index int64 0 0 | repo_id stringclasses 596 values | file_path stringlengths 31 168 | content stringlengths 1 6.2M |
|---|---|---|---|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_lancedb.py | from typing import Any
import pytest
from langchain_community.vectorstores import LanceDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def import_lancedb() -> Any:
try:
import lancedb
except ImportError as e:
raise ImportError(
"Could not import lancedb package. "
"Please install it with `pip install lancedb`."
) from e
return lancedb
@pytest.mark.requires("lancedb")
def test_lancedb_with_connection() -> None:
lancedb = import_lancedb()
embeddings = FakeEmbeddings()
db = lancedb.connect("/tmp/lancedb_connection")
texts = ["text 1", "text 2", "item 3"]
store = LanceDB(connection=db, embedding=embeddings)
store.add_texts(texts)
result = store.similarity_search("text 1")
result_texts = [doc.page_content for doc in result]
assert "text 1" in result_texts
store.delete(filter="text = 'text 1'")
assert store.get_table().count_rows() == 2
@pytest.mark.requires("lancedb")
def test_lancedb_without_connection() -> None:
embeddings = FakeEmbeddings()
texts = ["text 1", "text 2", "item 3"]
store = LanceDB(embedding=embeddings)
store.add_texts(texts)
result = store.similarity_search("text 1")
result_texts = [doc.page_content for doc in result]
assert "text 1" in result_texts
@pytest.mark.requires("lancedb")
def test_lancedb_add_texts() -> None:
embeddings = FakeEmbeddings()
store = LanceDB(embedding=embeddings)
store.add_texts(["text 2"])
result = store.similarity_search("text 2")
result_texts = [doc.page_content for doc in result]
assert "text 2" in result_texts
@pytest.mark.requires("lancedb")
def test_mmr() -> None:
embeddings = FakeEmbeddings()
store = LanceDB(embedding=embeddings)
store.add_texts(["text 1", "text 2", "item 3"])
result = store.max_marginal_relevance_search(query="text")
result_texts = [doc.page_content for doc in result]
assert "text 1" in result_texts
result = store.max_marginal_relevance_search_by_vector(
embeddings.embed_query("text")
)
result_texts = [doc.page_content for doc in result]
assert "text 1" in result_texts
@pytest.mark.requires("lancedb")
def test_lancedb_delete() -> None:
embeddings = FakeEmbeddings()
store = LanceDB(embedding=embeddings)
store.add_texts(["text 1", "text 2", "item 3"])
store.delete(filter="text = 'text 1'")
assert store.get_table().count_rows() == 2
@pytest.mark.requires("lancedb")
def test_lancedb_all_searches() -> None:
embeddings = FakeEmbeddings()
store = LanceDB(embedding=embeddings)
store.add_texts(["text 1", "text 2", "item 3"])
result_1 = store.similarity_search_with_relevance_scores(
"text 1", distance="cosine"
)
assert len(result_1[0]) == 2
assert "text 1" in result_1[0][0].page_content
result_2 = store.similarity_search_by_vector(embeddings.embed_query("text 1"))
assert "text 1" in result_2[0].page_content
result_3 = store.similarity_search_by_vector_with_relevance_scores(
embeddings.embed_query("text 1")
)
assert len(result_3[0]) == 2 # type: ignore
assert "text 1" in result_3[0][0].page_content # type: ignore
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/conftest.py | import os
from typing import Union
import pytest
from vcr.request import Request
# Those environment variables turn on Deep Lake pytest mode.
# It significantly makes tests run much faster.
# Need to run before `import deeplake`
os.environ["BUGGER_OFF"] = "true"
os.environ["DEEPLAKE_DOWNLOAD_PATH"] = "./testing/local_storage"
os.environ["DEEPLAKE_PYTEST_ENABLED"] = "true"
# This fixture returns a dictionary containing filter_headers options
# for replacing certain headers with dummy values during cassette playback
# Specifically, it replaces the authorization header with a dummy value to
# prevent sensitive data from being recorded in the cassette.
# It also filters request to certain hosts (specified in the `ignored_hosts` list)
# to prevent data from being recorded in the cassette.
@pytest.fixture(scope="module")
def vcr_config() -> dict:
skipped_host = ["pinecone.io"]
def before_record_response(response: dict) -> Union[dict, None]:
return response
def before_record_request(request: Request) -> Union[Request, None]:
for host in skipped_host:
if request.host.startswith(host) or request.host.endswith(host):
return None
return request
return {
"before_record_request": before_record_request,
"before_record_response": before_record_response,
"filter_headers": [
("authorization", "authorization-DUMMY"),
("X-OpenAI-Client-User-Agent", "X-OpenAI-Client-User-Agent-DUMMY"),
("Api-Key", "Api-Key-DUMMY"),
("User-Agent", "User-Agent-DUMMY"),
],
"ignore_localhost": True,
}
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_aerospike.py | """Test Aerospike functionality."""
