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Henrik Jess Nielsen
2026-06-01 23:40:55 +02:00
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215
docs/snippets/php/embeddings/basic_embeddings.php Normal file
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```php title="basic_embeddings.php"
<?php
declare(strict_types=1);
/**
* Basic Embedding Generation
*
* Generate vector embeddings for semantic search and similarity matching.
* Requires ONNX Runtime to be installed.
*/
require_once __DIR__ . '/vendor/autoload.php';
use Kreuzberg\Kreuzberg;
use Kreuzberg\Config\ExtractionConfig;
use Kreuzberg\Config\ChunkingConfig;
use Kreuzberg\Config\EmbeddingConfig;
$config = new ExtractionConfig(
chunking: new ChunkingConfig(
maxChunkSize: 512,
chunkOverlap: 50
),
embedding: new EmbeddingConfig(
model: 'all-MiniLM-L6-v2',
normalize: true
)
);
$kreuzberg = new Kreuzberg($config);
$result = $kreuzberg->extractFile('document.pdf');
echo "Embedding Generation Results:\n";
echo str_repeat('=', 60) . "\n";
echo "Chunks with embeddings: " . count($result->chunks ?? []) . "\n\n";
foreach ($result->chunks ?? [] as $chunk) {
echo "Chunk {$chunk->metadata->chunkIndex}:\n";
echo " Content length: " . strlen($chunk->content) . " chars\n";
if ($chunk->embedding !== null) {
echo " Embedding dimension: " . count($chunk->embedding) . "\n";
echo " First 5 values: [" . implode(', ', array_map(
fn($v) => number_format($v, 4),
array_slice($chunk->embedding, 0, 5)
)) . "...]\n";
}
echo "\n";
}
$models = [
'all-MiniLM-L6-v2',
'all-mpnet-base-v2',
'paraphrase-multilingual-MiniLM-L12-v2',
];
foreach ($models as $model) {
echo "Testing model: $model\n";
$config = new ExtractionConfig(
chunking: new ChunkingConfig(maxChunkSize: 256),
embedding: new EmbeddingConfig(
model: $model,
normalize: true
)
);
$kreuzberg = new Kreuzberg($config);
$start = microtime(true);
$result = $kreuzberg->extractFile('test_doc.pdf');
$elapsed = microtime(true) - $start;
$chunk = ($result->chunks ?? [])[0] ?? null;
if ($chunk && $chunk->embedding) {
echo " Dimension: " . count($chunk->embedding) . "\n";
echo " Time: " . number_format($elapsed, 3) . "s\n";
echo " Chunks: " . count($result->chunks ?? []) . "\n\n";
}
}
function cosineSimilarity(array $a, array $b): float
{
$dotProduct = 0.0;
$magnitudeA = 0.0;
$magnitudeB = 0.0;
for ($i = 0; $i < count($a); $i++) {
$dotProduct += $a[$i] * $b[$i];
$magnitudeA += $a[$i] * $a[$i];
$magnitudeB += $b[$i] * $b[$i];
}
return $dotProduct / (sqrt($magnitudeA) * sqrt($magnitudeB));
}
$config = new ExtractionConfig(
chunking: new ChunkingConfig(maxChunkSize: 512),
embedding: new EmbeddingConfig(model: 'all-MiniLM-L6-v2', normalize: true)
);
$kreuzberg = new Kreuzberg($config);
$result = $kreuzberg->extractFile('document.pdf');
echo "Chunk Similarity Analysis:\n";
echo str_repeat('=', 60) . "\n";
$chunks = $result->chunks ?? [];
if (count($chunks) >= 2) {
$referenceChunk = $chunks[0];
foreach (array_slice($chunks, 1, 5) as $chunk) {
if ($referenceChunk->embedding && $chunk->embedding) {
$similarity = cosineSimilarity(
$referenceChunk->embedding,
$chunk->embedding
);
echo "Chunk 0 vs Chunk {$chunk->metadata->chunkIndex}: ";
echo number_format($similarity, 4) . "\n";
}
}
}
echo "\n";
class SimpleVectorDB
{
private array $vectors = [];
public function add(string $id, array $embedding, string $content): void
{
$this->vectors[$id] = [
'embedding' => $embedding,
'content' => $content,
];
}
public function search(array $queryEmbedding, int $k = 5): array
{
$results = [];
foreach ($this->vectors as $id => $data) {
$similarity = $this->cosineSimilarity($queryEmbedding, $data['embedding']);
$results[] = [
'id' => $id,
'similarity' => $similarity,
'content' => $data['content'],
];
}
usort($results, fn($a, $b) => $b['similarity'] <=> $a['similarity']);
return array_slice($results, 0, $k);
}
private function cosineSimilarity(array $a, array $b): float
{
$dotProduct = 0.0;
$magA = 0.0;
$magB = 0.0;
for ($i = 0; $i < count($a); $i++) {
$dotProduct += $a[$i] * $b[$i];
$magA += $a[$i] * $a[$i];
$magB += $b[$i] * $b[$i];
}
return $dotProduct / (sqrt($magA) * sqrt($magB));
}
}
$db = new SimpleVectorDB();
$files = ['doc1.pdf', 'doc2.pdf', 'doc3.pdf'];
foreach ($files as $file) {
if (!file_exists($file)) continue;
$result = $kreuzberg->extractFile($file);
foreach ($result->chunks ?? [] as $chunk) {
if ($chunk->embedding) {
$id = $file . '_chunk_' . $chunk->metadata->chunkIndex;
$db->add($id, $chunk->embedding, $chunk->content);
}
}
}
echo "Vector database built\n";
echo "Ready for semantic search!\n";
$config = new ExtractionConfig(
chunking: new ChunkingConfig(maxChunkSize: 512),
embedding: new EmbeddingConfig(model: 'all-MiniLM-L6-v2', normalize: true)
);
$kreuzberg = new Kreuzberg($config);
$result = $kreuzberg->extractFile('export_doc.pdf');
$exportData = [];
foreach ($result->chunks ?? [] as $chunk) {
$exportData[] = [
'id' => uniqid('vec_', true),
'text' => $chunk->content,
'embedding' => $chunk->embedding,
'metadata' => [
'chunk_index' => $chunk->metadata->chunkIndex,
'source' => 'export_doc.pdf',
'timestamp' => time(),
],
];
}
file_put_contents('embeddings_export.json', json_encode($exportData));
echo "\nExported " . count($exportData) . " embeddings to embeddings_export.json\n";
```