diff --git a/docs/source/providers/vector_io/remote_pgvector.md b/docs/source/providers/vector_io/remote_pgvector.md
index 3e7d6e776..74f588a13 100644
--- a/docs/source/providers/vector_io/remote_pgvector.md
+++ b/docs/source/providers/vector_io/remote_pgvector.md
@@ -17,7 +17,7 @@ That means you'll get fast and efficient vector retrieval.
 To use PGVector in your Llama Stack project, follow these steps:
 
 1. Install the necessary dependencies.
-2. Configure your Llama Stack project to use Faiss.
+2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector).
 3. Start storing and querying vectors.
 
 ## Installation
diff --git a/llama_stack/providers/registry/vector_io.py b/llama_stack/providers/registry/vector_io.py
index c13e65bbc..e391341b4 100644
--- a/llama_stack/providers/registry/vector_io.py
+++ b/llama_stack/providers/registry/vector_io.py
@@ -395,7 +395,7 @@ That means you'll get fast and efficient vector retrieval.
 To use PGVector in your Llama Stack project, follow these steps:
 
 1. Install the necessary dependencies.
-2. Configure your Llama Stack project to use Faiss.
+2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector).
 3. Start storing and querying vectors.
 
 ## Installation