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World-class multimodal multilingual embeddings for search/RAG/agents.

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Segment long text into chunks and do tokenization.

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Premier tool for prompt engineering

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The data structure for multimodal data

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Tech blog

January 29, 2024

MyScale & Jina AI: Unleashing Great Potential for Your AI Applications

With full integration of Jina Embeddings v2 models, MyScale allows users to harness the capabilities of Jina AI within an SQL database.

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Scott Martens • 1 minutes read

MyScale has integrated Jina Embeddings v2 into its AI-focused SQL vector database. You can now use Jina AI's cutting-edge embedding models in conjunction with powerful and time-tested SQL database technologies to bring precise text matching and efficient semantic similarity to your data-driven applications.

Follow the link below to MyScale's website to see how you can supercharge your SQL with Jina Embeddings.

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August 26, 2024 • 13 minutes read

The What and Why of Text-Image Modality Gap in CLIP Models

You can't just use a CLIP model to retrieve text and images and sort the results by score. Why? Because of the modality gap. What is it, and where does it come from?

Futuristic black image with "modality gap" in 3D purple letters, additional text, and a dynamic glass sphere effect.

August 22, 2024 • 8 minutes read

Late Chunking in Long-Context Embedding Models

Chunking long documents while preserving contextual information is challenging. We introduce the "Late Chunking" that leverages long-context embedding models to generate contextual chunk embeddings for better retrieval applications.

Diagram illustrating the 'Late Chunking' and 'Long Document Model' processes in machine learning on a black background.

July 31, 2024 • 17 minutes read

Rephrased Labels Improve Zero-Shot Text Classification by 30%

When using embedding models for zero-shot classification, rephrasing the class label to "This is seriously about 'LABEL'" gives higher accuracy vs. using LABEL alone. But how, and why?

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