Documentation updates for Bedrock Converse API

- Added multimodal support documentation (images, video, documents)
- Added deprecation notices for existing Bedrock model implementations
- Updated feature comparison table
- Added warning notes about transitioning to Converse API

Author: tzolov

models/spring-ai-bedrock-converse/src/test/java/org/springframework/ai/bedrock/converse/client/BedrockNovaChatClientIT.java

@@ -17,7 +17,6 @@
 
 import java.io.IOException;
 import java.time.Duration;
-import java.util.function.Function;
 
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;

spring-ai-docs/src/main/antora/modules/ROOT/images/test.pdf.png

@@ -1,5 +1,21 @@
 = Amazon Bedrock
 
+[NOTE]
+====
+Following the Bedrock recommendations, Spring AI is transitioning to using Amazon Bedrock's Converse API for all Chat conversation implementations in Spring AI. 
+While the existing `InvokeModel API` supports conversation applications, we strongly recommend adopting the xref:api/chat/bedrock-converse.adoc[Bedrock Converse API] for several key benefits:
+
+- Unified Interface: Write your code once and use it with any supported Amazon Bedrock model
+- Model Flexibility: Seamlessly switch between different conversation models without code changes
+- Extended Functionality: Support for model-specific parameters through dedicated structures
+- Tool Support: Native integration with function calling and tool usage capabilities
+- Multimodal Capabilities: Built-in support for vision and other multimodal features
+- Future-Proof: Aligned with Amazon Bedrock's recommended best practices
+
+The Converse API does not support embedding operations, so these will remain in the current API and the embedding model functionality in the existing `InvokeModel API` will be maintained
+====
+
+
 link:https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html[Amazon Bedrock] is a managed service that provides foundation models from various AI providers, available through a unified API.
 
 Spring AI supports https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html[all the Chat and Embedding AI models] available through Amazon Bedrock by implementing the Spring interfaces `ChatModel`, `StreamingChatModel`, and  `EmbeddingModel`.

spring-ai-docs/src/main/antora/modules/ROOT/images/test.video.jpeg

@@ -15,14 +15,7 @@ TIP: The Bedrock Converse API provides a unified interface across multiple model
 [NOTE]
 ====
 Following the Bedrock recommendations, Spring AI is transitioning to using Amazon Bedrock's Converse API for all chat conversation implementations in Spring AI. 
-While the existing `InvokeModel API` supports conversation applications, we strongly recommend adopting the Converse API for several key benefits:
-
-- Unified Interface: Write your code once and use it with any supported Amazon Bedrock model
-- Model Flexibility: Seamlessly switch between different conversation models without code changes
-- Extended Functionality: Support for model-specific parameters through dedicated structures
-- Tool Support: Native integration with function calling and tool usage capabilities
-- Multimodal Capabilities: Built-in support for vision and other multimodal features
-- Future-Proof: Aligned with Amazon Bedrock's recommended best practices
+While the existing xref:api/bedrock-chat.adoc[InvokeModel API] supports conversation applications, we strongly recommend adopting the Converse API for all Char conversation models.
 
 The Converse API does not support embedding operations, so these will remain in the current API and the embedding model functionality in the existing `InvokeModel API` will be maintained
 ====
@@ -137,6 +130,118 @@ String response = ChatClient.create(this.chatModel)
         .content();
 ----
 
