Multimodal embedding model with 137M parameters that outperforms OpenAI text-embedding-3-small on both short and long context tasks. Features Matryoshka Representation Learning for flexible embedding dimensions.
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Elasticsearch Vector Search
Lucene KNN vector plugin for Elasticsearch search engine, enabling hybrid lexical+vector search, BM25 fusion, HNSW/IVF indexes for ANN. Used for enterprise search, RAG, multimodal apps. Integrated vs standalone like Weaviate: superior hybrid text handling but higher resource footprint.
Multimodal RAG
Retrieval-Augmented Generation extended to handle multiple modalities including text, images, video, and audio. Uses multimodal embeddings like Gemini Embedding 2 or CLIP to enable cross-modal search and generation.
BGE-VL
State-of-the-art multimodal embedding model from BAAI supporting text-to-image, image-to-text, and compositional visual search. Trained on the MegaPairs dataset with over 26 million retrieval triplets.
Qwen3 Embedding
Multilingual embedding model supporting over 100 languages and ranking #1 on MTEB multilingual leaderboard. Offers flexible model sizes from 0.6B to 8B parameters with user-defined instructions.
Jina Embeddings v4
Universal multimodal embedding model from Jina AI supporting text and images through unified pathway. Built on Qwen2.5-VL-3B-Instruct, outperforms proprietary models on visually rich document retrieval. This is a commercial API with free tier, though OSS weights available.
Deep Lake
Open-source database specializing in unstructured and multimodal data for AI/ML applications. Handles images, videos, and other data with decent vector operations, high recall for multimodal integration, and tight compatibility with PyTorch and TensorFlow.
