90 lines
2.8 KiB
Ruby
90 lines
2.8 KiB
Ruby
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require 'kreuzberg'
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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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embedding_config = Kreuzberg::EmbeddingConfig.new(
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model: { type: :preset, name: "balanced" },
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batch_size: 32,
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normalize: true,
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show_download_progress: true,
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cache_dir: "~/.cache/kreuzberg/embeddings"
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)
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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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embedding_config = Kreuzberg::EmbeddingConfig.new(
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model: {
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type: :custom,
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model_id: "BAAI/bge-small-en-v1.5",
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dimensions: 384
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},
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batch_size: 32,
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normalize: true,
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show_download_progress: true,
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cache_dir: nil # Uses default: .kreuzberg/embeddings/
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)
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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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embedding_config = Kreuzberg::EmbeddingConfig.new(
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model: {
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type: :custom,
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model_id: "sentence-transformers/all-mpnet-base-v2",
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dimensions: 768
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},
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batch_size: 16, # Larger model requires smaller batch size
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normalize: true,
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show_download_progress: true,
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cache_dir: "/var/cache/embeddings"
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)
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# Integration with ChunkingConfig
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# Add embeddings to your chunking configuration:
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chunking_config = Kreuzberg::ChunkingConfig.new(
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max_characters: 1024,
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overlap: 100,
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preset: "balanced",
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embedding: Kreuzberg::EmbeddingConfig.new(
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model: { type: :preset, name: "balanced" },
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batch_size: 32,
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normalize: true
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)
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)
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extraction_config = Kreuzberg::ExtractionConfig.new(
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chunking: chunking_config
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)
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# Key parameter explanations:
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#
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# batch_size: 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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# cache_dir: Where to store downloaded models
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# - nil: Uses .kreuzberg/embeddings/ in current directory
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# - String: Custom directory for model storage
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#
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# show_download_progress: Display download progress bar
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# - Useful for monitoring large model downloads
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