Update app.py

app.py

@@ -1,32 +1,39 @@
-from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+from llama_index import SimpleDirectoryReader, GPTListIndex, GPTVectorStoreIndex, StorageContext, LLMPredictor, PromptHelper, load_index_from_storage
 from langchain.chat_models import ChatOpenAI
 import gradio as gr
 import sys
 import os
 
-os.environ["OPENAI_API_KEY"] = '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'
+os.environ["OPENAI_API_KEY"] = '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'
 
 def construct_index(directory_path):
     max_input_size = 1000000
-    num_outputs = 456
+    num_outputs = 256
     max_chunk_overlap = 20
-    chunk_size_limit = 6048
+    chunk_size_limit = 2048
 
-    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+    prompt_helper = PromptHelper(max_input_size, num_outputs, chunk_overlap_ratio= 0.1, chunk_size_limit=chunk_size_limit)
 
-    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=1, model_name="gpt-4", max_tokens=num_outputs))
 
     documents = SimpleDirectoryReader(directory_path).load_data()
 
-    index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper,chunk_size_limit = 6048)
+    index = GPTVectorStoreIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper,chunk_size_limit = 2048)
 
-    index.save_to_disk('index.json')
+    
+    index.storage_context.persist(persist_dir="index.json")
 
     return index
 
 def chatbot(input_text):
-    index = GPTSimpleVectorIndex.load_from_disk('index.json')
-    response = index.query(input_text, response_mode="compact")
+
+    storage_context = StorageContext.from_defaults(persist_dir="index.json")
+    index = load_index_from_storage(storage_context)
+
+    query_engine = index.as_query_engine()
+    response = query_engine.query(input_text)
+
+    
     return response.response
 
 iface = gr.Interface(fn=chatbot,