Class swarmauri_standard.llms.CohereToolModel.CohereToolModel

swarmauri_standard.llms.CohereToolModel.CohereToolModel

CohereToolModel(**data)

Bases: LLMBase

A language model implementation for interacting with Cohere's API, specifically designed for tool-augmented conversations.

This class provides both synchronous and asynchronous methods for generating responses, handling tool calls, and managing conversations with the Cohere API. It supports streaming responses and batch processing of multiple conversations.

ATTRIBUTE	DESCRIPTION
api_key	The API key for authenticating with Cohere's API TYPE: SecretStr
allowed_models	List of supported Cohere model names TYPE: List[str]
name	The default model name to use TYPE: str
type	The type identifier for this model TYPE: Literal['CohereToolModel']
resource	The resource type identifier TYPE: str
timeout	Maximum timeout for API requests in seconds TYPE: float

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

Initialize the CohereToolModel with the provided configuration.

PARAMETER	DESCRIPTION
**data	Keyword arguments for configuring the model, including api_key TYPE: Dict[str, Any] DEFAULT: {}

Source code in swarmauri_standard/llms/CohereToolModel.py

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

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

api_key instance-attribute

api_key

allowed_models class-attribute instance-attribute

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

name class-attribute instance-attribute

name = 'command-a-03-2025'

type class-attribute instance-attribute

type = 'CohereToolModel'

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

predict

predict(
    conversation,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)

Generate a response for a conversation synchronously.

PARAMETER	DESCRIPTION
conversation	The conversation to generate a response for TYPE: Conversation
toolkit	Optional toolkit containing available tools TYPE: Optional[Toolkit] DEFAULT: None
temperature	Sampling temperature TYPE: float DEFAULT: 0.3
max_tokens	Maximum number of tokens to generate TYPE: int DEFAULT: 1024

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

Source code in swarmauri_standard/llms/CohereToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def predict(
    self,
    conversation: Conversation,
    toolkit: Optional[Toolkit] = None,
    temperature: float = 0.3,
    max_tokens: int = 1024,
) -> Conversation:
    """
    Generate a response for a conversation synchronously.

    Args:
        conversation (Conversation): The conversation to generate a response for
        toolkit (Optional[Toolkit]): Optional toolkit containing available tools
        temperature (float): Sampling temperature
        max_tokens (int): Maximum number of tokens to generate

    Returns:
        Conversation: The updated conversation with the model's response
    """
    conversation = self._ensure_conversation_has_message(conversation)
    formatted_messages = self._format_messages(conversation.history)
    tools = self._schema_convert_tools(toolkit.tools) if toolkit else None

    with DurationManager() as tool_timer:
        tool_payload = self._prepare_chat_payload(
            message=formatted_messages[-1]["message"],
            chat_history=(
                formatted_messages[:-1] if len(formatted_messages) > 1 else None
            ),
            tools=tools,
            force_single_step=True,
        )

        tool_response = self._client.post("/chat", json=tool_payload)
        tool_response.raise_for_status()
        tool_data = tool_response.json()

    tool_results = self._process_tool_calls(tool_data, toolkit)

    with DurationManager() as response_timer:
        response_payload = self._prepare_chat_payload(
            message=formatted_messages[-1]["message"],
            chat_history=(
                formatted_messages[:-1] if len(formatted_messages) > 1 else None
            ),
            tools=tools,
            tool_results=tool_results,
            temperature=temperature,
            force_single_step=True,
        )

        response = self._client.post("/chat", json=response_payload)
        response.raise_for_status()
        response_data = response.json()

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

    usage = self._prepare_usage_data(
        usage_data, tool_timer.duration, response_timer.duration
    )

    conversation.add_message(
        AgentMessage(content=response_data.get("text", ""), usage=usage)
    )
    return conversation

stream

stream(
    conversation,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)

Stream a response for a conversation synchronously.

PARAMETER	DESCRIPTION
conversation	The conversation to generate a response for TYPE: Conversation
toolkit	Optional toolkit containing available tools TYPE: Optional[Toolkit] DEFAULT: None
temperature	Sampling temperature TYPE: float DEFAULT: 0.3
max_tokens	Maximum number of tokens to generate TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
Iterator[str]	Iterator[str]: An iterator yielding response chunks

Source code in swarmauri_standard/llms/CohereToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def stream(
    self,
    conversation: Conversation,
    toolkit: Optional[Toolkit] = None,
    temperature: float = 0.3,
    max_tokens: int = 1024,
) -> Iterator[str]:
    """
    Stream a response for a conversation synchronously.

