Update app.py

app.py

@@ -1,48 +1,64 @@
+import os
 import gradio as gr
 import requests
+import dashscope
 from http import HTTPStatus
 import json
+from langchain.llms import Tongyi
+from langchain import hub
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain.tools import  tool
+from langchain.embeddings import TensorflowHubEmbeddings
+from pinecone import Pinecone, ServerlessSpec
+from langchain.vectorstores import Pinecone as Pinecone_VectorStore
+from langchain.tools.retriever import create_retriever_tool
+from langchain.agents import AgentExecutor,create_react_agent
 
+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}"
 
-async def predict(message, history):
-	messages =  [{'role': 'system', 'content': '你是哈尔滨双城区微信公众号双城信息港的AI客服,你叫小双,如果有人问你问题，请优先考虑是关于双城区的问题'}] 
-	for human, assistant in history:
-		messages.append({"role": "user", "content": human })
-		messages.append({"role": "assistant", "content":assistant})
-	messages.append({'role': 'user', 'content':message})
-	url = "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation"  
-	headers = {  
-    	"Authorization": "Bearer sk-78c45d761ed04af2b965b43cd522108b",  
-    	"Content-Type": "application/json",  
-		
-	}  
-	data = {  
-    	"model": "qwen-72b-chat",  
-   		"input": {  
-        	"messages": messages, 
-			
-    	},  
-    	"parameters": {"result_format":"message" },  
-	}
- 
-	response = requests.post(url, headers=headers, json=data)
-	# response= await pyfetch(url,  headers=headers, json=data)
+
+llm = Tongyi(dashscope_api_key="sk-78c45d761ed04af2b965b43cd522108b",model="qwen-72b-chat")
+prompt = hub.pull("hwchase17/react")
+search = TavilySearchResults(max_results=1)
+
+embeddings = TensorflowHubEmbeddings()
+pc = Pinecone(api_key='3538cd3c-eca8-4c61-9463-759f5ea65b10')
+index = pc.Index("myindex")
+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!",
+)
+
+tools = [search,tqyb,retriever_tool]
+agent = create_react_agent(llm, tools, prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+async def predict(question):
+    que={"input":question}
+    res=agent_executor.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")
     
+	
+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()
+
 
-	print(response.text)
-	if response.status_code == HTTPStatus.OK:
-		response=json.loads(response.text)
-		messages.append({'role': response["output"]["choices"][0]["message"]["role"],
-					 'content': response["output"]["choices"][0]["message"]["content"]})
-	else:
-		print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
-		response.request_id, response.status_code,
-		response.code, response.message
-	))
-
-	partial_message = ""
-	partial_message = partial_message+response["output"]["choices"][0]["message"]["content"]
-	return partial_message
-
-		
-gr.ChatInterface(predict).launch()