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package dev.langchain4j.service; import dev.langchain4j.agent.tool.DefaultToolExecutor; import dev.langchain4j.agent.tool.Tool; import dev.langchain4j.agent.tool.ToolSpecification; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.data.message.ChatMessage; import dev.langchain4j.data.message.ToolExecutionResultMessage; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.memory.ChatMemory; import dev.langchain4j.memory.chat.ChatMemoryProvider; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.chat.StreamingChatLanguageModel; import dev.langchain4j.model.input.structured.StructuredPrompt; import dev.langchain4j.model.moderation.Moderation; import dev.langchain4j.model.moderation.ModerationModel; import dev.langchain4j.model.output.Response; import dev.langchain4j.rag.DefaultRetrievalAugmentor; import dev.langchain4j.rag.RetrievalAugmentor; import dev.langchain4j.rag.content.retriever.ContentRetriever; import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; import dev.langchain4j.retriever.Retriever; import dev.langchain4j.spi.services.AiServicesFactory; import java.lang.reflect.Method; import java.util.*; import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.ExecutionException; import java.util.concurrent.Future; import static dev.langchain4j.agent.tool.ToolSpecifications.toolSpecificationFrom; import static dev.langchain4j.exception.IllegalConfigurationException.illegalConfiguration; import static dev.langchain4j.internal.ValidationUtils.ensureNotNull; import static dev.langchain4j.spi.ServiceHelper.loadFactories; import static java.util.stream.Collectors.toList; /** * AI Services provide a simpler and more flexible alternative to chains. * You can define your own API (a Java interface with one or more methods), * and AiServices will provide an implementation for it (we call this "AI Service"). * <p> * Currently, AI Services support: * <pre> * - Prompt templates for user and system messages using {@link UserMessage} and {@link SystemMessage} * - Structured prompts as method arguments (see {@link StructuredPrompt}) * - Shared or per-user (see {@link MemoryId}) chat memory * - RAG (see {@link RetrievalAugmentor}) * - Tools (see {@link Tool}) * - Various return types (output parsers), see below * - Streaming (use {@link TokenStream} as a return type) * - Auto-moderation using {@link Moderate} * </pre> * <p> * Here is the simplest example of an AI Service: * * <pre> * interface Assistant { * * String chat(String userMessage); * } * * Assistant assistant = AiServices.create(Assistant.class, model); * * String answer = assistant.chat("hello"); * System.out.println(answer); // Hello, how can I help you today? * </pre> * * <pre> * The return type of methods in your AI Service can be any of the following: * - a {@link String}, an {@link AiMessage} or a {@code Response<AiMessage>}, if you want to get the answer from the LLM as-is * - a {@code List<String>} or {@code Set<String>}, if you want to receive the answer as a collection of items or bullet points * - any {@link Enum} or a {@code boolean}, if you want to use the LLM for classification * - a primitive or boxed Java type: {@code int}, {@code Double}, etc., if you want to use the LLM for data extraction * - many default Java types: {@code Date}, {@code LocalDateTime}, {@code BigDecimal}, etc., if you want to use the LLM for data extraction * - any custom POJO, if you want to use the LLM for data extraction. * For POJOs, it is advisable to use the "json mode" feature if the LLM provider supports it. For OpenAI, this can be enabled by calling {@code responseFormat("json_object")} during model construction. * * </pre> * <p> * Let's see how we can classify the sentiment of a text: * <pre> * enum Sentiment { * POSITIVE, NEUTRAL, NEGATIVE * } * * interface SentimentAnalyzer { * * {@code @UserMessage}("Analyze sentiment of {{it}}") * Sentiment analyzeSentimentOf(String text); * } * * SentimentAnalyzer assistant = AiServices.create(SentimentAnalyzer.class, model); * * Sentiment sentiment = analyzeSentimentOf.chat("I love you"); * System.out.println(sentiment); // POSITIVE * </pre> * <p> * As demonstrated, you can put {@link UserMessage} and {@link SystemMessage} annotations above a method to define * templates for user and system messages, respectively. * In this example, the special {@code {{it}}} prompt template variable is used because there's only one method parameter. * However, you can use more parameters as demonstrated in the following example: * <pre> * interface Translator { * * {@code @SystemMessage}("You are a professional translator into {{language}}") * {@code @UserMessage}("Translate the following text: {{text}}") * String translate(@V("text") String text, @V("language") String language); * } * </pre> * <p> * See more examples <a href="https://github.com/langchain4j/langchain4j-examples/tree/main/other-examples/src/main/java">here</a>. * * @param <T> The interface for which AiServices will provide an implementation. */ public abstract class AiServices<T> { protected static final String DEFAULT = "default"; protected final AiServiceContext context; private boolean retrieverSet = false; private boolean contentRetrieverSet = false; private boolean retrievalAugmentorSet = false; protected AiServices(AiServiceContext context) { this.context = context; } /** * Creates an AI Service (an implementation of the provided interface), that is backed by the provided chat model. * This convenience method can be used to create simple AI Services. * For more complex cases, please use {@link #builder}. * * @param aiService The class of the interface to be implemented. * @param chatLanguageModel The chat model to be used under the hood. * @return An instance of the provided interface, implementing all its defined methods. */ public static <T> T create(Class<T> aiService, ChatLanguageModel chatLanguageModel) { return builder(aiService) .chatLanguageModel(chatLanguageModel) .build(); } /** * Creates an AI Service (an implementation of the provided interface), that is backed by the provided streaming chat model. * This convenience method can be used to create simple AI Services. * For more complex cases, please use {@link #builder}. * * @param aiService The class of the interface to be implemented. * @param streamingChatLanguageModel The streaming chat model to be used under the hood. * The return type of all methods should be {@link TokenStream}. * @return An instance of the provided interface, implementing all its defined methods. */ public static <T> T create(Class<T> aiService, StreamingChatLanguageModel streamingChatLanguageModel) { return builder(aiService) .streamingChatLanguageModel(streamingChatLanguageModel) .build(); } /** * Begins the construction of an AI Service. * * @param aiService The class of the interface to be implemented. * @return builder */ public static <T> AiServices<T> builder(Class<T> aiService) { AiServiceContext context = new AiServiceContext(aiService); for (AiServicesFactory factory : loadFactories(AiServicesFactory.class)) { return factory.create(context); } return new DefaultAiServices<>(context); } /** * Configures chat model that will be used under the hood of the AI Service. * <p> * Either {@link ChatLanguageModel} or {@link StreamingChatLanguageModel} should be configured, * but not both at the same time. * * @param chatLanguageModel Chat model that will be used under the hood of the AI Service. * @return builder */ public AiServices<T> chatLanguageModel(ChatLanguageModel chatLanguageModel) { context.chatModel = chatLanguageModel; return this; } /** * Configures streaming chat model that will be used under the hood of the AI Service. * The methods of the AI Service must return a {@link TokenStream} type. * <p> * Either {@link ChatLanguageModel} or {@link StreamingChatLanguageModel} should be configured, * but not both at the same time. * * @param streamingChatLanguageModel Streaming chat model that will be used under the hood of the AI Service. * @return builder */ public AiServices<T> streamingChatLanguageModel(StreamingChatLanguageModel streamingChatLanguageModel) { context.streamingChatModel = streamingChatLanguageModel; return this; } /** * Configures the chat memory that will be used to preserve conversation history between method calls. * <p> * Unless a {@link ChatMemory} or {@link ChatMemoryProvider} is configured, all method calls will be independent of each other. * In other words, the LLM will not remember the conversation from the previous method calls. * <p> * The same {@link ChatMemory} instance will be used for every method call. * <p> * If you want to have a separate {@link ChatMemory} for each user/conversation, configure {@link #chatMemoryProvider} instead. * <p> * Either a {@link ChatMemory} or a {@link ChatMemoryProvider} can be configured, but not both simultaneously. * * @param chatMemory An instance of chat memory to be used by the AI Service. * @return builder */ public AiServices<T> chatMemory(ChatMemory chatMemory) { context.chatMemories = new ConcurrentHashMap<>(); context.chatMemories.put(DEFAULT, chatMemory); return this; } /** * Configures the chat memory provider, which provides a dedicated instance of {@link ChatMemory} for each user/conversation. * To distinguish between users/conversations, one of the method's arguments should be a memory ID (of any data type) * annotated with {@link MemoryId}. * For each new (previously unseen) memoryId, an instance of {@link ChatMemory} will be automatically obtained * by invoking {@link ChatMemoryProvider#get(Object id)}. * Example: * <pre> * interface Assistant { * * String chat(@MemoryId int memoryId, @UserMessage String message); * } * </pre> * If you prefer to use the same (shared) {@link ChatMemory} for all users/conversations, configure a {@link #chatMemory} instead. * <p> * Either a {@link ChatMemory} or a {@link ChatMemoryProvider} can be configured, but not both simultaneously. * * @param chatMemoryProvider The provider of a {@link ChatMemory} for each new user/conversation. * @return builder */ public AiServices<T> chatMemoryProvider(ChatMemoryProvider chatMemoryProvider) { context.chatMemories = new ConcurrentHashMap<>(); context.chatMemoryProvider = chatMemoryProvider; return this; } /** * Configures a moderation model to be used for automatic content moderation. * If a method in the AI Service is annotated with {@link Moderate}, the moderation model will be invoked * to check the user content for any inappropriate or harmful material. * * @param moderationModel The moderation model to be used for content moderation. * @return builder * @see Moderate */ public AiServices<T> moderationModel(ModerationModel moderationModel) { context.moderationModel = moderationModel; return this; } /** * Configures the tools that the LLM can use. * A {@link ChatMemory} that can hold at least 3 messages is required for the tools to work properly. * * @param objectsWithTools One or more objects whose methods are annotated with {@link Tool}. * All these tools (methods annotated with {@link Tool}) will be accessible to the LLM. * Note that inherited methods are ignored. * @return builder * @see Tool */ public AiServices<T> tools(Object... objectsWithTools) { return tools(Arrays.asList(objectsWithTools)); } /** * Configures the tools that the LLM can use. * A {@link ChatMemory} that can hold at least 3 messages is required for the tools to work properly. * * @param objectsWithTools A list of objects whose methods are annotated with {@link Tool}. * All these tools (methods annotated with {@link Tool}) are accessible to the LLM. * Note that inherited methods are ignored. * @return builder * @see Tool */ public AiServices<T> tools(List<Object> objectsWithTools) { context.toolSpecifications = new ArrayList<>(); context.toolExecutors = new HashMap<>(); for (Object objectWithTool : objectsWithTools) { for (Method method : objectWithTool.getClass().getDeclaredMethods()) { if (method.isAnnotationPresent(Tool.class)) { ToolSpecification toolSpecification = toolSpecificationFrom(method); context.toolSpecifications.add(toolSpecification); context.toolExecutors.put(toolSpecification.name(), new DefaultToolExecutor(objectWithTool, method)); } } } return this; } /** * Deprecated. Use {@link #contentRetriever(ContentRetriever)} * (e.g. {@link EmbeddingStoreContentRetriever}) instead. * <br> * Configures a retriever that will be invoked on every method call to fetch relevant information * related to the current user message from an underlying source (e.g., embedding store). * This relevant information is automatically injected into the message sent to the LLM. * * @param retriever The retriever to be used by the AI Service. * @return builder */ @Deprecated public AiServices<T> retriever(Retriever<TextSegment> retriever) { if(contentRetrieverSet || retrievalAugmentorSet) { throw illegalConfiguration("Only one out of [retriever, contentRetriever, retrievalAugmentor] can be set"); } if (retriever != null) { AiServices<T> withContentRetriever = contentRetriever(retriever.toContentRetriever()); retrieverSet = true; return withContentRetriever; } return this; } /** * Configures a content retriever to be invoked on every method call for retrieving relevant content * related to the user's message from an underlying data source * (e.g., an embedding store in the case of an {@link EmbeddingStoreContentRetriever}). * The retrieved relevant content is then automatically incorporated into the message sent to the LLM. * <br> * This method provides a straightforward approach for those who do not require * a customized {@link RetrievalAugmentor}. * It configures a {@link DefaultRetrievalAugmentor} with the provided {@link ContentRetriever}. * * @param contentRetriever The content retriever to be used by the AI Service. * @return builder */ public AiServices<T> contentRetriever(ContentRetriever contentRetriever) { if(retrieverSet || retrievalAugmentorSet) { throw illegalConfiguration("Only one out of [retriever, contentRetriever, retrievalAugmentor] can be set"); } contentRetrieverSet = true; context.retrievalAugmentor = DefaultRetrievalAugmentor.builder() .contentRetriever(ensureNotNull(contentRetriever, "contentRetriever")) .build(); return this; } /** * Configures a retrieval augmentor to be invoked on every method call. * * @param retrievalAugmentor The retrieval augmentor to be used by the AI Service. * @return builder */ public AiServices<T> retrievalAugmentor(RetrievalAugmentor retrievalAugmentor) { if(retrieverSet || contentRetrieverSet) { throw illegalConfiguration("Only one out of [retriever, contentRetriever, retrievalAugmentor] can be set"); } retrievalAugmentorSet = true; context.retrievalAugmentor = ensureNotNull(retrievalAugmentor, "retrievalAugmentor"); return this; } /** * Constructs and returns the AI Service. * * @return An instance of the AI Service implementing the specified interface. */ public abstract T build(); protected void performBasicValidation() { if (context.chatModel == null && context.streamingChatModel == null) { throw illegalConfiguration("Please specify either chatLanguageModel or streamingChatLanguageModel"); } if (context.toolSpecifications != null && !context.hasChatMemory()) { throw illegalConfiguration( "Please set up chatMemory or chatMemoryProvider in order to use tools. " + "A ChatMemory that can hold at least 3 messages is required for the tools to work properly. " + "While the LLM can technically execute a tool without chat memory, if it only receives the " + "result of the tool's execution without the initial message from the user, it won't interpret " + "the result properly." ); } } public static List<ChatMessage> removeToolMessages(List<ChatMessage> messages) { return messages.stream() .filter(it -> !(it instanceof ToolExecutionResultMessage)) .filter(it -> !(it instanceof AiMessage && ((AiMessage) it).hasToolExecutionRequests())) .collect(toList()); } public static void verifyModerationIfNeeded(Future<Moderation> moderationFuture) { if (moderationFuture != null) { try { Moderation moderation = moderationFuture.get(); if (moderation.flagged()) { throw new ModerationException(String.format("Text \"%s\" violates content policy", moderation.flaggedText())); } } catch (InterruptedException | ExecutionException e) { throw new RuntimeException(e); } } } }