    Args:
        conversation (Conversation): The conversation to generate a response for
        toolkit (Optional[Toolkit]): Optional toolkit containing available tools
        temperature (float): Sampling temperature
        max_tokens (int): Maximum number of tokens to generate

    Returns:
        Iterator[str]: An iterator yielding response chunks
    """
    conversation = self._ensure_conversation_has_message(conversation)
    formatted_messages = self._format_messages(conversation.history)
    tools = self._schema_convert_tools(toolkit.tools) if toolkit else None

    # Handle tool call first
    tool_payload = self._prepare_chat_payload(
        message=formatted_messages[-1]["message"],
        chat_history=(
            formatted_messages[:-1] if len(formatted_messages) > 1 else None
        ),
        tools=tools,
        force_single_step=True,
    )
    with DurationManager() as prompt_timer:
        tool_response = self._client.post("/chat", json=tool_payload)
        tool_response.raise_for_status()
        tool_data = tool_response.json()

    tool_results = self._process_tool_calls(tool_data, toolkit)

    # Prepare streaming payload
    stream_payload = self._prepare_chat_payload(
        message=formatted_messages[-1]["message"],
        chat_history=(
            formatted_messages[:-1] if len(formatted_messages) > 1 else None
        ),
        tools=tools,
        tool_results=tool_results,
        temperature=temperature,
        force_single_step=True,
    )
    stream_payload["stream"] = True

    collected_content = []
    usage_data = {}
    with DurationManager() as completion_timer:
        with self._client.stream("POST", "/chat", json=stream_payload) as response:
            response.raise_for_status()
            for line in response.iter_lines():
                if line:
                    chunk = json.loads(line)
                    if "text" in chunk:
                        content = chunk["text"]
                        collected_content.append(content)
                        yield content
                    elif "usage" in chunk:
                        usage_data = chunk["usage"]

    full_content = "".join(collected_content)
    usage = self._prepare_usage_data(
        usage_data, prompt_timer.duration, completion_timer.duration
    )
    conversation.add_message(AgentMessage(content=full_content, usage=usage))

apredict async

apredict(
    conversation,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)

Generate a response for a conversation asynchronously.

PARAMETER	DESCRIPTION
conversation	The conversation to generate a response for TYPE: Conversation
toolkit	Optional toolkit containing available tools TYPE: Optional[Toolkit] DEFAULT: None
temperature	Sampling temperature TYPE: float DEFAULT: 0.3
max_tokens	Maximum number of tokens to generate TYPE: int DEFAULT: 1024

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

Source code in swarmauri_standard/llms/CohereToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def apredict(
    self,
    conversation: Conversation,
    toolkit: Optional[Toolkit] = None,
    temperature: float = 0.3,
    max_tokens: int = 1024,
) -> Conversation:
    """
    Generate a response for a conversation asynchronously.

    Args:
        conversation (Conversation): The conversation to generate a response for
        toolkit (Optional[Toolkit]): Optional toolkit containing available tools
        temperature (float): Sampling temperature
        max_tokens (int): Maximum number of tokens to generate

    Returns:
        Conversation: The updated conversation with the model's response
    """
    conversation = self._ensure_conversation_has_message(conversation)
    formatted_messages = self._format_messages(conversation.history)
    tools = self._schema_convert_tools(toolkit.tools) if toolkit else None

    with DurationManager() as tool_timer:
        tool_payload = self._prepare_chat_payload(
            message=formatted_messages[-1]["message"],
            chat_history=(
                formatted_messages[:-1] if len(formatted_messages) > 1 else None
            ),
            tools=tools,
            force_single_step=True,
        )

        tool_response = await self._async_client.post("/chat", json=tool_payload)
        tool_response.raise_for_status()
        tool_data = tool_response.json()

    tool_results = self._process_tool_calls(tool_data, toolkit)

    with DurationManager() as response_timer:
        response_payload = self._prepare_chat_payload(
            message=formatted_messages[-1]["message"],
            chat_history=(
                formatted_messages[:-1] if len(formatted_messages) > 1 else None
            ),
            tools=tools,
            tool_results=tool_results,
            temperature=temperature,
            force_single_step=True,
        )

        response = await self._async_client.post("/chat", json=response_payload)
        response.raise_for_status()
        response_data = response.json()

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

    usage = self._prepare_usage_data(
        usage_data, tool_timer.duration, response_timer.duration
    )

    conversation.add_message(
        AgentMessage(content=response_data.get("text", ""), usage=usage)
    )
    return conversation

astream async

astream(
    conversation,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)

Stream a response for a conversation asynchronously.

