ZeroShotAgent Class — langchain Architecture

Architecture documentation for the ZeroShotAgent class in base.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c["ZeroShotAgent"]
  37fcc3f3_2798_3648_915c_2bffdd19bff7["Agent"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|extends| 37fcc3f3_2798_3648_915c_2bffdd19bff7
  08e3637b_3cf5_6dc6_f338_f1122914aba8["base.py"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|defined in| 08e3637b_3cf5_6dc6_f338_f1122914aba8
  66bbb2d3_d68f_0d1b_d3bc_2f021869e9e1["_get_default_output_parser()"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|method| 66bbb2d3_d68f_0d1b_d3bc_2f021869e9e1
  13cb4024_722a_fbb1_520c_d8cbbbbeeaa5["_agent_type()"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|method| 13cb4024_722a_fbb1_520c_d8cbbbbeeaa5
  b1370dba_d2af_58c4_48b0_746779cf51ac["observation_prefix()"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|method| b1370dba_d2af_58c4_48b0_746779cf51ac
  41149540_6bef_0c8f_5ea3_26fe55c3b748["llm_prefix()"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|method| 41149540_6bef_0c8f_5ea3_26fe55c3b748
  a98a6213_7754_f1a3_d0c4_c6068b628192["create_prompt()"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|method| a98a6213_7754_f1a3_d0c4_c6068b628192
  f50a836d_579d_7ed4_e51f_6bb4bd0b8059["from_llm_and_tools()"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|method| f50a836d_579d_7ed4_e51f_6bb4bd0b8059
  1c25b3ee_1cc2_569b_6970_bd431e6f54ca["_validate_tools()"]
  efa737fc_e2bb_00c6_fe6e_ee6cb1f6cb6c -->|method| 1c25b3ee_1cc2_569b_6970_bd431e6f54ca

Relationship Graph

Source Code

libs/langchain/langchain_classic/agents/mrkl/base.py lines 45–176

class ZeroShotAgent(Agent):
    """Agent for the MRKL chain.

    Args:
        output_parser: Output parser for the agent.
    """

    output_parser: AgentOutputParser = Field(default_factory=MRKLOutputParser)

    @classmethod
    @override
    def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
        return MRKLOutputParser()

    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        return AgentType.ZERO_SHOT_REACT_DESCRIPTION

    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with.

        Returns:
            "Observation: "
        """
        return "Observation: "

    @property
    def llm_prefix(self) -> str:
        """Prefix to append the llm call with.

        Returns:
            "Thought: "
        """
        return "Thought:"

    @classmethod
    def create_prompt(
        cls,
        tools: Sequence[BaseTool],
        prefix: str = PREFIX,
        suffix: str = SUFFIX,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        input_variables: list[str] | None = None,
    ) -> PromptTemplate:
        """Create prompt in the style of the zero shot agent.

        Args:
            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.
            format_instructions: Instructions on how to use the tools.
            input_variables: List of input variables the final prompt will expect.


        Returns:
            A PromptTemplate with the template assembled from the pieces here.
        """
        tool_strings = render_text_description(list(tools))
        tool_names = ", ".join([tool.name for tool in tools])
        format_instructions = format_instructions.format(tool_names=tool_names)
        template = f"{prefix}\n\n{tool_strings}\n\n{format_instructions}\n\n{suffix}"
        if input_variables:
            return PromptTemplate(template=template, input_variables=input_variables)
        return PromptTemplate.from_template(template)

    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: BaseCallbackManager | None = None,
        output_parser: AgentOutputParser | None = None,
        prefix: str = PREFIX,
        suffix: str = SUFFIX,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        input_variables: list[str] | None = None,
        **kwargs: Any,
    ) -> Agent: