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(improvement)(common) Fix the issue with querying large models using the GLM interface. (#1226)
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lexluo09
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package dev.langchain4j.model.openai;
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import dev.ai4j.openai4j.OpenAiClient;
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import dev.ai4j.openai4j.OpenAiHttpException;
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import dev.ai4j.openai4j.chat.ChatCompletionRequest;
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import dev.ai4j.openai4j.chat.ChatCompletionResponse;
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import dev.langchain4j.agent.tool.ToolSpecification;
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import dev.langchain4j.data.message.AiMessage;
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import dev.langchain4j.data.message.ChatMessage;
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import dev.langchain4j.model.Tokenizer;
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import dev.langchain4j.model.chat.ChatLanguageModel;
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import dev.langchain4j.model.chat.TokenCountEstimator;
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import dev.langchain4j.model.chat.listener.ChatLanguageModelRequest;
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import dev.langchain4j.model.chat.listener.ChatLanguageModelResponse;
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import dev.langchain4j.model.listener.ModelListener;
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import dev.langchain4j.model.openai.spi.OpenAiChatModelBuilderFactory;
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import dev.langchain4j.model.output.Response;
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import lombok.Builder;
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import lombok.extern.slf4j.Slf4j;
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import java.net.Proxy;
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import java.time.Duration;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Map;
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import static dev.langchain4j.internal.RetryUtils.withRetry;
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import static dev.langchain4j.internal.Utils.getOrDefault;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.DEFAULT_USER_AGENT;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.OPENAI_DEMO_API_KEY;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.OPENAI_DEMO_URL;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.OPENAI_URL;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.aiMessageFrom;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.createModelListenerRequest;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.createModelListenerResponse;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.finishReasonFrom;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.toOpenAiMessages;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.toTools;
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import static dev.langchain4j.model.openai.InternalOpenAiHelper.tokenUsageFrom;
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import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO;
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import static dev.langchain4j.spi.ServiceHelper.loadFactories;
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import static java.time.Duration.ofSeconds;
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import static java.util.Collections.emptyList;
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import static java.util.Collections.singletonList;
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/**
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* Represents an OpenAI language model with a chat completion interface, such as gpt-3.5-turbo and gpt-4.
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* You can find description of parameters <a href="https://platform.openai.com/docs/api-reference/chat/create">here</a>.
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*/
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@Slf4j
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public class OpenAiChatModel implements ChatLanguageModel, TokenCountEstimator {
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public static final String ZHIPU = "bigmodel";
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private final OpenAiClient client;
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private final String baseUrl;
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private final String modelName;
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private final Double temperature;
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private final Double topP;
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private final List<String> stop;
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private final Integer maxTokens;
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private final Double presencePenalty;
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private final Double frequencyPenalty;
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private final Map<String, Integer> logitBias;
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private final String responseFormat;
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private final Integer seed;
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private final String user;
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private final Integer maxRetries;
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private final Tokenizer tokenizer;
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private final List<ModelListener<ChatLanguageModelRequest, ChatLanguageModelResponse>> listeners;
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@Builder
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public OpenAiChatModel(String baseUrl,
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String apiKey,
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String organizationId,
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String modelName,
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Double temperature,
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Double topP,
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List<String> stop,
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Integer maxTokens,
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Double presencePenalty,
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Double frequencyPenalty,
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Map<String, Integer> logitBias,
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String responseFormat,
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Integer seed,
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String user,
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Duration timeout,
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Integer maxRetries,
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Proxy proxy,
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Boolean logRequests,
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Boolean logResponses,
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Tokenizer tokenizer,
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Map<String, String> customHeaders,
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List<ModelListener<ChatLanguageModelRequest, ChatLanguageModelResponse>> listeners) {
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baseUrl = getOrDefault(baseUrl, OPENAI_URL);
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if (OPENAI_DEMO_API_KEY.equals(apiKey)) {
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baseUrl = OPENAI_DEMO_URL;
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}
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this.baseUrl = baseUrl;
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timeout = getOrDefault(timeout, ofSeconds(60));
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this.client = OpenAiClient.builder()
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.openAiApiKey(apiKey)
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.baseUrl(baseUrl)
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.organizationId(organizationId)
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.callTimeout(timeout)
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.connectTimeout(timeout)
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.readTimeout(timeout)
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.writeTimeout(timeout)
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.proxy(proxy)
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.logRequests(logRequests)
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.logResponses(logResponses)
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.userAgent(DEFAULT_USER_AGENT)
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.customHeaders(customHeaders)
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.build();
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this.modelName = getOrDefault(modelName, GPT_3_5_TURBO);
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this.temperature = getOrDefault(temperature, 0.7);
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this.topP = topP;
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this.stop = stop;
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this.maxTokens = maxTokens;
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this.presencePenalty = presencePenalty;
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this.frequencyPenalty = frequencyPenalty;
