Class swarmauri_standard.tool_llms.GeminiToolModel.GeminiToolModel

swarmauri_standard.tool_llms.GeminiToolModel.GeminiToolModel

GeminiToolModel(*args, **kwargs)

Bases: ToolLLMBase

A class that interacts with Gemini-based LLM APIs to process conversations, handle tool calls, and convert messages for compatible schema. This model supports synchronous and asynchronous operations.

ATTRIBUTE	DESCRIPTION
api_key	The API key used to authenticate requests to the Gemini API. TYPE: SecretStr
allowed_models	List of supported model names. TYPE: List[str]
name	The name of the Gemini model in use. TYPE: str
type	The model type, set to "GeminiToolModel". TYPE: Literal['GeminiToolModel']

Providers Resources: https://ai.google.dev/api/python/google/generativeai/protos/

Initializes the GeminiToolModel instance with the provided data.

PARAMETER	DESCRIPTION
*args	Variable length argument list. TYPE: Any DEFAULT: ()
**kwargs	Arbitrary keyword arguments containing initialization data. TYPE: Any DEFAULT: {}

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

def __init__(self, *args: Any, **kwargs: Any) -> None:
    """
    Initializes the GeminiToolModel instance with the provided data.

    Args:
        *args (Any): Variable length argument list.
        **kwargs (Any): Arbitrary keyword arguments containing initialization data.
    """
    super().__init__(*args, **kwargs)
    self.allowed_models = self.allowed_models or self.get_allowed_models()
    if not self.name and self.allowed_models:
        self.name = self.allowed_models[0]

api_key instance-attribute

api_key

name class-attribute instance-attribute

name = 'gemini-1.5-pro'

type class-attribute instance-attribute

type = 'GeminiToolModel'

timeout class-attribute instance-attribute

timeout = 600.0

BASE_URL class-attribute instance-attribute

BASE_URL = "https://generativelanguage.googleapis.com/v1beta/models"

allowed_models class-attribute instance-attribute

allowed_models = allowed_models or get_allowed_models()

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'

get_schema_converter

get_schema_converter()

Returns the schema converter class for Gemini API.

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

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

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

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

predict

predict(
    conversation,
    toolkit=None,
    tool_choice=None,
    multiturn=True,
    temperature=0.7,
    max_tokens=1024,
)

Generates model responses for a conversation synchronously.

PARAMETER	DESCRIPTION
conversation	The conversation instance. TYPE: Conversation
toolkit	Optional toolkit for handling tools. TYPE: Toolkit DEFAULT: None
tool_choice	Tool selection strategy (not used in Gemini but included for API compatibility) TYPE: Dict[str, Any] DEFAULT: None
multiturn	Whether to follow up a tool call with another LLM request. TYPE: bool DEFAULT: True
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit for generation. TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
IConversation	Updated conversation with model response. TYPE: Conversation

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def predict(
    self,
    conversation: Conversation,
    toolkit: Toolkit = None,
    tool_choice: Dict[str, Any] = None,
    multiturn: bool = True,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> Conversation:
    """
    Generates model responses for a conversation synchronously.

    Args:
        conversation (Conversation): The conversation instance.
        toolkit (Toolkit, optional): Optional toolkit for handling tools.
        tool_choice (Dict[str, Any], optional): Tool selection strategy (not used in Gemini but included for API compatibility)
        multiturn (bool): Whether to follow up a tool call with another LLM request.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit for generation.

    Returns:
        IConversation: Updated conversation with model response.
    """
    generation_config = {
        "temperature": temperature,
        "top_p": 0.95,
        "top_k": 0,
        "max_output_tokens": max_tokens,
    }

    tool_config = {
        "function_calling_config": {"mode": "ANY"},
    }

    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "contents": formatted_messages,
        "generation_config": generation_config,
        "safety_settings": self._safety_settings,
    }

    # Add tools if toolkit provided
    if toolkit:
        tools = self._schema_convert_tools(toolkit.tools)
        payload["tools"] = [tools]
        payload["tool_config"] = tool_config

    system_context = self._get_system_context(conversation.history)
    if system_context:
        payload["system_instruction"] = {"parts": [{"text": system_context}]}

    with httpx.Client(timeout=self.timeout) as client:
        response = client.post(
            f"{self.BASE_URL}/{self.name}:generateContent?key={self.api_key.get_secret_value()}",
            json=payload,
            headers=self._headers,
        )
        response.raise_for_status()
        tool_response = response.json()

