LLMs as Integration Endpoints: Building Apache Camel Routes With LangChain4j Chat

Building AI features is straightforward until you need to integrate them into a production system. In production, a simple 'call an LLM' often becomes: wiring HTTP/JMS/Kafka/file inputs, enforcing timeouts, retries, fallbacks, stitching context obtained from internal data sources, protecting secrets, making it testable without burning API credits, and ensuring the system is observable when issues occur overnight. wiring HTTP/JMS/Kafka/file inputs, enforcing timeouts, retries, fallbacks, stitching context obtained from internal data sources, protecting secrets, making it testable without burning API credits, and ensuring the system is observable when issues occur overnight. This is where Apache Camel excels. With Camel 4.5 and later, you can handle LLM calls as standard integration endpoints using camel-langchain4j-chat, powered by LangChain4j. In this tutorial, you will build a Java 21 and Gradle project that demonstrates: Single-message chat (CHAT_SINGLE_MESSAGE) Prompt templates + variables (CHAT_SINGLE_MESSAGE_WITH_PROMPT) Chat history (CHAT_MULTIPLE_MESSAGES) RAG with Camel’s Content Enricher (EIP + aggregator strategy) RAG via headers (simple “inject context” approach) A mock mode so everything executes in CI without API keys. Single-message chat (CHAT_SINGLE_MESSAGE) Prompt templates + variables (CHAT_SINGLE_MESSAGE_WITH_PROMPT) Chat history (CHAT_MULTIPLE_MESSAGES) RAG with Camel’s Content Enricher (EIP + aggregator strategy) RAG via headers (simple “inject context” approach) A mock mode so everything executes in CI without API keys. You’ll finish with a project you can reuse as a foundation for real integration flows, without turning your codebase into a “prompt spaghetti factory”. What We’re Building A small runnable CLI app that boots Camel Main (no Spring required) and runs five demos: single prompt history rag-enrich rag-header single single prompt prompt history history rag-enrich rag-enrich rag-header rag-header You can run them all or one at a time from the command line. Prerequisites Java 21 Basic Apache Camel familiarity (routes, direct: endpoints, ProducerTemplate) Optional: OpenAI API key (the tutorial runs fully without it) Java 21 Basic Apache Camel familiarity (routes, direct: endpoints, ProducerTemplate) direct: ProducerTemplate Optional: OpenAI API key (the tutorial runs fully without it) Architecture in One Picture Here’s the mental model: For RAG, Camel enriches the exchange before calling the LLM: Project Setup Tech Stack Java 21 Apache Camel 4.17.0 LangChain4j 1.10.0 Gradle 8.x OpenAI GPT-4o-mini (optional)' Java 21 Apache Camel 4.17.0 LangChain4j 1.10.0 Gradle 8.x OpenAI GPT-4o-mini (optional)' Folder Structure camel-langchain4j-chat-demo/ ├── build.gradle ├── settings.gradle ├── src/ │ ├── main/ │ │ ├── java/com/example/langchain4j/ │ │ │ ├── App.java │ │ │ ├── ChatRoutes.java │ │ │ ├── MockChatModel.java │ │ │ └── ModelFactory.java │ │ └── resources/ │ │ ├── application.properties │ │ └── logback.xml │ └── test/ │ └── java/com/example/langchain4j/ │ └── ChatRoutesTest.java └── README.md camel-langchain4j-chat-demo/ ├── build.gradle ├── settings.gradle ├── src/ │ ├── main/ │ │ ├── java/com/example/langchain4j/ │ │ │ ├── App.java │ │ │ ├── ChatRoutes.java │ │ │ ├── MockChatModel.java │ │ │ └── ModelFactory.java │ │ └── resources/ │ │ ├── application.properties │ │ └── logback.xml │ └── test/ │ └── java/com/example/langchain4j/ │ └── ChatRoutesTest.java └── README.md Step 1: Gradle Setup Create build.gradle: build.gradle plugins { id 'java' id 'application' }

group = 'com.example' version = '1.0.0'

java { toolchain { languageVersion = JavaLanguageVersion.of(21) } }

application { mainClass = 'com.example.langchain4j.App' }

repositories { mavenCentral() }

dependencies { implementation 'org.apache.camel:camel-core:4.17.0' implementation 'org.apache.camel:camel-main:4.17.0' implementation 'org.apache.camel:camel-langchain4j-chat:4.17.0' implementation 'dev.langchain4j:langchain4j-core:1.10.0' implementation 'dev.langchain4j:langchain4j-open-ai:1.10.0' implementation 'ch.qos.logback:logback-classic:1.5.15' testImplementation 'org.junit.jupiter:junit-jupiter:5.11.4' testImplementation 'org.apache.camel:camel-test-junit5:4.17.0' testImplementation 'org.assertj:assertj-core:3.27.3' }

