# Weighting sparse and dense vectorss

## Overview

Pinecone supports vectors with sparse and dense values, which allows you to perform semantic and keyword search over your data in one query and combine the results for more relevant results. This topic describes how to weight sparse versus dense vectors when querying your index.

To see sparse-dense embeddings in action, see the Ecommerce hybrid search example.

## Query a sparse-dense index with explicit weighting

Because Pinecone's index views your sparse-dense vector as a single vector, it does not offer a built-in parameter to adjust the weight of a query's dense part against its sparse part; the index is agnostic to density or sparsity of coordinates in your vectors. You may, however, incorporate a linear weighting scheme by customizing your query vector, as we demonstrate in the function below.

Examples

The following example transforms vector values using an alpha parameter.

``````def hybrid_score_norm(dense, sparse, alpha: float):
"""Hybrid score using a convex combination

alpha * dense + (1 - alpha) * sparse

Args:
dense: Array of floats representing
sparse: a dict of `indices` and `values`
alpha: scale between 0 and 1
"""
if alpha < 0 or alpha > 1:
raise ValueError("Alpha must be between 0 and 1")
hs = {
'indices': sparse['indices'],
'values':  [v * (1 - alpha) for v in sparse['values']]
}
return [v * alpha for v in dense], hs
``````

The following example transforms a vector using the above function, then queries a Pinecone index.

``````sparse_vector = {
'indices': [10, 45, 16],
'values':  [0.5, 0.5, 0.2]
}
dense_vector = [0.1, 0.2, 0.3]

hdense, hsparse = hybrid_score_norm(dense_vector, sparse_vector, alpha=0.75)

query_response = index.query(
namespace="example-namespace",
top_k=10,
vector=hdense,
sparse_vector=hsparse
)
``````