PARAMETER	DESCRIPTION
conversation	The conversation to generate a response for TYPE: Conversation
toolkit	Optional toolkit containing available tools TYPE: Optional[Toolkit] DEFAULT: None
temperature	Sampling temperature TYPE: float DEFAULT: 0.3
max_tokens	Maximum number of tokens to generate TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
AsyncIterator[str]	AsyncIterator[str]: An async iterator yielding response chunks

Source code in swarmauri_standard/llms/CohereToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def astream(
    self,
    conversation: Conversation,
    toolkit: Optional[Toolkit] = None,
    temperature: float = 0.3,
    max_tokens: int = 1024,
) -> AsyncIterator[str]:
    """
    Stream a response for a conversation asynchronously.

    Args:
        conversation (Conversation): The conversation to generate a response for
        toolkit (Optional[Toolkit]): Optional toolkit containing available tools
        temperature (float): Sampling temperature
        max_tokens (int): Maximum number of tokens to generate

    Returns:
        AsyncIterator[str]: An async iterator yielding response chunks
    """
    conversation = self._ensure_conversation_has_message(conversation)
    formatted_messages = self._format_messages(conversation.history)
    tools = self._schema_convert_tools(toolkit.tools) if toolkit else None

    # Handle tool call first
    tool_payload = self._prepare_chat_payload(
        message=formatted_messages[-1]["message"],
        chat_history=(
            formatted_messages[:-1] if len(formatted_messages) > 1 else None
        ),
        tools=tools,
        force_single_step=True,
    )
    with DurationManager() as prompt_timer:
        tool_response = await self._async_client.post("/chat", json=tool_payload)
        tool_response.raise_for_status()
        tool_data = tool_response.json()

    tool_results = self._process_tool_calls(tool_data, toolkit)

    # Prepare streaming payload
    stream_payload = self._prepare_chat_payload(
        message=formatted_messages[-1]["message"],
        chat_history=(
            formatted_messages[:-1] if len(formatted_messages) > 1 else None
        ),
        tools=tools,
        tool_results=tool_results,
        temperature=temperature,
        force_single_step=True,
    )
    stream_payload["stream"] = True

    collected_content = []
    usage_data = {}

    async with self._async_client.stream(
        "POST", "/chat", json=stream_payload
    ) as response:
        response.raise_for_status()
        with DurationManager() as completion_timer:
            async for line in response.aiter_lines():
                if line:
                    try:
                        chunk = json.loads(line)
                        if "text" in chunk:
                            content = chunk["text"]
                            collected_content.append(content)
                            yield content
                        elif "usage" in chunk:
                            usage_data = chunk["usage"]
                    except json.JSONDecodeError:
                        continue

    full_content = "".join(collected_content)
    usage = self._prepare_usage_data(
        usage_data, prompt_timer.duration, completion_timer.duration
    )
    conversation.add_message(AgentMessage(content=full_content, usage=usage))

batch

batch(
    conversations,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
)

Process multiple conversations in batch mode synchronously.

This method takes a list of conversations and processes them sequentially using the predict method. Each conversation is processed independently with the same parameters.

PARAMETER	DESCRIPTION
conversations	A list of conversation objects to process TYPE: List[Conversation]
toolkit	The toolkit containing available tools for the model TYPE: Optional[Toolkit] DEFAULT: None
temperature	The sampling temperature for response generation. Defaults to 0.3 TYPE: float DEFAULT: 0.3
max_tokens	The maximum number of tokens to generate for each response. Defaults to 1024 TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
List[Conversation]	List[Conversation]: A list of processed conversations with their respective responses

Source code in swarmauri_standard/llms/CohereToolModel.py

def batch(
    self,
    conversations: List[Conversation],
    toolkit: Optional[Toolkit] = None,
    temperature: float = 0.3,
    max_tokens: int = 1024,
) -> List[Conversation]:
    """
    Process multiple conversations in batch mode synchronously.

    This method takes a list of conversations and processes them sequentially using
    the predict method. Each conversation is processed independently with the same
    parameters.

