langchain.agents.openai_functions_agent.base.OpenAIFunctionsAgent

class langchain.agents.openai_functions_agent.base.OpenAIFunctionsAgent[source]

Bases: BaseSingleActionAgent

[Deprecated] An Agent driven by OpenAIs function powered API.

Parameters

  • llm – This should be an instance of ChatOpenAI, specifically a model that supports using functions.

  • tools – The tools this agent has access to.

  • prompt – The prompt for this agent, should support agent_scratchpad as one of the variables. For an easy way to construct this prompt, use `OpenAIFunctionsAgent.create_prompt(…)`[Deprecated] An Agent driven by OpenAIs function powered API.

  • llm – This should be an instance of ChatOpenAI, specifically a model that supports using functions.

  • tools – The tools this agent has access to.

  • prompt – The prompt for this agent, should support agent_scratchpad as one of the variables. For an easy way to construct this prompt, use OpenAIFunctionsAgent.create_prompt(…)

Notes

Deprecated since version 0.1.0: Use create_openai_functions_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 llm: BaseLanguageModel [Required]
param output_parser: Type[OpenAIFunctionsAgentOutputParser] = <class 'langchain.agents.output_parsers.openai_functions.OpenAIFunctionsAgentOutputParser'>
param prompt: BasePromptTemplate [Required]
param tools: Sequence[BaseTool] [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 – Steps the LLM has taken to date, along with observations

  • **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(system_message: Optional[SystemMessage] = SystemMessage(content='You are a helpful AI assistant.'), extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None) → BasePromptTemplate[source]

Create prompt for this agent.

Parameters

  • system_message – Message to use as the system message that will be the first in the prompt.

  • extra_prompt_messages – Prompt messages that will be placed between the system message and the new human input.

Returns

A prompt template to pass into this agent.

dict(**kwargs: Any) → Dict

Return dictionary representation of agent.

classmethod from_llm_and_tools(llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None, system_message: Optional[SystemMessage] = SystemMessage(content='You are a helpful AI assistant.'), **kwargs: Any) → BaseSingleActionAgent[source]

Construct an agent from an LLM and tools.

classmethod from_orm(obj: Any) → Model
get_allowed_tools() → List[str][source]

Get allowed tools.

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, with_functions: bool = True, **kwargs: Any) → Union[AgentAction, AgentFinish][source]

Given input, decided what to do.

Parameters

  • intermediate_steps – Steps the LLM has taken to date, along with observations

  • **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[source]

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 functions: List[dict]
property input_keys: List[str]

Get input keys. Input refers to user input here.

property return_values: List[str]

Return values of the agent.

Examples using OpenAIFunctionsAgent