const translation = {
  knowledge: 'Knowledge',
  documentCount: ' docs',
  wordCount: ' k words',
  appCount: ' linked apps',
  createDataset: 'Create Knowledge',
  createDatasetIntro: 'Import your own text data or write data in real-time via Webhook for LLM context enhancement.',
  deleteDatasetConfirmTitle: 'Delete this Knowledge?',
  deleteDatasetConfirmContent:
    '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.',
  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.',
  datasetDeleted: 'Knowledge deleted',
  datasetDeleteFailed: 'Failed to delete Knowledge',
  didYouKnow: 'Did you know?',
  intro1: 'The Knowledge can be integrated into the Dify application ',
  intro2: 'as a context',
  intro3: ',',
  intro4: 'or it ',
  intro5: 'can be created',
  intro6: ' as a standalone ChatGPT index plug-in to publish',
  unavailable: 'Unavailable',
  unavailableTip: 'Embedding model is not available, the default embedding model needs to be configured',
  datasets: 'KNOWLEDGE',
  datasetsApi: 'API ACCESS',
  retrieval: {
    semantic_search: {
      title: 'Vector Search',
      description: 'Generate query embeddings and search for the text chunk most similar to its vector representation.',
    },
    full_text_search: {
      title: 'Full-Text Search',
      description: 'Index all terms in the document, allowing users to search any term and retrieve relevant text chunk containing those terms.',
    },
    hybrid_search: {
      title: 'Hybrid Search',
      description: 'Execute full-text search and vector searches simultaneously, re-rank to select the best match for the user\'s query. Users can choose to set weights or configure to a Rerank model.',
      recommend: 'Recommend',
    },
    invertedIndex: {
      title: 'Inverted Index',
      description: 'Inverted Index is a structure used for efficient retrieval. Organized by terms, each term points to documents or web pages containing it.',
    },
    change: 'Change',
    changeRetrievalMethod: 'Change retrieval method',
  },
  docsFailedNotice: 'documents failed to be indexed',
  retry: 'Retry',
  indexingTechnique: {
    high_quality: 'HQ',
    economy: 'ECO',
  },
  indexingMethod: {
    semantic_search: 'VECTOR',
    full_text_search: 'FULL TEXT',
    hybrid_search: 'HYBRID',
    invertedIndex: 'INVERTED',
  },
  defaultRetrievalTip: 'Multi-path retrieval is used by default. Knowledge is retrieved from multiple knowledge bases and then re-ranked.',
  mixtureHighQualityAndEconomicTip: 'The Rerank model is required for mixture of high quality and economical knowledge bases.',
  inconsistentEmbeddingModelTip: 'The Rerank model is required if the Embedding models of the selected knowledge bases are inconsistent.',
  retrievalSettings: 'Retrieval Setting',
  rerankSettings: 'Rerank Setting',
  weightedScore: {
    title: 'Weighted Score',
    description: 'By adjusting the weights assigned, this rerank strategy determines whether to prioritize semantic or keyword matching.',
    semanticFirst: 'Semantic first',
    keywordFirst: 'Keyword first',
    customized: 'Customized',
    semantic: 'Semantic',
    keyword: 'Keyword',
  },
  nTo1RetrievalLegacy: 'N-to-1 retrieval will be officially deprecated from September. It is recommended to use the latest Multi-path retrieval to obtain better results. ',
  nTo1RetrievalLegacyLink: 'Learn more',
  nTo1RetrievalLegacyLinkText: ' N-to-1 retrieval will be officially deprecated in September.',
}

export default translation