= DeepSeek Chat

Spring AI supports the various AI language models from DeepSeek. You can interact with DeepSeek language models and create a multilingual conversational assistant based on DeepSeek models.

== Prerequisites

You will need to create an API key with DeepSeek to access DeepSeek language models.

Create an account at https://platform.deepseek.com/sign_up[DeepSeek registration page] and generate a token on the https://platform.deepseek.com/api_keys[API Keys page].

The Spring AI project defines a configuration property named `spring.ai.deepseek.api-key` that you should set to the value of the `API Key` obtained from the API Keys page.

You can set this configuration property in your `application.properties` file:

[source,properties]
----
spring.ai.deepseek.api-key=<your-deepseek-api-key>
----

For enhanced security when handling sensitive information like API keys, you can use Spring Expression Language (SpEL) to reference a custom environment variable:

[source,yaml]
----
# In application.yml
spring:
  ai:
    deepseek:
      api-key: ${DEEPSEEK_API_KEY}
----

[source,bash]
----
# In your environment or .env file
export DEEPSEEK_API_KEY=<your-deepseek-api-key>
----

You can also set this configuration programmatically in your application code:

[source,java]
----
// Retrieve API key from a secure source or environment variable
String apiKey = System.getenv("DEEPSEEK_API_KEY");
----

=== Add Repositories and BOM

Spring AI artifacts are published in the Spring Milestone and Snapshot repositories.
Refer to the xref:getting-started.adoc#artifact-repositories[Artifact 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 your 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

Spring AI provides Spring Boot auto-configuration for the DeepSeek Chat Model.
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-starter-model-deepseek</artifactId>
</dependency>
----

or to your Gradle `build.gradle` file.

[source,groovy]
----
dependencies {
    implementation 'org.springframework.ai:spring-ai-starter-model-deepseek'
}
----

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

=== Chat Properties

==== Retry Properties

The prefix `spring.ai.retry` is used as the property prefix that lets you configure the retry mechanism for the DeepSeek Chat model.

[cols="3,5,1"]
|====
| Property | Description | Default

| spring.ai.retry.max-attempts   | Maximum number of retry attempts. |  10
| spring.ai.retry.backoff.initial-interval | Initial sleep duration for the exponential backoff policy. |  2 sec.
| spring.ai.retry.backoff.multiplier | Backoff interval multiplier. |  5
| spring.ai.retry.backoff.max-interval | Maximum backoff duration. |  3 min.
| spring.ai.retry.on-client-errors | If false, throws a NonTransientAiException, and does not attempt a retry for `4xx` client error codes | false
| spring.ai.retry.exclude-on-http-codes | List of HTTP status codes that should not trigger a retry (e.g. to throw NonTransientAiException). | empty
| spring.ai.retry.on-http-codes | List of HTTP status codes that should trigger a retry (e.g. to throw TransientAiException). | empty
|====

==== Connection Properties

The prefix `spring.ai.deepseek` is used as the property prefix that lets you connect to DeepSeek.

[cols="3,5,1"]
|====
| Property | Description | Default

| spring.ai.deepseek.base-url   | The URL to connect to |  https://api.deepseek.com
| spring.ai.deepseek.api-key    | The API Key           |  -
|====

==== Configuration Properties

The prefix `spring.ai.deepseek.chat` is the property prefix that lets you configure the chat model implementation for DeepSeek.

[cols="3,5,1"]
|====
| Property | Description | Default

| spring.ai.deepseek.chat.enabled | Enables the DeepSeek chat model.  | true
| spring.ai.deepseek.chat.base-url | Optionally overrides the spring.ai.deepseek.base-url to provide a chat-specific URL | https://api.deepseek.com/
| spring.ai.deepseek.chat.api-key | Optionally overrides the spring.ai.deepseek.api-key to provide a chat-specific API key | -
| spring.ai.deepseek.chat.completions-path | The path to the chat completions endpoint | /chat/completions
| spring.ai.deepseek.chat.beta-prefix-path | The prefix path to the beta feature endpoint | /beta
| spring.ai.deepseek.chat.options.model | ID of the model to use. You can use either deepseek-reasoner or deepseek-chat. | deepseek-chat
| spring.ai.deepseek.chat.options.frequencyPenalty | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. | 0.0f
| spring.ai.deepseek.chat.options.maxTokens | The maximum number of tokens to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. | -
| spring.ai.deepseek.chat.options.presencePenalty | Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. |  0.0f
| spring.ai.deepseek.chat.options.stop | Up to 4 sequences where the API will stop generating further tokens. | -
| spring.ai.deepseek.chat.options.temperature | Which sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p, but not both. | 1.0F
| spring.ai.deepseek.chat.options.topP | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature, but not both. | 1.0F
| spring.ai.deepseek.chat.options.logprobs | Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of the message. | -
| spring.ai.deepseek.chat.options.topLogprobs | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used. | -
|====

