LangChain4j Agent

from("direct:chat")
    .to("langchain4j-agent:simple?agent=#simpleAgent")

var prompt = "What is Apache Camel";

String response = template.requestBody("direct:chat", prompt, String.class);

var prompt = "What is Apache Camel";
AiAgentBody body = new AiAgentBody(prompt);

String response = template.requestBody("direct:chat", body, String.class);

from("direct:chat")
    .to("langchain4j-agent:simple?agent=#simpleAgent")

var userPrompt = "Write a short story about a lost cat.";
var systemPrompt = "You are a whimsical storyteller. Your responses should be imaginative, descriptive, and always include a touch of magic. Start every story with 'Once upon a starlit night...";

String response = template.requestBodyAndHeader("direct:chat",
                userPrompt, "CamelLangChain4jAgentSystemMessage", systemPrompt , String.class);

var userPrompt = "Write a short story about a lost cat.";
var systemPrompt = "You are a whimsical storyteller. Your responses should be imaginative, descriptive, and always include a touch of magic. Start every story with 'Once upon a starlit night...";

AiAgentBody body = new AiAgentBody()
                .withUserMessage(userPrompt)
                .withSystemMessage(systemPrompt);

String response = template.requestBody("direct:chat", body, String.class);

// Define tool routes
from("langchain4j-tools:userDb?tags=users&description=Query user database&parameter.userId=string")
    .setBody(constant("{\"name\": \"John Doe\", \"id\": \"123\"}"));

from("langchain4j-tools:weather?tags=weather&description=Get weather information&parameter.city=string")
    .setBody(constant("{\"weather\": \"sunny\", \"temperature\": \"22°C\"}"));

// Agent with tools (using the created agent)
from("direct:chat")
    .to("langchain4j-agent:tools?agent=#simpleAgent&tags=users,weather");

var userPrompt = "Can you tell me the name of user 123 and the weather in New York?";
var systemPrompt = "You are a helpful assistant that can access user database and weather information. Use the available tools to provide accurate information.";

String response = template.requestBodyAndHeader("direct:chat",
                userPrompt, "CamelLangChain4jAgentSystemMessage", systemPrompt , String.class);

// Create the retrieval augmentor (shown below)
RetrievalAugmentor retrievalAugmentor = createRetrievalAugmentor();

// Create agent configuration with RAG
AgentConfiguration configuration = new AgentConfiguration()
    .withChatModel(chatModel)
    .withRetrievalAugmentor(retrievalAugmentor)
    .withInputGuardrailClasses(List.of())
    .withOutputGuardrailClasses(List.of());

Agent ragAgent = new AgentWithoutMemory(configuration);
context.getRegistry().bind("ragAgent", ragAgent);

// Use the RAG agent
from("direct:chat")
    .to("langchain4j-agent:rag?agent=#ragAgent")

// creating the retrieval Augmentor
EmbeddingStoreContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder()
                .embeddingStore(embeddingStore) // the embedding store should be defined
                .embeddingModel(embeddingModel) // the embedding model should be defined
                .maxResults(3)
                .minScore(0.6)
                .build();

RetrievalAugmentor retrievalAugmentor = DefaultRetrievalAugmentor.builder()
                .contentRetriever(contentRetriever)
                // other options or steps can be included for Advanced RAG
                .build();

// bind the retrievalAugmentor in the context
context.getRegistry().bind("retrievalAugmentor", retrievalAugmentor);

// using the producer
String response = template.requestBody("direct:chat", body, String.class);

from("direct:chat")
    .to("langchain4j-agent:memory?agent=#memoryAgent")

// Example of creating a Chat Memory Provider : Create a message window memory that keeps the last 10 messages
        ChatMemoryProvider chatMemoryProvider = memoryId -> MessageWindowChatMemory.builder()
                .id(memoryId)
                .maxMessages(10)
                .chatMemoryStore(store) // the Chat Memory store is previously created
                .build();

// bind the chat memory provider in the context
context.getRegistry().bind("chatMemoryProvider", chatMemoryProvider);


AiAgentBody request = new AiAgentBody("Hello!", null, "session-123");
String response = template.requestBody("direct:chat", request, String.class);

// Create agent configuration with guardrails
AgentConfiguration configuration = new AgentConfiguration()
    .withChatModel(chatModel)
    .withInputGuardrailClassesList("com.example.MyInputGuardrail")
    .withOutputGuardrailClassesList("com.example.MyOutputGuardrail1,com.example.MyOutputGuardrail2");

Agent safeAgent = new AgentWithoutMemory(configuration);
context.getRegistry().bind("safeAgent", safeAgent);

// Use the agent with guardrails
from("direct:agent-with-guardrails")
    .to("langchain4j-agent:safe?agent=#safeAgent")