Simple vectors storage and similarity search not working

Hello everyone and thanks for this awesome product,

I am trying to create a small database that will store 6 vectors and find the similarity against a queried input vector. I have managed to create the schema, create the vectors, store them, but when I query a vector against the database I get an empty result. I was expecting to get the list of closest vectors together with their distance. I don’t know exactly what is the problem. The following figure show my schema defintion, and the data importation, while underneath I have the similarity search.

nearVector = {
“vector”: [1.0, 2.0, 3.0, 4.0, 5.0]

result = (
client.query.get(“RandomVectorEntity”, [“id_number”, “vector”])


The output is just:
{‘data’: {‘Get’: {‘RandomVectorEntity’: }}}

Thank you very much for the awesome work

Upon further investigation, I found that there are no embeddings associate with the instances, so how I can assign the vector that I have defined in the begging, a list of 5 floats, as an embedding to each vector?

A further update is that I had to do the following:
data_import = client.data_object.create(instance, “RandomVectorEntity”, vector=vector_input[c])
so to have an instance associated with an embedding. Please confirm it if I am correct. Thank you.

yes that is correct. But if you import a lot of objects you should look into batch imports: Imports in detail | Weaviate - vector database