Faiss

Description

The Faiss online store provides support for materializing feature values and performing vector similarity search using Facebook AI Similarity Search (Faiss). Faiss is a library for efficient similarity search and clustering of dense vectors, making it well-suited for use cases involving embeddings and nearest-neighbor lookups.

Getting started

In order to use this online store, you'll need to install the Faiss dependency. E.g.

pip install 'feast[faiss]'

Example

feature_store.yaml

project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
  type: feast.infra.online_stores.faiss_online_store.FaissOnlineStore
  dimension: 128
  index_path: data/faiss_index
  index_type: IVFFlat    # optional, default: IVFFlat
  nlist: 100             # optional, default: 100

Note: Faiss is not registered as a named online store type. You must use the fully qualified class path as the type value.

The full set of configuration options is available in FaissOnlineStoreConfig.

Functionality Matrix

The set of functionality supported by online stores is described in detail here. Below is a matrix indicating which functionality is supported by the Faiss online store.

	Faiss
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
vector similarity search	yes

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