langgraph/libs/checkpoint-postgres/tests/test_store.py

# type: ignore

from contextlib import contextmanager
from typing import Any, Optional
from uuid import uuid4

import pytest
from langchain_core.embeddings import Embeddings
from psycopg import Connection

from langgraph.store.base import (
    GetOp,
    Item,
    ListNamespacesOp,
    MatchCondition,
    PutOp,
    SearchOp,
)
from langgraph.store.postgres import PostgresStore
from tests.conftest import (
    DEFAULT_URI,
    VECTOR_TYPES,
    CharacterEmbeddings,
)


@pytest.fixture(scope="function", params=["default", "pipe", "pool"])
def store(request) -> PostgresStore:
    database = f"test_{uuid4().hex[:16]}"
    uri_parts = DEFAULT_URI.split("/")
    uri_base = "/".join(uri_parts[:-1])
    query_params = ""
    if "?" in uri_parts[-1]:
        db_name, query_params = uri_parts[-1].split("?", 1)
        query_params = "?" + query_params

    conn_string = f"{uri_base}/{database}{query_params}"
    admin_conn_string = DEFAULT_URI

    with Connection.connect(admin_conn_string, autocommit=True) as conn:
        conn.execute(f"CREATE DATABASE {database}")
    try:
        with PostgresStore.from_conn_string(conn_string) as store:
            store.setup()

        if request.param == "pipe":
            with PostgresStore.from_conn_string(conn_string, pipeline=True) as store:
                yield store
        elif request.param == "pool":
            with PostgresStore.from_conn_string(
                conn_string, pool_config={"min_size": 1, "max_size": 10}
            ) as store:
                yield store
        else:  # default
            with PostgresStore.from_conn_string(conn_string) as store:
                yield store
    finally:
        with Connection.connect(admin_conn_string, autocommit=True) as conn:
            conn.execute(f"DROP DATABASE {database}")


def test_batch_order(store: PostgresStore) -> None:
    # Setup test data
    store.put(("test", "foo"), "key1", {"data": "value1"})
    store.put(("test", "bar"), "key2", {"data": "value2"})

    ops = [
        GetOp(namespace=("test", "foo"), key="key1"),
        PutOp(namespace=("test", "bar"), key="key2", value={"data": "value2"}),
        SearchOp(
            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
        ),
        ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
        GetOp(namespace=("test",), key="key3"),
    ]

    results = store.batch(ops)
    assert len(results) == 5
    assert isinstance(results[0], Item)
    assert isinstance(results[0].value, dict)
    assert results[0].value == {"data": "value1"}
    assert results[0].key == "key1"
    assert results[1] is None  # Put operation returns None
    assert isinstance(results[2], list)
    assert len(results[2]) == 1
    assert isinstance(results[3], list)
    assert len(results[3]) > 0  # Should contain at least our test namespaces
    assert results[4] is None  # Non-existent key returns None

    # Test reordered operations
    ops_reordered = [
        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
        GetOp(namespace=("test", "bar"), key="key2"),
        ListNamespacesOp(match_conditions=None, max_depth=None, limit=5, offset=0),
        PutOp(namespace=("test",), key="key3", value={"data": "value3"}),
        GetOp(namespace=("test", "foo"), key="key1"),
    ]

    results_reordered = store.batch(ops_reordered)
    assert len(results_reordered) == 5
    assert isinstance(results_reordered[0], list)
    assert len(results_reordered[0]) >= 2  # Should find at least our two test items
    assert isinstance(results_reordered[1], Item)
    assert results_reordered[1].value == {"data": "value2"}
    assert results_reordered[1].key == "key2"
    assert isinstance(results_reordered[2], list)
    assert len(results_reordered[2]) > 0
    assert results_reordered[3] is None  # Put operation returns None
    assert isinstance(results_reordered[4], Item)
    assert results_reordered[4].value == {"data": "value1"}
    assert results_reordered[4].key == "key1"


def test_batch_get_ops(store: PostgresStore) -> None:
    # Setup test data
    store.put(("test",), "key1", {"data": "value1"})
    store.put(("test",), "key2", {"data": "value2"})

    ops = [
        GetOp(namespace=("test",), key="key1"),
        GetOp(namespace=("test",), key="key2"),
        GetOp(namespace=("test",), key="key3"),  # Non-existent key
    ]

    results = store.batch(ops)

    assert len(results) == 3
    assert results[0] is not None
    assert results[1] is not None
    assert results[2] is None
    assert results[0].key == "key1"
    assert results[1].key == "key2"


def test_batch_put_ops(store: PostgresStore) -> None:
    ops = [
        PutOp(namespace=("test",), key="key1", value={"data": "value1"}),
        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
        PutOp(namespace=("test",), key="key3", value=None),  # Delete operation
    ]

    results = store.batch(ops)
    assert len(results) == 3
    assert all(result is None for result in results)

