Class swarmauri_standard.llms.LLM.LLM

swarmauri_standard.llms.LLM.LLM

LLM(**data)

Bases: LLMBase

Generic LLM class for interacting with various language model APIs. This class provides synchronous and asynchronous methods to send conversation data to the model, receive predictions, and stream responses.

ATTRIBUTE	DESCRIPTION
api_key	API key for authenticating requests to the API. TYPE: SecretStr
allowed_models	List of allowed model names that can be used. TYPE: List[str]
name	The default model name to use for predictions. TYPE: str
type	The type identifier for this class. TYPE: Literal[LLM]
BASE_URL	The base URL for API requests. TYPE: str
timeout	Timeout for API requests in seconds. TYPE: float

Initialize the LLM class with the provided data.

PARAMETER	DESCRIPTION
**data	Arbitrary keyword arguments containing initialization data. Should include api_key, and optionally name, BASE_URL, timeout. DEFAULT: {}

Source code in swarmauri_standard/llms/LLM.py

def __init__(self, **data) -> None:
    """
    Initialize the LLM class with the provided data.

    Args:
        **data: Arbitrary keyword arguments containing initialization data.
               Should include api_key, and optionally name, BASE_URL, timeout.
    """
    super().__init__(**data)

    # Set up headers for API requests
    self._headers = {
        "Authorization": f"Bearer {self.api_key.get_secret_value()}",
        "Content-Type": "application/json",
    }

    # Load allowed models and set default if needed
    if not self.allowed_models:
        self.allowed_models = self.get_allowed_models()

    if not self.name and self.allowed_models:
        self.name = self.allowed_models[0]

allowed_models instance-attribute

allowed_models = get_allowed_models()

name instance-attribute

name = allowed_models[0]

type class-attribute instance-attribute

type = 'LLMBase'

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'

api_key class-attribute instance-attribute

api_key = None

timeout class-attribute instance-attribute

timeout = 600.0

include_usage class-attribute instance-attribute

include_usage = True

BASE_URL class-attribute instance-attribute

BASE_URL = None

predict

predict(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    enable_json=False,
    stop=None,
)

Generates a response from the model based on the given conversation.

PARAMETER	DESCRIPTION
conversation	Conversation object with message history. TYPE: Conversation
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for the model's response. TYPE: int DEFAULT: 256
top_p	Cumulative probability for nucleus sampling. TYPE: float DEFAULT: 1.0
enable_json	Whether to format the response as JSON. TYPE: bool DEFAULT: False
stop	List of stop sequences for response termination. TYPE: Optional[List[str]] DEFAULT: None

RETURNS	DESCRIPTION
Conversation	Updated conversation with the model's response. TYPE: Conversation

Source code in swarmauri_standard/llms/LLM.py

@retry_on_status_codes((429, 529), max_retries=3)
def predict(
    self,
    conversation: Conversation,
    temperature: float = 0.7,
    max_tokens: int = 256,
    top_p: float = 1.0,
    enable_json: bool = False,
    stop: Optional[List[str]] = None,
) -> Conversation:
    """
    Generates a response from the model based on the given conversation.

    Args:
        conversation (Conversation): Conversation object with message history.
        temperature (float): Sampling temperature for response diversity.
        max_tokens (int): Maximum tokens for the model's response.
        top_p (float): Cumulative probability for nucleus sampling.
        enable_json (bool): Whether to format the response as JSON.
        stop (Optional[List[str]]): List of stop sequences for response termination.

    Returns:
        Conversation: Updated conversation with the model's response.
    """
    formatted_messages = self._format_messages(conversation.history)

    # Prepare the payload
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "top_p": top_p,
        "stop": stop or [],
    }

    # Add JSON response format if requested
    if enable_json:
        payload["response_format"] = {"type": "json_object"}

    # Make the API request and measure time
    with DurationManager() as prompt_timer:
        with httpx.Client(timeout=self.timeout) as client:
            response = client.post(
                self.BASE_URL, headers=self._headers, json=payload
            )
            response.raise_for_status()

    # Parse the response
    response_data = response.json()
    message_content = response_data["choices"][0]["message"]["content"]
    usage_data = response_data.get("usage", {})

    # Prepare usage data if tracking is enabled
    if self.include_usage:
        usage = self._prepare_usage_data(usage_data, prompt_timer.duration)
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))
    return conversation

apredict async

apredict(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    enable_json=False,
    stop=None,
)

Async method to generate a response from the model based on the given conversation.

