Lab 4 – Agentic workflows with LangChain4j

<dependency>
  <groupId>io.quarkiverse.langchain4j</groupId>
  <artifactId>quarkus-langchain4j-ollama</artifactId>
</dependency>

<dependency>
  <groupId>io.quarkiverse.langchain4j</groupId>
  <artifactId>quarkus-langchain4j-openai</artifactId>
</dependency>

package org.acme.agentic;

import java.math.BigDecimal;

/**
 * Simple DTO representing the market data returned by MarketDataResource.
 */
public record MarketDataSnapshot(String ticker, BigDecimal price, double pe, double dividendYield, String currency) {
}

package org.acme.agentic;

import java.math.BigDecimal;

import jakarta.ws.rs.GET;
import jakarta.ws.rs.Path;
import jakarta.ws.rs.PathParam;
import jakarta.ws.rs.Produces;
import jakarta.ws.rs.core.MediaType;

/**
 * Simple in-memory market data endpoint used by the InvestmentMemoFlow.
 * <p>
 * GET /market-data/{ticker}
 */
@Path("/market-data")
@Produces(MediaType.APPLICATION_JSON)
public class MarketDataResource {

    @GET
    @Path("/{ticker}")
    public MarketDataSnapshot marketData(@PathParam("ticker") String ticker) {
        String normalized = ticker.toUpperCase();

        BigDecimal price;
        double pe;
        double dividendYield;
        String currency = "USD";

        switch (normalized) {
            case "CSU.TO" -> {
                price = new BigDecimal("3170.25");
                pe = 28.4;
                dividendYield = 0.003;
                currency = "CAD";
            }
            case "RY.TO" -> {
                price = new BigDecimal("130.10");
                pe = 12.5;
                dividendYield = 0.043;
                currency = "CAD";
            }
            default -> {
                price = new BigDecimal("100.00");
                pe = 15.0;
                dividendYield = 0.0;
            }
        }

        return new MarketDataSnapshot(normalized, price, pe, dividendYield, currency);
    }
}

package org.acme.agentic;

import static io.serverlessworkflow.fluent.func.FuncWorkflowBuilder.workflow;
import static io.serverlessworkflow.fluent.func.dsl.FuncDSL.agent;
import static io.serverlessworkflow.fluent.func.dsl.FuncDSL.get;

import java.util.Map;

import jakarta.enterprise.context.ApplicationScoped;
import jakarta.inject.Inject;

import org.eclipse.microprofile.config.inject.ConfigProperty;

import io.quarkiverse.flow.Flow;
import io.serverlessworkflow.api.types.Workflow;

/**
 * Workflow that:
 * <p>
 * 1. Calls an HTTP endpoint to fetch market data for a ticker. 2. Pipes the result into a LangChain4j agent which
 * writes an InvestmentMemo.
 */
@ApplicationScoped
public class InvestmentMemoFlow extends Flow {

    private final InvestmentAnalystAgent analyst;

    @ConfigProperty(name = "org.acme.agentic.market-data.url")
    String marketDataUrl;

    @Inject
    public InvestmentMemoFlow(InvestmentAnalystAgent analyst) {
        this.analyst = analyst;
    }

    @Override
    public Workflow descriptor() {
        // tag::workflow[]
        return workflow("investment-memo").tasks(
                // 1) Fetch market data via HTTP and turn it into an InvestmentPrompt
                // tag::http-step[]
                get("fetchMarketData", marketDataUrl).outputAs((result, wf, tf) -> {
                    // This is the original task input, as sent by the workflow user
                    // It has the user's objective and horizon
                    // It could be a record, but we use as a Map to exemplify how to handle this type of object in the
                    // example.
                    final Map<String, Object> input = tf.input().asMap().orElseThrow();
                    // This is the task output before the outputAs filter
                    final String response = tf.rawOutput().asText().orElseThrow();
                    return new InvestmentPrompt(result.ticker(), input.get("objective").toString(),
                            input.get("horizon").toString(), response);
                }, MarketDataSnapshot.class),
                // end::http-step[]

                // 2) Call the LLM-backed investment analyst agent
                agent("investmentAnalyst", analyst::analyse, InvestmentPrompt.class)).build();
        // end::workflow[]
    }
}