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BAEL-9198 - Building an AI Chatbot in Java with Langchain4j and MongoDB Atlas
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‎libraries-llms-2/README.md

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## Relevant Articles

‎libraries-llms-2/pom.xml

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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<artifactId>libraries-llms-2</artifactId>
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<name>libraries-llms-2</name>
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<parent>
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<groupId>com.baeldung</groupId>
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<artifactId>parent-modules</artifactId>
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<version>1.0.0-SNAPSHOT</version>
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</parent>
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<dependencies>
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-starter-web</artifactId>
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<version>${spring-boot.version}</version>
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</dependency>
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<!-- dependencies for langchain4j -->
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-mongodb-atlas</artifactId>
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<version>${langchain4j.version}</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j</artifactId>
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<version>${langchain4j.version}</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-open-ai</artifactId>
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<version>${langchain4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-starter-test</artifactId>
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<version>${spring-boot.version}</version>
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<scope>test</scope>
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</dependency>
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</dependencies>
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<properties>
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<langchain4j.version>1.0.0-beta1</langchain4j.version>
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<spring-boot.version>3.3.2</spring-boot.version>
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</properties>
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</project>

‎libraries-llms-2/src/main/java/com/baeldung/chatbot/mongodb/assistants/ArticleBasedAssistant.java

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package com.baeldung.chatbot.mongodb.assistants;
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public interface ArticleBasedAssistant {
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String answer(String question);
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}

‎libraries-llms-2/src/main/java/com/baeldung/chatbot/mongodb/configuration/ChatBotConfiguration.java

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package com.baeldung.chatbot.mongodb.configuration;
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import com.baeldung.chatbot.mongodb.assistants.ArticleBasedAssistant;
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import com.mongodb.client.MongoClient;
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import com.mongodb.client.MongoClients;
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import com.mongodb.client.model.CreateCollectionOptions;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.model.chat.ChatLanguageModel;
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import dev.langchain4j.model.embedding.EmbeddingModel;
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import dev.langchain4j.model.openai.OpenAiChatModel;
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import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
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import dev.langchain4j.rag.content.retriever.ContentRetriever;
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
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import dev.langchain4j.service.AiServices;
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import dev.langchain4j.store.embedding.EmbeddingStore;
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import dev.langchain4j.store.embedding.mongodb.IndexMapping;
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import dev.langchain4j.store.embedding.mongodb.MongoDbEmbeddingStore;
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import org.bson.conversions.Bson;
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import org.springframework.beans.factory.annotation.Value;
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import org.springframework.context.annotation.Bean;
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import org.springframework.context.annotation.Configuration;
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import java.util.HashSet;
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import static dev.langchain4j.model.openai.OpenAiEmbeddingModelName.TEXT_EMBEDDING_3_SMALL;
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@Configuration
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public class ChatBotConfiguration {
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@Value("${app.mongodb.url}")
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private String mongodbUrl;
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@Value("${app.mongodb.db-name}")
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private String databaseName;
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@Value("${app.openai.apiKey}")
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private String apiKey;
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@Bean
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public MongoClient mongoClient() {
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return MongoClients.create(mongodbUrl);
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}
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@Bean
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public EmbeddingStore<TextSegment> embeddingStore(MongoClient mongoClient) {
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String collectionName = "embeddings";
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String indexName = "embedding";
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Long maxResultRatio = 10L;
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CreateCollectionOptions createCollectionOptions = new CreateCollectionOptions();
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Bson filter = null;
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IndexMapping indexMapping = IndexMapping.builder()
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.dimension(TEXT_EMBEDDING_3_SMALL.dimension())
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.metadataFieldNames(new HashSet<>())
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.build();
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Boolean createIndex = true;
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return new MongoDbEmbeddingStore(
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mongoClient,
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databaseName,
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collectionName,
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indexName,
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maxResultRatio,
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createCollectionOptions,
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filter,
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indexMapping,
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createIndex
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);
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}
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@Bean
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public EmbeddingModel embeddingModel() {
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return OpenAiEmbeddingModel.builder()
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.apiKey(apiKey)
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.modelName(TEXT_EMBEDDING_3_SMALL)
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.build();
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}
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@Bean
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public ContentRetriever contentRetriever(EmbeddingStore<TextSegment> embeddingStore, EmbeddingModel embeddingModel) {
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return EmbeddingStoreContentRetriever.builder()
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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.maxResults(10)
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.minScore(0.8)
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.build();
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}
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@Bean
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public ChatLanguageModel chatModel() {
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return OpenAiChatModel.builder()
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.apiKey(apiKey)
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.modelName("gpt-4o-mini")
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.build();
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}
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@Bean
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public ArticleBasedAssistant articleBasedAssistant(ChatLanguageModel chatModel, ContentRetriever contentRetriever) {
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return AiServices.builder(ArticleBasedAssistant.class)
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.chatLanguageModel(chatModel)
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.contentRetriever(contentRetriever)
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.build();
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}
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}

