feed back Changes in App.py

app.py

@@ -54,11 +54,12 @@ def setting_api_key(openai_api_key, serp_api_key):
 
 def setup_llm_embed():
     template = """<|system|>
-                Mention Clearly Before response " RAG Output"
+                you are Question answering system based AI, Machine Learning , Deep Learning , Generative AI, Data 
+                science, Data Analytics and Mathematics.
+                Mention Clearly Before response "Answer From RAG"
                 Please check if the following pieces of context has any mention of the keywords provided
                 in the question.Response as much as you could with context you get.
-                you are Question answering system based AI, Machine Learning , Deep Learning , Generative AI, Data 
-                science and Data Analytics.if the following pieces of Context does not relate to Question, 
+                If the following pieces of Context does not relate to Question, 
                 You must not answer on your own,you don't know the answer.
                 </s>
                 <|user|>
@@ -110,7 +111,7 @@ class CustomRetriever(BaseRetriever):
             self,
             vector_retriever: VectorIndexRetriever,
             keyword_retriever: KeywordTableSimpleRetriever,
-            mode: str = "AND",
+            mode: str = "OR",
     ) -> None:
 
         self._vector_retriever = vector_retriever
@@ -258,6 +259,7 @@ def file_nodes_vector():
         keyword_index = load_index_from_storage(storage_context_)
 
     except FileNotFoundError:
+        st.write("Generating New Embeddings ...")
         documents = SimpleDirectoryReader(input_dir="research_papers/").load_data()
         # LLM and Embedding Model Setup
 
@@ -266,10 +268,12 @@ def file_nodes_vector():
         Settings.embed_model = embed_model
 
         # Splitting Nodes
+        st.write("Splitting Content based on semantic !")
         nodes = semantic_split(embed_model, documents)
         ctx_vector = ctx_vector_func(llm, embed_model, nodes)
 
         # Creating Vector index and Keyword Index
+        st.write("Generating Vector Index ...")
 
         vector_index, keyword_index = create_vector_and_keyword_index(nodes, ctx_vector)
     return vector_index, keyword_index
@@ -326,11 +330,11 @@ def main():
         st.session_state.key_flag = False
 
     col_left, col_right = st.columns([1, 2])
-    with (col_left):
+    with col_left:
         st.write("""<h1 style="font-size: 15px;">Enter your OpenAI API key </h1>""", unsafe_allow_html=True)
         openai_api_key = st.text_input(placeholder="OpenAI api key ", label=" ", type="password")
 
-        st.write("""<h1 style="font-size: 15px;">Enter your SERP API key </h1>""", unsafe_allow_html=True)
+        st.write("""<h1 style="font-size: 15px;">Enter your SerpApi key </h1>""", unsafe_allow_html=True)
         serp_api_key = st.text_input(placeholder="Serp api key ", label=" ", type="password")
 
         set_keys_button = st.button("Set Keys ", type="primary")
@@ -356,34 +360,39 @@ def main():
             vector_index, keyword_index = file_nodes_vector()
             response = response_generation(query, st.session_state.cohere_api_key, vector_index, keyword_index)
             if str(response) in ["Empty Response", "RAG Output"] or not response:
-                with st.spinner("Getting Information from Articles, It will take some time."):
+                with st.spinner("Getting Information from Articles and Research papers ..."):
                     content_ = get_article_and_arxiv_content(query)
                     new_vector_index, new_keyword_index, new_nodes = func_add_new_article_content(content_)
                     response = response_generation(query, st.session_state.cohere_api_key, new_vector_index, new_keyword_index)
-                    st.write_stream(str(response))
 
+                if str(response) not in ["Empty Response", "RAG Output"]: 
+                    st.write_stream(str(response))
+                    col1, col2 = st.columns([1, 10])
+                    thumps_up_button = col1.button("👍")
+                    thumps_down_button = col2.button("👎")
+                    if thumps_up_button:
+                        st.write("Thank you for your positive feedback!")
+                        updating_vector(new_nodes, vector_index, keyword_index)
+                    elif thumps_down_button:
+                        st.write("""We're sorry , We will improve it.""")
+                else:
+                    st.write(f"RAG system Couldn't get the information related to {query}")
+
+            elif response:
+                st.write_stream(str(response))
                 col1, col2 = st.columns([1, 10])
                 thumps_up_button = col1.button("👍")
                 thumps_down_button = col2.button("👎")
                 if thumps_up_button:
                     st.write("Thank you for your positive feedback!")
-                    updating_vector(new_nodes, vector_index, keyword_index)
-                if thumps_down_button:
-                    st.write("""We're sorry , We will improve it.""")
-
-            else:
-                st.write_stream(str(response))
-                col1, col2 = st.columns([1, 10])
-                if col1.button("👍"):
-                    st.write("Thank you for your positive feedback!")
-                if col2.button("👎"):
+                elif thumps_down_button:
                     st.write("We're sorry , We will improve it.")
 
         elif generate_response_button and not str(query) and not st.session_state.key_flag:
             st.warning("Please set the necessary API keys and Enter the query")
 
         elif generate_response_button and str(query) and not st.session_state.key_flag:
-            st.warning("Please set the necessary API keys----")
+            st.warning("Please set the necessary API keys")
 
         elif generate_response_button and st.session_state.key_flag and not str(query):
             st.warning("Please Enter the query !")