Add Groq inference support via OpenAI client
- add dedicated groq chat page in the documentation. Explain how to re-configure the OpenAI client for accessing the Groq chat completion endpoint. - Doc: order the Chat and Embedding items in alphabetical order - Add Groq ITs. Resolves #996
This commit is contained in:
Christian Tzolov
committed by
Mark Pollack
Mark Pollack
parent
867bd173e5
commit
f61ccdd09d
@@ -0,0 +1,393 @@
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/*
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* Copyright 2023 - 2024 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;
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import static org.assertj.core.api.Assertions.assertThat;
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import java.io.IOException;
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import java.net.URL;
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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 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.Media;
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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.FunctionCallbackWrapper;
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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.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.ClassPathResource;
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import org.springframework.core.io.Resource;
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import org.springframework.util.MimeTypeUtils;
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import reactor.core.publisher.Flux;
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@SpringBootTest(classes = GroqWithOpenAiChatModelIT.Config.class)
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@EnabledIfEnvironmentVariable(named = "GROQ_API_KEY", matches = ".+")
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class GroqWithOpenAiChatModelIT {
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private static final Logger logger = LoggerFactory.getLogger(OpenAiChatModelIT.class);
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private static final String GROQ_BASE_URL = "https://api.groq.com/openai";
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private static final String DEFAULT_GROQ_MODEL = "llama3-70b-8192";
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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(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 = chatModel.call(prompt);
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assertThat(response.getResults()).hasSize(1);
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assertThat(response.getResults().get(0).getOutput().getContent()).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(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 = 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::getContent)
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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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@Disabled("Not supported by the current Groq API")
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void streamingWithTokenUsage() {
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var promptOptions = OpenAiChatOptions.builder().withStreamUsage(true).withSeed(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().getContent());
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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 = chatModel.call(prompt).getResult();
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Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
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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 = chatModel.call(prompt).getResult();
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ActorsFilms actorsFilms = outputConverter.convert(generation.getOutput().getContent());
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assertThat(actorsFilms.getActor()).isNotEmpty();
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}
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record ActorsFilmsRecord(String actor, List<String> movies) {
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}
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@Test
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void beanOutputConverterRecords() {
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BeanOutputConverter<ActorsFilmsRecord> outputConverter = new BeanOutputConverter<>(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 = chatModel.call(prompt).getResult();
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ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
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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<ActorsFilmsRecord> outputConverter = new BeanOutputConverter<>(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 = 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::getContent)
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.collect(Collectors.joining());
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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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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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.withFunctionCallbacks(List.of(FunctionCallbackWrapper.builder(new MockWeatherService())
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.withName("getCurrentWeather")
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.withDescription("Get the weather in location")
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.withResponseConverter((response) -> "" + response.temp() + response.unit())
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.build()))
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.build();
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ChatResponse response = chatModel.call(new Prompt(messages, promptOptions));
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logger.info("Response: {}", response);
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assertThat(response.getResult().getOutput().getContent()).containsAnyOf("30.0", "30");
