update doc

Signed-off-by: cmuhao <[email protected]>

docs/reference/online-stores/postgres.md

@@ -30,6 +30,8 @@ online_store:
     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
 ```
 {% endcode %}
 
@@ -60,3 +62,29 @@ Below is a matrix indicating which functionality is supported by the Postgres on
 | collocated by entity key                                  | no       |
 
 To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix).
+
+## PGVector
+The Postgres online store supports the use of [PGVector](https://pgvector.dev/) 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.
+
+Then you can use `retrieve_online_documents` to retrieve the top k closest vectors to a query vector.
+
+{% code title="python" %}
+```python
+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,
+)
+```
+{% endcode %}