diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc
index d2111bc5e..38dd88947 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc
@@ -95,7 +95,7 @@ spring:
 	  pgvector:
 		index-type: HNSW
 		distance-type: COSINE_DISTANCE
-		dimension: 1536
+		dimensions: 1536
 ----
 
 TIP: Check the list of xref:#pgvector-properties[configuration parameters] to learn about the default values and configuration options.
@@ -131,7 +131,7 @@ You can use the following properties in your Spring Boot configuration to custom
 
 |`spring.ai.vectorstore.pgvector.index-type`|  Nearest neighbor search index type. Options are `NONE` - exact nearest neighbor search, `IVFFlat` - index divides vectors into lists, and then searches a subset of those lists that are closest to the query vector. It has faster build times and uses less memory than HNSW, but has lower query performance (in terms of speed-recall tradeoff). `HNSW` - creates a multilayer graph. It has slower build times and uses more memory than IVFFlat, but has better query performance (in terms of speed-recall tradeoff). There’s no training step like IVFFlat, so the index can be created without any data in the table.| HNSW
 |`spring.ai.vectorstore.pgvector.distance-type`| Search distance type. Defaults to `COSINE_DISTANCE`. But if vectors are normalized to length 1, you can use `EUCLIDEAN_DISTANCE` or `NEGATIVE_INNER_PRODUCT` for best performance.| COSINE_DISTANCE
-|`spring.ai.vectorstore.pgvector.dimension`| Embeddings dimension. If not specified explicitly the PgVectorStore will retrieve the dimensions form the provided `EmbeddingClient`. Dimensions are set to the embedding column the on table creation. If you change the dimensions your would have to re-create the vector_store table as well. | -
+|`spring.ai.vectorstore.pgvector.dimensions`| Embeddings dimension. If not specified explicitly the PgVectorStore will retrieve the dimensions form the provided `EmbeddingClient`. Dimensions are set to the embedding column the on table creation. If you change the dimensions your would have to re-create the vector_store table as well. | -
 |`spring.ai.vectorstore.pgvector.remove-existing-vector-store-table` | Deletes the existing `vector_store` table on start up.  | false
 
 |===