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assertThat(response.getResult().getOutput().getContent()).containsAnyOf("10.0", "10");
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assertThat(response.getResult().getOutput().getContent()).containsAnyOf("15.0", "15");
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}
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@Test
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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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.withFunctionCallbacks(List.of(FunctionCallbackWrapper.builder(new MockWeatherService())
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.withName("getCurrentWeather")
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.withDescription("Get the weather in location")
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.withResponseConverter((response) -> "" + response.temp() + response.unit())
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.build()))
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.build();
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Flux<ChatResponse> response = 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::getContent)
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.collect(Collectors.joining());
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logger.info("Response: {}", content);
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assertThat(content).containsAnyOf("30.0", "30");
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assertThat(content).containsAnyOf("10.0", "10");
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assertThat(content).containsAnyOf("15.0", "15");
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}
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@Disabled("Groq does not support multi modality API")
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@ParameterizedTest(name = "{0} : {displayName} ")
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@ValueSource(strings = { "llama3-70b-8192" })
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void multiModalityEmbeddedImage(String modelName) throws IOException {
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var imageData = new ClassPathResource("/test.png");
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var userMessage = new UserMessage("Explain what do you see on this picture?",
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List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)));
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var response = chatModel
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.call(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().withModel(modelName).build()));
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logger.info(response.getResult().getOutput().getContent());
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assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple");
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assertThat(response.getResult().getOutput().getContent()).containsAnyOf("bowl", "basket");
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}
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@Disabled("Groq does not support multi modality API")
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@ParameterizedTest(name = "{0} : {displayName} ")
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@ValueSource(strings = { "llama3-70b-8192" })
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void multiModalityImageUrl(String modelName) throws IOException {
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var userMessage = new UserMessage("Explain what do you see on this picture?", List
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.of(new Media(MimeTypeUtils.IMAGE_PNG,
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new URL("https://docs.spring.io/spring-ai/reference/1.0-SNAPSHOT/_images/multimodal.test.png"))));
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ChatResponse response = chatModel
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.call(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().withModel(modelName).build()));
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logger.info(response.getResult().getOutput().getContent());
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assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple");
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assertThat(response.getResult().getOutput().getContent()).containsAnyOf("bowl", "basket");
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}
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@Disabled("Groq does not support multi modality API")
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@Test
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void streamingMultiModalityImageUrl() throws IOException {
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var userMessage = new UserMessage("Explain what do you see on this picture?", List
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.of(new Media(MimeTypeUtils.IMAGE_PNG,
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new URL("https://docs.spring.io/spring-ai/reference/1.0-SNAPSHOT/_images/multimodal.test.png"))));
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Flux<ChatResponse> response = chatModel.stream(new Prompt(List.of(userMessage)));
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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::getContent)
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.collect(Collectors.joining());
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logger.info("Response: {}", content);
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assertThat(content).contains("bananas", "apple");
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assertThat(content).containsAnyOf("bowl", "basket");
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}
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@ParameterizedTest(name = "{0} : {displayName} ")
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@ValueSource(strings = { "llama3-8b-8192", "llama3-70b-8192", "mixtral-8x7b-32768", "gemma-7b-it" })
