Jina Embeddings v2
Shares tags: build, models & apis, embeddings
AI Tool
Unlock the Power of Language with OpenAI Embeddings 3
Experience high-accuracy text embeddings for RAG and vector search.
Achieve optimal performance with our latest models: text-embedding-3-small and text-embedding-3-large.Enjoy significantly lower costs, with text-embedding-3-small priced 5x cheaper than previous versions.Benefit from enhanced multilingual support, allowing you to reach a broader audience effortlessly.
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overview
What are OpenAI Embeddings 3?
OpenAI Embeddings 3 delivers advanced text embedding capabilities that facilitate high-accuracy retrieval-augmented generation and semantic search. Whether you're developing AI systems or enhancing content recommendation, these models are designed to meet your needs.
- Supports clustering and cross-lingual applications.
- Dynamically control embedding dimensions via the API.
- Optimized for production AI environments.
features
Key Features
OpenAI Embeddings 3 introduces two innovative models to cater to various requirements. The text-embedding-3-small is ideal for low-latency applications, while text-embedding-3-large ensures the highest accuracy.
- Text-embedding-3-small: 5x cheaper with optimized latency.
- Text-embedding-3-large: Up to 3072 dimensions for top-tier accuracy.
- Improved multilingual performance for better global engagement.
use_cases
Use Cases
From AI systems to knowledge retrieval and content recommendation platforms, OpenAI Embeddings 3 can transform your applications. Leverage the versatility of these models to tackle a variety of natural language processing tasks.
- Production AI systems for diverse industries.
- Semantic search to enhance user experience.
- Effortless content recommendations based on user preferences.
Frequently Asked Questions
What are the main differences between the small and large models?
The text-embedding-3-small model is optimized for lower latency and cost, making it ideal for real-time applications, while the text-embedding-3-large model provides the highest accuracy for more demanding use cases.
Can I control the dimensions of the embeddings?
Yes, developers can dynamically adjust the embedding dimensions through the API, allowing you to balance accuracy with storage and compute costs.
What is the latest knowledge cutoff for these models?
The models do not include knowledge beyond September 2021, but their optimized performance makes them versatile for various NLP tasks.