Update app.py

app.py

@@ -1,30 +1,40 @@
-from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, ServiceContext, set_global_service_context, load_index_from_storage, StorageContext, PromptHelper
-from llama_index.llms import OpenAI
+from llama_index.core import (
+    Settings,
+    load_index_from_storage,
+    StorageContext,
+)
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.llms.openai import OpenAI 
 import gradio as gr
-import sys
 import os
-
-try:
-    from Config import openai_key
-    os.environ["OPENAI_API_KEY"] = openai_key
-except:
-    pass
+from Config import openai_key
 
 """
 Code adopted from Beebom article: "How to Train an AI Chatbot With Custom Knowledge Base Using ChatGPT API" by Arjun Sha
 https://beebom.com/how-train-ai-chatbot-custom-knowledge-base-chatgpt-api/
+Updated Feb 22, 2025 to use updated OpenAI API and Llama Index library
 """
 
 max_input_size = 4096
 num_outputs = 512
 chunk_size_limit = 600
-
-prompt_helper = PromptHelper(context_window=max_input_size, num_output=num_outputs, chunk_overlap_ratio=0.1, chunk_size_limit=chunk_size_limit)
-
-llm = OpenAI(model="gpt-3.5-turbo", temperature=0.5, max_tokens=num_outputs)
-
-service_context = ServiceContext.from_defaults(llm=llm, prompt_helper=prompt_helper)
-set_global_service_context(service_context)
+chunk_overlap = int(chunk_size_limit * 0.1) 
+
+os.environ["OPENAI_API_KEY"] = openai_key
+
+llm = OpenAI(
+    model="gpt-3.5-turbo",
+    temperature=0.5,
+    max_tokens=num_outputs
+)
+
+Settings.llm = llm
+Settings.node_parser = SentenceSplitter(
+    chunk_size=chunk_size_limit,
+    chunk_overlap=chunk_overlap
+)
+Settings.context_window = max_input_size
+Settings.num_output = num_outputs
 
 def retrieve_index(index_path):
     storage_context = StorageContext.from_defaults(persist_dir=index_path)