Class LlmRequest

src/google/adk/models/llm_request.py:50–300

LLM request class that allows passing in tools, output schema and system instructions to the model. Attributes: model: The model name. contents: The contents to send to the model. config: Additional config for the generate content request. tools_dict: The tools dictionary.

class LlmRequest(BaseModel):
  """LLM request class that allows passing in tools, output schema and system

  instructions to the model.

  Attributes:
    model: The model name.
    contents: The contents to send to the model.
    config: Additional config for the generate content request.
    tools_dict: The tools dictionary.
    cache_config: Context cache configuration for this request.
    cache_metadata: Cache metadata from previous requests, used for cache management.
  """

  model_config = ConfigDict(arbitrary_types_allowed=True)
  """The pydantic model config."""

  model: Optional[str] = None
  """The model name."""

  contents: list[types.Content] = Field(default_factory=list)
  """The contents to send to the model."""

  config: types.GenerateContentConfig = Field(
      default_factory=types.GenerateContentConfig
  )
  live_connect_config: types.LiveConnectConfig = Field(
      default_factory=types.LiveConnectConfig
  )
  """Additional config for the generate content request.

  tools in generate_content_config should not be set.
  """
  tools_dict: dict[str, BaseTool] = Field(default_factory=dict, exclude=True)
  """The tools dictionary."""

  cache_config: Optional[ContextCacheConfig] = None
  """Context cache configuration for this request."""

  cache_metadata: Optional[CacheMetadata] = None
  """Cache metadata from previous requests, used for cache management."""

  cacheable_contents_token_count: Optional[int] = None
  """Token count from previous request's prompt, used for cache size validation."""

  previous_interaction_id: Optional[str] = None
  """The ID of the previous interaction for stateful conversations.

  When using the interactions API, this ID is used to chain interactions
  together, allowing the API to maintain conversation state without sending
  the full history.
  """

  def append_instructions(
      self, instructions: Union[list[str], types.Content]
  ) -> list[types.Content]:
    """Appends instructions to the system instruction.