent	Maximum number of concurrent requests. TYPE: int DEFAULT: 5

RETURNS	DESCRIPTION
List[Conversation]	List[Conversation]: List of updated conversations.

Source code in swarmauri_standard/llms/AI21StudioModel.py

async def abatch(
    self,
    conversations: List,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    stop="\n",
    n=1,
    max_concurrent=5,
) -> List[Conversation]:
    """
    Processes a batch of conversations asynchronously, generating responses for each.

    Args:
        conversations (List): List of conversation objects.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum number of tokens in the response.
        top_p (float): Nucleus sampling parameter.
        stop (str): Stop sequence to halt generation.
        n (int): Number of completions to generate per conversation.
        max_concurrent (int): Maximum number of concurrent requests.

    Returns:
        List[Conversation]: List of updated conversations.
    """
    semaphore = asyncio.Semaphore(max_concurrent)

    async def process_conversation(conv) -> Conversation:
        async with semaphore:
            return await self.apredict(
                conv,
                temperature=temperature,
                max_tokens=max_tokens,
                top_p=top_p,
                stop=stop,
                n=n,
            )

    tasks = [process_conversation(conv) for conv in conversations]
    return await asyncio.gather(*tasks)

get_allowed_models

get_allowed_models()

Queries the LLMProvider API endpoint to get the list of allowed models.

RETURNS	DESCRIPTION
List[str]	List[str]: List of allowed model names.

Source code in swarmauri_standard/llms/AI21StudioModel.py

def get_allowed_models(self) -> List[str]:
    """
    Queries the LLMProvider API endpoint to get the list of allowed models.

    Returns:
        List[str]: List of allowed model names.
    """
    return ["jamba-large", "jamba-mini", "jamba-large-1.7", "jamba-mini-1.7"]

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: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

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: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

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

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

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

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger

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

def add_allowed_model(self, model: str) -> None:
    """
    Add a new model to the list of allowed models.

    Raises:
        ValueError: If the model is already in the allowed models list.
    """
    if model in self.allowed_models:
        raise ValueError(f"Model '{model}' is already allowed.")
    self.allowed_models.append(model)

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

def remove_allowed_model(self, model: str) -> None:
    """
    Remove a model from the list of allowed models.

    Raises:
        ValueError: If the model is not in the allowed models list.
    """
    if model not in self.allowed_models:
        raise ValueError(f"Model '{model}' is not in the allowed models list.")
    self.allowed_models.remove(model)