disable conversational memory with zephyr

streamlit_app.py

@@ -54,7 +54,7 @@ if 'uploaded' not in st.session_state:
     st.session_state['uploaded'] = False
 
 if 'memory' not in st.session_state:
-    st.session_state['memory'] = ConversationBufferWindowMemory(k=4)
+    st.session_state['memory'] = None
 
 if 'binary' not in st.session_state:
     st.session_state['binary'] = None
@@ -117,12 +117,14 @@ def clear_memory():
 def init_qa(model, api_key=None):
     ## For debug add: callbacks=[PromptLayerCallbackHandler(pl_tags=["langchain", "chatgpt", "document-qa"])])
     if model == 'chatgpt-3.5-turbo':
+        st.session_state['memory'] = ConversationBufferWindowMemory(k=4)
         if api_key:
             chat = ChatOpenAI(model_name="gpt-3.5-turbo",
                               temperature=0,
                               openai_api_key=api_key,
                               frequency_penalty=0.1)
             embeddings = OpenAIEmbeddings(openai_api_key=api_key)
+
         else:
             chat = ChatOpenAI(model_name="gpt-3.5-turbo",
                               temperature=0,
@@ -134,11 +136,13 @@ def init_qa(model, api_key=None):
                               model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
         embeddings = HuggingFaceEmbeddings(
             model_name="all-MiniLM-L6-v2")
+        st.session_state['memory'] = ConversationBufferWindowMemory(k=4)
 
     elif model == 'zephyr-7b-beta':
         chat = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta",
                               model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
         embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        st.session_state['memory'] = None
     else:
         st.error("The model was not loaded properly. Try reloading. ")
         st.stop()
@@ -255,7 +259,8 @@ with st.sidebar:
         'Reset chat memory.',
         key="reset-memory-button",
         on_click=clear_memory,
-        help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.")
+        help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.",
+        disabled=model in st.session_state['rqa'] and st.session_state['rqa'][model].memory is None)
 
 left_column, right_column = st.columns([1, 1])
 
@@ -267,8 +272,8 @@ with right_column:
         ":warning: Do not upload sensitive data. We **temporarily** store text from the uploaded PDF documents solely for the purpose of processing your request, and we **do not assume responsibility** for any subsequent use or handling of the data submitted to third parties LLMs.")
 
     uploaded_file = st.file_uploader("Upload an article",
-                                 type=("pdf", "txt"),
-                                 on_change=new_file,
+                                     type=("pdf", "txt"),
+                                     on_change=new_file,
                                      disabled=st.session_state['model'] is not None and st.session_state['model'] not in
                                               st.session_state['api_keys'],
                                      help="The full-text is extracted using Grobid. ")
@@ -335,8 +340,8 @@ if uploaded_file and not st.session_state.loaded_embeddings:
 
             st.session_state['doc_id'] = hash = st.session_state['rqa'][model].create_memory_embeddings(tmp_file.name,
                                                                                                         chunk_size=chunk_size,
-                                                                                                    perc_overlap=0.1,
-                                                                                                    include_biblio=True)
+                                                                                                        perc_overlap=0.1,
+                                                                                                        include_biblio=True)
             st.session_state['loaded_embeddings'] = True
             st.session_state.messages = []
 
@@ -389,7 +394,7 @@ with right_column:
         elif mode == "LLM":
             with st.spinner("Generating response..."):
                 _, text_response = st.session_state['rqa'][model].query_document(question, st.session_state.doc_id,
-                                                                             context_size=context_size)
+                                                                                 context_size=context_size)
 
         if not text_response:
             st.error("Something went wrong. Contact Luca Foppiano ([email protected]) to report the issue.")