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void validateCallResponseMetadata(String model) {
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// @formatter:off
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ChatResponse response = ChatClient.create(chatModel).prompt()
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.options(OpenAiChatOptions.builder().withModel(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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@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(GROQ_BASE_URL, System.getenv("GROQ_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().withModel(DEFAULT_GROQ_MODEL).build());
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}
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}
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}
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@@ -4,11 +4,6 @@
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* xref:api/index.adoc[]
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** xref:api/chatclient.adoc[]
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** xref:api/chatmodel.adoc[]
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*** xref:api/chat/openai-chat.adoc[OpenAI]
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**** xref:api/chat/functions/openai-chat-functions.adoc[Function Calling]
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*** xref:api/chat/ollama-chat.adoc[Ollama]
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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[Function Calling]
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*** xref:api/bedrock-chat.adoc[Amazon Bedrock]
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**** xref:api/chat/bedrock/bedrock-anthropic3.adoc[Anthropic3]
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**** xref:api/chat/bedrock/bedrock-anthropic.adoc[Anthropic2]
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@@ -16,37 +11,43 @@
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**** xref:api/chat/bedrock/bedrock-cohere.adoc[Cohere]
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**** xref:api/chat/bedrock/bedrock-titan.adoc[Titan]
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**** xref:api/chat/bedrock/bedrock-jurassic2.adoc[Jurassic2]
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*** xref:api/chat/huggingface.adoc[Hugging Face]
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*** xref:api/chat/anthropic-chat.adoc[Anthropic 3]
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**** xref:api/chat/functions/anthropic-chat-functions.adoc[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[Function Calling]
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*** xref:api/chat/google-vertexai.adoc[Google VertexAI]
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**** xref:api/chat/vertexai-palm2-chat.adoc[VertexAI PaLM2 ]
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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[Function Calling]
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*** xref:api/chat/groq-chat.adoc[Groq]
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*** xref:api/chat/huggingface.adoc[Hugging Face]
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*** xref:api/chat/mistralai-chat.adoc[Mistral AI]
|
||||
**** xref:api/chat/functions/mistralai-chat-functions.adoc[Function Calling]
|
||||
*** xref:api/chat/zhipuai-chat.adoc[ZhiPu AI]
|
||||
**** xref:api/chat/functions/zhipuai-chat-functions.adoc[Function Calling]
|
||||
*** xref:api/chat/anthropic-chat.adoc[Anthropic 3]
|
||||
**** xref:api/chat/functions/anthropic-chat-functions.adoc[Function Calling]
|
||||
*** xref:api/chat/watsonx-ai-chat.adoc[Watsonx.AI]
|
||||
*** xref:api/chat/minimax-chat.adoc[MiniMax]
|
||||
**** xref:api/chat/functions/minimax-chat-functions.adoc[Function Calling]
|
||||
*** xref:api/chat/moonshot-chat.adoc[Moonshot AI]
|
||||
**** xref:api/chat/functions/moonshot-chat-functions.adoc[Function Calling]
|
||||
*** xref:api/chat/ollama-chat.adoc[Ollama]
|
||||
*** xref:api/chat/openai-chat.adoc[OpenAI]
|
||||
**** xref:api/chat/functions/openai-chat-functions.adoc[Function Calling]
|
||||
*** xref:api/chat/qianfan-chat.adoc[QianFan]
|
||||
*** xref:api/chat/zhipuai-chat.adoc[ZhiPu AI]
|
||||
**** xref:api/chat/functions/zhipuai-chat-functions.adoc[Function Calling]
|
||||
*** xref:api/chat/watsonx-ai-chat.adoc[Watsonx.AI]
|
||||
** xref:api/embeddings.adoc[]
|
||||
*** xref:api/embeddings/openai-embeddings.adoc[OpenAI]
|
||||
*** xref:api/embeddings/ollama-embeddings.adoc[Ollama]
|
||||
*** xref:api/embeddings/azure-openai-embeddings.adoc[Azure OpenAI]
|
||||
*** xref:api/embeddings/postgresml-embeddings.adoc[PostgresML]
|
||||
*** xref:api/embeddings/vertexai-embeddings.adoc[Google VertexAI PaLM2]
|
||||
*** xref:api/bedrock.adoc[Amazon Bedrock]
|
||||
**** xref:api/embeddings/bedrock-cohere-embedding.adoc[Cohere]
|
||||
**** xref:api/embeddings/bedrock-titan-embedding.adoc[Titan]
|
||||
*** xref:api/embeddings/onnx.adoc[Transformers (ONNX)]
|
||||
*** xref:api/embeddings/azure-openai-embeddings.adoc[Azure OpenAI]
|
||||
*** xref:api/embeddings/vertexai-embeddings.adoc[Google VertexAI PaLM2]
|
||||
*** xref:api/embeddings/mistralai-embeddings.adoc[Mistral AI]
|
||||
*** xref:api/embeddings/minimax-embeddings.adoc[MiniMax]
|
||||
*** xref:api/embeddings/zhipuai-embeddings.adoc[ZhiPu AI]
|
||||
*** xref:api/embeddings/ollama-embeddings.adoc[Ollama]
|
||||
*** xref:api/embeddings/onnx.adoc[(ONNX) Transformers]
|
||||
*** xref:api/embeddings/openai-embeddings.adoc[OpenAI]
|
||||
*** xref:api/embeddings/postgresml-embeddings.adoc[PostgresML]
|
||||
*** xref:api/embeddings/qianfan-embeddings.adoc[QianFan]
|
||||
*** xref:api/embeddings/zhipuai-embeddings.adoc[ZhiPu AI]
|
||||
** xref:api/imageclient.adoc[]
|
||||
*** xref:api/image/azure-openai-image.adoc[Azure OpenAI]
|
||||
*** xref:api/image/openai-image.adoc[OpenAI]
|
||||
|
||||
@@ -0,0 +1,255 @@
|
||||
= Groq Chat
|
||||
|
||||
Spring AI supports https://groq.com/[Groq] - fast AI inference engine by reusing the existing xref::api/chat/openai-chat.adoc[OpenAI] client.
