feat: Add Perplexity AI integration and documentation updates
- Introduced `PerplexityWithOpenAiChatModelIT` integration test for Perplexity AI with OpenAI Chat Model. - Includes various test cases for role-based prompts, streaming responses, token usage validation, and output converters. - Added tests for function calls and metadata validation. - Updated Antora navigation (`nav.adoc`) to include Perplexity AI documentation link. - Enhanced chat model comparison documentation to highlight Perplexity AI integration. - Added a dedicated `perplexity-chat.adoc` page under `spring-ai-docs` to provide detailed documentation for integrating Perplexity AI. - Covers API prerequisites, auto-configuration, and runtime options. - Explains configuration properties such as `spring.ai.openai.base-url`, `spring.ai.openai.chat.model`, and `spring.ai.openai.chat.options.*`. - Provides examples for environment variable setup and runtime overrides. - Highlights limitations like lack of multimodal support and explicit function calling. - Includes a sample Spring Boot controller demonstrating integration usage. - Links to Perplexity documentation for further reference.
This commit is contained in:
Alexandros Pappas
committed by
Christian Tzolov
Christian Tzolov
parent
0b00e6f446
commit
dcc8d5b620
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/*
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* Copyright 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.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.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.ai.retry.RetryUtils;
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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 org.springframework.web.client.RestClient;
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import org.springframework.web.reactive.function.client.WebClient;
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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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* Unlike other proxy implementations (e.g., NVIDIA), Perplexity operates differently:
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*
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* - Perplexity includes integrated real-time web search results as part of its response
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* rather than through explicit function calls. Consequently, no `toolCalls` or function
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* call mechanisms are exposed in the API responses
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*
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* For more information on Perplexity's behavior, refer to its API documentation:
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* <a href="https://docs.perplexity.ai/api-reference/chat-completions">perplexity-api</a>
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*/
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@SpringBootTest(classes = PerplexityWithOpenAiChatModelIT.Config.class)
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@EnabledIfEnvironmentVariable(named = "PERPLEXITY_API_KEY", matches = ".+")
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// @Disabled("Requires Perplexity credits")
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class PerplexityWithOpenAiChatModelIT {
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private static final Logger logger = LoggerFactory.getLogger(PerplexityWithOpenAiChatModelIT.class);
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private static final String PERPLEXITY_BASE_URL = "https://api.perplexity.ai";
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private static final String PERPLEXITY_COMPLETIONS_PATH = "/chat/completions";
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private static final String DEFAULT_PERPLEXITY_MODEL = "llama-3.1-sonar-small-128k-online";
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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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// Ensure the SystemMessage comes before UserMessage to comply with Perplexity
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// API's sequence rules
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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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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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Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
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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().getContent()).contains("Blackbeard");
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}
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@Test
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void streamRoleTest() {
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// Ensure the SystemMessage comes before UserMessage to comply with Perplexity
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// API's sequence rules
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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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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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Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
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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::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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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 the 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().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 = this.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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@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 = this.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 = 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::getContent)
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.filter(c -> c != null)
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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(FunctionCallback.builder()
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.description("Get the weather in location")
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.function("getCurrentWeather", new MockWeatherService())
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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.getResults().stream().mapToLong(r -> r.getOutput().getToolCalls().size()).sum()).isZero();
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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(FunctionCallback.builder()
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.description("Get the weather in location")
