deploy

app.py

@@ -33,15 +33,15 @@ text_splitter = SentenceSplitter(
   tokenizer=tiktoken.encoding_for_model("gpt-3.5-turbo").encode
 )
 
+# cria um serviço de contexto para configurar a criação do indice
+service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model, text_splitter=text_splitter)
+
 # verifica se a pasta storage existe localmente
 PERSIST_DIR = "./storage"
 if not os.path.exists(PERSIST_DIR):
     # caso não exista lê os documentos da pasta e cria um índice
     documents = SimpleDirectoryReader("./data").load_data()
 
-    # cria um serviço de contexto para configurar a criação do indice
-    service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model, text_splitter=text_splitter)
-
     # cria um indice utilizando um contexto de serviços
     index = VectorStoreIndex.from_documents(documents, service_context=service_context)
 
@@ -50,7 +50,7 @@ if not os.path.exists(PERSIST_DIR):
 else:
     # caso a pasta exista, lê o índice existente
     storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-    index = load_index_from_storage(storage_context)
+    index = load_index_from_storage(storage_context, service_context=service_context)
 
 # define um prompt
 text_qa_template = PromptTemplate('''

storage/graph_store.json

@@ -0,0 +1 @@
+{"graph_dict": {}}

storage/image__vector_store.json

@@ -0,0 +1 @@
+{"embedding_dict": {}, "text_id_to_ref_doc_id": {}, "metadata_dict": {}}

storage/index_store.json

@@ -0,0 +1 @@
+{"index_store/data": {"a7a9eb12-e1ac-4cba-87c8-602a34181c4c": {"__type__": "vector_store", "__data__": "{\"index_id\": \"a7a9eb12-e1ac-4cba-87c8-602a34181c4c\", \"summary\": null, \"nodes_dict\": {\"4919b298-1b7c-4470-a901-36f09a02b53d\": \"4919b298-1b7c-4470-a901-36f09a02b53d\", \"019da7cd-7cf0-42d6-b613-c01ebbe6a289\": \"019da7cd-7cf0-42d6-b613-c01ebbe6a289\", \"c2708a9c-5e57-4191-a519-e801f26cd279\": \"c2708a9c-5e57-4191-a519-e801f26cd279\", \"552931d3-3811-4179-8bff-4f8c84e2162d\": \"552931d3-3811-4179-8bff-4f8c84e2162d\", \"a24d6f39-ebb2-4314-967f-bc1cb7d77daf\": \"a24d6f39-ebb2-4314-967f-bc1cb7d77daf\", \"b0c718f2-6f3e-4be6-b974-573f2e3a44f8\": \"b0c718f2-6f3e-4be6-b974-573f2e3a44f8\", \"d951070b-2a90-4b81-a8b9-df48d61d8c23\": \"d951070b-2a90-4b81-a8b9-df48d61d8c23\", \"80baf0e6-273c-43d7-8ebb-c28477f29dc0\": \"80baf0e6-273c-43d7-8ebb-c28477f29dc0\", \"cf942a4e-2832-4ccf-ba6d-ed01d49e60d2\": \"cf942a4e-2832-4ccf-ba6d-ed01d49e60d2\", \"b7c84b60-0dd9-44d4-9f9a-5ef2570bec7f\": \"b7c84b60-0dd9-44d4-9f9a-5ef2570bec7f\", \"98e11407-92c3-4702-8afb-8131c91c992c\": \"98e11407-92c3-4702-8afb-8131c91c992c\", \"66699631-c95f-4ba9-b591-06307b173430\": \"66699631-c95f-4ba9-b591-06307b173430\", \"860fcb36-0f9c-4ba6-bc1c-6faff3aff8e6\": \"860fcb36-0f9c-4ba6-bc1c-6faff3aff8e6\", \"295e9342-e854-4ed8-9605-2745645f29a8\": \"295e9342-e854-4ed8-9605-2745645f29a8\", \"d245cc2a-7aac-47fa-8cbd-6e85bba048e6\": \"d245cc2a-7aac-47fa-8cbd-6e85bba048e6\", \"21932082-06af-4fea-9a6b-34046c3917aa\": \"21932082-06af-4fea-9a6b-34046c3917aa\", \"e051bc4e-5b21-4f0b-8359-e31a361b64e4\": \"e051bc4e-5b21-4f0b-8359-e31a361b64e4\", \"07e1a0d5-6b25-4d9d-b8e3-37d1a0047a02\": \"07e1a0d5-6b25-4d9d-b8e3-37d1a0047a02\", \"0ee91c33-5180-4604-a3b4-3b24266c50cd\": \"0ee91c33-5180-4604-a3b4-3b24266c50cd\", \"fdef88a2-e496-41f8-ae17-eb7d7135493e\": \"fdef88a2-e496-41f8-ae17-eb7d7135493e\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}