Class swarmauri_standard.tool_llms.ToolLLM.ToolLLM

swarmauri_standard.tool_llms.ToolLLM.ToolLLM

ToolLLM(**data)

Bases: ToolLLMBase

Initialize the OpenAIToolModel class with the provided data.

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

Source code in swarmauri_standard/tool_llms/ToolLLM.py

def __init__(self, **data: dict[str, Any]) -> None:
    """
    Initialize the OpenAIToolModel class with the provided data.

    Args:
        **data: Arbitrary keyword arguments containing initialization data.
    """
    super().__init__(**data)
    self._headers = {
        "Authorization": f"Bearer {self.api_key.get_secret_value()}",
        "Content-Type": "application/json",
    }
    self.allowed_models = self.allowed_models or self.get_allowed_models()

allowed_models instance-attribute

allowed_models = allowed_models or get_allowed_models()

type class-attribute instance-attribute

type = 'ToolLLMBase'

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="forbid", 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

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

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

version class-attribute instance-attribute

version = '0.1.0'

api_key class-attribute instance-attribute

api_key = None

timeout class-attribute instance-attribute

timeout = 600.0

BASE_URL class-attribute instance-attribute

BASE_URL = None

get_schema_converter

get_schema_converter()

Source code in swarmauri_standard/tool_llms/ToolLLM.py

def get_schema_converter(self) -> Type["SchemaConverterBase"]:
    return OpenAISchemaConverter()

predict

predict(
    conversation,
    toolkit,
    tool_choice,
    multiturn=True,
    temperature=0.7,
    max_tokens=1024,
)

Makes a synchronous prediction using the Groq model.

PARAMETER	DESCRIPTION
conversation	Conversation instance with message history. TYPE: Conversation
toolkit	Optional toolkit for tool conversion. TYPE: Tookit
tool_choice	Tool selection strategy. TYPE: dict[str, Any]
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit. TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
IConversation	Updated conversation with agent responses and tool calls. TYPE: IConversation

Source code in swarmauri_standard/tool_llms/ToolLLM.py

def predict(
    self,
    conversation: Conversation,
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    multiturn: bool = True,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> IConversation:
    """
    Makes a synchronous prediction using the Groq model.

    Parameters:
        conversation (Conversation): Conversation instance with message history.
        toolkit (Tookit): Optional toolkit for tool conversion.
        tool_choice (dict[str, Any]): Tool selection strategy.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit.

    Returns:
        IConversation: Updated conversation with agent responses and tool calls.
    """
    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "tools": self._schema_convert_tools(toolkit.tools) if toolkit else None,
        "tool_choice": tool_choice or "auto",
    }

    with httpx.Client(timeout=self.timeout) as client:
        response = client.post(self.BASE_URL, headers=self._headers, json=payload)
        response.raise_for_status()
        tool_response = response.json()

    messages = [formatted_messages[-1], tool_response["choices"][0]["message"]]
    tool_calls = tool_response["choices"][0]["message"].get("tool_calls", [])
    messages = self._process_tool_calls(tool_calls, toolkit, messages)

    # Add tool messages to Conversation to enable Conversation hooks
    tool_messages = [
        FunctionMessage(
            tool_call_id=m["tool_call_id"], name=m["name"], content=m["content"]
        )
        for m in messages
        if m["role"] == "tool"
    ]

    conversation.add_messages(tool_messages)

    if multiturn:
        payload["messages"] = messages
        payload.pop("tools", None)
        payload.pop("tool_choice", None)

        with httpx.Client(timeout=self.timeout) as client:
            response = client.post(
                self.BASE_URL, headers=self._headers, json=payload
            )
            response.raise_for_status()

        agent_response = response.json()

        agent_message = AgentMessage(
            content=agent_response["choices"][0]["message"]["content"]
        )
        conversation.add_message(agent_message)
    return conversation

apredict async

apredict(
    conversation,
    toolkit,
    tool_choice,
    multiturn=True,
    temperature=0.7,
    max_tokens=1024,
)

Makes an asynchronous prediction using the OpenAI model.

PARAMETER	DESCRIPTION
conversation	Conversation instance with message history. TYPE: Conversation
toolkit	Optional toolkit for tool conversion. TYPE: Tookit
tool_choice	Tool selection strategy. TYPE: dict[str, Any]
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit. TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
Conversation	Updated conversation with agent responses and tool calls. TYPE: IConversation

Source code in swarmauri_standard/tool_llms/ToolLLM.py

async def apredict(
    self,
    conversation: Conversation,
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    multiturn: bool = True,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> IConversation:
    """
    Makes an asynchronous prediction using the OpenAI model.

    Parameters:
        conversation (Conversation): Conversation instance with message history.
        toolkit (Tookit): Optional toolkit for tool conversion.
        tool_choice (dict[str, Any]): Tool selection strategy.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit.