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.function("getCurrentWeather", new MockWeatherService())
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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::getContent)
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.collect(Collectors.joining());
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logger.info("Response: {}", content);
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assertThat(content).doesNotContain("toolCalls");
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}
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@Test
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void validateCallResponseMetadata() {
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ChatResponse response = ChatClient.create(this.chatModel)
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.prompt()
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.options(OpenAiChatOptions.builder().withModel(DEFAULT_PERPLEXITY_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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logger.info(response.toString());
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assertThat(response.getMetadata().getId()).isNotEmpty();
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assertThat(response.getMetadata().getModel()).containsIgnoringCase(DEFAULT_PERPLEXITY_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(PERPLEXITY_BASE_URL, System.getenv("PERPLEXITY_API_KEY"), PERPLEXITY_COMPLETIONS_PATH,
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"/v1/embeddings", RestClient.builder(), WebClient.builder(),
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RetryUtils.DEFAULT_RESPONSE_ERROR_HANDLER);
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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,
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OpenAiChatOptions.builder().withModel(DEFAULT_PERPLEXITY_MODEL).build());
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}
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}
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}
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@@ -31,6 +31,7 @@
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//// **** xref:api/chat/functions/moonshot-chat-functions.adoc[Function Calling]
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*** xref:api/chat/nvidia-chat.adoc[NVIDIA]
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*** xref:api/chat/ollama-chat.adoc[Ollama]
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*** xref:api/chat/perplexity-chat.adoc[Perplexity AI]
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*** OCI Generative AI
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**** xref:api/chat/oci-genai/cohere-chat.adoc[Cohere]
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*** xref:api/chat/openai-chat.adoc[OpenAI]
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@@ -31,6 +31,7 @@ This table compares various Chat Models supported by Spring AI, detailing their
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| xref::api/chat/oci-genai/cohere-chat.adoc[OCI GenAI/Cohere] | text ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::yes.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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| xref::api/chat/ollama-chat.adoc[Ollama] | 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::yes.svg[width=16] ^a| image::yes.svg[width=16]
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| xref::api/chat/openai-chat.adoc[OpenAI] | text, image, audio ^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/perplexity-chat.adoc[Perplexity (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::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::yes.svg[width=16]
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| xref::api/chat/qianfan-chat.adoc[QianFan] | 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::no.svg[width=12] ^a| image::no.svg[width=12] ^a| image::no.svg[width=12]
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| xref::api/chat/zhipuai-chat.adoc[ZhiPu AI] | text ^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/watsonx-ai-chat.adoc[Watsonx.AI] | text ^a| image::no.svg[width=12] ^a| image::yes.svg[width=16] ^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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@@ -0,0 +1,217 @@
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= Perplexity Chat
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https://perplexity.ai/[Perplexity AI] provides a unique AI service that integrates its language models with real-time search capabilities. It offers a variety of models and supports streaming responses for conversational AI.
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Spring AI integrates with Perplexity AI by reusing the existing xref::api/chat/openai-chat.adoc[OpenAI] client. To get started, you'll need to obtain a https://docs.perplexity.ai/guides/getting-started[Perplexity API Key], configure the base URL, and select one of the supported https://docs.perplexity.ai/guides/model-cards[models].
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image::spring-ai-perplexity-integration.jpg[w=800,align="center"]
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NOTE: The Perplexity API is not fully compatible with the OpenAI API.
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Perplexity combines realtime web search results with its language model responses.
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Unlike OpenAI, Perplexity does not expose `toolCalls` - `function call` mechanisms.
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Additionally, currently Perplexity doesn’t support multimodal messages.
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|
||||
Check the https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/proxy/PerplexityWithOpenAiChatModelIT.java[PerplexityWithOpenAiChatModelIT.java] tests for examples of using Perplexity with Spring AI.
|
||||
|
||||
|
||||
== Prerequisites
|
||||
|
||||
* **Create an API Key**:
|
||||
Visit https://docs.perplexity.ai/guides/getting-started[here] to create an API Key. Configure it using the `spring.ai.openai.api-key` property in your Spring AI project.
|
||||
|
||||
* **Set the Perplexity Base URL**:
|
||||
Set the `spring.ai.openai.base-url` property to `https://api.perplexity.ai`.
|
||||
|
||||
* **Select a Perplexity Model**:
|
||||
Use the `spring.ai.openai.chat.model=<model name>` property to specify the model. Refer to https://docs.perplexity.ai/guides/model-cards[Supported Models] for available options.
|
||||
|
||||
* **Set the chat completions path**:
|
||||
Set the `spring.ai.openai.chat.completions-path` to `/chat/completions` . Refer to https://docs.perplexity.ai/api-reference/chat-completions[chat completions api] for more details.
