Class swarmauri_standard.agents.RagAgent.RagAgent

swarmauri_standard.agents.RagAgent.RagAgent

Bases: AgentRetrieveMixin, AgentVectorStoreMixin, AgentSystemContextMixin, AgentConversationMixin, AgentBase

RagAgent (Retriever-And-Generator Agent) extends DocumentAgentBase, specialized in retrieving documents based on input queries and generating responses.

llm instance-attribute

llm

conversation class-attribute instance-attribute

conversation = MaxSystemContextConversation(
    system_context=""
)

vector_store instance-attribute

vector_store

system_context class-attribute instance-attribute

system_context = SystemMessage(content='')

type class-attribute instance-attribute

type = 'RagAgent'

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=AGENT.value)

version class-attribute instance-attribute

version = '0.1.0'

llm_kwargs class-attribute instance-attribute

llm_kwargs = {}

last_retrieved class-attribute instance-attribute

last_retrieved = Field(default_factory=list)

exec

exec(
    input_data="",
    top_k=5,
    preamble=True,
    fixed=False,
    llm_kwargs={},
)

Source code in swarmauri_standard/agents/RagAgent.py

def exec(
    self,
    input_data: Optional[Union[str, IMessage]] = "",
    top_k: int = 5,
    preamble: bool = True,
    fixed: bool = False,
    llm_kwargs: Optional[Dict] = {},
) -> Any:
    llm_kwargs = llm_kwargs or self.llm_kwargs

    try:
        self._prepare_context(input_data, top_k, preamble, fixed)
        if llm_kwargs:
            self.llm.predict(conversation=self.conversation, **llm_kwargs)
        else:
            self.llm.predict(conversation=self.conversation)
        return self.conversation.get_last().content
    except Exception as e:
        print(f"RagAgent error: {e}")
        raise e

aexec async

aexec(
    input_data="",
    top_k=5,
    preamble=True,
    fixed=False,
    llm_kwargs={},
)

Source code in swarmauri_standard/agents/RagAgent.py

async def aexec(
    self,
    input_data: Optional[Union[str, IMessage]] = "",
    top_k: int = 5,
    preamble: bool = True,
    fixed: bool = False,
    llm_kwargs: Optional[Dict] = {},
) -> Any:
    llm_kwargs = llm_kwargs or self.llm_kwargs

    try:
        self._prepare_context(input_data, top_k, preamble, fixed)
        if llm_kwargs:
            await self.llm.apredict(conversation=self.conversation, **llm_kwargs)
        else:
            await self.llm.apredict(conversation=self.conversation)
        return self.conversation.get_last().content
    except Exception as e:
        print(f"RagAgent error: {e}")
        raise e

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

batch

batch(inputs, llm_kwargs=None)

File: AgentBase.py Class: AgentBase Method: batch

Default batch implementation: calls exec on each input in inputs. Subclasses can override for optimized bulk behavior.

Source code in swarmauri_base/agents/AgentBase.py

def batch(
    self,
    inputs: List[Union[str, IMessage]],
    llm_kwargs: Optional[Dict] = None,
) -> List[Any]:
    """
    File: AgentBase.py
    Class: AgentBase
    Method: batch

    Default batch implementation: calls `exec` on each input in `inputs`.
    Subclasses can override for optimized bulk behavior.
    """
    llm_kwargs = llm_kwargs or self.llm_kwargs or {}
    results: List[Any] = []
    for inp in inputs:
        results.append(self.exec(inp, llm_kwargs=llm_kwargs))
    return results

abatch async

abatch(inputs, llm_kwargs=None)

File: AgentBase.py Class: AgentBase Method: abatch

Default async batch implementation: concurrently calls aexec on all inputs. Subclasses can override for more efficient implementations.

Source code in swarmauri_base/agents/AgentBase.py

async def abatch(
    self,
    inputs: List[Union[str, IMessage]],
    llm_kwargs: Optional[Dict] = None,
) -> List[Any]:
    """
    File: AgentBase.py
    Class: AgentBase
    Method: abatch

    Default async batch implementation: concurrently calls `aexec` on all inputs.
    Subclasses can override for more efficient implementations.
    """
    llm_kwargs = llm_kwargs or self.llm_kwargs or {}
    tasks = [self.aexec(inp, llm_kwargs=llm_kwargs) for inp in inputs]
    return await asyncio.gather(*tasks)

set_system_context

set_system_context(value)

Source code in swarmauri_base/agents/AgentSystemContextMixin.py

@field_validator("system_context", mode="before")
def set_system_context(cls, value: Union[str, SystemMessage]) -> SystemMessage:
    if isinstance(value, str):
        return SystemMessage(content=value)
    return value