Class swarmauri_standard.llms.GroqToolModel.GroqToolModel

swarmauri_standard.llms.GroqToolModel.GroqToolModel

GroqToolModel(**data)

Bases: LLMBase

GroqToolModel provides an interface to interact with Groq's large language models for tool usage.

This class supports synchronous and asynchronous predictions, streaming of responses, and batch processing. It communicates with the Groq API to manage conversations, format messages, and handle tool-related functions.

ATTRIBUTE	DESCRIPTION
api_key	API key to authenticate with Groq API. TYPE: SecretStr
allowed_models	List of permissible model names. TYPE: List[str]
name	Default model name for predictions. TYPE: str
type	Type identifier for the model. TYPE: Literal['GroqToolModel']
timeout	Request timeout in seconds. TYPE: float

Provider Documentation: https://console.groq.com/docs/tool-use#models

Initialize the GroqAIAudio class with the provided data.

PARAMETER	DESCRIPTION
**data	Arbitrary keyword arguments containing initialization data. TYPE: Any DEFAULT: {}

Source code in swarmauri_standard/llms/GroqToolModel.py

def __init__(self, **data: Any):
    """
    Initialize the GroqAIAudio class with the provided data.

    Args:
        **data (Any): Arbitrary keyword arguments containing initialization data.
    """
    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,
    )

    self.allowed_models = self.allowed_models or self.get_allowed_models()
    self.name = self.allowed_models[0]

api_key instance-attribute

api_key

type class-attribute instance-attribute

type = 'GroqToolModel'

timeout class-attribute instance-attribute

timeout = 600.0

allowed_models class-attribute instance-attribute

allowed_models = allowed_models or get_allowed_models()

name class-attribute instance-attribute

name = allowed_models[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

predict

predict(
    conversation,
    toolkit=None,
    tool_choice=None,
    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: Optional[Any] DEFAULT: None
tool_choice	Tool selection strategy. TYPE: Optional[Union[str, Dict[str, Any]]] DEFAULT: None
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: Conversation

Source code in swarmauri_standard/llms/GroqToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def predict(
    self,
    conversation: Conversation,
    toolkit: Optional[Any] = None,
    tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> Conversation:
    """
    Makes a synchronous prediction using the Groq model.

    Parameters:
        conversation (Conversation): Conversation instance with message history.
        toolkit (Optional[Any]): Optional toolkit for tool conversion.
        tool_choice (Optional[Union[str, 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)

    if toolkit and not tool_choice:
        tool_choice = "auto"

    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,
    }

    response = self._client.post(self._BASE_URL, 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.pop("tools", None)
    payload.pop("tool_choice", None)

    response = self._client.post(self._BASE_URL, 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=None,
    tool_choice=None,
    temperature=0.7,
    max_tokens=1024,
)

Makes an asynchronous prediction using the Groq model.

PARAMETER	DESCRIPTION
conversation	Conversation instance with message history. TYPE: Conversation
toolkit	Optional toolkit for tool conversion. TYPE: Optional[Any] DEFAULT: None
tool_choice	Tool selection strategy. TYPE: Optional[Union[str, Dict[str, Any]]] DEFAULT: None
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: Conversation

Source code in swarmauri_standard/llms/GroqToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def apredict(
    self,
    conversation: Conversation,
    toolkit: Optional[Any] = None,
    tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> Conversation:
    """
    Makes an asynchronous prediction using the Groq model.

    Parameters:
        conversation (Conversation): Conversation instance with message history.
        toolkit (Optional[Any]): Optional toolkit for tool conversion.
        tool_choice (Optional[Union[str, 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)

    if toolkit and not tool_choice:
        tool_choice = "auto"

    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,
    }

    response = await self._async_client.post(self._BASE_URL, 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.pop("tools", None)
    payload.pop("tool_choice", None)

    response = await self._async_client.post(self._BASE_URL, 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=None,
    tool_choice=None,
    temperature=0.7,
    max_tokens=1024,
)

Streams response from Groq model in real-time.

