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A sparse vector query executes search using sparse vector representations, typically generated by learned sparse retrieval models. You must provide precalculated token-weight pairs as your query vectors, where each pair represents a term and its corresponding relevance score.
Currently, LambdaDB does not support built-in natural language processing models for automatic sparse vector generation.

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Parameters

ParameterDescriptionTypeRequired
fieldThe name of the vector field to search againststring✓
queryVectorQuery vector as token-weight pairsobject✓

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Examples

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Token-based sparse vector query

{
  "sparseVector": {
    "field": "example_sparse_field",
    "queryVector": {
      "LambdaDB": 0.5,
      "awesome": 0.3,
      "AI": 0.2
    }
  }
}

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Index-based sparse vector query

If you prefer to use the index-based format with separate values and index arrays, you can specify index positions as keys in the queryVector object: 
{
  "sparseVector": {
    "field": "example_sparse_field",
    "queryVector": {
      "10": 0.5,
      "45": 0.3,
      "234": 0.2
    }
  }
}