docs/reference/online-stores/qdrant.md
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
write_batch_size: 100
{% endcode %}
The full set of configuration options is available in QdrantOnlineStoreConfig.
| Qdrant | |
|---|---|
| write feature values to the online store | yes |
| read feature values from the online store | yes |
| update infrastructure (e.g. tables) in the online store | yes |
| teardown infrastructure (e.g. tables) in the online store | yes |
| generate a plan of infrastructure changes | no |
| support for on-demand transforms | yes |
| readable by Python SDK | yes |
| readable by Java | no |
| readable by Go | no |
| support for entityless feature views | yes |
| support for concurrent writing to the same key | no |
| support for ttl (time to live) at retrieval | no |
| support for deleting expired data | no |
| collocated by feature view | yes |
| collocated by feature service | no |
| collocated by entity key | no |
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(
features=["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.