PARAMETER	DESCRIPTION
conversation	Conversation object with message history. TYPE: Conversation
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for the model's response. TYPE: int DEFAULT: 256
top_p	Cumulative probability for nucleus sampling. TYPE: float DEFAULT: 1.0
enable_json	Whether to format the response as JSON. TYPE: bool DEFAULT: False
stop	List of stop sequences for response termination. TYPE: Optional[List[str]] DEFAULT: None

RETURNS	DESCRIPTION
Conversation	Updated conversation with the model's response. TYPE: Conversation

Source code in swarmauri_standard/llms/LLM.py

@retry_on_status_codes((429, 529), max_retries=3)
async def apredict(
    self,
    conversation: Conversation,
    temperature: float = 0.7,
    max_tokens: int = 256,
    top_p: float = 1.0,
    enable_json: bool = False,
    stop: Optional[List[str]] = None,
) -> Conversation:
    """
    Async method to generate a response from the model based on the given conversation.

    Args:
        conversation (Conversation): Conversation object with message history.
        temperature (float): Sampling temperature for response diversity.
        max_tokens (int): Maximum tokens for the model's response.
        top_p (float): Cumulative probability for nucleus sampling.
        enable_json (bool): Whether to format the response as JSON.
        stop (Optional[List[str]]): List of stop sequences for response termination.

    Returns:
        Conversation: Updated conversation with the model's response.
    """
    formatted_messages = self._format_messages(conversation.history)

    # Prepare the payload
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "top_p": top_p,
        "stop": stop or [],
    }

    # Add JSON response format if requested
    if enable_json:
        payload["response_format"] = {"type": "json_object"}

    # Make the async API request and measure time
    with DurationManager() as prompt_timer:
        async with httpx.AsyncClient(timeout=self.timeout) as client:
            response = await client.post(
                self.BASE_URL, headers=self._headers, json=payload
            )
            response.raise_for_status()

    # Parse the response
    response_data = response.json()
    message_content = response_data["choices"][0]["message"]["content"]
    usage_data = response_data.get("usage", {})

    # Prepare usage data if tracking is enabled
    if self.include_usage:
        usage = self._prepare_usage_data(usage_data, prompt_timer.duration)
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

    return conversation

stream

stream(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    enable_json=False,
    stop=None,
)

Streams response text from the model in real-time.

PARAMETER	DESCRIPTION
conversation	Conversation object with message history. TYPE: Conversation
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for the model's response. TYPE: int DEFAULT: 256
top_p	Cumulative probability for nucleus sampling. TYPE: float DEFAULT: 1.0
enable_json	Whether to format the response as JSON. TYPE: bool DEFAULT: False
stop	List of stop sequences for response termination. TYPE: Optional[List[str]] DEFAULT: None

YIELDS	DESCRIPTION
str	Partial response content from the model. TYPE:: str

Source code in swarmauri_standard/llms/LLM.py

@retry_on_status_codes((429, 529), max_retries=3)
def stream(
    self,
    conversation: Conversation,
    temperature: float = 0.7,
    max_tokens: int = 256,
    top_p: float = 1.0,
    enable_json: bool = False,
    stop: Optional[List[str]] = None,
) -> Generator[str, None, None]:
    """
    Streams response text from the model in real-time.

    Args:
        conversation (Conversation): Conversation object with message history.
        temperature (float): Sampling temperature for response diversity.
        max_tokens (int): Maximum tokens for the model's response.
        top_p (float): Cumulative probability for nucleus sampling.
        enable_json (bool): Whether to format the response as JSON.
        stop (Optional[List[str]]): List of stop sequences for response termination.

