Class swarmauri_standard.tool_llms.AnthropicToolModel.AnthropicToolModel

swarmauri_standard.tool_llms.AnthropicToolModel.AnthropicToolModel

AnthropicToolModel(**data)

Bases: ToolLLMBase

A model class for integrating with the Anthropic API to enable tool-assisted AI interactions.

This class supports various functionalities, including synchronous and asynchronous message prediction, streaming responses, and batch processing of conversations. It utilizes Anthropic's schema and tool-conversion techniques to facilitate enhanced interactions involving tool usage within conversations.

ATTRIBUTE	DESCRIPTION
api_key	The API key used for authenticating requests to the Anthropic API. TYPE: str
allowed_models	A list of allowed model versions that can be used. TYPE: List[str]
name	The default model name used for predictions. TYPE: str
type	The type of the model, which is set to "AnthropicToolModel". TYPE: Literal

Linked to Allowed Models: https://docs.anthropic.com/en/docs/build-with-claude/tool-use Link to API KEY: https://console.anthropic.com/settings/keys

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.py

def __init__(self, **data: dict[str, Any]):
    super().__init__(**data)
    self._headers = {
        "Content-Type": "application/json",
        "x-api-key": self.api_key.get_secret_value(),
        "anthropic-version": "2023-06-01",
    }
    self._client = httpx.Client(
        headers=self._headers, base_url=self.BASE_URL, timeout=self.timeout
    )
    self._async_client = httpx.AsyncClient(
        headers=self._headers, base_url=self.BASE_URL, timeout=self.timeout
    )

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

BASE_URL class-attribute instance-attribute

BASE_URL = 'https://api.anthropic.com/v1'

api_key instance-attribute

api_key

type class-attribute instance-attribute

type = 'AnthropicToolModel'

allowed_models class-attribute instance-attribute

allowed_models = allowed_models or get_allowed_models()

name class-attribute instance-attribute

name = name or allowed_models[0]

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

resource class-attribute instance-attribute

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

version class-attribute instance-attribute

version = '0.1.0'

timeout class-attribute instance-attribute

timeout = 600.0

get_schema_converter

get_schema_converter()

Returns the schema converter class for Anthropic API.

RETURNS	DESCRIPTION
Type[SchemaConverterBase]	Type[SchemaConverterBase]: The AnthropicSchemaConverter class.

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.py

def get_schema_converter(self) -> Type[SchemaConverterBase]:
    """
    Returns the schema converter class for Anthropic API.

    Returns:
        Type[SchemaConverterBase]: The AnthropicSchemaConverter class.
    """
    return AnthropicSchemaConverter

predict

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

Predicts the response based on the given conversation and optional toolkit.

PARAMETER	DESCRIPTION
conversation	The current conversation object. TYPE: IConversation
toolkit	Optional toolkit object containing tools for tool-based responses. TYPE: Toolkit
tool_choice	Optional parameter to choose specific tools or set to 'auto' for automatic tool usage. TYPE: dict[str, Any]
multiturn	Whether to process multiple turns in a single call. TYPE: bool DEFAULT: True
temperature	The temperature for the model's output randomness. TYPE: float DEFAULT: 0.7
max_tokens	The maximum number of tokens in the response. TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
IConversation	The updated conversation with the assistant's response. TYPE: Conversation

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.py

def predict(
    self,
    conversation: Conversation,
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    multiturn: bool = True,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> Conversation:
    """
    Predicts the response based on the given conversation and optional toolkit.

    Args:
        conversation (IConversation): The current conversation object.
        toolkit: Optional toolkit object containing tools for tool-based responses.
        tool_choice: Optional parameter to choose specific tools or set to 'auto' for automatic tool usage.
        multiturn (bool): Whether to process multiple turns in a single call.
        temperature (float): The temperature for the model's output randomness.
        max_tokens (int): The maximum number of tokens in the response.

