Text queries retrieve images, screenshots retrieve documentation, and PDFs, charts, and infographics index into the same vector space. The model supports Matryoshka truncation, so you can drop from 1,024 to 512 or 256 dimensions when storage matters, with minimal quality loss.

jina-clip-v2 is one of several Jina embedding models now available on EIS. For workloads that also span video and audio, jina-embeddings-v5-omni covers all four modalities in a single index: nearly 100 languages and a 0.67B parameter base small enough to run on conventional GPU servers. jina-clip-v2 remains the focused option for cross-modal retrieval between text and images.

How multimodal search works in jina-clip-v2

jina-clip-v2 is a dual-encoder model where separate text and image encoders produce embeddings in the same vector space. This allows text and images to be retrieved interchangeably. A query like “red sports car” can return matching images, an image can surface relevant product descriptions or documentation, and screenshots can map directly to tickets, dashboards, or logs. This isn’t a stitched pipeline of models. It’s a single, shared embedding space across modalities, combining a multilingual Jina-XLM-RoBERTa text encoder with an EVA02-L vision encoder.

Multilingual and document-aware by design

Unlike traditional CLIP models that focus primarily on short English captions, jina-clip-v2 is trained on multilingual text-text and text-image pairs, across 89 languages, and on visually complex datasets at progressively higher resolutions.

EIS allows you to run managed models directly inside Elasticsearch. There’s no separate model hosting layer to provision, no GPU infrastructure to manage, and no external embedding service to maintain.

With jina-clip-v2 on EIS, you can:

• Generate text and image embeddings where your data already lives.

• Index multimodal vectors alongside structured and unstructured content.

• Combine vector search with BM25 using hybrid retrieval.

• Power multimodal retrieval augmented generation (RAG) pipelines grounded in images and documents.

How to run multimodal search with jina-clip-v2 on EIS

The jina-clip-v2 endpoint is preconfigured on Elastic Inference Service. To generate embeddings, call the inference endpoint from the Elasticsearch dev console:

This is the response:

Using jina-clip-v2 embeddings in a search query:

Get endpoint config

Basic text request

Multimodal batch (text + image as separate vectors)

The example below shows how to send both a text and an image input as separate items, each producing its own embedding:

Create custom endpoint with minimum dimensions

Multimodal search in Elasticsearch, from text to images to RAG

By making jina-clip-v2 available on EIS, multimodal search becomes a first-class capability inside Elasticsearch.

Text and images can be indexed into the same vector space. Queries can retrieve across modalities and languages. Hybrid search can combine lexical precision with multimodal semantics. RAG systems can ground responses in charts, screenshots, and document layouts, not just plain text.

All Elastic Cloud trials have access to Elastic Inference Service. Try it now on Elastic Cloud Serverless or Elastic Cloud Hosted, or use EIS via Cloud Connect with your self-managed cluster.

Jina AI, which recently joined Elastic via acquisition, is a leader in open-source multilingual and multimodal embeddings, rerankers, and small language models. Jina brings deep expertise in search foundation models that help developers build high-quality retrieval and RAG systems across text, images, code, and long multilingual content.

Elastic Inference Service (EIS) makes it easy for developers to add fast, high-quality, and reliable semantic retrieval for search, RAG, and agentic applications with predictable, consumption-based pricing. EIS offers fully-managed GPU-powered inference with ready-to-use models, no additional setup or hosting complexity required.

jina-embeddings-v3 supports high-quality multilingual retrieval with long-context reasoning and task-tuned modes for RAG and agents. It provides developers fast dense embeddings across a broad range of languages without the operational overhead.

Why jina-embeddings-v3?

jina-embeddings-v3 is a text embedding model that supports 32 languages and up to 8192-token context, high relevance at lower cost, and GPU-powered inference through EIS.

Key capabilities

• Multilinguality: Closes the language gap and aligns meaning across 32 languages, including Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese.

• Parameter-efficiency: Delivers higher performance with only 570M parameters, achieving comparable performance to much larger LLM-based embeddings at lower costs.

• Dimensionality control: Default in 1024 dimensions, and with Matryoshka representation support, it lets developers dial the embedding size all the way down to 32 dimensions, giving flexibility to balance accuracy, latency, and storage based on your needs.

• Task-specific optimization: Features task-specific Low-Rank Adaptation (LoRA) adapters, enabling it to generate high-quality embeddings for various tasks including query-document retrieval, clustering, classification, and text matching.

Get started

The response:

What’s next

Alongside these new models, EIS continues to evolve to support more users and simplify semantic search across environments.

Cloud Connect for EIS: Cloud Connect for EIS will soon bring EIS to self-managed environments, reducing operational overhead and enabling hybrid architectures and scaling where it works best for you.

semantic_text defaults to jina-embeddings-v3 on EIS: semantic_text currently uses ELSER as the embeddings model behind the scenes, but will default to the jina-embeddings-v3 endpoint on EIS in the near future. This change will provide built-in inference at ingestion time, making it easier to adopt multilingual search without additional configuration.

More models: We’re expanding the EIS model catalog to meet the rising inference demands of our customers. In the coming months, we’ll introduce new models that support an even broader set of search and inference workloads. Hot on the heels of jina-embeddings-v3, the next models to follow are jina-reranker-v2-base-multilingualand jina-reranker-v3. Both Jina AI models greatly improve precision through multilingual reranking for RAG and AI agents.

Conclusion

With jina-embeddings-v3 on EIS, you can build multilingual, high-precision retrieval pipelines without managing models, GPUs, or infrastructure. You get fast dense retrieval and tight integration with Elasticsearch’s relevance stack, all in one platform.

Whether you are building global RAG systems, search, or agentic workflows that need reliable context, Elastic now gives you a high-performance model out-of-the-box, and the operational simplicity to move from prototype to production with confidence.

All Elastic Cloud trials have access to the Elastic Inference Service. Try it now on Elastic Cloud Serverless and Elastic Cloud Hosted.