[ "dev.langchain4j.rag.DefaultRetrievalAugmentor.builder" ]
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package org.mfusco; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; import static java.time.Duration.ofSeconds; public class MortgageChat { private final ChatLanguageModel model; private final PersonExtractor extractor; private final DroolsMortgageCalculator droolsMortgageCalculator = new DroolsMortgageCalculator(); private final Assistant assistant; public MortgageChat(String openAiApiKey) { model = OpenAiChatModel.builder() .apiKey(openAiApiKey) .timeout(ofSeconds(60)) .build(); extractor = AiServices.create(PersonExtractor.class, model); assistant = AiServices.builder(Assistant.class) .chatLanguageModel(model) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) .tools(droolsMortgageCalculator) .build(); } public String chat(String text) { return text.endsWith("?") ? assistant.chat(text) : extractPerson(text); } private String extractPerson(String text) { Person person = extractor.extractPersonFrom(text); droolsMortgageCalculator.register(person); return person.toString(); } }
[ "dev.langchain4j.service.AiServices.builder", "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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package com.moyz.adi.common.service; import com.moyz.adi.common.helper.LLMContext; import com.moyz.adi.common.interfaces.TriConsumer; import com.moyz.adi.common.util.AdiPgVectorEmbeddingStore; import com.moyz.adi.common.vo.AnswerMeta; import com.moyz.adi.common.vo.PromptMeta; import dev.langchain4j.data.document.Document; import dev.langchain4j.data.document.DocumentSplitter; import dev.langchain4j.data.document.splitter.DocumentSplitters; import dev.langchain4j.data.embedding.Embedding; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.input.Prompt; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.openai.OpenAiTokenizer; import dev.langchain4j.model.output.Response; import dev.langchain4j.store.embedding.EmbeddingMatch; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import lombok.extern.slf4j.Slf4j; import org.apache.commons.lang3.StringUtils; import org.apache.commons.lang3.tuple.ImmutablePair; import org.apache.commons.lang3.tuple.Pair; import org.apache.commons.lang3.tuple.Triple; import org.springframework.beans.factory.annotation.Value; import org.springframework.stereotype.Service; import java.util.List; import java.util.Map; import java.util.regex.Matcher; import java.util.regex.Pattern; import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO; import static java.util.stream.Collectors.joining; @Slf4j @Service public class RAGService { @Value("${spring.datasource.url}") private String dataBaseUrl; @Value("${spring.datasource.username}") private String dataBaseUserName; @Value("${spring.datasource.password}") private String dataBasePassword; private static final PromptTemplate promptTemplate = PromptTemplate.from("尽可能准确地回答下面的问题: {{question}}\n\n根据以下知识库的内容:\n{{information}}"); private EmbeddingModel embeddingModel; private EmbeddingStore<TextSegment> embeddingStore; public void init() { log.info("initEmbeddingModel"); embeddingModel = new AllMiniLmL6V2EmbeddingModel(); embeddingStore = initEmbeddingStore(); } private EmbeddingStore<TextSegment> initEmbeddingStore() { // 正则表达式匹配 String regex = "jdbc:postgresql://([^:/]+):(\\d+)/(\\w+).+"; Pattern pattern = Pattern.compile(regex); Matcher matcher = pattern.matcher(dataBaseUrl); String host = ""; String port = ""; String databaseName = ""; if (matcher.matches()) { host = matcher.group(1); port = matcher.group(2); databaseName = matcher.group(3); System.out.println("Host: " + host); System.out.println("Port: " + port); System.out.println("Database: " + databaseName); } else { throw new RuntimeException("parse url error"); } AdiPgVectorEmbeddingStore embeddingStore = AdiPgVectorEmbeddingStore.builder() .host(host) .port(Integer.parseInt(port)) .database(databaseName) .user(dataBaseUserName) .password(dataBasePassword) .dimension(384) .createTable(true) .dropTableFirst(false) .table("adi_knowledge_base_embedding") .build(); return embeddingStore; } private EmbeddingStoreIngestor getEmbeddingStoreIngestor() { DocumentSplitter documentSplitter = DocumentSplitters.recursive(1000, 0, new OpenAiTokenizer(GPT_3_5_TURBO)); EmbeddingStoreIngestor embeddingStoreIngestor = EmbeddingStoreIngestor.builder() .documentSplitter(documentSplitter) .embeddingModel(embeddingModel) .embeddingStore(embeddingStore) .build(); return embeddingStoreIngestor; } /** * 对文档切块并向量化 * * @param document 知识库文档 */ public void ingest(Document document) { getEmbeddingStoreIngestor().ingest(document); } public Prompt retrieveAndCreatePrompt(String kbUuid, String question) { // Embed the question Embedding questionEmbedding = embeddingModel.embed(question).content(); // Find relevant embeddings in embedding store by semantic similarity // You can play with parameters below to find a sweet spot for your specific use case int maxResults = 3; double minScore = 0.6; List<EmbeddingMatch<TextSegment>> relevantEmbeddings = ((AdiPgVectorEmbeddingStore) embeddingStore).findRelevantByKbUuid(kbUuid, questionEmbedding, maxResults, minScore); // Create a prompt for the model that includes question and relevant embeddings String information = relevantEmbeddings.stream() .map(match -> match.embedded().text()) .collect(joining("\n\n")); if (StringUtils.isBlank(information)) { return null; } return promptTemplate.apply(Map.of("question", question, "information", Matcher.quoteReplacement(information))); } /** * 召回并提问 * * @param kbUuid 知识库uuid * @param question 用户的问题 * @param modelName LLM model name * @return */ public Pair<String, Response<AiMessage>> retrieveAndAsk(String kbUuid, String question, String modelName) { Prompt prompt = retrieveAndCreatePrompt(kbUuid, question); if (null == prompt) { return null; } Response<AiMessage> response = new LLMContext(modelName).getLLMService().chat(prompt.toUserMessage()); return new ImmutablePair<>(prompt.text(), response); } }
[ "dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder" ]
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package dev.zbendhiba.demo.telegram.openapi; import java.util.List; import dev.langchain4j.chain.ConversationalRetrievalChain; import dev.langchain4j.data.embedding.Embedding; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.openai.OpenAiChatModel; import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO; import dev.langchain4j.retriever.EmbeddingStoreRetriever; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; import jakarta.enterprise.context.ApplicationScoped; import static java.time.Duration.ofSeconds; import org.apache.camel.builder.RouteBuilder; import org.apache.camel.component.telegram.model.IncomingMessage; import org.eclipse.microprofile.config.inject.ConfigProperty; @ApplicationScoped public class Routes extends RouteBuilder { @ConfigProperty(name="open-api-key") String openApiKey; private EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel(); private EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>(); @Override public void configure() throws Exception { // REST endpoint to add a bio rest("data") .post("/camel-split-ingest/") .to("direct:camel-split-ingest") .post("/langchain4j-split-ingest/") .to("direct:langchain4j-split-ingest"); // Ingest Data from("direct:camel-split-ingest") .wireTap("direct:processBio") .transform().simple("Thanks"); from("direct:processBio") // split into paragraphs and use OpenApiTokenizer .split(body().tokenize("\\s*\\n\\s*\\n")) .setHeader("paragraphNumber", simple("${exchangeProperty.CamelSplitIndex}")) // Process each paragraph using the OpenAiTokenizerProcessor .process(new CamelSplitterProcessor()) .to("direct:processTokenizedPart") .end(); // Embed paragraphs into Vector Database from("direct:processTokenizedPart") .process(exchange -> { embed(exchange.getIn().getBody(List.class)); }); from("direct:process-langchain4j-split-ingest") .process(new LangchainSplitterProcessor()) .to("direct:processTokenizedPart"); from("direct:langchain4j-split-ingest") .wireTap("direct:process-langchain4j-split-ingest") .transform().simple("Thanks"); ChatLanguageModel model = OpenAiChatModel.builder() .apiKey(openApiKey) .modelName(GPT_3_5_TURBO) .temperature(0.3) .timeout(ofSeconds(3000)) .build(); ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder() .chatLanguageModel(model) .retriever(EmbeddingStoreRetriever.from(embeddingStore, embeddingModel)) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) .promptTemplate(PromptTemplate .from("Answer the following question to the best of your ability: {{question}}\n\nBase your answer on the following information:\n{{information}}")) .build(); from("telegram:bots?timeout=30000") .log("Text received in Telegram : ${body}") // this is just a Hello World, we suppose that we receive only text messages from user .filter(simple("${body} != '/start'")) .process(e->{ IncomingMessage incomingMessage = e.getMessage().getBody(IncomingMessage.class); var openapiMessage = chain.execute(incomingMessage.getText()); e.getMessage().setBody(openapiMessage); }) .log("Text to send to user based on response from ChatGPT : ${body}") .to("telegram:bots") .end(); } public void embed(List<TextSegment> textSegments ) { List<Embedding> embeddings = embeddingModel.embedAll(textSegments).content(); embeddingStore.addAll(embeddings, textSegments); } }
[ "dev.langchain4j.chain.ConversationalRetrievalChain.builder", "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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package eu.luminis.faqlangchain.service; import java.io.File; import java.io.FileNotFoundException; import java.time.Duration; import java.util.Arrays; import java.util.stream.Collectors; import com.fasterxml.jackson.databind.JsonNode; import dev.langchain4j.data.document.Document; import dev.langchain4j.data.document.DocumentSplitter; import dev.langchain4j.data.document.splitter.DocumentSplitters; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.springframework.beans.factory.annotation.Qualifier; import org.springframework.beans.factory.annotation.Value; import org.springframework.core.io.FileSystemResource; import org.springframework.http.HttpStatus; import org.springframework.http.MediaType; import org.springframework.http.client.MultipartBodyBuilder; import org.springframework.stereotype.Service; import org.springframework.util.ResourceUtils; import org.springframework.web.reactive.function.BodyInserters; import org.springframework.web.reactive.function.client.WebClient; import reactor.core.publisher.Mono; @Service public class IngestService { private static final Logger LOGGER = LoggerFactory.getLogger(IngestService.class); private final WebClient webClient; private final EmbeddingStore<TextSegment> embeddingStore; private final EmbeddingModel embeddingModel; public IngestService(@Value("${unstructured.apiKey}") String unstructuredApiKey, @Qualifier("openaiModel") EmbeddingModel embeddingModel, @Qualifier("inMemoryEmbeddingStore") EmbeddingStore<TextSegment> embeddingStore) { this.embeddingModel = embeddingModel; this.embeddingStore = embeddingStore; this.webClient = WebClient.builder() .baseUrl("https://api.unstructured.io/general/v0/") .defaultHeader("unstructured-api-key", unstructuredApiKey) .build(); } public boolean ingestPDF() throws FileNotFoundException { LOGGER.info("Ingesting PDF"); File file = ResourceUtils.getFile("classpath:data/faq.pdf"); MultipartBodyBuilder builder = new MultipartBodyBuilder(); builder.part("files", new FileSystemResource(file)); builder.part("strategy", "ocr_only"); builder.part("ocr_languages", "eng"); Mono<Object> mono = webClient.post() .uri("general") .contentType(MediaType.MULTIPART_FORM_DATA) .body(BodyInserters.fromMultipartData(builder.build())) .exchangeToMono(response -> { if (response.statusCode().equals(HttpStatus.OK)) { return response.bodyToMono(UnstructuredResponse[].class); } else { LOGGER.error("Something went wrong when uploading file to Unstructured API. Received status code {}", response.statusCode()); return response.bodyToMono(JsonNode.class); } }); Object response = mono.block(Duration.ofMinutes(1)); if (response instanceof JsonNode jsonNode) { LOGGER.error("Response: {}", jsonNode); return false; } if (response instanceof UnstructuredResponse[] unstructuredResponses) { String text = Arrays.stream(unstructuredResponses).map(UnstructuredResponse::getText).collect(Collectors.joining(" ")); Document document = Document.from(text); DocumentSplitter documentSplitter = DocumentSplitters.recursive(300); EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder() .documentSplitter(documentSplitter) .embeddingModel(embeddingModel) .embeddingStore(embeddingStore) .build(); ingestor.ingest(document); LOGGER.info("Ingestion of PDF finished"); return true; } return false; } }
[ "dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder" ]
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package org.agoncal.fascicle.langchain4j.accessing.vertexai; import dev.langchain4j.model.vertexai.VertexAiChatModel; // tag::adocSkip[] /** * @author Antonio Goncalves * http://www.antoniogoncalves.org * -- */ // end::adocSkip[] public class MusicianService { public static void main(String[] args) { MusicianService musicianService = new MusicianService(); musicianService.useVertexAiLanguageModelBuilder(); } private static final String AZURE_OPENAI_KEY = System.getenv("AZURE_OPENAI_KEY"); private static final String AZURE_OPENAI_ENDPOINT = System.getenv("AZURE_OPENAI_ENDPOINT"); private static final String AZURE_OPENAI_DEPLOYMENT_NAME = System.getenv("AZURE_OPENAI_DEPLOYMENT_NAME"); private static final String PROMPT = "When was the first Beatles album released?"; // ################################### // ### AZURE OPENAI LANGUAGE MODEL ### // ################################### public void useVertexAiLanguageModelBuilder() { System.out.println("### useVertexAiLanguageModelBuilder"); // tag::adocSnippet[] VertexAiChatModel model = VertexAiChatModel.builder() .endpoint(AZURE_OPENAI_ENDPOINT) .temperature(0.3) .build(); // end::adocSnippet[] String completion = model.generate(PROMPT); } }
[ "dev.langchain4j.model.vertexai.VertexAiChatModel.builder" ]
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package com.example.application; import com.example.application.services.BookingTools; import com.example.application.services.CustomerSupportAgent; import com.vaadin.flow.component.page.AppShellConfigurator; import com.vaadin.flow.theme.Theme; import dev.langchain4j.data.document.DocumentSplitter; import dev.langchain4j.data.document.parser.TextDocumentParser; import dev.langchain4j.data.document.splitter.DocumentSplitters; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.memory.chat.TokenWindowChatMemory; import dev.langchain4j.model.Tokenizer; import dev.langchain4j.model.chat.StreamingChatLanguageModel; import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.openai.OpenAiStreamingChatModel; import dev.langchain4j.model.openai.OpenAiTokenizer; import dev.langchain4j.rag.content.retriever.ContentRetriever; import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; import dev.langchain4j.service.AiServices; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; import org.springframework.beans.factory.annotation.Value; import org.springframework.boot.CommandLineRunner; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.context.annotation.Bean; import org.springframework.core.io.Resource; import org.springframework.core.io.ResourceLoader; import java.io.IOException; import static dev.langchain4j.data.document.loader.FileSystemDocumentLoader.loadDocument; import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO; import static dev.langchain4j.model.openai.OpenAiModelName.GPT_4; @SpringBootApplication @Theme(value = "customer-service-chatbot") public class Application implements AppShellConfigurator { public static void main(String[] args) { SpringApplication.run(Application.class, args); } @Bean EmbeddingModel embeddingModel() { return new AllMiniLmL6V2EmbeddingModel(); } @Bean EmbeddingStore<TextSegment> embeddingStore() { return new InMemoryEmbeddingStore<>(); } @Bean Tokenizer tokenizer() { return new OpenAiTokenizer(GPT_3_5_TURBO); } // In the real world, ingesting documents would often happen separately, on a CI server or similar @Bean CommandLineRunner docsToEmbeddings( EmbeddingModel embeddingModel, EmbeddingStore<TextSegment> embeddingStore, Tokenizer tokenizer, ResourceLoader resourceLoader ) throws IOException { return args -> { Resource resource = resourceLoader.getResource("classpath:terms-of-service.txt"); var termsOfUse = loadDocument(resource.getFile().toPath(), new TextDocumentParser()); DocumentSplitter documentSplitter = DocumentSplitters.recursive(200, 0, tokenizer); EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder() .documentSplitter(documentSplitter) .embeddingModel(embeddingModel) .embeddingStore(embeddingStore) .build(); ingestor.ingest(termsOfUse); }; } @Bean StreamingChatLanguageModel chatLanguageModel() { return OpenAiStreamingChatModel.builder() .apiKey(ApiKeys.OPENAI_API_KEY) .modelName(GPT_3_5_TURBO) .build(); } @Bean ContentRetriever retriever( EmbeddingStore<TextSegment> embeddingStore, EmbeddingModel embeddingModel ) { return EmbeddingStoreContentRetriever.builder() .embeddingStore(embeddingStore) .embeddingModel(embeddingModel) .maxResults(2) .minScore(0.6) .build(); } @Bean CustomerSupportAgent customerSupportAgent( StreamingChatLanguageModel chatLanguageModel, Tokenizer tokenizer, ContentRetriever retriever, BookingTools tools ) { return AiServices.builder(CustomerSupportAgent.class) .streamingChatLanguageModel(chatLanguageModel) .chatMemoryProvider(chatId -> TokenWindowChatMemory.builder() .id(chatId) .maxTokens(1000, tokenizer) .build()) .contentRetriever(retriever) .tools(tools) .build(); } }
