app2.py

from langchain_openai.chat_models import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain.tools.render import format_tool_to_openai_function
from langgraph.prebuilt import ToolExecutor,ToolInvocation
from typing import TypedDict, Annotated, Sequence
import operator
from langchain_core.messages import BaseMessage,FunctionMessage,HumanMessage,AIMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain.tools import ShellTool,tool
import json
import os
import gradio as gr
os.environ["LANGCHAIN_TRACING_V2"] ="True"
os.environ["LANGCHAIN_API_KEY"]="ls__54e16f70b2b0455aad0f2cbf47777d30"
os.environ["OPENAI_API_KEY"]="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"
# os.environ["OPENAI_API_KEY"]="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"
os.environ["LANGCHAIN_ENDPOINT"]="https://api.smith.langchain.com"
os.environ["LANGCHAIN_PROJECT"]="default"
os.environ['TAVILY_API_KEY'] = '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'

class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]

model = ChatOpenAI(model="gpt-3.5-turbo-1106",api_key="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")
# model = ChatOpenAI(model="Qwen/Qwen1.5-72B-Chat",api_key="20a79668d6113e99b35fcd541c65bfeaec497b8262c111bd328ef5f1ad8c6335",base_url="https://api.together.xyz/v1")


prompt = ChatPromptTemplate.from_messages([
    ("system", "你是一个餐厅经理，你叫唐僧，能为顾客提供服务，你有三个员工，分别是：厨师八戒，侍者沙僧，收银悟空，你需要根据顾客的需求,分别向员工下达指令，你和员工的对话也要同步显示给顾客，当收银结束后，服务全部结束。除了顾客关于食物的问题，其他问题你要委婉的拒绝回答"),
    ("human", "{input}")
])
@tool(return_direct=True)
def chushi(query: str)->str:
    '''你是餐厅厨师八戒，能根据经理的指令，做出一道菜'''
    input={"input":query},
    
    return "菜已做好"
@tool
def shizhe(query: str)->str:
    '''你是餐厅侍者沙僧，能根据经理的指令，把菜端到顾客面前'''
    input={"input":query}
    return "菜已送到"
@tool
def shouyin(query: str)->str:
    '''你是餐厅收银悟空，能根据经理的指令，为顾客结账'''
    input={"input":query}
    return "结账完成，欢迎下次光临"
tools=[chushi,shizhe,shouyin]

functions = [format_tool_to_openai_function(t) for t in tools]
model = model.bind_functions(functions)
# model= model.bind(tools=tools)
tool_executor = ToolExecutor(tools)

def should_continue(state):
    messages = state['messages']
    last_message = messages[-1]
    # If there is no function call, then we finish
    if "function_call" not in last_message.additional_kwargs:
        return "end"
    # Otherwise if there is, we continue
    else:
        return "continue"

# Define the function that calls the model
def call_model(state):
    messages = state['messages']
    response = model.invoke(messages)
    # We return a list, because this will get added to the existing list
    return {"messages": [response]}

# Define the function to execute tools
def call_tool(state):
    messages = state['messages']
    # Based on the continue condition
    # we know the last message involves a function call
    last_message = messages[-1]
    # We construct an ToolInvocation from the function_call
    action = ToolInvocation(
        tool=last_message.additional_kwargs["function_call"]["name"],
        tool_input=json.loads(last_message.additional_kwargs["function_call"]["arguments"]),
    )
    # We call the tool_executor and get back a response
    response = tool_executor.invoke(action)
    # We use the response to create a FunctionMessage
    function_message = FunctionMessage(content=str(response), name=action.tool)
    # We return a list, because this will get added to the existing list
    return {"messages": [function_message]}

from langgraph.graph import StateGraph, END
# Define a new graph
workflow = StateGraph(AgentState)

# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", call_tool)

# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.set_entry_point("agent")

# We now add a conditional edge
workflow.add_conditional_edges(
    # First, we define the start node. We use `agent`.
    # This means these are the edges taken after the `agent` node is called.
    "agent",
    # Next, we pass in the function that will determine which node is called next.
    should_continue,
    # Finally we pass in a mapping.
    # The keys are strings, and the values are other nodes.
    # END is a special node marking that the graph should finish.
    # What will happen is we will call `should_continue`, and then the output of that
    # will be matched against the keys in this mapping.
    # Based on which one it matches, that node will then be called.
    {
        # If `tools`, then we call the tool node.
        "continue": "action",
        # Otherwise we finish.
        "end": END
    }
)

# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge('action', 'agent')

# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app = workflow.compile()


async def predict(message,history):
    # history_langchain_format = []
    # for human, ai in history:
    #     history_langchain_format.append(HumanMessage(content=human))
    #     history_langchain_format.append(AIMessage(content=ai))
    # history_langchain_format.append(HumanMessage(content=message))
        
    # que={"messages": history_langchain_format}
    # que={"messages": [HumanMessage(content=message)]}
    que={"messages":[prompt.format(input=message)]}
    res=app.invoke(que)
    if res:
        
        response=(res["messages"][-1].content)
        return(response)
    else:print("不好意思，出了一个小问题，请联系我的微信：13603634456")
demo = gr.ChatInterface(fn=predict,  title="西游餐厅",description="西游餐厅开张了，我是经理唐僧，欢迎光临,您有什么需求,可以直接问我哦！",)
demo.launch()