Observability

Chat Model Observability

Certain implementations of ChatLanguageModel and StreamingChatLanguageModel (see "Observability" column") allow configuring ChatModelListener(s) to listen for events such as:

• Requests to the LLM
• Response from the LLM
• Errors

These events include various attributes, as described in the OpenTelemetry Generative AI Semantic Conventions, such as:

• Request:
  • Messages
  • Model
  • Temperature
  • Top P
  • Max Tokens
  • Tools
  • Response Format
  • etc
• Response:
  • Assistant Message
  • ID
  • Model
  • Token Usage
  • Finish Reason
  • etc

Here is an example of using ChatModelListener:

ChatModelListener listener = new ChatModelListener() {

    @Override
    public void onRequest(ChatModelRequestContext requestContext) {
        ChatRequest chatRequest = requestContext.chatRequest();

        List<ChatMessage> messages = chatRequest.messages();
        System.out.println(messages);

        ChatRequestParameters parameters = chatRequest.parameters();
        System.out.println(parameters.modelName());
        System.out.println(parameters.temperature());
        System.out.println(parameters.topP());
        System.out.println(parameters.topK());
        System.out.println(parameters.frequencyPenalty());
        System.out.println(parameters.presencePenalty());
        System.out.println(parameters.maxOutputTokens());
        System.out.println(parameters.stopSequences());
        System.out.println(parameters.toolSpecifications());
        System.out.println(parameters.toolChoice());
        System.out.println(parameters.responseFormat());

        if (parameters instanceof OpenAiChatRequestParameters openAiParameters) {
            System.out.println(openAiParameters.maxCompletionTokens());
            System.out.println(openAiParameters.logitBias());
            System.out.println(openAiParameters.parallelToolCalls());
            System.out.println(openAiParameters.seed());
            System.out.println(openAiParameters.user());
            System.out.println(openAiParameters.store());
            System.out.println(openAiParameters.metadata());
            System.out.println(openAiParameters.serviceTier());
            System.out.println(openAiParameters.reasoningEffort());
        }

        System.out.println(requestContext.modelProvider());

        Map<Object, Object> attributes = requestContext.attributes();
        attributes.put("my-attribute", "my-value");
    }

    @Override
    public void onResponse(ChatModelResponseContext responseContext) {
        ChatResponse chatResponse = responseContext.chatResponse();

        AiMessage aiMessage = chatResponse.aiMessage();
        System.out.println(aiMessage);

        ChatResponseMetadata metadata = chatResponse.metadata();
        System.out.println(metadata.id());
        System.out.println(metadata.modelName());
        System.out.println(metadata.finishReason());

        if (metadata instanceof OpenAiChatResponseMetadata openAiMetadata) {
            System.out.println(openAiMetadata.created());
            System.out.println(openAiMetadata.serviceTier());
            System.out.println(openAiMetadata.systemFingerprint());
        }

        TokenUsage tokenUsage = metadata.tokenUsage();
        System.out.println(tokenUsage.inputTokenCount());
        System.out.println(tokenUsage.outputTokenCount());
        System.out.println(tokenUsage.totalTokenCount());
        if (tokenUsage instanceof OpenAiTokenUsage openAiTokenUsage) {
            System.out.println(openAiTokenUsage.inputTokensDetails().cachedTokens());
            System.out.println(openAiTokenUsage.outputTokensDetails().reasoningTokens());
        }

        ChatRequest chatRequest = responseContext.chatRequest();
        System.out.println(chatRequest);

        System.out.println(responseContext.modelProvider());

        Map<Object, Object> attributes = responseContext.attributes();
        System.out.println(attributes.get("my-attribute"));
    }

    @Override
    public void onError(ChatModelErrorContext errorContext) {
        Throwable error = errorContext.error();
        error.printStackTrace();

        ChatRequest chatRequest = errorContext.chatRequest();
        System.out.println(chatRequest);

        System.out.println(errorContext.modelProvider());

        Map<Object, Object> attributes = errorContext.attributes();
        System.out.println(attributes.get("my-attribute"));
    }
};

ChatLanguageModel model = OpenAiChatModel.builder()
        .apiKey(System.getenv("OPENAI_API_KEY"))
        .modelName(GPT_4_O_MINI)
        .listeners(List.of(listener))
        .build();

model.chat("Tell me a joke about Java");

The attributes map allows passing information between the onRequest, onResponse, and onError methods of the same ChatModelListener, as well as between multiple ChatModelListeners.

How listeners work

• Listeners are specified as a List<ChatModelListener> and are called in the order of iteration.
• Listeners are called synchronously and in the same thread. See more details about the streaming case below. The second listener is not called until the first one returns.
• The ChatModelListener.onRequest() method is called right before calling the LLM provider API.
• The ChatModelListener.onRequest() method is called only once per request. If an error occurs while calling the LLM provider API and a retry happens, ChatModelListener.onRequest() will not be called for every retry.
• The ChatModelListener.onResponse() method is called only once, immediately after receiving a successful response from the LLM provider.
• The ChatModelListener.onError() method is called only once. If an error occurs while calling the LLM provider API and a retry happens, ChatModelListener.onError() will not be called for every retry.
• If an exception is thrown from one of the ChatModelListener methods, it will be logged at the WARN level. The execution of subsequent listeners will continue as usual.
• The ChatRequest provided via ChatModelRequestContext, ChatModelResponseContext, and ChatModelErrorContext is the final request, containing both the default ChatRequestParameters configured on the ChatLanguageModel and the request-specific ChatRequestParameters merged together.
• For StreamingChatLanguageModel, the ChatModelListener.onResponse() and ChatModelListener.onError() are called on a different thread than the ChatModelListener.onRequest(). The thread context is currently not propagated automatically, so you might want to use the attributes map to propagate any necessary data from ChatModelListener.onRequest() to ChatModelListener.onResponse() or ChatModelListener.onError().
• For StreamingChatLanguageModel, the ChatModelListener.onResponse() is called before the StreamingChatResponseHandler.onCompleteResponse() is called. The ChatModelListener.onError() is called before the StreamingChatResponseHandler.onError() is called.

Observability in Spring Boot Application

See more details here.