Class swarmauri_standard.llms.CohereModel.CohereModel

swarmauri_standard.llms.CohereModel.CohereModel

CohereModel(**data)

Bases: LLMBase

This class provides both synchronous and asynchronous methods for interacting with Cohere's chat endpoints, supporting single messages, streaming, and batch processing.

ATTRIBUTE	DESCRIPTION
api_key	The authentication key for accessing Cohere's API. TYPE: SecretStr
allowed_models	List of supported Cohere model identifiers. TYPE: List[str]
name	The default model name to use (defaults to "command"). TYPE: str
type	The type identifier for this model class. TYPE: Literal['CohereModel']
timeout	Timeout for API requests in seconds. TYPE: float

Link to Allowed Models: https://docs.cohere.com/docs/models Link to API Key: https://dashboard.cohere.com/api-keys

Initialize the CohereModel with the provided configuration.

PARAMETER	DESCRIPTION
**data	Keyword arguments for model configuration, must include 'api_key'. TYPE: Dict[str, Any] DEFAULT: {}

Source code in swarmauri_standard/llms/CohereModel.py

def __init__(self, **data: Dict[str, Any]) -> None:
    """
    Initialize the CohereModel with the provided configuration.

    Args:
        **data (Dict[str, Any]): Keyword arguments for model configuration, must include 'api_key'.
    """
    super().__init__(**data)
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": f"Bearer {self.api_key.get_secret_value()}",
    }
    self._client = httpx.Client(
        headers=headers, base_url=self._BASE_URL, timeout=self.timeout
    )

api_key instance-attribute

api_key

allowed_models class-attribute instance-attribute

allowed_models = [
    "command-a-03-2025",
    "command-r7b-12-2024",
    "command-a-translate-08-2025",
    "command-a-reasoning-08-2025",
    "command-a-vision-07-2025",
    "command-r-plus-04-2024",
    "command-r-plus",
    "command-r-08-2024",
    "command-r-03-2024",
    "command-r",
    "command",
    "command-nightly",
    "command-light",
    "command-light-nightly",
]

name class-attribute instance-attribute

name = 'command-a-03-2025'

type class-attribute instance-attribute

type = 'CohereModel'

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

resource class-attribute instance-attribute

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

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

get_headers

get_headers()

Generate the HTTP headers needed for API requests.

RETURNS	DESCRIPTION
Dict[str, str]	Dict[str, str]: Headers dictionary with authorization and content type.

Source code in swarmauri_standard/llms/CohereModel.py

def get_headers(self) -> Dict[str, str]:
    """
    Generate the HTTP headers needed for API requests.

    Returns:
        Dict[str, str]: Headers dictionary with authorization and content type.
    """
    return {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": f"Bearer {self.api_key}",
    }

predict

predict(conversation, temperature=0.7, max_tokens=256)

Generate a single prediction from the model synchronously.

PARAMETER	DESCRIPTION
conversation	The conversation object containing message history TYPE: Conversation
temperature	Sampling temperature. Defaults to 0.7 TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens in response. Defaults to 256 TYPE: int DEFAULT: 256

RETURNS	DESCRIPTION
Conversation	The updated conversation object with the model's response added TYPE: Conversation

RAISES	DESCRIPTION
HTTPError	If the API request fails

Source code in swarmauri_standard/llms/CohereModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def predict(
    self,
    conversation: Conversation,
    temperature: float = 0.7,
    max_tokens: int = 256,
) -> Conversation:
    """
    Generate a single prediction from the model synchronously.

    Args:
        conversation (Conversation): The conversation object containing message history
        temperature (float, optional): Sampling temperature. Defaults to 0.7
        max_tokens (int, optional): Maximum tokens in response. Defaults to 256

    Returns:
        Conversation: The updated conversation object with the model's response added

    Raises:
        httpx.HTTPError: If the API request fails
    """
    chat_history, system_message, message = self._format_messages(
        conversation.history
    )

    if not message:
        if conversation.history:
            message = conversation.history[-1].content
        else:
            message = ""

    payload = {
        "message": message,
        "chat_history": chat_history,
        "model": self.name,
        "temperature": temperature,
        "max_tokens": max_tokens,
    }

    if system_message:
        payload["preamble"] = system_message

    with DurationManager() as prompt_timer:
        response = self._client.post("/chat", json=payload)
        response.raise_for_status()
        data = response.json()

    with DurationManager() as completion_timer:
        message_content = data["text"]

    usage_data = data.get("usage", {})

    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))

    return conversation

apredict async

apredict(conversation, temperature=0.7, max_tokens=256)

Generate a single prediction from the model asynchronously.

