首先,你需要继承LLM这个基类来自动变成上述提到的Runable,然后下面是必须重写实现的方法: 这里可以看到,LLM这个基类已经帮我们实现了invoke的方法,但它是需要调用_call方法的,返回的是字符串 但其实,更多的情况下比如Tongyi等模型,是重写_generate函数来实现invoke调用,因为_generate函数返回的LLMResult结构体,可以附带更多...
name ="数字相加计算工具"description ="当你被要求计算2个数字相加时,使用此工具"def_run(self, a, b):returna["title"] + b["title"]# 加入到工具合集tools = [SumNumberTool()]# 使用reAct的提示词prompt = hub.pull("hwchase17/structured-chat-agent")# 创建Agentagent = create_structured_chat_...
llm_with_tools.invoke(query).tool_calls [{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_rcdMie7E89Xx06lEKKxJyB5N', 'type': 'tool_call'}, {'name': 'add', 'args': {'a': 11, 'b': 49}, 'id': 'call_nheGN8yfvSJsnIuGZaXihou3', 'type': 'tool_...
invoke({"input": "I'd like to figure out what games are tonight"}) print(f"message1: {message1}") 此外,它的工具包中,还封装了这些 __all__ = [ "AINAppOps", "AINOwnerOps", "AINRuleOps", "AINTransfer", "AINValueOps", "AIPluginTool", "APIOperation", "ArxivQueryRun", "...
agent_executor.invoke({"input":"你帮我算下 3.941592623412424 + 4.3434532535353的结果"}) 6. 总结 本文主要聊了LangChain和LangChainHub的发展变迁,还介绍了LangChainHub的使用,希望对你有帮助! ===>>>关于我<<<=== 本篇完结!欢迎点赞 关注 收藏!!! 原文链接:https:...
比如:structured-chat-agent(https://smith.langchain.com/hub/hwchase17/structured-chat-agent),提示词写的还是有点复杂的,大致意思就是:告诉LLM,它可以使用一堆什么格式的工具,然后先推理,选择合适的工具,执行之后,进行观察,观察完了之后,继续推理,如果有答案了,就回复用户。
message1 = chain.invoke({"input": "I'd like to figure out what games are tonight"}) print(f"message1: {message1}") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 此外,它的工具包中,还封装了这些 ...
agent_executor.invoke( { "input": "Who is Diljit Dosanjh and what is his age as of today? What is that raised to the 0.43 power?" } ) Entering new AgentExecutor chain... I need to find out who Diljit Dosanjh is and his age, then calculate the power. ...
# Chain the prompt and the model togetherchain=prompt|model# Invoke the chain with the input parametersresponse=chain.invoke({"question":"what is the WiFi password","time_of_day":"afternoon","style":"professional"})# Print the responseresponse ...
Invoke graph Now we can run the graph! LangGraph Studio lets you run your graph with different inputs and configurations. Start a new run To start a new run: In the dropdown menu (top-left corner of the left-hand pane), select a graph. In our example the graph is calledagent. The...