types

DeploymentDefinition

Bases: BaseModel

Parameters:

Name	Type	Description	Default
name	str		required

Source code in llama_deploy/types/apiserver.py

class DeploymentDefinition(BaseModel):
    name: str

Status

Bases: BaseModel

Parameters:

Name	Type	Description	Default
status	StatusEnum		required
status_message	str		required
max_deployments	int | None		None
deployments	list[str] | None		None

Source code in llama_deploy/types/apiserver.py

class Status(BaseModel):
    status: StatusEnum
    status_message: str
    max_deployments: int | None = None
    deployments: list[str] | None = None

ActionTypes

Bases: str, Enum

Action types for messages. Different consumers will handle (or ignore) different action types.

Source code in llama_deploy/types/core.py

class ActionTypes(str, Enum):
    """
    Action types for messages.
    Different consumers will handle (or ignore) different action types.
    """

    NEW_TASK = "new_task"
    COMPLETED_TASK = "completed_task"
    REQUEST_FOR_HELP = "request_for_help"
    NEW_TOOL_CALL = "new_tool_call"
    COMPLETED_TOOL_CALL = "completed_tool_call"
    TASK_STREAM = "task_stream"
    SEND_EVENT = "send_event"

ChatMessage

Bases: BaseModel

Chat message.

Parameters:

Name	Type	Description	Default
role	MessageRole		<MessageRole.USER: 'user'>
blocks	list[Annotated[Union[TextBlock, ImageBlock, AudioBlock], FieldInfo]]	Built-in mutable sequence. If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.	<dynamic>

Source code in llama-index-core/llama_index/core/base/llms/types.py

class ChatMessage(BaseModel):
    """Chat message."""

    role: MessageRole = MessageRole.USER
    additional_kwargs: dict[str, Any] = Field(default_factory=dict)
    blocks: list[ContentBlock] = Field(default_factory=list)

    def __init__(self, /, content: Any | None = None, **data: Any) -> None:
        """Keeps backward compatibility with the old `content` field.

        If content was passed and contained text, store a single TextBlock.
        If content was passed and it was a list, assume it's a list of content blocks and store it.
        """
        if content is not None:
            if isinstance(content, str):
                data["blocks"] = [TextBlock(text=content)]
            elif isinstance(content, list):
                data["blocks"] = content

        super().__init__(**data)

    @model_validator(mode="after")
    def legacy_additional_kwargs_image(self) -> Self:
        """Provided for backward compatibility.

        If `additional_kwargs` contains an `images` key, assume the value is a list
        of ImageDocument and convert them into image blocks.
        """
        if documents := self.additional_kwargs.get("images"):
            documents = cast(list[ImageDocument], documents)
            for doc in documents:
                img_base64_bytes = doc.resolve_image(as_base64=True).read()
                self.blocks.append(ImageBlock(image=img_base64_bytes))
        return self

    @property
    def content(self) -> str | None:
        """Keeps backward compatibility with the old `content` field.

        Returns:
            The cumulative content of the TextBlock blocks, None if there are none.
        """
        content = ""
        for block in self.blocks:
            if isinstance(block, TextBlock):
                content += block.text

        return content or None

    @content.setter
    def content(self, content: str) -> None:
        """Keeps backward compatibility with the old `content` field.

        Raises:
            ValueError: if blocks contains more than a block, or a block that's not TextBlock.
        """
        if not self.blocks:
            self.blocks = [TextBlock(text=content)]
        elif len(self.blocks) == 1 and isinstance(self.blocks[0], TextBlock):
            self.blocks = [TextBlock(text=content)]
        else:
            raise ValueError(
                "ChatMessage contains multiple blocks, use 'ChatMessage.blocks' instead."
            )

    def __str__(self) -> str:
        return f"{self.role.value}: {self.content}"

    @classmethod
    def from_str(
        cls,
        content: str,
        role: Union[MessageRole, str] = MessageRole.USER,
        **kwargs: Any,
    ) -> Self:
        if isinstance(role, str):
            role = MessageRole(role)
        return cls(role=role, blocks=[TextBlock(text=content)], **kwargs)

    def _recursive_serialization(self, value: Any) -> Any:
        if isinstance(value, BaseModel):
            value.model_rebuild()  # ensures all fields are initialized and serializable
            return value.model_dump()  # type: ignore
        if isinstance(value, dict):
            return {
                key: self._recursive_serialization(value)
                for key, value in value.items()
            }
        if isinstance(value, list):
            return [self._recursive_serialization(item) for item in value]
        return value

    @field_serializer("additional_kwargs", check_fields=False)
    def serialize_additional_kwargs(self, value: Any, _info: Any) -> Any:
        return self._recursive_serialization(value)

content property writable

content: str | None

Keeps backward compatibility with the old content field.

