Bases: IToolPredict
, ComponentBase
resource = Field(default=TOOL_LLM.value, frozen=True)
model_config = ConfigDict(
extra="forbid", arbitrary_types_allowed=True
)
id = Field(default_factory=generate_id)
Add a new model to the list of allowed models.
RAISES |
DESCRIPTION |
ValueError
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If the model is already in the allowed models list.
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Source code in swarmauri_base/tool_llms/ToolLLMBase.py
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44 | 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)
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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.
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Source code in swarmauri_base/tool_llms/ToolLLMBase.py
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55 | 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)
|
Source code in swarmauri_base/tool_llms/ToolLLMBase.py
| @abstractmethod
def get_schema_converter(self) -> Type["SchemaConverterBase"]:
raise NotImplementedError(
"get_schema_converter() not implemented in subclass yet."
)
|
Source code in swarmauri_base/tool_llms/ToolLLMBase.py
| @abstractmethod
def predict(self, *args, **kwargs):
raise NotImplementedError("predict() not implemented in subclass yet.")
|
apredict(*args, **kwargs)
Source code in swarmauri_base/tool_llms/ToolLLMBase.py
| @abstractmethod
async def apredict(self, *args, **kwargs):
raise NotImplementedError("apredict() not implemented in subclass yet.")
|
Source code in swarmauri_base/tool_llms/ToolLLMBase.py
| @abstractmethod
def stream(self, *args, **kwargs):
raise NotImplementedError("stream() not implemented in subclass yet.")
|
Source code in swarmauri_base/tool_llms/ToolLLMBase.py
| @abstractmethod
async def astream(self, *args, **kwargs):
raise NotImplementedError("astream() not implemented in subclass yet.")
|
Source code in swarmauri_base/tool_llms/ToolLLMBase.py
| @abstractmethod
def batch(self, *args, **kwargs):
raise NotImplementedError("batch() not implemented in subclass yet.")
|
Source code in swarmauri_base/tool_llms/ToolLLMBase.py
| @abstractmethod
async def abatch(self, *args, **kwargs):
raise NotImplementedError("abatch() not implemented in subclass yet.")
|
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
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585 | @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(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
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654 | @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(toml_data)
Validate a model from a TOML string.
Source code in swarmauri_base/TomlMixin.py
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24 | @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(
fields_to_exclude=None, api_key_placeholder=None
)
Return a TOML representation of the model.
Source code in swarmauri_base/TomlMixin.py
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56 | 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(yaml_data)
Validate a model from a YAML string.
Source code in swarmauri_base/YamlMixin.py
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23 | @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(
fields_to_exclude=None, api_key_placeholder=None
)
Return a YAML representation of the model.
Source code in swarmauri_base/YamlMixin.py
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55 | 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
|