Update app.py

app.py

@@ -1,71 +1,118 @@
-#有2个embedding库,HuggingFaceHubEmbeddings和HuggingFaceBgeEmbeddings，分别是云端和本地的，
-#对应pinecone有2个index,分别是myindex01和myindex
-import gradio as gr
-import requests
-import dashscope
-from http import HTTPStatus
-import json
-# from langchain.llms import Tongyi
-from langchain_community.llms import Tongyi,ChatGLM ,OpenAI
-from langchain import hub
+
+from langchain_openai.chat_models import ChatOpenAI
 from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain.tools import  tool
-# from langchain_community.embeddings import TensorflowHubEmbeddings 
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from langchain_community.embeddings import HuggingFaceHubEmbeddings
-from langchain_community.llms import HuggingFaceHub
-from pinecone import Pinecone, ServerlessSpec
-# from langchain.vectorstores import Pinecone as Pinecone_VectorStore
-from langchain_community.vectorstores import Pinecone as Pinecone_VectorStore
-from langchain.tools.retriever import create_retriever_tool
-from langchain.agents import AgentExecutor,create_react_agent
-from getpass import getpass
+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
+from langchain.tools import ShellTool
+import json
 import os
-os.environ['TAVILY_API_KEY'] = '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'
-@tool
-def tqyb(query: str) -> str:
-    """这是天气预报api,示例query=北京"""
-    url=f"https://api.seniverse.com/v3/weather/now.json?key=SWtPLxs4A2GhenWC-&location={query}&language=zh-Hans&unit=c"
-    response = requests.get(url)    
-    # 检查请求是否成功  
-    if response.status_code == 200:  
-        res=response.json()
-        return res  # 假设API返回的是JSON格式数据  
-    else:  
-        return f"请求失败，状态码：{response.status_code}"
-
-os.environ['OPENAI_API_KEY']="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"
-
-
-# llm = HuggingFaceHub(repo_id="Qwen/Qwen1.5-0.5B", model_kwargs={"temperature": 0.5, "max_length": 64})
-llm = Tongyi(dashscope_api_key="sk-78c45d761ed04af2b965b43cd522108b",model="qwen-72b")
-prompt = hub.pull("hwchase17/react")
-search = TavilySearchResults(max_results=1)
-
-model_name = "BAAI/bge-small-en"
-model_kwargs = {"device": "cpu"}
-encode_kwargs = {"normalize_embeddings": True}
-# embeddings = HuggingFaceBgeEmbeddings(
-#     model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
-# )
-embeddings = HuggingFaceHubEmbeddings()
-pc = Pinecone(api_key='3538cd3c-eca8-4c61-9463-759f5ea65b10')
-index = pc.Index("myindex01")
-vectorstore = Pinecone_VectorStore(index, embeddings.embed_query, "text")
-db=vectorstore.as_retriever()
-retriever_tool = create_retriever_tool(
-    db,
-    "shuangcheng_search",
-    "关于双城的区情信息检索工具，如果问题与双城的区情有关，你必须使用这个工具!",
+import gradio as gr
+os.environ["LANGCHAIN_TRACING_V2"] ="True"
+os.environ["LANGCHAIN_API_KEY"]="ls__54e16f70b2b0455aad0f2cbf47777d30"
+os.environ["OPENAI_API_KEY"]="20a79668d6113e99b35fcd541c65bfeaec497b8262c111bd328ef5f1ad8c6335"
+# os.environ["OPENAI_API_KEY"]="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"
+os.environ["LANGCHAIN_ENDPOINT"]="https://api.smith.langchain.com"
+os.environ["LANGCHAIN_PROJECT"]="default"
+
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], operator.add]
+
+model = ChatOpenAI(model="gpt-3.5-turbo-1106",api_key="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")
+shell_tool = ShellTool()
+tools = [TavilySearchResults(max_results=1),shell_tool]
+
+
+functions = [format_tool_to_openai_function(t) for t in tools]
+model = model.bind_functions(functions)
+
+tool_executor = ToolExecutor(tools)
+# Define the function that determines whether to continue or not
+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
+    }
 )
 
-tools = [search,tqyb,retriever_tool]
-agent = create_react_agent(llm, tools, prompt)
-agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+# 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()
+
+# inputs = {"messages": [HumanMessage(content="查询你的cast命令版本")]}
+# app.invoke(inputs)
 
 async def predict(question):
-    que={"input":question}
-    res=agent_executor.invoke(que)
+    que={"messages": [HumanMessage(content=question)]}
+    res=app.invoke(que)
     if res:
         return(res["output"])
     else:print("不好意思，出了一个小问题，请联系我的微信：13603634456")
@@ -74,5 +121,7 @@ async def predict(question):
 gr.Interface(
     predict,inputs="textbox",
     outputs="textbox",
-    title="定制版AI专家BOT",
-    description="这是一个定制版的AI专家BOT，你可以通过输入问题，让AI为你回答。\n目前提供三个示例工具：\n1.天气预报(函数调用API)\n2.双城区情检索（增强型检索RAG）\n3.搜索引擎").launch()
+    title="定制版AI专家BOT-0.1版",
+    description="这是一个定制版的AI专家BOT，你可以通过输入问题，让AI为你回答。\n目前提供三个示例工具：\n1.bash命令行执行工具，可以将人类语言转化为bash命令，然后执行。\n2.搜索引擎").launch()
+
+