Updated code

app.py

@@ -214,13 +214,9 @@ if st.session_state["vector_db"] and st.session_state["llm"]:
                 for item in response:
                     full_response += item
                 placeholder.markdown(full_response)
-                # The following logic will work in the way given below.
-                # -- Check if intermediary steps are present in the output of the given prompt.
-                # -- If not, we can conclude that, agent has used internet search as tool.
-                # -- Check if intermediary steps are present in the output of the prompt.
-                # -- If intermediary steps are present, it means agent has used exising custom knowledge base for iformation retrival and therefore we need to give souce docs as output along with LLM's reponse.
                 if response:
                     st.text("-------------------------------------")
+                    #Getting source docs
                     docs= st.session_state["ensemble_retriver"].get_relevant_documents(prompt)   
                     source_doc_list= []  
                     for doc in docs:
@@ -237,6 +233,6 @@ if st.session_state["vector_db"] and st.session_state["llm"]:
                             st.write("---")  # Add a separator between entries
                     message = {"role": "assistant", "content": full_response, "Source":merged_source_doc}
                     st.session_state.messages.append(message)
-                    
+
         end = timeit.default_timer()
         print(f"Time to retrieve response: {end - start}")

utils.py

@@ -251,8 +251,8 @@ def load_text_chunks(text_chunks_pkl_dir):
 def load_ensemble_retriver(text_chunks, embeddings, chroma_vectorstore):
     """Load ensemble retiriever with BM25 and Chroma as individual retrievers"""
     bm25_retriever = BM25Retriever.from_documents(text_chunks)
-    bm25_retriever.k = 1
-    chroma_retriever = chroma_vectorstore.as_retriever(search_kwargs={"k": 1})  
+    bm25_retriever.k = 2
+    chroma_retriever = chroma_vectorstore.as_retriever(search_kwargs={"k": 2})  
     ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, chroma_retriever], weights=[0.3, 0.7])
     return ensemble_retriever