diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/postgresml-embeddings.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/postgresml-embeddings.adoc
index 67666d1da..16aed87ef 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/postgresml-embeddings.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/postgresml-embeddings.adoc
@@ -6,14 +6,87 @@ Embeddings are a numeric representation of text.
 They are used to represent words and sentences as vectors, an array of numbers.
 Embeddings can be used to find similar pieces of text, by comparing the similarity of the numeric vectors using a distance measure, or they can be used as input features for other machine learning models, since most algorithms can't use text directly.
 
-Many pretrained LLMs can be used to generate embeddings from text within PostgresML.
+Many pre-trained LLMs can be used to generate embeddings from text within PostgresML.
 You can browse all the https://huggingface.co/models?library=sentence-transformers[models] available to find the best solution on Hugging Face.
 
-== Getting Started
+== Auto-configuration
 
-=== Configure the PostgresML Embeddings Client Manually
+Spring AI provides Spring Boot auto-configuration for the Azure PostgresML Embedding Client.
+To enable it add the following dependency to your project's Maven `pom.xml` file:
+
+[source, xml]
+----
+<dependency>
+    <groupId>org.springframework.ai</groupId>
+    <artifactId>spring-ai-postgresml-spring-boot-starter</artifactId>
+    <version>0.8.0-SNAPSHOT</version>
+</dependency>
+----
+
+or to your Gradle `build.gradle` build file.
+
+[source,groovy]
+----
+dependencies {
+    implementation 'org.springframework.ai:spring-ai-postgresml-spring-boot-starter:0.8.0-SNAPSHOT'
+}
+----
+
+NOTE: Refer to the xref:getting-started.adoc#_dependency_management[Dependency Management] section to add Milestone and/or Snapshot Repositories to your build file.
+
+Use the `spring.ai.postgresml.embedding.options.*` properties to configure your `PostgresMlEmbeddingClient`. links
+
+=== Embedding Properties
+
+The prefix `spring.ai.postgres.embedding` is property prefix that configures the `EmbeddingClient` implementation for PostgresML embeddings.
+
+[cols="3,5,1"]
+|====
+| Property | Description | Default
+| spring.ai.postgres.embedding.options.transformer  | The Huggingface transformer model to use for the embedding.  | distilbert-base-uncased
+| spring.ai.postgres.embedding.options.kwargs   | Additional transformer specific options.  | empty map
+| spring.ai.postgres.embedding.options.vectorType   | PostgresML vector type to use for the embedding. Two options are supported: `PG_ARRAY` and `PG_VECTOR`. | PG_ARRAY
+| spring.ai.postgres.embedding.options.metadataMode   | Document metadata aggregation mode  | EMBED
+|====
+
+=== Sample Controller
+
+This will create a `EmbeddingClient` implementation that you can inject into your class.
+Here is an example of a simple `@Controller` class that uses the `EmbeddingClient` implementation.
+
+[source,application.properties]
+----
+spring.ai.postgres.embedding.options.transformer=distilbert-base-uncased
+spring.ai.postgres.embedding.options.vectorType=PG_ARRAY
+spring.ai.postgres.embedding.options.metadataMode=EMBED
+spring.ai.postgres.embedding.options.kwargs.device=cpu
+----
+
+[source,java]
+----
+@RestController
+public class EmbeddingController {
+
+    private final EmbeddingClient embeddingClient;
+
+    @Autowired
+    public EmbeddingController(EmbeddingClient embeddingClient) {
+        this.embeddingClient = embeddingClient;
+    }
+
+    @GetMapping("/ai/embedding")
+    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
+        EmbeddingResponse embeddingResponse = this.embeddingClient.embedForResponse(List.of(message));
+        return Map.of("embedding", embeddingResponse);
+    }
+}
+----
+
+== Manual configuration
+
+Instead of using the Spring Boot auto-configuration, you can create the `PostgresMlEmbeddingClient` manually.
+For this add the `spring-ai-postgresml` dependency to your project's Maven `pom.xml` file:
 