    # Verify the puts worked
    item1 = store.get(("test",), "key1")
    item2 = store.get(("test",), "key2")
    item3 = store.get(("test",), "key3")

    assert item1 and item1.value == {"data": "value1"}
    assert item2 and item2.value == {"data": "value2"}
    assert item3 is None


def test_batch_search_ops(store: PostgresStore) -> None:
    # Setup test data
    test_data = [
        (("test", "foo"), "key1", {"data": "value1", "tag": "a"}),
        (("test", "bar"), "key2", {"data": "value2", "tag": "a"}),
        (("test", "baz"), "key3", {"data": "value3", "tag": "b"}),
    ]
    for namespace, key, value in test_data:
        store.put(namespace, key, value)

    ops = [
        SearchOp(namespace_prefix=("test",), filter={"tag": "a"}, limit=10, offset=0),
        SearchOp(namespace_prefix=("test",), filter=None, limit=2, offset=0),
        SearchOp(namespace_prefix=("test", "foo"), filter=None, limit=10, offset=0),
    ]

    results = store.batch(ops)
    assert len(results) == 3

    # First search should find items with tag "a"
    assert len(results[0]) == 2
    assert all(item.value["tag"] == "a" for item in results[0])

    # Second search should return first 2 items
    assert len(results[1]) == 2

    # Third search should only find items in test/foo namespace
    assert len(results[2]) == 1
    assert results[2][0].namespace == ("test", "foo")


def test_batch_list_namespaces_ops(store: PostgresStore) -> None:
    # Setup test data with various namespaces
    test_data = [
        (("test", "documents", "public"), "doc1", {"content": "public doc"}),
        (("test", "documents", "private"), "doc2", {"content": "private doc"}),
        (("test", "images", "public"), "img1", {"content": "public image"}),
        (("prod", "documents", "public"), "doc3", {"content": "prod doc"}),
    ]
    for namespace, key, value in test_data:
        store.put(namespace, key, value)

    ops = [
        ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
        ListNamespacesOp(match_conditions=None, max_depth=2, limit=10, offset=0),
        ListNamespacesOp(
            match_conditions=[MatchCondition("suffix", "public")],
            max_depth=None,
            limit=10,
            offset=0,
        ),
    ]

    results = store.batch(ops)
    assert len(results) == 3

    # First operation should list all namespaces
    assert len(results[0]) == len(test_data)

    # Second operation should only return namespaces up to depth 2
    assert all(len(ns) <= 2 for ns in results[1])

    # Third operation should only return namespaces ending with "public"
    assert all(ns[-1] == "public" for ns in results[2])


class TestPostgresStore:
    @pytest.fixture(autouse=True)
    def setup(self) -> None:
        with PostgresStore.from_conn_string(DEFAULT_URI) as store:
            store.setup()

    def test_basic_store_ops(self) -> None:
        with PostgresStore.from_conn_string(DEFAULT_URI) as store:
            namespace = ("test", "documents")
            item_id = "doc1"
            item_value = {"title": "Test Document", "content": "Hello, World!"}

            store.put(namespace, item_id, item_value)
            item = store.get(namespace, item_id)

            assert item
            assert item.namespace == namespace
            assert item.key == item_id
            assert item.value == item_value

            # Test update
            updated_value = {"title": "Updated Document", "content": "Hello, Updated!"}
            store.put(namespace, item_id, updated_value)
            updated_item = store.get(namespace, item_id)

            assert updated_item.value == updated_value
            assert updated_item.updated_at > item.updated_at

            # Test get from non-existent namespace
            different_namespace = ("test", "other_documents")
            item_in_different_namespace = store.get(different_namespace, item_id)
            assert item_in_different_namespace is None

