Class swarmauri_standard.llms.MistralToolModel.MistralToolModel

swarmauri_standard.llms.MistralToolModel.MistralToolModel

MistralToolModel(**data)

Bases: LLMBase

A model class for interacting with the Mistral API for tool-assisted conversation and prediction.

This class provides methods for synchronous and asynchronous communication with the Mistral API. It supports processing single and batch conversations, as well as streaming responses.

ATTRIBUTE	DESCRIPTION
api_key	The API key for authenticating requests with the Mistral API. TYPE: SecretStr
allowed_models	A list of supported model names for the Mistral API. TYPE: List[str]
name	The default model name to use for predictions. TYPE: str
type	The type identifier for the model. TYPE: Literal['MistralToolModel']
timeout	Maximum time to wait for API responses in seconds. TYPE: float

Provider resources: https://docs.mistral.ai/capabilities/function_calling/#available-models

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

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

Source code in swarmauri_standard/llms/MistralToolModel.py

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

    Parameters:
        **data (Any): 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 = [
    "mistral-medium-2508",
    "codestral-2508",
    "devstral-medium-2507",
    "mistral-ocr-2505",
    "ministral-8b-2410",
    "mistral-medium-2505",
    "codestral-2501",
    "mistral-large-2411",
    "pixtral-large-2411",
    "mistral-small-2407",
    "mistral-embed",
    "codestral-embed",
    "mistral-moderation-2411",
    "mistral-small-2506",
    "devstral-small-2507",
    "mistral-small-2501",
    "devstral-small-2505",
    "pixtral-12b-2409",
    "open-mistral-nemo",
]

name class-attribute instance-attribute

name = ''

type class-attribute instance-attribute

type = 'MistralToolModel'

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_allowed_models

get_allowed_models()

Get a list of allowed models for the Mistral API.

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

Source code in swarmauri_standard/llms/MistralToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def get_allowed_models(self) -> List[str]:
    """
    Get a list of allowed models for the Mistral API.

    Returns:
        List[str]: List of allowed model names.
    """
    response = self._client.get("models")
    response.raise_for_status()
    response_data = response.json()

    tool_models = [
        model["id"]
        for model in response_data["data"]
        if model["capabilities"]["function_calling"]
    ]

    return tool_models

predict

predict(
    conversation,
    toolkit=None,
    tool_choice=None,
    temperature=0.7,
    max_tokens=1024,
    safe_prompt=False,
)

Make a synchronous prediction using the Mistral API.

PARAMETER	DESCRIPTION
conversation	The conversation object. TYPE: Conversation
toolkit	The toolkit for tool assistance. TYPE: Optional[Any] DEFAULT: None
tool_choice	The tool choice strategy (default is "auto"). TYPE: Optional[str] DEFAULT: None
temperature	The temperature for response variability. TYPE: float DEFAULT: 0.7
max_tokens	The maximum number of tokens for the response. TYPE: int DEFAULT: 1024
safe_prompt	Whether to use a safer prompt. TYPE: bool DEFAULT: False

RETURNS	DESCRIPTION
Conversation	The updated conversation object. TYPE: Conversation

Source code in swarmauri_standard/llms/MistralToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def predict(
    self,
    conversation: Conversation,
    toolkit: Optional[Any] = None,
    tool_choice: Optional[str] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
    safe_prompt: bool = False,
) -> Conversation:
    """
    Make a synchronous prediction using the Mistral API.

    Args:
        conversation (Conversation): The conversation object.
        toolkit (Optional[Any]): The toolkit for tool assistance.
        tool_choice (Optional[str]): The tool choice strategy (default is "auto").
        temperature (float): The temperature for response variability.
        max_tokens (int): The maximum number of tokens for the response.
        safe_prompt (bool): Whether to use a safer prompt.

    Returns:
        Conversation: The updated conversation object.
    """
    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,
        "safe_prompt": safe_prompt,
    }

    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", [])

    if tool_calls:
        for tool_call in tool_calls:
            func_name = tool_call["function"]["name"]
            func_call = toolkit.get_tool_by_name(func_name)
            func_args = json.loads(tool_call["function"]["arguments"])
            func_result = func_call(**func_args)

            messages.append(
                {
                    "tool_call_id": tool_call["id"],
                    "role": "tool",
                    "name": func_name,
                    "content": json.dumps(func_result),
                }
            )

    payload["messages"] = messages

    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,
    safe_prompt=False,
)

Make an asynchronous prediction using the Mistral API.