[ "dev.langchain4j.service.AiServices.builder", "dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder", "dev.langchain4j.memory.chat.TokenWindowChatMemory.builder", "dev.langchain4j.model.openai.OpenAiStreamingChatModel.builder", "dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder" ]
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package com.tencent.supersonic.headless.core.chat.parser.llm; import com.tencent.supersonic.common.util.JsonUtil; import com.tencent.supersonic.headless.core.config.OptimizationConfig; import com.tencent.supersonic.headless.core.chat.query.llm.s2sql.LLMReq; import com.tencent.supersonic.headless.core.chat.query.llm.s2sql.LLMReq.SqlGenerationMode; import com.tencent.supersonic.headless.core.chat.query.llm.s2sql.LLMResp; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.input.Prompt; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.output.Response; import lombok.extern.slf4j.Slf4j; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.springframework.beans.factory.InitializingBean; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Service; import java.util.HashMap; import java.util.List; import java.util.Map; @Service @Slf4j public class TwoPassSqlGeneration implements SqlGeneration, InitializingBean { private static final Logger keyPipelineLog = LoggerFactory.getLogger("keyPipeline"); @Autowired private ChatLanguageModel chatLanguageModel; @Autowired private SqlExamplarLoader sqlExamplarLoader; @Autowired private OptimizationConfig optimizationConfig; @Autowired private SqlPromptGenerator sqlPromptGenerator; @Override public LLMResp generation(LLMReq llmReq, Long dataSetId) { keyPipelineLog.info("dataSetId:{},llmReq:{}", dataSetId, llmReq); List<Map<String, String>> sqlExamples = sqlExamplarLoader.retrieverSqlExamples(llmReq.getQueryText(), optimizationConfig.getText2sqlExampleNum()); String linkingPromptStr = sqlPromptGenerator.generateLinkingPrompt(llmReq, sqlExamples); Prompt prompt = PromptTemplate.from(JsonUtil.toString(linkingPromptStr)).apply(new HashMap<>()); keyPipelineLog.info("step one request prompt:{}", prompt.toSystemMessage()); Response<AiMessage> response = chatLanguageModel.generate(prompt.toSystemMessage()); keyPipelineLog.info("step one model response:{}", response.content().text()); String schemaLinkStr = OutputFormat.getSchemaLink(response.content().text()); String generateSqlPrompt = sqlPromptGenerator.generateSqlPrompt(llmReq, schemaLinkStr, sqlExamples); Prompt sqlPrompt = PromptTemplate.from(JsonUtil.toString(generateSqlPrompt)).apply(new HashMap<>()); keyPipelineLog.info("step two request prompt:{}", sqlPrompt.toSystemMessage()); Response<AiMessage> sqlResult = chatLanguageModel.generate(sqlPrompt.toSystemMessage()); String result = sqlResult.content().text(); keyPipelineLog.info("step two model response:{}", result); Map<String, Double> sqlMap = new HashMap<>(); sqlMap.put(result, 1D); keyPipelineLog.info("schemaLinkStr:{},sqlMap:{}", schemaLinkStr, sqlMap); LLMResp llmResp = new LLMResp(); llmResp.setQuery(llmReq.getQueryText()); llmResp.setSqlRespMap(OutputFormat.buildSqlRespMap(sqlExamples, sqlMap)); return llmResp; } @Override public void afterPropertiesSet() { SqlGenerationFactory.addSqlGenerationForFactory(SqlGenerationMode.TWO_PASS_AUTO_COT, this); } }
[ "dev.langchain4j.model.input.PromptTemplate.from" ]
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package com.sg.chatbot.service; import org.springframework.http.codec.ServerSentEvent; import dev.langchain4j.memory.chat.TokenWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.openai.OpenAiStreamingChatModel; import dev.langchain4j.model.openai.OpenAiTokenizer; import dev.langchain4j.service.AiServices; import dev.langchain4j.service.TokenStream; import org.springframework.stereotype.Service; import reactor.core.publisher.Flux; import reactor.core.publisher.Sinks; @Service public class ChatService { private String openaiApiKey = "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"; private Assistant assistant; private StreamingAssistant streamingAssistant; interface Assistant { String chat(String message); } interface StreamingAssistant { TokenStream chat(String message); } public ChatService(){ if (openaiApiKey == null) { System.err .println("ERROR: OPENAI_API_KEY environment variable is not set. Please set it to your OpenAI API key."); } var memory = TokenWindowChatMemory.withMaxTokens(2000, new OpenAiTokenizer("gpt-3.5-turbo")); assistant = AiServices.builder(Assistant.class) .chatLanguageModel(OpenAiChatModel.withApiKey(openaiApiKey)) .chatMemory(memory) .build(); streamingAssistant = AiServices.builder(StreamingAssistant.class) .streamingChatLanguageModel(OpenAiStreamingChatModel.withApiKey(openaiApiKey)) .chatMemory(memory) .build(); } public String chat(String message) { System.out.println(message); return assistant.chat(message); } public Flux<ServerSentEvent<String>> chatStream(String message) { Sinks.Many<String> sink = Sinks.many().unicast().onBackpressureBuffer(); streamingAssistant.chat(message) .onNext(sink::tryEmitNext) .onComplete(c -> sink.tryEmitComplete()) .onError(sink::tryEmitError) .start(); return sink.asFlux().map(mes -> ServerSentEvent.<String>builder() .event("chat") .data(mes) .build()); } }
[ "dev.langchain4j.service.AiServices.builder" ]
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package dev.langchain4j.model.azure; import com.azure.ai.openai.models.*; import dev.langchain4j.agent.tool.ToolExecutionRequest; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.model.Tokenizer; import dev.langchain4j.model.output.Response; import dev.langchain4j.model.output.TokenUsage; import java.util.List; import static dev.langchain4j.model.azure.InternalAzureOpenAiHelper.finishReasonFrom; import static java.util.Collections.singletonList; /** * This class needs to be thread safe because it is called when a streaming result comes back * and there is no guarantee that this thread will be the same as the one that initiated the request, * in fact it almost certainly won't be. */ class AzureOpenAiStreamingResponseBuilder { private final StringBuffer contentBuilder = new StringBuffer(); private final StringBuffer toolNameBuilder = new StringBuffer(); private final StringBuffer toolArgumentsBuilder = new StringBuffer(); private volatile CompletionsFinishReason finishReason; private final Integer inputTokenCount; public AzureOpenAiStreamingResponseBuilder(Integer inputTokenCount) { this.inputTokenCount = inputTokenCount; } public void append(ChatCompletions completions) { if (completions == null) { return; } List<ChatChoice> choices = completions.getChoices(); if (choices == null || choices.isEmpty()) { return; } ChatChoice chatCompletionChoice = choices.get(0); if (chatCompletionChoice == null) { return; } CompletionsFinishReason finishReason = chatCompletionChoice.getFinishReason(); if (finishReason != null) { this.finishReason = finishReason; } com.azure.ai.openai.models.ChatResponseMessage delta = chatCompletionChoice.getDelta(); if (delta == null) { return; } String content = delta.getContent(); if (content != null) { contentBuilder.append(content); return; } FunctionCall functionCall = delta.getFunctionCall(); if (functionCall != null) { if (functionCall.getName() != null) { toolNameBuilder.append(functionCall.getName()); } if (functionCall.getArguments() != null) { toolArgumentsBuilder.append(functionCall.getArguments()); } } } public void append(Completions completions) { if (completions == null) { return; } List<Choice> choices = completions.getChoices(); if (choices == null || choices.isEmpty()) { return; } Choice completionChoice = choices.get(0); if (completionChoice == null) { return; } CompletionsFinishReason completionsFinishReason = completionChoice.getFinishReason(); if (completionsFinishReason != null) { this.finishReason = completionsFinishReason; } String token = completionChoice.getText(); if (token != null) { contentBuilder.append(token); } } public Response<AiMessage> build(Tokenizer tokenizer, boolean forcefulToolExecution) { String content = contentBuilder.toString(); if (!content.isEmpty()) { return Response.from( AiMessage.from(content), tokenUsage(content, tokenizer), finishReasonFrom(finishReason) ); } String toolName = toolNameBuilder.toString(); if (!toolName.isEmpty()) { ToolExecutionRequest toolExecutionRequest = ToolExecutionRequest.builder() .name(toolName) .arguments(toolArgumentsBuilder.toString()) .build(); return Response.from( AiMessage.from(toolExecutionRequest), tokenUsage(toolExecutionRequest, tokenizer, forcefulToolExecution), finishReasonFrom(finishReason) ); } return null; } private TokenUsage tokenUsage(String content, Tokenizer tokenizer) { if (tokenizer == null) { return null; } int outputTokenCount = tokenizer.estimateTokenCountInText(content); return new TokenUsage(inputTokenCount, outputTokenCount); } private TokenUsage tokenUsage(ToolExecutionRequest toolExecutionRequest, Tokenizer tokenizer, boolean forcefulToolExecution) { if (tokenizer == null) { return null; } int outputTokenCount = 0; if (forcefulToolExecution) { // OpenAI calculates output tokens differently when tool is executed forcefully outputTokenCount += tokenizer.estimateTokenCountInForcefulToolExecutionRequest(toolExecutionRequest); } else { outputTokenCount = tokenizer.estimateTokenCountInToolExecutionRequests(singletonList(toolExecutionRequest)); } return new TokenUsage(inputTokenCount, outputTokenCount); } }
[ "dev.langchain4j.agent.tool.ToolExecutionRequest.builder" ]
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package dev.nano.sbot.configuration; import dev.langchain4j.chain.ConversationalRetrievalChain; import dev.langchain4j.data.document.Document; import dev.langchain4j.data.document.splitter.DocumentSplitters; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.retriever.EmbeddingStoreRetriever; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; import dev.nano.sbot.retriever.EmbeddingStoreLoggingRetriever; import lombok.RequiredArgsConstructor; import lombok.extern.slf4j.Slf4j; import org.springframework.beans.factory.annotation.Value; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import java.time.Duration; import java.util.List; import static dev.nano.sbot.constant.Constants.PROMPT_TEMPLATE_2; @Configuration @RequiredArgsConstructor @Slf4j public class LangChainConfiguration { @Value("${langchain.api.key}") private String apiKey; @Value("${langchain.timeout}") private Long timeout; private final List<Document> documents; @Bean public ConversationalRetrievalChain chain() { EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel(); EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>(); EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder() .documentSplitter(DocumentSplitters.recursive(500, 0)) .embeddingModel(embeddingModel) .embeddingStore(embeddingStore) .build(); log.info("Ingesting Spring Boot Resources ..."); ingestor.ingest(documents); log.info("Ingested {} documents", documents.size()); EmbeddingStoreRetriever retriever = EmbeddingStoreRetriever.from(embeddingStore, embeddingModel); EmbeddingStoreLoggingRetriever loggingRetriever = new EmbeddingStoreLoggingRetriever(retriever); /*MessageWindowChatMemory chatMemory = MessageWindowChatMemory.builder() .maxMessages(10) .build();*/ log.info("Building ConversationalRetrievalChain ..."); ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder() .chatLanguageModel(OpenAiChatModel.builder() .apiKey(apiKey) .timeout(Duration.ofSeconds(timeout)) .build() ) .promptTemplate(PromptTemplate.from(PROMPT_TEMPLATE_2)) //.chatMemory(chatMemory) .retriever(loggingRetriever) .build(); log.info("Spring Boot knowledge base is ready!"); return chain; } }
[ "dev.langchain4j.chain.ConversationalRetrievalChain.builder", "dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder", "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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package com.nexus.backend.service; import com.nexus.backend.dto.UserTender; import com.nexus.backend.entity.Act; import com.nexus.backend.entity.Tender; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.input.Prompt; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.openai.OpenAiChatModel; import org.springframework.stereotype.Service; import java.util.HashMap; import java.util.Map; import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO; @Service public class AiService { public void testGpt(){ PromptTemplate promptTemplate = PromptTemplate .from("Tell me a {{adjective}} joke about {{content}}.."); Map<String, Object> variables = new HashMap<>(); variables.put("adjective", "funny"); variables.put("content", "computers"); Prompt prompt = promptTemplate.apply(variables); ChatLanguageModel model = OpenAiChatModel.builder() .apiKey("KEY").modelName(GPT_3_5_TURBO) .temperature(0.3) .build(); String response = model.generate(prompt.text()); System.out.println(response); } public String checkIfCompliant(Act act, UserTender userTender) { PromptTemplate promptTemplate = PromptTemplate .from("This is a government act with a set of compliances {{act}}, With keeping this above act in mind, tell me if my tender/plan seems broadly compliant or not. " + "Consider this tender/plan: {{tender}}" + "Let me know if there are any shortcomings and where the tender/plan is not compliant. Also tell me about penalties."); Map<String, Object> variables = new HashMap<>(); variables.put("act", act); variables.put("tender", userTender); Prompt prompt = promptTemplate.apply(variables); ChatLanguageModel model = OpenAiChatModel.builder() .apiKey("API_KEY") .modelName(GPT_3_5_TURBO) .temperature(0.3) .build(); String response = model.generate(prompt.text()); System.out.println(response); return response; } public void Summarise(){ } public String checkIfTenderIsCompliant(Tender tender, String userTender) { PromptTemplate promptTemplate = PromptTemplate .from("This is a government Tender with a set of compliances {{tender}}. With keeping this above act in mind, tell me if my tender seems broadly compliant or not. " + "Consider this tender/plan: {{userTender}}" + "Let me know if there are any shortcomings and where the tender is not compliant. Also tell me about penalties."); Map<String, Object> variables = new HashMap<>(); variables.put("tender", tender.toString()); variables.put("userTender", userTender.toString()); Prompt prompt = promptTemplate.apply(variables); ChatLanguageModel model = OpenAiChatModel.builder() .apiKey("KEY") .modelName(GPT_3_5_TURBO) .temperature(0.3) .build(); String response = model.generate(prompt.text()); System.out.println(response); return response; } }
