An Easter egg hidden on Jina AI website is the use of Reranker for article recommendations. Go to any blog post page, hit 𝐒𝐡𝐢𝐟𝐭+𝟐, and you will get the top 5 related articles from all the posts we've published so far. Its implementation is deadly simple - it grabs the current post as the `query` and uses the content of all 240 posts as `documents`, sending them to our Reranker API asking for `top_k=5`. Inefficient? Maybe, but our reranker is built for long-context and low-latency, so let it be! With the recent release of Reranker V2, we gave this easter egg another try (/fry), and the results are interesting. Comparing V2 to V1, we can observe the following: 1. V2 is faster than V1. The video below is not sped up. Remember, we simply send the raw content of all articles on the site directly to the Reranker API, which is ~1M tokens in one rerank request. 2. V2 gives much better recommendations than V1. We examined a challenging case, "Artificial General Intelligence is Cursed, And Science Fiction Isn't Helping." Many rerankers fail to recommend related articles from the pool; our V2 recommends 5/5 related articles, whereas V1 only recommends 3/5, and the scoring of V1 is not entirely accurate. https://lnkd.in/eUMe9U8U This is how we implemented this feature. It's incredibly simple—probably too simple—but hey, it works! We're not big fans of over-engineering anyway. Reranker v2 API: https://jina.ai/reranker Reranker v2 Release Note: https://lnkd.in/eZWQtJYU
Interesting usage of AI in applied manners!
Senior ML Engineer, Gen AI Specialist at IBM
2wHan Xiao the reranker seems really slow on cpu and mac m1. Do you have any idea to improve the latency ?