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docs/snippets/java/config/embedding_config.java

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+import kreuzberg.config.EmbeddingConfig;
+import kreuzberg.config.EmbeddingModelType;
+import kreuzberg.config.ChunkingConfig;
+import kreuzberg.config.ExtractionConfig;
+
+public class EmbeddingConfigExample {
+    public static void main(String[] args) {
+        // Example 1: Preset model (recommended)
+        // Fast, balanced, or quality preset configurations optimized for common use cases.
+        EmbeddingConfig embeddingConfig = EmbeddingConfig.builder()
+            .model(EmbeddingModelType.preset("balanced"))
+            .batchSize(32)
+            .normalize(true)
+            .showDownloadProgress(true)
+            .cacheDir("~/.cache/kreuzberg/embeddings")
+            .build();
+
+        // Available presets:
+        // - "fast" (384 dims): Quick prototyping, development, resource-constrained
+        // - "balanced" (768 dims): Production, general-purpose RAG, English documents
+        // - "quality" (1024 dims): Complex documents, maximum accuracy
+        // - "multilingual" (768 dims): International documents, 100+ languages
+
+
+        // Example 2: Custom ONNX model (requires embeddings feature)
+        // Direct access to specific ONNX embedding models from HuggingFace with custom dimensions.
+        embeddingConfig = EmbeddingConfig.builder()
+            .model(EmbeddingModelType.custom("BAAI/bge-small-en-v1.5", 384))
+            .batchSize(32)
+            .normalize(true)
+            .showDownloadProgress(true)
+            .cacheDir(null)  // Uses default: .kreuzberg/embeddings/
+            .build();
+
+        // Popular ONNX-compatible models:
+        // - "BAAI/bge-small-en-v1.5" (384 dims): Fast, efficient
+        // - "BAAI/bge-base-en-v1.5" (768 dims): Balanced quality/speed
+        // - "BAAI/bge-large-en-v1.5" (1024 dims): High quality, slower
+        // - "sentence-transformers/paraphrase-multilingual-mpnet-base-v2" (768 dims): Multilingual support
+
+
+        // Example 3: Alternative Custom ONNX Model
+        // For advanced users wanting different ONNX embedding models.
+        embeddingConfig = EmbeddingConfig.builder()
+            .model(EmbeddingModelType.custom("sentence-transformers/all-mpnet-base-v2", 768))
+            .batchSize(16)  // Larger model requires smaller batch size
+            .normalize(true)
+            .showDownloadProgress(true)
+            .cacheDir("/var/cache/embeddings")
+            .build();
+
+
+        // Integration with ChunkingConfig
+        // Add embeddings to your chunking configuration:
+        ChunkingConfig chunkingConfig = ChunkingConfig.builder()
+            .maxChars(1024)
+            .maxOverlap(100)
+            .preset("balanced")
+            .embedding(EmbeddingConfig.builder()
+                .model(EmbeddingModelType.preset("balanced"))
+                .batchSize(32)
+                .normalize(true)
+                .build())
+            .build();
+
+        ExtractionConfig extractionConfig = ExtractionConfig.builder()
+            .chunking(chunkingConfig)
+            .build();
+    }
+}
+
+// Key parameter explanations:
+//
+// batchSize: Number of texts to embed at once (32-128 typical)
+//   - Larger batches are faster but use more memory
+//   - Smaller batches for resource-constrained environments
+//
+// normalize: Whether to normalize vectors (L2 norm)
+//   - true (recommended): Enables cosine similarity in vector DBs
+//   - false: Raw embedding values
+//
+// cacheDir: Where to store downloaded models
+//   - null: Uses .kreuzberg/embeddings/ in current directory
+//   - String path: Custom directory for model storage
+//
+// showDownloadProgress: Display download progress bar
+//   - Useful for monitoring large model downloads