Nomad changes

docs/snippets/php/utils/chunking_rag.php

@@ -0,0 +1,80 @@
+```php title="chunking_rag.php"
+<?php
+
+declare(strict_types=1);
+
+/**
+ * Chunking for RAG (Retrieval-Augmented Generation)
+ *
+ * Advanced chunking configuration optimized for RAG systems with embeddings.
+ * Demonstrates how to process documents into chunks with embeddings for
+ * vector database storage and semantic search.
+ */
+
+require_once __DIR__ . '/vendor/autoload.php';
+
+use Kreuzberg\Kreuzberg;
+use Kreuzberg\Config\ExtractionConfig;
+use Kreuzberg\Config\ChunkingConfig;
+use Kreuzberg\Config\EmbeddingConfig;
+
+$config = new ExtractionConfig(
+    chunking: new ChunkingConfig(
+        maxChars: 500,
+        maxOverlap: 50,
+        embedding: new EmbeddingConfig(
+            model: 'balanced',
+            normalize: true,
+            batchSize: 16
+        )
+    )
+);
+
+$kreuzberg = new Kreuzberg($config);
+$result = $kreuzberg->extractFile('research_paper.pdf');
+
+echo "RAG Chunking Results:\n";
+echo str_repeat('=', 60) . "\n";
+
+$chunksWithEmbeddings = [];
+foreach ($result->chunks ?? [] as $chunk) {
+    if ($chunk->embedding !== null) {
+        $chunksWithEmbeddings[] = [
+            'content' => substr($chunk->content, 0, 100) . '...',
+            'embedding_dims' => count($chunk->embedding),
+            'full_content' => $chunk->content,
+            'embedding' => $chunk->embedding,
+        ];
+    }
+}
+
+echo "Chunks with embeddings: " . count($chunksWithEmbeddings) . "\n\n";
+
+echo "Sample chunks for vector database:\n";
+echo str_repeat('=', 60) . "\n";
+
+foreach (array_slice($chunksWithEmbeddings, 0, 3) as $index => $chunk) {
+    echo "Chunk " . ($index + 1) . ":\n";
+    echo "  Content preview: {$chunk['content']}\n";
+    echo "  Embedding dimensions: {$chunk['embedding_dims']}\n";
+    echo "  Ready for vector DB: Yes\n\n";
+}
+
+$vectorDbRecords = array_map(
+    fn($chunk, $idx) => [
+        'id' => sprintf('doc_%s_chunk_%d', md5('research_paper.pdf'), $idx),
+        'content' => $chunk['full_content'],
+        'embedding' => $chunk['embedding'],
+        'metadata' => [
+            'source' => 'research_paper.pdf',
+            'chunk_index' => $idx,
+            'char_count' => strlen($chunk['full_content']),
+        ],
+    ],
+    $chunksWithEmbeddings,
+    array_keys($chunksWithEmbeddings)
+);
+
+echo "Prepared " . count($vectorDbRecords) . " records for vector database\n";
+echo "Each record contains: id, content, embedding, and metadata\n";
+```