Supersonic, Subatomic intelligent applications. Infusing Generative AI in your Java Apps with LangChain4j Kevin Dubois(@kevindubois) Principal Developer Advocate

Kevin Dubois ★ Principal Developer Advocate at Red Hat ★ Java Champion ★ Based in Belgium 󰎐 ★ 🗣 Speak English, Dutch, French, Italian ★ Open Source Contributor (Quarkus, Camel, Knative, ..) @kevindubois@mastodon.social youtube.com/@thekevindubois linkedin.com/in/kevindubois github.com/kdubois @kevindubois.com

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Left / Right of the model

Set goals App developer IT operations Data engineer Data scientists ML engineer Gather and prepare data Develop model Integrate models in app dev Model monitoring & management Retrain models Business leadership AI as a team initiative

Some Definitions

Boring Stuff Artificial Intelligence Artificial Intelligence “Ability of a machine to imitate human intelligent behaviour”

Machine Learning Artificial Intelligence “Application of AI to automatically learn and improve from experience” Machine Learning

Boring Stuff Deep Learning Artificial Intelligence “Vast volumes of data and complex algorithms to train a model using Neural Networks.” Machine Learning Deep Learning

Boring Stuff Generative AI Artificial Intelligence “Focuses on creating new and original content ” Machine Learning Deep Learning Generative AI

▸ Transformers (recognize, predict and generate human language) ▸ Trained in a huge amount of data (trillions of tokens) ▸ Relationship between words and phrases ▸ Fine-tuned for specific domains What are Large Language Models (LLMs)?

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LLM Langchain4j

Dependency io.quarkiverse.langchain4j quarkus-langchain4j-openai 0.16.2

Prompts ▸ Interacting with the model for asking questions ▸ Interpreting messages to get important information ▸ Populating Java classes from natural language ▸ Structuring output

@RegisterAiService interface Assistant { String chat(String message); } -------------------- @Inject private final Assistant assistant; quarkus.langchain4j.openai.api-key=sk-... Configure API key Define Ai Service Create the instance

Prompting @SystemMessage("You are a professional poet") @UserMessage(""" Write a poem about {topic}. The poem should be {lines} lines long. """) String writeAPoem(String topic, int lines); Add context to the calls Main message to send Placeholder

class TransactionInfo { @Description("full name") public String name; @Description("IBAN value") public String iban; @Description("Date of the transaction") public LocalDate transactionDate; @Description("Amount in dollars of the transaction") public double amount; } interface TransactionExtractor { @UserMessage("Extract information about a transaction from {{it}}") TransactionInfo extractTransaction(String text); } Marshalling objects

Chains & Memory ▸ Create conversations ▸ Refer to past answers ▸ Manage concurrent interactions

@RegisterAiService(chatMemoryProviderSupplier = BeanChatMemoryProviderSupplier.class) interface AiServiceWithMemory { String chat(@UserMessage String msg); } --------------------------------- @Inject private AiServiceWithMemory ai; String userMessage1 = "Can you give a brief explanation of Kubernetes?"; String answer1 = ai.chat(userMessage1); String userMessage2 = "Can you give me a YAML example to deploy an app for this?"; String answer2 = ai.chat(userMessage2); Possibility to customize memory provider Remember previous interactions

@RegisterAiService(/*chatMemoryProviderSupplier = BeanChatMemoryProviderSupplier.class*/) interface AiServiceWithMemory { String chat(@MemoryId Integer id, @UserMessage String msg); } --------------------------------- @Inject private AiServiceWithMemory ai; String answer1 = ai.chat(1,"I'm Frank"); String answer2 = ai.chat(2,"I'm Betty"); String answer3 = ai.chat(1,"Who Am I?"); default memory provider Refers to conversation with id == 1, ie. Frank keep track of multiple parallel conversations

Tools ▸ Mixing business code with model ▸ Delegating to external services

@RegisterAiService(tools = EmailService.class) public interface MyAiService { @SystemMessage("You are a professional poet") @UserMessage("Write a poem about {topic}. Then send this poem by email.") String writeAPoem(String topic); @ApplicationScoped public class EmailService { @Inject Mailer mailer; @Tool("send the given content by email") public void sendAnEmail(String content) { mailer.send(Mail.withText("letter@quarkus.io", "A poem", content)); } } Describe when to use the tool Register the tool Ties it back to the tool description

Embedding Documents ▸ Adding terms of use rules into the model ▸ Asking questions of legal documents ▸ Natural queries

@Inject RedisEmbeddingStore store; EmbeddingModel embeddingModel; public void ingest(List documents) { EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder() .embeddingStore(store) .embeddingModel(embeddingModel) .documentSplitter(recursive(500, 0)) .build(); ingestor.ingest(documents); } Document from CSV, spreadsheet, text.. Ingested documents stored in Redis Ingest documents

@ApplicationScoped public class DocumentRetriever implements Retriever { private final EmbeddingStoreRetriever retriever; DocumentRetriever(RedisEmbeddingStore store, EmbeddingModel model) { retriever = EmbeddingStoreRetriever.from(store, model, 20); } @Override public List findRelevant(String s) { return retriever.findRelevant(s); } } CDI injection Augmentation interface

@RegisterAiService(retrieverSupplier = BeanRetrieverSupplier.class) public interface MyAiService { (..) } Look for the Retriever bean

Easier way to retrieve docs: Easy RAG! $ quarkus extension add langchain4j-easy-rag quarkus.langchain4j.easy-rag.path=src/main/resources/catalog Path to documents

Local Models ▸ Use models on-prem ▸ Evolve a model privately ▸ Sentiment Analysis of private data

Run LLMs locally and build AI applications podman-desktop.io Download now at: Supported platforms: Podman AI Lab

Fault Tolerance ▸ Gracefully handle model failures ▸ Retries, Fallback

@RegisterAiService() public interface AiService { @SystemMessage("You are a Java developer") @UserMessage("Create a class about {topic}") @Fallback(fallbackMethod = "fallback") @Retry(maxRetries = 3, delay = 2000) public String chat(String topic); default String fallback(String topic){ return "I'm sorry, I wasn't able create a class about topic: " + topic; } } Handle Failure $ quarkus ext add smallrye-fault-tolerance Add MicroProfile Fault Tolerance dependency Retry up to 3 times

Observability ▸ Collect metrics about your AI-infused app ▸ Trace through requests to see how long they took, and where they happened

$ quarkus ext add micrometer opentelemetry micrometer-registry prometheus

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