PHP code example of neuron-core / llm-classifier

    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

    

neuron-core / llm-classifier example snippets

use NeuronAI\Providers\OpenAI\OpenAI;
use NeuronCore\Classifier\Calibration\Calibrator;
use NeuronCore\Classifier\Calibration\Grader\ExactMatchGrader;
use NeuronCore\Classifier\Calibration\Grader\LlmJudgeGrader;
use NeuronCore\Classifier\Calibration\GraderResolver;
use NeuronCore\Classifier\Calibration\SeedCorpus;

// The models we want to route BETWEEN — they take the test.
$panel = [
    new OpenAI(apiKey: $cheapKey,  model: 'gpt-4o-mini'),
    new OpenAI(apiKey: $premiumKey, model: 'gpt-4o'),
];

// A separate "judge" model grades the answers. Keep it OUT of the panel.
$judge = new OpenAI(apiKey: $key, model: 'gpt-4o');

$artifact = (new Calibrator(
    panel:    $panel,
    corpus:   SeedCorpus::fromFile('seed.csv'),
    graders:  new GraderResolver([
        // Mechanical check: the answer must match exactly.
        'math' => new ExactMatchGrader(),
        // No single right answer: let the judge compare to a rubric.
        'writing' => new LlmJudgeGrader($judge),
    ]),
    language: 'en',
    fasttext: 'cc.en.300.vec',   // download once from https://fasttext.cc/docs/en/crawl-vectors.html#models
))->run();

$artifact->writeTo('storage/model.script'); // ship this file with your app

use NeuronCore\Classifier\Classifier;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\OpenAI\OpenAI;

// Load ONCE — e.g. on app boot, or under Octane/RoadRunner/FrankenPHP workers.
$scorer = Classifier::load('storage/model.script');

// Use on every request:

// 1) Guard first: how much of this prompt does the classifier actually recognize?
//    Low coverage = out-of-domain → don't trust the score, send to the strong model.
if ($scorer->coverage($userPrompt) < 0.4) {
    $model = 'o1';   // unfamiliar territory → safest, most capable model
} else {
    // 2) In-domain: route by difficulty. overall() returns ONE score in 0..1.
    $score = $scorer->overall($userPrompt);

    // Pick the model that's cheap enough for how easy this prompt is.
    $model = match (true) {
        $score < 0.33 => 'gpt-4o-mini',   // easy  → cheap & fast
        $score < 0.70 => 'gpt-4o',        // medium → solid all-rounder
        default       => 'o1',            // hard  → most capable
    };
}

$provider = new OpenAI(apiKey: $key, model: $model);
$answer = $provider->chat(new UserMessage($userPrompt))->getContent();

if ($scorer->coverage($userPrompt) < 0.4) {   // too many unknown words
    $model = 'o1';                              // don't trust the score → strongest model
} else {
    $score = $scorer->classify($userPrompt)['math'] ?? 0.0;
    // …route by score…
}

use NeuronCore\Classifier\Classifier;

$scorer = Classifier::load('storage/model.bin');
$score  = $scorer->overall($userPrompt); // 0 = easy, 1 = hard

bash
# 1) one-time: download the fastText vectors
curl -O https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.vec.gz
gunzip cc.en.300.vec.gz
mv cc.en.300.vec storage/

# 2) Run calibration will generate the model file -> storage/model.bin
php script/routerbench.php