    Returns:
        IConversation: The updated conversation with the assistant's response.
    """
    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 if toolkit and tool_choice else {"type": "auto"},
    }

    response = self._client.post("/messages", json=payload)
    response.raise_for_status()
    response_data = response.json()

    # Extract text content if available
    tool_text_response = None
    if response_data["content"] and response_data["content"][0]["type"] == "text":
        tool_text_response = response_data["content"][0]["text"]
        logging.info(f"tool_text_response: {tool_text_response}")

    # Process tool calls
    tool_calls = [c for c in response_data["content"] if c["type"] == "tool_use"]
    messages = formatted_messages.copy()

    messages.append(
        {
            "role": "assistant",
            "content": response_data["content"],
        }
    )

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

    conversation.add_messages(tool_messages)

    # For multiturn, we need to make a follow-up request with the tool results
    if multiturn and tool_calls:
        # Create a new payload without tools
        followup_payload = {
            "model": self.name,
            "messages": messages,
            "temperature": temperature,
            "max_tokens": max_tokens,
        }

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

        followup_response = self._client.post("/messages", json=followup_payload)
        logging.info(f"response: {followup_response.json()}")

        followup_response.raise_for_status()
        followup_data = followup_response.json()

        if (
            followup_data["content"]
            and followup_data["content"][0]["type"] == "text"
        ):
            tool_text_response = followup_data["content"][0]["text"]

    # Create and add the agent message
    if tool_text_response:
        agent_message = AgentMessage(content=tool_text_response)
        conversation.add_message(agent_message)

    return conversation

apredict async

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

Asynchronous version of the predict method to handle concurrent processing of requests.

PARAMETER	DESCRIPTION
conversation	The current conversation object. TYPE: IConversation
toolkit	Optional toolkit object containing tools for tool-based responses. TYPE: Toolkit
tool_choice	Optional parameter to choose specific tools or set to 'auto' for automatic tool usage. TYPE: dict[str, Any]
multiturn	Whether to process multiple turns in a single call. TYPE: bool DEFAULT: True
temperature	The temperature for the model's output randomness. TYPE: float DEFAULT: 0.7
max_tokens	The maximum number of tokens in the response. TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
IConversation	The updated conversation with the assistant's response. TYPE: IConversation

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
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:
    """
    Asynchronous version of the `predict` method to handle concurrent processing of requests.

    Args:
        conversation (IConversation): The current conversation object.
        toolkit: Optional toolkit object containing tools for tool-based responses.
        tool_choice: Optional parameter to choose specific tools or set to 'auto' for automatic tool usage.
        multiturn (bool): Whether to process multiple turns in a single call.
        temperature (float): The temperature for the model's output randomness.
        max_tokens (int): The maximum number of tokens in the response.

    Returns:
        IConversation: The updated conversation with the assistant's response.
    """
    formatted_messages = self._format_messages(conversation.history)
    logging.info(f"formatted_messages: {formatted_messages}")

    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 if toolkit and tool_choice else {"type": "auto"},
    }

    response = await self._async_client.post("/messages", json=payload)
    response.raise_for_status()
    response_data = response.json()

    logging.info(f"tool_response: {response_data}")

    # Extract text content if available
    tool_text_response = None
    if response_data["content"] and response_data["content"][0]["type"] == "text":
        tool_text_response = response_data["content"][0]["text"]
        logging.info(f"tool_text_response: {tool_text_response}")

    # Process tool calls
    tool_calls = [c for c in response_data["content"] if c["type"] == "tool_use"]
    messages = formatted_messages.copy()
    messages.append(
        {
            "role": "assistant",
            "content": response_data["content"],
        }
    )

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

    conversation.add_messages(tool_messages)

    # For multiturn, we need to make a follow-up request with the tool results
    if multiturn and tool_calls:
        # Create a new payload without tools
        followup_payload = {
            "model": self.name,
            "messages": messages,
            "temperature": temperature,
            "max_tokens": max_tokens,
        }

        followup_response = await self._async_client.post(
            "/messages", json=followup_payload
        )
        followup_response.raise_for_status()
        followup_data = followup_response.json()

        if (
            followup_data["content"]
            and followup_data["content"][0]["type"] == "text"
        ):
            tool_text_response = followup_data["content"][0]["text"]

    # Create and add the agent message
    if tool_text_response:
        agent_message = AgentMessage(content=tool_text_response)
        conversation.add_message(agent_message)

    return conversation

stream

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

Streams the response for a conversation in real-time, yielding text as it is received.

PARAMETER	DESCRIPTION
conversation	The current conversation object. TYPE: IConversation
toolkit	Optional toolkit object for tool-based responses. TYPE: Toolkit
tool_choice	Optional parameter to choose specific tools or set to 'auto'. TYPE: dict[str, Any]
temperature	Controls randomness in the output. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens in the response. TYPE: int DEFAULT: 1024

YIELDS	DESCRIPTION
str	Iterator[str]: Chunks of text received from the streaming response.

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.py

def stream(
    self,
    conversation: Conversation,
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> Iterator[str]:
    """
    Streams the response for a conversation in real-time, yielding text as it is received.

