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Boost robustness and performance with model soups: averaging weights. No extra cost, better results.

Size bias refers to how the length of text inputs affects similarity, regardless of semantic relevance. It explains why search systems sometimes return long, barely-relevant documents instead of shorter, more precise matches to your query.

Introducing jina-reranker-m0, our new multilingual multimodal reranker for retrieving visual documents, with SOTA performance on multilingual long documents and code searching tasks.

Standard LLM or reasoning model, which is better for DeepSearch? In this post, we explored using DeepSeek-R1 in the DeepSearch implementation for choosing the next action.

Our DeepSearch works with private PDFs and visual documents right out of the box. Discover how DeepSearch can unlock valuable insights from your enterprise data.

Nailing these two details takes your DeepSearch from mid to GOAT: selecting the best snippets from lengthy webpages and ranking URLs before crawling.

We investigate embedding models on new "needle-in-haystack" tasks and find that beyond 4K tokens, they're just rolling dice - even with exact lexical matches or query expansion, they can't tell signal from noise in long context.

This idea either extremely smart or extremely stupid—no in-between. Read till the end and find out why this could be useful.

QPS out, depth in. DeepSearch is the new norm. Find answers through read-search-reason loops. Learn what it is and how to build it.

Search has changed a lot since embedding models were introduced. Is there still a role for lexical techniques like query expansion in AI? We think so.