[ "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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package eu.luminis.faqlangchain.config; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.inprocess.InProcessEmbeddingModel; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.openai.OpenAiEmbeddingModel; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; import dev.langchain4j.store.embedding.weaviate.WeaviateEmbeddingStore; import org.springframework.beans.factory.annotation.Qualifier; import org.springframework.beans.factory.annotation.Value; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import static dev.langchain4j.model.inprocess.InProcessEmbeddingModelType.*; import static dev.langchain4j.model.openai.OpenAiModelName.*; import static java.time.Duration.*; @Configuration public class QuestionAnsweringConfig { @Value("${openai.apiKey}") private String openaiApiKey; @Qualifier("openaiModel") @Bean public EmbeddingModel openaiEmbeddingModel() { return OpenAiEmbeddingModel.builder() .apiKey(openaiApiKey) .modelName(TEXT_EMBEDDING_ADA_002) .build(); } @Qualifier("inMemoryModel") @Bean public EmbeddingModel inMemoryEmbeddingModel() { return new InProcessEmbeddingModel(ALL_MINILM_L6_V2); } @Qualifier("openaiChatModel") @Bean public ChatLanguageModel openaiChatModel() { return OpenAiChatModel.builder() .apiKey(openaiApiKey) .modelName(GPT_3_5_TURBO) .temperature(0.7) .timeout(ofSeconds(15)) .maxRetries(3) .logResponses(true) .logRequests(true) .build(); } @Qualifier("inMemoryEmbeddingStore") @Bean public EmbeddingStore<TextSegment> inMemoryEmbeddingStore() { return new InMemoryEmbeddingStore<>(); } @Qualifier("weaviateEmbeddingStore") @Bean public EmbeddingStore<TextSegment> weaviateEmbeddingStore(@Value("${weaviate.apiKey}") String apiKey, @Value("${weaviate.host}") String host) { return WeaviateEmbeddingStore.builder() .apiKey(apiKey) .scheme("https") .host(host) .build(); } }
[ "dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder", "dev.langchain4j.model.openai.OpenAiChatModel.builder", "dev.langchain4j.store.embedding.weaviate.WeaviateEmbeddingStore.builder" ]
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package com.example.demo; import java.time.Duration; import java.time.LocalDate; import java.util.Arrays; import java.util.List; import dev.langchain4j.memory.ChatMemory; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.input.structured.StructuredPrompt; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.output.structured.Description; import dev.langchain4j.service.AiServices; import dev.langchain4j.service.MemoryId; import dev.langchain4j.service.SystemMessage; import dev.langchain4j.service.UserMessage; import dev.langchain4j.service.V; public class AiServicesExamples { static Duration duration = Duration.ofSeconds(60); static ChatLanguageModel model = OpenAiChatModel.builder().apiKey(ApiKeys.OPENAI_API_KEY).timeout(duration).build(); ////////////////// SIMPLE EXAMPLE ////////////////////// static class Simple_AI_Service_Example { interface Assistant { String chat(String message); } public static void main(String[] args) { Assistant assistant = AiServices.create(Assistant.class, model); String userMessage = "Translate 'Plus-Values des cessions de valeurs mobilières, de droits sociaux et gains assimilés'"; String answer = assistant.chat(userMessage); System.out.println(answer); } } ////////////////// WITH MESSAGE AND VARIABLES ////////////////////// static class AI_Service_with_System_and_User_Messages_Example { interface TextUtils { @SystemMessage("You are a professional translator into {{language}}") @UserMessage("Translate the following text: {{text}}") String translate(@V("text") String text, @V("language") String language); @SystemMessage("Summarize every message from user in {{n}} bullet points. Provide only bullet points.") List<String> summarize(@UserMessage String text, @V("n") int n); } public static void main(String[] args) { TextUtils utils = AiServices.create(TextUtils.class, model); String translation = utils.translate("Hello, how are you?", "italian"); System.out.println(translation); // Ciao, come stai? String text = "AI, or artificial intelligence, is a branch of computer science that aims to create " + "machines that mimic human intelligence. This can range from simple tasks such as recognizing " + "patterns or speech to more complex tasks like making decisions or predictions."; List<String> bulletPoints = utils.summarize(text, 3); System.out.println(bulletPoints); } } ////////////////////EXTRACTING DIFFERENT DATA TYPES //////////////////// static class Sentiment_Extracting_AI_Service_Example { enum Sentiment { POSITIVE, NEUTRAL, NEGATIVE; } interface SentimentAnalyzer { @UserMessage("Analyze sentiment of {{it}}") Sentiment analyzeSentimentOf(String text); @UserMessage("Does {{it}} have a positive sentiment?") boolean isPositive(String text); } public static void main(String[] args) { SentimentAnalyzer sentimentAnalyzer = AiServices.create(SentimentAnalyzer.class, model); Sentiment sentiment = sentimentAnalyzer.analyzeSentimentOf("It is amazing!"); System.out.println(sentiment); // POSITIVE boolean positive = sentimentAnalyzer.isPositive("It is bad!"); System.out.println(positive); // false } } static class POJO_Extracting_AI_Service_Example { static class Person { private String firstName; private String lastName; private LocalDate birthDate; @Override public String toString() { return "Person {" + " firstName = \"" + firstName + "\"" + ", lastName = \"" + lastName + "\"" + ", birthDate = " + birthDate + " }"; } } interface PersonExtractor { @UserMessage("Extract information about a person from {{it}}") Person extractPersonFrom(String text); } public static void main(String[] args) { PersonExtractor extractor = AiServices.create(PersonExtractor.class, model); String text = "In 1968, amidst the fading echoes of Independence Day, " + "a child named John arrived under the calm evening sky. " + "This newborn, bearing the surname Doe, marked the start of a new journey."; Person person = extractor.extractPersonFrom(text); System.out.println(person); // Person { firstName = "John", lastName = "Doe", birthDate = 1968-07-04 } } } ////////////////////// DESCRIPTIONS //////////////////////// static class POJO_With_Descriptions_Extracting_AI_Service_Example { static class Recipe { @Description("short title, 3 words maximum") private String title; @Description("short description, 2 sentences maximum") private String description; @Description("each step should be described in 6 to 8 words, steps should rhyme with each other") private List<String> steps; private Integer preparationTimeMinutes; @Override public String toString() { return "Recipe {" + " title = \"" + title + "\"" + ", description = \"" + description + "\"" + ", steps = " + steps + ", preparationTimeMinutes = " + preparationTimeMinutes + " }"; } } @StructuredPrompt("Create a recipe of a {{dish}} that can be prepared using only {{ingredients}}") static class CreateRecipePrompt { private String dish; private List<String> ingredients; } interface Chef { Recipe createRecipeFrom(String... ingredients); Recipe createRecipe(CreateRecipePrompt prompt); } public static void main(String[] args) { Chef chef = AiServices.create(Chef.class, model); Recipe recipe = chef.createRecipeFrom("cucumber", "tomato", "feta", "onion", "olives", "lemon"); System.out.println(recipe); CreateRecipePrompt prompt = new CreateRecipePrompt(); prompt.dish = "oven dish"; prompt.ingredients = Arrays.asList("cucumber", "tomato", "feta", "onion", "olives", "potatoes"); Recipe anotherRecipe = chef.createRecipe(prompt); System.out.println(anotherRecipe); } } ////////////////////////// WITH MEMORY ///////////////////////// static class ServiceWithMemoryExample { interface Assistant { String chat(String message); } public static void main(String[] args) { ChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(10); Assistant assistant = AiServices.builder(Assistant.class) .chatLanguageModel(model) .chatMemory(chatMemory) .build(); String answer = assistant.chat("Hello! My name is Klaus."); System.out.println(answer); // Hello Klaus! How can I assist you today? String answerWithName = assistant.chat("What is my name?"); System.out.println(answerWithName); // Your name is Klaus. } } static class ServiceWithMemoryForEachUserExample { interface Assistant { String chat(@MemoryId int memoryId, @UserMessage String userMessage); } public static void main(String[] args) { Assistant assistant = AiServices.builder(Assistant.class) .chatLanguageModel(model) .chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(10)) .build(); System.out.println(assistant.chat(1, "Hello, my name is Klaus")); // Hi Klaus! How can I assist you today? System.out.println(assistant.chat(2, "Hello, my name is Francine")); // Hello Francine! How can I assist you today? System.out.println(assistant.chat(1, "What is my name?")); // Your name is Klaus. System.out.println(assistant.chat(2, "What is my name?")); // Your name is Francine. } } }
[ "dev.langchain4j.service.AiServices.builder", "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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import dev.langchain4j.agent.tool.Tool; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.output.Response; import dev.langchain4j.service.AiServices; public class _04_Agents { static class Calculator { @Tool("Calculates the length of a string") int stringLength(String s) { return s.length(); } @Tool("Calculates the sum of two numbers") int add(int a, int b) { return a + b; } } interface Assistant { Response<AiMessage> chat(String userMessage); } public static void main(String[] args) { String openAiKey = System.getenv("OPENAI_API_KEY"); var assistant = AiServices.builder(Assistant.class) .chatLanguageModel(OpenAiChatModel.withApiKey(openAiKey)) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) .tools(new Calculator()) .build(); var question = "What is the sum of the numbers of letters in the words 'language' and 'model'"; var response = assistant.chat(question); System.out.println(response.content().text()); System.out.println("\n\n########### TOKEN USAGE ############\n"); System.out.println(response.tokenUsage()); } }
[ "dev.langchain4j.service.AiServices.builder" ]
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package me.nzuguem.bot.configurations.llm; import dev.langchain4j.memory.ChatMemory; import dev.langchain4j.memory.chat.ChatMemoryProvider; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import jakarta.annotation.PreDestroy; import jakarta.enterprise.context.RequestScoped; import java.util.Map; import java.util.concurrent.ConcurrentHashMap; @RequestScoped public class ChatMemoryBean implements ChatMemoryProvider { private final Map<Object, ChatMemory> memories = new ConcurrentHashMap<>(); @Override public ChatMemory get(Object memoryId) { return memories.computeIfAbsent(memoryId, id -> MessageWindowChatMemory.builder() .maxMessages(20) .id(memoryId) .build()); } @PreDestroy public void close() { memories.clear(); } }
[ "dev.langchain4j.memory.chat.MessageWindowChatMemory.builder" ]
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package net.savantly.mainbot.config; import java.time.Duration; import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.context.annotation.Primary; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.openai.OpenAiChatModel; import lombok.RequiredArgsConstructor; import net.savantly.mainbot.service.replicate.ReplicateClient; @Configuration @RequiredArgsConstructor public class ChatModelConfig { private final OpenAIConfig openAIConfig; @Bean @Primary @ConditionalOnProperty(prefix = "openai", name = "enabled", havingValue = "true") public ChatLanguageModel getChatModel(ReplicateClient replicateClient) { return getOpenAiChatModel(); // return new ReplicateChatLanguageModel(replicateClient); } public ChatLanguageModel getOpenAiChatModel() { String apiKey = openAIConfig.getApiKey(); return OpenAiChatModel.builder() .apiKey(apiKey) // https://platform.openai.com/account/api-keys .modelName(openAIConfig.getChatModelId()) .temperature(0.1) .logResponses(false) .logRequests(false) .timeout(Duration.ofSeconds(openAIConfig.getTimeoutSeconds())) .build(); } }
[ "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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package io.quarkiverse.langchain4j.workshop.chat; import dev.langchain4j.memory.ChatMemory; import dev.langchain4j.memory.chat.ChatMemoryProvider; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import jakarta.enterprise.context.ApplicationScoped; import java.util.Map; import java.util.concurrent.ConcurrentHashMap; @ApplicationScoped public class ChatMemoryBean implements ChatMemoryProvider { private final Map<Object, ChatMemory> memories = new ConcurrentHashMap<>(); @Override public ChatMemory get(Object memoryId) { return memories.computeIfAbsent(memoryId, id -> MessageWindowChatMemory.builder() .maxMessages(3) .id(memoryId) .build()); } public void clear(Object session) { memories.remove(session); } }
[ "dev.langchain4j.memory.chat.MessageWindowChatMemory.builder" ]
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package io.quarkiverse.langchain4j.workshop.chat; import dev.langchain4j.data.document.Document; import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; import dev.langchain4j.data.document.parser.TextDocumentParser; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import io.quarkiverse.langchain4j.redis.RedisEmbeddingStore; import io.quarkus.runtime.StartupEvent; import jakarta.enterprise.context.ApplicationScoped; import jakarta.enterprise.event.Observes; import jakarta.inject.Inject; import java.io.File; import java.util.List; import static dev.langchain4j.data.document.splitter.DocumentSplitters.recursive; @ApplicationScoped public class DocumentIngestor { /** * The embedding store (the database). * The bean is provided by the quarkus-langchain4j-redis extension. */ @Inject RedisEmbeddingStore store; /** * The embedding model (how the vector of a document is computed). * The bean is provided by the LLM (like openai) extension. */ @Inject EmbeddingModel embeddingModel; public void ingest(@Observes StartupEvent event) { System.out.printf("Ingesting documents...%n"); List<Document> documents = FileSystemDocumentLoader.loadDocuments(new File("src/main/resources/catalog").toPath(), new TextDocumentParser()); var ingestor = EmbeddingStoreIngestor.builder() .embeddingStore(store) .embeddingModel(embeddingModel) .documentSplitter(recursive(500, 0)) .build(); ingestor.ingest(documents); System.out.printf("Ingested %d documents.%n", documents.size()); } }
[ "dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder" ]
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package com.example.demo; import java.time.Duration; import dev.langchain4j.chain.ConversationalChain; import dev.langchain4j.model.openai.OpenAiChatModel; public class _07_ConversationalChain { public static void main(String[] args) { Duration duration = Duration.ofSeconds(60); OpenAiChatModel model = OpenAiChatModel.builder().apiKey(ApiKeys.OPENAI_API_KEY).timeout(duration).build(); ConversationalChain chain = ConversationalChain.builder().chatLanguageModel(model) // .chatMemory(...) // you can override default chat memory .build(); String userMessage1 = "Can you give a brief explanation of the Agile methodology, 3 lines max?"; System.out.println("[User]: " + userMessage1); String answer1 = chain.execute(userMessage1); System.out.println("[LLM]: " + answer1); String userMessage2 = "What are good tools for that? 3 lines max."; System.out.println("[User]: " + userMessage2); String answer2 = chain.execute(userMessage2); System.out.println("[LLM]: " + answer2); } }
[ "dev.langchain4j.model.openai.OpenAiChatModel.builder", "dev.langchain4j.chain.ConversationalChain.builder" ]
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package org.mf.langchain.service; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.model.localai.LocalAiChatModel; import dev.langchain4j.model.localai.LocalAiStreamingChatModel; import org.jetbrains.annotations.Nullable; import org.mf.langchain.util.LanguageModel; import org.mf.langchain.StreamLanguageModel; import org.springframework.stereotype.Service; import java.time.Duration; import java.util.function.Consumer; @Service public class LangChainService { private final LanguageModel lm; private final StreamLanguageModel slm; LangChainService() { lm = new LanguageModel(LocalAiChatModel.builder() .modelName("phi-2") .baseUrl("http://localhost:8080") .build()); slm = new StreamLanguageModel(LocalAiStreamingChatModel.builder() .modelName("phi-2") .baseUrl("http://localhost:8080") .timeout(Duration.ofDays(1)) .temperature(0.8) .build()); } public String Generate(String prompt) { return lm.RunBlocking(prompt); } public void GenerateStream(String prompt, Consumer<String> onNext, Consumer<Throwable> onError, @Nullable Consumer<AiMessage> onComplete) { slm.generate(prompt, onNext, onError, onComplete); } }
