= Llama Chat

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.

Trained on 2 trillion tokens from public data sources, Llama-Chat provides extensive knowledge for insightful conversations.
Rigorous testing, including over 1,000 hours of red-teaming and annotation, ensures both performance and safety, making it a reliable choice for AI-driven dialogues.

The https://aws.amazon.com/bedrock/llama/[AWS Llama Model Page] and https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html[Amazon Bedrock User Guide] contains detailed information on how to use the AWS hosted model.

== Prerequisites

Refer to the xref:api/bedrock.adoc[Spring AI documentation on Amazon Bedrock] for setting up API access.

=== Add Repositories and BOM

Spring AI artifacts are published in Spring Milestone and Snapshot repositories.   Refer to the xref:getting-started.adoc#repositories[Repositories] section to add these repositories to your build system.

To help with dependency management, Spring AI provides a BOM (bill of materials) to ensure that a consistent version of Spring AI is used throughout the entire project. Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build system.


== Auto-configuration

Add the `spring-ai-bedrock-ai-spring-boot-starter` dependency to your project's Maven `pom.xml` file:

[source,xml]
----
<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-bedrock-ai-spring-boot-starter</artifactId>
</dependency>
----

or to your Gradle `build.gradle` build file.

[source,gradle]
----
dependencies {
    implementation 'org.springframework.ai:spring-ai-bedrock-ai-spring-boot-starter'
}
----

TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.

=== Enable Llama Chat Support

By default the Bedrock Llama model is disabled.
To enable it set the `spring.ai.bedrock.llama.chat.enabled` property to `true`.
Exporting environment variable is one way to set this configuration property:

[source,shell]
----
export SPRING_AI_BEDROCK_LLAMA_CHAT_ENABLED=true
----

=== Chat Properties

The prefix `spring.ai.bedrock.aws` is the property prefix to configure the connection to AWS Bedrock.

[cols="3,3,3"]
|====
| Property | Description | Default

| spring.ai.bedrock.aws.region     | AWS region to use.  | us-east-1
| spring.ai.bedrock.aws.timeout    | AWS timeout to use. | 5m
| spring.ai.bedrock.aws.access-key | AWS access key.  | -
| spring.ai.bedrock.aws.secret-key | AWS secret key.  | -
|====


The prefix `spring.ai.bedrock.llama.chat` is the property prefix that configures the chat model implementation for Llama.

[cols="2,5,1"]
|====
| Property | Description | Default

| spring.ai.bedrock.llama.chat.enabled              | Enable or disable support for Llama  | false
| spring.ai.bedrock.llama.chat.model                | The model id to use (See Below) | meta.llama3-70b-instruct-v1:0
| spring.ai.bedrock.llama.chat.options.temperature          | Controls the randomness of the output. Values can range over [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied, while a value closer to 0.0 will typically result in less surprising responses from the model. This value specifies default to be used by the backend while making the call to the model. | 0.7
| spring.ai.bedrock.llama.chat.options.top-p                | The maximum cumulative probability of tokens to consider when sampling. The model uses combined Top-k and nucleus sampling. Nucleus sampling considers the smallest set of tokens whose probability sum is at least topP. | AWS Bedrock default
| spring.ai.bedrock.llama.chat.options.max-gen-len          | Specify the maximum number of tokens to use in the generated response. The model truncates the response once the generated text exceeds maxGenLen. | 300
|====

Look at https://github.com/spring-projects/spring-ai/blob/4ba9a3cd689b9fd3a3805f540debe398a079c6ef/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/api/LlamaChatBedrockApi.java#L164[LlamaChatBedrockApi#LlamaChatModel]  for other model IDs. The other value supported is `meta.llama2-13b-chat-v1`.
Model ID values can also be found in the https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html[AWS Bedrock documentation for base model IDs].

TIP: All properties prefixed with `spring.ai.bedrock.llama.chat.options` can be overridden at runtime by adding a request specific <<chat-options>> to the `Prompt` call.

== Runtime Options [[chat-options]]

The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatOptions.java[BedrockLlChatOptions.java] provides model configurations, such as temperature, topK, topP, etc.

On start-up, the default options can be configured with the `BedrockLlamaChatModel(api, options)` constructor or the `spring.ai.bedrock.llama.chat.options.*` properties.

At run-time you can override the default options by adding new, request specific, options to the `Prompt` call.
For example to override the default temperature for a specific request:

[source,java]
----
ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        BedrockLlamaChatOptions.builder()
            .withTemperature(0.4)
        .build()
    ));
----

TIP: In addition to the model specific https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatOptions.java[BedrockLlamaChatOptions] you can use a portable https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/prompt/ChatOptions.java[ChatOptions] instance, created with the https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/prompt/ChatOptionsBuilder.java[ChatOptionsBuilder#builder()].