|
||||
|
||||
Example environment variables configuration:
|
||||
|
||||
[source,shell]
|
||||
----
|
||||
export SPRING_AI_OPENAI_API_KEY=<INSERT PERPLEXITY API KEY HERE>
|
||||
export SPRING_AI_OPENAI_BASE_URL=https://api.perplexity.ai
|
||||
export SPRING_AI_OPENAI_CHAT_MODEL=llama-3.1-sonar-small-128k-online
|
||||
----
|
||||
|
||||
=== 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` or Gradle `build.gradle` build files:
|
||||
|
||||
[tabs]
|
||||
======
|
||||
Maven::
|
||||
+
|
||||
[source, xml]
|
||||
----
|
||||
<dependency>
|
||||
<groupId>org.springframework.ai</groupId>
|
||||
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
|
||||
</dependency>
|
||||
----
|
||||
|
||||
Gradle::
|
||||
+
|
||||
[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", stripes=even]
|
||||
|====
|
||||
| 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", stripes=even]
|
||||
|====
|
||||
| Property | Description | Default
|
||||
|
||||
| spring.ai.openai.base-url | The URL to connect to. Must be set to `https://api.perplexity.ai` | -
|
||||
| spring.ai.openai.chat.api-key | Your Perplexity 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", stripes=even]
|
||||
|====
|
||||
| Property | Description | Default
|
||||
|
||||
| spring.ai.openai.chat.model | One of the supported https://docs.perplexity.ai/guides/model-cards[Perplexity models]. Example: `llama-3.1-sonar-small-128k-online`. | -
|
||||
| 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.perplexity.ai` | -
|
||||
| spring.ai.openai.chat.completions-path | Must be set to `/chat/completions` | `/v1/chat/completions`
|
||||
| spring.ai.openai.chat.options.temperature | The amount of randomness in the response, valued between 0 inclusive and 2 exclusive. Higher values are more random, and lower values are more deterministic. Required range: `0 < x < 2`. | 0.2
|
||||
| spring.ai.openai.chat.options.frequencyPenalty | A multiplicative penalty greater than 0. Values greater than 1.0 penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. A value of 1.0 means no penalty. Incompatible with presence_penalty. Required range: `x > 0`. | 1
|
||||
| spring.ai.openai.chat.options.maxTokens | The maximum number of completion tokens returned by the API. The total number of tokens requested in max_tokens plus the number of prompt tokens sent in messages must not exceed the context window token limit of model requested. If left unspecified, then the model will generate tokens until either it reaches its stop token or the end of its context window. | -
|
||||
| spring.ai.openai.chat.options.presencePenalty | A value 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. Incompatible with `frequency_penalty`. Required range: `-2 < x < 2` | 0
|
||||
| spring.ai.openai.chat.options.topP | The nucleus sampling threshold, valued between 0 and 1 inclusive. For each subsequent token, the model considers the results of the tokens with top_p probability mass. We recommend either altering top_k or top_p, but not both. Required range: `0 < x < 1` | 0.9
|
||||
| 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("llama-3.1-sonar-large-128k-online")
|
||||
.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
|
||||
|
||||
NOTE: Perplexity does not support explicit function calling. Instead, it integrates search results directly into responses.
|
||||
|
||||
== Multimodal
|
||||
|
||||
NOTE: Currently, the Perplexity 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=<PERPLEXITY_API_KEY>
|
||||
spring.ai.openai.base-url=https://api.perplexity.ai
|
||||
spring.ai.openai.chat.completions-path=/chat/completions
|
||||
spring.ai.openai.chat.options.model=llama-3.1-sonar-small-128k-online
|
||||
spring.ai.openai.chat.options.temperature=0.7
|
||||
|
||||
# The Perplexity API doesn't support embeddings, so we need to disable it.
|
||||
spring.ai.openai.embedding.enabled=false
|
||||
----
|
||||
|
||||
TIP: replace the `api-key` with your Perplexity Api key.
|
||||
|
||||
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", this.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 this.chatModel.stream(prompt);
|
||||
}
|
||||
}
|
||||
----
|
||||
|
||||
== Supported Models
|
||||
|
||||
Perplexity supports several models optimized for search-enhanced conversational AI. Refer to https://docs.perplexity.ai/guides/model-cards[Supported Models] for details.
|
||||
|
||||
== References
|
||||
|
||||
* https://docs.perplexity.ai/home[Documentation Home]
|
||||
* https://docs.perplexity.ai/api-reference/chat-completions[API Reference]
|
||||
* https://docs.perplexity.ai/guides/getting-started[Getting Started]
|
||||
* https://docs.perplexity.ai/guides/rate-limits[Rate Limits]
|
||||
Reference in New Issue
Block a user