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Updated 2 months ago

Hi all

Hi all
I created a qdrant vector db locally with this
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from qdrant_client import QdrantClient
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import VectorStoreIndex, StorageContext

# For local instance
client = QdrantClient(path="./qdrant_data")

vector_store = QdrantVectorStore(
    "hr", client=client, enable_hybrid=True
)

Settings.chunk_size = 2048
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)


then later on i try to do retrieval with
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from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from llama_index.core import load_index_from_storage

# Create a local Qdrant vector store
client = QdrantClient(path="./qdrant_data") 

vector_store = QdrantVectorStore(client=client, collection_name="hr") 

storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Load the persisted index
index = load_index_from_storage(storage_context=storage_context)

but i keep running into
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ValueError: No index in storage context, check if you specified the right persist_dir.
L
T
2 comments
you dont need to persist or load with most vector db integrations.

To "load" just do VectorStoreIndex.from_vector_store(vector_store)
Ye found that out a while ago
Not sure if I wasn't looking in the right places, but didn't come across it in the docs (would be nice if it was casually slid in there)
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