    # Check if there are tool calls
    has_tool_calls = False
    if "candidates" in tool_response and tool_response["candidates"]:
        content = tool_response["candidates"][0]["content"]
        formatted_messages.append(content)

        if "parts" in content:
            tool_calls = content["parts"]
            for part in tool_calls:
                if "functionCall" in part:
                    has_tool_calls = True
                    break

    # Process tool calls if present
    if has_tool_calls and toolkit:
        messages, tool_messages = self._process_tool_calls(
            tool_calls, toolkit, formatted_messages
        )
        if tool_messages:
            conversation.add_messages(tool_messages)

        # For multiturn, follow up with another request if there were tool calls
        if multiturn:
            payload["contents"] = messages
            if "tools" in payload:
                payload.pop("tools")
            if "tool_config" in payload:
                payload.pop("tool_config")

            with httpx.Client(timeout=self.timeout) as client:
                response = client.post(
                    f"{self.BASE_URL}/{self.name}:generateContent?key={self.api_key.get_secret_value()}",
                    json=payload,
                    headers=self._headers,
                )
                response.raise_for_status()
                agent_response = response.json()

            if "candidates" in agent_response and agent_response["candidates"]:
                content = agent_response["candidates"][0]["content"]["parts"][0][
                    "text"
                ]
                conversation.add_message(AgentMessage(content=content))
    else:
        # If no tool calls, just add the assistant's message
        if "candidates" in tool_response and tool_response["candidates"]:
            content = tool_response["candidates"][0]["content"]["parts"][0]["text"]
            conversation.add_message(AgentMessage(content=content))

    return conversation

apredict async

apredict(
    conversation,
    toolkit=None,
    tool_choice=None,
    multiturn=True,
    temperature=0.7,
    max_tokens=1024,
)

Asynchronously generates model responses for a conversation.

PARAMETER	DESCRIPTION
conversation	The conversation instance. TYPE: Conversation
toolkit	Optional toolkit for handling tools. TYPE: Toolkit DEFAULT: None
tool_choice	Tool selection strategy (not used in Gemini but included for API compatibility) TYPE: Dict[str, Any] DEFAULT: None
multiturn	Whether to follow up a tool call with another LLM request. TYPE: bool DEFAULT: True
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit for generation. TYPE: int DEFAULT: 1024

RETURNS	DESCRIPTION
Conversation	Updated conversation with model response. TYPE: Conversation

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def apredict(
    self,
    conversation: Conversation,
    toolkit: Toolkit = None,
    tool_choice: Dict[str, Any] = None,
    multiturn: bool = True,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> Conversation:
    """
    Asynchronously generates model responses for a conversation.

    Args:
        conversation (Conversation): The conversation instance.
        toolkit (Toolkit, optional): Optional toolkit for handling tools.
        tool_choice (Dict[str, Any], optional): Tool selection strategy (not used in Gemini but included for API compatibility)
        multiturn (bool): Whether to follow up a tool call with another LLM request.
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit for generation.

    Returns:
        Conversation: Updated conversation with model response.
    """
    generation_config = {
        "temperature": temperature,
        "top_p": 0.95,
        "top_k": 0,
        "max_output_tokens": max_tokens,
    }

    tool_config = {
        "function_calling_config": {"mode": "ANY"},
    }

    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "contents": formatted_messages,
        "generation_config": generation_config,
        "safety_settings": self._safety_settings,
    }

    # Add tools if toolkit provided
    if toolkit:
        tools = self._schema_convert_tools(toolkit.tools)
        payload["tools"] = [tools]
        payload["tool_config"] = tool_config

    system_context = self._get_system_context(conversation.history)
    if system_context:
        payload["system_instruction"] = {"parts": [{"text": system_context}]}

    async with httpx.AsyncClient(timeout=self.timeout) as client:
        response = await client.post(
            f"{self.BASE_URL}/{self.name}:generateContent?key={self.api_key.get_secret_value()}",
            json=payload,
            headers=self._headers,
        )
        response.raise_for_status()
        tool_response = response.json()

