Skip to main content

watsonx.ai

Maven Dependency​

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-watsonx</artifactId>
    <version>1.8.0-beta15</version>
</dependency>

WatsonxChatModel​

The WatsonxChatModel class allows you to create an instance of the ChatModel interface fully encapsulated within LangChain4j.
To create an instance, you must specify the mandatory parameters:

  • url(...) – IBM Cloud endpoint URL (as String, URI, or CloudRegion);
  • apiKey(...) – IBM Cloud IAM API key;
  • projectId(...) – IBM Cloud Project ID (or use spaceId(...));
  • modelName(...) – Foundation model ID for inference;

Example​

import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.watsonx.WatsonxChatModel;
import com.ibm.watsonx.ai.CloudRegion;

ChatModel chatModel = WatsonxChatModel.builder()
    .url(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("ibm/granite-3-3-8b-instruct")
    .temperature(0.7)
    .maxOutputTokens(0)
    .build();

String answer = chatModel.chat("Hello from watsonx.ai");
System.out.println(answer);

How to create an IBM Cloud API Key​

You can create an API key at https://cloud.ibm.com/iam/apikeys by clicking Create +.

How to find your Project ID​

  1. Visit https://dataplatform.cloud.ibm.com/projects/?context=wx
  2. Open your project
  3. Go to the Manage tab
  4. Copy the Project ID from the Details section

How to find the model name​

Available foundation models are listed here.

WatsonxStreamingChatModel​

The WatsonxStreamingChatModel provides streaming support for IBM watsonx.ai within LangChain4j. It’s useful when you want to process tokens as they are generated, ideal for real-time applications such as chat UIs or long text generation.

Streaming uses the same configuration structure and parameters as the non-streaming WatsonxChatModel. The main difference is that responses are delivered incrementally through a handler interface.

Example​

import dev.langchain4j.model.chat.StreamingChatModel;
import dev.langchain4j.model.chat.StreamingChatResponseHandler;
import dev.langchain4j.model.chat.ChatResponse;
import dev.langchain4j.model.watsonx.WatsonxStreamingChatModel;
import com.ibm.watsonx.ai.CloudRegion;

StreamingChatModel model = WatsonxStreamingChatModel.builder()
    .url(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("ibm/granite-3-3-8b-instruct")
    .maxOutputTokens(0)
    .build();

model.chat("What is the capital of Italy?", new StreamingChatResponseHandler() {

    @Override
    public void onPartialResponse(String partialResponse) {
        System.out.println("Partial: " + partialResponse);
    }

    @Override
    public void onCompleteResponse(ChatResponse completeResponse) {
        System.out.println("Complete: " + completeResponse);
    }

    @Override
    public void onError(Throwable error) {
        error.printStackTrace();
    }
});

Tool Integration​

Both WatsonxChatModel and WatsonxStreamingChatModel support LangChain4j Tools, allowing the model to call Java methods annotated with @Tool.

Here’s an example using the synchronous model (WatsonxChatModel), but the same approach applies to the streaming variant.

static class Tools {

    @Tool
    LocalDate currentDate() {
        return LocalDate.now();
    }

    @Tool
    LocalTime currentTime() {
        return LocalTime.now();
    }
}

interface AiService {
    String chat(String userMessage);
}

ChatModel chatModel = WatsonxChatModel.builder()
    .url(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("mistralai/mistral-small-3-1-24b-instruct-2503")
    .maxOutputTokens(0)
    .build();

AiService aiService = AiServices.builder(AiService.class)
        .chatModel(model)
        .tools(new Tools())
        .build();

String answer = aiService.chat("What is the date today?");
System.out.println(answer);

NOTE: Ensure your selected model supports tool use.

Enabling Thinking / Reasoning Output​

Some foundation models can include internal reasoning (also referred to as thinking) steps as part of their responses.
Depending on the model, this reasoning may be embedded in the same text as the final response, or returned separately in a dedicated field from watsonx.ai.

To correctly enable and capture this behavior, you must configure the thinking(...) builder method according to the model’s output format.
This ensures that LangChain4j can automatically extract the reasoning and response content from the model output.

