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Model Use Cases

Ideal for advanced RAG systems, long-form document retrieval, recommendation engines, and knowledge search requiring higher precision and semantic depth.

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Key Features

  • High-Dimensional Embeddings: Captures nuanced semantic relationships, improving recall and precision in large document collections.
  • Superior Retrieval Accuracy: Delivers stronger relevance matching for complex queries and diverse content types.
  • Multilingual Semantic Support: Maintains strong embedding quality across multiple languages for global applications.
  • Optimized for Knowledge Systems: Well suited for enterprise search, knowledge bases, and large-scale AI assistant platforms.

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