[ "dev.langchain4j.model.localai.LocalAiChatModel.builder", "dev.langchain4j.model.localai.LocalAiStreamingChatModel.builder" ]
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import dev.langchain4j.agent.tool.Tool; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.output.Response; import dev.langchain4j.service.AiServices; public class _04_Agents { static class Calculator { @Tool("Calculates the length of a string") int stringLength(String s) { return s.length(); } @Tool("Calculates the sum of two numbers") int add(int a, int b) { return a + b; } } interface Assistant { Response<AiMessage> chat(String userMessage); } public static void main(String[] args) { String openAiKey = System.getenv("OPENAI_API_KEY"); var assistant = AiServices.builder(Assistant.class) .chatLanguageModel(OpenAiChatModel.withApiKey(openAiKey)) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) .tools(new Calculator()) .build(); var question = "What is the sum of the numbers of letters in the words 'language' and 'model'"; var response = assistant.chat(question); System.out.println(response.content().text()); System.out.println("\n\n########### TOKEN USAGE ############\n"); System.out.println(response.tokenUsage()); } }
[ "dev.langchain4j.service.AiServices.builder" ]
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package io.quarkiverse.langchain4j.workshop.chat; import dev.langchain4j.data.document.Document; import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; import dev.langchain4j.data.document.parser.TextDocumentParser; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import io.quarkiverse.langchain4j.redis.RedisEmbeddingStore; import io.quarkus.runtime.StartupEvent; import jakarta.enterprise.context.ApplicationScoped; import jakarta.enterprise.event.Observes; import jakarta.inject.Inject; import java.io.File; import java.util.List; import static dev.langchain4j.data.document.splitter.DocumentSplitters.recursive; @ApplicationScoped public class DocumentIngestor { /** * The embedding store (the database). * The bean is provided by the quarkus-langchain4j-redis extension. */ @Inject RedisEmbeddingStore store; /** * The embedding model (how the vector of a document is computed). * The bean is provided by the LLM (like openai) extension. */ @Inject EmbeddingModel embeddingModel; public void ingest(@Observes StartupEvent event) { System.out.printf("Ingesting documents...%n"); List<Document> documents = FileSystemDocumentLoader.loadDocuments(new File("src/main/resources/catalog").toPath(), new TextDocumentParser()); var ingestor = EmbeddingStoreIngestor.builder() .embeddingStore(store) .embeddingModel(embeddingModel) .documentSplitter(recursive(500, 0)) .build(); ingestor.ingest(documents); System.out.printf("Ingested %d documents.%n", documents.size()); } }
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package io.quarkiverse.langchain4j.samples; import java.util.function.Supplier; import dev.langchain4j.memory.ChatMemory; import dev.langchain4j.memory.chat.ChatMemoryProvider; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.store.memory.chat.InMemoryChatMemoryStore; public class CustomProvider implements Supplier<ChatMemoryProvider> { private final InMemoryChatMemoryStore store = new InMemoryChatMemoryStore(); @Override public ChatMemoryProvider get() { return new ChatMemoryProvider() { @Override public ChatMemory get(Object memoryId) { return MessageWindowChatMemory.builder() .maxMessages(20) .id(memoryId) .chatMemoryStore(store) .build(); } }; } }
[ "dev.langchain4j.memory.chat.MessageWindowChatMemory.builder" ]
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package dev.onurb.travelassistant; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; import java.io.IOException; import java.time.Duration; import java.util.Scanner; public class TravelAgency { public static void main(String[] args) throws IOException { String apiKey = System.getenv("OPENAPI_KEY"); TravelAssistant assistant = AiServices.builder(TravelAssistant.class) .chatLanguageModel(OpenAiChatModel.builder().apiKey(apiKey).timeout(Duration.ofMinutes(3)).build()) .tools(new TripServices()) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) .build(); String input = readInput(); while (!"bye".equalsIgnoreCase(input)) { String answer = assistant.chat(input); System.out.println("\u001B[33m" + answer + "\u001B[37m"); input = readInput(); } } private static String readInput() { Scanner in = new Scanner(System.in); System.out.print("> "); return in.nextLine(); } }
[ "dev.langchain4j.service.AiServices.builder", "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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/* * Copyright 2024 Google LLC * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * https://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package gemini.workshop; import dev.langchain4j.agent.tool.P; import dev.langchain4j.agent.tool.Tool; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.vertexai.VertexAiGeminiChatModel; import dev.langchain4j.service.AiServices; public class Step8b_FunctionCalling { record WeatherForecast(String location, String forecast, int temperature) {} static class WeatherForecastService { @Tool("Get the weather forecast for a location") WeatherForecast getForecast(@P("Location to get the forecast for") String location) { if (location.equals("Paris")) { return new WeatherForecast("Paris", "Sunny", 20); } else if (location.equals("London")) { return new WeatherForecast("London", "Rainy", 15); } else { return new WeatherForecast("Unknown", "Unknown", 0); } } } interface WeatherAssistant { String chat(String userMessage); } public static void main(String[] args) { ChatLanguageModel model = VertexAiGeminiChatModel.builder() .project(System.getenv("PROJECT_ID")) .location(System.getenv("LOCATION")) .modelName("gemini-1.0-pro") .maxOutputTokens(100) .build(); WeatherForecastService weatherForecastService = new WeatherForecastService(); WeatherAssistant assistant = AiServices.builder(WeatherAssistant.class) .chatLanguageModel(model) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) .tools(weatherForecastService) .build(); System.out.println(assistant.chat("What is the weather in Paris?")); System.out.println(assistant.chat("What is the weather in London?")); System.out.println(assistant.chat("Is the temperature warmer in Paris or London?")); } }
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package com.hillarocket.application.handler; import com.vaadin.flow.server.auth.AnonymousAllowed; import dev.hilla.BrowserCallable; import dev.langchain4j.memory.chat.TokenWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.openai.OpenAiStreamingChatModel; import dev.langchain4j.model.openai.OpenAiTokenizer; import dev.langchain4j.service.AiServices; import dev.langchain4j.service.TokenStream; import jakarta.annotation.PostConstruct; import org.springframework.beans.factory.annotation.Value; import reactor.core.publisher.Flux; import reactor.core.publisher.Sinks; @BrowserCallable @AnonymousAllowed public class OpenApiHandler { @Value("${openai.api.key}") private String OPENAI_API_KEY; private Assistant assistant; private StreamingAssistant streamingAssistant; interface Assistant { String chat(String message); } interface StreamingAssistant { TokenStream chat(String message); } @PostConstruct public void init() { if (OPENAI_API_KEY == null) { System.err.println("ERROR: OPENAI_API_KEY environment variable is not set. Please set it to your OpenAI API key."); } var memory = TokenWindowChatMemory.withMaxTokens(2000, new OpenAiTokenizer("gpt-3.5-turbo")); assistant = AiServices.builder(Assistant.class) .chatLanguageModel(OpenAiChatModel.withApiKey(OPENAI_API_KEY)) .chatMemory(memory) .build(); streamingAssistant = AiServices.builder(StreamingAssistant.class) .streamingChatLanguageModel(OpenAiStreamingChatModel.withApiKey(OPENAI_API_KEY)) .chatMemory(memory) .build(); } public String chat(String message) { return assistant.chat(message); } public Flux<String> chatStream(String message) { Sinks.Many<String> sink = Sinks.many().unicast().onBackpressureBuffer(); streamingAssistant.chat(message) .onNext(sink::tryEmitNext) .onComplete(c -> sink.tryEmitComplete()) .onError(sink::tryEmitError) .start(); return sink.asFlux(); } }
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package _Engenharia; import dev.langchain4j.chain.ConversationalRetrievalChain; import dev.langchain4j.data.document.Document; //import dev.langchain4j.data.document.splitter.ParagraphSplitter; !!!!!!!!!!!!!!!DANDO ERRO, substitui temporariamente!!!!!!!!!!!!!!!!!!!!! import dev.langchain4j.data.document.splitter.DocumentSplitters; //Substituição import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.huggingface.HuggingFaceChatModel; import dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel; import dev.langchain4j.retriever.EmbeddingStoreRetriever; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; import java.net.URISyntaxException; import java.net.URL; import java.nio.file.Path; import java.nio.file.Paths; import static dev.langchain4j.data.document.FileSystemDocumentLoader.loadDocument; import static java.time.Duration.ofSeconds; import java.io.File; public class Assistente { // You can get your own HuggingFace API key here: https://huggingface.co/settings/tokens public static final String hfApiKey = "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"; private static String pergunta; public String fazerPergunta() throws Exception { Document document = loadDocument(toPath("template.txt")); //Usa documento criado com todos os dados do documento selecionado (Esse documento e criado dentro do pacote _Engenharia) //escolhendo um modelo para vetorizar meu texto EmbeddingModel embeddingModel = HuggingFaceEmbeddingModel.builder() .accessToken(hfApiKey) .modelId("sentence-transformers/all-MiniLM-L6-v2") .waitForModel(true) .timeout(ofSeconds(60)) .build(); EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>(); //estou aplicando o modelo de vetorização escolhido ao meu texto EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder() // .splitter(new ParagraphSplitter()) !!!!!!!!!!!!!!!DANDO ERRO, substitui temporariamente!!!!!!!!!!!!!!!!!!!!! .documentSplitter(DocumentSplitters.recursive(500)) //Substituição .embeddingModel(embeddingModel) .embeddingStore(embeddingStore) .build(); ingestor.ingest(document); //aqui eu escolho o modelo da inferência (a pergunta) ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder() .chatLanguageModel(HuggingFaceChatModel.withAccessToken(hfApiKey)) .retriever(EmbeddingStoreRetriever.from(embeddingStore, embeddingModel)) // .chatMemory() // you can override default chat memory // .promptTemplate() // you can override default prompt template .build(); //aqui eu faço a inferência String answer = chain.execute(pergunta); File delete_file = new File("src/main/java/_Engenharia/template.txt"); //Apaga o documento depois da resposta delete_file.delete(); //Caso erro na resposta o arquivo NAO e deletado return answer; // Charlie is a cheerful carrot living in VeggieVille... //exemplo para continuar a pesquisa //https://github.com/langchain4j/langchain4j/blob/7307f43d9823af619f1e3196252d212f3df04ddc/langchain4j/src/main/java/dev/langchain4j/model/huggingface/HuggingFaceChatModel.java } private static Path toPath(String fileName) { try { URL fileUrl = Assistente.class.getResource(fileName); return Paths.get(fileUrl.toURI()); } catch (URISyntaxException e) { throw new RuntimeException(e); } } public void setPergunta(String p) { pergunta = p; } }
[ "dev.langchain4j.chain.ConversationalRetrievalChain.builder", "dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel.builder", "dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder" ]
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package com.kchandrakant; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.input.Prompt; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.openai.OpenAiChatModel; import java.util.HashMap; import java.util.Map; import static dev.langchain4j.model.openai.OpenAiModelName.GPT_3_5_TURBO; import static java.time.Duration.ofSeconds; public class PromptTemplates { public static void main(String[] args) { // Create a prompt template PromptTemplate promptTemplate = PromptTemplate.from("Tell me a {{adjective}} joke about {{content}}.."); // Generate prompt using the prompt template and user variables Map<String, Object> variables = new HashMap<>(); variables.put("adjective", "funny"); variables.put("content", "humans"); Prompt prompt = promptTemplate.apply(variables); System.out.println(prompt.text()); // Create an instance of a model ChatLanguageModel model = OpenAiChatModel.builder() .apiKey(ApiKeys.OPENAI_API_KEY) .modelName(GPT_3_5_TURBO) .temperature(0.3) .build(); // Start interacting String response = model.generate(prompt.text()); System.out.println(response); } }
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package com.azure.migration.java.copilot.service; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.rag.content.retriever.ContentRetriever; import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; import dev.langchain4j.service.AiServices; import dev.langchain4j.store.embedding.EmbeddingStore; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; @Configuration public class Configure { @Bean ServiceAnalysisAgent chooseServiceAnalysisAgent(ChatLanguageModel chatLanguageModel) { return AiServices.builder(ServiceAnalysisAgent.class) .chatLanguageModel(chatLanguageModel) .build(); } @Bean ConfigureResourceAgent configureResourceAgent(ChatLanguageModel chatLanguageModel,ContentRetriever contentRetriever) { return AiServices.builder(ConfigureResourceAgent.class) .chatLanguageModel(chatLanguageModel) .contentRetriever(contentRetriever) .build(); } @Bean WorkflowChatAgent configureWorkflowChatAgent(ChatLanguageModel chatLanguageModel, ContentRetriever contentRetriever, MigrationWorkflowTools migrationWorkflowTools) { return AiServices.builder(WorkflowChatAgent.class) .chatLanguageModel(chatLanguageModel) .tools(migrationWorkflowTools) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) .build(); } @Bean ContentRetriever contentRetriever(EmbeddingStore<TextSegment> embeddingStore, EmbeddingModel embeddingModel) { // You will need to adjust these parameters to find the optimal setting, which will depend on two main factors: // - The nature of your data // - The embedding model you are using int maxResults = 5; double minScore = 0.6; return EmbeddingStoreContentRetriever.builder() .embeddingStore(embeddingStore) .embeddingModel(embeddingModel) .maxResults(maxResults) .minScore(minScore) .build(); } @Bean EmbeddingModel embeddingModel() { return new AllMiniLmL6V2EmbeddingModel(); } }
[ "dev.langchain4j.service.AiServices.builder", "dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder" ]
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package com.example.application.services; import com.vaadin.flow.server.auth.AnonymousAllowed; import dev.hilla.BrowserCallable; import dev.langchain4j.memory.chat.TokenWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.openai.OpenAiStreamingChatModel; import dev.langchain4j.model.openai.OpenAiTokenizer; import dev.langchain4j.service.AiServices; import dev.langchain4j.service.TokenStream; import jakarta.annotation.PostConstruct; import org.springframework.beans.factory.annotation.Value; import org.springframework.stereotype.Service; import reactor.core.publisher.Flux; import reactor.core.publisher.Sinks; @Service @BrowserCallable @AnonymousAllowed public class ChatService { @Value("${openai.api.key}") private String OPENAI_API_KEY; private Assistant assistant; private StreamingAssistant streamingAssistant; interface Assistant { String chat(String message); } interface StreamingAssistant { TokenStream chat(String message); } @PostConstruct public void init() { var memory = TokenWindowChatMemory.withMaxTokens(2000, new OpenAiTokenizer("gpt-3.5-turbo")); assistant = AiServices.builder(Assistant.class) .chatLanguageModel(OpenAiChatModel.withApiKey(OPENAI_API_KEY)) .chatMemory(memory) .build(); streamingAssistant = AiServices.builder(StreamingAssistant.class) .streamingChatLanguageModel(OpenAiStreamingChatModel.withApiKey(OPENAI_API_KEY)) .chatMemory(memory) .build(); } public String chat(String message) { return assistant.chat(message); } public Flux<String> chatStream(String message) { Sinks.Many<String> sink = Sinks.many().unicast().onBackpressureBuffer(); streamingAssistant.chat(message) .onNext(sink::tryEmitNext) .onComplete(sink::tryEmitComplete) .onError(sink::tryEmitError) .start(); return sink.asFlux(); } }
