ZeroShotAgent#
- class langchain.agents.mrkl.base.ZeroShotAgent[source]#
Bases:
Agent
Deprecated since version 0.1.0: Use
create_react_agent
instead.Agent for the MRKL chain.
- Parameters:
output_parser β Output parser for the agent.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- 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], prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', suffix: str = 'Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: List[str] | None = None) PromptTemplate [source]#
Create prompt in the style of the zero shot agent.
- Parameters:
tools (Sequence[BaseTool]) β List of tools the agent will have access to, used to format the prompt.
prefix (str) β String to put before the list of tools. Defaults to PREFIX.
suffix (str) β String to put after the list of tools. Defaults to SUFFIX.
format_instructions (str) β Instructions on how to use the tools. Defaults to FORMAT_INSTRUCTIONS
input_variables (List[str] | None) β List of input variables the final prompt will expect. Defaults to None.
- Returns:
A PromptTemplate with the template assembled from the pieces here.
- Return type:
- classmethod from_llm_and_tools(llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: BaseCallbackManager | None = None, output_parser: AgentOutputParser | None = None, prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', suffix: str = 'Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: List[str] | None = None, **kwargs: Any) Agent [source]#
Construct an agent from an LLM and tools.
- Parameters:
llm (BaseLanguageModel) β The LLM to use as the agent LLM.
tools (Sequence[BaseTool]) β The tools to use.
callback_manager (BaseCallbackManager | None) β The callback manager to use. Defaults to None.
output_parser (AgentOutputParser | None) β The output parser to use. Defaults to None.
prefix (str) β The prefix to use. Defaults to PREFIX.
suffix (str) β The suffix to use. Defaults to SUFFIX.
format_instructions (str) β The format instructions to use. Defaults to FORMAT_INSTRUCTIONS.
input_variables (List[str] | None) β The input variables to use. Defaults to None.
kwargs (Any) β Additional parameters to pass to the agent.
- 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.
- Returns:
β
- Return type:
βThought
- property observation_prefix: str#
Prefix to append the observation with.
- Returns:
β
- Return type:
βObservation
- property return_values: List[str]#
Return values of the agent.