Optimization

These Java examples are real runnable files. Edit the source file first; this page is rebuilt from the checked-in example and its metadata header.

Java AxGen Optimization

Runs a baseline OpenAI prediction and applies an optimizer artifact.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: beginner
Run: npm run example -- java src/examples/java/optimization/AxgenOptimizationExample.java
Source: src/examples/java/optimization/AxgenOptimizationExample.java

Java

import dev.axllm.ax.*;
import java.nio.file.*;
import java.util.*;

public final class AxgenOptimizationExample {
  static String apiKey() {
    String apiKey = System.getenv("OPENAI_API_KEY");
    if (apiKey == null || apiKey.isBlank()) apiKey = System.getenv("OPENAI_APIKEY");
    if (apiKey == null || apiKey.isBlank()) {
      throw new IllegalStateException("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.");
    }
    return apiKey;
  }

  static OpenAICompatibleClient client() {
    return new OpenAICompatibleClient(
        Map.of("api_key", apiKey(), "model", System.getenv().getOrDefault("AX_OPENAI_MODEL", "gpt-5.4-mini"), "model_config", Map.of("temperature", 0.0)));
  }

  static final class ExampleOptimizer implements OptimizerEngine {
    public String name() { return "example"; }
    public String version() { return "1"; }
    public Map<String, Object> optimize(Map<String, Object> request) {
      return Map.of("componentMap", Map.of("priority::instruction", "Classify operational risk. Use high for production-impacting urgency."), "metadata", Map.of("source", "axgen"));
    }
  }

  public static void main(String[] args) throws Exception {
    AxGen program = new AxGen(Ax.s("emailText:string -> priority:class \"high, normal, low\", rationale:string"), Map.of("id", "priority", "instruction", "Classify the email priority."));
    Map<String, Object> baseline = program.forward(client(), Map.of("emailText", "Production checkout is failing for enterprise customers."));
    Map<String, Object> artifact = program.optimizeWith(new ExampleOptimizer(), List.of(Map.of("emailText", "URGENT: checkout is down", "priority", "high")), Map.of("apply", false));
    program.applyOptimization(Json.stringify(artifact));
    Map<String, Object> after = program.forward(client(), Map.of("emailText", "Production checkout is failing for enterprise customers."));
    System.out.println(Json.stringify(Map.of("baseline", baseline, "after", after)));
  }
}

Java GEPA Optimization

Pairs a real OpenAI baseline with a local GEPA optimization pass.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: intermediate
Run: npm run example -- java src/examples/java/optimization/GepaOptimizationExample.java
Source: src/examples/java/optimization/GepaOptimizationExample.java

Java

import dev.axllm.ax.*;
import java.nio.file.*;
import java.util.*;

public final class GepaOptimizationExample {
  static String apiKey() {
    String apiKey = System.getenv("OPENAI_API_KEY");
    if (apiKey == null || apiKey.isBlank()) apiKey = System.getenv("OPENAI_APIKEY");
    if (apiKey == null || apiKey.isBlank()) {
      throw new IllegalStateException("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.");
    }
    return apiKey;
  }

  static OpenAICompatibleClient client() {
    return new OpenAICompatibleClient(
        Map.of("api_key", apiKey(), "model", System.getenv().getOrDefault("AX_OPENAI_MODEL", "gpt-5.4-mini"), "model_config", Map.of("temperature", 0.0)));
  }

  static final class LocalEvaluator implements OptimizerEvaluator {
    public Map<String, Object> evaluate(Map<String, Object> candidateMap, Map<String, Object> options) {
      return Map.of("rows", List.of(Map.of("prediction", Map.of("answer", "Ax composes typed LLM programs."), "scores", Map.of("quality", 0.9), "scalar", 0.9)), "avg", 0.9, "count", 1);
    }
  }

  public static void main(String[] args) throws Exception {
    AxGen program = new AxGen(Ax.s("emailText:string -> priority:class \"high, normal, low\", rationale:string"), Map.of("id", "priority", "instruction", "Classify the email priority."));
    Map<String, Object> baseline = program.forward(client(), Map.of("emailText", "Production checkout is failing for enterprise customers."));
    Map<String, Object> request = Map.of("programKind", "axgen", "components", List.of(Map.of("id", "priority::instruction", "owner", "priority", "kind", "instruction", "current", "Classify priority clearly.")), "dataset", Map.of("train", List.of(Map.of("emailText", "URGENT: checkout is down"))), "options", Map.of("numTrials", 0, "maxMetricCalls", 4, "seed", 7));
    Map<String, Object> artifact = new AxGEPA(null, Map.of("seed", 7)).optimize(request, new LocalEvaluator());
    System.out.println(Json.stringify(Map.of("baseline", baseline, "artifact", artifact)));
  }
}

Java Optimization Artifact Reuse

Saves and reapplies an optimizer artifact after a real OpenAI baseline.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: advanced
Run: npm run example -- java src/examples/java/optimization/ArtifactOptimizationExample.java
Source: src/examples/java/optimization/ArtifactOptimizationExample.java

Java

import dev.axllm.ax.*;
import java.nio.file.*;
import java.util.*;

public final class ArtifactOptimizationExample {
  static String apiKey() {
    String apiKey = System.getenv("OPENAI_API_KEY");
    if (apiKey == null || apiKey.isBlank()) apiKey = System.getenv("OPENAI_APIKEY");
    if (apiKey == null || apiKey.isBlank()) {
      throw new IllegalStateException("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.");
    }
    return apiKey;
  }

  static OpenAICompatibleClient client() {
    return new OpenAICompatibleClient(
        Map.of("api_key", apiKey(), "model", System.getenv().getOrDefault("AX_OPENAI_MODEL", "gpt-5.4-mini"), "model_config", Map.of("temperature", 0.0)));
  }

  static final class ExampleOptimizer implements OptimizerEngine {
    public String name() { return "example"; }
    public String version() { return "1"; }
    public Map<String, Object> optimize(Map<String, Object> request) {
      return Map.of("componentMap", Map.of("priority::instruction", "Classify operational risk. Use high for production-impacting urgency."), "metadata", Map.of("source", "artifact"));
    }
  }

  public static void main(String[] args) throws Exception {
    AxGen program = new AxGen(Ax.s("emailText:string -> priority:class \"high, normal, low\", rationale:string"), Map.of("id", "priority", "instruction", "Classify the email priority."));
    Map<String, Object> baseline = program.forward(client(), Map.of("emailText", "Production checkout is failing for enterprise customers."));
    Map<String, Object> artifact = program.optimizeWith(new ExampleOptimizer(), List.of(Map.of("emailText", "URGENT: checkout is down", "priority", "high")), Map.of("apply", false));
    program.applyOptimization(Json.stringify(artifact));
    Map<String, Object> after = program.forward(client(), Map.of("emailText", "Production checkout is failing for enterprise customers."));
    System.out.println(Json.stringify(Map.of("baseline", baseline, "after", after)));
  }
}