            # Test delete
            store.delete(namespace, item_id)
            deleted_item = store.get(namespace, item_id)
            assert deleted_item is None

    def test_list_namespaces(self) -> None:
        with PostgresStore.from_conn_string(DEFAULT_URI) as store:
            # Create test data with various namespaces
            test_namespaces = [
                ("test", "documents", "public"),
                ("test", "documents", "private"),
                ("test", "images", "public"),
                ("test", "images", "private"),
                ("prod", "documents", "public"),
                ("prod", "documents", "private"),
            ]

            # Insert test data
            for namespace in test_namespaces:
                store.put(namespace, "dummy", {"content": "dummy"})

            # Test listing with various filters
            all_namespaces = store.list_namespaces()
            assert len(all_namespaces) == len(test_namespaces)

            # Test prefix filtering
            test_prefix_namespaces = store.list_namespaces(prefix=["test"])
            assert len(test_prefix_namespaces) == 4
            assert all(ns[0] == "test" for ns in test_prefix_namespaces)

            # Test suffix filtering
            public_namespaces = store.list_namespaces(suffix=["public"])
            assert len(public_namespaces) == 3
            assert all(ns[-1] == "public" for ns in public_namespaces)

            # Test max depth
            depth_2_namespaces = store.list_namespaces(max_depth=2)
            assert all(len(ns) <= 2 for ns in depth_2_namespaces)

            # Test pagination
            paginated_namespaces = store.list_namespaces(limit=3)
            assert len(paginated_namespaces) == 3

            # Cleanup
            for namespace in test_namespaces:
                store.delete(namespace, "dummy")

    def test_search(self) -> None:
        with PostgresStore.from_conn_string(DEFAULT_URI) as store:
            # Create test data
            test_data = [
                (
                    ("test", "docs"),
                    "doc1",
                    {"title": "First Doc", "author": "Alice", "tags": ["important"]},
                ),
                (
                    ("test", "docs"),
                    "doc2",
                    {"title": "Second Doc", "author": "Bob", "tags": ["draft"]},
                ),
                (
                    ("test", "images"),
                    "img1",
                    {"title": "Image 1", "author": "Alice", "tags": ["final"]},
                ),
            ]

            for namespace, key, value in test_data:
                store.put(namespace, key, value)

            # Test basic search
            all_items = store.search(["test"])
            assert len(all_items) == 3

            # Test namespace filtering
            docs_items = store.search(["test", "docs"])
            assert len(docs_items) == 2
            assert all(item.namespace == ("test", "docs") for item in docs_items)

            # Test value filtering
            alice_items = store.search(["test"], filter={"author": "Alice"})
            assert len(alice_items) == 2
            assert all(item.value["author"] == "Alice" for item in alice_items)

            # Test pagination
            paginated_items = store.search(["test"], limit=2)
            assert len(paginated_items) == 2

            offset_items = store.search(["test"], offset=2)
            assert len(offset_items) == 1

            # Cleanup
            for namespace, key, _ in test_data:
                store.delete(namespace, key)


@contextmanager
def _create_vector_store(
    vector_type: str,
    distance_type: str,
    fake_embeddings: Embeddings,
    text_fields: Optional[list[str]] = None,
) -> PostgresStore:
    """Create a store with vector search enabled."""
    database = f"test_{uuid4().hex[:16]}"
    uri_parts = DEFAULT_URI.split("/")
    uri_base = "/".join(uri_parts[:-1])
    query_params = ""
    if "?" in uri_parts[-1]:
        db_name, query_params = uri_parts[-1].split("?", 1)
        query_params = "?" + query_params

    conn_string = f"{uri_base}/{database}{query_params}"
    admin_conn_string = DEFAULT_URI

    index_config = {
        "dims": fake_embeddings.dims,
        "embed": fake_embeddings,
        "ann_index_config": {
            "vector_type": vector_type,
        },
        "distance_type": distance_type,
        "text_fields": text_fields,
    }

    with Connection.connect(admin_conn_string, autocommit=True) as conn:
        conn.execute(f"CREATE DATABASE {database}")
    try:
        with PostgresStore.from_conn_string(
            conn_string,
            index=index_config,
        ) as store:
            store.setup()
            yield store
    finally:
        with Connection.connect(admin_conn_string, autocommit=True) as conn:
            conn.execute(f"DROP DATABASE {database}")


