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Add documentation how to use the OpenAI client to access DeepSeek
Signed-off-by: Alexandros Pappas <[email protected]>
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‎models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/proxy/DeepSeekWithOpenAiChatModelIT.java‎

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/*
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* Copyright 2024-2025 the original author or authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.springframework.ai.openai.chat.proxy;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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import java.util.Map;
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import java.util.stream.Collectors;
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import org.junit.jupiter.api.Disabled;
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import org.junit.jupiter.api.Test;
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import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
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import org.junit.jupiter.params.ParameterizedTest;
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import org.junit.jupiter.params.provider.ValueSource;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import reactor.core.publisher.Flux;
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import org.springframework.ai.chat.client.ChatClient;
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import org.springframework.ai.chat.messages.AssistantMessage;
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import org.springframework.ai.chat.messages.Message;
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import org.springframework.ai.chat.messages.UserMessage;
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import org.springframework.ai.chat.model.ChatResponse;
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import org.springframework.ai.chat.model.Generation;
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import org.springframework.ai.chat.prompt.Prompt;
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import org.springframework.ai.chat.prompt.PromptTemplate;
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import org.springframework.ai.chat.prompt.SystemPromptTemplate;
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import org.springframework.ai.converter.BeanOutputConverter;
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import org.springframework.ai.converter.ListOutputConverter;
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import org.springframework.ai.converter.MapOutputConverter;
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import org.springframework.ai.model.function.FunctionCallback;
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import org.springframework.ai.openai.OpenAiChatModel;
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import org.springframework.ai.openai.OpenAiChatOptions;
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import org.springframework.ai.openai.api.OpenAiApi;
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import org.springframework.ai.openai.api.tool.MockWeatherService;
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import org.springframework.ai.openai.chat.ActorsFilms;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.beans.factory.annotation.Value;
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import org.springframework.boot.SpringBootConfiguration;
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import org.springframework.boot.test.context.SpringBootTest;
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import org.springframework.context.annotation.Bean;
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import org.springframework.core.convert.support.DefaultConversionService;
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import org.springframework.core.io.Resource;
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import static org.assertj.core.api.Assertions.assertThat;
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/**
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* @author Alexandros Pappas
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*
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* The DeepSeek API uses an API format compatible with OpenAI, allowing developers to
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* easily integrate it into existing systems that use the OpenAI SDK5.
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*
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* For more information on DeepSeek behavior, refer to its API documentation:
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* <a href="https://api-docs.deepseek.com/">DeepSeek API</a>
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*/
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@SpringBootTest(classes = DeepSeekWithOpenAiChatModelIT.Config.class)
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@EnabledIfEnvironmentVariable(named = "DEEPSEEK_API_KEY", matches = ".+")
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@Disabled("Requires DeepSeek credits")
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class DeepSeekWithOpenAiChatModelIT {
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private static final Logger logger = LoggerFactory.getLogger(DeepSeekWithOpenAiChatModelIT.class);
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private static final String DEEPSEEK_BASE_URL = "https://api.deepseek.com";
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private static final String DEFAULT_DEEPSEEK_MODEL = "deepseek-chat";
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@Value("classpath:/prompts/system-message.st")
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private Resource systemResource;
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@Autowired
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private OpenAiChatModel chatModel;
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@Test
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void roleTest() {
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UserMessage userMessage = new UserMessage(
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"Tell me about 3 famous pirates from the Golden Age of Piracy and what they did.");
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SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(this.systemResource);
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Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", "Bob", "voice", "pirate"));
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Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
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ChatResponse response = this.chatModel.call(prompt);
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assertThat(response.getResults()).hasSize(1);
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assertThat(response.getResults().get(0).getOutput().getText()).contains("Blackbeard");
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}
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@Test
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void streamRoleTest() {
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UserMessage userMessage = new UserMessage(
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"Tell me about 3 famous pirates from the Golden Age of Piracy and what they did.");
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SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(this.systemResource);
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Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", "Bob", "voice", "pirate"));
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Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
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Flux<ChatResponse> flux = this.chatModel.stream(prompt);
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List<ChatResponse> responses = flux.collectList().block();
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assertThat(responses.size()).isGreaterThan(1);
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String stitchedResponseContent = responses.stream()
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.map(ChatResponse::getResults)
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.flatMap(List::stream)
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.map(Generation::getOutput)
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.map(AssistantMessage::getText)
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.collect(Collectors.joining());
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assertThat(stitchedResponseContent).contains("Blackbeard");
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}
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@Test
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void streamingWithTokenUsage() {
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var promptOptions = OpenAiChatOptions.builder().streamUsage(true).seed(1).build();
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var prompt = new Prompt("List two colors of the Polish flag. Be brief.", promptOptions);
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var streamingTokenUsage = this.chatModel.stream(prompt).blockLast().getMetadata().getUsage();
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var referenceTokenUsage = this.chatModel.call(prompt).getMetadata().getUsage();
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assertThat(streamingTokenUsage.getPromptTokens()).isGreaterThan(0);
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assertThat(streamingTokenUsage.getGenerationTokens()).isGreaterThan(0);
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assertThat(streamingTokenUsage.getTotalTokens()).isGreaterThan(0);
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assertThat(streamingTokenUsage.getPromptTokens()).isEqualTo(referenceTokenUsage.getPromptTokens());
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assertThat(streamingTokenUsage.getGenerationTokens()).isEqualTo(referenceTokenUsage.getGenerationTokens());
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assertThat(streamingTokenUsage.getTotalTokens()).isEqualTo(referenceTokenUsage.getTotalTokens());
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}
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@Test
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void listOutputConverter() {
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DefaultConversionService conversionService = new DefaultConversionService();
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ListOutputConverter outputConverter = new ListOutputConverter(conversionService);
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String format = outputConverter.getFormat();
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String template = """
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List five {subject}
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{format}
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""";
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PromptTemplate promptTemplate = new PromptTemplate(template,
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Map.of("subject", "ice cream flavors", "format", format));
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Prompt prompt = new Prompt(promptTemplate.createMessage());
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Generation generation = this.chatModel.call(prompt).getResult();
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List<String> list = outputConverter.convert(generation.getOutput().getText());
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assertThat(list).hasSize(5);
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}
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@Test
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void mapOutputConverter() {
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MapOutputConverter outputConverter = new MapOutputConverter();
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String format = outputConverter.getFormat();
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String template = """
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Provide me a List of {subject}
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{format}
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""";
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PromptTemplate promptTemplate = new PromptTemplate(template,
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Map.of("subject", "numbers from 1 to 9 under they key name 'numbers'", "format", format));
