langchain.agents.format_scratchpad.tools — 🦜🔗 LangChain 0.2.0

import json
from typing import List, Sequence, Tuple

from langchain_core.agents import AgentAction
from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    ToolMessage,
)

from langchain.agents.output_parsers.tools import ToolAgentAction


def _create_tool_message(
    agent_action: ToolAgentAction, observation: str
) -> ToolMessage:
    """将代理动作和观察转换为函数消息。
参数：
    agent_action：代理发送的工具调用请求
    observation：工具调用的结果
返回：
    对应于原始工具调用的FunctionMessage
"""
    if not isinstance(observation, str):
        try:
            content = json.dumps(observation, ensure_ascii=False)
        except Exception:
            content = str(observation)
    else:
        content = observation
    return ToolMessage(
        tool_call_id=agent_action.tool_call_id,
        content=content,
        additional_kwargs={"name": agent_action.tool},
    )


[docs]def format_to_tool_messages(
    intermediate_steps: Sequence[Tuple[AgentAction, str]],
) -> List[BaseMessage]:
    """将（AgentAction，工具输出）元组转换为FunctionMessages。

参数：
    intermediate_steps：LLM迄今为止所采取的步骤，以及观察结果

返回：
    发送到LLM进行下一次预测的消息列表
"""
    messages = []
    for agent_action, observation in intermediate_steps:
        if isinstance(agent_action, ToolAgentAction):
            new_messages = list(agent_action.message_log) + [
                _create_tool_message(agent_action, observation)
            ]
            messages.extend([new for new in new_messages if new not in messages])
        else:
            messages.append(AIMessage(content=agent_action.log))
    return messages
Source code for langchain.agents.format_scratchpad.tools
