qdrant_client.get_fastembed_vector_params() with upload_collection about qdrant-client HOT 4 CLOSED

Fastembed creates a named vector with this name fast-bge-small-en-v1.5 in your case, however you are uploading unnamed vectors, that's why you they are not added to your collection.

You don't see any error on python client side, because upload_collection has param wait set to False by default, which means that client does not wait for the response from the server.

Here is a link to an example with upsert

You can check accepted param types of vectors in upload_collection signature.
You can also find examples of usage it with various input types in tests

from qdrant-client.

Hi @uguraydrd

Could you show please how you upload points to your collection?

from qdrant-client.

Hi @joein,

Of course. I do this:

metadata = data["col"].to_dict(orient="records")
model = qdrant_client.embedding_models[qdrant_client.embedding_model_name]

qdrant_client.upload_collection(
    collection_name="test",
    vectors=[
        arr.tolist()
        for arr in list(
            model.passage_embed(
                texts=data,
                batch_size=128,
            )
        )
    ],
    ids=range(len(data)),
    payload=metadata,
    batch_size=128,
)

from qdrant-client.

Thank you very much for your information. Finally, It works for me, but normally, I could not find or see this anything on your tutorials unfortunately . When I analyzed qdrant_client.add() deeply, I noticed the situation you mentioned.

I want to put the working code here, maybe it will be useful to other people.

metadata = data["col"].to_dict(orient="records")
model = qdrant_client.embedding_models[qdrant_client.embedding_model_name]
vector_field_name = qdrant_client.get_vector_field_name()

qdrant_client.upload_collection(
    collection_name="test",
    vectors=[
        {self.vector_field_name: arr.tolist()}
        for arr in self.model.passage_embed(
            texts=data,
            batch_size=128,
        )
    ],
    ids=range(len(data)),
    payload=metadata,
    batch_size=128,
)

from qdrant-client.