PARAMETER	DESCRIPTION
conversation	The conversation object. TYPE: Conversation
toolkit	The toolkit for tool assistance. TYPE: Optional[Any] DEFAULT: None
tool_choice	The tool choice strategy. TYPE: Optional[str] DEFAULT: None
temperature	The temperature for response variability. TYPE: float DEFAULT: 0.7
max_tokens	The maximum number of tokens for the response. TYPE: int DEFAULT: 1024
safe_prompt	Whether to use a safer prompt. TYPE: bool DEFAULT: False

RETURNS	DESCRIPTION
Conversation	The updated conversation object. TYPE: Conversation

Source code in swarmauri_standard/llms/MistralToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def apredict(
    self,
    conversation: Conversation,
    toolkit: Optional[Any] = None,
    tool_choice: Optional[str] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
    safe_prompt: bool = False,
) -> Conversation:
    """
    Make an asynchronous prediction using the Mistral API.

    Args:
        conversation (Conversation): The conversation object.
        toolkit (Optional[Any]): The toolkit for tool assistance.
        tool_choice (Optional[str]): The tool choice strategy.
        temperature (float): The temperature for response variability.
        max_tokens (int): The maximum number of tokens for the response.
        safe_prompt (bool): Whether to use a safer prompt.

    Returns:
        Conversation: The updated conversation object.
    """
    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,
        "safe_prompt": safe_prompt,
    }

    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", [])

    if tool_calls:
        for tool_call in tool_calls:
            func_name = tool_call["function"]["name"]
            func_call = toolkit.get_tool_by_name(func_name)
            func_args = json.loads(tool_call["function"]["arguments"])
            func_result = func_call(**func_args)

            messages.append(
                {
                    "tool_call_id": tool_call["id"],
                    "role": "tool",
                    "name": func_name,
                    "content": json.dumps(func_result),
                }
            )

    payload["messages"] = messages

    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,
    safe_prompt=False,
)

Stream a response from the Mistral API.

This method sends a conversation and optional toolkit information to the Mistral API and returns a generator that yields response content as it is received.

PARAMETER	DESCRIPTION
conversation	The conversation object containing the message history. TYPE: Conversation
toolkit	The toolkit for tool assistance, providing external tools to be invoked. TYPE: Optional[Any] DEFAULT: None
tool_choice	The tool choice strategy, such as "auto" or "manual". TYPE: Optional[str] DEFAULT: None
temperature	The sampling temperature for response variability. TYPE: float DEFAULT: 0.7
max_tokens	The maximum number of tokens to generate in the response. TYPE: int DEFAULT: 1024
safe_prompt	Whether to use a safer prompt, reducing potential harmful content. TYPE: bool DEFAULT: False

YIELDS	DESCRIPTION
str	Iterator[str]: A streaming generator that yields the response content as text.

Example

for response_text in model.stream(conversation): print(response_text)

Source code in swarmauri_standard/llms/MistralToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def stream(
    self,
    conversation: Conversation,
    toolkit: Optional[Any] = None,
    tool_choice: Optional[str] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
    safe_prompt: bool = False,
) -> Iterator[str]:
    """
    Stream a response from the Mistral API.

    This method sends a conversation and optional toolkit information to the Mistral API
    and returns a generator that yields response content as it is received.

    Args:
        conversation (Conversation): The conversation object containing the message history.
        toolkit (Optional[Any]): The toolkit for tool assistance, providing external tools to be invoked.
        tool_choice (Optional[str]): The tool choice strategy, such as "auto" or "manual".
        temperature (float): The sampling temperature for response variability.
        max_tokens (int): The maximum number of tokens to generate in the response.
        safe_prompt (bool): Whether to use a safer prompt, reducing potential harmful content.

    Yields:
        Iterator[str]: A streaming generator that yields the response content as text.

    Example:
        for response_text in model.stream(conversation):
            print(response_text)
    """
    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,
        "safe_prompt": safe_prompt,
    }

    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", [])

    if tool_calls:
        for tool_call in tool_calls:
            func_name = tool_call["function"]["name"]
            func_call = toolkit.get_tool_by_name(func_name)
            func_args = json.loads(tool_call["function"]["arguments"])
            func_result = func_call(**func_args)

            messages.append(
                {
                    "tool_call_id": tool_call["id"],
                    "role": "tool",
                    "name": func_name,
                    "content": json.dumps(func_result),
                }
            )

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

    logging.info(f"messages: {messages}")

    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,
    safe_prompt=False,
)

Asynchronously stream a response from the Mistral API.

This method sends a conversation and optional toolkit information to the Mistral API and returns an asynchronous generator that yields response content as it is received.

PARAMETER	DESCRIPTION
conversation	The conversation object containing the message history. TYPE: Conversation
toolkit	The toolkit for tool assistance, providing external tools to be invoked. TYPE: Optional[Any] DEFAULT: None
tool_choice	The tool choice strategy, such as "auto" or "manual". TYPE: Optional[str] DEFAULT: None
temperature	The sampling temperature for response variability. TYPE: float DEFAULT: 0.7
max_tokens	The maximum number of tokens to generate in the response. TYPE: int DEFAULT: 1024
safe_prompt	Whether to use a safer prompt, reducing potential harmful content. TYPE: bool DEFAULT: False

YIELDS	DESCRIPTION
AsyncIterator[str]	AsyncIterator[str]: An asynchronous streaming generator that yields the response content as text.