@pytest.fixture(
    scope="function",
    params=[
        (vector_type, distance_type)
        for vector_type in VECTOR_TYPES
        for distance_type in (
            ["hamming"] if vector_type == "bit" else ["l2", "inner_product", "cosine"]
        )
    ],
    ids=lambda p: f"{p[0]}_{p[1]}",
)
def vector_store(
    request,
    fake_embeddings: Embeddings,
) -> PostgresStore:
    """Create a store with vector search enabled."""
    vector_type, distance_type = request.param
    with _create_vector_store(vector_type, distance_type, fake_embeddings) as store:
        yield store


def test_vector_store_initialization(
    vector_store: PostgresStore, fake_embeddings: CharacterEmbeddings
) -> None:
    """Test store initialization with embedding config."""
    # Store should be initialized with embedding config
    assert vector_store.index_config is not None
    assert vector_store.index_config["dims"] == fake_embeddings.dims
    assert vector_store.index_config["embed"] == fake_embeddings


def test_vector_insert_with_auto_embedding(vector_store: PostgresStore) -> None:
    """Test inserting items that get auto-embedded."""
    docs = [
        ("doc1", {"text": "short text"}),
        ("doc2", {"text": "longer text document"}),
        ("doc3", {"text": "longest text document here"}),
        ("doc4", {"description": "text in description field"}),
        ("doc5", {"content": "text in content field"}),
        ("doc6", {"body": "text in body field"}),
    ]

    for key, value in docs:
        vector_store.put(("test",), key, value)

    results = vector_store.search(("test",), query="long text")
    assert len(results) > 0

    doc_order = [r.key for r in results]
    assert "doc2" in doc_order
    assert "doc3" in doc_order


def test_vector_update_with_embedding(vector_store: PostgresStore) -> None:
    """Test that updating items properly updates their embeddings."""
    vector_store.put(("test",), "doc1", {"text": "zany zebra Xerxes"})
    vector_store.put(("test",), "doc2", {"text": "something about dogs"})
    vector_store.put(("test",), "doc3", {"text": "text about birds"})

    results_initial = vector_store.search(("test",), query="Zany Xerxes")
    assert len(results_initial) > 0
    assert results_initial[0].key == "doc1"
    initial_score = results_initial[0].score

    vector_store.put(("test",), "doc1", {"text": "new text about dogs"})

    results_after = vector_store.search(("test",), query="Zany Xerxes")
    after_score = next((r.score for r in results_after if r.key == "doc1"), 0.0)
    assert after_score < initial_score

    results_new = vector_store.search(("test",), query="new text about dogs")
    for r in results_new:
        if r.key == "doc1":
            assert r.score > after_score

    # Don't index this one
    vector_store.put(("test",), "doc4", {"text": "new text about dogs"}, index=False)
    results_new = vector_store.search(("test",), query="new text about dogs", limit=3)
    assert not any(r.key == "doc4" for r in results_new)


def test_vector_search_with_filters(vector_store: PostgresStore) -> None:
    """Test combining vector search with filters."""
    # Insert test documents
    docs = [
        ("doc1", {"text": "red apple", "color": "red", "score": 4.5}),
        ("doc2", {"text": "red car", "color": "red", "score": 3.0}),
        ("doc3", {"text": "green apple", "color": "green", "score": 4.0}),
        ("doc4", {"text": "blue car", "color": "blue", "score": 3.5}),
    ]

    for key, value in docs:
        vector_store.put(("test",), key, value)

    results = vector_store.search(("test",), query="apple", filter={"color": "red"})
    assert len(results) == 2
    assert results[0].key == "doc1"

    results = vector_store.search(("test",), query="car", filter={"color": "red"})
    assert len(results) == 2
    assert results[0].key == "doc2"

    results = vector_store.search(
        ("test",), query="bbbbluuu", filter={"score": {"$gt": 3.2}}
    )
    assert len(results) == 3
    assert results[0].key == "doc4"

