小迭代，user_question

app.py

@@ -336,26 +336,25 @@ def app():
                     st.markdown(message["content"][0])
 
             #这里的session.state就是保存了这个对话会话的一些基本信息和设置
-            if user_question:
-                retrieved_chunks_for_user = searchVDB(user_question, st.session_state.embeddings_df, st.session_state.faiss_index)
-                #retrieved_chunks_for_user = []
-                prompt = decorate_user_question(user_question, retrieved_chunks_for_user)
-                st.session_state.messages.append({"role": "user", "content": [user_question, prompt]})
-                with st.chat_message("user"):
-                    st.markdown(user_question)
-                # Display assistant response in chat message container
-                with st.chat_message("assistant"):
-                    message_placeholder = st.empty()
-                    full_response = ""
-                    for response in openai.ChatCompletion.create(
-                        model=st.session_state["openai_model"],
-                        messages=[{"role": m["role"], "content": m["content"][1]} for m in st.session_state.messages],
-                        stream=True,
-                    ):
-                        full_response += response.choices[0].delta.get("content", "")
-                        message_placeholder.markdown(full_response + "▌")
-                    message_placeholder.markdown(full_response)
-                st.session_state.messages.append({"role": "assistant", "content": [full_response,1]})
+            retrieved_chunks_for_user = searchVDB(user_question, st.session_state.embeddings_df, st.session_state.faiss_index)
+            #retrieved_chunks_for_user = []
+            prompt = decorate_user_question(user_question, retrieved_chunks_for_user)
+            st.session_state.messages.append({"role": "user", "content": [user_question, prompt]})
+            with st.chat_message("user"):
+                st.markdown(user_question)
+            # Display assistant response in chat message container
+            with st.chat_message("assistant"):
+                message_placeholder = st.empty()
+                full_response = ""
+                for response in openai.ChatCompletion.create(
+                    model=st.session_state["openai_model"],
+                    messages=[{"role": m["role"], "content": m["content"][1]} for m in st.session_state.messages],
+                    stream=True,
+                ):
+                    full_response += response.choices[0].delta.get("content", "")
+                    message_placeholder.markdown(full_response + "▌")
+                message_placeholder.markdown(full_response)
+            st.session_state.messages.append({"role": "assistant", "content": [full_response,1]})