PARAMETER	DESCRIPTION
conversation	The conversation object containing message history TYPE: Conversation
temperature	Sampling temperature. Defaults to 0.7 TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens in response. Defaults to 256 TYPE: int DEFAULT: 256

RETURNS	DESCRIPTION
Conversation	The updated conversation object with the model's response added TYPE: Conversation

RAISES	DESCRIPTION
HTTPError	If the API request fails

Source code in swarmauri_standard/llms/CohereModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def apredict(
    self,
    conversation: Conversation,
    temperature: float = 0.7,
    max_tokens: int = 256,
) -> Conversation:
    """
    Generate a single prediction from the model asynchronously.

    Args:
        conversation (Conversation): The conversation object containing message history
        temperature (float, optional): Sampling temperature. Defaults to 0.7
        max_tokens (int, optional): Maximum tokens in response. Defaults to 256

    Returns:
        Conversation: The updated conversation object with the model's response added

    Raises:
        httpx.HTTPError: If the API request fails
    """
    chat_history, system_message, message = self._format_messages(
        conversation.history
    )

    if not message:
        if conversation.history:
            message = conversation.history[-1].content
        else:
            message = ""

    payload = {
        "message": message,
        "chat_history": chat_history,
        "model": self.name,
        "temperature": temperature,
        "max_tokens": max_tokens,
    }

    if system_message:
        payload["preamble"] = system_message

    async with httpx.AsyncClient(
        headers=self.get_headers(), base_url=self._BASE_URL
    ) as client:
        with DurationManager() as prompt_timer:
            response = await client.post("/chat", json=payload)
            response.raise_for_status()
            data = response.json()

        with DurationManager() as completion_timer:
            message_content = data["text"]

        usage_data = data.get("usage", {})

    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))

    return conversation

stream

stream(conversation, temperature=0.7, max_tokens=256)

Stream responses from the model synchronously, yielding content as it becomes available.

This method processes the conversation and streams the model's response piece by piece, allowing for real-time processing of the output. At the end of streaming, it adds the complete response to the conversation history.

PARAMETER	DESCRIPTION
conversation	The conversation object containing message history TYPE: Conversation
temperature	Sampling temperature. Controls randomness in the response. Higher values (e.g., 0.8) create more diverse outputs, while lower values (e.g., 0.2) make outputs more deterministic. Defaults to 0.7. TYPE: float DEFAULT: 0.7
max_tokens	Maximum number of tokens to generate in the response. Defaults to 256. TYPE: int DEFAULT: 256

YIELDS	DESCRIPTION
str	Chunks of the model's response as they become available. TYPE:: str

RETURNS	DESCRIPTION
None	The method updates the conversation object in place after completion. TYPE: Iterator[str]

Source code in swarmauri_standard/llms/CohereModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def stream(
    self,
    conversation: Conversation,
    temperature: float = 0.7,
    max_tokens: int = 256,
) -> Iterator[str]:
    """
    Stream responses from the model synchronously, yielding content as it becomes available.

    This method processes the conversation and streams the model's response piece by piece,
    allowing for real-time processing of the output. At the end of streaming, it adds the
    complete response to the conversation history.

    Args:
        conversation (Conversation): The conversation object containing message history
        temperature (float, optional): Sampling temperature. Controls randomness in the response.
            Higher values (e.g., 0.8) create more diverse outputs, while lower values (e.g., 0.2)
            make outputs more deterministic. Defaults to 0.7.
        max_tokens (int, optional): Maximum number of tokens to generate in the response.
            Defaults to 256.

    Yields:
        str: Chunks of the model's response as they become available.

    Returns:
        None: The method updates the conversation object in place after completion.
    """
    chat_history, system_message, message = self._format_messages(
        conversation.history
    )

    if not message and conversation.history:
        message = conversation.history[-1].content

    payload = {
        "message": message or "",
        "chat_history": chat_history,
        "model": self.name,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "stream": True,
    }

    if system_message:
        payload["preamble"] = system_message

    collected_content = []
    usage_data = {}

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

    with DurationManager() as completion_timer:
        for line in response.iter_lines():
            if line:
                chunk = json.loads(line)
                if "text" in chunk:
                    content = chunk["text"]
                    collected_content.append(content)
                    yield content
                elif "usage" in chunk:
                    usage_data = chunk["usage"]

    message_content = "".join(collected_content)

    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)

Stream responses from the model asynchronously, yielding content as it becomes available.

This method is the asynchronous version of stream(). It processes the conversation and streams the model's response piece by piece using async/await syntax. The method creates and manages its own AsyncClient instance to prevent event loop issues.

PARAMETER	DESCRIPTION
conversation	The conversation object containing message history TYPE: Conversation
temperature	Sampling temperature. Controls randomness in the response. Higher values (e.g., 0.8) create more diverse outputs, while lower values (e.g., 0.2) make outputs more deterministic. Defaults to 0.7. TYPE: float DEFAULT: 0.7
max_tokens	Maximum number of tokens to generate in the response. Defaults to 256. TYPE: int DEFAULT: 256

YIELDS	DESCRIPTION
str	Chunks of the model's response as they become available. TYPE:: AsyncIterator[str]

RETURNS	DESCRIPTION
None	The method updates the conversation object in place after completion. TYPE: AsyncIterator[str]

Source code in swarmauri_standard/llms/CohereModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def astream(
    self,
    conversation: Conversation,
    temperature: float = 0.7,
    max_tokens: int = 256,
) -> AsyncIterator[str]:
    """
    Stream responses from the model asynchronously, yielding content as it becomes available.