Returns:

Type	Description
str | None	The cumulative content of the TextBlock blocks, None if there are none.

legacy_additional_kwargs_image

legacy_additional_kwargs_image() -> Self

Provided for backward compatibility.

If additional_kwargs contains an images key, assume the value is a list of ImageDocument and convert them into image blocks.

Source code in llama-index-core/llama_index/core/base/llms/types.py

@model_validator(mode="after")
def legacy_additional_kwargs_image(self) -> Self:
    """Provided for backward compatibility.

    If `additional_kwargs` contains an `images` key, assume the value is a list
    of ImageDocument and convert them into image blocks.
    """
    if documents := self.additional_kwargs.get("images"):
        documents = cast(list[ImageDocument], documents)
        for doc in documents:
            img_base64_bytes = doc.resolve_image(as_base64=True).read()
            self.blocks.append(ImageBlock(image=img_base64_bytes))
    return self

EventDefinition

Bases: BaseModel

The definition of event.

To be used as payloads for service endpoints when wanting to send serialized Events.

Parameters:

Name	Type	Description	Default
agent_id	str		required
event_obj_str	str		required

Attributes:

Name	Type	Description
event_object_str	str	serialized string of event.

Source code in llama_deploy/types/core.py

class EventDefinition(BaseModel):
    """The definition of event.

    To be used as payloads for service endpoints when wanting to send serialized
    Events.

    Attributes:
        event_object_str (str): serialized string of event.
    """

    agent_id: str
    event_obj_str: str

HumanResponse

Bases: BaseModel

A simple human response.

Parameters:

Name	Type	Description	Default
result	str		required

Attributes:

Name	Type	Description
response	str	The human response.

Source code in llama_deploy/types/core.py

class HumanResponse(BaseModel):
    """
    A simple human response.

    Attributes:
        response (str):
            The human response.
    """

    result: str

ServiceDefinition

Bases: BaseModel

The definition of a service, bundles useful information describing the service.

Parameters:

Name	Type	Description	Default
service_name	str	The name of the service.	required
description	str	A description of the service and it's purpose.	required
prompt	list[ChatMessage]	Specific instructions for the service.	<dynamic>
host	str | None		None
port	int | None		None

Attributes:

Name	Type	Description
service_name	str	The name of the service.
description	str	A description of the service and it's purpose.
prompt	list[ChatMessage]	Specific instructions for the service.
host	str | None	The host of the service, if its a network service.
port	int | None	The port of the service, if its a network service.

Source code in llama_deploy/types/core.py

class ServiceDefinition(BaseModel):
    """
    The definition of a service, bundles useful information describing the service.

    Attributes:
        service_name (str):
            The name of the service.
        description (str):
            A description of the service and it's purpose.
        prompt (list[ChatMessage]):
            Specific instructions for the service.
        host (str | None):
            The host of the service, if its a network service.
        port (int | None):
            The port of the service, if its a network service.
    """

    service_name: str = Field(description="The name of the service.")
    description: str = Field(
        description="A description of the service and it's purpose."
    )
    prompt: list[ChatMessage] = Field(
        default_factory=list, description="Specific instructions for the service."
    )
    host: str | None = None
    port: int | None = None

SessionDefinition

Bases: BaseModel

The definition of a session.

Parameters:

Name	Type	Description	Default
session_id	str		'0f36b17c-d5a0-4447-9097-c701209e0971'
task_ids	list[str]	Built-in mutable sequence. If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.	<dynamic>

Attributes:

Name	Type	Description
session_id	str	The session ID. Defaults to a random UUID.
task_definitions	list[str]	The task ids in order, representing the session.
state	dict	The current session state.

Source code in llama_deploy/types/core.py

class SessionDefinition(BaseModel):
    """
    The definition of a session.

    Attributes:
        session_id (str):
            The session ID. Defaults to a random UUID.
        task_definitions (list[str]):
            The task ids in order, representing the session.
        state (dict):
            The current session state.
    """

    session_id: str = Field(default_factory=generate_id)
    task_ids: list[str] = Field(default_factory=list)
    state: dict = Field(default_factory=dict)

    @property
    def current_task_id(self) -> str | None:
        if len(self.task_ids) == 0:
            return None

        return self.task_ids[-1]

TaskDefinition

Bases: BaseModel

The definition and state of a task.

Parameters:

Name	Type	Description	Default
input	str		required
task_id	str		'd705268b-100f-4306-8b4b-452e0b90f27b'
session_id	str | None		None
agent_id	str | None		None

Attributes:

Name	Type	Description
input	str	The task input.
session_id	str	The session ID that the task belongs to.
task_id	str	The task ID. Defaults to a random UUID.
agent_id	str	The agent ID that the task should be sent to. If blank, the orchestrator decides.

