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Isaacus just shipped a new state-of-the-art model, this time focused on reranking for legal RAG.
Although Kanon 2 Embedder already represents the frontier of legal-domain retrieval, we know that not everyone is ready to re-embed their entire corpus. We also knew there was still accuracy left on the table for teams handling highly sensitive legal work.
Enter Kanon 2 Reranker: the world’s best legal reranking model.
We tested it across both production RAG pipelines and standalone retrieval tasks, and the results were remarkable.
Not only does it outperform the competition in a category where there are still very few serious alternatives, it also delivers major retrieval accuracy gains over our standalone embedder. Those improvements translated into exceptional downstream performance.
In our final test, we compared Voyage AI by MongoDB 2.5 Rerank with Kanon 2 Reranker on Legal RAG Bench, using identical embedding models, generative models, and pipeline hyperparameters. The only difference was the reranker.
The result: Kanon 2 Reranker decisively outperformed Voyage 2.5 Rerank.
On holdout questions, the head-to-head margin was one of the most extreme we have seen: for every 1 question Voyage got right and we got wrong, there were 6 questions we got right and Voyage got wrong.
We share an example in the blog post where Voyage Rerank actually underperforms Kanon 2 Embedder on its own, delivering the wrong context to the LLM. In that case, not using a reranker at all would have led to the correct answer.
All in all, I’m immensely proud of the performance gains we’ve achieved.
But as we always say, the best benchmark is your own data.
So redeem your free credits, give Kanon 2 Reranker a try, and see firsthand the difference our models can make:
https://huggingface.co/blog/isaacus/kanon-2-reranker
Although Kanon 2 Embedder already represents the frontier of legal-domain retrieval, we know that not everyone is ready to re-embed their entire corpus. We also knew there was still accuracy left on the table for teams handling highly sensitive legal work.
Enter Kanon 2 Reranker: the world’s best legal reranking model.
We tested it across both production RAG pipelines and standalone retrieval tasks, and the results were remarkable.
Not only does it outperform the competition in a category where there are still very few serious alternatives, it also delivers major retrieval accuracy gains over our standalone embedder. Those improvements translated into exceptional downstream performance.
In our final test, we compared Voyage AI by MongoDB 2.5 Rerank with Kanon 2 Reranker on Legal RAG Bench, using identical embedding models, generative models, and pipeline hyperparameters. The only difference was the reranker.
The result: Kanon 2 Reranker decisively outperformed Voyage 2.5 Rerank.
On holdout questions, the head-to-head margin was one of the most extreme we have seen: for every 1 question Voyage got right and we got wrong, there were 6 questions we got right and Voyage got wrong.
We share an example in the blog post where Voyage Rerank actually underperforms Kanon 2 Embedder on its own, delivering the wrong context to the LLM. In that case, not using a reranker at all would have led to the correct answer.
All in all, I’m immensely proud of the performance gains we’ve achieved.
But as we always say, the best benchmark is your own data.
So redeem your free credits, give Kanon 2 Reranker a try, and see firsthand the difference our models can make:
https://huggingface.co/blog/isaacus/kanon-2-reranker
