[[ChatModel]]
= Chat Model API

The Chat Model API offers developers the ability to integrate AI-powered chat completion capabilities into their applications. It leverages pre-trained language models, such as GPT (Generative Pre-trained Transformer), to generate human-like responses to user inputs in natural language.

The API typically works by sending a prompt or partial conversation to the AI model, which then generates a completion or continuation of the conversation based on its training data and understanding of natural language patterns. The completed response is then returned to the application, which can present it to the user or use it for further processing.

The `Spring AI Chat Model API` is designed to be a simple and portable interface for interacting with various xref:concepts.adoc#_models[AI Models], allowing developers to switch between different models with minimal code changes.
This design aligns with Spring's philosophy of modularity and interchangeability.

Also with the help of companion classes like `Prompt` for input encapsulation and `ChatResponse` for output handling, the Chat Model API unifies the communication with AI Models.
It manages the complexity of request preparation and response parsing, offering a direct and simplified API interaction.

== API Overview

This section provides a guide to the Spring AI Chat Model API interface and associated classes.

=== ChatModel

Here is the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/ChatModel.java[ChatModel] interface definition:

[source,java]
----
public interface ChatModel extends Model<Prompt, ChatResponse> {

	default String call(String message) {// implementation omitted
	}

    @Override
	ChatResponse call(Prompt prompt);
}

----

The `call` method with a `String` parameter simplifies initial use, avoiding the complexities of the more sophisticated `Prompt` and `ChatResponse` classes.
In real-world applications, it is more common to use the `call` method that takes a `Prompt` instance and returns an `ChatResponse`.

=== StreamingChatModel

Here is the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/StreamingChatModel.java[StreamingChatModel] interface definition:

[source,java]
----
public interface StreamingChatModel extends StreamingModel<Prompt, ChatResponse> {
    @Override
	Flux<ChatResponse> stream(Prompt prompt);
}
----

The `stream` method takes a `Prompt` request similar to `ChatModel` but it streams the responses using the reactive Flux API.

=== Prompt

The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/prompt/Prompt.java[Prompt] is a `ModelRequest` that encapsulates a list of https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/messages/Message.java[Message] objects and optional model request options.
The following listing shows a truncated version of the Prompt class, excluding constructors and other utility methods:

[source,java]
----
public class Prompt implements ModelRequest<List<Message>> {

    private final List<Message> messages;

    private ChatOptions modelOptions;

	@Override
	public ChatOptions getOptions() {..}

	@Override
	public List<Message> getInstructions() {...}

    // constructors and utility methods omitted
}
----

==== Message

The `Message` interface encapsulates a textual message, a collection of attributes as a `Map`, and a categorization known as `MessageType`. The interface is defined as follows:

[source,java]
----
public interface Message extends Node<String> {

   String getContent();

   List<Media> getMedia();

   MessageType getMessageType();
}
----


and the Node interface is

```java

public interface Node<T> {

    T getContent();

    Map<String, Object> getMetadata();
}
```

The `Message` interface has various implementations that correspond to the categories of messages that an AI model can process.
Some models, like OpenAI's chat completion endpoint, distinguish between message categories based on conversational roles, effectively mapped by the `MessageType`.

For instance, OpenAI recognizes message categories for distinct conversational roles such as `system`,`user`, `function` or `assistant`.

While the term `MessageType` might imply a specific message format, in this context it effectively designates the role a message plays in the dialogue.

For AI models that do not use specific roles, the `UserMessage` implementation acts as a standard category, typically representing user-generated inquiries or instructions.
To understand the practical application and the relationship between `Prompt` and `Message`, especially in the context of these roles or message categories, see the detailed explanations in the xref:api/prompt.adoc[Prompts] section.