    # Multiple filters
    results = vector_store.search(
        ("test",), query="apple", filter={"score": {"$gte": 4.0}, "color": "green"}
    )
    assert len(results) == 1
    assert results[0].key == "doc3"


def test_vector_search_pagination(vector_store: PostgresStore) -> None:
    """Test pagination with vector search."""
    # Insert multiple similar documents
    for i in range(5):
        vector_store.put(("test",), f"doc{i}", {"text": f"test document number {i}"})

    # Test with different page sizes
    results_page1 = vector_store.search(("test",), query="test", limit=2)
    results_page2 = vector_store.search(("test",), query="test", limit=2, offset=2)

    assert len(results_page1) == 2
    assert len(results_page2) == 2
    assert results_page1[0].key != results_page2[0].key

    # Get all results
    all_results = vector_store.search(("test",), query="test", limit=10)
    assert len(all_results) == 5


def test_vector_search_edge_cases(vector_store: PostgresStore) -> None:
    """Test edge cases in vector search."""
    vector_store.put(("test",), "doc1", {"text": "test document"})

    results = vector_store.search(("test",), query="")
    assert len(results) == 1

    results = vector_store.search(("test",), query=None)
    assert len(results) == 1

    long_query = "test " * 100
    results = vector_store.search(("test",), query=long_query)
    assert len(results) == 1

    special_query = "test!@#$%^&*()"
    results = vector_store.search(("test",), query=special_query)
    assert len(results) == 1


@pytest.mark.parametrize(
    "vector_type,distance_type",
    [
        ("vector", "cosine"),
        ("vector", "inner_product"),
        ("halfvec", "cosine"),
        ("halfvec", "inner_product"),
    ],
)
def test_embed_with_path_sync(
    request: Any,
    fake_embeddings: CharacterEmbeddings,
    vector_type: str,
    distance_type: str,
) -> None:
    """Test vector search with specific text fields in Postgres store."""
    with _create_vector_store(
        vector_type,
        distance_type,
        fake_embeddings,
        text_fields=["key0", "key1", "key3"],
    ) as store:
        # This will have 2 vectors representing it
        doc1 = {
            # Omit key0 - check it doesn't raise an error
            "key1": "xxx",
            "key2": "yyy",
            "key3": "zzz",
        }
        # This will have 3 vectors representing it
        doc2 = {
            "key0": "uuu",
            "key1": "vvv",
            "key2": "www",
            "key3": "xxx",
        }
        store.put(("test",), "doc1", doc1)
        store.put(("test",), "doc2", doc2)

        # doc2.key3 and doc1.key1 both would have the highest score
        results = store.search(("test",), query="xxx")
        assert len(results) == 2
        assert results[0].key != results[1].key
        ascore = results[0].score
        bscore = results[1].score
        assert ascore == pytest.approx(bscore, abs=1e-3)

        # ~Only match doc2
        results = store.search(("test",), query="uuu")
        assert len(results) == 2
        assert results[0].key != results[1].key
        assert results[0].key == "doc2"
        assert results[0].score > results[1].score
        assert ascore == pytest.approx(results[0].score, abs=1e-3)

        # ~Only match doc1
        results = store.search(("test",), query="zzz")
        assert len(results) == 2
        assert results[0].key != results[1].key
        assert results[0].key == "doc1"
        assert results[0].score > results[1].score
        assert ascore == pytest.approx(results[0].score, abs=1e-3)

        # Un-indexed - will have low results for both. Not zero (because we're projecting)
        # but less than the above.
        results = store.search(("test",), query="www")
        assert len(results) == 2
        assert results[0].key != results[1].key
        assert results[0].score < ascore
        assert results[1].score < ascore


@pytest.mark.parametrize(
    "vector_type,distance_type",
    [
        ("vector", "cosine"),
        ("vector", "inner_product"),
        ("halfvec", "cosine"),
        ("halfvec", "inner_product"),
    ],
)
def test_embed_with_path_operation_config(
    request: Any,
    fake_embeddings: CharacterEmbeddings,
    vector_type: str,
    distance_type: str,
) -> None:
    """Test operation-level field configuration for vector search."""

