74 lines
3.3 KiB
TypeScript
74 lines
3.3 KiB
TypeScript
const translation = {
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knowledge: 'Knowledge',
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documentCount: ' docs',
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wordCount: ' k words',
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appCount: ' linked apps',
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createDataset: 'Create Knowledge',
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createDatasetIntro: 'Import your own text data or write data in real-time via Webhook for LLM context enhancement.',
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deleteDatasetConfirmTitle: 'Delete this Knowledge?',
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deleteDatasetConfirmContent:
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'Deleting the Knowledge is irreversible. Users will no longer be able to access your Knowledge, and all prompt configurations and logs will be permanently deleted.',
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datasetUsedByApp: 'The knowledge is being used by some apps. Apps will no longer be able to use this Knowledge, and all prompt configurations and logs will be permanently deleted.',
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datasetDeleted: 'Knowledge deleted',
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datasetDeleteFailed: 'Failed to delete Knowledge',
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didYouKnow: 'Did you know?',
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intro1: 'The Knowledge can be integrated into the Dify application ',
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intro2: 'as a context',
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intro3: ',',
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intro4: 'or it ',
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intro5: 'can be created',
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intro6: ' as a standalone ChatGPT index plug-in to publish',
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unavailable: 'Unavailable',
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unavailableTip: 'Embedding model is not available, the default embedding model needs to be configured',
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datasets: 'KNOWLEDGE',
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datasetsApi: 'API ACCESS',
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retrieval: {
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semantic_search: {
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title: 'Vector Search',
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description: 'Generate query embeddings and search for the text chunk most similar to its vector representation.',
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},
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full_text_search: {
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title: 'Full-Text Search',
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description: 'Index all terms in the document, allowing users to search any term and retrieve relevant text chunk containing those terms.',
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},
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hybrid_search: {
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title: 'Hybrid Search',
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description: 'Execute full-text search and vector searches simultaneously, re-rank to select the best match for the user\'s query. Configuration of the Rerank model APIs necessary.',
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recommend: 'Recommend',
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},
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invertedIndex: {
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title: 'Inverted Index',
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description: 'Inverted Index is a structure used for efficient retrieval. Organized by terms, each term points to documents or web pages containing it.',
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},
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change: 'Change',
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changeRetrievalMethod: 'Change retrieval method',
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},
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docsFailedNotice: 'documents failed to be indexed',
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retry: 'Retry',
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indexingTechnique: {
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high_quality: 'HQ',
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economy: 'ECO',
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},
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indexingMethod: {
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semantic_search: 'VECTOR',
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full_text_search: 'FULL TEXT',
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hybrid_search: 'HYBRID',
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},
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mixtureHighQualityAndEconomicTip: 'The Rerank model is required for mixture of high quality and economical knowledge bases.',
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inconsistentEmbeddingModelTip: 'The Rerank model is required if the Embedding models of the selected knowledge bases are inconsistent.',
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retrievalSettings: 'Retrieval Setting',
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rerankSettings: 'Rerank Setting',
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weightedScore: {
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title: 'Weighted Score',
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description: 'By adjusting the weights assigned， this rerank strategy determines whether to prioritize semantic or keyword matching.',
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semanticFirst: 'Semantic first',
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keywordFirst: 'Keyword first',
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customized: 'Customized',
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semantic: 'Semantic',
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keyword: 'Keyword',
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},
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nTo1RetrievalLegacy: 'According to product planning, N-to-1 retrieval will be officially deprecated in September. Until then you can still use it normally.',
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}
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export default translation
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