PostgreSQL (contrib)

Description

The PostgreSQL online store provides support for materializing feature values into a PostgreSQL database for serving online features.

• Only the latest feature values are persisted

• sslmode, sslkey_path, sslcert_path, and sslrootcert_path are optional

Getting started

In order to use this online store, you'll need to run pip install 'feast[postgres]'. You can get started by then running feast init -t postgres.

Example

project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
    type: postgres
    host: DB_HOST
    port: DB_PORT
    database: DB_NAME
    db_schema: DB_SCHEMA
    user: DB_USERNAME
    password: DB_PASSWORD
    sslmode: verify-ca
    sslkey_path: /path/to/client-key.pem
    sslcert_path: /path/to/client-cert.pem
    sslrootcert_path: /path/to/server-ca.pem
    pgvector_enabled: false
    vector_len: 512

The full set of configuration options is available in PostgreSQLOnlineStoreConfig.

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 Postgres online store.

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

PGVector

The Postgres online store supports the use of PGVector for storing feature values. To enable PGVector, set pgvector_enabled: true in the online store configuration.

The vector_len parameter can be used to specify the length of the vector. The default value is 512.

Please make sure to follow the instructions in the repository, which, as the time of this writing, requires you to run CREATE EXTENSION vector; in the database.

Then you can use retrieve_online_documents to retrieve the top k closest vectors to a query vector. For the Retrieval Augmented Generation (RAG) use-case, you have to embed the query prior to passing the query vector.

from feast import FeatureStore
from feast.infra.online_stores.postgres import retrieve_online_documents

feature_store = FeatureStore(repo_path=".")

query_vector = [0.1, 0.2, 0.3, 0.4, 0.5]
top_k = 5

feature_values = retrieve_online_documents(
    feature_store=feature_store,
    feature_view_name="document_fv:embedding_float",
    query_vector=query_vector,
    top_k=top_k,
)