    Args:
        conversation (IConversation): The current conversation object.
        toolkit: Optional toolkit object for tool-based responses.
        tool_choice: Optional parameter to choose specific tools or set to 'auto'.
        temperature (float): Controls randomness in the output.
        max_tokens (int): Maximum tokens in the response.

    Yields:
        Iterator[str]: Chunks of text received from the streaming response.
    """
    formatted_messages = self._format_messages(conversation.history)

    # First, handle any tool calls that might be needed
    tool_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 if toolkit and tool_choice else {"type": "auto"},
    }

    response = self._client.post("/messages", json=tool_payload)
    response.raise_for_status()
    tool_response_data = response.json()

    tool_calls = [
        c for c in tool_response_data["content"] if c["type"] == "tool_use"
    ]
    messages = formatted_messages.copy()

    if tool_calls:
        messages.append(
            {
                "role": "assistant",
                "content": tool_response_data["content"],
            }
        )

        messages, tool_messages = self._process_tool_calls(
            tool_calls, toolkit, messages
        )
        conversation.add_messages(tool_messages)

    stream_payload = {
        "model": self.name,
        "messages": messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "stream": True,
    }

    message_content = ""

    with self._client.stream("POST", "/messages", json=stream_payload) as response:
        response.raise_for_status()

        for line in response.iter_lines():
            if not line:
                continue

            line_text = line.decode("utf-8") if isinstance(line, bytes) else line

            if not line_text.startswith("data: "):
                continue

            json_str = line_text.removeprefix("data: ").strip()
            if not json_str or json_str == "[DONE]":
                continue

            try:
                event = json.loads(json_str)

                if event["type"] == "content_block_delta":
                    if "delta" in event and "text" in event["delta"].get(
                        "type", ""
                    ):
                        delta_text = event["delta"].get("text", "")
                        if delta_text:
                            message_content += delta_text
                            yield delta_text

                elif event["type"] == "tool_use":
                    logging.info(f"Tool use event in stream: {event}")

            except json.JSONDecodeError as e:
                logging.warning(
                    f"Error parsing stream event: {e}\nLine: {line_text}"
                )

    if message_content:
        conversation.add_message(AgentMessage(content=message_content))

    return conversation

astream async

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

Asynchronously streams the response for a conversation, yielding text in real-time.

PARAMETER	DESCRIPTION
conversation	The current conversation object. TYPE: IConversation
toolkit	Optional toolkit object for tool-based responses. TYPE: Toolkit
tool_choice	Optional parameter to choose specific tools or set to 'auto'. TYPE: dict[str, Any]
temperature	Controls randomness in the output. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens in the response. TYPE: int DEFAULT: 1024

YIELDS	DESCRIPTION
AsyncIterator[str]	AsyncIterator[str]: Chunks of text received from the streaming response.

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.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 the response for a conversation, yielding text in real-time.

    Args:
        conversation (IConversation): The current conversation object.
        toolkit: Optional toolkit object for tool-based responses.
        tool_choice: Optional parameter to choose specific tools or set to 'auto'.
        temperature (float): Controls randomness in the output.
        max_tokens (int): Maximum tokens in the response.

    Yields:
        AsyncIterator[str]: Chunks of text received from the streaming response.
    """
    formatted_messages = self._format_messages(conversation.history)

    tool_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 if toolkit and tool_choice else {"type": "auto"},
    }

    response = await self._async_client.post("/messages", json=tool_payload)
    response.raise_for_status()
    tool_response_data = response.json()

    tool_calls = [
        c for c in tool_response_data["content"] if c["type"] == "tool_use"
    ]
    messages = formatted_messages.copy()

    if tool_calls:
        messages.append(
            {
                "role": "assistant",
                "content": tool_response_data["content"],
            }
        )

        messages, tool_messages = self._process_tool_calls(
            tool_calls, toolkit, messages
        )
        conversation.add_messages(tool_messages)

    stream_payload = {
        "model": self.name,
        "messages": messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "stream": True,
    }

    message_content = ""

    async with self._async_client.stream(
        "POST", "/messages", json=stream_payload
    ) as response:
        response.raise_for_status()

        async for line in response.aiter_lines():
            if not line:
                continue

            if not line.startswith("data: "):
                continue

            json_str = line.removeprefix("data: ").strip()
            if not json_str or json_str == "[DONE]":
                continue

            try:
                event = json.loads(json_str)

                if event["type"] == "content_block_delta":
                    if (
                        "delta" in event
                        and event["delta"].get("type") == "text_delta"
                    ):
                        delta_text = event["delta"].get("text", "")
                        if delta_text:
                            message_content += delta_text
                            yield delta_text

            except json.JSONDecodeError as e:
                logging.warning(f"Error parsing stream event: {e}\nLine: {line}")

    if message_content:
        conversation.add_message(AgentMessage(content=message_content))

batch

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

Processes a batch of conversations in a synchronous manner.

