langgraph：基于agent和qwen2.5构造一个多功能机器人，私有化部署

template = """你的工作是从用户那里获取他们想要创建哪种类型的提示模板的信息。
您应该从他们那里获得以下信息：
-提示的目的是什么-将向提示模板传递哪些变量-输出不应该做什么的任何限制-输出必须遵守的任何要求
如果你无法辨别这些信息，请他们澄清！不要试图疯狂猜测。
在您能够辨别所有信息后，调用相关工具。
"""
def get_messages_info(messages):return [SystemMessage(content=template)] + messages
class PromptInstructions(BaseModel):"""关于如何提示LLM的说明prompt"""objective: strvariables: List[str]constrains:List[str]requirements: List[str]
llm =ChatOpenAI(model="qwen2.5", openai_api_key="ollama", openai_api_base="http://localhost:11434/v1/")llm_with_tool = llm.bind_tools([PromptInstructions])# 根据用户的输入 调用 需要的工具def info_chain(state):messages = get_messages_info(state['messages'])response = llm_with_tool.invoke(messages)return {"messages":[response]}

from langchain_core.messages import AIMessage,HumanMessage, ToolMessage
prompt_system = """根据以下要求，编写一个好的提示模板:{reqs}"""
def get_prompt_messages(messages:list):tool_call = Noneother_msgs = []for m in messages:if isinstance(m, AIMessage) and m.tool_calls:tool_call = m.tool_calls[0]['args']elif isinstance(m, ToolMessage):continueelif tool_call is not None:other_msgs.append(m)return [SystemMessage(content=prompt_system.format(reqs=tool_call))] + other_msgs

def prompt_gen_chain(state):messages = get_prompt_messages(state["messages"])response = llm.invoke(messages)return {"messages":[response]}

from typing import Literal
from langgraph.graph import END
# 定义一个函数，用于获取当前状态def get_state(state)-> Literal["add_tool_message","info","__end__"]:messages = state["messages"]if isinstance(messages[-1],AIMessage) and messages[-1].tool_calls:return "add_tool_message"elif not isinstance(messages[-1], HumanMessage):return ENDreturn "info"

# Create the graphfrom langgraph.checkpoint.memoryimport MemorySaverfrom langgraph.graph import StateGraph,STARTfrom langgraph.graph.message import add_messagesfrom typing import Annotatedfrom typing_extensions import TypedDict

class State(TypedDict):messages: Annotated[list, add_messages]
memory = MemorySaver()workflow = StateGraph(State)workflow.add_node("info",info_chain)workflow.add_node("prompt",prompt_gen_chain)
# 工具调用用来做提示词生成@workflow.add_nodedef add_tool_message(state:State):return {"messages":[ToolMessage(content="Prompt generated!",tool_call_id= state["messages"][-1].tool_calls[0]['id'])]}
workflow.add_edge(START,"info")workflow.add_conditional_edges("info",get_state)workflow.add_edge("add_tool_message","prompt")workflow.add_edge("prompt",END)graph = workflow.compile(checkpointer=memory)

import uuid
config = {"configurable":{"thread_id":str(uuid.uuid4())}}
while True:user = input("User (q/Q to quit): ")print(f"User (q/Q to quit): {user}")if user in {"q", "Q"}:print("AI: Byebye")breakoutput = Nonefor output in graph.stream({"messages":[HumanMessage(content=user)]}, config=config, stream_mode="updates"):last_message = next(iter(output.values()))["messages"][-1]last_message.pretty_print()if output and "prompt" in output:print("Done!")

- 提示的目的是什么？- 将向提示模板传递哪些变量？（例如，在这个例子中提到的数组）- 输出不应该做什么的任何限制。- 输出必须遵守的任何要求。

1、写一个快速排序算法 2、“1,3,2,5“ 3、no 4、no