After parsing the nodes and persisting a vectorindex (specifically weaviate) I'm unable to load it
# Parse the nodes
docstore = SimpleDocumentStore()
docstore.add_documents(nodes)
storage_context = StorageContext.from_defaults(
docstore=docstore,
vector_store=vector_store,
)
# Create vecorstore
index = VectorStoreIndex(
nodes=nodes,
service_context=ServiceContext.from_defaults(llm=llm, embed_model=embedding),
storage_context=storage_context,
)
# Persist
index.storage_context.persist("../index/frcb/")
# Reload
from llama_index.storage import StorageContext
from llama_index.storage.index_store import SimpleIndexStore
from llama_index.storage.docstore import SimpleDocumentStore
storage_context_loaded = StorageContext.from_defaults(
vector_store=vector_store,
docstore=SimpleDocumentStore.from_persist_path("../index/frcb/docstore.json"),
index_store=SimpleIndexStore.from_persist_path("../index/frcb/index_store.json"),
persist_dir="../index/frcb/",
)
from llama_index.indices.loading import load_index_from_storage
index_loaded = load_index_from_storage(
storage_context=storage_context_loaded,
service_context=ServiceContext.from_defaults(llm=llm, embed_model=embedding),
)
I know it's not retrieving properly because I'm getting
Empty Response
engine_loaded = index_loaded.as_query_engine(
similarity_top_k=10,
)
final_engine_loaded = SubQuestionQueryEngine.from_defaults(
query_engine_tools=[
QueryEngineTool(
query_engine=engine_loaded,
metadata=ToolMetadata(
name="free_form_question_answer",
description="financial insights.",
),
)
],
question_gen=question_gen,
service_context=ServiceContext.from_defaults(llm=llm, embed_model=embedding),
use_async=True,
)