== Sample Controller

https://start.spring.io/[Create] a new Spring Boot project and add the `spring-ai-bedrock-ai-spring-boot-starter` to your pom (or gradle) dependencies.

Add a `application.properties` file, under the `src/main/resources` directory, to enable and configure the Anthropic chat model:

[source]
----
spring.ai.bedrock.aws.region=eu-central-1
spring.ai.bedrock.aws.timeout=1000ms
spring.ai.bedrock.aws.access-key=${AWS_ACCESS_KEY_ID}
spring.ai.bedrock.aws.secret-key=${AWS_SECRET_ACCESS_KEY}

spring.ai.bedrock.llama.chat.enabled=true
spring.ai.bedrock.llama.chat.options.temperature=0.8
----

TIP: replace the `regions`, `access-key` and `secret-key` with your AWS credentials.

This will create a `BedrockLlamaChatModel` implementation that you can inject into your class.
Here is an example of a simple `@Controller` class that uses the chat model for text generations.

[source,java]
----
@RestController
public class ChatController {

    private final BedrockLlamaChatModel chatModel;

    @Autowired
    public ChatController(BedrockLlamaChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @GetMapping("/ai/generate")
    public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", chatModel.call(message));
    }

    @GetMapping("/ai/generateStream")
	public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        Prompt prompt = new Prompt(new UserMessage(message));
        return chatModel.stream(prompt);
    }
}
----

== Manual Configuration

The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatModel.java[BedrockLlamaChatModel] implements the `ChatModel` and `StreamingChatModel` and uses the <<low-level-api>> to connect to the Bedrock Anthropic service.

Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:

[source,xml]
----
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-bedrock</artifactId>
</dependency>
----

or to your Gradle `build.gradle` build file.

[source,gradle]
----
dependencies {
    implementation 'org.springframework.ai:spring-ai-bedrock'
}
----

TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.

Next, create an https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatModel.java[BedrockLlamaChatModel] and use it for text generations:

[source,java]
----
LlamaChatBedrockApi api = new LlamaChatBedrockApi(LlamaChatModel.LLAMA2_70B_CHAT_V1.id(),
	EnvironmentVariableCredentialsProvider.create(),
	Region.US_EAST_1.id(),
	new ObjectMapper(),
	Duration.ofMillis(1000L));

BedrockLlamaChatModel chatModel = new BedrockLlamaChatModel(api,
    BedrockLlamaChatOptions.builder()
        .withTemperature(0.5f)
        .withMaxGenLen(100)
        .withTopP(0.9f).build());

ChatResponse response = chatModel.call(
    new Prompt("Generate the names of 5 famous pirates."));

// Or with streaming responses
Flux<ChatResponse> response = chatModel.stream(
    new Prompt("Generate the names of 5 famous pirates."));
----

== Low-level LlamaChatBedrockApi Client [[low-level-api]]

https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/api/LlamaChatBedrockApi.java[LlamaChatBedrockApi] provides is lightweight Java client on top of AWS Bedrock https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-meta.html[Meta Llama 2 and Llama 2 Chat models].

Following class diagram illustrates the LlamaChatBedrockApi interface and building blocks:

image::bedrock/bedrock-llama-chat-api.jpg[LlamaChatBedrockApi Class Diagram]

The LlamaChatBedrockApi supports the `meta.llama3-8b-instruct-v1:0`,`meta.llama3-70b-instruct-v1:0`,`meta.llama2-13b-chat-v1` and `meta.llama2-70b-chat-v1` models for both synchronous (e.g. `chatCompletion()`) and streaming (e.g. `chatCompletionStream()`) responses.

Here is a simple snippet how to use the api programmatically:

[source,java]
----
LlamaChatBedrockApi llamaChatApi = new LlamaChatBedrockApi(
        LlamaChatModel.LLAMA3_70B_INSTRUCT_V1.id(),
        Region.US_EAST_1.id(),
        Duration.ofMillis(1000L));

LlamaChatRequest request = LlamaChatRequest.builder("Hello, my name is")
		.withTemperature(0.9f)
		.withTopP(0.9f)
		.withMaxGenLen(20)
		.build();

LlamaChatResponse response = llamaChatApi.chatCompletion(request);

// Streaming response
Flux<LlamaChatResponse> responseStream = llamaChatApi.chatCompletionStream(request);
List<LlamaChatResponse> responses = responseStream.collectList().block();
----

Follow the https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/api/LlamaChatBedrockApi.java[LlamaChatBedrockApi.java]'s JavaDoc for further information.