+== Multimodal
+
+Multimodality refers to a model's ability to simultaneously understand and process information from various sources, including text, images, video, pdf, doc, html, md and more data formats. 
+
+The Bedrock Converse API supports multimodal inputs, including text and image inputs, and can generate a text response based on the combined input.
+
+You need a model that supports multimodal inputs, such as the Anthropic Claude or Amazon Nova models.
+
+=== Images
+
+For link:https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html[models] that support vision multimodality, such as Amazon Nova, Anthropic Claude, Llama 3.2, the Bedrock Converse API Amazon allows you to include multiple images in the payload. Those models can analyze the passed images and answer questions, classify an image, as well as summarize images based on provided instructions.
+
+Currently, Bedrock Converse supports the `base64` encoded images of `image/jpeg`, `image/png`, `image/gif` and `image/webp` mime types.
+
+Spring AI's `Message` interface supports multimodal AI models by introducing the `Media`` type.
+It contains data and information about media attachments in messages, using Spring's `org.springframework.util.MimeType` and a `java.lang.Object` for the raw media data.
+
+Below is a simple code example, demonstrating the combination of user text with an image.
+
+[source,java]
+----
+String response = ChatClient.create(chatModel)
+    .prompt()
+    .user(u -> u.text("Explain what do you see on this picture?")
+        .media(Media.Format.IMAGE_PNG, new ClassPathResource("/test.png")))
+    .call()
+    .content();
+
+logger.info(response);
+----
+
+It takes as an input the `test.png` image:
+
+image::multimodal.test.png[Multimodal Test Image, 200, 200, align="left"]
+
+along with the text message "Explain what do you see on this picture?", and generates a response something like:
+
+----
+The image shows a close-up view of a wire fruit basket containing several pieces of fruit.
+...
+----
+
+=== Video
+
+The link:https://docs.aws.amazon.com/nova/latest/userguide/modalities-video.html[Amazon Nova models] allow you to include a single video in the payload, which can be provided either in base64 format or through an Amazon S3 URI.
+
+Currently, Bedrock Nova supports the images of `video/x-matros`, `video/quicktime`, `video/mp4`, `video/video/webm`, `video/x-flv`, `video/mpeg`, `video/x-ms-wmv` and `image/3gpp` mime types.
+
+Spring AI's `Message` interface supports multimodal AI models by introducing the `Media`` type.
+It contains data and information about media attachments in messages, using Spring's `org.springframework.util.MimeType` and a `java.lang.Object` for the raw media data.
+
+Below is a simple code example, demonstrating the combination of user text with a video.
+
+[source,java]
+----
+String response = ChatClient.create(chatModel)
+    .prompt()
+    .user(u -> u.text("Explain what do you see in this video?")
+        .media(Media.Format.VIDEO_MP4, new ClassPathResource("/test.video.mp4")))
+    .call()
+    .content();
+
+logger.info(response);
+----
+
+It takes as an input the `test.video.mp4` image:
+
+image::test.video.jpeg[Multimodal Test Video, 200, 200, align="left"]
+
+along with the text message "Explain what do you see in this video?", and generates a response something like:
+
+----
+The video shows a group of baby chickens, also known as chicks, huddled together on a surface 
+...
+----
+
+=== Documents
+
+For some models, Bedrock allows you to include documents in the payload through Converse API document support, which can be provided in bytes. 
+The document support has two different variants as explained below:
+
+- **Text document types** (txt, csv, html, md, and so on), where the emphasis is on text understanding. These use case include answering based on textual elements of the document.
+- **Media document types** (pdf, docx, xlsx), where the emphasis is on vision-based understanding to answer questions. These use cases include answering questions based on charts, graphs, and so on.
+
+Currently the Anthropic link:https://docs.anthropic.com/en/docs/build-with-claude/pdf-support[PDF support (beta)] and Amazon Bedrock Nova models support document multimodality.
+
+Below is a simple code example, demonstrating the combination of user text with a media document.
+
+[source,java]
+----
+String response = ChatClient.create(chatModel)
+    .prompt()
+    .user(u -> u.text(
+            "You are a very professional document summarization specialist. Please summarize the given document.")
+        .media(Media.Format.DOC_PDF, new ClassPathResource("/spring-ai-reference-overview.pdf")))
+    .call()
+    .content();
+
+logger.info(response);
+----
+
+image::test.pdf.png[Multimodal Test PNG, 200, 200, align="left"]
+
+along with the text message "You are a very professional document summarization specialist. Please summarize the given document.", and generates a response something like:
+
+----
+**Introduction:**
+- Spring AI is designed to simplify the development of applications with artificial intelligence (AI) capabilities, aiming to avoid unnecessary complexity.
+...
+----
+
+
 == Sample Controller
 
 Create a new Spring Boot project and add the `spring-ai-bedrock-converse-spring-boot-starter` to your dependencies.

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/bedrock.adoc

@@ -1,5 +1,18 @@
 = Bedrock Anthropic 2 Chat
 
+[NOTE]
+====
+Following the Bedrock recommendations, Spring AI is transitioning to using Amazon Bedrock's Converse API for all chat conversation implementations in Spring AI. 
+While the existing `InvokeModel API` supports conversation applications, we strongly recommend adopting the xref:api/chat/bedrock-converse.adoc[Bedrock Converse API] for several key benefits:
+
+- Unified Interface: Write your code once and use it with any supported Amazon Bedrock model
+- Model Flexibility: Seamlessly switch between different conversation models without code changes
+- Extended Functionality: Support for model-specific parameters through dedicated structures
+- Tool Support: Native integration with function calling and tool usage capabilities
+- Multimodal Capabilities: Built-in support for vision and other multimodal features
+- Future-Proof: Aligned with Amazon Bedrock's recommended best practices
+====
+
 NOTE: The Anthropic 2 Chat API is deprecated and replaced by the new Anthropic Claude 3 Message API.
 Please use the xref:api/chat/bedrock/bedrock-anthropic3.adoc[Anthropic Claude 3 Message API] for new projects.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock-converse.adoc