test { useJUnitPlatform() } plugins { id 'java' id 'application' }

group = 'com.example' version = '1.0.0'

java { toolchain { languageVersion = JavaLanguageVersion.of(21) } }

application { mainClass = 'com.example.langchain4j.App' }

repositories { mavenCentral() }

dependencies { implementation 'org.apache.camel:camel-core:4.17.0' implementation 'org.apache.camel:camel-main:4.17.0' implementation 'org.apache.camel:camel-langchain4j-chat:4.17.0' implementation 'dev.langchain4j:langchain4j-core:1.10.0' implementation 'dev.langchain4j:langchain4j-open-ai:1.10.0' implementation 'ch.qos.logback:logback-classic:1.5.15' testImplementation 'org.junit.jupiter:junit-jupiter:5.11.4' testImplementation 'org.apache.camel:camel-test-junit5:4.17.0' testImplementation 'org.assertj:assertj-core:3.27.3' }

test { useJUnitPlatform() } And settings.gradle: settings.gradle rootProject.name = 'camel-langchain4j-chat-demo' rootProject.name = 'camel-langchain4j-chat-demo' Step 2: Configuration and Logging application.properties application.properties # Application Mode (mock or openai) app.mode=openai

# OpenAI Configuration (only used when app.mode=openai) openai.apiKey=sk-***** openai.modelName=gpt-4o-mini openai.temperature=0.3

# Apache Camel Configuration camel.main.name=camel-langchain4j-chat-demo camel.main.duration-max-seconds=0 camel.main.shutdown-timeout=30

# Logging Configuration logging.level.root=INFO logging.level.org.apache.camel=INFO logging.level.com.example.langchain4j=DEBUG # Application Mode (mock or openai) app.mode=openai

# OpenAI Configuration (only used when app.mode=openai) openai.apiKey=sk-***** openai.modelName=gpt-4o-mini openai.temperature=0.3

# Apache Camel Configuration camel.main.name=camel-langchain4j-chat-demo camel.main.duration-max-seconds=0 camel.main.shutdown-timeout=30

# Logging Configuration logging.level.root=INFO logging.level.org.apache.camel=INFO logging.level.com.example.langchain4j=DEBUG logback.xml logback.xml %d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n

%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n

Step 3: Production-Friendly “Mock Mode” (No API Key Required) If you want this to be more than a toy demo, you need a way to run it without external dependencies. That’s why we implement a deterministic MockChatModel: deterministic MockChatModel MockChatModel.java MockChatModel.java package com.example.langchain4j;

import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.data.message.ChatMessage; import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.chat.request.ChatRequest; import dev.langchain4j.model.chat.response.ChatResponse;

/** * Mock implementation of ChatModel for testing and demo without API keys. */ public class MockChatModel implements ChatModel {

@Override public ChatResponse chat(ChatRequest request) { StringBuilder response = new StringBuilder("[MOCK] Responding to: "); if (request != null && request.messages() != null && !request.messages().isEmpty()) { ChatMessage lastMessage = request.messages().get(request.messages().size() - 1); // Use text() method which should exist String userText = ""; try { userText = (String) lastMessage.getClass().getMethod("text").invoke(lastMessage); } catch (Exception e) { userText = lastMessage.toString(); } // Generate deterministic response based on content if (userText.contains("recipe") || userText.contains("dish")) { response.append("Here's a delicious recipe with your requested ingredients!"); } else if (userText.contains("Apache Camel")) { response.append("Apache Camel is a powerful integration framework!"); } else if (userText.contains("capital")) { response.append("Paris is the capital of France."); } else { response.append("I understand your question about: ").append(userText.substring(0, Math.min(50, userText.length()))); } } else { response.append("Hello! I'm a mock AI assistant."); } return ChatResponse.builder() .aiMessage(new AiMessage(response.toString())) .build(); } } package com.example.langchain4j;

import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.data.message.ChatMessage; import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.chat.request.ChatRequest; import dev.langchain4j.model.chat.response.ChatResponse;

/** * Mock implementation of ChatModel for testing and demo without API keys. */ public class MockChatModel implements ChatModel {

