Qdrant is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage vectors with additional payload and extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.

In order to use this online store, you'll need to run pip install 'feast[qdrant]'.

{% code title="feature_store.yaml" %}

project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
    type: qdrant
    host: localhost
    port: 6333
    vector_len: 384
    write_batch_size: 100

{% endcode %}

The full set of configuration options is available in QdrantOnlineStoreConfig.

To compare this set of functionality against other online stores, please see the full functionality matrix.

The Qdrant online store supports retrieving document vectors for a given list of entity keys. The document vectors are returned as a dictionary where the key is the entity key and the value is the document vector. The document vector is a dense vector of floats.

{% code title="python" %}

from feast import FeatureStore

feature_store = FeatureStore(repo_path="feature_store.yaml")

query_vector = [1.0, 2.0, 3.0, 4.0, 5.0]
top_k = 5

# Retrieve the top k closest features to the query vector
# Since Qdrant supports multiple vectors per entry,
# the vector to use can be specified in the repo config.
# Reference: https://qdrant.tech/documentation/concepts/vectors/#named-vectors
feature_values = feature_store.retrieve_online_documents(
    feature="my_feature",
    query=query_vector,
    top_k=top_k
)

{% endcode %}

These APIs are subject to change in future versions of Feast to improve performance and usability.