fil/docs/snippets/csharp/config/embedding_config.cs

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