Class swarmauri_standard.chains.PromptContextChain.PromptContextChain

swarmauri_standard.chains.PromptContextChain.PromptContextChain

PromptContextChain(**data)

Bases: PromptContextChainBase

Source code in swarmauri_base/chains/PromptContextChainBase.py

def __init__(self, **data: Any):
    super().__init__(**data)
    # Now that the instance is created, we can safely access `prompt_matrix.shape`
    self.response_matrix = PromptMatrixBase(
        matrix=[
            [None for _ in range(self.prompt_matrix.shape[1])]
            for _ in range(self.prompt_matrix.shape[0])
        ]
    )

type class-attribute instance-attribute

type = 'PromptContextChain'

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

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

resource = Field(default=CHAIN.value)

version class-attribute instance-attribute

version = '0.1.0'

steps class-attribute instance-attribute

steps = Field(default_factory=list)

context class-attribute instance-attribute

context = Field(default_factory=dict)

prompt_matrix instance-attribute

prompt_matrix

agents class-attribute instance-attribute

agents = Field(default_factory=list)

llm_kwargs class-attribute instance-attribute

llm_kwargs = Field(default_factory=dict)

response_matrix class-attribute instance-attribute

response_matrix = PromptMatrixBase(
    matrix=[
        [None for _ in (range(shape[1]))]
        for _ in (range(shape[0]))
    ]
)

current_step_index class-attribute instance-attribute

current_step_index = 0

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

update

update(**kwargs)

Source code in swarmauri_base/chains/ChainContextBase.py

def update(self, **kwargs):
    self.context.update(kwargs)

get_value

get_value(key)

Source code in swarmauri_base/chains/ChainContextBase.py

def get_value(self, key: str) -> Any:
    return self.context.get(key)

build_dependencies

build_dependencies()

Build the chain steps in the correct order by resolving dependencies first.

Source code in swarmauri_base/chains/PromptContextChainBase.py

def build_dependencies(self) -> List[ChainStepBase]:
    """
    Build the chain steps in the correct order by resolving dependencies first.
    """
    steps = []

    for i in range(self.prompt_matrix.shape[1]):
        try:
            sequence = np.array(self.prompt_matrix.matrix)[:, i].tolist()
            execution_order = self.resolve_dependencies(sequence=sequence)
            for j in execution_order:
                prompt = sequence[j]
                if prompt:
                    ref = f"Agent_{j}_Step_{i}_response"  # Using a unique reference string
                    step = ChainStepBase(
                        key=f"Agent_{j}_Step_{i}",
                        method=self._execute_prompt,
                        args=[j, prompt, ref],
                        ref=ref,
                    )
                    steps.append(step)
        except Exception as e:
            print(str(e))
    return steps

resolve_dependencies

resolve_dependencies(sequence)

Resolve dependencies within a specific sequence of the prompt matrix.

PARAMETER	DESCRIPTION
sequence	The sequence of prompts to resolve dependencies for. TYPE: List[Optional[str]]

RETURNS	DESCRIPTION
List[int]	List[int]: The execution order of the agents for the given sequence.

Source code in swarmauri_base/chains/PromptContextChainBase.py

def resolve_dependencies(self, sequence: List[Optional[str]]) -> List[int]:
    """
    Resolve dependencies within a specific sequence of the prompt matrix.

    Args:
        sequence (List[Optional[str]]): The sequence of prompts to resolve dependencies for.

    Returns:
        List[int]: The execution order of the agents for the given sequence.
    """

    return [x for x in range(0, len(sequence), 1)]

execute

execute(build_dependencies=True)

Execute the chain of prompts based on the state of the prompt matrix. Iterates through each sequence in the prompt matrix, resolves dependencies, and executes prompts in the resolved order.

Source code in swarmauri_base/chains/PromptContextChainBase.py

def execute(self, build_dependencies=True) -> None:
    """
    Execute the chain of prompts based on the state of the prompt matrix.
    Iterates through each sequence in the prompt matrix, resolves dependencies,
    and executes prompts in the resolved order.
    """
    if build_dependencies:
        self.steps = self.build_dependencies()
        self.current_step_index = 0

    while self.current_step_index < len(self.steps):
        step = self.steps[self.current_step_index]
        method = step.method
        args = step.args
        ref = step.ref
        result = method(*args)
        self.context[ref] = result
        prompt_index = self._extract_step_number(ref)
        self._update_response_matrix(args[0], prompt_index, result)
        self.current_step_index += 1  # Move to the next step
    else:
        print("All steps have been executed.")

execute_next_step

execute_next_step()

Execute the next step in the steps list if available.

Source code in swarmauri_base/chains/PromptContextChainBase.py

def execute_next_step(self):
    """
    Execute the next step in the steps list if available.
    """
    if self.current_step_index < len(self.steps):
        step = self.steps[self.current_step_index]
        method = step.method
        args = step.args
        ref = step.ref
        result = method(*args)
        self.context[ref] = result
        prompt_index = self._extract_step_number(ref)
        self._update_response_matrix(args[0], prompt_index, result)
        self.current_step_index += 1  # Move to the next step
    else:
        print("All steps have been executed.")