Example

async for response_text in model.astream(conversation): print(response_text)

Source code in swarmauri_standard/llms/MistralToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def astream(
    self,
    conversation: Conversation,
    toolkit: Optional[Any] = None,
    tool_choice: Optional[str] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
    safe_prompt: bool = False,
) -> AsyncIterator[str]:
    """
    Asynchronously stream a response from the Mistral API.

    This method sends a conversation and optional toolkit information to the Mistral API
    and returns an asynchronous generator that yields response content as it is received.

    Args:
        conversation (Conversation): The conversation object containing the message history.
        toolkit (Optional[Any]): The toolkit for tool assistance, providing external tools to be invoked.
        tool_choice (Optional[str]): The tool choice strategy, such as "auto" or "manual".
        temperature (float): The sampling temperature for response variability.
        max_tokens (int): The maximum number of tokens to generate in the response.
        safe_prompt (bool): Whether to use a safer prompt, reducing potential harmful content.

    Yields:
        AsyncIterator[str]: An asynchronous streaming generator that yields the response content as text.

    Example:
        async for response_text in model.astream(conversation):
            print(response_text)
    """
    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,
        "safe_prompt": safe_prompt,
    }

    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", [])

    if tool_calls:
        for tool_call in tool_calls:
            func_name = tool_call["function"]["name"]
            func_call = toolkit.get_tool_by_name(func_name)
            func_args = json.loads(tool_call["function"]["arguments"])
            func_result = func_call(**func_args)

            messages.append(
                {
                    "tool_call_id": tool_call["id"],
                    "role": "tool",
                    "name": func_name,
                    "content": json.dumps(func_result),
                }
            )

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

    logging.info(f"messages: {messages}")

    response = await self._async_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,
    safe_prompt=False,
)

Synchronously processes multiple conversations and generates responses for each.

PARAMETER	DESCRIPTION
conversations	List of conversations to process. TYPE: List[Conversation]
toolkit	The toolkit for tool assistance. TYPE: Optional[Any] DEFAULT: None
tool_choice	The tool choice strategy. TYPE: Optional[str] DEFAULT: None
temperature	Sampling temperature for response generation. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for the response. TYPE: int DEFAULT: 1024
safe_prompt	If True, enables safe prompting. TYPE: bool DEFAULT: False

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

Source code in swarmauri_standard/llms/MistralToolModel.py

def batch(
    self,
    conversations: List[Conversation],
    toolkit: Optional[Any] = None,
    tool_choice: Optional[str] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
    safe_prompt: bool = False,
) -> List[Conversation]:
    """
    Synchronously processes multiple conversations and generates responses for each.

    Args:
        conversations (List[Conversation]): List of conversations to process.
        toolkit (Optional[Any]): The toolkit for tool assistance.
        tool_choice (Optional[str]): The tool choice strategy.
        temperature (float): Sampling temperature for response generation.
        max_tokens (int): Maximum tokens for the response.
        safe_prompt (bool): If True, enables safe prompting.

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

abatch async

abatch(
    conversations,
    toolkit=None,
    tool_choice=None,
    temperature=0.7,
    max_tokens=1024,
    safe_prompt=False,
    max_concurrent=5,
)

Asynchronously processes multiple conversations with controlled concurrency.

PARAMETER	DESCRIPTION
conversations	List of conversations to process. TYPE: List[Conversation]
toolkit	The toolkit for tool assistance. TYPE: Optional[Any] DEFAULT: None
tool_choice	The tool choice strategy. TYPE: Optional[str] DEFAULT: None
temperature	Sampling temperature for response generation. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for the response. TYPE: int DEFAULT: 1024
safe_prompt	If True, enables safe prompting. TYPE: bool DEFAULT: False
max_concurrent	Maximum number of concurrent tasks. TYPE: int DEFAULT: 5

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

Source code in swarmauri_standard/llms/MistralToolModel.py

async def abatch(
    self,
    conversations: List[Conversation],
    toolkit: Optional[Any] = None,
    tool_choice: Optional[str] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
    safe_prompt: bool = False,
    max_concurrent: int = 5,
) -> List[Conversation]:
    """
    Asynchronously processes multiple conversations with controlled concurrency.

    Args:
        conversations (List[Conversation]): List of conversations to process.
        toolkit (Optional[Any]): The toolkit for tool assistance.
        tool_choice (Optional[str]): The tool choice strategy.
        temperature (float): Sampling temperature for response generation.
        max_tokens (int): Maximum tokens for the response.
        safe_prompt (bool): If True, enables safe prompting.
        max_concurrent (int): Maximum number of concurrent tasks.

    Returns:
        List[Conversation]: List of updated conversations with generated responses.
    """
    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,
                safe_prompt=safe_prompt,
            )

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

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)