|
||||
For this you need to use the OpenAI client but set the base-url to: https://api.groq.com/openai and select one of the
|
||||
provided https://console.groq.com/docs/models[Groq models]: `llama3-8b-8192`, `llama3-70b-8192`, `mixtral-8x7b-32768`, `gemma-7b-it`.
|
||||
|
||||
Check the https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/GroqWithOpenAiChatModelIT.java[GroqWithOpenAiChatModelIT.java] tests
|
||||
for examples of using Groq with Spring AI.
|
||||
|
||||
NOTE: The Groq API is not fully compatible with the OpenAI API.
|
||||
Be aware for the following https://console.groq.com/docs/openai[compatability constrains].
|
||||
Additionally, currently Groq doesn't support multimodal messages.
|
||||
|
||||
== Prerequisites
|
||||
|
||||
* Create an API Key.
|
||||
Please visit https://console.groq.com/keys[here] to create an API Key.
|
||||
The Spring AI project defines a configuration property named `spring.ai.openai.api-key` that you should set to the value of the `API Key` obtained from groq.com.
|
||||
* Set the Groq URL.
|
||||
You have to set the `spring.ai.openai.base-url` property to `https://api.groq.com/openai`.
|
||||
* Select a https://console.groq.com/docs/models[Groq Model].
|
||||
The avalable https://console.groq.com/docs/models[model] names are `llama3-8b-8192`, `llama3-70b-8192`, `mixtral-8x7b-32768`, `gemma-7b-it`.
|
||||
Use the `spring.ai.openai.chat.model=<model name>` property to set the Model.
|
||||
|
||||
Exporting an environment variable is one way to set that configuration property:
|
||||
|
||||
[source,shell]
|
||||
----
|
||||
export SPRING_AI_OPENAI_API_KEY=<INSERT GROQ API KEY HERE>
|
||||
export SPRING_AI_OPENAI_BASE_URL=https://api.groq.com/openai
|
||||
export SPRING_AI_OPENAI_CHAT_MODEL=llama3-70b-8192
|
||||
----
|
||||
|
||||
=== Add Repositories and BOM
|
||||
|
||||
Spring AI artifacts are published in Spring Milestone and Snapshot repositories.
|
||||
Refer to the xref:getting-started.adoc#repositories[Repositories] section to add these repositories to your build system.
|
||||
|
||||
To help with dependency management, Spring AI provides a BOM (bill of materials) to ensure that a consistent version of Spring AI is used throughout the entire project. Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build system.
|
||||
|
||||
|
||||
== Auto-configuration
|
||||
|
||||
Spring AI provides Spring Boot auto-configuration for the OpenAI Chat Client.
|
||||
To enable it add the following dependency to your project's Maven `pom.xml` file:
|
||||
|
||||
[source, xml]
|
||||
----
|
||||
<dependency>
|
||||
<groupId>org.springframework.ai</groupId>
|
||||
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
|
||||
</dependency>
|
||||
----
|
||||
|
||||
or to your Gradle `build.gradle` build file.
|
||||
|
||||
[source,groovy]
|
||||
----
|
||||
dependencies {
|
||||
implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'
|
||||
}
|
||||
----
|
||||
|
||||
TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
|
||||
|
||||
=== Chat Properties
|
||||
|
||||
==== Retry Properties
|
||||
|
||||
The prefix `spring.ai.retry` is used as the property prefix that lets you configure the retry mechanism for the OpenAI chat model.
|
||||
|
||||
[cols="3,5,1"]
|
||||
|====
|
||||
| Property | Description | Default
|
||||
|
||||
| spring.ai.retry.max-attempts | Maximum number of retry attempts. | 10
|
||||
| spring.ai.retry.backoff.initial-interval | Initial sleep duration for the exponential backoff policy. | 2 sec.
