在ZERO_SHOT_REACT_DESCRIPTION基础上,增强了语言模型的对话能力。可以基于用户输入从多种数据源中快速检索并提取信息。 对话增强生成型 CONVERSATION_REACT_DESCRIPTION是langchain中一个基于记忆的智能代理类型,该类型利用momery处理上下文对话,每轮对话都能携带记忆,更好的回答用户问题。 结构化对话
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION:在聊天过程中接入工具性代理,相当于OpenAI Plugin 广告 大模型应用开发 动手做AI Agent GPT大语言模型应用 智 京东 ¥44.90 去购买 7、Callback模块 回调模块允许接到LLM应用程序的各个阶段,鉴于LLM的幻觉问题,这对于日志记录、监视、流式处理和其他任务非常有用,现...
AgentType.SELF_ASK_WITH_SEARCH: SelfAskWithSearchAgent, AgentType.CONVERSATIONAL_REACT_DESCRIPTION: ConversationalAgent, AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION: ChatAgent, AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION: ConversationalChatAgent, AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION: Struct...
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...
各个工具如何工作?AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION 到底是什么?agent.run () 的结果输出(仅在 verbose=True 时出现)更有帮助。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 >EnteringnewAgentExecutorchain...Thought:Ineed to use a search engine to find Olivia Wilde's boyfriend and a...
"OPENAI_API_KEY"] = "sk- KixPLDte96E5POAK6ZJmT3BlbkFJ8mcZmSRxWsFULKJqfzar"# load llmllm = OpenAI(temperature=)# load external tooltools = load_tools(["llm-math"], llm=llm)# init agentagent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)llm...
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, # 使用chatreactAgent handle_parsing_errors=True, # 处理出错设置为True可以有效规避莫名bug verbose = True) # 开启日志打印 agent("What is the 25% of 300?") 1. 2. 3. 4. 5. 6.
agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True) async def run(): response = await agent_chain.arun(input="打开百度,并看一下网页标题") print(response) if __name__ == '__main__': asyncio.run(run...
CHAT_ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True, verbose=True) try: result = agent1("今天是几号?") print(result) except: print("exception on external access") tool 装饰器要求: Requires: - Function must be of type (str) -> str - Function must have a docstring,必须要有...
LangChain开发了chat-zero-shot-react-description Agent 来严格约束输出的数据格式,以防 ChatGPT 在遵循特定的输出数据格式上表现得不好,有很多“无法解析 LLM 输出”的报错 围绕LangChain开发生态项目:举例,LangChain 本身是一个没有 UI 的库,但社区成员 Rodrigo Nader 为它构建了一个开源的 UI,叫做 LangFlow,...