==== Chat Options

Represents the options that can be passed to the AI model. The `ChatOptions` class is a subclass of `ModelOptions` and is used to define few portable options that can be passed to the AI model.
The `ChatOptions` class is defined as follows:


[source,java]
----
public interface ChatOptions extends ModelOptions {

	Float getTemperature();
	void setTemperature(Float temperature);
	Float getTopP();
	void setTopP(Float topP);
	Integer getTopK();
	void setTopK(Integer topK);
}
----

Additionally, every model specific ChatModel/StreamingChatModel implementation can have its own options that can be passed to the AI model. For example, the OpenAI Chat Completion model has its own options like `presencePenalty`, `frequencyPenalty`, `bestOf` etc.

This is a powerful feature that allows developers to use model specific options when starting the application and then override them with at runtime using the Prompt request:

image::chat-options-flow.jpg[align="center", width="800px"]

=== ChatResponse

The structure of the `ChatResponse` class is as follows:

[source,java]
----
public class ChatResponse implements ModelResponse<Generation> {

    private final ChatResponseMetadata chatResponseMetadata;
	private final List<Generation> generations;

	@Override
	public ChatResponseMetadata getMetadata() {...}

    @Override
	public List<Generation> getResults() {...}

    // other methods omitted
}
----

The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/ChatResponse.java[ChatResponse] class holds the AI Model's output, with each `Generation` instance containing one of potentially multiple outputs resulting from a single prompt.

The `ChatResponse` class also carries a `ChatResponseMetadata` metadata about the AI Model's response.

=== Generation

Finally, the https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/Generation.java[Generation] class extends from the `ModelResult` to represent the output assistant message response and related metadata about this result:

[source,java]
----
public class Generation implements ModelResult<AssistantMessage> {

	private AssistantMessage assistantMessage;
	private ChatGenerationMetadata chatGenerationMetadata;

	@Override
	public AssistantMessage getOutput() {...}

	@Override
	public ChatGenerationMetadata getMetadata() {...}

    // other methods omitted
}
----

== Available Implementations

The `ChatModel` and `StreamingChatModel` implementations are provided for the following Model providers:

image::spring-ai-chat-completions-clients.jpg[align="center", width="800px"]

* xref:api/chat/openai-chat.adoc[OpenAI Chat Completion] (streaming & function-calling support)
* xref:api/chat/azure-openai-chat.adoc[Microsoft Azure Open AI Chat Completion] (streaming & function-calling support)
* xref:api/chat/ollama-chat.adoc[Ollama Chat Completion]
* xref:api/chat/huggingface.adoc[HuggingFace Chat Completion] (no streaming support)
* xref:api/chat/vertexai-palm2-chat.adoc[Google Vertex AI PaLM2 Chat Completion] (no streaming support)
* xref:api/chat/vertexai-gemini-chat.adoc[Google Vertex AI Gemini Chat Completion] (streaming, multi-modality & function-calling support)
* xref:api/bedrock.adoc[Amazon Bedrock]
** xref:api/chat/bedrock/bedrock-cohere.adoc[Cohere Chat Completion]
** xref:api/chat/bedrock/bedrock-llama.adoc[Llama Chat Completion]
** xref:api/chat/bedrock/bedrock-titan.adoc[Titan Chat Completion]
** xref:api/chat/bedrock/bedrock-anthropic.adoc[Anthropic Chat Completion]
** xref:api/chat/bedrock/bedrock-jurassic2.adoc[Jurassic2 Chat Completion]
* xref:api/chat/mistralai-chat.adoc[Mistral AI Chat Completion] (streaming & function-calling support)
* xref:api/chat/anthropic-chat.adoc[Anthropic Chat Completion] (streaming)

== Chat Model API

The Spring AI Chat Model API is build on top of the Spring AI `Generic Model API` providing Chat specific abstractions and implementations. Following class diagram illustrates the main classes and interfaces of the Spring AI Chat Model API.

image::spring-ai-chat-api.jpg[align="center", width="900px"]

// == Best Practices
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// == Troubleshooting
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