Elastic has introduced a new family of advanced multilingual embedding models designed to improve the speed, efficiency, and accuracy of semantic search and AI-driven applications. The company announced the release of jina-embeddings-v5-text, a set of compact yet powerful embedding models built natively for Elasticsearch that deliver strong performance across multiple semantic and search-related tasks. Despite the fact that the models are comparatively smaller in terms of size, with models having 0.2 billion and 0.6 billion parameters, these models have been proven to be more effective than the larger alternatives with 7 billion to 14 billion parameters on the Multilingual Massive Text Embedding Benchmark (MMTEB) task. This is a clear indication of the dedication of Elastic towards providing efficient AI infrastructure that requires less computation while still providing high-quality results. The new architecture enables faster query processing, lower infrastructure costs, and new use cases, such as edge scenarios or environments with limited compute and memory resources. The models are optimized for a number of key tasks that are used in modern AI and search applications, including retrieval, text matching, classification, and clustering. These capabilities allow organizations to perform natural language searches, identify duplicate or paraphrased content, categorize and analyze documents, and group information based on semantic similarity. In addition to performance gains, Elastic is emphasizing flexibility and accessibility in deployment.
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The jina-embeddings-v5-text models are available as open-weight models through Hugging Face for self-hosted implementations using frameworks such as vLLM, llama.cpp, or MLX, while also being integrated into Elastic’s GPU-powered Elastic Inference Service (EIS). Through EIS, enterprises can run inference workloads without managing complex infrastructure, allowing them to deploy advanced AI-powered search and retrieval capabilities more easily across cloud and on-premises environments. According to Steve Kearns, general manager, Search at Elastic, “Vector search, RAG, and AI agents depend on high-quality retrieval,” said Steve Kearns, general manager, Search, Elastic. “With the addition of the Jina v5’s multilingual embeddings, Elasticsearch continues to be the platform of choice for end-to-end context engineering.” In general, the launch is a continuation of Elastic’s strategy to combine search technology with AI capabilities, which will enable enterprises to develop more intelligent applications that can turn massive amounts of data into valuable insights, including workloads like generative AI and retrieval-augmented generation.