Build Your Retrieval Augmented Generation (RAG) Project with Jina Embeddings

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Retrieval Augmented Generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. We are excited to introduce Jina AI's new Embedding model, designed to enhance the RAG's capabilities.

📖 Best-in-class: Developed from our cutting-edge academic research and rigorously tested against the SOTA
models to ensure unparalleled performance.

⛰️ 8192 token length: Pioneering the first open-source embedding model with an 8192-token length, enabling the representation of an entire chapter in a single vector.

💬 Multilingual Support: Offering bilingual models for German-English, Chinese-English, among others, ideal for
cross-lingual.

🧩Seamless integration：Fully compatible with OpenAl's API. Effortlessly integrates with over 10 vector databases and RAG systems for a smooth user experience.

💰5x more affordable：Start with free trials and enjoy a straightforward pricing structure. Get access to powerful embeddings for just 20% of OpenAl's cost.

To ensure our development aligns with the needs of professionals like you, we've created a brief form. Your input will be instrumental in shaping a product that truly resonates with the community and addresses the nuances of using RAG in various industries. We'll contact you after receiving your form to provide a 30-minute free consulting call to support your project.

We also provide the open-source version here.

Your Name *

Email Address *

Which company/organization are you in? *

How deeply have you incorporated RAG into your project?
*

Exploration/Feasibility Check Stage

Prototype Stage

Development Stage

Already completed an RAG project

How necessary do you think the RAG project can be for your current business?
*

Indispensable - eg: it can reduce the cost of the project / improve engineering efficiency/ improve the accuracy

Better to have - eg: it can bring incremental opportunities

Moderately Necessary - The RAG project is beneficial to our business but not essential.

Not Sure - Still explore the value behind

Please briefly describe how you are using/going to use the RAG framework:
*

What are the primary challenges you face with the current implementation of RAG?
*

Wrong or bad semantic matches

Inaccurate match scores

Hallucinations in generative models

Cost of embedding solution

Cost of text-generation (LLM) solution

Privacy of your data, w.r.t. SaaS solution

Incompatibility with your existing tech stack

Other:

Which embedding model you are using now？ *

Have you considered finding a better Embedding model to get better results from RAG *

Yes

How do you anticipate our Embedding model could assist in your RAG implementation? Are there specific metrics or results you hope to achieve with an improved Embedding model?

Thanks for your feedback, we'd like to contact you to discuss how to help with your RAG project. If you are going to use the Jina Embeddings model, we'd like to hear your further comments and to build deeper collaborations.

Please feel free to contact lisa.li@jina.ai if you have any questions.

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