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Prompt prompt = new Prompt(promptTemplate.createMessage());
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Generation generation = this.chatModel.call(prompt).getResult();
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Map<String, Object> result = outputConverter.convert(generation.getOutput().getText());
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assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
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}
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@Test
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void beanOutputConverter() {
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BeanOutputConverter<ActorsFilms> outputConverter = new BeanOutputConverter<>(ActorsFilms.class);
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String format = outputConverter.getFormat();
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String template = """
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Generate the filmography for a random actor.
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{format}
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""";
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PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
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Prompt prompt = new Prompt(promptTemplate.createMessage());
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Generation generation = this.chatModel.call(prompt).getResult();
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ActorsFilms actorsFilms = outputConverter.convert(generation.getOutput().getText());
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assertThat(actorsFilms.getActor()).isNotEmpty();
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}
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@Test
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void beanOutputConverterRecords() {
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BeanOutputConverter<DeepSeekWithOpenAiChatModelIT.ActorsFilmsRecord> outputConverter = new BeanOutputConverter<>(
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DeepSeekWithOpenAiChatModelIT.ActorsFilmsRecord.class);
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String format = outputConverter.getFormat();
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String template = """
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Generate the filmography of 5 movies for Tom Hanks.
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{format}
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""";
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PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
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Prompt prompt = new Prompt(promptTemplate.createMessage());
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Generation generation = this.chatModel.call(prompt).getResult();
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DeepSeekWithOpenAiChatModelIT.ActorsFilmsRecord actorsFilms = outputConverter
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.convert(generation.getOutput().getText());
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logger.info("" + actorsFilms);
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assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
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assertThat(actorsFilms.movies()).hasSize(5);
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}
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@Test
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void beanStreamOutputConverterRecords() {
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BeanOutputConverter<DeepSeekWithOpenAiChatModelIT.ActorsFilmsRecord> outputConverter = new BeanOutputConverter<>(
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DeepSeekWithOpenAiChatModelIT.ActorsFilmsRecord.class);
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String format = outputConverter.getFormat();
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String template = """
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Generate the filmography of 5 movies for Tom Hanks.
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{format}
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""";
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PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
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Prompt prompt = new Prompt(promptTemplate.createMessage());
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String generationTextFromStream = this.chatModel.stream(prompt)
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.collectList()
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.block()
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.stream()
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.map(ChatResponse::getResults)
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.flatMap(List::stream)
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.map(Generation::getOutput)
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.map(AssistantMessage::getText)
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.collect(Collectors.joining());
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DeepSeekWithOpenAiChatModelIT.ActorsFilmsRecord actorsFilms = outputConverter.convert(generationTextFromStream);
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logger.info("" + actorsFilms);
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assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
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assertThat(actorsFilms.movies()).hasSize(5);
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}
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@Test
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@Disabled("The current version of the deepseek-chat model's Function Calling capability is unstable, which may result in looped calls or empty responses.")
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void functionCallTest() {
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UserMessage userMessage = new UserMessage("What's the weather like in San Francisco, Tokyo, and Paris?");
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List<Message> messages = new ArrayList<>(List.of(userMessage));
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var promptOptions = OpenAiChatOptions.builder()
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.functionCallbacks(List.of(FunctionCallback.builder()
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.function("getCurrentWeather", new MockWeatherService())
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.description("Get the weather in location")
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.inputType(MockWeatherService.Request.class)
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.build()))
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.build();
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ChatResponse response = this.chatModel.call(new Prompt(messages, promptOptions));
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logger.info("Response: {}", response);
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assertThat(response.getResult().getOutput().getText()).contains("30", "10", "15");
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}
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@Test
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@Disabled("The current version of the deepseek-chat model's Function Calling capability is unstable, which may result in looped calls or empty responses.")
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void streamFunctionCallTest() {
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UserMessage userMessage = new UserMessage(
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"What's the weather like in San Francisco, Tokyo, and Paris? Return the temperature in Celsius.");
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List<Message> messages = new ArrayList<>(List.of(userMessage));
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var promptOptions = OpenAiChatOptions.builder()
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.functionCallbacks(List.of(FunctionCallback.builder()
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.function("getCurrentWeather", new MockWeatherService())
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.description("Get the weather in location")
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.inputType(MockWeatherService.Request.class)
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.build()))
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.build();
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Flux<ChatResponse> response = this.chatModel.stream(new Prompt(messages, promptOptions));
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String content = response.collectList()
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.block()
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.stream()
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.map(ChatResponse::getResults)
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.flatMap(List::stream)
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.map(Generation::getOutput)
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.map(AssistantMessage::getText)
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.collect(Collectors.joining());
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logger.info("Response: {}", content);
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assertThat(content).contains("30", "10", "15");
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}
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@ParameterizedTest(name = "{0} : {displayName} ")
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@ValueSource(strings = { "deepseek-chat", "deepseek-reasoner" })
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void validateCallResponseMetadata(String model) {
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// @formatter:off
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ChatResponse response = ChatClient.create(this.chatModel).prompt()
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.options(OpenAiChatOptions.builder().model(model).build())
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.user("Tell me about 3 famous pirates from the Golden Age of Piracy and what they did")
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.call()
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.chatResponse();
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// @formatter:on
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logger.info(response.toString());
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assertThat(response.getMetadata().getId()).isNotEmpty();
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assertThat(response.getMetadata().getModel()).containsIgnoringCase(model);
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assertThat(response.getMetadata().getUsage().getPromptTokens()).isPositive();
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assertThat(response.getMetadata().getUsage().getGenerationTokens()).isPositive();
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assertThat(response.getMetadata().getUsage().getTotalTokens()).isPositive();
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}
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record ActorsFilmsRecord(String actor, List<String> movies) {
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}
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@SpringBootConfiguration
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static class Config {
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@Bean
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public OpenAiApi chatCompletionApi() {
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return new OpenAiApi(DEEPSEEK_BASE_URL, System.getenv("DEEPSEEK_API_KEY"));
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}
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@Bean
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public OpenAiChatModel openAiClient(OpenAiApi openAiApi) {
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return new OpenAiChatModel(openAiApi, OpenAiChatOptions.builder().model(DEFAULT_DEEPSEEK_MODEL).build());
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}
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}
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}