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this.logitBias = logitBias;
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this.responseFormat = responseFormat;
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this.seed = seed;
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this.user = user;
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this.maxRetries = getOrDefault(maxRetries, 3);
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this.tokenizer = getOrDefault(tokenizer, OpenAiTokenizer::new);
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this.listeners = listeners == null ? emptyList() : new ArrayList<>(listeners);
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}
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public String modelName() {
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return modelName;
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}
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@Override
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public Response<AiMessage> generate(List<ChatMessage> messages) {
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return generate(messages, null, null);
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}
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@Override
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public Response<AiMessage> generate(List<ChatMessage> messages, List<ToolSpecification> toolSpecifications) {
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return generate(messages, toolSpecifications, null);
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}
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@Override
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public Response<AiMessage> generate(List<ChatMessage> messages, ToolSpecification toolSpecification) {
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return generate(messages, singletonList(toolSpecification), toolSpecification);
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}
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private Response<AiMessage> generate(List<ChatMessage> messages,
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List<ToolSpecification> toolSpecifications,
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ToolSpecification toolThatMustBeExecuted
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) {
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ChatCompletionRequest.Builder requestBuilder = ChatCompletionRequest.builder()
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.model(modelName)
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.messages(toOpenAiMessages(messages))
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.topP(topP)
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.stop(stop)
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.maxTokens(maxTokens)
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.presencePenalty(presencePenalty)
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.frequencyPenalty(frequencyPenalty)
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.logitBias(logitBias)
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.responseFormat(responseFormat)
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.seed(seed)
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.user(user);
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if (!(baseUrl.contains(ZHIPU))) {
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requestBuilder.temperature(temperature);
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}
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if (toolSpecifications != null && !toolSpecifications.isEmpty()) {
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requestBuilder.tools(toTools(toolSpecifications));
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}
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if (toolThatMustBeExecuted != null) {
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requestBuilder.toolChoice(toolThatMustBeExecuted.name());
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}
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ChatCompletionRequest request = requestBuilder.build();
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ChatLanguageModelRequest modelListenerRequest =
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createModelListenerRequest(request, messages, toolSpecifications);
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listeners.forEach(listener -> {
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try {
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listener.onRequest(modelListenerRequest);
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} catch (Exception e) {
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log.warn("Exception while calling model listener", e);
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}
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});
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try {
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ChatCompletionResponse chatCompletionResponse =
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withRetry(() -> client.chatCompletion(request).execute(), maxRetries);
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Response<AiMessage> response = Response.from(
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aiMessageFrom(chatCompletionResponse),
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tokenUsageFrom(chatCompletionResponse.usage()),
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finishReasonFrom(chatCompletionResponse.choices().get(0).finishReason())
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);
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ChatLanguageModelResponse modelListenerResponse = createModelListenerResponse(
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chatCompletionResponse.id(),
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chatCompletionResponse.model(),
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response
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);
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listeners.forEach(listener -> {
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try {
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listener.onResponse(modelListenerResponse, modelListenerRequest);
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} catch (Exception e) {
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log.warn("Exception while calling model listener", e);
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}
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});
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return response;
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} catch (RuntimeException e) {
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Throwable error;
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if (e.getCause() instanceof OpenAiHttpException) {
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error = e.getCause();
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} else {
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error = e;
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}
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listeners.forEach(listener -> {
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try {
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listener.onError(error, null, modelListenerRequest);
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} catch (Exception e2) {
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log.warn("Exception while calling model listener", e2);
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}
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});
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throw e;
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}
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}
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@Override
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public int estimateTokenCount(List<ChatMessage> messages) {
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return tokenizer.estimateTokenCountInMessages(messages);
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}
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public static OpenAiChatModel withApiKey(String apiKey) {
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return builder().apiKey(apiKey).build();
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}
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public static OpenAiChatModelBuilder builder() {
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for (OpenAiChatModelBuilderFactory factory : loadFactories(OpenAiChatModelBuilderFactory.class)) {
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return factory.get();
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}
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return new OpenAiChatModelBuilder();
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}
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public static class OpenAiChatModelBuilder {
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public OpenAiChatModelBuilder() {
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// This is public so it can be extended
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// By default with Lombok it becomes package private
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}
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public OpenAiChatModelBuilder modelName(String modelName) {
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this.modelName = modelName;
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return this;
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}
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}
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}
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