@Override public ChatResponse chat(ChatRequest request) { StringBuilder response = new StringBuilder("[MOCK] Responding to: "); if (request != null && request.messages() != null && !request.messages().isEmpty()) { ChatMessage lastMessage = request.messages().get(request.messages().size() - 1); // Use text() method which should exist String userText = ""; try { userText = (String) lastMessage.getClass().getMethod("text").invoke(lastMessage); } catch (Exception e) { userText = lastMessage.toString(); } // Generate deterministic response based on content if (userText.contains("recipe") || userText.contains("dish")) { response.append("Here's a delicious recipe with your requested ingredients!"); } else if (userText.contains("Apache Camel")) { response.append("Apache Camel is a powerful integration framework!"); } else if (userText.contains("capital")) { response.append("Paris is the capital of France."); } else { response.append("I understand your question about: ").append(userText.substring(0, Math.min(50, userText.length()))); } } else { response.append("Hello! I'm a mock AI assistant."); } return ChatResponse.builder() .aiMessage(new AiMessage(response.toString())) .build(); } } Why this matters in production: CI/CD runs don’t require an LLM. Unit tests are stable. Engineers can work offline. You can validate flow logic (routing, RAG, templates) before spending money. CI/CD runs don’t require an LLM. Unit tests are stable. Engineers can work offline. You can validate flow logic (routing, RAG, templates) before spending money. Step 4: Choosing a Model at Runtime (OpenAI or Mock) ModelFactory.java ModelFactory.java package com.example.langchain4j;

import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.openai.OpenAiChatModel; import org.slf4j.Logger; import org.slf4j.LoggerFactory;

import java.time.Duration;

/** * Factory to create ChatModel instances based on configuration. */ public class ModelFactory { private static final Logger log = LoggerFactory.getLogger(ModelFactory.class);

public static ChatModel createChatModel(String mode, String apiKey, String modelName, Double temperature) { if ("openai".equalsIgnoreCase(mode) && apiKey != null && !apiKey.isEmpty()) { log.info("Creating OpenAI chat model with model: {}", modelName); return OpenAiChatModel.builder() .apiKey(apiKey) .modelName(modelName) .temperature(temperature) .timeout(Duration.ofSeconds(60)) .build(); } else { log.info("Creating Mock chat model (API key not provided or mode is mock)"); return new MockChatModel(); } } } package com.example.langchain4j;

import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.openai.OpenAiChatModel; import org.slf4j.Logger; import org.slf4j.LoggerFactory;

import java.time.Duration;

/** * Factory to create ChatModel instances based on configuration. */ public class ModelFactory { private static final Logger log = LoggerFactory.getLogger(ModelFactory.class);

public static ChatModel createChatModel(String mode, String apiKey, String modelName, Double temperature) { if ("openai".equalsIgnoreCase(mode) && apiKey != null && !apiKey.isEmpty()) { log.info("Creating OpenAI chat model with model: {}", modelName); return OpenAiChatModel.builder() .apiKey(apiKey) .modelName(modelName) .temperature(temperature) .timeout(Duration.ofSeconds(60)) .build(); } else { log.info("Creating Mock chat model (API key not provided or mode is mock)"); return new MockChatModel(); } } } Step 5: The Camel Routes (Where AI Becomes “Just Another Endpoint”) The Camel component URI looks like: langchain4j-chat:chatId?chatModel=#beanName&chatOperation=OPERATION langchain4j-chat:chatId?chatModel=#beanName&chatOperation=OPERATION chatId is just an identifier chatModel=#chatModel references a bean in the Camel registry chatOperation controls the behavior chatId is just an identifier chatId chatModel=#chatModel references a bean in the Camel registry chatModel=#chatModel chatOperation controls the behavior chatOperation ChatRoutes.java ChatRoutes.java package com.example.langchain4j;

import dev.langchain4j.data.message.AiMessage; import dev.langchain4j.data.message.ChatMessage; import dev.langchain4j.data.message.SystemMessage; import dev.langchain4j.data.message.UserMessage; import dev.langchain4j.rag.content.Content; import org.apache.camel.AggregationStrategy; import org.apache.camel.Exchange; import org.apache.camel.builder.RouteBuilder; import org.apache.camel.component.langchain4j.chat.LangChain4jChat;

import java.util.ArrayList; import java.util.List; import java.util.Map;

/** * Apache Camel routes demonstrating camel-langchain4j-chat component usage. */ public class ChatRoutes extends RouteBuilder {

