OpenAI

note

If you are using Quarkus, please refer to the Quarkus LangChain4j documentation.

OpenAI Documentation

• OpenAI API Documentation
• OpenAI API Reference

Maven Dependency

Plain Java

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-open-ai</artifactId>
    <version>0.36.2</version>
</dependency>

Spring Boot

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
    <version>0.36.2</version>
</dependency>

API Key

To use OpenAI models, you will need an API key. You can create one here.

note

If you don't have your own OpenAI API key, don't worry. You can temporarily use demo key, which we provide for free for demonstration purposes:

String apiKey = "demo";

Be aware that when using the demo key, all requests to the OpenAI API go through our proxy, which injects the real key before forwarding your request to the OpenAI API. We do not collect or use your data in any way. The demo key has a quota, is restricted to the gpt-4o-mini model, and should only be used for demonstration purposes.

Creating OpenAiChatModel

Plain Java

ChatLanguageModel model = OpenAiChatModel.builder()
        .apiKey(System.getenv("OPENAI_API_KEY"))
        ...
        .build();

This will create an instance of OpenAiChatModel with default model parameters (e.g. gpt-3.5-turbo model name, 0.7 temperature, etc.). Default model parameters can be customized by providing values in the builder.

Spring Boot

Add to the application.properties:

langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
langchain4j.open-ai.chat-model.base-url=...
langchain4j.open-ai.chat-model.custom-headers=...
langchain4j.open-ai.chat-model.frequency-penalty=...
langchain4j.open-ai.chat-model.log-requests=...
langchain4j.open-ai.chat-model.log-responses=...
langchain4j.open-ai.chat-model.logit-bias=...
langchain4j.open-ai.chat-model.max-retries=...
langchain4j.open-ai.chat-model.max-completion-tokens=...
langchain4j.open-ai.chat-model.max-tokens=...
langchain4j.open-ai.chat-model.model-name=...
langchain4j.open-ai.chat-model.organization-id=...
langchain4j.open-ai.chat-model.parallel-tool-calls=...
langchain4j.open-ai.chat-model.presence-penalty=...
langchain4j.open-ai.chat-model.proxy.host=...
langchain4j.open-ai.chat-model.proxy.port=...
langchain4j.open-ai.chat-model.proxy.type=...
langchain4j.open-ai.chat-model.response-format=...
langchain4j.open-ai.chat-model.seed=...
langchain4j.open-ai.chat-model.stop=...
langchain4j.open-ai.chat-model.strict-schema=...
langchain4j.open-ai.chat-model.strict-tools=...
langchain4j.open-ai.chat-model.temperature=...
langchain4j.open-ai.chat-model.timeout=...
langchain4j.open-ai.chat-model.top-p=
langchain4j.open-ai.chat-model.user=...

See the description of some of the parameters above here.

This configuration will create an OpenAiChatModel bean, which can be either used by an AI Service or autowired where needed, for example:

@RestController
class ChatLanguageModelController {

    ChatLanguageModel chatLanguageModel;

    ChatLanguageModelController(ChatLanguageModel chatLanguageModel) {
        this.chatLanguageModel = chatLanguageModel;
    }

    @GetMapping("/model")
    public String model(@RequestParam(value = "message", defaultValue = "Hello") String message) {
        return chatLanguageModel.generate(message);
    }
}

Structured Outputs

The Structured Outputs feature is supported for both tools and response format.

See more info on Structured Outputs here.

Structured Outputs for Tools

To enable Structured Outputs feature for tools, set .strictTools(true) when building the model:

OpenAiChatModel.builder()
    ...
    .strictTools(true)
    .build(),

Please note that this will automatically make all tool parameters mandatory (required in json schema) and set additionalProperties=false for each object in json schema. This is due to the current OpenAI limitations.