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package org.acme; import dev.langchain4j.data.document.Document; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import io.quarkus.logging.Log; import io.quarkus.runtime.Startup; import jakarta.enterprise.context.ApplicationScoped; import jakarta.inject.Inject; import jakarta.json.Json; import jakarta.json.JsonArray; import jakarta.json.JsonReader; import jakarta.json.JsonValue; import org.eclipse.microprofile.config.inject.ConfigProperty; import java.io.File; import java.io.FileNotFoundException; import java.io.FileReader; import java.util.ArrayList; import java.util.List; import static dev.langchain4j.data.document.splitter.DocumentSplitters.recursive; @ApplicationScoped public class IngestData { @Inject EmbeddingStore<TextSegment> store; @Inject EmbeddingModel embeddingModel; @Inject @ConfigProperty(name = "data.file") File dataFile; @Inject @ConfigProperty(name = "max.entries", defaultValue = "99999") Integer maxEntries; @Startup public void init() { List<Document> documents = new ArrayList<>(); try(JsonReader reader = Json.createReader(new FileReader(dataFile))) { JsonArray results = reader.readArray(); Log.info("Ingesting news reports..."); int i = 0; for (JsonValue newsEntry : results) { i++; if(i > maxEntries) { break; } String content = newsEntry.asJsonObject().getString("content", null); if(content != null && !content.isEmpty()) { Document doc = new Document(content); documents.add(doc); continue; } String fullDescription = newsEntry.asJsonObject().getString("full_description", null); if(fullDescription != null && !fullDescription.isEmpty()) { Document doc = new Document(fullDescription); documents.add(doc); continue; } String description = newsEntry.asJsonObject().getString("description", null); if(description != null && !description.isEmpty()) { Document doc = new Document(description); documents.add(doc); continue; } } var ingestor = EmbeddingStoreIngestor.builder() .embeddingStore(store) .embeddingModel(embeddingModel) .documentSplitter(recursive(1000, 50)) .build(); ingestor.ingest(documents); Log.infof("Ingested %d news articles.", documents.size()); } catch (FileNotFoundException e) { throw new RuntimeException(e); } } }
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package com.sivalabs.demo.langchain4j; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.ollama.OllamaChatModel; public class OllamaChatDemo { public static void main(String[] args) { ChatLanguageModel model = OllamaChatModel.builder() .baseUrl("http://localhost:11434") .modelName("llama2") .build(); String answer = model.generate("List all the movies directed by Quentin Tarantino"); System.out.println(answer); } }
[ "dev.langchain4j.model.ollama.OllamaChatModel.builder" ]
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package com.ramesh.langchain; import java.util.Scanner; import dev.langchain4j.agent.tool.Tool; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; /*** * This project demostrates the use of LangCHain Services which uses custom tools to generate the final output */ public class ServiceWithToolsLive { // Open AI Key and Chat GPT Model to use public static String OPENAI_API_KEY = "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"; public static String OPENAI_MODEL = "gpt-3.5-turbo"; public static void main(String[] args) { System.out.println("Using a custom Calculator as LangChain \"tool\""); // Building a Custom LangChain Assistant using LangChain AiServices System.out.println("Building a Custom Assistant using LangChain AiServices"); Assistant assistant = AiServices.builder(Assistant.class) .chatLanguageModel(OpenAiChatModel.withApiKey(OPENAI_API_KEY)).tools(new Calculator()) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)).build(); while (true) { // get 2 words for which the total characters count is calculated Scanner scanner = new Scanner(System.in); System.out.print("Enter Word 1:"); String word1 = scanner.nextLine(); System.out.print("Enter Word 2:"); String word2 = scanner.nextLine(); String question = "What is the sum of the numbers of letters in the words \"" + word1 + "\" and \"" + word2 + "\"?"; System.out.println("Prompting ChatGPT :" + question); // when a prompt having 2 words are sent LLM via LAngChain Assistant // the Calcualtor functions are called to get the final answers System.out.println("Invoking Custom Assistant Class chat() and getting response from ChatGPT..."); String answer = assistant.chat(question); System.out.println("ChatGPT Response...\n"); System.out.println(answer); } } // a custom tool static class Calculator { @Tool("Calculates the length of a string") int stringLength(String s) { return s.length(); } @Tool("Calculates the sum of two numbers") int add(int a, int b) { return a + b; } } interface Assistant { String chat(String userMessage); } }
[ "dev.langchain4j.service.AiServices.builder" ]
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package ${{ values.basePackage }}; import java.io.IOException; import java.nio.file.Path; import dev.langchain4j.data.document.Document; import dev.langchain4j.data.document.DocumentParser; import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; import dev.langchain4j.data.document.parser.TextDocumentParser; import dev.langchain4j.data.document.splitter.DocumentSplitters; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.memory.ChatMemory; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.rag.content.retriever.ContentRetriever; import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; import dev.langchain4j.service.AiServices; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.context.annotation.Bean; import org.springframework.util.ResourceUtils; import org.springframework.web.bind.annotation.PostMapping; import org.springframework.web.bind.annotation.RequestBody; import org.springframework.web.bind.annotation.RestController; @SpringBootApplication public class DemoApplication { public static void main(String[] args) { SpringApplication.run(DemoApplication.class, args); } @Bean ChatAgent chatAgent(ChatLanguageModel chatLanguageModel) { ChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(10); return AiServices.builder(ChatAgent.class) .chatLanguageModel(chatLanguageModel) .chatMemory(chatMemory) .build(); } @Bean DocumentAgent documentAgent(ChatLanguageModel chatLanguageModel, EmbeddingModel embeddingModel, EmbeddingStore<TextSegment> embeddingStore) throws IOException { Path documentPath = ResourceUtils.getFile("classpath:documents/story.md").toPath(); DocumentParser documentParser = new TextDocumentParser(); Document document = FileSystemDocumentLoader.loadDocument(documentPath, documentParser); EmbeddingStoreIngestor dataIngestor = EmbeddingStoreIngestor.builder() .embeddingStore(embeddingStore) .embeddingModel(embeddingModel) .documentSplitter(DocumentSplitters.recursive(300, 10)) .build(); dataIngestor.ingest(document); ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder() .embeddingStore(embeddingStore) .embeddingModel(embeddingModel) .maxResults(3) .minScore(0.5) .build(); ChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(10); return AiServices.builder(DocumentAgent.class) .chatLanguageModel(chatLanguageModel) .contentRetriever(contentRetriever) .chatMemory(chatMemory) .build(); } } @RestController class ChatController { private final ChatAgent chatAgent; ChatController(ChatAgent chatAgent) { this.chatAgent = chatAgent; } @PostMapping("/chat") String chat(@RequestBody String prompt) { return chatAgent.answer(prompt); } } @RestController class DocumentController { private final DocumentAgent documentAgent; DocumentController(DocumentAgent documentAgent) { this.documentAgent = documentAgent; } @PostMapping("/chat/doc") String chat(@RequestBody String prompt) { return documentAgent.answer(prompt); } } interface ChatAgent { String answer(String prompt); } interface DocumentAgent { String answer(String prompt); }
[ "dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever.builder", "dev.langchain4j.service.AiServices.builder", "dev.langchain4j.store.embedding.EmbeddingStoreIngestor.builder" ]
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package com.docuverse.backend.configuration; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.openai.OpenAiEmbeddingModel; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; import io.github.cdimascio.dotenv.Dotenv; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import static dev.langchain4j.model.openai.OpenAiModelName.TEXT_EMBEDDING_ADA_002; import static java.time.Duration.ofSeconds; @Configuration public class EmbeddingModelConfiguration { Dotenv dotenv = Dotenv.load(); @Bean public EmbeddingModel embeddingModel() { return OpenAiEmbeddingModel.builder() .apiKey(dotenv.get("OPENAI_API_KEY")) .modelName(TEXT_EMBEDDING_ADA_002) .timeout(ofSeconds(15)) .logRequests(false) .logResponses(false) .build(); } }
[ "dev.langchain4j.model.openai.OpenAiEmbeddingModel.builder" ]
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package io.quarkiverse.langchain4j.openai.runtime; import static io.quarkiverse.langchain4j.runtime.OptionalUtil.firstOrDefault; import java.nio.file.Path; import java.nio.file.Paths; import java.util.Optional; import java.util.function.Supplier; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.chat.DisabledChatLanguageModel; import dev.langchain4j.model.chat.DisabledStreamingChatLanguageModel; import dev.langchain4j.model.chat.StreamingChatLanguageModel; import dev.langchain4j.model.embedding.DisabledEmbeddingModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.image.DisabledImageModel; import dev.langchain4j.model.image.ImageModel; import dev.langchain4j.model.moderation.DisabledModerationModel; import dev.langchain4j.model.moderation.ModerationModel; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.openai.OpenAiEmbeddingModel; import dev.langchain4j.model.openai.OpenAiModerationModel; import dev.langchain4j.model.openai.OpenAiStreamingChatModel; import io.quarkiverse.langchain4j.openai.QuarkusOpenAiClient; import io.quarkiverse.langchain4j.openai.QuarkusOpenAiImageModel; import io.quarkiverse.langchain4j.openai.runtime.config.ChatModelConfig; import io.quarkiverse.langchain4j.openai.runtime.config.EmbeddingModelConfig; import io.quarkiverse.langchain4j.openai.runtime.config.ImageModelConfig; import io.quarkiverse.langchain4j.openai.runtime.config.LangChain4jOpenAiConfig; import io.quarkiverse.langchain4j.openai.runtime.config.ModerationModelConfig; import io.quarkiverse.langchain4j.runtime.NamedModelUtil; import io.quarkus.runtime.ShutdownContext; import io.quarkus.runtime.annotations.Recorder; import io.smallrye.config.ConfigValidationException; @Recorder public class OpenAiRecorder { private static final String DUMMY_KEY = "dummy"; public Supplier<ChatLanguageModel> chatModel(LangChain4jOpenAiConfig runtimeConfig, String modelName) { LangChain4jOpenAiConfig.OpenAiConfig openAiConfig = correspondingOpenAiConfig(runtimeConfig, modelName); if (openAiConfig.enableIntegration()) { String apiKey = openAiConfig.apiKey(); if (DUMMY_KEY.equals(apiKey)) { throw new ConfigValidationException(createApiKeyConfigProblems(modelName)); } ChatModelConfig chatModelConfig = openAiConfig.chatModel(); var builder = OpenAiChatModel.builder() .baseUrl(openAiConfig.baseUrl()) .apiKey(apiKey) .timeout(openAiConfig.timeout()) .maxRetries(openAiConfig.maxRetries()) .logRequests(firstOrDefault(false, chatModelConfig.logRequests(), openAiConfig.logRequests())) .logResponses(firstOrDefault(false, chatModelConfig.logResponses(), openAiConfig.logResponses())) .modelName(chatModelConfig.modelName()) .temperature(chatModelConfig.temperature()) .topP(chatModelConfig.topP()) .presencePenalty(chatModelConfig.presencePenalty()) .frequencyPenalty(chatModelConfig.frequencyPenalty()) .responseFormat(chatModelConfig.responseFormat().orElse(null)); openAiConfig.organizationId().ifPresent(builder::organizationId); if (chatModelConfig.maxTokens().isPresent()) { builder.maxTokens(chatModelConfig.maxTokens().get()); } return new Supplier<>() { @Override public ChatLanguageModel get() { return builder.build(); } }; } else { return new Supplier<>() { @Override public ChatLanguageModel get() { return new DisabledChatLanguageModel(); } }; } } public Supplier<StreamingChatLanguageModel> streamingChatModel(LangChain4jOpenAiConfig runtimeConfig, String modelName) { LangChain4jOpenAiConfig.OpenAiConfig openAiConfig = correspondingOpenAiConfig(runtimeConfig, modelName); if (openAiConfig.enableIntegration()) { String apiKey = openAiConfig.apiKey(); if (DUMMY_KEY.equals(apiKey)) { throw new ConfigValidationException(createApiKeyConfigProblems(modelName)); } ChatModelConfig chatModelConfig = openAiConfig.chatModel(); var builder = OpenAiStreamingChatModel.builder() .baseUrl(openAiConfig.baseUrl()) .apiKey(apiKey) .timeout(openAiConfig.timeout()) .logRequests(firstOrDefault(false, chatModelConfig.logRequests(), openAiConfig.logRequests())) .logResponses(firstOrDefault(false, chatModelConfig.logResponses(), openAiConfig.logResponses())) .modelName(chatModelConfig.modelName()) .temperature(chatModelConfig.temperature()) .topP(chatModelConfig.topP()) .presencePenalty(chatModelConfig.presencePenalty()) .frequencyPenalty(chatModelConfig.frequencyPenalty()) .responseFormat(chatModelConfig.responseFormat().orElse(null)); openAiConfig.organizationId().ifPresent(builder::organizationId); if (chatModelConfig.maxTokens().isPresent()) { builder.maxTokens(chatModelConfig.maxTokens().get()); } return new Supplier<>() { @Override public StreamingChatLanguageModel get() { return builder.build(); } }; } else { return new Supplier<>() { @Override public StreamingChatLanguageModel get() { return new DisabledStreamingChatLanguageModel(); } }; } } public Supplier<EmbeddingModel> embeddingModel(LangChain4jOpenAiConfig runtimeConfig, String modelName) { LangChain4jOpenAiConfig.OpenAiConfig openAiConfig = correspondingOpenAiConfig(runtimeConfig, modelName); if (openAiConfig.enableIntegration()) { String apiKeyOpt = openAiConfig.apiKey(); if (DUMMY_KEY.equals(apiKeyOpt)) { throw new ConfigValidationException(createApiKeyConfigProblems(modelName)); } EmbeddingModelConfig embeddingModelConfig = openAiConfig.embeddingModel(); var builder = OpenAiEmbeddingModel.builder() .baseUrl(openAiConfig.baseUrl()) .apiKey(apiKeyOpt) .timeout(openAiConfig.timeout()) .maxRetries(openAiConfig.maxRetries()) .logRequests(firstOrDefault(false, embeddingModelConfig.logRequests(), openAiConfig.logRequests())) .logResponses(firstOrDefault(false, embeddingModelConfig.logResponses(), openAiConfig.logResponses())) .modelName(embeddingModelConfig.modelName()); if (embeddingModelConfig.user().isPresent()) { builder.user(embeddingModelConfig.user().get()); } openAiConfig.organizationId().ifPresent(builder::organizationId); return new Supplier<>() { @Override public EmbeddingModel get() { return builder.build(); } }; } else { return new Supplier<>() { @Override public EmbeddingModel get() { return new DisabledEmbeddingModel(); } }; } } public Supplier<ModerationModel> moderationModel(LangChain4jOpenAiConfig runtimeConfig, String modelName) { LangChain4jOpenAiConfig.OpenAiConfig openAiConfig = correspondingOpenAiConfig(runtimeConfig, modelName); if (openAiConfig.enableIntegration()) { String apiKey = openAiConfig.apiKey(); if (DUMMY_KEY.equals(apiKey)) { throw new ConfigValidationException(createApiKeyConfigProblems(modelName)); } ModerationModelConfig moderationModelConfig = openAiConfig.moderationModel(); var builder = OpenAiModerationModel.builder() .baseUrl(openAiConfig.baseUrl()) .apiKey(apiKey) .timeout(openAiConfig.timeout()) .maxRetries(openAiConfig.maxRetries()) .logRequests(firstOrDefault(false, moderationModelConfig.logRequests(), openAiConfig.logRequests())) .logResponses(firstOrDefault(false, moderationModelConfig.logResponses(), openAiConfig.logResponses())) .modelName(moderationModelConfig.modelName()); openAiConfig.organizationId().ifPresent(builder::organizationId); return new Supplier<>() { @Override public ModerationModel get() { return builder.build(); } }; } else { return new Supplier<>() { @Override public ModerationModel get() { return new DisabledModerationModel(); } }; } } public Supplier<ImageModel> imageModel(LangChain4jOpenAiConfig runtimeConfig, String modelName) { LangChain4jOpenAiConfig.OpenAiConfig openAiConfig = correspondingOpenAiConfig(runtimeConfig, modelName); if (openAiConfig.enableIntegration()) { String apiKey = openAiConfig.apiKey(); if (DUMMY_KEY.equals(apiKey)) { throw new ConfigValidationException(createApiKeyConfigProblems(modelName)); } ImageModelConfig imageModelConfig = openAiConfig.imageModel(); var builder = QuarkusOpenAiImageModel.builder() .baseUrl(openAiConfig.baseUrl()) .apiKey(apiKey) .timeout(openAiConfig.timeout()) .maxRetries(openAiConfig.maxRetries()) .logRequests(firstOrDefault(false, imageModelConfig.logRequests(), openAiConfig.logRequests())) .logResponses(firstOrDefault(false, imageModelConfig.logResponses(), openAiConfig.logResponses())) .modelName(imageModelConfig.modelName()) .size(imageModelConfig.size()) .quality(imageModelConfig.quality()) .style(imageModelConfig.style()) .responseFormat(imageModelConfig.responseFormat()) .user(imageModelConfig.user()); openAiConfig.organizationId().ifPresent(builder::organizationId); // we persist if the directory was set explicitly and the boolean flag was not set to false // or if the boolean flag was set explicitly to true Optional<Path> persistDirectory = Optional.empty(); if (imageModelConfig.persist().isPresent()) { if (imageModelConfig.persist().get()) { persistDirectory = imageModelConfig.persistDirectory().or(new Supplier<>() { @Override public Optional<? extends Path> get() { return Optional.of(Paths.get(System.getProperty("java.io.tmpdir"), "dall-e-images")); } }); } } else { if (imageModelConfig.persistDirectory().isPresent()) { persistDirectory = imageModelConfig.persistDirectory(); } } builder.persistDirectory(persistDirectory); return new Supplier<>() { @Override public ImageModel get() { return builder.build(); } }; } else { return new Supplier<>() { @Override public ImageModel get() { return new DisabledImageModel(); } }; } } private LangChain4jOpenAiConfig.OpenAiConfig correspondingOpenAiConfig(LangChain4jOpenAiConfig runtimeConfig, String modelName) { LangChain4jOpenAiConfig.OpenAiConfig openAiConfig; if (NamedModelUtil.isDefault(modelName)) { openAiConfig = runtimeConfig.defaultConfig(); } else { openAiConfig = runtimeConfig.namedConfig().get(modelName); } return openAiConfig; } private ConfigValidationException.Problem[] createApiKeyConfigProblems(String modelName) { return createConfigProblems("api-key", modelName); } private ConfigValidationException.Problem[] createConfigProblems(String key, String modelName) { return new ConfigValidationException.Problem[] { createConfigProblem(key, modelName) }; } private ConfigValidationException.Problem createConfigProblem(String key, String modelName) { return new ConfigValidationException.Problem(String.format( "SRCFG00014: The config property quarkus.langchain4j.openai%s%s is required but it could not be found in any config source", NamedModelUtil.isDefault(modelName) ? "." : ("." + modelName + "."), key)); } public void cleanUp(ShutdownContext shutdown) { shutdown.addShutdownTask(new Runnable() { @Override public void run() { QuarkusOpenAiClient.clearCache(); } }); } }
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package io.quarkiverse.langchain4j.sample; import java.util.function.Supplier; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.openai.OpenAiChatModel; public class MyChatModelSupplier implements Supplier<ChatLanguageModel> { @Override public ChatLanguageModel get() { return OpenAiChatModel.builder() .apiKey("...") .build(); } }
[ "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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package com.tencent.supersonic.headless.core.chat.parser.llm; import com.tencent.supersonic.common.util.JsonUtil; import com.tencent.supersonic.headless.core.config.OptimizationConfig; import com.tencent.supersonic.headless.core.chat.query.llm.s2sql.LLMReq; import com.tencent.supersonic.headless.core.chat.query.llm.s2sql.LLMReq.SqlGenerationMode; import com.tencent.supersonic.headless.core.chat.query.llm.s2sql.LLMResp; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.input.Prompt; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.output.Response; import org.apache.commons.lang3.tuple.Pair; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.springframework.beans.factory.InitializingBean; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Service; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.concurrent.CopyOnWriteArrayList; @Service public class TwoPassSCSqlGeneration implements SqlGeneration, InitializingBean { private static final Logger keyPipelineLog = LoggerFactory.getLogger("keyPipeline"); @Autowired private ChatLanguageModel chatLanguageModel; @Autowired private SqlExamplarLoader sqlExamplarLoader; @Autowired private OptimizationConfig optimizationConfig; @Autowired private SqlPromptGenerator sqlPromptGenerator; @Override public LLMResp generation(LLMReq llmReq, Long dataSetId) { //1.retriever sqlExamples and generate exampleListPool keyPipelineLog.info("dataSetId:{},llmReq:{}", dataSetId, llmReq); List<Map<String, String>> sqlExamples = sqlExamplarLoader.retrieverSqlExamples(llmReq.getQueryText(), optimizationConfig.getText2sqlExampleNum()); List<List<Map<String, String>>> exampleListPool = sqlPromptGenerator.getExampleCombos(sqlExamples, optimizationConfig.getText2sqlFewShotsNum(), optimizationConfig.getText2sqlSelfConsistencyNum()); //2.generator linking prompt,and parallel generate response. List<String> linkingPromptPool = sqlPromptGenerator.generatePromptPool(llmReq, exampleListPool, false); List<String> linkingResults = new CopyOnWriteArrayList<>(); linkingPromptPool.parallelStream().forEach( linkingPrompt -> { Prompt prompt = PromptTemplate.from(JsonUtil.toString(linkingPrompt)).apply(new HashMap<>()); keyPipelineLog.info("step one request prompt:{}", prompt.toSystemMessage()); Response<AiMessage> linkingResult = chatLanguageModel.generate(prompt.toSystemMessage()); String result = linkingResult.content().text(); keyPipelineLog.info("step one model response:{}", result); linkingResults.add(OutputFormat.getSchemaLink(result)); } ); List<String> sortedList = OutputFormat.formatList(linkingResults); Pair<String, Map<String, Double>> linkingMap = OutputFormat.selfConsistencyVote(sortedList); //3.generator sql prompt,and parallel generate response. List<String> sqlPromptPool = sqlPromptGenerator.generateSqlPromptPool(llmReq, sortedList, exampleListPool); List<String> sqlTaskPool = new CopyOnWriteArrayList<>(); sqlPromptPool.parallelStream().forEach(sqlPrompt -> { Prompt linkingPrompt = PromptTemplate.from(JsonUtil.toString(sqlPrompt)).apply(new HashMap<>()); keyPipelineLog.info("step two request prompt:{}", linkingPrompt.toSystemMessage()); Response<AiMessage> sqlResult = chatLanguageModel.generate(linkingPrompt.toSystemMessage()); String result = sqlResult.content().text(); keyPipelineLog.info("step two model response:{}", result); sqlTaskPool.add(result); }); //4.format response. Pair<String, Map<String, Double>> sqlMapPair = OutputFormat.selfConsistencyVote(sqlTaskPool); keyPipelineLog.info("linkingMap:{} sqlMap:{}", linkingMap, sqlMapPair.getRight()); LLMResp llmResp = new LLMResp(); llmResp.setQuery(llmReq.getQueryText()); llmResp.setSqlRespMap(OutputFormat.buildSqlRespMap(sqlExamples, sqlMapPair.getRight())); return llmResp; } @Override public void afterPropertiesSet() { SqlGenerationFactory.addSqlGenerationForFactory(SqlGenerationMode.TWO_PASS_AUTO_COT_SELF_CONSISTENCY, this); } }
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package org.example; import dev.langchain4j.data.message.ChatMessage; import dev.langchain4j.data.message.UserMessage; import dev.langchain4j.memory.chat.ChatMemoryProvider; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; import dev.langchain4j.store.memory.chat.ChatMemoryStore; import java.io.IOException; import java.nio.file.Files; import java.nio.file.Paths; import java.nio.file.StandardOpenOption; import java.util.ArrayList; import java.util.List; public class _09_AIServices_06_ChatMemoryPersisted { public static void main(String[] args) { OpenAiChatModel model = OpenAiChatModel.withApiKey(ApiKeys.OPENAI_DEMO); FileStore store = new FileStore(); ChatMemoryProvider provider = memoryId -> MessageWindowChatMemory.builder() .id(memoryId) .maxMessages(10) .chatMemoryStore(store) .build(); ChatAssistant assistant = AiServices.builder(ChatAssistant.class) .chatLanguageModel(model) .chatMemoryProvider(provider) .build(); System.out.println(assistant.chat(1, "Hello my name is Michael")); System.out.println(assistant.chat(2, "Hello my name is Karl")); // System.out.println(assistant.chat(1, "What is my name?")); // System.out.println(assistant.chat(2, "What is my name?")); } } class FileStore implements ChatMemoryStore { public static final String PATH = "src/main/resources/messages_%s.txt"; @Override public List<ChatMessage> getMessages(Object memoryId) { List<ChatMessage> chatMessages = new ArrayList<>(); String file = PATH.formatted(memoryId); try { if (!Files.exists(Paths.get(file))) { Files.createFile(Paths.get(file)); } for (String s : Files.readAllLines(Paths.get(file))) { chatMessages.add(UserMessage.from(s)); } } catch (IOException e) { throw new RuntimeException(e); } return chatMessages; } @Override public void updateMessages(Object memoryId, List<ChatMessage> messages) { String file = PATH.formatted(memoryId); for (ChatMessage message : messages) { try { Files.writeString(Paths.get(file), message.text() + "\n", StandardOpenOption.APPEND); } catch (IOException e) { throw new RuntimeException(e); } } } @Override public void deleteMessages(Object memoryId) { System.out.println("Not implemented"); } }
[ "dev.langchain4j.service.AiServices.builder", "dev.langchain4j.memory.chat.MessageWindowChatMemory.builder" ]
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package org.agoncal.fascicle.langchain4j.vectordb.pgvector; import dev.langchain4j.data.embedding.Embedding; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.store.embedding.EmbeddingMatch; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.pgvector.PgVectorEmbeddingStore; import java.util.List; // tag::adocSkip[] /** * @author Antonio Goncalves * http://www.antoniogoncalves.org * -- */ // end::adocSkip[] public class MusicianService { public static void main(String[] args) { MusicianService musicianService = new MusicianService(); musicianService.usePGVectorToStoreEmbeddings(); } public void usePGVectorToStoreEmbeddings() { System.out.println("### usePGVectorToStoreEmbeddings"); // tag::adocSnippet[] EmbeddingStore<TextSegment> embeddingStore = PgVectorEmbeddingStore.builder() .host("localhost") .port(5432) .createTable(true) .dropTableFirst(true) .dimension(384) .table("langchain4j_collection") .user("agoncal") .password("agoncal") .database("agoncal") .build(); // end::adocSnippet[] EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel(); TextSegment segment1 = TextSegment.from("I've been to France twice."); Embedding embedding1 = embeddingModel.embed(segment1).content(); embeddingStore.add(embedding1, segment1); TextSegment segment2 = TextSegment.from("New Delhi is the capital of India."); Embedding embedding2 = embeddingModel.embed(segment2).content(); embeddingStore.add(embedding2, segment2); Embedding queryEmbedding = embeddingModel.embed("Did you ever travel abroad?").content(); List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.findRelevant(queryEmbedding, 1); EmbeddingMatch<TextSegment> embeddingMatch = relevant.get(0); System.out.println(embeddingMatch.score()); System.out.println(embeddingMatch.embedded().text()); } }
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package com.ramesh.langchain; import static dev.langchain4j.data.document.FileSystemDocumentLoader.loadDocument; import static java.time.Duration.ofSeconds; import dev.langchain4j.chain.ConversationalRetrievalChain; import dev.langchain4j.data.document.Document; import dev.langchain4j.data.document.splitter.DocumentSplitters; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.embedding.AllMiniLmL6V2QuantizedEmbeddingModel; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.retriever.EmbeddingStoreRetriever; import dev.langchain4j.store.embedding.EmbeddingStore; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; /*** * This project demonstrates how to use LangChain to ingest data from a document and * get responses for prompts from the same, by creating a LangChain Chain */ public class ChainWithDocumentLive { // Open AI Key and Chat GPT Model to use public static String OPENAI_API_KEY = "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"; public static String OPENAI_MODEL = "gpt-3.5-turbo"; public static void main(String[] args) { // embedding model to yse EmbeddingModel embeddingModel = new AllMiniLmL6V2QuantizedEmbeddingModel(); // embeddings will be stored in memory EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>(); //Creating instance of EmbeddingStoreIngestor System.out.println("Creating instance of EmbeddingStoreIngestor..."); EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder() .documentSplitter(DocumentSplitters.recursive(500, 0)) .embeddingModel(embeddingModel) .embeddingStore(embeddingStore) .build(); // ingesting input data System.out.println("Loading content from simpsons_adventures.txt and ingesting..."); Document document = loadDocument(".\\simpsons_adventures.txt"); ingestor.ingest(document); // building the chat model ChatLanguageModel chatModel = OpenAiChatModel.builder() .apiKey(OPENAI_API_KEY) .timeout(ofSeconds(60)) .build(); // Building LangChain with Embeddings Retriever System.out.println("Building LangChain with Embeddings Retriever..."); ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder() .chatLanguageModel(chatModel) .retriever(EmbeddingStoreRetriever.from(embeddingStore, embeddingModel)) .chatMemory(MessageWindowChatMemory.withMaxMessages(10)) .promptTemplate(PromptTemplate.from("Answer the following question to the best of your ability: {{question}}\n\nBase your answer on the following information:\n{{information}}")) .build(); // prompting ChatGPT System.out.println("Prompting ChatGPT \"Who is Simpson?\"..."); System.out.println("\nFetching response from ChatGPT via the created LangChain...\n"); // executing the LangChain chain String answer = chain.execute("Who is Simpson?"); System.out.println(answer); } }
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package io.quarkiverse.langchain4j.samples; import static dev.langchain4j.data.document.splitter.DocumentSplitters.recursive; import java.util.List; import jakarta.enterprise.context.ApplicationScoped; import jakarta.inject.Inject; import dev.langchain4j.data.document.Document; import dev.langchain4j.model.embedding.EmbeddingModel; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import io.quarkiverse.langchain4j.pinecone.PineconeEmbeddingStore; @ApplicationScoped public class IngestorExampleWithPinecone { /** * The embedding store (the database). * The bean is provided by the quarkus-langchain4j-pinecone extension. */ @Inject PineconeEmbeddingStore store; /** * The embedding model (how is computed the vector of a document). * The bean is provided by the LLM (like openai) extension. */ @Inject EmbeddingModel embeddingModel; public void ingest(List<Document> documents) { EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder() .embeddingStore(store) .embeddingModel(embeddingModel) .documentSplitter(recursive(500, 0)) .build(); // Warning - this can take a long time... ingestor.ingest(documents); } }
