tracking retrieved context

app.py

@@ -55,10 +55,12 @@ else:
     feedback_option = (
         "thumbs" if st.sidebar.toggle(label="`Faces` ⇄ `Thumbs`", value=False) else "faces"
     )
+
     with st.sidebar:
         model_name = st.selectbox("**Model**", options=["llama-3.1-70b-versatile","gemma2-9b-it","gemma-7b-it","llama-3.2-3b-preview", "llama3-70b-8192", "mixtral-8x7b-32768"])
         temp = st.slider("**Temperature**", min_value=0.0, max_value=1.0, step=0.001)
         n_docs = st.number_input("**Number of retireved documents**", min_value=0, max_value=10, value=5, step=1)
+
     if st.sidebar.button("Clear message history"):
         print("Clearing message history")
         memory.clear()
@@ -103,7 +105,8 @@ else:
                                             for i, doc in enumerate(used_docs)
                                         ]
                                     )
-                st.download_button(
+                with st.sidebar:
+                    st.download_button(
                     label="Consulted Documents",
                     data=docs_content,
                     file_name="Consulted_documents.txt",
@@ -119,7 +122,7 @@ else:
                     collection_name="chat-history",
                     payload=[
                         {"text": prompt, "type": "question", "question_ID": run_id},
-                        {"text": full_response, "type": "answer", "question_ID": run_id}
+                        {"text": full_response, "type": "answer", "question_ID": run_id, "used_docs":used_docs}
                     ],
                     vectors=[
                         question_embedding,