    Returns:
        Conversation: Updated conversation with agent responses and tool calls.
    """
    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "tools": self._schema_convert_tools(toolkit.tools) if toolkit else None,
        "tool_choice": tool_choice or "auto",
    }

    async with httpx.AsyncClient(timeout=self.timeout) as client:
        response = await client.post(
            self.BASE_URL, headers=self._headers, json=payload
        )
        response.raise_for_status()
        tool_response = response.json()

    messages = [formatted_messages[-1], tool_response["choices"][0]["message"]]
    tool_calls = tool_response["choices"][0]["message"].get("tool_calls", [])
    messages = self._process_tool_calls(tool_calls, toolkit, messages)

    # Add tool messages to Conversation to enable Conversation hooks
    tool_messages = [
        FunctionMessage(
            tool_call_id=m["tool_call_id"], name=m["name"], content=m["content"]
        )
        for m in messages
        if m["role"] == "tool"
    ]

    conversation.add_messages(tool_messages)

    if multiturn:
        payload["messages"] = messages
        payload.pop("tools", None)
        payload.pop("tool_choice", None)

        async with httpx.AsyncClient(timeout=self.timeout) as client:
            response = await client.post(
                self.BASE_URL, headers=self._headers, json=payload
            )
            response.raise_for_status()

        agent_response = response.json()

        agent_message = AgentMessage(
            content=agent_response["choices"][0]["message"]["content"]
        )
        conversation.add_message(agent_message)
    return conversation

stream

stream(
    conversation,
    toolkit,
    tool_choice,
    temperature=0.7,
    max_tokens=1024,
)

Streams response from OpenAI model in real-time.

PARAMETER	DESCRIPTION
conversation	Conversation instance with message history. TYPE: Conversation
toolkit	Optional toolkit for tool conversion. TYPE: Tookit
tool_choice	Tool selection strategy. TYPE: dict[str, Any]
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit. TYPE: int DEFAULT: 1024

YIELDS	DESCRIPTION
str	Iterator[str]: Streamed response content.

Source code in swarmauri_standard/tool_llms/ToolLLM.py

def stream(
    self,
    conversation: Conversation,
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> Iterator[str]:
    """
    Streams response from OpenAI model in real-time.

    Parameters:
        conversation (Conversation): Conversation instance with message history.
        toolkit (Tookit): Optional toolkit for tool conversion.
        tool_choice: Tool selection strategy.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit.

    Yields:
        Iterator[str]: Streamed response content.
    """

    formatted_messages = self._format_messages(conversation.history)

    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "tools": self._schema_convert_tools(toolkit.tools) if toolkit else [],
        "tool_choice": tool_choice or "auto",
    }

    with httpx.Client(timeout=self.timeout) as client:
        response = client.post(self.BASE_URL, headers=self._headers, json=payload)
        response.raise_for_status()

    tool_response = response.json()

    messages = [formatted_messages[-1], tool_response["choices"][0]["message"]]
    tool_calls = tool_response["choices"][0]["message"].get("tool_calls", [])

    messages = self._process_tool_calls(tool_calls, toolkit, messages)

    payload["messages"] = messages
    payload["stream"] = True
    payload.pop("tools", None)
    payload.pop("tool_choice", None)

    with httpx.Client(timeout=self.timeout) as client:
        response = client.post(self.BASE_URL, headers=self._headers, json=payload)
        response.raise_for_status()

    message_content = ""

    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"]:
                    delta = chunk["choices"][0]["delta"]["content"]
                    message_content += delta
                    yield delta
        except json.JSONDecodeError:
            pass

    conversation.add_message(AgentMessage(content=message_content))

astream async

astream(
    conversation,
    toolkit,
    tool_choice,
    temperature=0.7,
    max_tokens=1024,
)

Asynchronously streams response from Groq model.

PARAMETER	DESCRIPTION
conversation	Conversation instance with message history. TYPE: Conversation
toolkit	Optional toolkit for tool conversion. TYPE: Tookit
tool_choice	Tool selection strategy. TYPE: dict[str, Any]
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit. TYPE: int DEFAULT: 1024

YIELDS	DESCRIPTION
AsyncIterator[str]	AsyncIterator[str]: Streamed response content.

Source code in swarmauri_standard/tool_llms/ToolLLM.py

async def astream(
    self,
    conversation: Conversation,
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> AsyncIterator[str]:
    """
    Asynchronously streams response from Groq model.

    Parameters:
        conversation (Conversation): Conversation instance with message history.
        toolkit (Tookit): Optional toolkit for tool conversion.
        tool_choice (dict[str, Any]): Tool selection strategy.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit.

