Update app.py

app.py

@@ -152,7 +152,7 @@ def create_vector_database(loaded_documents):
     """
 
     # Split loaded documents into chunks
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=40)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=40, length_function = len)
     chunked_documents = text_splitter.split_documents(loaded_documents)
 
     # Initialize HuggingFace embeddings
@@ -229,7 +229,7 @@ def create_chain(llm, prompt, CONDENSE_QUESTION_PROMPT, db):
         chain_type="stuff",
         retriever=db.as_retriever(search_kwargs={"k": 3}),
         return_source_documents=True,
-        max_tokens_limit=512,
+        max_tokens_limit=256,
         combine_docs_chain_kwargs={"prompt": prompt},
         condense_question_prompt=CONDENSE_QUESTION_PROMPT,
         memory=memory,
@@ -314,7 +314,7 @@ def main():
                 # Check if the extension is in FILE_LOADER_MAPPING
                 if ext in FILE_LOADER_MAPPING:
                     loader_class, loader_args = FILE_LOADER_MAPPING[ext]
-                    st.write(f"loader_class: {loader_class}")
+                    # st.write(f"loader_class: {loader_class}")
 
                     # Save the uploaded file to the temporary directory
                     file_path = os.path.join(td, uploaded_file.name)
@@ -327,7 +327,7 @@ def main():
                 else:
                     st.warning(f"Unsupported file extension: {ext}")
 
-        st.write(f"loaded_documents: {loaded_documents}")  
+        # st.write(f"loaded_documents: {loaded_documents}")  
         st.write("Chat with the Document:")
         query = st.text_input("Ask a question:")
 
@@ -339,9 +339,9 @@ def main():
                     prompt = set_custom_prompt()
                     CONDENSE_QUESTION_PROMPT = set_custom_prompt_condense()
                     db = create_vector_database(loaded_documents)
-                    st.write(f"db: {db}") 
+                    # st.write(f"db: {db}") 
                     response = retrieve_bot_answer(query,loaded_documents)
-                    st.write(f"response: {response}") 
+                    # st.write(f"response: {response}") 
                     # Display bot response
                     st.write("Bot Response:")
                     st.write(response)