SelfAskWithSearchAgent#

class langchain.agents.self_ask_with_search.base.SelfAskWithSearchAgent[source]#

Bases: Agent

Deprecated since version 0.1.0: Use create_self_ask_with_search() instead.

Agent for the self-ask-with-search paper.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param allowed_tools: List[str] | None = None#

Allowed tools for the agent. If None, all tools are allowed.

param llm_chain: LLMChain [Required]#

LLMChain to use for agent.

param output_parser: AgentOutputParser [Optional]#

Output parser to use for agent.

async aplan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None, **kwargs: Any) → AgentAction | AgentFinish#

Async given input, decided what to do.

Parameters:
Returns:

Action specifying what tool to use.

Return type:

AgentAction | AgentFinish

classmethod create_prompt(tools: Sequence[BaseTool]) → BasePromptTemplate[source]#

Prompt does not depend on tools.

Parameters:

tools (Sequence[BaseTool])

Return type:

BasePromptTemplate

classmethod from_llm_and_tools(llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: BaseCallbackManager | None = None, output_parser: AgentOutputParser | None = None, **kwargs: Any) → Agent#

Construct an agent from an LLM and tools.

Parameters:
Returns:

Agent object.

Return type:

Agent

get_allowed_tools() → List[str] | None#

Get allowed tools.

Return type:

List[str] | None

get_full_inputs(intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) → Dict[str, Any]#

Create the full inputs for the LLMChain from intermediate steps.

Parameters:

  • intermediate_steps (List[Tuple[AgentAction, str]]) – Steps the LLM has taken to date, along with observations.

  • **kwargs (Any) – User inputs.

Returns:

Full inputs for the LLMChain.

Return type:

Dict[str, Any]

plan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None, **kwargs: Any) → AgentAction | AgentFinish#

Given input, decided what to do.

Parameters:
Returns:

Action specifying what tool to use.

Return type:

AgentAction | AgentFinish

return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) → AgentFinish#

Return response when agent has been stopped due to max iterations.

Parameters:

  • early_stopping_method (str) – Method to use for early stopping.

  • intermediate_steps (List[Tuple[AgentAction, str]]) – Steps the LLM has taken to date, along with observations.

  • **kwargs (Any) – User inputs.

Returns:

Agent finish object.

Return type:

AgentFinish

Raises:

ValueError – If early_stopping_method is not in [‘force’, ‘generate’].

save(file_path: Path | str) → None#

Save the agent.

Parameters:

file_path (Path | str) – Path to file to save the agent to.

Return type:

None

Example: .. code-block:: python

# If working with agent executor agent.agent.save(file_path=”path/agent.yaml”)

tool_run_logging_kwargs() → Dict#

Return logging kwargs for tool run.

Return type:

Dict

property llm_prefix: str#

Prefix to append the LLM call with.

property observation_prefix: str#

Prefix to append the observation with.

property return_values: List[str]#

Return values of the agent.