Source code in llama_deploy/types/core.py

class TaskDefinition(BaseModel):
    """
    The definition and state of a task.

    Attributes:
        input (str):
            The task input.
        session_id (str):
            The session ID that the task belongs to.
        task_id (str):
            The task ID. Defaults to a random UUID.
        agent_id (str):
            The agent ID that the task should be sent to.
            If blank, the orchestrator decides.
    """

    input: str
    task_id: str = Field(default_factory=generate_id)
    session_id: str | None = None
    agent_id: str | None = None

TaskResult

Bases: BaseModel

The result of a task.

Parameters:

Name	Type	Description	Default
task_id	str		required
history	list[ChatMessage]		required
result	str		required

Attributes:

Name	Type	Description
task_id	str	The task ID.
history	list[ChatMessage]	The task history.
result	str	The task result.
data	dict	Additional data about the task or result.
is_last	bool	If not true, there are more results to be streamed.
index	int	The index of the task in the session.

Source code in llama_deploy/types/core.py

class TaskResult(BaseModel):
    """
    The result of a task.

    Attributes:
        task_id (str):
            The task ID.
        history (list[ChatMessage]):
            The task history.
        result (str):
            The task result.
        data (dict):
            Additional data about the task or result.
        is_last (bool):
            If not true, there are more results to be streamed.
        index (int):
            The index of the task in the session.
    """

    task_id: str
    history: list[ChatMessage]
    result: str
    data: dict = Field(default_factory=dict)

TaskStream

Bases: BaseModel

A stream of data generated by a task.

Parameters:

Name	Type	Description	Default
task_id	str		required
session_id	str | None		required
data	dict		required
index	int		required

Attributes:

Name	Type	Description
task_id	str	The associated task ID.
data	list[dict]	The stream data.
index	int	The index of the stream data.

Source code in llama_deploy/types/core.py

class TaskStream(BaseModel):
    """
    A stream of data generated by a task.

    Attributes:
        task_id (str):
            The associated task ID.
        data (list[dict]):
            The stream data.
        index (int):
            The index of the stream data.
    """

    task_id: str
    session_id: str | None
    data: dict
    index: int

ToolCall

Bases: BaseModel

A tool call.

Parameters:

Name	Type	Description	Default
id_	str		'6f029859-f5e7-4e06-a23d-39c4d05c9906'
tool_call_bundle	ToolCallBundle		required
source_id	str		required

Attributes:

Name	Type	Description
id_	str	The tool call ID. Defaults to a random UUID.
tool_call_bundle	ToolCallBundle	The tool call bundle.
source_id	str	The source ID.

Source code in llama_deploy/types/core.py

class ToolCall(BaseModel):
    """
    A tool call.

    Attributes:
        id_ (str):
            The tool call ID. Defaults to a random UUID.
        tool_call_bundle (ToolCallBundle):
            The tool call bundle.
        source_id (str):
            The source ID.
    """

    id_: str = Field(default_factory=generate_id)
    tool_call_bundle: ToolCallBundle
    source_id: str

ToolCallBundle

Bases: BaseModel

A bundle of information for a tool call.

Parameters:

Name	Type	Description	Default
tool_name	str		required
tool_args	list[Any]		required
tool_kwargs	dict[str, Any]		required

Attributes:

Name	Type	Description
tool_name	str	The name of the tool.
tool_args	list[Any]	The tool arguments.
tool_kwargs	dict[str, Any]	The tool keyword arguments

Source code in llama_deploy/types/core.py

class ToolCallBundle(BaseModel):
    """
    A bundle of information for a tool call.

    Attributes:
        tool_name (str):
            The name of the tool.
        tool_args (list[Any]):
            The tool arguments.
        tool_kwargs (dict[str, Any]):
            The tool keyword arguments
    """

    tool_name: str
    tool_args: list[Any]
    tool_kwargs: dict[str, Any]

ToolCallResult

Bases: BaseModel

A tool call result.

Parameters:

Name	Type	Description	Default
id_	str		required
tool_message	ChatMessage		required
result	str		required

Attributes:

Name	Type	Description
id_	str	The tool call ID. Should match the ID of the tool call.
tool_message	ChatMessage	The tool message.
result	str	The tool result.

Source code in llama_deploy/types/core.py

class ToolCallResult(BaseModel):
    """
    A tool call result.

    Attributes:
        id_ (str):
            The tool call ID. Should match the ID of the tool call.
        tool_message (ChatMessage):
            The tool message.
        result (str):
            The tool result.
    """

    id_: str
    tool_message: ChatMessage
    result: str