docs/reference/online-stores/faiss.md
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.
In order to use this online store, you'll need to install the Faiss dependency. E.g.
pip install 'feast[faiss]'
{% code title="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
{% endcode %}
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.
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.