Working with Sparse Vectors

sparse = {
    "indices": [10, 45, 102, 2048],
    "values": [0.5, 0.3, 0.8, 0.1],
}

index.upsert(vectors=[{
    "id": "doc1",
    "values": dense,           # required, even for sparse-heavy
    "sparse_values": sparse,
    "metadata": {"source": "wiki"},
}])

res = index.query(
    vector=dense_q,
    sparse_vector={"indices": [...], "values": [...]},
    top_k=10,
)

def valid_sparse(s):
    return (
        isinstance(s.get("indices"), list)
        and isinstance(s.get("values"), list)
        and len(s["indices"]) == len(s["values"])
        and all(isinstance(i, int) and i >= 0 for i in s["indices"])
        and len(set(s["indices"])) == len(s["indices"])  # no dupes
    )