STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION 是什么如何用 structure什么意,C语言提供两种结合不同类型的对象来创建数据类型的机制:结构(structure),用关键字struct声明,将多个对象集合到一个单元中;联合(union),用关键字union声明,允许用几种不同的类型来引用
{serpapi.com注册可以获得key}" llm = OpenAI(model_name="text-davinci-003", temperature=0) tools = load_tools(["serpapi", "llm-math"], llm=llm) agent = initialize_agent( tools, llm, agent="zero-shot-react-description", verbose=True) agent.run("珠穆拉玛峰旁边的西北方向的山峰叫什么...
However, STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION is slower than LLMSingleActionAgent and hence the latter is good at completing specific tasks quickly. As for the question where should one use these agents, LLMSingleActionAgent should be used when you have to complete a specific task quickly...
tools=[Tool(name="Pressrelease",func=lambda q:str(index.as_query_engine().query(q)),description="useful pressreleases for answering relevnat questions",return_direct=True,),] 然后,LLM选择要用于 RAG 实施的正确代理类型。在这种情况下,LLM可以使用chat-zero-shot-react-description代理。通过此代理,L...
tools, llm, agent="zero-shot-react-description", verbose=True) print("问题:") print("答案:" + agent.run("告诉我'鸡你太美'是什么意思")) 2、执行结果 问题: > Entering new AgentExecutor chain... I should try to find an answer online ...
agent=initialize_agent(tools,llm,agent="chat-zero-shot-react-description",verbose=True) You can see the agent going throughthoughts,actions, andobservation, use the tool (in this scenario, querying your indexed documents); and return a result: ...
Bash command.If you expect output it should be printed out.""",func=bash.run,)# math_tool=_get_llm_math(llm)# search_tool=_get_serpapi()tools=[python_repl_util,command_tool]agent=initialize_agent(tools,llm,agent="zero-shot-react-description",verbose=True)agent.run("给我播放一首音乐"...
so we need to pass that in.tools = load_tools(["serpapi","llm-math"],llm=llm)# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.agent = initialize_agent(tools, llm,agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,verbose=True...
(tools,llm,agent="zero-shot-react-description",verbose=True)print("问题:")print("答案:"+agent.run("查询这周天气"))print("问题:")print("答案:"+agent.run("告诉我'鸡你太美'是什么意思"))print("问题:")print("答案:"+agent.run("告诉我'hello world'是什么意思"))print("问题:")print(...
agent = initialize_agent( agent = AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, #agent = AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, tools = tools, #tools = load_knowledge_tools(), llm = llm_agent, verbose = True, max_iterations = 3, early_stopping_method = 'generate', memory...