|
||||
| spring.ai.retry.backoff.multiplier | Backoff interval multiplier. | 5
|
||||
| spring.ai.retry.backoff.max-interval | Maximum backoff duration. | 3 min.
|
||||
| spring.ai.retry.on-client-errors | If false, throw a NonTransientAiException, and do not attempt retry for `4xx` client error codes | false
|
||||
| spring.ai.retry.exclude-on-http-codes | List of HTTP status codes that should not trigger a retry (e.g. to throw NonTransientAiException). | empty
|
||||
| spring.ai.retry.on-http-codes | List of HTTP status codes that should trigger a retry (e.g. to throw TransientAiException). | empty
|
||||
|====
|
||||
|
||||
==== Connection Properties
|
||||
|
||||
The prefix `spring.ai.openai` is used as the property prefix that lets you connect to OpenAI.
|
||||
|
||||
[cols="3,5,1"]
|
||||
|====
|
||||
| Property | Description | Default
|
||||
|
||||
| spring.ai.openai.base-url | The URL to connect to. Must be set to `https://api.groq.com/openai` | -
|
||||
| spring.ai.openai.api-key | The Groq API Key | -
|
||||
|====
|
||||
|
||||
|
||||
==== Configuration Properties
|
||||
|
||||
The prefix `spring.ai.openai.chat` is the property prefix that lets you configure the chat model implementation for OpenAI.
|
||||
|
||||
[cols="3,5,1"]
|
||||
|====
|
||||
| Property | Description | Default
|
||||
|
||||
| spring.ai.openai.chat.enabled | Enable OpenAI chat model. | true
|
||||
| spring.ai.openai.chat.base-url | Optional overrides the spring.ai.openai.base-url to provide chat specific url. Must be set to `https://api.groq.com/openai` | -
|
||||
| spring.ai.openai.chat.api-key | Optional overrides the spring.ai.openai.api-key to provide chat specific api-key | -
|
||||
| spring.ai.openai.chat.options.model | The avalable https://console.groq.com/docs/models[model] names are `llama3-8b-8192`, `llama3-70b-8192`, `mixtral-8x7b-32768`, `gemma-7b-it`. | -
|
||||
| spring.ai.openai.chat.options.temperature | The sampling temperature to use that controls the apparent creativity of generated completions. Higher values will make output more random while lower values will make results more focused and deterministic. It is not recommended to modify temperature and top_p for the same completions request as the interaction of these two settings is difficult to predict. | 0.8
|
||||
| spring.ai.openai.chat.options.frequencyPenalty | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. | 0.0f
|
||||
| spring.ai.openai.chat.options.maxTokens | The maximum number of tokens to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. | -
|
||||
| spring.ai.openai.chat.options.n | How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs. | 1
|
||||
| spring.ai.openai.chat.options.presencePenalty | Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. | -
|
||||
| spring.ai.openai.chat.options.responseFormat | An object specifying the format that the model must output. Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is valid JSON.| -
|
||||
| spring.ai.openai.chat.options.seed | This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. | -
|
||||
| spring.ai.openai.chat.options.stop | Up to 4 sequences where the API will stop generating further tokens. | -
|
||||
| spring.ai.openai.chat.options.topP | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both. | -
|
||||
| spring.ai.openai.chat.options.tools | A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. | -
|
||||
| spring.ai.openai.chat.options.toolChoice | Controls which (if any) function is called by the model. none means the model will not call a function and instead generates a message. auto means the model can pick between generating a message or calling a function. Specifying a particular function via {"type: "function", "function": {"name": "my_function"}} forces the model to call that function. none is the default when no functions are present. auto is the default if functions are present. | -
|
||||
| spring.ai.openai.chat.options.user | A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. | -
|
||||
| spring.ai.openai.chat.options.functions | List of functions, identified by their names, to enable for function calling in a single prompt requests. Functions with those names must exist in the functionCallbacks registry. | -
|
||||
| spring.ai.openai.chat.options.stream-usage | (For streaming only) Set to add an additional chunk with token usage statistics for the entire request. The `choices` field for this chunk is an empty array and all other chunks will also include a usage field, but with a null value. | false
|
||||
|====
|
||||
|
||||
TIP: All properties prefixed with `spring.ai.openai.chat.options` can be overridden at runtime by adding a request specific <<chat-options>> to the `Prompt` call.
