langchain.agents.mrkl.base.ZeroShotAgent

class langchain.agents.mrkl.base.ZeroShotAgent[source]

Bases: Agent

[Deprecated] MRKL链的代理。

Notes

Deprecated since version 0.1.0: Use create_react_agent instead.

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]

给定输入，决定要做什么。

参数：
intermediate_steps: LLM到目前为止所采取的步骤，

以及观察结果

callbacks: 要运行的回调函数。 **kwargs: 用户输入。

返回：

指定要使用的工具。

Parameters
Return type

Union[AgentAction, AgentFinish]

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

Parameters

  • _fields_set (Optional[SetStr]) –

  • values (Any) –

Return type

Model

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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

  • update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model

  • self (Model) –

Returns

new model instance

Return type

Model

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]

创建与零shot代理风格相似的提示。

参数：

tools：代理将可以访问的工具列表，用于格式化提示。 prefix：工具列表之前要放置的字符串。 suffix：工具列表之后要放置的字符串。 input_variables：最终提示将期望的输入变量列表。

返回：

从这里的各个部分组装而成的PromptTemplate。

Parameters

  • tools (Sequence[BaseTool]) –

  • prefix (str) –

  • suffix (str) –

  • format_instructions (str) –

  • input_variables (Optional[List[str]]) –

Return type

PromptTemplate

dict(**kwargs: Any) → Dict

返回代理的字典表示。

Parameters

kwargs (Any) –

Return type

Dict

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]

从LLM和工具构建一个代理。

Parameters
Return type

Agent

classmethod from_orm(obj: Any) → Model
Parameters

obj (Any) –

Return type

Model

get_allowed_tools() → Optional[List[str]]
Return type

Optional[List[str]]

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

为LLMChain从中间步骤创建完整的输入。

Parameters

  • intermediate_steps (List[Tuple[AgentAction, str]]) –

  • kwargs (Any) –

Return type

Dict[str, Any]

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().

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

  • encoder (Optional[Callable[[Any], Any]]) –

  • models_as_dict (bool) –

  • dumps_kwargs (Any) –

Return type

unicode

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
Parameters

  • path (Union[str, Path]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model
Parameters

obj (Any) –

Return type

Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
Parameters

  • b (Union[str, bytes]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

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

给定输入，决定要做什么。

参数：
intermediate_steps: LLM到目前为止所采取的步骤，

以及观察结果

callbacks: 要运行的回调函数。 **kwargs: 用户输入。

返回：

指定要使用的工具。

Parameters
Return type

Union[AgentAction, AgentFinish]

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

当代理由于达到最大迭代次数而停止时返回响应。

Parameters

  • early_stopping_method (str) –

  • intermediate_steps (List[Tuple[AgentAction, str]]) –

  • kwargs (Any) –

Return type

AgentFinish

save(file_path: Union[Path, str]) → None

保存代理。

参数：

file_path：保存代理的文件路径。

示例： .. code-block:: python

# 如果使用代理执行器 agent.agent.save(file_path=”path/agent.yaml”)

Parameters

file_path (Union[Path, str]) –

Return type

None

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny
Parameters

  • by_alias (bool) –

  • ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode
Parameters

  • by_alias (bool) –

  • ref_template (unicode) –

  • dumps_kwargs (Any) –

Return type

unicode

tool_run_logging_kwargs() → Dict
Return type

Dict

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) → Model
Parameters

value (Any) –

Return type

Model

property input_keys: List[str]

返回输入的键。

元数据 私有

property llm_prefix: str

在llm调用前附加的前缀。

property observation_prefix: str

要附加到观测值前面的前缀。

property return_values: List[str]

代理的返回值。