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