NOTE: You can override the common `spring.ai.deepseek.base-url` and `spring.ai.deepseek.api-key` for the `ChatModel` implementations.
The `spring.ai.deepseek.chat.base-url` and `spring.ai.deepseek.chat.api-key` properties, if set, take precedence over the common properties.
This is useful if you want to use different DeepSeek accounts for different models and different model endpoints.

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

== Runtime Options [[chat-options]]

The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-deepseek/src/main/java/org/springframework/ai/deepseek/DeepSeekChatOptions.java[DeepSeekChatOptions.java] provides model configurations, such as the model to use, the temperature, the frequency penalty, etc.

On startup, the default options can be configured with the `DeepSeekChatModel(api, options)` constructor or the `spring.ai.deepseek.chat.options.*` properties.

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

[source,java]
----
ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates. Please provide the JSON response without any code block markers such as ```json```.",
        DeepSeekChatOptions.builder()
            .withModel(DeepSeekApi.ChatModel.DEEPSEEK_CHAT.getValue())
            .withTemperature(0.8f)
        .build()
    ));
----

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

== Sample Controller (Auto-configuration)

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

Add an `application.properties` file under the `src/main/resources` directory to enable and configure the DeepSeek Chat model:

[source,application.properties]
----
spring.ai.deepseek.api-key=YOUR_API_KEY
spring.ai.deepseek.chat.options.model=deepseek-chat
spring.ai.deepseek.chat.options.temperature=0.8
----

TIP: Replace the `api-key` with your DeepSeek credentials.

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

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

    private final DeepSeekChatModel chatModel;

    @Autowired
    public ChatController(DeepSeekChatModel 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) {
        var prompt = new Prompt(new UserMessage(message));
        return chatModel.stream(prompt);
    }
}
----

== Chat Prefix Completion
The chat prefix completion follows the Chat Completion API, where users provide an assistant's prefix message for the model to complete the rest of the message.

When using prefix completion, the user must ensure that the last message in the messages list is a DeepSeekAssistantMessage.

Below is a complete Java code example for chat prefix completion. In this example, we set the prefix message of the assistant to "```python\n" to force the model to output Python code, and set the stop parameter to ['```'] to prevent additional explanations from the model.

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

    private final DeepSeekChatModel chatModel;

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

    @GetMapping("/ai/generatePythonCode")
    public String generate(@RequestParam(value = "message", defaultValue = "Please write quick sort code") String message) {
		UserMessage userMessage = new UserMessage(message);
		Message assistantMessage = DeepSeekAssistantMessage.prefixAssistantMessage("```python\\n");
		Prompt prompt = new Prompt(List.of(userMessage, assistantMessage), ChatOptions.builder().stopSequences(List.of("```")).build());
		ChatResponse response = chatModel.call(prompt);
		return response.getResult().getOutput().getText();
    }
}
----

== Reasoning Model (deepseek-reasoner)
The `deepseek-reasoner` is a reasoning model developed by DeepSeek. Before delivering the final answer, the model first generates a Chain of Thought (CoT) to enhance the accuracy of its responses. Our API provides users with access to the CoT content generated by `deepseek-reasoner`, enabling them to view, display, and distill it.

You can use the `DeepSeekAssistantMessage` to get the CoT content generated by `deepseek-reasoner`.
[source,java]
----
public void deepSeekReasonerExample() {
    DeepSeekChatOptions promptOptions = DeepSeekChatOptions.builder()
            .model(DeepSeekApi.ChatModel.DEEPSEEK_REASONER.getValue())
            .build();
    Prompt prompt = new Prompt("9.11 and 9.8, which is greater?", promptOptions);
    ChatResponse response = chatModel.call(prompt);

