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docs/snippets/csharp/config/embedding_config.cs

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+using Kreuzberg.Config;
+
+public class EmbeddingConfigExample
+{
+    public static void Main()
+    {
+        // Example 1: Preset model (recommended)
+        // Fast, balanced, or quality preset configurations optimized for common use cases.
+        var embeddingConfig = new EmbeddingConfig
+        {
+            Model = new EmbeddingModelType.Preset
+            {
+                Name = "balanced"
+            },
+            BatchSize = 32,
+            Normalize = true,
+            ShowDownloadProgress = true,
+            CacheDir = "~/.cache/kreuzberg/embeddings"
+        };
+
+        // 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 = new EmbeddingConfig
+        {
+            Model = new EmbeddingModelType.Custom
+            {
+                ModelId = "BAAI/bge-small-en-v1.5",
+                Dimensions = 384
+            },
+            BatchSize = 32,
+            Normalize = true,
+            ShowDownloadProgress = true,
+            CacheDir = null  // Uses default: .kreuzberg/embeddings/
+        };
+
+        // 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 = new EmbeddingConfig
+        {
+            Model = new EmbeddingModelType.Custom
+            {
+                ModelId = "sentence-transformers/all-mpnet-base-v2",
+                Dimensions = 768
+            },
+            BatchSize = 16,  // Larger model requires smaller batch size
+            Normalize = true,
+            ShowDownloadProgress = true,
+            CacheDir = "/var/cache/embeddings"
+        };
+
+
+        // Integration with ChunkingConfig
+        // Add embeddings to your chunking configuration:
+        var chunkingConfig = new ChunkingConfig
+        {
+            MaxChars = 1024,
+            MaxOverlap = 100,
+            Preset = "balanced",
+            Embedding = new EmbeddingConfig
+            {
+                Model = new EmbeddingModelType.Preset
+                {
+                    Name = "balanced"
+                },
+                BatchSize = 32,
+                Normalize = true
+            }
+        };
+
+        var extractionConfig = new ExtractionConfig
+        {
+            Chunking = chunkingConfig
+        };
+    }
+}
+
+// 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