There are two main configuration modes:

  • ExtractionTags → for models that return reasoning and response in the same text block (e.g ibm/granite-3-3-8b-instruct).
  • ThinkingEffort → for models that already separate reasoning and response automatically (e.g openai/gpt-oss-120b).

Models that return reasoning and response together​

Use ExtractionTags when the model outputs reasoning and response in the same text string.
The tags define XML-like markers used to separate the reasoning from the final response.

Example tags:

  • Reasoning tag: <think> — contains the model's internal reasoning.
  • Response tag: <response> — contains the user-facing answer.

Behavior​

  • If both tags are specified, they are used directly to extract reasoning and response segments.
  • If only the reasoning tag is specified, everything outside that tag is considered the response.

Example for ibm/granite-3-3-8b-instruct​

ChatModel chatModel = WatsonxChatModel.builder()
    .url(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("ibm/granite-3-3-8b-instruct")
    .maxOutputTokens(0)
    .thinking(ExtractionTags.of("think", "response"))
    .build();

ChatResponse chatResponse = chatModel.chat(
    UserMessage.userMessage("Why is the sky blue?")
);

AiMessage aiMessage = chatResponse.aiMessage();

System.out.println(aiMessage.thinking());
System.out.println(aiMessage.text());

Models that return reasoning and response separately.​

For models that already return reasoning and response as separate fields, use the ThinkingEffort to control how much reasoning the model applies during generation. Alternatively, enable it using the boolean flag.

Example for openai/gpt-oss-120b​

ChatModel chatModel = WatsonxChatModel.builder()
    .url(CloudRegion.DALLAS)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("openai/gpt-oss-120b")
    .thinking(ThinkingEffort.HIGH)
    .build();

or

ChatModel chatModel = WatsonxChatModel.builder()
    .url(CloudRegion.DALLAS)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("openai/gpt-oss-120b")
    .thinking(true)
    .build();

Streaming Example​

StreamingChatModel model = WatsonxStreamingChatModel.builder()
    .url(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("ibm/granite-3-3-8b-instruct")
    .thinking(ExtractionTags.of("think", "response"))
    .build();

List<ChatMessage> messages = List.of(
    UserMessage.userMessage("Why is the sky blue?")
);

ChatRequest chatRequest = ChatRequest.builder()
    .messages(messages)
    .build();

model.chat(chatRequest, new StreamingChatResponseHandler() {

    @Override
    public void onPartialResponse(String partialResponse) {
        ...
    }

    @Override
    public void onPartialThinking(PartialThinking partialThinking) {
        ...
    }
});

Notes:

  • Ensure that the selected model supports reasoning output.
  • Use ExtractionTags for models that embed reasoning and response in a single text string.
  • Use ThinkingEffort or thinking(true) for models that already separate reasoning and response automatically.

WatsonxEmbeddingModel​

The WatsonxEmbeddingModel enables you to generate embeddings using IBM watsonx.ai and integrate them with LangChain4j's vector-based operations such as search, retrieval-augmented generation (RAG), and similarity comparison.

It implements the LangChain4j EmbeddingModel interface.

EmbeddingModel embeddingModel = WatsonxEmbeddingModel.builder()
    .url(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("ibm/granite-embedding-278m-multilingual")
    .build();

System.out.println(embeddingModel.embed("Hello from watsonx.ai"));

🔗 View available embedding model IDs

WatsonxScoringModel​

The WatsonxScoringModel provides a LangChain4j-compatible implementation of a ScoringModel using IBM watsonx.ai Rerank (cross-encoder) models.

It is particularly useful for ranking a list of documents (or text segments) based on their relevance to a user query.

Example: LangChain4j Integration​

ScoringModel scoringModel = WatsonxScoringModel.builder()
    .url(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("cross-encoder/ms-marco-minilm-l-12-v2")
    .build();

ScoringModel model = new WatsonxScoringModel(rerankService);

var scores = scoringModel.scoreAll(
    List.of(
        TextSegment.from("Example_1"),
        TextSegment.from("Example_2")
    ),
    "Hello from watsonx.ai"
);

System.out.println(scores);

🔗 View available rerank model IDs

Quarkus​

See more details here.

Examples​