NetraEmbed from Cognitive-Lab is a state-of-the-art multilingual multimodal embedding model powered by a Gemma3-4B-IT backbone with SigLIP vision encoder, designed for visual document retrieval via BiEncoder architecture that encodes images of documents and text queries into compact single dense vectors supporting Matryoshka dimensions of 768 (fastest, 95% accuracy retention), 1536 (balanced), or 2560 (maximum accuracy) for flexible inference without model reloading. It achieves groundbreaking performance on Nayana-IR Bench (22 languages) with 0.716 NDCG@5 on cross-lingual tasks—152% improvement over ColPali-v1.3—and 0.738 on monolingual, while being 250x more storage-efficient (~10KB per document vs. 2.5MB multi-vector) than traditional approaches, preserving visual elements like charts, tables, and layouts without OCR errors. Ideal for scalable semantic search across millions of multilingual PDFs/scans using cosine similarity in vector DBs like FAISS, Milvus, or Pinecone, it enables enterprise-grade cross-lingual document discovery for revenue charts, hierarchies, or diagrams in diverse scripts.
