Elastic made two Jina Rerankers available on Elastic Inference Service (EIS), a GPU-accelerated inference-as-a-service that makes it easy to run fast, high-quality inference without complex setup or hosting. These rerankers bring low-latency, high-precision multilingual reranking to the Elastic ecosystem.
As generative AI prototypes move into production-ready search and RAG systems, users run into relevance and inference latency limits, particularly for multilingual use cases. Rerankers improve search quality by reordering results based on semantic relevance, helping surface the most accurate matches for a query. They improve relevance across aggregated, multi-query results, without reindexing or pipeline changes. This makes them especially valuable for hybrid search, RAG, and context-engineering workflows where better context boosts downstream accuracy.
By delivering GPU-accelerated Jina rerankers as a managed service, Elastic enables teams to improve search and RAG accuracy without managing model infrastructure.
“Search relevance is foundational to AI-driven experiences,” said Steve Kearns, general manager, Search at Elastic. “By bringing these Jina reranker models to Elastic Inference Service, we are enabling teams to deliver fast and accurate multilingual search, RAG, and agentic AI experiences, available out of the box with minimal setup.”
The two new Jina reranker models are optimized for different production needs:
Jina Reranker v2 (jina-reranker-v2-base-multilingual)
Built for scalable, agentic workflows.
- Low-latency inference at scale: Low-latency inference with strong multilingual performance that can outperform larger rerankers.
- Support for agentic use cases: Ability to select relevant SQL tables and external functions that best match user queries, enabling more advanced agent-driven workflows.
- Unbounded candidate support: Scores documents independently to handle arbitrarily large candidate sets. These scores remain consistent across batches, so developers can rerank results incrementally without relying on strict top-k limits.
Jina Reranker v3 (jina-reranker-v3)
Optimized for high-precision shortlist reranking.
- Lightweight, production-friendly architecture: Optimized for low-latency inference and efficient deployment in production settings.
- Strong multilingual performance: Benchmarks show that v3 delivers state-of-the-art multilingual performance, outperforming much larger alternatives, and maintains stable top-k rankings under permutation.
- Cost-efficient, cross-document reranking: v3 reranks up to 64 documents together in a single inference call, reasoning across the full candidate set to improve ordering when results are similar or overlapping. By batching candidates instead of scoring them individually, v3 significantly reduces inference usage, making it a strong fit for RAG and agentic workflows with defined top-k results.
These models extend Elastic’s growing catalogue of ready-to-use models available on EIS, which includes the open source multilingual and multimodal embeddings, rerankers, and small language models built by Jina and acquired by Elastic last year. EIS has an expanding catalogue of ready-to-use models on managed GPUs, with additional models expected to be added over time.
All Elastic Cloud trials have access to the Elastic Inference Service.
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