    with _create_vector_store(
        vector_type,
        distance_type,
        fake_embeddings,
        text_fields=["key17"],  # Default fields that won't match our test data
    ) as store:
        doc3 = {
            "key0": "aaa",
            "key1": "bbb",
            "key2": "ccc",
            "key3": "ddd",
        }
        doc4 = {
            "key0": "eee",
            "key1": "bbb",  # Same as doc3.key1
            "key2": "fff",
            "key3": "ggg",
        }

        store.put(("test",), "doc3", doc3, index=["key0", "key1"])
        store.put(("test",), "doc4", doc4, index=["key1", "key3"])

        results = store.search(("test",), query="aaa")
        assert len(results) == 2
        assert results[0].key == "doc3"
        assert len(set(r.key for r in results)) == 2
        assert results[0].score > results[1].score

        results = store.search(("test",), query="ggg")
        assert len(results) == 2
        assert results[0].key == "doc4"
        assert results[0].score > results[1].score

        results = store.search(("test",), query="bbb")
        assert len(results) == 2
        assert results[0].key != results[1].key
        assert results[0].score == pytest.approx(results[1].score, abs=1e-3)

        results = store.search(("test",), query="ccc")
        assert len(results) == 2
        assert all(
            r.score < 0.9 for r in results
        )  # Unindexed field should have low scores

        # Test index=False behavior
        doc5 = {
            "key0": "hhh",
            "key1": "iii",
        }
        store.put(("test",), "doc5", doc5, index=False)
        results = store.search(("test",))
        assert len(results) == 3
        assert all(r.score is None for r in results)
        assert any(r.key == "doc5" for r in results)

        results = store.search(("test",), query="hhh")
        # TODO: We don't currently fill in additional results if there are not enough
        # returned during vector search.
        # assert len(results) == 3
        # doc5_result = next(r for r in results if r.key == "doc5")
        # assert doc5_result.score is None


def _cosine_similarity(X: list[float], Y: list[list[float]]) -> list[float]:
    """
    Compute cosine similarity between a vector X and a matrix Y.
    Lazy import numpy for efficiency.
    """

    similarities = []
    for y in Y:
        dot_product = sum(a * b for a, b in zip(X, y))
        norm1 = sum(a * a for a in X) ** 0.5
        norm2 = sum(a * a for a in y) ** 0.5
        similarity = dot_product / (norm1 * norm2) if norm1 > 0 and norm2 > 0 else 0.0
        similarities.append(similarity)

    return similarities


def _inner_product(X: list[float], Y: list[list[float]]) -> list[float]:
    """
    Compute inner product between a vector X and a matrix Y.
    Lazy import numpy for efficiency.
    """

    similarities = []
    for y in Y:
        similarity = sum(a * b for a, b in zip(X, y))
        similarities.append(similarity)

    return similarities


def _neg_l2_distance(X: list[float], Y: list[list[float]]) -> list[float]:
    """
    Compute l2 distance between a vector X and a matrix Y.
    Lazy import numpy for efficiency.
    """

    similarities = []
    for y in Y:
        similarity = sum((a - b) ** 2 for a, b in zip(X, y)) ** 0.5
        similarities.append(-similarity)

    return similarities


@pytest.mark.parametrize(
    "vector_type,distance_type",
    [
        ("vector", "cosine"),
        ("vector", "inner_product"),
        ("halfvec", "l2"),
    ],
)
@pytest.mark.parametrize("query", ["aaa", "bbb", "ccc", "abcd", "poisson"])
def test_scores(
    fake_embeddings: CharacterEmbeddings,
    vector_type: str,
    distance_type: str,
    query: str,
) -> None:
    """Test operation-level field configuration for vector search."""
    with _create_vector_store(
        vector_type,
        distance_type,
        fake_embeddings,
        text_fields=["key0"],
    ) as store:
        doc = {
            "key0": "aaa",
        }
        store.put(("test",), "doc", doc, index=["key0", "key1"])

        results = store.search((), query=query)
        vec0 = fake_embeddings.embed_query(doc["key0"])
        vec1 = fake_embeddings.embed_query(query)
        if distance_type == "cosine":
            similarities = _cosine_similarity(vec1, [vec0])
        elif distance_type == "inner_product":
            similarities = _inner_product(vec1, [vec0])
        elif distance_type == "l2":
            similarities = _neg_l2_distance(vec1, [vec0])

        assert len(results) == 1
        assert results[0].score == pytest.approx(similarities[0], abs=1e-3)