langchain.agents.conversational.base.ConversationalAgent

class langchain.agents.conversational.base.ConversationalAgent

Bases: Agent

[Deprecated] 一个除了使用工具外还能进行对话的代理程序。

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 ai_prefix: str = 'AI'

用于AI输出之前的前缀。

param allowed_tools: Optional[List[str]] = None

param llm_chain: langchain.chains.llm.LLMChain [Required]

param output_parser: langchain.agents.agent.AgentOutputParser [Optional]

代理的输出解析器。

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

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

• callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –

• kwargs (Any) –

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 = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nTOOLS:\n------\n\nAssistant has access to the following tools:', suffix: str = 'Begin!\n\nPrevious conversation history:\n{chat_history}\n\nNew input: {input}\n{agent_scratchpad}', format_instructions: str = 'To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\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```\n\nWhen you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None) → PromptTemplate

创建与零次代理相似的提示。

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。 ai_prefix：AI输出之前要使用的字符串。 human_prefix：人类输出之前要使用的字符串。 input_variables：最终提示将期望的输入变量列表。

返回：

从这里的各部分组装而成的PromptTemplate。

Parameters

• tools (Sequence[BaseTool]) –

• prefix (str) –

• suffix (str) –

• format_instructions (str) –

• ai_prefix (str) –

• human_prefix (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 = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nTOOLS:\n------\n\nAssistant has access to the following tools:', suffix: str = 'Begin!\n\nPrevious conversation history:\n{chat_history}\n\nNew input: {input}\n{agent_scratchpad}', format_instructions: str = 'To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\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```\n\nWhen you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None, **kwargs: Any) → Agent

从LLM和工具构建一个代理。

Parameters

• llm (BaseLanguageModel) –

• tools (Sequence[BaseTool]) –

• callback_manager (Optional[BaseCallbackManager]) –

• output_parser (Optional[AgentOutputParser]) –

• prefix (str) –

• suffix (str) –

• format_instructions (str) –

• ai_prefix (str) –

• human_prefix (str) –

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

• kwargs (Any) –

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

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

• callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –

• kwargs (Any) –

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

用于在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。