|
||||
|
||||
== Runtime Options [[chat-options]]
|
||||
|
||||
The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatOptions.java[OpenAiChatOptions.java] provides model configurations, such as the model to use, the temperature, the frequency penalty, etc.
|
||||
|
||||
On start-up, the default options can be configured with the `OpenAiChatModel(api, options)` constructor or the `spring.ai.openai.chat.options.*` properties.
|
||||
|
||||
At run-time you can override the default options by adding new, request specific, options to the `Prompt` call.
|
||||
For example to override the default model and temperature for a specific request:
|
||||
|
||||
[source,java]
|
||||
----
|
||||
ChatResponse response = chatModel.call(
|
||||
new Prompt(
|
||||
"Generate the names of 5 famous pirates.",
|
||||
OpenAiChatOptions.builder()
|
||||
.withModel("mixtral-8x7b-32768")
|
||||
.withTemperature(0.4)
|
||||
.build()
|
||||
));
|
||||
----
|
||||
|
||||
TIP: In addition to the model specific https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatOptions.java[OpenAiChatOptions] you can use a portable https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/prompt/ChatOptions.java[ChatOptions] instance, created with the https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/prompt/ChatOptionsBuilder.java[ChatOptionsBuilder#builder()].
|
||||
|
||||
== Function Calling
|
||||
|
||||
Groq API endpoints support https://console.groq.com/docs/tool-use[function calling].
|
||||
You can register custom Java functions with the OpenAiChatModel and have the OpenAI model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
|
||||
This is a powerful technique to connect the LLM capabilities with external tools and APIs.
|
||||
Read more about xref:api/chat/functions/openai-chat-functions.adoc[OpenAI Function Calling].
|
||||
|
||||
TIP: Currently only the `llama3-70b` model is recommend for tool use.
|
||||
|
||||
== Multimodal
|
||||
|
||||
NOTE: Currently the Groq API doesn't support media content.
|
||||
|
||||
== Sample Controller
|
||||
|
||||
https://start.spring.io/[Create] a new Spring Boot project and add the `spring-ai-openai-spring-boot-starter` to your pom (or gradle) dependencies.
|
||||
|
||||
Add a `application.properties` file, under the `src/main/resources` directory, to enable and configure the OpenAi chat model:
|
||||
|
||||
[source,application.properties]
|
||||
----
|
||||
spring.ai.openai.api-key=<GROQ_API_KEY>
|
||||
spring.ai.openai.base-url=https://api.groq.com/openai
|
||||
spring.ai.openai.chat.options.model=llama3-70b-8192
|
||||
spring.ai.openai.chat.options.temperature=0.7
|
||||
----
|
||||
|
||||
TIP: replace the `api-key` with your OpenAI credentials.
|
||||
|
||||
This will create a `OpenAiChatModel` implementation that you can inject into your class.
|
||||
Here is an example of a simple `@Controller` class that uses the chat model for text generations.
|
||||
|
||||
[source,java]
|
||||
----
|
||||
@RestController
|
||||
public class ChatController {
|
||||
|
||||
private final OpenAiChatModel chatModel;
|
||||
|
||||
@Autowired
|
||||
public ChatController(OpenAiChatModel chatModel) {
|
||||
this.chatModel = chatModel;
|
||||
}
|
||||
|
||||
@GetMapping("/ai/generate")
|
||||
public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
|
||||
return Map.of("generation", chatModel.call(message));
|
||||
}
|
||||
|
||||
@GetMapping("/ai/generateStream")
|
||||
public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
|
||||
Prompt prompt = new Prompt(new UserMessage(message));
|
||||
return chatModel.stream(prompt);
|
||||
}
|
||||
}
|
||||
----
|
||||
|
||||
== Manual Configuration
|
||||
|
||||
The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatModel.java[OpenAiChatModel] implements the `ChatModel` and `StreamingChatModel` and uses the <<low-level-api>> to connect to the OpenAI service.
