优化UI

app.py

@@ -45,7 +45,7 @@ def web_loader(file, openai_key, puzhiai_key, zilliz_uri, user, password):
     global chain
     chain = RetrievalQAWithSourcesChain.from_chain_type(
         ZhipuAILLM(model="glm-3-turbo", temperature=0.1, zhipuai_api_key=puzhiai_key),
-        chain_type="map_reduce",
+        chain_type="refine",
         retriever=docsearch.as_retriever(),
     )
     return "success to load data"
@@ -66,26 +66,10 @@ if __name__ == "__main__":
     with block as demo:
         gr.Markdown(
             """
-        <h1><center>Langchain And Zilliz Cloud Example</center></h1>
-        This is how to use Zilliz Cloud as vector store in LangChain.
-        The purpose of this example is to allow you to input multiple URLs (separated by newlines) and then ask questions about the content of the corresponding web pages.
+        <h1><center>Langchain And Zilliz App</center></h1>
         
-        v.2.27.9.59
+        v.2.27.11.21
         
-        ## 📋 Prerequisite:
-
-        1. 🔑 To obtain an OpenAI key, please visit https://platform.openai.com/account/api-keys.
-        2. 💻 Create a Zilliz Cloud account to get free credits for usage by visiting https://cloud.zilliz.com.
-        3. 🗄️ Create a database in Zilliz Cloud.
-
-        ## 📝 Steps for usage:
-
-        1. 🖋️ Fill in the url list input box with multiple URLs.
-        2. 🔑 Fill in the OpenAI API key in the openai api key input box.
-        3. 🌩️ Fill in the Zilliz Cloud connection parameters, including the connection URL, corresponding username, and password.
-        4. 🚀 Click the Load Data button to load the data. When the load status text box prompts that the data has been successfully loaded, proceed to the next step.
-        5. ❓ In the question input box, enter the relevant question about the web page.
-        6. 🔍 Click the Generate button to search for the answer to the question. The final answer will be displayed in the question answer text box.
         """
         )
         # url_list_text = gr.Textbox(