Jina

Jina offers a collection of AI-powered tools to boost search, in particular for multimodal data. The project includes a variety of tools like embeddings, rerankers, LLM-readers and prompt optimizers. Jina hopes to advance search AI technology so people can better wrangle complex data.

Jina's embeddings are multimodal and bilingual, using long-context embeddings for search and RAG (Retrieve-Augment-Generate) solutions. Its reranker API is perhaps the most interesting, able to optimize search relevance and RAG results. It can work in more than 100 languages and can be used for code search and tabular data search. The reranker takes the initial search results and re-runs them to try to better match the user's intent.

For those who want fine-tuned embeddings, Jina has an auto fine-tuning feature. It lets you specify a domain and get a fine-tuned model ready to go without having to gather lots of training data. The fine-tuning is automated, so it's accessible to those who don't have a lot of technical expertise.

Jina also has tools for developers, including open-source projects like DocArray for managing multimodal data structures, RunGPT for serving large multimodal models, and VectorDB, a lightweight Python vector database. There are also tools for generating interesting art and for improving LLMs like DALL-E Flow and ThinkGPT.

Jina charges for its APIs based on the number of tokens sent in requests, with a free trial that gets you 1 million tokens to start. That pricing applies to all products in Jina's search foundation. On-premises deployment options are available for AWS Sagemaker, Microsoft Azure and Google Cloud Services, so customers can use it wherever they want.

Expect good performance from Jina's tools, and it's a good option for those who want to improve their search and RAG abilities. The project's AI and multimodal data focus means it's a good option for those who need to search more complex data.

Published on July 17, 2024