    Yields:
        AsyncIterator[str]: Streamed response content.
    """
    formatted_messages = self._format_messages(conversation.history)

    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "tools": self._schema_convert_tools(toolkit.tools) if toolkit else [],
        "tool_choice": tool_choice or "auto",
    }

    async with httpx.AsyncClient(timeout=self.timeout) as client:
        response = await client.post(
            self.BASE_URL, headers=self._headers, json=payload
        )
        response.raise_for_status()

    tool_response = response.json()

    messages = [formatted_messages[-1], tool_response["choices"][0]["message"]]
    tool_calls = tool_response["choices"][0]["message"].get("tool_calls", [])

    messages = self._process_tool_calls(tool_calls, toolkit, messages)

    payload["messages"] = messages
    payload["stream"] = True
    payload.pop("tools", None)
    payload.pop("tool_choice", None)

    async with httpx.AsyncClient(timeout=self.timeout) as client:
        agent_response = await client.post(
            self.BASE_URL, headers=self._headers, json=payload
        )
        agent_response.raise_for_status()

    message_content = ""
    async for line in agent_response.aiter_lines():
        json_str = line.replace("data: ", "")
        try:
            if json_str:
                chunk = json.loads(json_str)
                if chunk["choices"][0]["delta"]:
                    delta = chunk["choices"][0]["delta"]["content"]
                    message_content += delta
                    yield delta
        except json.JSONDecodeError:
            pass
    conversation.add_message(AgentMessage(content=message_content))

batch

batch(
    conversations,
    toolkit,
    tool_choice,
    temperature=0.7,
    max_tokens=1024,
)

Processes a batch of conversations and generates responses for each sequentially.

PARAMETER	DESCRIPTION
conversations	List of conversations to process. TYPE: List[Conversation]
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
tool_choice dict[str, Any])	Tool selection strategy.
toolkit	Optional toolkit for tool conversion. TYPE: Tookit
max_tokens	Maximum tokens for each response. TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
List[Conversation]	List[IConversation]: List of updated conversations with model responses.

Source code in swarmauri_standard/tool_llms/ToolLLM.py

def batch(
    self,
    conversations: List[Conversation],
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> List[Conversation]:
    """
    Processes a batch of conversations and generates responses for each sequentially.

    Args:
        conversations (List[Conversation]): List of conversations to process.
        temperature (float): Sampling temperature for response diversity.
        tool_choice dict[str, Any]): Tool selection strategy.
        toolkit (Tookit): Optional toolkit for tool conversion.
        max_tokens (int): Maximum tokens for each response.

    Returns:
        List[IConversation]: List of updated conversations with model responses.
    """
    return [
        self.predict(
            conv,
            toolkit=toolkit,
            tool_choice=tool_choice,
            temperature=temperature,
            max_tokens=max_tokens,
        )
        for conv in conversations
    ]

abatch async

abatch(
    conversations,
    toolkit,
    tool_choice,
    temperature=0.7,
    max_tokens=1024,
    max_concurrent=5,
)

Async method for processing a batch of conversations concurrently.

PARAMETER	DESCRIPTION
conversations	List of conversations to process. TYPE: List[Conversation]
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
tool_choice	Tool selection strategy. TYPE: dict[str, Any]
toolkit	Optional toolkit for tool conversion.s TYPE: Tookit
max_tokens	Maximum tokens for each response. TYPE: int DEFAULT: 1024
max_concurrent	Maximum number of concurrent requests. TYPE: int DEFAULT: 5

RETURNS	DESCRIPTION
List[Conversation]	List[Conversation]: List of updated conversations with model responses.

Source code in swarmauri_standard/tool_llms/ToolLLM.py

async def abatch(
    self,
    conversations: List[Conversation],
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    temperature: float = 0.7,
    max_tokens: int = 1024,
    max_concurrent: int = 5,
) -> List[Conversation]:
    """
    Async method for processing a batch of conversations concurrently.

    Args:
        conversations (List[Conversation]): List of conversations to process.
        temperature (float): Sampling temperature for response diversity.
        tool_choice (dict[str, Any]): Tool selection strategy.
        toolkit (Tookit): Optional toolkit for tool conversion.s
        max_tokens (int): Maximum tokens for each response.
        max_concurrent (int): Maximum number of concurrent requests.

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

    async def process_conversation(conv):
        async with semaphore:
            return await self.apredict(
                conv,
                toolkit=toolkit,
                tool_choice=tool_choice,
                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()

Get the list of allowed models for the OpenAI API.

RETURNS	DESCRIPTION
List[str]	List[str]: List of allowed models.

Source code in swarmauri_standard/tool_llms/ToolLLM.py

def get_allowed_models(self) -> List[str]:
    """
    Get the list of allowed models for the OpenAI API.

    Returns:
        List[str]: List of allowed models.
    """
    pass

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/tool_llms/ToolLLMBase.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/tool_llms/ToolLLMBase.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)