langchain.agents.mrkl.base
.ZeroShotAgent¶
- class langchain.agents.mrkl.base.ZeroShotAgent[source]¶
Bases:
Agent
Agent for the MRKL chain.
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: Optional[List[str]] = None¶
- param llm_chain: langchain.chains.llm.LLMChain [Required]¶
- param output_parser: langchain.agents.agent.AgentOutputParser [Optional]¶
- async aplan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) Union[AgentAction, AgentFinish] ¶
Given input, decided what to do.
- Parameters
intermediate_steps – Steps the LLM has taken to date, along with observations
callbacks – Callbacks to run.
**kwargs – User inputs.
- Returns
Action specifying what tool to use.
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model ¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model ¶
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model
- Returns
new model instance
- 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: Optional[List[str]] = None) PromptTemplate [source]¶
Create prompt in the style of the zero shot agent.
- Parameters
tools – List of tools the agent will have access to, used to format the prompt.
prefix – String to put before the list of tools.
suffix – String to put after the list of tools.
input_variables – List of input variables the final prompt will expect.
- Returns
A PromptTemplate with the template assembled from the pieces here.
- dict(**kwargs: Any) Dict ¶
Return dictionary representation of agent.
- classmethod from_llm_and_tools(llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = 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: Optional[List[str]] = None, **kwargs: Any) Agent [source]¶
Construct an agent from an LLM and tools.
- classmethod from_orm(obj: Any) Model ¶
- get_allowed_tools() Optional[List[str]] ¶
- get_full_inputs(intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) Dict[str, Any] ¶
Create the full inputs for the LLMChain from intermediate steps.
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode ¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- classmethod parse_obj(obj: Any) Model ¶
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- plan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) Union[AgentAction, AgentFinish] ¶
Given input, decided what to do.
- Parameters
intermediate_steps – Steps the LLM has taken to date, along with observations
callbacks – Callbacks to run.
**kwargs – User inputs.
- Returns
Action specifying what tool to use.
- 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.
- save(file_path: Union[Path, str]) None ¶
Save the agent.
- Parameters
file_path – Path to file to save the agent to.
Example: .. code-block:: python
# If working with agent executor agent.agent.save(file_path=”path/agent.yaml”)
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny ¶
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode ¶
- tool_run_logging_kwargs() Dict ¶
- classmethod update_forward_refs(**localns: Any) None ¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- classmethod validate(value: Any) Model ¶
- 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.