Qdrant

Description

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.

Getting started

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

Example

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

The full set of configuration options is available in QdrantOnlineStoreConfig.

Functionality Matrix

	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.

Retrieving online document vectors

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.

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
)

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