    Yields:
        str: Partial response content from the model.
    """
    formatted_messages = self._format_messages(conversation.history)

    # Prepare the payload with stream flag
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "top_p": top_p,
        "stream": True,
        "stop": stop or [],
    }

    if enable_json:
        payload["response_format"] = {"type": "json_object"}

    if self.include_usage:
        payload["stream_options"] = {"include_usage": True}

    # Start timing for prompt processing
    with DurationManager() as prompt_timer:
        with httpx.Client(timeout=self.timeout) as client:
            response = client.post(
                self.BASE_URL, headers=self._headers, json=payload
            )
            response.raise_for_status()

    # Process the streaming response
    message_content = ""
    usage_data = {}

    with DurationManager() as completion_timer:
        for line in response.iter_lines():
            if line.startswith("data: "):
                json_str = line.replace("data: ", "")  # Remove 'data: ' prefix

                if json_str.strip() == "[DONE]":
                    break

                try:
                    if json_str:
                        chunk = json.loads(json_str)
                        if (
                            "choices" in chunk
                            and chunk["choices"]
                            and "delta" in chunk["choices"][0]
                        ):
                            delta = chunk["choices"][0]["delta"]
                            if "content" in delta:
                                content = delta["content"]
                                message_content += content
                                yield content

                        # Collect usage data if available
                        if (
                            self.include_usage
                            and "usage" in chunk
                            and chunk["usage"] is not None
                        ):
                            usage_data = chunk["usage"]

                except json.JSONDecodeError:
                    pass

    # Add the complete message to the conversation
    if self.include_usage:
        usage = self._prepare_usage_data(
            usage_data, prompt_timer.duration, completion_timer.duration
        )
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

astream async

astream(
    conversation,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    enable_json=False,
    stop=None,
)

Async generator that streams response text from the model in real-time.

PARAMETER	DESCRIPTION
conversation	Conversation object with message history. TYPE: Conversation
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for the model's response. TYPE: int DEFAULT: 256
top_p	Cumulative probability for nucleus sampling. TYPE: float DEFAULT: 1.0
enable_json	Whether to format the response as JSON. TYPE: bool DEFAULT: False
stop	List of stop sequences for response termination. TYPE: Optional[List[str]] DEFAULT: None

YIELDS	DESCRIPTION
str	Partial response content from the model. TYPE:: AsyncGenerator[str, None]

Source code in swarmauri_standard/llms/LLM.py

@retry_on_status_codes((429, 529), max_retries=3)
async def astream(
    self,
    conversation: Conversation,
    temperature: float = 0.7,
    max_tokens: int = 256,
    top_p: float = 1.0,
    enable_json: bool = False,
    stop: Optional[List[str]] = None,
) -> AsyncGenerator[str, None]:
    """
    Async generator that streams response text from the model in real-time.

    Args:
        conversation (Conversation): Conversation object with message history.
        temperature (float): Sampling temperature for response diversity.
        max_tokens (int): Maximum tokens for the model's response.
        top_p (float): Cumulative probability for nucleus sampling.
        enable_json (bool): Whether to format the response as JSON.
        stop (Optional[List[str]]): List of stop sequences for response termination.

    Yields:
        str: Partial response content from the model.
    """
    formatted_messages = self._format_messages(conversation.history)

    # Prepare the payload with stream flag
    payload = {
        "model": self.name,
        "messages": formatted_messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
        "top_p": top_p,
        "stream": True,
        "stop": stop or [],
    }

    if enable_json:
        payload["response_format"] = {"type": "json_object"}

    if self.include_usage:
        payload["stream_options"] = {"include_usage": True}

    # Start timing for prompt processing
    with DurationManager() as prompt_timer:
        async with httpx.AsyncClient(timeout=self.timeout) as client:
            response = await client.post(
                self.BASE_URL, headers=self._headers, json=payload
            )
            response.raise_for_status()

    # Process the streaming response asynchronously
    message_content = ""
    usage_data = {}

    with DurationManager() as completion_timer:
        async for line in response.aiter_lines():
            if line.startswith("data: "):
                json_str = line[6:]  # Remove 'data: ' prefix

                if json_str.strip() == "[DONE]":
                    break

                try:
                    if json_str:
                        chunk = json.loads(json_str)
                        if (
                            "choices" in chunk
                            and chunk["choices"]
                            and "delta" in chunk["choices"][0]
                        ):
                            delta = chunk["choices"][0]["delta"]
                            if "content" in delta:
                                content = delta["content"]
                                message_content += content
                                yield content

                        # Collect usage data if available
                        if (
                            self.include_usage
                            and "usage" in chunk
                            and chunk["usage"] is not None
                        ):
                            usage_data = chunk["usage"]

                except json.JSONDecodeError:
                    pass

    # Add the complete message to the conversation
    if self.include_usage:
        usage = self._prepare_usage_data(
            usage_data, prompt_timer.duration, completion_timer.duration
        )
        conversation.add_message(AgentMessage(content=message_content, usage=usage))
    else:
        conversation.add_message(AgentMessage(content=message_content))

batch

batch(
    conversations,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    enable_json=False,
    stop=None,
)

