Embeddings new_releases clip-v2 release!
Top-performing multimodal multilingual long-context embeddings for search, RAG, agents applications.
Embedding API
Try our world-class embedding models to improve your search and RAG systems. Start with a free trial!
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Example inputs
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upload
Request
Bash
Language
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curl https://api.jina.ai/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer " \
-d @- <<EOFEOF
{
"normalized": true,
"embedding_type": "float",
"input": [
"Organic skincare for sensitive skin with aloe vera and chamomile: Imagine the soothing embrace of nature with our organic skincare range, crafted specifically for sensitive skin. Infused with the calming properties of aloe vera and chamomile, each product provides gentle nourishment and protection. Say goodbye to irritation and hello to a glowing, healthy complexion.",
"Bio-Hautpflege für empfindliche Haut mit Aloe Vera und Kamille: Erleben Sie die wohltuende Wirkung unserer Bio-Hautpflege, speziell für empfindliche Haut entwickelt. Mit den beruhigenden Eigenschaften von Aloe Vera und Kamille pflegen und schützen unsere Produkte Ihre Haut auf natürliche Weise. Verabschieden Sie sich von Hautirritationen und genießen Sie einen strahlenden Teint.",
"Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla: Descubre el poder de la naturaleza con nuestra línea de cuidado de la piel orgánico, diseñada especialmente para pieles sensibles. Enriquecidos con aloe vera y manzanilla, estos productos ofrecen una hidratación y protección suave. Despídete de las irritaciones y saluda a una piel radiante y saludable.",
"针对敏感肌专门设计的天然有机护肤产品:体验由芦荟和洋甘菊提取物带来的自然呵护。我们的护肤产品特别为敏感肌设计,温和滋润,保护您的肌肤不受刺激。让您的肌肤告别不适,迎来健康光彩。",
"新しいメイクのトレンドは鮮やかな色と革新的な技術に焦点を当てています: 今シーズンのメイクアップトレンドは、大胆な色彩と革新的な技術に注目しています。ネオンアイライナーからホログラフィックハイライターまで、クリエイティビティを解き放ち、毎回ユニークなルックを演出しましょう。"
]
}
EOFEOF
key
API key
Available tokens
0
clip-v2: Multilingual Multimodal Embeddings
jina-clip-v2 is a 0.9B CLIP-style model that brings three major advances: multilingual support for 89 languages, high image resolution at 512x512, and Matryoshka representation learning for truncated embeddings.
v3: Frontier Multilingual Embeddings
jina-embeddings-v3
is a frontier multilingual text embedding model with 570M parameters and 8192 token-length, outperforming the latest proprietary embeddings from OpenAI and Cohere on MTEB. Read our blog post and research paper below.Three Ways to Purchase
Subscribe to our API, purchase through cloud providers, or obtain a commercial license for your organization.
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With a commercial license for on-prem deployment
On-premises deployment
Deploy Jina Embeddings models in AWS Sagemaker and Microsoft Azure, and soon in Google Cloud Services, or contact our sales team to get customized Kubernetes deployments for your Virtual Private Cloud and on-premises servers.
AWS SageMaker
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Microsoft Azure
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Google Cloud
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API Integrations
Our Embedding API is natively integrated with various renowned databases, vector stores, RAG, and LLMOps frameworks. To begin, just copy and paste your API key into any of the listed integrations for a quick and seamless start.
Vector Store
LLMOps
RAG
Observability
MongoDB
DataStax
Qdrant
Pinecone
Chroma
Weaviate
Milvus
Epsilla
MyScale
LlamaIndex
Haystack
Langchain
Dify
SuperDuperDB
DashVector
Portkey
Baseten
TiDB
LanceDB
Carbon
Our Publications
Understand how our frontier search models were trained from scratch, check out our latest publications. Meet our team at EMNLP, SIGIR, ICLR, NeurIPS, and ICML!