|
||||
|
||||
Add the `spring-ai-openai` dependency to your project's Maven `pom.xml` file:
|
||||
|
||||
[source, xml]
|
||||
----
|
||||
<dependency>
|
||||
<groupId>org.springframework.ai</groupId>
|
||||
<artifactId>spring-ai-openai</artifactId>
|
||||
</dependency>
|
||||
----
|
||||
|
||||
or to your Gradle `build.gradle` build file.
|
||||
|
||||
[source,groovy]
|
||||
----
|
||||
dependencies {
|
||||
implementation 'org.springframework.ai:spring-ai-openai'
|
||||
}
|
||||
----
|
||||
|
||||
TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
|
||||
|
||||
Next, create a `OpenAiChatModel` and use it for text generations:
|
||||
|
||||
[source,java]
|
||||
----
|
||||
var openAiApi = new OpenAiApi("https://api.groq.com/openai", System.getenv("GROQ_API_KEY"));
|
||||
var openAiChatOptions = OpenAiChatOptions.builder()
|
||||
.withModel("llama3-70b-8192")
|
||||
.withTemperature(0.4)
|
||||
.withMaxTokens(200)
|
||||
.build();
|
||||
var chatModel = new OpenAiChatModel(openAiApi, openAiChatOptions);
|
||||
|
||||
|
||||
ChatResponse response = chatModel.call(
|
||||
new Prompt("Generate the names of 5 famous pirates."));
|
||||
|
||||
// Or with streaming responses
|
||||
Flux<ChatResponse> response = chatModel.stream(
|
||||
new Prompt("Generate the names of 5 famous pirates."));
|
||||
----
|
||||
|
||||
The `OpenAiChatOptions` provides the configuration information for the chat requests.
|
||||
The `OpenAiChatOptions.Builder` is fluent options builder.
|
||||
@@ -7,10 +7,11 @@ image::function-calling-basic-flow.jpg[Function calling, width=700, align="cente
|
||||
|
||||
Spring AI currently supports Function invocation for the following AI Models
|
||||
|
||||
* OpenAI: Refer to the xref:api/chat/functions/openai-chat-functions.adoc[Open AI function invocation docs].
|
||||
* VertexAI Gemini: Refer to the xref:api/chat/functions/vertexai-gemini-chat-functions.adoc[Vertex AI Gemini function invocation docs].
|
||||
* Azure OpenAI: Refer to the xref:api/chat/functions/azure-open-ai-chat-functions.adoc[Azure OpenAI function invocation docs].
|
||||
* Mistral AI: Refer to the xref:api/chat/functions/mistralai-chat-functions.adoc[Mistral AI function invocation docs].
|
||||
* Anthropic Claude: Refer to the xref:api/chat/functions/anthropic-chat-functions.adoc[Anthropic Claude function invocation docs].
|
||||
* Azure OpenAI: Refer to the xref:api/chat/functions/azure-open-ai-chat-functions.adoc[Azure OpenAI function invocation docs].
|
||||
* Google VertexAI Gemini: Refer to the xref:api/chat/functions/vertexai-gemini-chat-functions.adoc[Vertex AI Gemini function invocation docs].
|
||||
* Groq: Refer to the xref:api/chat/groq-chat.adoc#_function_calling[Groq function invocation docs].
|
||||
* Mistral AI: Refer to the xref:api/chat/functions/mistralai-chat-functions.adoc[Mistral AI function invocation docs].
|
||||
* MiniMax : Refer to the xref:api/chat/functions/minimax-chat-functions.adoc[MiniMax function invocation docs].
|
||||
* OpenAI: Refer to the xref:api/chat/functions/openai-chat-functions.adoc[Open AI function invocation docs].
|
||||
* ZhiPu AI : Refer to the xref:api/chat/functions/zhipuai-chat-functions.adoc[ZhiPu AI function invocation docs].
|
||||
Reference in New Issue
Block a user