    # Check if there are tool calls
    has_tool_calls = False
    if "candidates" in tool_response and tool_response["candidates"]:
        content = tool_response["candidates"][0]["content"]
        formatted_messages.append(content)

        if "parts" in content:
            tool_calls = content["parts"]
            for part in tool_calls:
                if "functionCall" in part:
                    has_tool_calls = True
                    break

    # Process tool calls if present
    if has_tool_calls and toolkit:
        messages, tool_messages = self._process_tool_calls(
            tool_calls, toolkit, formatted_messages
        )
        if tool_messages:
            conversation.add_messages(tool_messages)

        # For multiturn, follow up with another request if there were tool calls
        if multiturn:
            payload["contents"] = messages
            if "tools" in payload:
                payload.pop("tools")
            if "tool_config" in payload:
                payload.pop("tool_config")

            async with httpx.AsyncClient(timeout=self.timeout) as client:
                response = await client.post(
                    f"{self.BASE_URL}/{self.name}:generateContent?key={self.api_key.get_secret_value()}",
                    json=payload,
                    headers=self._headers,
                )
                response.raise_for_status()
                agent_response = response.json()

            if "candidates" in agent_response and agent_response["candidates"]:
                content = agent_response["candidates"][0]["content"]["parts"][0][
                    "text"
                ]
                conversation.add_message(AgentMessage(content=content))
    else:
        # If no tool calls, just add the assistant's message
        if "candidates" in tool_response and tool_response["candidates"]:
            content = tool_response["candidates"][0]["content"]["parts"][0]["text"]
            conversation.add_message(AgentMessage(content=content))

    return conversation

stream

stream(
    conversation,
    toolkit=None,
    tool_choice=None,
    temperature=0.7,
    max_tokens=1024,
)

Streams response generation in real-time.

PARAMETER	DESCRIPTION
conversation	The conversation instance. TYPE: Conversation
toolkit	Optional toolkit for handling tools. TYPE: Toolkit DEFAULT: None
tool_choice	Tool selection strategy (not used in Gemini but included for API compatibility) TYPE: Dict[str, Any] DEFAULT: None
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit for generation. TYPE: int DEFAULT: 1024

YIELDS	DESCRIPTION
str	Iterator[str]: Streamed text chunks from the model response.

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

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

    Args:
        conversation (Conversation): The conversation instance.
        toolkit (Toolkit, optional): Optional toolkit for handling tools.
        tool_choice (Dict[str, Any], optional): Tool selection strategy (not used in Gemini but included for API compatibility)
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit for generation.

    Yields:
        Iterator[str]: Streamed text chunks from the model response.
    """
    generation_config = {
        "temperature": temperature,
        "top_p": 0.95,
        "top_k": 0,
        "max_output_tokens": max_tokens,
    }

    tool_config = {
        "function_calling_config": {"mode": "ANY"},
    }

    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "contents": formatted_messages,
        "generation_config": generation_config,
        "safety_settings": self._safety_settings,
    }

    # Add tools if toolkit provided
    if toolkit:
        tools = self._schema_convert_tools(toolkit.tools)
        payload["tools"] = [tools]
        payload["tool_config"] = tool_config

    system_context = self._get_system_context(conversation.history)
    if system_context:
        payload["system_instruction"] = {"parts": [{"text": system_context}]}

    # First, handle tool calls
    with httpx.Client(timeout=self.timeout) as client:
        response = client.post(
            f"{self.BASE_URL}/{self.name}:generateContent?key={self.api_key.get_secret_value()}",
            json=payload,
            headers=self._headers,
        )
        response.raise_for_status()
        tool_response = response.json()

    # Check if there are tool calls
    has_tool_calls = False
    if "candidates" in tool_response and tool_response["candidates"]:
        content = tool_response["candidates"][0]["content"]
        formatted_messages.append(content)

        if "parts" in content:
            tool_calls = content["parts"]
            for part in tool_calls:
                if "functionCall" in part:
                    has_tool_calls = True
                    break

    # Process tool calls if present
    if has_tool_calls and toolkit:
        messages, tool_messages = self._process_tool_calls(
            tool_calls, toolkit, formatted_messages
        )
        if tool_messages:
            conversation.add_messages(tool_messages)

