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app.py

@@ -34,18 +34,10 @@ for message in st.session_state.messages:
 
 if prompt := st.chat_input("Disclaimer: I am still a product in developement"):
     app = conversational_ai()
-    # if prompt.startswith("/add"):
-    #     with st.chat_message("user"):
-    #         st.markdown(prompt)
-    #         st.session_state.messages.append({"role": "user", "content": prompt})
-    #     prompt = prompt.replace("/add", "").strip()
-    #     with st.chat_message("assistant"):
-    #         message_placeholder = st.empty()
-    #         message_placeholder.markdown("Adding to knowledge base...")
-    #         app.add(prompt)
-    #         message_placeholder.markdown(f"Added {prompt} to knowledge base!")
-    #         st.session_state.messages.append({"role": "assistant", "content": f"Added {prompt} to knowledge base!"})
-    #         st.stop()
+    # app.reset()
+    # print(len(app.db.get()["metadatas"]))
+    # print(len(app.get_data_sources()))
+    # quit()
 
     with st.chat_message("user"):
         st.markdown(prompt)
@@ -57,13 +49,19 @@ if prompt := st.chat_input("Disclaimer: I am still a product in developement"):
 
         print("Querying the Agent.")
         cntxt = app.search(prompt)
-        print(*cntxt, sep="\n=======================\n")
-        full_response = app.llm.query(
-            input_query=prompt,
-            contexts=[i["context"] for i in cntxt if i["metadata"]["score"] <= 0.6],
-        )
+        relevant_c = [i["context"] for i in cntxt if i["metadata"]["score"] <= 0.6]
+        print(*relevant_c, sep="\n=======================\n")
+        if len(relevant_c) != 0:
+            full_response = app.llm.query(
+                input_query=prompt,
+                contexts=relevant_c,
+            )
+            full_response = full_response.rpartition("Answer:")[-1].strip()
+        else:
+            full_response = (
+                "Sorry but I don't have relevant knowledge to asnwer that query."
+            )
         # print(f"##FULL:\n\n{full_response}")
-        full_response = full_response.rpartition("Answer:")[-1].strip()
         print(f"#ANSWER\n\n{full_response}")
 
         msg_placeholder.markdown(full_response)

db/main/737ff09d-da7b-4fe3-943e-a4f166193732/data_level0.bin

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db/main/737ff09d-da7b-4fe3-943e-a4f166193732/header.bin

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db/main/737ff09d-da7b-4fe3-943e-a4f166193732/index_metadata.pickle

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db/main/737ff09d-da7b-4fe3-943e-a4f166193732/link_lists.bin

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db/main/chroma.sqlite3

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