Update app.py

app.py

@@ -1,18 +1,25 @@
-import os
+#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.llms import Tongyi
+from langchain_community.llms import Tongyi,ChatGLM ,OpenAI
 from langchain import hub
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain.tools import  tool
-from langchain.embeddings import TensorflowHubEmbeddings
+# 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.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
-
+import os
 os.environ['TAVILY_API_KEY'] = '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'
 @tool
 def tqyb(query: str) -> str:
@@ -26,14 +33,23 @@ def tqyb(query: str) -> str:
     else:  
         return f"请求失败，状态码：{response.status_code}"
 
+os.environ['OPENAI_API_KEY']="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"
 
-llm = Tongyi(dashscope_api_key="sk-78c45d761ed04af2b965b43cd522108b",model="qwen-72b-chat")
+
+llm = HuggingFaceHub(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_kwargs={"temperature": 0.5, "max_length": 64})
+# llm = Tongyi(dashscope_api_key="sk-78c45d761ed04af2b965b43cd522108b",model="qwen-72b-chat")
 prompt = hub.pull("hwchase17/react")
 search = TavilySearchResults(max_results=1)
 
-embeddings = TensorflowHubEmbeddings()
+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("myindex")
+index = pc.Index("myindex01")
 vectorstore = Pinecone_VectorStore(index, embeddings.embed_query, "text")
 db=vectorstore.as_retriever()
 retriever_tool = create_retriever_tool(
@@ -49,9 +65,8 @@ agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 async def predict(question):
     que={"input":question}
     res=agent_executor.invoke(que)
-    print(res)
     if res:
-        return(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")
     
 	
@@ -60,5 +75,3 @@ gr.Interface(
     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()
-
-