‎libraries-llms-2/src/main/java/com/baeldung/chatbot/mongodb/controllers/ChatBotController.java

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package com.baeldung.chatbot.mongodb.controllers;
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import com.baeldung.chatbot.mongodb.assistants.ArticleBasedAssistant;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.web.bind.annotation.*;
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@RestController
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public class ChatBotController {
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private final ArticleBasedAssistant assistant;
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@Autowired
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public ChatBotController(ArticleBasedAssistant assistant) {
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this.assistant = assistant;
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}
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@GetMapping("/chat-bot")
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public String answer(@RequestParam("question") String question) {
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return assistant.answer(question);
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}
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}

‎libraries-llms-2/src/main/java/com/baeldung/chatbot/mongodb/repositories/ArticlesRepository.java

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package com.baeldung.chatbot.mongodb.repositories;
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import com.fasterxml.jackson.databind.JsonNode;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import dev.langchain4j.data.document.Document;
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import dev.langchain4j.data.document.DocumentSplitter;
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import dev.langchain4j.data.document.Metadata;
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import dev.langchain4j.data.document.splitter.DocumentSplitters;
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import dev.langchain4j.data.embedding.Embedding;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.model.embedding.EmbeddingModel;
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import dev.langchain4j.model.openai.OpenAiEmbeddingModelName;
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import dev.langchain4j.model.openai.OpenAiTokenizer;
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import dev.langchain4j.store.embedding.EmbeddingStore;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.beans.factory.annotation.Value;
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import org.springframework.stereotype.Component;
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import java.io.*;
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import java.util.ArrayList;
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import java.util.Iterator;
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import java.util.List;
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@Component
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public class ArticlesRepository {
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private static final Logger log = LoggerFactory.getLogger(ArticlesRepository.class);
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private final EmbeddingStore<TextSegment> embeddingStore;
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private final EmbeddingModel embeddingModel;
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private final ObjectMapper objectMapper = new ObjectMapper();
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@Autowired
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public ArticlesRepository(@Value("${app.load-articles}") Boolean shouldLoadArticles,
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EmbeddingStore<TextSegment> embeddingStore, EmbeddingModel embeddingModel) throws IOException {
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this.embeddingStore = embeddingStore;
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this.embeddingModel = embeddingModel;
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if (shouldLoadArticles) {
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loadArticles();
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}
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}
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private void loadArticles() throws IOException {
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String resourcePath = "articles.json";
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int maxTokensPerChunk = 8000;
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int overlapTokens = 800;
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List<TextSegment> documents = loadJsonDocuments(resourcePath, maxTokensPerChunk, overlapTokens);
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log.info("Documents to store: " + documents.size());
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for (TextSegment document : documents) {
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Embedding embedding = embeddingModel.embed(document.text()).content();
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embeddingStore.add(embedding, document);
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}
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log.info("Documents are uploaded");
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}
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private List<TextSegment> loadJsonDocuments(String resourcePath, int maxTokensPerChunk, int overlapTokens) throws IOException {
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InputStream inputStream = ArticlesRepository.class.getClassLoader().getResourceAsStream(resourcePath);
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if (inputStream == null) {
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throw new FileNotFoundException("Resource not found: " + resourcePath);
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}
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BufferedReader reader = new BufferedReader(new InputStreamReader(inputStream));
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int batchSize = 500;
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List<Document> batch = new ArrayList<>();
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List<TextSegment> textSegments = new ArrayList<>();
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String line;
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while ((line = reader.readLine()) != null) {
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JsonNode jsonNode = objectMapper.readTree(line);
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String title = jsonNode.path("title").asText(null);
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String body = jsonNode.path("body").asText(null);
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JsonNode metadataNode = jsonNode.path("metadata");
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if (body != null) {
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addDocumentToBatch(title, body, metadataNode, batch);
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if (batch.size() >= batchSize) {
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textSegments.addAll(splitIntoChunks(batch, maxTokensPerChunk, overlapTokens));
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batch.clear();
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}
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}
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}
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if (!batch.isEmpty()) {
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textSegments.addAll(splitIntoChunks(batch, maxTokensPerChunk, overlapTokens));
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}
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return textSegments;
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}
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private void addDocumentToBatch(String title, String body, JsonNode metadataNode, List<Document> batch) {
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String text = (title != null ? title + "\n\n" + body : body);
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Metadata metadata = new Metadata();
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if (metadataNode != null && metadataNode.isObject()) {
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Iterator<String> fieldNames = metadataNode.fieldNames();
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while (fieldNames.hasNext()) {
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String fieldName = fieldNames.next();
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metadata.put(fieldName, metadataNode.path(fieldName).asText());
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}
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}
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Document document = Document.from(text, metadata);
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batch.add(document);
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}
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private List<TextSegment> splitIntoChunks(List<Document> documents, int maxTokensPerChunk, int overlapTokens) {
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OpenAiTokenizer tokenizer = new OpenAiTokenizer(OpenAiEmbeddingModelName.TEXT_EMBEDDING_3_SMALL);
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DocumentSplitter splitter = DocumentSplitters.recursive(
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maxTokensPerChunk,
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overlapTokens,
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tokenizer
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);
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List<TextSegment> allSegments = new ArrayList<>();
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for (Document document : documents) {
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List<TextSegment> segments = splitter.split(document);
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allSegments.addAll(segments);
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}
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return allSegments;
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}
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}

