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watsonx.ai

Maven Dependency​

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-watsonx</artifactId>
    <version>1.11.0-beta19</version>
</dependency>

Authentication​

Watsonx.ai supports authentication via the Authenticator interface.

This allows to use different authentication mechanisms depending on your deployment:

  • IBMCloudAuthenticator – authenticates with IBM Cloud using an API key. This is the simplest approach and is used when you provide the apiKey(...) builder method.
  • CP4DAuthenticator – authenticates with Cloud Pak for Data deployments.
  • Custom authenticators – any implementation of the Authenticator interface can be used.

The WatsonxChatModel, WatsonxStreamingChatModel, and other service builders accept either a shortcut via .apiKey(...) or a full Authenticator instance via .authenticator(...).

Example​

import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.watsonx.WatsonxChatModel;
import com.ibm.watsonx.ai.core.auth.cp4d.CP4DAuthenticator;
import com.ibm.watsonx.ai.core.auth.cp4d.AuthMode;
import com.ibm.watsonx.ai.CloudRegion;

WatsonxChatModel.builder()
    .baseUrl(CloudRegion.FRANKFURT)
    .apiKey("your-api-key") // Simple IBM Cloud authentication
    .projectId("your-project-id")
    .modelName("ibm/granite-4-h-small")
    .build();

WatsonxChatModel.builder()
    .baseUrl("https://my-instance-url")
    .authenticator( // For Cloud Pak for Data deployments
        CP4DAuthenticator.builder()
            .baseUrl("https://my-instance-url")
            .username("username")
            .apiKey("api-key")
            .authMode(AuthMode.LEGACY)
            .build()
    )
    .projectId("my-project-id")
    .modelName("ibm/granite-4-h-small")
    .build();

Custom HttpClient and SSL Configuration​

Using a custom HttpClient​

All services and authenticators support a custom HttpClient instance through the builder pattern. This is particularly useful for Cloud Pak for Data environments where you may need to configure custom TLS/SSL settings, proxy configuration, or other HTTP client properties.

HttpClient httpClient = HttpClient.newBuilder()
    .sslContext(createCustomSSLContext())
    .executor(ExecutorProvider.ioExecutor())
    .build();

EmbeddingModel embeddingModel = WatsonxEmbeddingModel.builder()
    .baseUrl("https://my-instance-url")
    .modelName("ibm/granite-embedding-278m-multilingual")
    .projectId("project-id")
    .httpClient(httpClient) // Custom HttpClient
    .authenticator(
        CP4DAuthenticator.builder()
            .baseUrl("https://my-instance-url")
            .username("username")
            .apiKey("api-key")
            .httpClient(httpClient) // Custom HttpClient
            .build()
    )
    .build();

Note: When using a custom HttpClient with Cloud Pak for Data, make sure to set it on both the service builder and the authenticator builder to ensure consistent HTTP behavior across all requests.

Disabling SSL verification​

If you only need to disable SSL certificate verification, you can use the verifySsl(false) option instead of providing a custom HttpClient:

EmbeddingModel embeddingModel = WatsonxEmbeddingModel.builder()
    .baseUrl("https://my-instance-url")
    .modelName("ibm/granite-embedding-278m-multilingual")
    .projectId("project-id")
    .verifySsl(false) // Disable SSL verification
    .authenticator(
        CP4DAuthenticator.builder()
            .baseUrl("https://my-instance-url")
            .username("username")
            .apiKey("api-key")
            .verifySsl(false) // Disable SSL verification
            .build()
    )
    .build();

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

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:

  • baseUrl(...) – 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;

You can authenticate using either .apiKey(...) or a full Authenticator instance via .authenticator(...).