-Add the `spring-ai-postgresml` dependency to your project's Maven `pom.xml` file:
 [source, xml]
 ----
 <dependency>
@@ -60,18 +133,14 @@ Then you don’t have to call the `afterPropertiesSet()` manually:
 ----
 @Bean
 public EmbeddingClient embeddingClient(JdbcTemplate jdbcTemplate) {
-
     return new PostgresMlEmbeddingClient(jdbcTemplate,
         PostgresMlEmbeddingOptions.builder()
-                .withTransformer("distilbert-base-uncased")
-                .withVectorType(VectorType.PG_VECTOR)
-                .withKwargs(Map.of("device", "cpu"))
-                .withMetadataMode(MetadataMode.EMBED)
+             ....
             .build());
 }
 ----
 
-==== OpenAiEmbeddingOptions
+=== EmbeddingOptions
 
 Use the https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/postgresml/PostgresMlEmbeddingOptions.java[PostgresMlEmbeddingOptions.java] to configure the `PostgresMlEmbeddingClient` with options, such as the model to use and etc.
 
@@ -94,76 +163,4 @@ EmbeddingResponse embeddingResponse = embeddingClient.call(
                 .build()));
 ----
 
-=== PostgresMlEmbeddingClient Auto-configuration
-
-Spring AI provides Spring Boot auto-configuration for the Azure PostgresML Embedding Client.
-To enable it add the following dependency to your project's Maven `pom.xml` file:
-
-[source, xml]
-----
-<dependency>
-    <groupId>org.springframework.ai</groupId>
-    <artifactId>spring-ai-postgresml-spring-boot-starter</artifactId>
-    <version>0.8.0-SNAPSHOT</version>
-</dependency>
-----
-
-or to your Gradle `build.gradle` build file.
-
-[source,groovy]
-----
-dependencies {
-    implementation 'org.springframework.ai:spring-ai-postgresml-spring-boot-starter:0.8.0-SNAPSHOT'
-}
-----
-
-NOTE: Refer to the xref:getting-started.adoc#_dependency_management[Dependency Management] section to add Milestone and/or Snapshot Repositories to your build file.
-
-Use the `spring.ai.postgresml.embedding.options.*` properties to configure your `PostgresMlEmbeddingClient`. links
-
-==== Sample Embedding Controller
-
-This will create a `EmbeddingClient` implementation that you can inject into your class.
-Here is an example of a simple `@Controller` class that uses the `EmbeddingClient` implementation.
-
-[source,application.properties]
-----
-spring.ai.postgres.embedding.options.transformer=distilbert-base-uncased
-spring.ai.postgres.embedding.options.vectorType=PG_ARRAY
-spring.ai.postgres.embedding.options.metadataMode=EMBED
-spring.ai.postgres.embedding.options.kwargs.device=cpu
-----
-
-[source,java]
-----
-@RestController
-public class EmbeddingController {
-
-    private final EmbeddingClient embeddingClient;
-
-    @Autowired
-    public EmbeddingController(EmbeddingClient embeddingClient) {
-        this.embeddingClient = embeddingClient;
-    }
-
-    @GetMapping("/ai/embedding")
-    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
-        EmbeddingResponse embeddingResponse = this.embeddingClient.embedForResponse(List.of(message));
-        return Map.of("embedding", embeddingResponse);
-    }
-}
-----
-
-== PostgresML Embedding Properties
-
-The prefix `spring.ai.postgres.embedding` is property prefix that configures the `EmbeddingClient` implementation for PostgresML embeddings.
-
-[cols="3,5,1"]
-|====
-| Property | Description | Default
-| spring.ai.postgres.embedding.options.transformer  | The Huggingface transformer model to use for the embedding.  | distilbert-base-uncased
-| spring.ai.postgres.embedding.options.kwargs   | Additional transformer specific options.  | empty map
-| spring.ai.postgres.embedding.options.vectorType   | PostgresML vector type to use for the embedding. Two options are supported: `PG_ARRAY` and `PG_VECTOR`. | PG_ARRAY
-| spring.ai.postgres.embedding.options.metadataMode   | Document metadata aggregation mode  | EMBED
-|====