Generation

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

C++ Typed Generation

Runs a small typed generation program against OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: beginner
Run: npm run example -- cpp src/examples/cpp/generation/basic_generation.cpp
Source: src/examples/cpp/generation/basic_generation.cpp

C++

#include "axllm/axllm.hpp"
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <sstream>


int main() {
  const char* key = std::getenv("OPENAI_API_KEY");
  if (key == nullptr || std::string(key).empty()) key = std::getenv("OPENAI_APIKEY");
  if (key == nullptr || std::string(key).empty()) {
    std::cerr << "Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.\n";
    return 2;
  }
  const char* model = std::getenv("AX_OPENAI_MODEL");
  axllm::OpenAICompatibleClient client(axllm::object({
      {"api_key", key},
      {"model", model == nullptr || std::string(model).empty() ? "gpt-5.4-mini" : model},
      {"model_config", axllm::object({{"temperature", 0}})},
  }));
  axllm::AxGen program = axllm::ax("question:string -> answer:string");
  axllm::Value output = program.forward(client, axllm::object({{"question", "In one sentence, explain Ax as a language-agnostic LLM programming library."}}));
  std::cout << axllm::stringify(output) << "\n";
}

C++ Structured Extraction

Extracts structured fields and labels from support text with OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: intermediate
Run: npm run example -- cpp src/examples/cpp/generation/structured_generation.cpp
Source: src/examples/cpp/generation/structured_generation.cpp

C++

#include "axllm/axllm.hpp"
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <sstream>


int main() {
  const char* key = std::getenv("OPENAI_API_KEY");
  if (key == nullptr || std::string(key).empty()) key = std::getenv("OPENAI_APIKEY");
  if (key == nullptr || std::string(key).empty()) {
    std::cerr << "Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.\n";
    return 2;
  }
  const char* model = std::getenv("AX_OPENAI_MODEL");
  axllm::OpenAICompatibleClient client(axllm::object({
      {"api_key", key},
      {"model", model == nullptr || std::string(model).empty() ? "gpt-5.4-mini" : model},
      {"model_config", axllm::object({{"temperature", 0}})},
  }));
  axllm::AxGen program = axllm::ax("ticket:string -> priority:class \"high, normal, low\", summary:string, labels:string[]");
  axllm::Value output = program.forward(client, axllm::object({{"ticket", "Checkout has failed for enterprise customers since 09:00. Support wants a concise summary and tags."}}));
  std::cout << axllm::stringify(output) << "\n";
}

C++ Contextual Generation

Answers from supplied context and returns compact citations with OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: advanced
Run: npm run example -- cpp src/examples/cpp/generation/context_generation.cpp
Source: src/examples/cpp/generation/context_generation.cpp

C++

#include "axllm/axllm.hpp"
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <sstream>


int main() {
  const char* key = std::getenv("OPENAI_API_KEY");
  if (key == nullptr || std::string(key).empty()) key = std::getenv("OPENAI_APIKEY");
  if (key == nullptr || std::string(key).empty()) {
    std::cerr << "Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.\n";
    return 2;
  }
  const char* model = std::getenv("AX_OPENAI_MODEL");
  axllm::OpenAICompatibleClient client(axllm::object({
      {"api_key", key},
      {"model", model == nullptr || std::string(model).empty() ? "gpt-5.4-mini" : model},
      {"model_config", axllm::object({{"temperature", 0}})},
  }));
  axllm::AxGen program = axllm::ax("context:string, question:string -> answer:string, citations:string[]");
  axllm::Value output = program.forward(client, axllm::object({{"context", "Ax uses signatures, ai(), ax(), agent(), flow(), and optimize()."}, {"question", "How should a new developer think about Ax?"}}));
  std::cout << axllm::stringify(output) << "\n";
}