Type Alias RecommendChunksRequest

RecommendChunksRequest: {
    filters?: ChunkFilter | null;
    limit?: number | null;
    metadata?: unknown;
    negative_chunk_ids?: string[] | null;
    negative_tracking_ids?: string[] | null;
    positive_chunk_ids?: string[] | null;
    positive_tracking_ids?: string[] | null;
    recommend_type?: RecommendType | null;
    slim_chunks?: boolean | null;
    strategy?: RecommendationStrategy | null;
    user_id?: string | null;
}

Type declaration

  • Optionalfilters?: ChunkFilter | null
  • Optionallimit?: number | null

    The number of chunks to return. This is the number of chunks which will be returned in the response. The default is 10.

  • Optionalmetadata?: unknown

    Metadata is any metadata you want to associate w/ the event that is created from this request

  • Optionalnegative_chunk_ids?: string[] | null

    The ids of the chunks to be used as negative examples for the recommendation. The chunks in this array will be used to filter out similar chunks.

  • Optionalnegative_tracking_ids?: string[] | null

    The tracking_ids of the chunks to be used as negative examples for the recommendation. The chunks in this array will be used to filter out similar chunks.

  • Optionalpositive_chunk_ids?: string[] | null

    The ids of the chunks to be used as positive examples for the recommendation. The chunks in this array will be used to find similar chunks.

  • Optionalpositive_tracking_ids?: string[] | null

    The tracking_ids of the chunks to be used as positive examples for the recommendation. The chunks in this array will be used to find similar chunks.

  • Optionalrecommend_type?: RecommendType | null
  • Optionalslim_chunks?: boolean | null

    Set slim_chunks to true to avoid returning the content and chunk_html of the chunks. This is useful for when you want to reduce amount of data over the wire for latency improvement (typicall 10-50ms). Default is false.

  • Optionalstrategy?: RecommendationStrategy | null
  • Optionaluser_id?: string | null

    User ID is the id of the user who is making the request. This is used to track user interactions with the recommendation results.

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