Weaviate让AI问答在速度和准确性间自由切换，开发者可根据需求选择是否启用结果评估功能。
Weaviate Cloud更新了Ask Mode功能，允许用户通过result_evaluation参数控制结果评估流程。用户可选择将参数设置为"none"跳过评估，获得更快更便宜的回答；或设置为"llm"启用评估，使用额外模型调用来验证答案准确性。评估模式新增is_partial_answer和missing_information字段，提供答案完整性的信号。
Skip the evaluation and get a faster, cheaper answer. Or get a precise set of sources that match yo...
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 ⚡