PARAMETER	DESCRIPTION
conversation	Conversation instance with message history. TYPE: Conversation
toolkit	Optional toolkit for tool conversion. TYPE: Optional[Any] DEFAULT: None
tool_choice	Tool selection strategy. TYPE: Optional[Union[str, Dict[str, Any]]] DEFAULT: None
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/llms/GroqToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def stream(
    self,
    conversation: Conversation,
    toolkit: Optional[Any] = None,
    tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> Iterator[str]:
    """
    Streams response from Groq model in real-time.

    Parameters:
        conversation (Conversation): Conversation instance with message history.
        toolkit (Optional[Any]): Optional toolkit for tool conversion.
        tool_choice (Optional[Union[str, Dict[str, Any]]]): 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",
    }

    response = self._client.post(self._BASE_URL, 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)

    response = self._client.post(self._BASE_URL, 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=None,
    tool_choice=None,
    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: Optional[Any] DEFAULT: None
tool_choice	Tool selection strategy. TYPE: Optional[Union[str, Dict[str, Any]]] DEFAULT: None
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/llms/GroqToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def astream(
    self,
    conversation: Conversation,
    toolkit: Optional[Any] = None,
    tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
    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 (Optional[Any]): Optional toolkit for tool conversion.
        tool_choice (Optional[Union[str, 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",
    }

    response = self._client.post(self._BASE_URL, 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)

    response = self._client.post(self._BASE_URL, json=payload)
    response.raise_for_status()
    message_content = ""

    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"]:
                    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=None,
    tool_choice=None,
    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]
toolkit	Optional toolkit for tool conversion. TYPE: Optional[Any] DEFAULT: None
tool_choice	Tool selection strategy. TYPE: Optional[Union[str, Dict[str, Any]]] DEFAULT: None
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for each response. TYPE: int DEFAULT: 1024

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

Source code in swarmauri_standard/llms/GroqToolModel.py

def batch(
    self,
    conversations: List[Conversation],
    toolkit: Optional[Any] = None,
    tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
    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.
        toolkit (Optional[Any]): Optional toolkit for tool conversion.
        tool_choice (Optional[Union[str, Dict[str, Any]]]): Tool selection strategy.
        temperature (float): Sampling temperature for response diversity.
        max_tokens (int): Maximum tokens for each response.

    Returns:
        List[Conversation]: List of updated conversations with model responses.
    """
    if toolkit and not tool_choice:
        tool_choice = "auto"

    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=None,
    tool_choice=None,
    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]
toolkit	Optional toolkit for tool conversion. TYPE: Optional[Any] DEFAULT: None
tool_choice	Tool selection strategy. TYPE: Optional[Union[str, Dict[str, Any]]] DEFAULT: None
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
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/llms/GroqToolModel.py

async def abatch(
    self,
    conversations: List[Conversation],
    toolkit: Optional[Any] = None,
    tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
    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.
        toolkit (Optional[Any]): Optional toolkit for tool conversion.
        tool_choice (Optional[Union[str, Dict[str, Any]]]): Tool selection strategy.
        temperature (float): Sampling temperature for response diversity.
        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.
    """
    if toolkit and not tool_choice:
        tool_choice = "auto"

    semaphore = asyncio.Semaphore(max_concurrent)

    async def process_conversation(conv: Conversation) -> Conversation:
        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()

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/GroqToolModel.py

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

    Returns:
        List[str]: List of allowed model names.
    """
    models_data = [
        "moonshotai/kimi-k2-instruct",
        "moonshotai/kimi-k2-instruct-0905",
        "openai/gpt-oss-20b",
        "openai/gpt-oss-120b",
        "meta-llama/llama-4-maverick-17b-128e-instruct",
        "meta-llama/llama-4-scout-17b-16e-instruct",
        "llama-3.3-70b-versatile",
        "llama-3.1-8b-instant",
    ]
    return models_data

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)