Structured Outputs for Response Format

To enable the Structured Outputs feature for response formatting when using AI Services, set .responseFormat("json_schema") and .strictJsonSchema(true) when building the model:

OpenAiChatModel.builder()
    ...
    .responseFormat("json_schema")
    .strictJsonSchema(true)
    .build();

In this case AI Service will not append "You must answer strictly in the following JSON format: ..." string to the end of the last UserMessage, but will create a JSON schema from the given POJO and pass it to the LLM. Please note that this works only when method return type is a POJO. If the return type is something else, (like an enum or a List<String>), the old behaviour is applied (with "You must answer strictly ..."). Other return types will be supported in the near future.

Creating OpenAiStreamingChatModel

Plain Java

OpenAiStreamingChatModel model = OpenAiStreamingChatModel.builder()
        .apiKey(System.getenv("OPENAI_API_KEY"))
        ...
        .build();

Spring Boot

Add to the application.properties:

langchain4j.open-ai.streaming-chat-model.api-key=${OPENAI_API_KEY}
langchain4j.open-ai.streaming-chat-model.base-url=...
langchain4j.open-ai.streaming-chat-model.custom-headers=...
langchain4j.open-ai.streaming-chat-model.frequency-penalty=...
langchain4j.open-ai.streaming-chat-model.log-requests=...
langchain4j.open-ai.streaming-chat-model.log-responses=...
langchain4j.open-ai.streaming-chat-model.logit-bias=...
langchain4j.open-ai.streaming-chat-model.max-retries=...
langchain4j.open-ai.streaming-chat-model.max-completion-tokens=...
langchain4j.open-ai.streaming-chat-model.max-tokens=...
langchain4j.open-ai.streaming-chat-model.model-name=...
langchain4j.open-ai.streaming-chat-model.organization-id=...
langchain4j.open-ai.streaming-chat-model.parallel-tool-calls=...
langchain4j.open-ai.streaming-chat-model.presence-penalty=...
langchain4j.open-ai.streaming-chat-model.proxy.host=...
langchain4j.open-ai.streaming-chat-model.proxy.port=...
langchain4j.open-ai.streaming-chat-model.proxy.type=...
langchain4j.open-ai.streaming-chat-model.response-format=...
langchain4j.open-ai.streaming-chat-model.seed=...
langchain4j.open-ai.streaming-chat-model.stop=...
langchain4j.open-ai.streaming-chat-model.strict-schema=...
langchain4j.open-ai.streaming-chat-model.strict-tools=...
langchain4j.open-ai.streaming-chat-model.temperature=...
langchain4j.open-ai.streaming-chat-model.timeout=...
langchain4j.open-ai.streaming-chat-model.top-p=...
langchain4j.open-ai.streaming-chat-model.user=...

Creating OpenAiModerationModel

Plain Java

ModerationModel model = OpenAiModerationModel.builder()
        .apiKey(System.getenv("OPENAI_API_KEY"))
        ...
        .build();

Spring Boot

Add to the application.properties:

langchain4j.open-ai.moderation-model.api-key=${OPENAI_API_KEY}
langchain4j.open-ai.moderation-model.base-url=...
langchain4j.open-ai.moderation-model.custom-headers=...
langchain4j.open-ai.moderation-model.log-requests=...
langchain4j.open-ai.moderation-model.log-responses=...
langchain4j.open-ai.moderation-model.max-retries=...
langchain4j.open-ai.moderation-model.model-name=...
langchain4j.open-ai.moderation-model.organization-id=...
langchain4j.open-ai.moderation-model.proxy.host=...
langchain4j.open-ai.moderation-model.proxy.port=...
langchain4j.open-ai.moderation-model.proxy.type=...
langchain4j.open-ai.moderation-model.timeout=...

Creating OpenAiTokenizer

Plain Java

Tokenizer tokenizer = new OpenAiTokenizer();
// or
Tokenizer tokenizer = new OpenAiTokenizer("gpt-4o");

Spring Boot

The OpenAiTokenizer bean is created automatically by the Spring Boot starter.

Examples

• OpenAI Examples