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Minor: improve tips display (#5631)
### What problem does this PR solve? 1. Add the missing translations.  2. Shorten overly long tips.  ### Type of change - [ ] Bug Fix (non-breaking change which fixes an issue) - [ ] New Feature (non-breaking change which adds functionality) - [ ] Documentation Update - [ ] Refactoring - [ ] Performance Improvement - [x] Other (please describe):
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@ -54,7 +54,7 @@ const GraphRagItems = ({ marginBottom = false }: GraphRagItemsProps) => {
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(title: React.ReactNode | string) => {
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return {
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title: typeof title === 'string' ? t(title) : title,
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overlayInnerStyle: { width: '50vw' },
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overlayInnerStyle: { width: '32vw' },
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};
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},
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[t],
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@ -67,7 +67,7 @@ const GraphRagItems = ({ marginBottom = false }: GraphRagItemsProps) => {
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label={t('useGraphRag')}
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initialValue={false}
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valuePropName="checked"
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tooltip={renderWideTooltip('useGraphRagTip')}
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tooltip={t('useGraphRagTip')}
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>
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<Switch />
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</Form.Item>
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@ -520,6 +520,7 @@ This auto-tag feature enhances retrieval by adding another layer of domain-speci
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'This optimizes user queries using context in a multi-round conversation. When enabled, it will consume additional LLM tokens.',
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howUseId: 'How to use chat ID?',
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description: 'Description of assistant',
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descriptionPlaceholder: 'e.g. A chat assistant for resume.',
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useKnowledgeGraph: 'Use knowledge graph',
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useKnowledgeGraphTip:
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'It will retrieve descriptions of relevant entities,relations and community reports, which will enhance inference of multi-hop and complex question.',
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@ -504,6 +504,8 @@ export default {
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'在多輪對話的中,對去知識庫查詢的問題進行最佳化。會呼叫大模型額外消耗token。',
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howUseId: '如何使用聊天ID?',
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description: '助理描述',
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descriptionPlaceholder:
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'例如 你是一個專業的簡曆助手,隻能回答簡曆的問題。',
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useKnowledgeGraph: '使用知識圖譜',
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useKnowledgeGraph提示:
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'它將檢索相關實體、關係和社區報告的描述,這將增強多跳和複雜問題的推理。',
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@ -520,6 +520,8 @@ General:实体和关系提取提示来自 GitHub - microsoft/graphrag:基于
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'在多轮对话的中,对去知识库查询的问题进行优化。会调用大模型额外消耗token。',
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howUseId: '如何使用聊天ID?',
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description: '助理描述',
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descriptionPlaceholder:
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'例如 你是一个专业的简历助手,只能回答简历的问题。',
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useKnowledgeGraph: '使用知识图谱',
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useKnowledgeGraphTip:
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'它将检索相关实体、关系和社区报告的描述,这将增强多跳和复杂问题的推理。',
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