‎spring-ai-docs/src/main/antora/modules/ROOT/images/spring-ai-deepseek-integration.jpg‎

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‎spring-ai-docs/src/main/antora/modules/ROOT/nav.adoc‎

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**** xref:api/chat/functions/anthropic-chat-functions.adoc[Anthropic Function Calling]
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*** xref:api/chat/azure-openai-chat.adoc[Azure OpenAI]
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**** xref:api/chat/functions/azure-open-ai-chat-functions.adoc[Azure OpenAI Function Calling]
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*** xref:api/chat/deepseek-chat.adoc[DeepSeek AI]
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*** xref:api/chat/google-vertexai.adoc[Google VertexAI]
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**** xref:api/chat/vertexai-gemini-chat.adoc[VertexAI Gemini]
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***** xref:api/chat/functions/vertexai-gemini-chat-functions.adoc[Gemini Function Calling]

‎spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/comparison.adoc‎

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| xref::api/chat/anthropic-chat.adoc[Anthropic Claude] | text, pdf, image ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12]
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| xref::api/chat/azure-openai-chat.adoc[Azure OpenAI] | text, image ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::no.svg[width=12] ^a| image::yes.svg[width=16]
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| xref::api/chat/deepseek-chat.adoc[DeepSeek (OpenAI-proxy)] | text ^a| image::no.svg[width=12] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16]
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| xref::api/chat/vertexai-gemini-chat.adoc[Google VertexAI Gemini] | text, pdf, image, audio, video ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::no.svg[width=12] ^a| image::yes.svg[width=16]
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| xref::api/chat/groq-chat.adoc[Groq (OpenAI-proxy)] | text, image ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::yes.svg[width=16] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::yes.svg[width=16]
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| xref::api/chat/huggingface.adoc[HuggingFace] | text ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12]

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