@tool("lower_case", return_direct=True) def to_lower_case(input:str) -> str: """Returns the input as all lower case.""" return input.lower() 输出 StructuredTool(name='lower_case', description='lower_case(input: str) -> str - Returns the input as all lower case.', args_schema=...
Input should be a fully formed question.", return_direct=True, ), ] agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True ) agent.run( "What did biden say about ketanji brown jackson in the state of the union address?" ) agent.run("Why use...
#实际AgentExecutor中的部分相关代码:for agent_action in actions: if run_manager: run_manager.on_agent_action(agent_action, color="green") # Otherwise we lookup the tool if agent_action.tool in name_to_tool_map: tool = name_to_tool_map[agent_action.tool] return_direct = tool.return_dire...
FLARE Direct(直接 FLARE):该方法直接使用语言模型生成的内容作为搜索查询。它迭代地生成下一句以了解未来的主题,并且在存在不确定的令牌时,它会检索相关文档以重新生成下一句。 不同于传统方法只检索一次,然后用于生成,FLARE 采取迭代过程。它包括使用即将出现的句子预测作为查询来检索相关文档。如果初始生成的置信度较...
Action Input:根据需要使用的Tool,从用户的输入里提取相关的内容,可以输入到Tool里面 Observation:就是观察通过使用 Tool 得到的一个输出结果。 Thought:就是再看一眼用户的输入,判断一下该怎么做。 Final Answer:就是 Thought 在看到 Obersavation 之后,给出的最终输出。
[agent_action.tool]tool_run_kwargs =self.agent.tool_run_logging_kwargs()ifreturn_direct:tool_run_kwargs["llm_prefix"] =""# We then call the tool on the tool input to get an observationobservation = tool.run(agent_action.tool_input,verbose=self.verbose,color=color,callbacks=run_manager...
return_direct(bool), 默认关闭,打开时tool会返回执行结果 args_schema(Pydantic BaseModel), 可选,但推荐使用,可用于提供更多信息(例如,少量示例)或验证预期参数。 定义工具有两种主要方法,我们将在下面的示例中介绍这两种方法。 # Import things that are needed genericallyfromlangchain.chainsimportLLMMathChainfrom...
return_direct=tool.return_direct color=color_mapping[agent_action.tool] tool_run_kwargs=self.agent.tool_run_logging_kwargs()ifreturn_direct: tool_run_kwargs["llm_prefix"] =""# We then call the tool on the tool input togetan observation ...
Exploiting the exogenous and regional nature of the Great East Japan Earthquake of 2011, this paper provides a systematic quantification of the role of input-output linkages as a mechanism for the propagation and amplification of shocks. We document that the disruption caused by the earthquake and ...
", input_variables=["order"] ) answer_order_llm = LLMChain(llm = ChatOpenAI(temperature=0), prompt=answer_order_info) @tool("Search Order", return_direct=True) def search_order(input:str)->str: """ useful for when you need to answer questions about customers orders """ pattern = ...