Example​

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

ChatModel chatModel = WatsonxChatModel.builder()
    .baseUrl(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("ibm/granite-4-h-small")
    .temperature(0.7)
    .maxOutputTokens(0)
    .build();

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

🔗 View available models

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()
    .baseUrl(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("ibm/granite-4-h-small")
    .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();
    }
});

🔗 View available models

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()
    .baseUrl(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()
    .baseUrl(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()
    .baseUrl(CloudRegion.DALLAS)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .modelName("openai/gpt-oss-120b")
    .thinking(ThinkingEffort.HIGH)
    .build();

or

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

Streaming Example​

StreamingChatModel model = WatsonxStreamingChatModel.builder()
    .baseUrl(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.

WatsonxModelCatalog​

The WatsonxModelCatalog provides a programmatic way to discover and list all available foundation models on IBM watsonx.ai. It implements the LangChain4j ModelCatalog interface, allowing you to retrieve detailed information about each model.

Example​

import dev.langchain4j.model.catalog.ModelCatalog;
import dev.langchain4j.model.catalog.ModelDescription;
import dev.langchain4j.model.watsonx.WatsonxModelCatalog;
import com.ibm.watsonx.ai.CloudRegion;

ModelCatalog modelCatalog = WatsonxModelCatalog.builder()
    .baseUrl(CloudRegion.FRANKFURT)
    .build();

var models = modelCatalog.listModels();

WatsonxModerationModel​

The WatsonxModerationModel provides a LangChain4j implementation of the ModerationModel interface using IBM watsonx.ai.
It allows to automatically detect and flag sensitive, unsafe, or policy-violating content in text through detectors.

One or multiple detectors can be used to identify different types of content, such as:

  • Pii – Detects Personally Identifiable Information (e.g., emails, phone numbers)
  • Hap – Detects hate, abuse, or profanity
  • GraniteGuardian – Detects risky or harmful language

Example​

ModerationModel model = WatsonxModerationModel.builder()
    .baseUrl(CloudRegion.FRANKFURT)
    .apiKey("your-api-key")
    .projectId("your-project-id")
    .detectors(Hap.ofDefaults(), GraniteGuardian.ofDefaults())
    .build();

Response<Moderation> response = model.moderate("...");

Metadata​

Each moderation response includes a metadata map that provides additional context about the detection.

KeyDescription
detectionThe detected label or category assigned by the detector
detection_typeThe type of detector that triggered the flag
startThe starting character index of the detected segment
endThe ending character index of the detected segment
scoreThe confidence score of the detection

These metadata values are available via Response.metadata():

Map<String, Object> metadata = response.metadata();
System.out.println("Detection type: " + metadata.get("detection_type"));
System.out.println("Score: " + metadata.get("score"));

Configuration via Environment Variables​

The LangChain4j watsonx integration allows customization of internal HTTP behavior through environment variables.
These settings are optional and sensible defaults are used when variables are not explicitly defined.

Retry Configuration​

HTTP requests are automatically retried in case of transient failures or expired authentication tokens.
Retry behavior can be customized using the following environment variables:

Environment VariableDescriptionDefault
WATSONX_RETRY_TOKEN_EXPIRED_MAX_RETRIESMaximum number of retries when an authentication token has expired (HTTP 401 / 403)1
WATSONX_RETRY_STATUS_CODES_MAX_RETRIESMaximum number of retries for transient HTTP status codes (429, 503, 504, 520)10
WATSONX_RETRY_STATUS_CODES_BACKOFF_ENABLEDEnables exponential backoff for transient retriestrue
WATSONX_RETRY_STATUS_CODES_INITIAL_INTERVAL_MSInitial retry interval in milliseconds (used as base for exponential backoff)20

HTTP IO Executor Configuration​

Streaming responses and HTTP response processing are handled by an internal IO executor.
By default, a single-threaded executor is used to ensure sequential processing of streaming events.

This behavior can be customized using the following environment variable:

Environment VariableDescriptionDefault
WATSONX_IO_EXECUTOR_THREADSNumber of threads used for HTTP IO and SSE stream parsing1

Quarkus​

See more details here.

Examples​