        # Now stream with tool results included
        payload["contents"] = messages
        if "tools" in payload:
            payload.pop("tools")
        if "tool_config" in payload:
            payload.pop("tool_config")

        with httpx.Client(timeout=self.timeout) as client:
            response = client.post(
                f"{self.BASE_URL}/{self.name}:streamGenerateContent?alt=sse&key={self.api_key.get_secret_value()}",
                json=payload,
                headers=self._headers,
            )
            response.raise_for_status()

        full_response = ""
        for line in response.iter_lines():
            line_str = line.decode("utf-8") if isinstance(line, bytes) else line
            if not line_str or line_str.startswith("data: [DONE]"):
                continue

            json_str = line_str.replace("data: ", "")
            if json_str:
                try:
                    response_data = json.loads(json_str)
                    if (
                        "candidates" in response_data
                        and response_data["candidates"]
                    ):
                        candidate = response_data["candidates"][0]
                        if (
                            "content" in candidate
                            and "parts" in candidate["content"]
                        ):
                            parts = candidate["content"]["parts"]
                            if parts and "text" in parts[0]:
                                chunk = parts[0]["text"]
                                full_response += chunk
                                yield chunk
                except json.JSONDecodeError:
                    pass

        conversation.add_message(AgentMessage(content=full_response))
    else:
        # If no tool calls, just stream the response directly
        if "candidates" in tool_response and tool_response["candidates"]:
            content = tool_response["candidates"][0]["content"]["parts"][0]["text"]
            conversation.add_message(AgentMessage(content=content))
            yield content

astream async

astream(
    conversation,
    toolkit=None,
    tool_choice=None,
    temperature=0.7,
    max_tokens=1024,
)

Asynchronously streams response generation in real-time.

PARAMETER	DESCRIPTION
conversation	The conversation instance. TYPE: Conversation
toolkit	Optional toolkit for handling tools. TYPE: Toolkit DEFAULT: None
tool_choice	Tool selection strategy (not used in Gemini but included for API compatibility) TYPE: Dict[str, Any] DEFAULT: None
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit for generation. TYPE: int DEFAULT: 1024

YIELDS	DESCRIPTION
AsyncIterator[str]	AsyncIterator[str]: Asynchronously streamed text chunks from the model response.

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def astream(
    self,
    conversation: Conversation,
    toolkit: Toolkit = None,
    tool_choice: Dict[str, Any] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> AsyncIterator[str]:
    """
    Asynchronously streams response generation in real-time.

    Args:
        conversation (Conversation): The conversation instance.
        toolkit (Toolkit, optional): Optional toolkit for handling tools.
        tool_choice (Dict[str, Any], optional): Tool selection strategy (not used in Gemini but included for API compatibility)
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit for generation.

    Yields:
        AsyncIterator[str]: Asynchronously streamed text chunks from the model response.
    """
    generation_config = {
        "temperature": temperature,
        "top_p": 0.95,
        "top_k": 0,
        "max_output_tokens": max_tokens,
    }

    tool_config = {
        "function_calling_config": {"mode": "ANY"},
    }

    formatted_messages = self._format_messages(conversation.history)
    payload = {
        "contents": formatted_messages,
        "generation_config": generation_config,
        "safety_settings": self._safety_settings,
    }

    # Add tools if toolkit provided
    if toolkit:
        tools = self._schema_convert_tools(toolkit.tools)
        payload["tools"] = [tools]
        payload["tool_config"] = tool_config

    system_context = self._get_system_context(conversation.history)
    if system_context:
        payload["system_instruction"] = {"parts": [{"text": system_context}]}

    # First, handle tool calls
    async with httpx.AsyncClient(timeout=self.timeout) as client:
        response = await client.post(
            f"{self.BASE_URL}/{self.name}:generateContent?key={self.api_key.get_secret_value()}",
            json=payload,
            headers=self._headers,
        )
        response.raise_for_status()
        tool_response = response.json()

    # Check if there are tool calls
    has_tool_calls = False
    if "candidates" in tool_response and tool_response["candidates"]:
        content = tool_response["candidates"][0]["content"]
        formatted_messages.append(content)

        if "parts" in content:
            tool_calls = content["parts"]
            for part in tool_calls:
                if "functionCall" in part:
                    has_tool_calls = True
                    break