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

PARAMETER	DESCRIPTION
conversations	List of conversations to process. TYPE: List[Conversation]
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for each response. TYPE: int DEFAULT: 256
top_p	Cumulative probability for nucleus sampling. TYPE: float DEFAULT: 1.0
enable_json	Whether to format the response as JSON. TYPE: bool DEFAULT: False
stop	List of stop sequences for response termination. TYPE: Optional[List[str]] DEFAULT: None

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

Source code in swarmauri_standard/llms/LLM.py

def batch(
    self,
    conversations: List[Conversation],
    temperature: float = 0.7,
    max_tokens: int = 256,
    top_p: float = 1.0,
    enable_json: bool = False,
    stop: Optional[List[str]] = None,
) -> List[Conversation]:
    """
    Processes a batch of conversations and generates responses for each sequentially.

    Args:
        conversations (List[Conversation]): List of conversations to process.
        temperature (float): Sampling temperature for response diversity.
        max_tokens (int): Maximum tokens for each response.
        top_p (float): Cumulative probability for nucleus sampling.
        enable_json (bool): Whether to format the response as JSON.
        stop (Optional[List[str]]): List of stop sequences for response termination.

    Returns:
        List[Conversation]: List of updated conversations with model responses.
    """
    results = []
    for conversation in conversations:
        result_conversation = self.predict(
            conversation,
            temperature=temperature,
            max_tokens=max_tokens,
            top_p=top_p,
            enable_json=enable_json,
            stop=stop,
        )
        results.append(result_conversation)
    return results

abatch async

abatch(
    conversations,
    temperature=0.7,
    max_tokens=256,
    top_p=1.0,
    enable_json=False,
    stop=None,
    max_concurrent=5,
)

Async method for processing a batch of conversations concurrently.

PARAMETER	DESCRIPTION
conversations	List of conversations to process. TYPE: List[Conversation]
temperature	Sampling temperature for response diversity. TYPE: float DEFAULT: 0.7
max_tokens	Maximum tokens for each response. TYPE: int DEFAULT: 256
top_p	Cumulative probability for nucleus sampling. TYPE: float DEFAULT: 1.0
enable_json	Whether to format the response as JSON. TYPE: bool DEFAULT: False
stop	List of stop sequences for response termination. TYPE: Optional[List[str]] DEFAULT: None
max_concurrent	Maximum number of concurrent requests. TYPE: int DEFAULT: 5

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

Source code in swarmauri_standard/llms/LLM.py

async def abatch(
    self,
    conversations: List[Conversation],
    temperature: float = 0.7,
    max_tokens: int = 256,
    top_p: float = 1.0,
    enable_json: bool = False,
    stop: Optional[List[str]] = None,
    max_concurrent: int = 5,
) -> List[Conversation]:
    """
    Async method for processing a batch of conversations concurrently.

    Args:
        conversations (List[Conversation]): List of conversations to process.
        temperature (float): Sampling temperature for response diversity.
        max_tokens (int): Maximum tokens for each response.
        top_p (float): Cumulative probability for nucleus sampling.
        enable_json (bool): Whether to format the response as JSON.
        stop (Optional[List[str]]): List of stop sequences for response termination.
        max_concurrent (int): Maximum number of concurrent requests.

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

    async def process_conversation(conv: Conversation) -> Conversation:
        async with semaphore:
            return await self.apredict(
                conv,
                temperature=temperature,
                max_tokens=max_tokens,
                top_p=top_p,
                enable_json=enable_json,
                stop=stop,
            )

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

get_allowed_models

get_allowed_models()

Returns a list of allowed models for this LLM provider.

This default implementation returns a static list. Provider-specific subclasses should override this to query their respective APIs.

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

Source code in swarmauri_standard/llms/LLM.py

def get_allowed_models(self) -> List[str]:
    """
    Returns a list of allowed models for this LLM provider.

    This default implementation returns a static list. Provider-specific
    subclasses should override this to query their respective APIs.

    Returns:
        List[str]: List of allowed model names.
    """
    return ["gpt-4", "gpt-3.5-turbo", "claude-3-5-sonnet-20240229"]

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)