Remove-np-ninf

[icfly2]

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Numpy 2 dropped support for the np.INF and NINF and instead it recomends np.inf and - np.inf

WIP: I'm still looking for the contribution guide, inparticular the how to run the tests locally bit, as I'm not familiar with poetry. Tests pass in the runners but I'm pretty sure the current test suite doesn't cover the changed code.

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[icfly2]

I can show that if you upgrade numpy to 2.x it fails the current test suite, but with the change, the client doesn't fail the tests. This requires the client without fastembed, as that is pinned to below 2 for numpy:

qdrant-client on  dev is 📦 v1.11.2 via 🐍 v3.10.13 
❯ poetry run pytest -x
============================== test session starts ==============================
platform linux -- Python 3.12.3, pytest-7.4.4, pluggy-1.5.0
rootdir: /home/rum/qdrant-client
configfile: pyproject.toml
plugins: asyncio-0.21.2, anyio-4.4.0, timeout-2.3.1
asyncio: mode=Mode.STRICT
collected 370 items                                                             

qdrant_client/hybrid/test_reranking.py .                                  [  0%]
qdrant_client/local/tests/test_datetimes.py ..................            [  5%]
qdrant_client/local/tests/test_distances.py .                             [  5%]
qdrant_client/local/tests/test_payload_filters.py ..                      [  5%]
qdrant_client/local/tests/test_payload_utils.py ...                       [  6%]
qdrant_client/local/tests/test_referenced_vectors.py .................... [ 12%]
..........                                                                [ 14%]
qdrant_client/local/tests/test_vectors.py .                               [ 15%]
tests/test_async_qdrant_client.py .....                                   [ 16%]
tests/test_fastembed.py .ssssss                                           [ 18%]
tests/test_in_memory.py .F

=================================== FAILURES ====================================
_______________ test_sparse_in_memory_key_filter_returns_results ________________

qdrant = <qdrant_client.qdrant_client.QdrantClient object at 0x7f58e905e8d0>

    def test_sparse_in_memory_key_filter_returns_results(qdrant: QdrantClient):
        qdrant.create_collection(
            collection_name="test_collection",
            vectors_config={},
            sparse_vectors_config={"text": models.SparseVectorParams()},
        )
    
        operation_info = qdrant.upsert(
            collection_name="test_collection",
            wait=True,
            points=[
                models.PointStruct(
                    id=1,
                    vector={
                        "text": models.SparseVector(
                            indices=[0, 1, 2, 3], values=[0.05, 0.61, 0.76, 0.74]
                        )
                    },
                    payload={"city": "Berlin"},
                ),
                models.PointStruct(
                    id=2,
                    vector={
                        "text": models.SparseVector(
                            indices=[0, 1, 2, 3], values=[0.19, 0.81, 0.75, 0.11]
                        )
                    },
                    payload={"city": ["Berlin", "London"]},
                ),
                models.PointStruct(
                    id=3,
                    vector={
                        "text": models.SparseVector(
                            indices=[0, 1, 2, 3], values=[0.36, 0.55, 0.47, 0.94]
                        )
                    },
                    payload={"city": ["Berlin", "Moscow"]},
                ),
                models.PointStruct(
                    id=4,
                    vector={
                        "text": models.SparseVector(
                            indices=[0, 1, 2, 3], values=[0.18, 0.01, 0.85, 0.80]
                        )
                    },
                    payload={"city": ["London", "Moscow"]},
                ),
            ],
        )
    
        assert operation_info.operation_id == 0
        assert operation_info.status == models.UpdateStatus.COMPLETED
    
>       search_result = qdrant.search(
            collection_name="test_collection",
            query_vector=models.NamedSparseVector(
                name="text",
                vector=models.SparseVector(indices=[0, 1, 2, 3], values=[0.2, 0.1, 0.9, 0.7]),
            ),
            query_filter=models.Filter(
                must=[models.FieldCondition(key="city", match=models.MatchValue(value="London"))]
            ),
            limit=3,
        )

tests/test_in_memory.py:110: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
qdrant_client/qdrant_client.py:387: in search
    return self._client.search(
qdrant_client/local/qdrant_local.py:204: in search
    return collection.search(
qdrant_client/local/local_collection.py:618: in search
    if sparse_scoring and score == np.NINF:

[joein]

Thank you for addressing this