    # Process tool calls if present
    if has_tool_calls and toolkit:
        messages, tool_messages = self._process_tool_calls(
            tool_calls, toolkit, formatted_messages
        )
        if tool_messages:
            conversation.add_messages(tool_messages)

        # Now stream with tool results included
        payload["contents"] = messages
        if "tools" in payload:
            payload.pop("tools")
        if "tool_config" in payload:
            payload.pop("tool_config")

        async with httpx.AsyncClient(timeout=self.timeout) as client:
            response = await client.post(
                f"{self.BASE_URL}/{self.name}:streamGenerateContent?alt=sse&key={self.api_key.get_secret_value()}",
                json=payload,
                headers=self._headers,
            )
            response.raise_for_status()

        full_response = ""
        async for line in response.aiter_lines():
            if not line or line == "data: [DONE]":
                continue

            json_str = line.replace("data: ", "")
            if json_str:
                try:
                    response_data = json.loads(json_str)
                    if (
                        "candidates" in response_data
                        and response_data["candidates"]
                    ):
                        candidate = response_data["candidates"][0]
                        if (
                            "content" in candidate
                            and "parts" in candidate["content"]
                        ):
                            parts = candidate["content"]["parts"]
                            if parts and "text" in parts[0]:
                                chunk = parts[0]["text"]
                                full_response += chunk
                                yield chunk
                except json.JSONDecodeError:
                    pass

        conversation.add_message(AgentMessage(content=full_response))
    else:
        # If no tool calls, just stream the response directly
        if "candidates" in tool_response and tool_response["candidates"]:
            content = tool_response["candidates"][0]["content"]["parts"][0]["text"]
            conversation.add_message(AgentMessage(content=content))
            yield content

batch

batch(
    conversations,
    toolkit=None,
    tool_choice=None,
    temperature=0.7,
    max_tokens=1024,
)

Processes multiple conversations synchronously.

PARAMETER	DESCRIPTION
conversations	List of conversation instances. TYPE: List[Conversation]
toolkit	Optional toolkit for handling tools. TYPE: Toolkit DEFAULT: None
tool_choice	Tool selection strategy (not used in Gemini but included for API compatibility) TYPE: Dict[str, Any] DEFAULT: None
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit for generation. TYPE: int DEFAULT: 1024

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

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

def batch(
    self,
    conversations: List[Conversation],
    toolkit: Toolkit = None,
    tool_choice: Dict[str, Any] = None,
    temperature: float = 0.7,
    max_tokens: int = 1024,
) -> List[Conversation]:
    """
    Processes multiple conversations synchronously.

    Args:
        conversations (List[Conversation]): List of conversation instances.
        toolkit (Toolkit, optional): Optional toolkit for handling tools.
        tool_choice (Dict[str, Any], optional): Tool selection strategy (not used in Gemini but included for API compatibility)
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit for generation.

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

abatch async

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

Asynchronously processes multiple conversations with concurrency control.

PARAMETER	DESCRIPTION
conversations	List of conversation instances. TYPE: List[Conversation]
toolkit	Optional toolkit for handling tools. TYPE: Toolkit DEFAULT: None
tool_choice	Tool selection strategy (not used in Gemini but included for API compatibility) TYPE: Dict[str, Any] DEFAULT: None
temperature	Sampling temperature. TYPE: float DEFAULT: 0.7
max_tokens	Maximum token limit for generation. TYPE: int DEFAULT: 1024
max_concurrent	Maximum number of concurrent asynchronous tasks. TYPE: int DEFAULT: 5

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

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

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

    Args:
        conversations (List[Conversation]): List of conversation instances.
        toolkit (Toolkit, optional): Optional toolkit for handling tools.
        tool_choice (Dict[str, Any], optional): Tool selection strategy (not used in Gemini but included for API compatibility)
        temperature (float): Sampling temperature.
        max_tokens (int): Maximum token limit for generation.
        max_concurrent (int): Maximum number of concurrent asynchronous tasks.

    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,
                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 Gemini API.

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

Source code in swarmauri_standard/tool_llms/GeminiToolModel.py

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

    Returns:
        List[str]: A list of allowed model names.
    """
    return ["gemini-1.5-pro", "gemini-1.5-flash"]

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)