ReActTextWorldAgent#
- class langchain.agents.react.base.ReActTextWorldAgent[source]#
Bases:
ReActDocstoreAgent
Deprecated since version 0.1.0.
Agent for the ReAct TextWorld chain.
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 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:
intermediate_steps (List[Tuple[AgentAction, str]]) β Steps the LLM has taken to date, along with observations.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to run.
**kwargs (Any) β User inputs.
- Returns:
Action specifying what tool to use.
- Return type:
- classmethod create_prompt(tools: Sequence[BaseTool]) BasePromptTemplate [source]#
Return default prompt.
- Parameters:
tools (Sequence[BaseTool])
- Return type:
- 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:
llm (BaseLanguageModel) β Language model to use.
tools (Sequence[BaseTool]) β Tools to use.
callback_manager (BaseCallbackManager | None) β Callback manager to use.
output_parser (AgentOutputParser | None) β Output parser to use.
kwargs (Any) β Additional arguments.
- Returns:
Agent object.
- Return type:
- 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]]) β Steps the LLM has taken to date, along with observations.
**kwargs (Any) β 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:
intermediate_steps (List[Tuple[AgentAction, str]]) β Steps the LLM has taken to date, along with observations.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to run.
**kwargs (Any) β User inputs.
- Returns:
Action specifying what tool to use.
- Return type:
- 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) β Method to use for early stopping.
intermediate_steps (List[Tuple[AgentAction, str]]) β Steps the LLM has taken to date, along with observations.
**kwargs (Any) β User inputs.
- Returns:
Agent finish object.
- Return type:
- Raises:
ValueError β If early_stopping_method is not in [βforceβ, βgenerateβ].
- save(file_path: Path | str) None #
Save the agent.
- Parameters:
file_path (Path | str) β 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=βpath/agent.yamlβ)
- 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.