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Jina Embeddings v3

Multilingual embedding model with on-the-fly dimension switching (512/1024/2048) under Apache 2.0 for flexible RAG.

View on GitHub Official site
Embedding Models CC BY-NC 4.0 Medium setup 1,142 stars

Overview

Plain English

Multilingual embedding model with on-the-fly dimension switching (512/1024/2048) under Apache 2.0 for flexible RAG.

Technical

Multilingual embedding model with on-the-fly dimension switching (512/1024/2048) under Apache 2.0 for flexible RAG.

Technical scorecard

License CC BY-NC 4.0
Commercial use No
OpenAI-compatible API No
REST API No
Fine-tuning support No
Quantization support No
Docker available No
GUI / no-code available No
Telemetry None
Offline after setup Yes

Data & Privacy

Does it send data online?

After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.

Does it store history?

Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.

License checks?

The listed license needs review before commercial use.

Telemetry?

None

Last verified: Apr 8, 2026. Maintainer verification should be treated as directory guidance, not legal advice.

Setup & Installation

Medium

A developer can usually get this running with standard docs.

Prerequisites

Python, Docker, Bare Metal 

# Start with the official project documentation
# https://huggingface.co/jinaai/jina-embeddings-v3

Hardware Requirements

RAM8 GB minimum / 16 GB recommended
Hardware tagsCPU Only, NVIDIA GPU (CUDA)
Model formatsSafetensors
Primary languagePython

Works Well With

RAGFlow RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. PrivateGPT Secure document Q&A system designed for sensitive data with no internet connection required for full operation. Milvus Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search. FAISS (Meta) A library for efficient similarity search and clustering of dense vectors.
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