Update app.py

app.py

@@ -22,11 +22,10 @@ from langchain_core.runnables import RunnablePassthrough
 
 def create_retriever_from_chroma(vectorstore_path="docs/chroma/", search_type='mmr', k=7, chunk_size=250, chunk_overlap=20):
     data_path = "data"
-    model_name = "sentence-transformers/all-mpnet-base-v2"
-    model_kwargs = {'device': 'cpu'}
+    model_name = "Alibaba-NLP/gte-base-en-v1.5"
+    model_kwargs = {'device': 'cpu',
+                   "trust_remote_code" : 'True'}
     encode_kwargs = {'normalize_embeddings': True}
-
-    # Initialize embeddings
     embeddings = HuggingFaceEmbeddings(
         model_name=model_name,
         model_kwargs=model_kwargs,
@@ -104,6 +103,12 @@ def handle_userinput(user_question,retriever):
     st.session_state.messages.append({"role": "user", "content": user_question})
     st.chat_message("user").write(user_question)
     docs = retriever.invoke(user_question)
+
+    with st.sidebar:
+        st.subheader("Your documents")
+        with st.spinner("Processing"):
+            for doc in docs:
+                st.write(f"Document: {doc}")
     
     doc_txt = [doc.page_content for doc in docs]
     
@@ -112,11 +117,7 @@ def handle_userinput(user_question,retriever):
     st.session_state.messages.append({"role": "assistant", "content": response})
     st.chat_message("assistant").write(response)
     
-    with st.sidebar:
-        st.subheader("Your documents")
-        with st.spinner("Processing"):
-            for doc in docs:
-                st.write(f"Document: {doc}")
+
                 
             
 
@@ -134,16 +135,18 @@ def create_conversational_rag_chain(retriever):
         temperature=0.0,
         top_p=0.9,
         n_ctx=22000,
+        n_batch=2000
         max_tokens=200,
         repeat_penalty=1.7,
+        last_n_tokens_size = 200,
         # callback_manager=callback_manager,
         verbose=False,
     )
 
-    template = """Answer the question: {question} based only on the following context:
+   template = """Answer the question based only on the following context:
     {context}
 
-    
+    Question: {question}
     """
     prompt = ChatPromptTemplate.from_template(template)