query()
:
top_k
: the number of results to return.n_probes
: the number of clusters to search for each query vector.filters
: a list of metadata filters to apply to the query.include
: a list of item fields to return (e.g., ["distance", "metadata"]
).greedy
: whether to perform a greedy search (higher recall but slower).query()
:
age
field is greater than 18
, you can use the following filter:
age
field is greater than 18
. You can also use other comparison operators such as $lt
, $gte
, $lte
, $eq
, and $neq
.
For more details on metadata filters, see the Metadata Filtering guide.
embedding_model
during index creation, you can automatically generate embeddings for queries by providing query_contents
to the query()
call:
sentence-transformers
for embedding generation. You can use any model from the HuggingFace Model Hub that is compatible with sentence-transformers
.
get()
, which retrieves and decrypts item added via upsert()
. For more details, see the Get Items guide.
50,000
vectors in your index, you should train the index. Without doing so, queries will run slower. For more details, see Training an Encrypted Index.