Update app.py

app.py

@@ -3,7 +3,6 @@ import textwrap
 
 from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document
 from llama_index.vector_stores import DeepLakeVectorStore
-import deeplake
 from langchain.chat_models import ChatOpenAI
 import chainlit as cl
 os.environ["OPENAI_API_KEY"] = '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'
@@ -27,7 +26,7 @@ from llama_index import load_index_from_storage, load_indices_from_storage, load
 
 
 
-dataset_path ="hub://cxcxxaaaaaz/text_embedding" # if we comment this out and don't pass the path then GPTDeepLakeIndex will create dataset in memory
+#dataset_path ="hub://cxcxxaaaaaz/text_embedding" # if we comment this out and don't pass the path then GPTDeepLakeIndex will create dataset in memory
 from llama_index.storage.storage_context import StorageContext
 from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document
 from llama_index.vector_stores import ChromaVectorStore
@@ -69,10 +68,10 @@ node_parser = SimpleNodeParser(
 )
 
 prompt_helper = PromptHelper(
-  context_window=4096,
+  context_window=2000,
   num_output=256,
   chunk_overlap_ratio=0.1,
-  chunk_size_limit=20
+  chunk_size_limit=200
 )
 
 import tiktoken
@@ -93,9 +92,8 @@ service_context = ServiceContext.from_defaults(
 from llama_index import set_global_service_context
 
 
-index = VectorStoreIndex.from_documents([], vectorstore=vector_store, storage_context=storage_context, service_context=service_context)
-
 
+index = VectorStoreIndex.from_documents([], vectorstore=vector_store, storage_context=storage_context, service_context=service_context)