How much compute should a single search get?

Every retrieval app has to balance quality, latency, and cost. The problem is that not every request needs the same amount of compute. A support lookup may be straightforward, while an 800-token multi-step research question may need much more work. Configure everything for the hardest query and you overpay for the easy ones. Optimize only for speed and the difficult queries may fall apart.

Weaviate's Query Agent now gives developers control over this tradeoff with an 𝗲𝗳𝗳𝗼𝗿𝘁 parameter in Search Mode. For each request, you explicitly choose medium, high, or ultrahigh.

Weaviate's Query Agent now has an 𝗲𝗳𝗳𝗼𝗿𝘁 parameter in Search Mode, so you pick per request: medium, high, or ultrahigh.

response = qa. search("What are Listwise Rerankers?", effort="ultrahigh")

The 𝗲𝗳𝗳𝗼𝗿𝘁 setting controls how much work Search Mode puts into refining the results from the initial agentic search. Higher effort can improve retrieval accuracy, with additional latency and cost.

With this, you're not hand-authoring query decomposition logic, you're not maintaining two retrieval stacks, and you're not paying maximum compute on every trivial lookup. Query Agent also knows your schema, so "find me some vintage shoes under $70" becomes a hard price filter plus semantic search, without you parsing that yourself.

The benchmarks in our blog show why this control matters. On BRIGHT Biology, recall improves from roughly 𝟰𝟯% 𝗮𝘁 𝗺𝗲𝗱𝗶𝘂𝗺 𝗲𝗳𝗳𝗼𝗿𝘁, 𝘁𝗼 𝟱𝟬% 𝗮𝘁 𝗵𝗶𝗴𝗵, 𝗮𝗻𝗱 𝟱𝟳% 𝗮𝘁 𝘂𝗹𝘁𝗿𝗮𝗵𝗶𝗴𝗵. The gains vary by dataset, but the general pattern is that harder, reasoning-heavy searches benefit more from additional effort.

Read more in our docs: https://weaviate.io/blog/search-mode-effort?utm_source=channels&utm_medium=w_social&utm_campaign=query_agent&utm_content=808276882