    This method is the asynchronous version of `stream()`. It processes the conversation and
    streams the model's response piece by piece using async/await syntax. The method creates
    and manages its own AsyncClient instance to prevent event loop issues.

    Args:
        conversation (Conversation): The conversation object containing message history
        temperature (float, optional): Sampling temperature. Controls randomness in the response.
            Higher values (e.g., 0.8) create more diverse outputs, while lower values (e.g., 0.2)
            make outputs more deterministic. Defaults to 0.7.
        max_tokens (int, optional): Maximum number of tokens to generate in the response.
            Defaults to 256.

    Yields:
        str: Chunks of the model's response as they become available.

    Returns:
        None: The method updates the conversation object in place after completion.
    """

    chat_history, system_message, message = self._format_messages(
        conversation.history
    )

    if not message:
        if conversation.history:
            message = conversation.history[-1].content
        else:
            message = ""

    payload = {
        "message": message,
        "chat_history": chat_history,
        "model": self.name,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "stream": True,
    }

    if system_message:
        payload["preamble"] = system_message

    collected_content = []
    usage_data = {}

    async with httpx.AsyncClient(
        headers=self.get_headers(), base_url=self._BASE_URL
    ) as client:
        with DurationManager() as prompt_timer:
            response = await client.post("/chat", json=payload)
            response.raise_for_status()

        with DurationManager() as completion_timer:
            async for line in response.aiter_lines():
                if line:
                    try:
                        chunk = json.loads(line)
                        if "text" in chunk:
                            content = chunk["text"]
                            collected_content.append(content)
                            yield content
                        elif "usage" in chunk:
                            usage_data = chunk["usage"]
                    except json.JSONDecodeError:
                        continue

        message_content = "".join(collected_content)

    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)

Process multiple conversations synchronously.

PARAMETER	DESCRIPTION
conversations	List of conversation objects to process TYPE: List[Conversation]
temperature	Sampling temperature. Defaults to 0.7 TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens in response. Defaults to 256 TYPE: int DEFAULT: 256

RETURNS	DESCRIPTION
List[Conversation]	List[Conversation]: List of updated conversation objects with model responses added

Source code in swarmauri_standard/llms/CohereModel.py

def batch(
    self,
    conversations: List[Conversation],
    temperature: float = 0.7,
    max_tokens: int = 256,
) -> List[Conversation]:
    """
    Process multiple conversations synchronously.

    Args:
        conversations (List[Conversation]): List of conversation objects to process
        temperature (float, optional): Sampling temperature. Defaults to 0.7
        max_tokens (int, optional): Maximum tokens in response. Defaults to 256

    Returns:
        List[Conversation]: List of updated conversation objects with model responses added
    """
    return [
        self.predict(conv, temperature=temperature, max_tokens=max_tokens)
        for conv in conversations
    ]

abatch async

abatch(
    conversations,
    temperature=0.7,
    max_tokens=256,
    max_concurrent=5,
)

Process multiple conversations asynchronously with concurrency control.

PARAMETER	DESCRIPTION
conversations	List of conversation objects to process TYPE: List[Conversation]
temperature	Sampling temperature. Defaults to 0.7 TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens in response. Defaults to 256 TYPE: int DEFAULT: 256
max_concurrent	Maximum number of concurrent requests. Defaults to 5 TYPE: int DEFAULT: 5

RETURNS	DESCRIPTION
List[Conversation]	List[Conversation]: List of updated conversation objects with model responses added

Source code in swarmauri_standard/llms/CohereModel.py

async def abatch(
    self,
    conversations: List[Conversation],
    temperature: float = 0.7,
    max_tokens: int = 256,
    max_concurrent: int = 5,
) -> List[Conversation]:
    """
    Process multiple conversations asynchronously with concurrency control.

    Args:
        conversations (List[Conversation]): List of conversation objects to process
        temperature (float, optional): Sampling temperature. Defaults to 0.7
        max_tokens (int, optional): Maximum tokens in response. Defaults to 256
        max_concurrent (int, optional): Maximum number of concurrent requests. Defaults to 5

    Returns:
        List[Conversation]: List of updated conversation objects with model responses added
    """
    semaphore = asyncio.Semaphore(max_concurrent)

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

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

get_allowed_models

get_allowed_models()

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

RETURNS	DESCRIPTION
List[str]	List[str]: List of allowed model identifiers from the API.

Source code in swarmauri_standard/llms/CohereModel.py

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

    Returns:
        List[str]: List of allowed model identifiers from the API.
    """
    response = self._client.get("/models")
    response.raise_for_status()
    data = response.json()
    return data.get("models", [])

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)