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title, short_description, description
| title | short_description | description |
|---|---|---|
| Feast | Use Qdrant as the online vector store in Feast to serve embedding features and similarity lookups in production ML systems at scale. | Configure Qdrant as the online vector store in the Feast feature store to serve embedding features and similarity searches for production ML systems. |
Feast
Feast (Feature Store) is an open-source feature store that helps teams operate production ML systems at scale by allowing them to define, manage, validate, and serve features for production AI/ML.
Qdrant is available as a supported vectorstore in Feast to integrate in your workflows.
Insatallation
To use the Qdrant online store, you need to install Feast with the qdrant extra.
pip install 'feast[qdrant]'
Usage
An example config with Qdrant could look like:
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: qdrant
host: xyz-example.eu-central.aws.cloud.qdrant.io
port: 6333
api_key: <your-own-key>
vector_len: 384
# Reference: https://qdrant.tech/documentation/manage-data/vectors/#named-vectors
# vector_name: text-vec
write_batch_size: 100
You can refer to the Feast documentation for the full list of configuration options.
Retrieving Documents
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 being the vector.
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
feature_values = feature_store.retrieve_online_documents(
feature="my_feature",
query=query_vector,
top_k=top_k
)