    Args:
        conversations (List[Conversation]): A list of conversation objects to process
        toolkit (Optional[Toolkit]): The toolkit containing available tools for the model
        temperature (float): The sampling temperature for response generation.
            Defaults to 0.3
        max_tokens (int): The maximum number of tokens to generate for each
            response. Defaults to 1024

    Returns:
        List[Conversation]: A list of processed conversations with their respective responses
    """
    return [
        self.predict(
            conv, toolkit=toolkit, temperature=temperature, max_tokens=max_tokens
        )
        for conv in conversations
    ]

abatch async

abatch(
    conversations,
    toolkit=None,
    temperature=0.3,
    max_tokens=1024,
    max_concurrent=5,
)

Process multiple conversations in batch mode asynchronously.

This method processes multiple conversations concurrently while limiting the maximum number of simultaneous requests using a semaphore. This helps prevent overwhelming the API service while still maintaining efficient processing.

PARAMETER	DESCRIPTION
conversations	A list of conversation objects to process TYPE: List[Conversation]
toolkit	The toolkit containing available tools for the model TYPE: Optional[Toolkit] DEFAULT: None
temperature	The sampling temperature for response generation. Defaults to 0.3 TYPE: float DEFAULT: 0.3
max_tokens	The maximum number of tokens to generate for each response. Defaults to 1024 TYPE: int DEFAULT: 1024
max_concurrent	The maximum number of conversations to process simultaneously. Defaults to 5 TYPE: int DEFAULT: 5

RETURNS	DESCRIPTION
List[Conversation]	List[Conversation]: A list of processed conversations with their respective responses

Note

The max_concurrent parameter helps control API usage and prevent rate limiting while still allowing for parallel processing of multiple conversations.

Source code in swarmauri_standard/llms/CohereToolModel.py

async def abatch(
    self,
    conversations: List[Conversation],
    toolkit: Optional[Toolkit] = None,
    temperature: float = 0.3,
    max_tokens: int = 1024,
    max_concurrent: int = 5,
) -> List[Conversation]:
    """
    Process multiple conversations in batch mode asynchronously.

    This method processes multiple conversations concurrently while limiting the
    maximum number of simultaneous requests using a semaphore. This helps prevent
    overwhelming the API service while still maintaining efficient processing.

    Args:
        conversations (List[Conversation]): A list of conversation objects to process
        toolkit (Optional[Toolkit]): The toolkit containing available tools for the model
        temperature (float): The sampling temperature for response generation.
            Defaults to 0.3
        max_tokens (int): The maximum number of tokens to generate for each
            response. Defaults to 1024
        max_concurrent (int): The maximum number of conversations to process
            simultaneously. Defaults to 5

    Returns:
        List[Conversation]: A list of processed conversations with their respective responses

    Note:
        The max_concurrent parameter helps control API usage and prevent rate limiting
        while still allowing for parallel processing of multiple conversations.
    """
    semaphore = asyncio.Semaphore(max_concurrent)

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

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

get_allowed_models

get_allowed_models()

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

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

Source code in swarmauri_standard/llms/CohereToolModel.py

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

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

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

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

Source code in swarmauri_base/DynamicBase.py

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

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

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

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

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

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

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

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

Source code in swarmauri_base/DynamicBase.py

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

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

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

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

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

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

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

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

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

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

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

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

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

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

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

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

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

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

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

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

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

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

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

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

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

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

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

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

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

add_allowed_model

add_allowed_model(model)

Add a new model to the list of allowed models.

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

Source code in swarmauri_base/llms/LLMBase.py

def add_allowed_model(self, model: str) -> None:
    """
    Add a new model to the list of allowed models.

    Raises:
        ValueError: If the model is already in the allowed models list.
    """
    if model in self.allowed_models:
        raise ValueError(f"Model '{model}' is already allowed.")
    self.allowed_models.append(model)

remove_allowed_model

remove_allowed_model(model)

Remove a model from the list of allowed models.

RAISES	DESCRIPTION
ValueError	If the model is not in the allowed models list.

Source code in swarmauri_base/llms/LLMBase.py

def remove_allowed_model(self, model: str) -> None:
    """
    Remove a model from the list of allowed models.

    Raises:
        ValueError: If the model is not in the allowed models list.
    """
    if model not in self.allowed_models:
        raise ValueError(f"Model '{model}' is not in the allowed models list.")
    self.allowed_models.remove(model)