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/** A retriever that replaces the functionality of a traditional query. */
@@ -58,13 +59,13 @@ export class KnnRetriever extends RetrieverBase {
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/** The name of the vector field to search against. */
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field: String
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/** Query vector. Must have the same number of dimensions as the vector field you are searching against. You must provide a query_vector_builder or query_vector, but not both. */
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query_vector?: float[]
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query_vector?: QueryVector
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/** Defines a model to build a query vector. */
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query_vector_builder?: QueryVectorBuilder
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/** Number of nearest neighbors to return as top hits. */
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k: integer
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k: long
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/** Number of nearest neighbor candidates to consider per shard. */
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num_candidates: integer
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num_candidates: long
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/** Query to filter the documents that can match. The kNN search will return the top k documents that also match this filter. */
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filter?: QueryContainer|QueryContainer[]
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/** The minimum similarity required for a document to be considered a match. */
@@ -75,7 +76,7 @@ export class RRFRetriever extends RetrieverBase {
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/** A list of child retrievers to specify which sets of returned top documents will have the RRF formula applied to them. */
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retrievers: RetrieverContainer[]
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/** This value determines how much influence documents in individual result sets per query have over the final ranked result set. */
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rank_constant?: integer
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rank_constant?: long
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/** This value determines the size of the individual result sets per query. */
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