Multi Vector > 10000 documents throws scoring error

To reproduce:

from redisvl.query import MultiVectorQuery, Vector

from redisvl.index import SearchIndex
from redisvl.schema import IndexSchema
import numpy as np
from redis import Redis

client = Redis(host="localhost", port=6379, decode_responses=True)

test_index = SearchIndex(
    IndexSchema.from_dict(
        {
            "index": {
                "name": "idx:test",
                "prefix": "test:",
                "storage_type": "json",
            },
            "fields": [
                {"name": "id", "type": "numeric"},
                {
                    "name": "embedding_1",
                    "type": "vector",
                    "attrs": {
                        "dims": 512,
                        "distance_metric": "cosine",
                        "algorithm": "hnsw",
                        "datatype": "float32",
                    },
                },
                {
                    "name": "embedding_2",
                    "type": "vector",
                    "attrs": {
                        "dims": 512,
                        "distance_metric": "cosine",
                        "algorithm": "hnsw",
                        "datatype": "float32",
                    },
                },
            ],
        },
    ),
    client,
    validate_on_load=True,
)

test_index.create(overwrite=True)

TEST_DOCS_COUNT = 10001

test_data = [
    {
        "id": i,
        "embedding_1": np.random.rand(512),
        "embedding_2": np.random.rand(512),
    }
    for i, _ in enumerate(range(TEST_DOCS_COUNT))
]

test_index.load(test_data)

embedding_1 = np.random.rand(512)
embedding_2 = np.random.rand(512)

query_vectors = [
    Vector(
        vector=embedding_1,
        field_name="embedding_1",
        dtype="float32",
        weight=0.7,
    ),
    Vector(
        vector=embedding_2,
        field_name="embedding_2",
        dtype="float32",
        weight=0.2,
    ),
]
query = MultiVectorQuery(
    vectors=query_vectors,
    num_results=10,
    return_fields=["id"],
)

results = test_index.query(query)
print(results)

With a TEST_DOCS_COUNT > 10,000 redis throws:
Error while aggregating: Could not find the value for a parameter name, consider using EXISTS if applicable for distance_1

<=10,000 works fine. I assume this is to do with the same logic that sets the max offset to 10,000 by default.

The workaround I'm using for now is the unstable feature case() to assign defaults for missing scores.

Is RediSearch limited by some design constraint to this number? Seems low for many applications of vector search, or maybe I'm missing something.

Start by reproducing the example through MultiVectorQuery and SearchIndex.query with more than 10,000 documents, then trace how the vector scores are assembled and passed to RediSearch. Compare the failing case with the <=10,000-document case and the referenced case() workaround. Done means the query returns results without the missing distance_1 scoring error at larger document counts.