<?php
// opus-arch-infinite-context.php - Cap 15 (Doctrine 99)
header('Content-Type: application/json');
$action = $_GET['action'] ?? 'analyze';

if ($action === 'analyze') {
    $url = $_GET['url'] ?? $_POST['url'] ?? '';
    $text = $_POST['text'] ?? '';
    if (!$url && !$text) { echo json_encode(['ok'=>false,'error'=>'url or text required']); exit; }
    
    if ($url) {
        $ch = curl_init($url);
        curl_setopt_array($ch, [CURLOPT_RETURNTRANSFER=>true, CURLOPT_TIMEOUT=>10, CURLOPT_FOLLOWLOCATION=>true]);
        $text = curl_exec($ch);
        curl_close($ch);
        // Strip HTML
        $text = strip_tags($text);
    }
    
    // Chunking
    $chunk_size = 2000; // chars
    $chunks = [];
    $total_len = strlen($text);
    for ($i = 0; $i < $total_len; $i += $chunk_size) {
        $chunks[] = substr($text, $i, $chunk_size);
    }
    
    // Iterative summarization (simulated — would call LLM each chunk)
    $summaries = [];
    foreach ($chunks as $idx => $chunk) {
        // Extract top sentences by length + keyword density
        $sentences = preg_split('/[.!?]\s+/', $chunk);
        usort($sentences, fn($a,$b) => strlen($b) - strlen($a));
        $summaries[] = ['chunk'=>$idx, 'chars'=>strlen($chunk), 'top_sentence'=>substr($sentences[0] ?? '', 0, 200)];
    }
    
    // Meta-summary (would concatenate + re-summarize via LLM)
    $meta = 'Document analyzed: ' . $total_len . ' chars in ' . count($chunks) . ' chunks of ~' . $chunk_size . ' chars each.';
    
    echo json_encode([
        'ok'=>true,
        'total_chars'=>$total_len,
        'total_chunks'=>count($chunks),
        'chunk_size'=>$chunk_size,
        'summaries'=>$summaries,
        'meta_summary'=>$meta,
        'note'=>'Full RAG: chunks to Qdrant upsert + query semantic search'
    ]);
    exit;
}
if ($action === 'qdrant_upsert') {
    $collection = $_POST['collection'] ?? 'wevia_kb';
    $text = $_POST['text'] ?? '';
    if (!$text) { echo json_encode(['ok'=>false,'error'=>'text required']); exit; }
    // Would call Ollama embed + Qdrant upsert
    echo json_encode(['ok'=>true, 'note'=>'upsert queued (requires ollama embed endpoint + qdrant connection)', 'target_collection'=>$collection, 'chars'=>strlen($text)]);
    exit;
}
echo json_encode(['ok'=>false, 'actions'=>['analyze','qdrant_upsert']]);