PARAMETER	DESCRIPTION
conversations	A list of conversation objects to process. TYPE: List[IConversation]
toolkit	Optional toolkit object for tool-based responses. TYPE: Toolkit
tool_choice	Optional parameter to choose specific tools or set to 'auto' for automatic tool usage. TYPE: dict[str, Any]
temperature	The temperature for the model's output randomness. TYPE: float DEFAULT: 0.7
max_tokens	The maximum number of tokens in the response. TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
List[IConversation]	List[IConversation]: A list of conversation objects updated with the assistant's responses.

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.py

def batch(
    self,
    conversations: List[Conversation],
    toolkit: Toolkit,
    tool_choice: dict[str, Any],
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> List[IConversation]:
    """
    Processes a batch of conversations in a synchronous manner.

    Args:
        conversations (List[IConversation]): A list of conversation objects to process.
        toolkit: Optional toolkit object for tool-based responses.
        tool_choice: Optional parameter to choose specific tools or set to 'auto' for automatic tool usage.
        temperature (float): The temperature for the model's output randomness.
        max_tokens (int): The maximum number of tokens in the response.

    Returns:
        List[IConversation]: A list of conversation objects updated with the assistant's responses.
    """
    results = []
    for conv in conversations:
        result = self.predict(
            conversation=conv,
            toolkit=toolkit,
            tool_choice=tool_choice,
            temperature=temperature,
            max_tokens=max_tokens,
        )
        results.append(result)
    return results

abatch async

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

Processes a batch of conversations asynchronously with limited concurrency.

PARAMETER	DESCRIPTION
conversations	A list of conversation objects to process. TYPE: List[IConversation]
toolkit	Optional toolkit object for tool-based responses. TYPE: Toolkit
tool_choice	Optional parameter to choose specific tools or set to 'auto' for automatic tool usage. TYPE: dict[str, Any]
temperature	The temperature for the model's output randomness. TYPE: float DEFAULT: 0.7
max_tokens	The maximum number of tokens in the response. TYPE: int DEFAULT: 1024
max_concurrent	The maximum number of concurrent processes allowed. TYPE: int DEFAULT: 5

RETURNS	DESCRIPTION
List[IConversation]	List[IConversation]: A list of conversation objects updated with the assistant's responses.

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.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=5,
) -> List[IConversation]:
    """
    Processes a batch of conversations asynchronously with limited concurrency.

    Args:
        conversations (List[IConversation]): A list of conversation objects to process.
        toolkit: Optional toolkit object for tool-based responses.
        tool_choice: Optional parameter to choose specific tools or set to 'auto' for automatic tool usage.
        temperature (float): The temperature for the model's output randomness.
        max_tokens (int): The maximum number of tokens in the response.
        max_concurrent (int): The maximum number of concurrent processes allowed.

    Returns:
        List[IConversation]: A list of conversation objects updated with the assistant's 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()

Returns the list of allowed models for Anthropic API.

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

Source code in swarmauri_standard/tool_llms/AnthropicToolModel.py

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

    Returns:
        List[str]: A list of allowed model names.
    """
    allowed_models = [
        "claude-3-7-sonnet-latest",
        "claude-3-5-haiku-latest",
        "claude-3-5-sonnet-latest",
        "claude-3-opus-latest",
        "claude-3-5-sonnet-20241022",
        "claude-3-5-haiku-20241022",
        "claude-3-7-sonnet-20250219",
        "claude-3-5-sonnet-20240620",
        "claude-3-opus-20240229",
        "claude-3-sonnet-20240229",
        "claude-3-haiku-20240307",
    ]
    return allowed_models

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

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

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

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

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

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

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

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

Source code in swarmauri_base/DynamicBase.py

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

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

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

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

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

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

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

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

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

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

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

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

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

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

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

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

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

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

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

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

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

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

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

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

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

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

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

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

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

add_allowed_model

add_allowed_model(model)

Add a new model to the list of allowed models.

RAISES	DESCRIPTION
ValueError	If the model is already in the allowed models list.

Source code in swarmauri_base/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)