    // Get the CoT content generated by deepseek-reasoner, only available when using deepseek-reasoner model
    DeepSeekAssistantMessage deepSeekAssistantMessage = (DeepSeekAssistantMessage) response.getResult().getOutput();
    String reasoningContent = deepSeekAssistantMessage.getReasoningContent();
    String text = deepSeekAssistantMessage.getText();
}
----
== Reasoning Model Multi-round Conversation
In each round of the conversation, the model outputs the CoT (reasoning_content) and the final answer (content). In the next round of the conversation, the CoT from previous rounds is not concatenated into the context, as illustrated in the following diagram:

image::deepseek_r1_multiround_example.png[Multimodal Test Image, align="center"]

Please note that if the reasoning_content field is included in the sequence of input messages, the API will return a 400 error. Therefore, you should remove the reasoning_content field from the API response before making the API request, as demonstrated in the API example.
[source,java]
----
public String deepSeekReasonerMultiRoundExample() {
    List<Message> messages = new ArrayList<>();
    messages.add(new UserMessage("9.11 and 9.8, which is greater?"));
    DeepSeekChatOptions promptOptions = DeepSeekChatOptions.builder()
            .model(DeepSeekApi.ChatModel.DEEPSEEK_REASONER.getValue())
            .build();

    Prompt prompt = new Prompt(messages, promptOptions);
    ChatResponse response = chatModel.call(prompt);

    DeepSeekAssistantMessage deepSeekAssistantMessage = (DeepSeekAssistantMessage) response.getResult().getOutput();
    String reasoningContent = deepSeekAssistantMessage.getReasoningContent();
    String text = deepSeekAssistantMessage.getText();

    messages.add(new AssistantMessage(Objects.requireNonNull(text)));
    messages.add(new UserMessage("How many Rs are there in the word 'strawberry'?"));
    Prompt prompt2 = new Prompt(messages, promptOptions);
    ChatResponse response2 = chatModel.call(prompt2);

    DeepSeekAssistantMessage deepSeekAssistantMessage2 = (DeepSeekAssistantMessage) response2.getResult().getOutput();
    String reasoningContent2 = deepSeekAssistantMessage2.getReasoningContent();
    return deepSeekAssistantMessage2.getText();
}
----

== Manual Configuration

The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-deepseek/src/main/java/org/springframework/ai/deepseek/DeepSeekChatModel.java[DeepSeekChatModel] implements the `ChatModel` and `StreamingChatModel` and uses the <<low-level-api>> to connect to the DeepSeek service.

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

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

or to your Gradle `build.gradle` file.

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

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 a `DeepSeekChatModel` and use it for text generation:

[source,java]
----
var deepSeekApi = new DeepSeekApi(System.getenv("DEEPSEEK_API_KEY"));

var chatModel = new DeepSeekChatModel(deepSeekApi, DeepSeekChatOptions.builder()
                .withModel(DeepSeekApi.ChatModel.DEEPSEEK_CHAT.getValue())
                .withTemperature(0.4f)
                .withMaxTokens(200)
                .build());

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

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

The `DeepSeekChatOptions` provides the configuration information for the chat requests.
The `DeepSeekChatOptions.Builder` is a fluent options builder.

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

The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-deepseek/src/main/java/org/springframework/ai/deepseek/api/DeepSeekApi.java[DeepSeekApi] is a lightweight Java client for link:https://platform.deepseek.com/api-docs/[DeepSeek API].

Here is a simple snippet showing how to use the API programmatically:

[source,java]
----
DeepSeekApi deepSeekApi =
    new DeepSeekApi(System.getenv("DEEPSEEK_API_KEY"));

ChatCompletionMessage chatCompletionMessage =
    new ChatCompletionMessage("Hello world", Role.USER);

// Sync request
ResponseEntity<ChatCompletion> response = deepSeekApi.chatCompletionEntity(
    new ChatCompletionRequest(List.of(chatCompletionMessage), DeepSeekApi.ChatModel.DEEPSEEK_CHAT.getValue(), 0.7, false));

// Streaming request
Flux<ChatCompletionChunk> streamResponse = deepSeekApi.chatCompletionStream(
    new ChatCompletionRequest(List.of(chatCompletionMessage), DeepSeekApi.ChatModel.DEEPSEEK_CHAT.getValue(), 0.7, true));
----

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

==== DeepSeekApi Samples
* The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-deepseek/src/test/java/org/springframework/ai/deepseek/api/DeepSeekApiIT.java[DeepSeekApiIT.java] test provides some general examples of how to use the lightweight library.