‎libraries-llms-2/src/main/resources/application.properties

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app.mongodb.url=mongodb://wikiuser:password@localhost:27017/admin
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app.mongodb.db-name=chatbot_db
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app.openai.apiKey=${OPENAI_API_KEY}
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app.load-articles=false

‎libraries-llms-2/src/test/docker/mongodb/docker-compose.yml

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version: '3.1'
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services:
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my-mongodb:
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image: mongodb/mongodb-atlas-local:7.0.9
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container_name: my-mongodb
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environment:
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- MONGODB_INITDB_ROOT_USERNAME=wikiuser
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- MONGODB_INITDB_ROOT_PASSWORD=password
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ports:
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- 27017:27017

‎libraries-llms-2/src/test/java/com/baeldung/chatbot/mongodb/ChatBotLiveTest.java

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package com.baeldung.chatbot.mongodb;
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import com.baeldung.chatbot.mongodb.configuration.ChatBotConfiguration;
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import com.baeldung.chatbot.mongodb.controllers.ChatBotController;
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import com.baeldung.chatbot.mongodb.repositories.ArticlesRepository;
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import org.junit.jupiter.api.Assertions;
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import org.junit.jupiter.api.Test;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.boot.test.autoconfigure.web.servlet.AutoConfigureMockMvc;
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import org.springframework.boot.test.context.SpringBootTest;
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import org.springframework.test.web.servlet.MockMvc;
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import static org.springframework.test.web.servlet.request.MockMvcRequestBuilders.get;
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@AutoConfigureMockMvc
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@SpringBootTest(classes = {ChatBotConfiguration.class, ArticlesRepository.class, ChatBotController.class})
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class ChatBotLiveTest {
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Logger log = LoggerFactory.getLogger(ChatBotLiveTest.class);
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@Autowired
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private MockMvc mockMvc;
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@Test
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void givenChatBotApi_whenCallingGetEndpointWithQuestion_thenExpectedAnswersIsPresent() throws Exception {
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String chatResponse = mockMvc
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.perform(get("/chat-bot")
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.param("question", "Steps to implement Spring boot app and MongoDB"))
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.andReturn()
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.getResponse()
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.getContentAsString();
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log.info(chatResponse);
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Assertions.assertTrue(chatResponse.contains("Step 1"));
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}
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}

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