Sparse vector query - LambdaDB Documentation

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.

Parameters

Parameter	Description	Type	Required
field	The name of the vector field to search against	string	✓
queryVector	Query vector as token-weight pairs	object	✓

Examples

Token-based sparse vector query

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

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
    }
  }
}