Learning about Embeddings
Where to start with embeddings? We've got you covered. Learn about embeddings from the ground up with our comprehensive guide.
The Evolution of Embeddings Poster
Discover the ideal poster for your space, featuring captivating infographics or breathtaking visuals tracing the evolution of text embedding models since 1950.
Rate Limit
Columns
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Product | API Endpoint | Descriptionarrow_upward | w/o API Key | w/ API Key | w/ Premium API Key | Average Latency | Token Usage Counting | Allowed Request | |
---|---|---|---|---|---|---|---|---|---|
Embedding API | https://api.jina.ai/v1/embeddings | Convert text/images to fixed-length vectors | block | 500 RPM & 1,000,000 TPM | 2,000 RPM & 5,000,000 TPM | bolt depends on the input size help | Count the number of tokens in the input request. | POST | |
Reranker API | https://api.jina.ai/v1/rerank | Tokenize and segment long text | block | 500 RPM & 1,000,000 TPM | 2,000 RPM & 5,000,000 TPM | bolt depends on the input size help | Count the number of tokens in the input request. | POST | |
Reader API | https://r.jina.ai | Convert URL to LLM-friendly text | 20 RPM | 200 RPM | 1000 RPM | 4.6s | Count the number of tokens in the output response. | GET/POST | |
Reader API | https://s.jina.ai | Search the web and convert results to LLM-friendly text | block | 40 RPM | 100 RPM | 8.7s | Count the number of tokens in the output response. | GET/POST | |
Reader API | https://g.jina.ai | Grounding a statement with web knowledge | block | 10 RPM | 30 RPM | 22.7s | Count the total number of tokens in the whole process. | GET/POST | |
Classifier API (Zero-shot) | https://api.jina.ai/v1/classify | Classify inputs using zero-shot classification | block | 200 RPM & 500,000 TPM | 1,000 RPM & 3,000,000 TPM | bolt depends on the input size | Tokens counted as: input_tokens + label_tokens | POST | |
Classifier API (Few-shot) | https://api.jina.ai/v1/classify | Classify inputs using a trained few-shot classifier | block | 20 RPM & 200,000 TPM | 60 RPM & 1,000,000 TPM | bolt depends on the input size | Tokens counted as: input_tokens | POST | |
Classifier API | https://api.jina.ai/v1/train | Train a classifier using labeled examples | block | 20 RPM & 200,000 TPM | 60 RPM & 1,000,000 TPM | bolt depends on the input size | Tokens counted as: input_tokens × num_iters | POST | |
Segmenter API | https://segment.jina.ai | Tokenize and segment long text | 20 RPM | 200 RPM | 1,000 RPM | 0.3s | Token is not counted as usage. | GET/POST |
CC BY-NC License Self-Check
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Embeddings-related common questions
How were the jina-embeddings-v2 models trained?
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What is jina-clip-v1, can I use it for search text and image?
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Which languages do your models support?
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What is the maximum length for a single sentence input?
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What is the maximum number of sentences I can include in a single request?
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How do I send images to the jina-clip-v1 model?
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How do Jina Embeddings models compare to OpenAI's text-embedding-ada-002 model?
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How seamless is the transition from OpenAI's text-embedding-ada-002 to your solution?
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How tokens are calculated when using jina-clip-v1?
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Do you provide models for embedding images or audio?
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Can Jina Embedding models be fine-tuned with private or company data?
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Can your endpoints be hosted privately on AWS, Azure, or GCP?
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API-related common questions
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Can I use the same API key for embedding, reranking, reader, fine-tuning APIs?
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Can I monitor the token usage of my API key?
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What should I do if I forget my API key?
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Do API keys expire?
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Why is the first request for some models slow?
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Is user input data used for training your models?
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Billing-related common questions
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Is billing based on the number of sentences or requests?
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Is there a free trial available for new users?
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Are tokens charged for failed requests?
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What payment methods are accepted?
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Is invoicing available for token purchases?
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