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import dev.langchain4j.data.document.FileSystemDocumentLoader; import dev.langchain4j.data.document.splitter.DocumentSplitters; import dev.langchain4j.data.segment.TextSegment; import dev.langchain4j.memory.chat.MessageWindowChatMemory; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.openai.OpenAiEmbeddingModel; import dev.langchain4j.retriever.EmbeddingStoreRetriever; import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; import java.net.URISyntaxException; import java.net.URL; import java.nio.file.Path; import java.nio.file.Paths; import java.util.List; import java.util.Map; import java.util.Scanner; import static java.util.stream.Collectors.joining; public class _03_Retrieval { private static final String RETRIEVER_DOCUMENT_NAME = ""; public static void main(String[] args) { var openAiKey = System.getenv("OPENAI_API_KEY"); var embeddingModel = OpenAiEmbeddingModel.withApiKey(openAiKey); var embeddingStore = new InMemoryEmbeddingStore<TextSegment>(); // 0 - Ingesting the document and store in vectorized form var ingestor = EmbeddingStoreIngestor.builder() .documentSplitter(DocumentSplitters.recursive(500, 0)) .embeddingModel(embeddingModel) .embeddingStore(embeddingStore) .build(); var filePath = toPath(RETRIEVER_DOCUMENT_NAME); var document = FileSystemDocumentLoader.loadDocument(filePath); ingestor.ingest(document); var chatModel = OpenAiChatModel.withApiKey(openAiKey); var chatMemory = MessageWindowChatMemory.withMaxMessages(10); var retriever = EmbeddingStoreRetriever.from(embeddingStore, embeddingModel); var promptTemplate = PromptTemplate.from(""" Answer the following question to the best of your ability: {{question}} Base your answer on the following information: {{information}}"""); try (Scanner scanner = new Scanner(System.in)) { while (true) { System.out.println("\nEnter your question: "); // 1 - Retrieving the question from the user String question = scanner.nextLine(); if (question.equals("exit")) { break; } // 2, 3 - Retrieving the most relevant segments according to the question var relevantSegments = retriever.findRelevant(question); var prompt = promptTemplate.apply( Map.of( "question", question, "information", format(relevantSegments))); chatMemory.add(prompt.toUserMessage()); // 4 - Send the prompt to the model var response = chatModel.generate(chatMemory.messages()); chatMemory.add(response.content()); // 5 - Printing answer to the user System.out.println(response.content().text()); System.out.println("\n\n########### TOKEN USAGE ############\n"); System.out.println(response.tokenUsage()); } } } private static String format(List<TextSegment> relevantSegments) { return relevantSegments.stream() .map(TextSegment::text) .map(segment -> "..." + segment + "...") .collect(joining("\n\n")); } private static Path toPath(String fileName) { try { URL fileUrl = _03_Retrieval.class.getResource(fileName); return Paths.get(fileUrl.toURI()); } catch (URISyntaxException e) { throw new RuntimeException(e); } } }
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package org.example; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.output.structured.Description; import dev.langchain4j.service.AiServices; import dev.langchain4j.service.SystemMessage; import dev.langchain4j.service.UserMessage; import java.util.List; public class _09_AIServices_04_PokemonTrainer { public static void main(String[] args) { // Zet logger op debug OpenAiChatModel model = OpenAiChatModel.builder() .apiKey(ApiKeys.OPENAI_DEMO) .logRequests(true) .build(); PokemonTrainerGeneratorService trainerGenerator = AiServices.create(PokemonTrainerGeneratorService.class, model); Trainer trainer = trainerGenerator.generate("Generate a low level trainer named 'Kelvin' with 2 bug and 2 fire pokemon"); System.out.println(trainer); } } interface PokemonTrainerGeneratorService { @SystemMessage("You generate random pokemon trainers with random pokemon, in accordance to the user message") Trainer generate(@UserMessage String text); } record Trainer(String name, List<Pokemon> team) { } record Pokemon(String name // , @Description("All uppercase") String type , String type , int level , int hp , @Description("Random number of moves between 1 and 4") List<String> moves) {}
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import dev.ai4j.openai4j.Model; import dev.langchain4j.data.message.UserMessage; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.openai.OpenAiChatModel; public class _00_Model { public static void main(String[] args) { String openAiKey = System.getenv("OPENAI_API_KEY"); ChatLanguageModel chatModel = OpenAiChatModel.builder() .modelName(Model.GPT_3_5_TURBO.stringValue()) .apiKey(openAiKey) .build(); var prompt = "Write hello world example in Java printing 'Hello TDC Future 2023'"; var response = chatModel.generate(UserMessage.from(prompt)); System.out.println(response.content().text()); System.out.println("\n\n########### TOKEN USAGE ############\n"); System.out.println(response.tokenUsage()); } }
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package com.example.application.services; import com.vaadin.flow.server.auth.AnonymousAllowed; import dev.hilla.BrowserCallable; import dev.langchain4j.memory.chat.TokenWindowChatMemory; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.model.openai.OpenAiStreamingChatModel; import dev.langchain4j.model.openai.OpenAiTokenizer; import dev.langchain4j.service.AiServices; import dev.langchain4j.service.TokenStream; import jakarta.annotation.PostConstruct; import org.springframework.beans.factory.annotation.Value; import reactor.core.publisher.Flux; import reactor.core.publisher.Sinks; @BrowserCallable @AnonymousAllowed public class ChatService { @Value("${openai.api.key}") private String OPENAI_API_KEY; private Assistant assistant; private StreamingAssistant streamingAssistant; interface Assistant { String chat(String message); } interface StreamingAssistant { TokenStream chat(String message); } @PostConstruct public void init() { var memory = TokenWindowChatMemory.withMaxTokens(2000, new OpenAiTokenizer("gpt-3.5-turbo")); assistant = AiServices.builder(Assistant.class) .chatLanguageModel(OpenAiChatModel.withApiKey(OPENAI_API_KEY)) .chatMemory(memory) .build(); streamingAssistant = AiServices.builder(StreamingAssistant.class) .streamingChatLanguageModel(OpenAiStreamingChatModel.withApiKey(OPENAI_API_KEY)) .chatMemory(memory) .build(); } public String chat(String message) { return assistant.chat(message); } public Flux<String> chatStream(String message) { Sinks.Many<String> sink = Sinks.many().unicast().onBackpressureBuffer(); streamingAssistant.chat(message) .onNext(sink::tryEmitNext) .onComplete(sink::tryEmitComplete) .onError(sink::tryEmitError) .start(); return sink.asFlux(); } }
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import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.input.Prompt; import dev.langchain4j.model.input.PromptTemplate; import dev.langchain4j.model.input.structured.StructuredPrompt; import dev.langchain4j.model.input.structured.StructuredPromptProcessor; import dev.langchain4j.model.openai.OpenAiChatModel; import java.util.HashMap; import java.util.List; import java.util.Map; import static java.time.Duration.ofSeconds; import static java.util.Arrays.asList; public class _03_PromptTemplate { static class Simple_Prompt_Template_Example { public static void main(String[] args) { ChatLanguageModel model = OpenAiChatModel.builder() .apiKey(ApiKeys.OPENAI_API_KEY) .timeout(ofSeconds(60)) .build(); String template = "Create a recipe for a {{dishType}} with the following ingredients: {{ingredients}}"; PromptTemplate promptTemplate = PromptTemplate.from(template); Map<String, Object> variables = new HashMap<>(); variables.put("dishType", "oven dish"); variables.put("ingredients", "potato, tomato, feta, olive oil"); Prompt prompt = promptTemplate.apply(variables); String response = model.generate(prompt.text()); System.out.println(response); } } static class Structured_Prompt_Template_Example { @StructuredPrompt({ "Create a recipe of a {{dish}} that can be prepared using only {{ingredients}}.", "Structure your answer in the following way:", "Recipe name: ...", "Description: ...", "Preparation time: ...", "Required ingredients:", "- ...", "- ...", "Instructions:", "- ...", "- ..." }) static class CreateRecipePrompt { String dish; List<String> ingredients; CreateRecipePrompt(String dish, List<String> ingredients) { this.dish = dish; this.ingredients = ingredients; } } public static void main(String[] args) { ChatLanguageModel model = OpenAiChatModel.builder() .apiKey(ApiKeys.OPENAI_API_KEY) .timeout(ofSeconds(60)) .build(); Structured_Prompt_Template_Example.CreateRecipePrompt createRecipePrompt = new Structured_Prompt_Template_Example.CreateRecipePrompt( "salad", asList("cucumber", "tomato", "feta", "onion", "olives") ); Prompt prompt = StructuredPromptProcessor.toPrompt(createRecipePrompt); String recipe = model.generate(prompt.text()); System.out.println(recipe); } } }
[ "dev.langchain4j.model.openai.OpenAiChatModel.builder" ]
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/* * Copyright 2024 Google LLC * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * https://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package gemini.workshop; import dev.langchain4j.model.chat.ChatLanguageModel; import dev.langchain4j.model.vertexai.VertexAiGeminiChatModel; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.data.message.ImageContent; import dev.langchain4j.data.message.TextContent; import dev.langchain4j.data.message.UserMessage; import dev.langchain4j.model.output.Response; public class Step3_Multimodal { static final String CAT_IMAGE_URL = "https://upload.wikimedia.org/wikipedia/commons/e/e9/" + "Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png"; public static void main(String[] args) { ChatLanguageModel model = VertexAiGeminiChatModel.builder() .project(System.getenv("PROJECT_ID")) .location(System.getenv("LOCATION")) .modelName("gemini-1.0-pro-vision") .build(); UserMessage userMessage = UserMessage.from( ImageContent.from(CAT_IMAGE_URL), TextContent.from("Describe the picture") ); Response<AiMessage> response = model.generate(userMessage); System.out.println(response.content().text()); } }
[ "dev.langchain4j.model.vertexai.VertexAiGeminiChatModel.builder" ]
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package dev.langchain4j.model.openai; import dev.ai4j.openai4j.chat.*; import dev.ai4j.openai4j.completion.CompletionChoice; import dev.ai4j.openai4j.completion.CompletionResponse; import dev.langchain4j.agent.tool.ToolExecutionRequest; import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.model.Tokenizer; import dev.langchain4j.model.output.Response; import dev.langchain4j.model.output.TokenUsage; import java.util.List; import java.util.Map; import java.util.concurrent.ConcurrentHashMap; import static dev.langchain4j.model.openai.InternalOpenAiHelper.finishReasonFrom; import static java.util.Collections.singletonList; import static java.util.stream.Collectors.toList; /** * This class needs to be thread safe because it is called when a streaming result comes back * and there is no guarantee that this thread will be the same as the one that initiated the request, * in fact it almost certainly won't be. */ public class OpenAiStreamingResponseBuilder { private final StringBuffer contentBuilder = new StringBuffer(); private final StringBuffer toolNameBuilder = new StringBuffer(); private final StringBuffer toolArgumentsBuilder = new StringBuffer(); private final Map<Integer, ToolExecutionRequestBuilder> indexToToolExecutionRequestBuilder = new ConcurrentHashMap<>(); private volatile String finishReason; private final Integer inputTokenCount; public OpenAiStreamingResponseBuilder(Integer inputTokenCount) { this.inputTokenCount = inputTokenCount; } public void append(ChatCompletionResponse partialResponse) { if (partialResponse == null) { return; } List<ChatCompletionChoice> choices = partialResponse.choices(); if (choices == null || choices.isEmpty()) { return; } ChatCompletionChoice chatCompletionChoice = choices.get(0); if (chatCompletionChoice == null) { return; } String finishReason = chatCompletionChoice.finishReason(); if (finishReason != null) { this.finishReason = finishReason; } Delta delta = chatCompletionChoice.delta(); if (delta == null) { return; } String content = delta.content(); if (content != null) { contentBuilder.append(content); return; } if (delta.functionCall() != null) { FunctionCall functionCall = delta.functionCall(); if (functionCall.name() != null) { toolNameBuilder.append(functionCall.name()); } if (functionCall.arguments() != null) { toolArgumentsBuilder.append(functionCall.arguments()); } } if (delta.toolCalls() != null && !delta.toolCalls().isEmpty()) { ToolCall toolCall = delta.toolCalls().get(0); ToolExecutionRequestBuilder toolExecutionRequestBuilder = indexToToolExecutionRequestBuilder.computeIfAbsent(toolCall.index(), idx -> new ToolExecutionRequestBuilder()); if (toolCall.id() != null) { toolExecutionRequestBuilder.idBuilder.append(toolCall.id()); } FunctionCall functionCall = toolCall.function(); if (functionCall.name() != null) { toolExecutionRequestBuilder.nameBuilder.append(functionCall.name()); } if (functionCall.arguments() != null) { toolExecutionRequestBuilder.argumentsBuilder.append(functionCall.arguments()); } } } public void append(CompletionResponse partialResponse) { if (partialResponse == null) { return; } List<CompletionChoice> choices = partialResponse.choices(); if (choices == null || choices.isEmpty()) { return; } CompletionChoice completionChoice = choices.get(0); if (completionChoice == null) { return; } String finishReason = completionChoice.finishReason(); if (finishReason != null) { this.finishReason = finishReason; } String token = completionChoice.text(); if (token != null) { contentBuilder.append(token); } } public Response<AiMessage> build(Tokenizer tokenizer, boolean forcefulToolExecution) { String content = contentBuilder.toString(); if (!content.isEmpty()) { return Response.from( AiMessage.from(content), tokenUsage(content, tokenizer), finishReasonFrom(finishReason) ); } String toolName = toolNameBuilder.toString(); if (!toolName.isEmpty()) { ToolExecutionRequest toolExecutionRequest = ToolExecutionRequest.builder() .name(toolName) .arguments(toolArgumentsBuilder.toString()) .build(); return Response.from( AiMessage.from(toolExecutionRequest), tokenUsage(singletonList(toolExecutionRequest), tokenizer, forcefulToolExecution), finishReasonFrom(finishReason) ); } if (!indexToToolExecutionRequestBuilder.isEmpty()) { List<ToolExecutionRequest> toolExecutionRequests = indexToToolExecutionRequestBuilder.values().stream() .map(it -> ToolExecutionRequest.builder() .id(it.idBuilder.toString()) .name(it.nameBuilder.toString()) .arguments(it.argumentsBuilder.toString()) .build()) .collect(toList()); return Response.from( AiMessage.from(toolExecutionRequests), tokenUsage(toolExecutionRequests, tokenizer, forcefulToolExecution), finishReasonFrom(finishReason) ); } return null; } private TokenUsage tokenUsage(String content, Tokenizer tokenizer) { if (tokenizer == null) { return null; } int outputTokenCount = tokenizer.estimateTokenCountInText(content); return new TokenUsage(inputTokenCount, outputTokenCount); } private TokenUsage tokenUsage(List<ToolExecutionRequest> toolExecutionRequests, Tokenizer tokenizer, boolean forcefulToolExecution) { if (tokenizer == null) { return null; } int outputTokenCount = 0; if (forcefulToolExecution) { // OpenAI calculates output tokens differently when tool is executed forcefully for (ToolExecutionRequest toolExecutionRequest : toolExecutionRequests) { outputTokenCount += tokenizer.estimateTokenCountInForcefulToolExecutionRequest(toolExecutionRequest); } } else { outputTokenCount = tokenizer.estimateTokenCountInToolExecutionRequests(toolExecutionRequests); } return new TokenUsage(inputTokenCount, outputTokenCount); } private static class ToolExecutionRequestBuilder { private final StringBuffer idBuilder = new StringBuffer(); private final StringBuffer nameBuilder = new StringBuffer(); private final StringBuffer argumentsBuilder = new StringBuffer(); } }
[ "dev.langchain4j.agent.tool.ToolExecutionRequest.builder" ]
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