@@ -1,5 +1,18 @@
 = Bedrock Anthropic 3
 
+[NOTE]
+====
+Following the Bedrock recommendations, Spring AI is transitioning to using Amazon Bedrock's Converse API for all chat conversation implementations in Spring AI. 
+While the existing `InvokeModel API` supports conversation applications, we strongly recommend adopting the xref:api/chat/bedrock-converse.adoc[Bedrock Converse API] for several key benefits:
+
+- Unified Interface: Write your code once and use it with any supported Amazon Bedrock model
+- Model Flexibility: Seamlessly switch between different conversation models without code changes
+- Extended Functionality: Support for model-specific parameters through dedicated structures
+- Tool Support: Native integration with function calling and tool usage capabilities
+- Multimodal Capabilities: Built-in support for vision and other multimodal features
+- Future-Proof: Aligned with Amazon Bedrock's recommended best practices
+====
+
 link:https://www.anthropic.com/[Anthropic Claude] is a family of foundational AI models that can be used in a variety of applications.
 
 The Claude model has the following high level features

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-anthropic.adoc

@@ -1,5 +1,18 @@
 = Cohere Chat
 
+[NOTE]
+====
+Following the Bedrock recommendations, Spring AI is transitioning to using Amazon Bedrock's Converse API for all Chat conversation implementations in Spring AI. 
+While the existing `InvokeModel API` supports conversation applications, we strongly recommend adopting the xref:api/chat/bedrock-converse.adoc[Bedrock Converse API] for several key benefits:
+
+- Unified Interface: Write your code once and use it with any supported Amazon Bedrock model
+- Model Flexibility: Seamlessly switch between different conversation models without code changes
+- Extended Functionality: Support for model-specific parameters through dedicated structures
+- Tool Support: Native integration with function calling and tool usage capabilities
+- Multimodal Capabilities: Built-in support for vision and other multimodal features
+- Future-Proof: Aligned with Amazon Bedrock's recommended best practices
+====
+
 Provides Bedrock Cohere chat model.
 Integrate generative AI capabilities into essential apps and workflows that improve business outcomes.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-anthropic3.adoc

@@ -1,5 +1,18 @@
 = Jurassic-2 Chat
 
+[NOTE]
+====
+Following the Bedrock recommendations, Spring AI is transitioning to using Amazon Bedrock's Converse API for all Chat conversation implementations in Spring AI. 
+While the existing `InvokeModel API` supports conversation applications, we strongly recommend adopting the xref:api/chat/bedrock-converse.adoc[Bedrock Converse API] for several key benefits:
+
+- Unified Interface: Write your code once and use it with any supported Amazon Bedrock model
+- Model Flexibility: Seamlessly switch between different conversation models without code changes
+- Extended Functionality: Support for model-specific parameters through dedicated structures
+- Tool Support: Native integration with function calling and tool usage capabilities
+- Multimodal Capabilities: Built-in support for vision and other multimodal features
+- Future-Proof: Aligned with Amazon Bedrock's recommended best practices
+====
+
 https://aws.amazon.com/bedrock/jurassic/[AI21 Labs Jurassic on Amazon Bedrock
 ] Jurassic is AI21 Labs’ family of reliable FMs for the enterprise, powering sophisticated language generation tasks – such as question answering, text generation, search, and summarization – across thousands of live applications.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-cohere.adoc

@@ -1,5 +1,18 @@
 = Llama Chat
 
+[NOTE]
+====
+Following the Bedrock recommendations, Spring AI is transitioning to using Amazon Bedrock's Converse API for all Chat conversation implementations in Spring AI. 
+While the existing `InvokeModel API` supports conversation applications, we strongly recommend adopting the xref:api/chat/bedrock-converse.adoc[Bedrock Converse API] for several key benefits:
+
+- Unified Interface: Write your code once and use it with any supported Amazon Bedrock model
+- Model Flexibility: Seamlessly switch between different conversation models without code changes
+- Extended Functionality: Support for model-specific parameters through dedicated structures
+- Tool Support: Native integration with function calling and tool usage capabilities
+- Multimodal Capabilities: Built-in support for vision and other multimodal features
+- Future-Proof: Aligned with Amazon Bedrock's recommended best practices
+====
+
 https://ai.meta.com/llama/[Meta's Llama Chat] is part of the Llama collection of large language models.
 It excels in dialogue-based applications with a parameter scale ranging from 7 billion to 70 billion.
 Leveraging public datasets and over 1 million human annotations, Llama Chat offers context-aware dialogues.