I am using the Attu client to manage the data from Milvus and all the data looks good inside.
But when i am trying to query the same data with the python client, the embeddings results i get are in a float8 format instead of a float32 one.
Why is that? Is there a limitation or conversion on the retrieval process?
Here is a snippet of the query i am running:
<code>self.milvusClient.query(
collection_name='someCollection',
output_fields=['embedding'],
filter='objectId in [' + ','.join(objectIdList) + ']',
limit=limit
)
</code>
<code>self.milvusClient.query(
collection_name='someCollection',
output_fields=['embedding'],
filter='objectId in [' + ','.join(objectIdList) + ']',
limit=limit
)
</code>
self.milvusClient.query(
collection_name='someCollection',
output_fields=['embedding'],
filter='objectId in [' + ','.join(objectIdList) + ']',
limit=limit
)
Also here is a snippet of the creation command for the embedding field:
<code>
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=512, is_primary=False, auto_id=False)
index_params.add_index(
field_name="embedding",
index_type="IVF_FLAT",
metric_type="L2",
params={
"nlist": 2048
}
)
</code>
<code>
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=512, is_primary=False, auto_id=False)
index_params.add_index(
field_name="embedding",
index_type="IVF_FLAT",
metric_type="L2",
params={
"nlist": 2048
}
)
</code>
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=512, is_primary=False, auto_id=False)
index_params.add_index(
field_name="embedding",
index_type="IVF_FLAT",
metric_type="L2",
params={
"nlist": 2048
}
)
Value stored: -0.24403861165046692
Value retrieved: -0.24403861
Thank you!