@Override public void configure() throws Exception { // Demo 1: CHAT_SINGLE_MESSAGE - Simple question/answer from("direct:single") .log("Demo 1: Single message - Input: ${body}") .to("langchain4j-chat:demo?chatModel=#chatModel&chatOperation=CHAT_SINGLE_MESSAGE") .log("Demo 1: Response: ${body}");

// Demo 2: CHAT_SINGLE_MESSAGE_WITH_PROMPT - Using prompt template with variables from("direct:prompt") .log("Demo 2: Prompt template - Variables: ${body}") .process(exchange -> { // Set the prompt template in header String template = "Create a recipe for a {{dishType}} with these ingredients: {{ingredients}}"; exchange.getIn().setHeader("CamelLangChain4jChatPromptTemplate", template); }) .to("langchain4j-chat:demo?chatModel=#chatModel&chatOperation=CHAT_SINGLE_MESSAGE_WITH_PROMPT") .log("Demo 2: Response: ${body}");

// Demo 3: CHAT_MULTIPLE_MESSAGES - Chat with history/context from("direct:history") .log("Demo 3: Multiple messages with history") .process(exchange -> { // Build a conversation with system message, previous context, and user question List messages = new ArrayList<>(); messages.add(new SystemMessage("You are a helpful AI assistant specialized in Apache Camel.")); messages.add(new UserMessage("What is Apache Camel?")); messages.add(new AiMessage("Apache Camel is an open-source integration framework based on enterprise integration patterns.")); messages.add(new UserMessage("What are some key features?")); exchange.getIn().setBody(messages); }) .to("langchain4j-chat:demo?chatModel=#chatModel&chatOperation=CHAT_MULTIPLE_MESSAGES") .log("Demo 3: Response: ${body}");

// Demo 4: RAG using Content Enricher pattern with LangChain4jRagAggregatorStrategy from("direct:rag-enrich") .log("Demo 4: RAG with Content Enricher - Question: ${body}") .enrich("direct:rag-source", new LangChain4jRagAggregatorStrategy()) .to("langchain4j-chat:demo?chatModel=#chatModel&chatOperation=CHAT_SINGLE_MESSAGE") .log("Demo 4: Response: ${body}");

// RAG knowledge source route from("direct:rag-source") .log("Fetching RAG knowledge...") .process(exchange -> { // Simulate fetching relevant documents/snippets List knowledgeSnippets = new ArrayList<>(); knowledgeSnippets.add("Apache Camel 4.x introduced the concept of lightweight mode for faster startup."); knowledgeSnippets.add("The camel-langchain4j-chat component supports multiple chat operations including single message, prompt templates, and chat history."); knowledgeSnippets.add("LangChain4j integration allows Camel routes to interact with various LLM providers like OpenAI, Azure OpenAI, and more."); exchange.getIn().setBody(knowledgeSnippets); });

// Demo 5: RAG using CamelLangChain4jChatAugmentedData header from("direct:rag-header") .log("Demo 5: RAG with header - Question: ${body}") .process(exchange -> { String question = exchange.getIn().getBody(String.class); // Create augmented data content List augmentedData = new ArrayList<>(); augmentedData.add(Content.from("Apache Camel version 4.0 was released in 2023 with major improvements.")); augmentedData.add(Content.from("The LangChain4j component enables AI-powered integration patterns in Camel routes.")); // Set augmented data in header exchange.getIn().setHeader("CamelLangChain4jChatAugmentedData", augmentedData); // Reset body to the question exchange.getIn().setBody(question); }) .to("langchain4j-chat:demo?chatModel=#chatModel&chatOperation=CHAT_SINGLE_MESSAGE") .log("Demo 5: Response: ${body}"); } /** * Custom aggregation strategy for RAG pattern using Content Enricher. */ private static class LangChain4jRagAggregatorStrategy implements AggregationStrategy { @Override public Exchange aggregate(Exchange original, Exchange resource) { String question = original.getIn().getBody(String.class); List knowledgeSnippets = resource.getIn().getBody(List.class); // Build augmented prompt with context StringBuilder augmentedPrompt = new StringBuilder(); augmentedPrompt.append("Context:\n"); for (String snippet : knowledgeSnippets) { augmentedPrompt.append("- ").append(snippet).append("\n"); } augmentedPrompt.append("\nQuestion: ").append(question); original.getIn().setBody(augmentedPrompt.toString()); return original; } } } package com.example.langchain4j;