Weaviate Ask Mode Update
Developers can now optimize between response speed and answer verification in Weaviate Cloud, reducing costs when source precision isn't critical.
Weaviate Cloud now offers customizable result evaluation in Ask Mode. Users can set result_evaluation to 'none' for faster, cheaper responses or 'llm' to verify answers with additional model calls. The 'llm' option adds fields like is_partial_answer and missing_information to assess answer completeness.
Skip the evaluation and get a faster, cheaper answer. Or get a precise set of sources that match your answer. Now you have full control between latency and verifiability. 𝗔𝘀𝗸 𝗠𝗼𝗱𝗲 turns a natural-language question into searches or aggregations across your Weaviate Cloud collections, then generates an answer grounded in the retrieved data. Previously, the result evaluation step always ran. Now you can customise the behavior of your ask mode run, using 𝗿𝗲𝘀𝘂𝗹𝘁_𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 in Python or 𝗿𝗲𝘀𝘂𝗹𝘁𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 in JavaScript/TypeScript: - Set it to "𝘯𝘰𝘯𝘦" to retrieve data and generate the final answer. This is the default, faster and cheaper workflow. - Set it to "𝘭𝘭𝘮" to evaluate the answer against the retrieved context using an additional model call. This retains the sources relevant to or used in the answer, and report possible incompleteness or missing information. The workflow becomes: 1. Retrieve data 2. Generate an answer 3. Optionally evaluate the answer and refine its sources Since the evaluation requires an additional LLM call, it adds latency and cost. But it also enables extra fields: 𝘪𝘴_𝘱𝘢𝘳𝘵𝘪𝘢𝘭_𝘢𝘯𝘴𝘸𝘦𝘳 and 𝘮𝘪𝘴𝘴𝘪𝘯𝘨_𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯, giving you useful signals about whether the answer is complete and what may still be missing. You can now choose the right tradeoff for each application: faster responses when source refinement isn't required, or additional evaluation when docs.weaviate.io/query-agent/gu… eness signals matter. Read the Ask Mode documentation: https://t.co/6Q5aptisKk 💬 0 🔄 1 ❤️ 4 👀 350 📊 1 ⚡
