langchain.agents.agent
.Agent¶
- class langchain.agents.agent.Agent[source]¶
Bases:
BaseSingleActionAgent
[Deprecated] Agent that calls the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include a variable called “agent_scratchpad” where the agent can put its intermediary work.
Notes
Deprecated since version langchain==0.1.0: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. 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 output_parser: AgentOutputParser [Required]¶
- async aplan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) Union[AgentAction, AgentFinish] [source]¶
Given input, decided what to do.
- Parameters
intermediate_steps (List[Tuple[AgentAction, str]]) – Steps the LLM has taken to date, along with observations
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to run.
**kwargs (Any) – User inputs.
- Returns
Action specifying what tool to use.
- 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
- abstract classmethod create_prompt(tools: Sequence[BaseTool]) BasePromptTemplate [source]¶
Create a prompt for this class.
- Parameters
tools (Sequence[BaseTool]) –
- Return type
- dict(**kwargs: Any) Dict [source]¶
Return dictionary representation of agent.
- 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, **kwargs: Any) Agent [source]¶
Construct an agent from an LLM and tools.
- Parameters
llm (BaseLanguageModel) –
tools (Sequence[BaseTool]) –
callback_manager (Optional[BaseCallbackManager]) –
output_parser (Optional[AgentOutputParser]) –
kwargs (Any) –
- Return type
- classmethod from_orm(obj: Any) Model ¶
- Parameters
obj (Any) –
- Return type
Model
- get_full_inputs(intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) Dict[str, Any] [source]¶
Create the full inputs for the LLMChain from intermediate steps.
- 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] [source]¶
Given input, decided what to do.
- Parameters
intermediate_steps (List[Tuple[AgentAction, str]]) – Steps the LLM has taken to date, along with observations
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to run.
**kwargs (Any) – User inputs.
- Returns
Action specifying what tool to use.
- Return type
Union[AgentAction, AgentFinish]
- return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) AgentFinish [source]¶
Return response when agent has been stopped due to max iterations.
- Parameters
early_stopping_method (str) –
intermediate_steps (List[Tuple[AgentAction, str]]) –
kwargs (Any) –
- Return type
- save(file_path: Union[Path, str]) None ¶
Save the agent.
- Parameters
file_path (Union[Path, str]) – 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=”path/agent.yaml”)
- 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
- 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
- abstract property llm_prefix: str¶
Prefix to append the LLM call with.
- abstract property observation_prefix: str¶
Prefix to append the observation with.
- property return_values: List[str]¶
Return values of the agent.