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New translations ai-assistant.md (Chinese Simplified)
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uid: ai-assistant
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title: AI 助手
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author: Morten Lønskov
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updated: 2026-03-19
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updated: 2026-04-15
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applies_to:
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products:
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- product: Tabular Editor 2
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### Azure OpenAI
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选择 **Azure OpenAI** 作为提供商。 输入你的 API 密钥,以及 Azure OpenAI 资源的服务端点 URL。 将模型名称设置为与你的部署名称保持一致。
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Select **Azure OpenAI** as the provider and configure three fields:
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- **API key** — the access key for your Azure OpenAI resource
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- **Service endpoint** — the endpoint URL for your resource, for example `https://your-resource.openai.azure.com`. Use the resource URL, not the `privatelink` alias; the SSL certificate is issued for `*.openai.azure.com` and connecting directly to `*.privatelink.openai.azure.com` fails certificate validation
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- **Model name** — the **deployment name**, not the underlying model name and not the resource name
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Azure OpenAI requires the deployment name in every API call. A deployment name is chosen when the deployment is created, so it can be any string. Deployments are often named after the model they serve (for example `gpt-4o`), but that is a convention, not a requirement. If you enter the resource name or a raw model name that does not exist as a deployment, the request fails.
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#### Finding your deployment name
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In the [Azure AI Foundry portal](https://ai.azure.com):
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1. Sign in and select your Azure OpenAI resource
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2. Open **Deployments** (or **Models + endpoints** if the resource has been upgraded to Foundry)
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3. Copy the value from the **Name** column
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Deployments created before your organization adopted Azure AI Foundry may not appear in the portal. List them from the Azure CLI:
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```bash
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az cognitiveservices account deployment list --name "<resource-name>" --resource-group "<resource-group>" --output table
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```
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See [Create and deploy an Azure OpenAI resource](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/create-resource#deploy-a-model) for more details.
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For 403 errors, SSL failures or "DeploymentNotFound" responses, see @azure-openai-connection-errors.
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### 自定义(OpenAI 兼容)
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其他减少 token 使用量的方法:
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- 围绕特定的表、度量值或列提出更聚焦的问题,而不是对整个模型提出泛泛的问题
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- Ask focused questions about specific tables, measures or columns rather than broad questions about the entire model. A vague prompt such as _"Set display folders on all measures"_ forces the assistant to retrieve metadata for the entire model. A specific prompt such as _"Set display folders on the measures I have selected"_ limits the context to the current selection and uses far fewer tokens
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- 切换话题时开启新对话,避免累积过长的对话历史
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- 进行探索性提问时,使用更小或成本更低的模型
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