import inspect
import os
import subprocess
import time
from typing import Any, Generator
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores.aerospike import (
Aerospike,
)
from langchain_community.vectorstores.utils import DistanceStrategy
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
pytestmark = pytest.mark.requires("aerospike_vector_search")
TEST_INDEX_NAME = "test-index"
TEST_NAMESPACE = "test"
TEST_AEROSPIKE_HOST_PORT = ("localhost", 5002)
TEXT_KEY = "_text"
VECTOR_KEY = "_vector"
ID_KEY = "_id"
EUCLIDEAN_SCORE = 1.0
DIR_PATH = os.path.dirname(os.path.realpath(__file__)) + "/docker-compose/aerospike"
FEAT_KEY_PATH = DIR_PATH + "/features.conf"
def compose_up() -> None:
subprocess.run(["docker", "compose", "up", "-d"], cwd=DIR_PATH)
time.sleep(10)
def compose_down() -> None:
subprocess.run(["docker", "compose", "down"], cwd=DIR_PATH)
@pytest.fixture(scope="class", autouse=True)
def docker_compose() -> Generator[None, None, None]:
try:
import aerospike_vector_search # noqa
except ImportError:
pytest.skip("aerospike_vector_search not installed")
if not os.path.exists(FEAT_KEY_PATH):
pytest.skip(
"Aerospike feature key file not found at path {}".format(FEAT_KEY_PATH)
)
compose_up()
yield
compose_down()
@pytest.fixture(scope="class")
def seeds() -> Generator[Any, None, None]:
try:
from aerospike_vector_search.types import HostPort
except ImportError:
pytest.skip("aerospike_vector_search not installed")
yield HostPort(
host=TEST_AEROSPIKE_HOST_PORT[0],
port=TEST_AEROSPIKE_HOST_PORT[1],
)
@pytest.fixture(scope="class")
@pytest.mark.requires("aerospike_vector_search")
def admin_client(seeds: Any) -> Generator[Any, None, None]:
try:
from aerospike_vector_search.admin import Client as AdminClient
except ImportError:
pytest.skip("aerospike_vector_search not installed")
with AdminClient(seeds=seeds) as admin_client:
yield admin_client
@pytest.fixture(scope="class")
@pytest.mark.requires("aerospike_vector_search")
def client(seeds: Any) -> Generator[Any, None, None]:
try:
from aerospike_vector_search import Client
except ImportError:
pytest.skip("aerospike_vector_search not installed")
with Client(seeds=seeds) as client:
yield client
@pytest.fixture
def embedder() -> Any:
return ConsistentFakeEmbeddings()
@pytest.fixture
def aerospike(
client: Any, embedder: ConsistentFakeEmbeddings
) -> Generator[Aerospike, None, None]:
yield Aerospike(
client,
embedder,
TEST_NAMESPACE,
vector_key=VECTOR_KEY,
text_key=TEXT_KEY,
id_key=ID_KEY,
)
def get_func_name() -> str:
"""
Used to get the name of the calling function. The name is used for the index
and set name in Aerospike tests for debugging purposes.
"""
return inspect.stack()[1].function
"""
TODO: Add tests for delete()
"""
class TestAerospike:
def test_from_text(
self,
client: Any,
admin_client: Any,
embedder: ConsistentFakeEmbeddings,
) -> None:
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike = Aerospike.from_texts(
["foo", "bar", "baz", "bay", "bax", "baw", "bav"],
embedder,
client=client,
namespace=TEST_NAMESPACE,
index_name=index_name,
ids=["1", "2", "3", "4", "5", "6", "7"],
set_name=set_name,
)
expected = [
Document(
page_content="foo",
metadata={
ID_KEY: "1",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
},
),
Document(
page_content="bar",
metadata={
ID_KEY: "2",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
},
),
Document(
page_content="baz",
metadata={
ID_KEY: "3",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],
},
),
]
actual = aerospike.search(
"foo", k=3, index_name=index_name, search_type="similarity"
)
assert actual == expected
def test_from_documents(
self,
client: Any,
admin_client: Any,
embedder: ConsistentFakeEmbeddings,
) -> None:
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
documents = [
Document(
page_content="foo",
metadata={
ID_KEY: "1",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
},
),
Document(
page_content="bar",
metadata={
ID_KEY: "2",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
},
),
Document(
page_content="baz",
metadata={
ID_KEY: "3",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],
},
),
Document(
page_content="bay",
metadata={
ID_KEY: "4",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0],
},
),
Document(
page_content="bax",
metadata={
ID_KEY: "5",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0],
},
),
Document(
page_content="baw",
metadata={
ID_KEY: "6",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0],
},
),
Document(
page_content="bav",
metadata={
ID_KEY: "7",
"_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0],
},
),
]
aerospike = Aerospike.from_documents(
documents,
embedder,
client=client,
namespace=TEST_NAMESPACE,
index_name=index_name,
ids=["1", "2", "3", "4", "5", "6", "7"],
set_name=set_name,
)
actual = aerospike.search(
"foo", k=3, index_name=index_name, search_type="similarity"
)
expected = documents[:3]
assert actual == expected
def test_delete(self, aerospike: Aerospike, admin_client: Any, client: Any) -> None:
"""Test end to end construction and search."""
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
)
assert client.exists(namespace=TEST_NAMESPACE, set_name=set_name, key="1")
assert client.exists(namespace=TEST_NAMESPACE, set_name=set_name, key="2")
assert client.exists(namespace=TEST_NAMESPACE, set_name=set_name, key="3")
aerospike.delete(["1", "2", "3"], set_name=set_name)
assert not client.exists(namespace=TEST_NAMESPACE, set_name=set_name, key="1")
assert not client.exists(namespace=TEST_NAMESPACE, set_name=set_name, key="2")
assert not client.exists(namespace=TEST_NAMESPACE, set_name=set_name, key="3")
def test_search_blocking(self, aerospike: Aerospike, admin_client: Any) -> None:
"""Test end to end construction and search."""
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
) # Blocks until all vectors are indexed
expected = [Document(page_content="foo", metadata={ID_KEY: "1"})]
actual = aerospike.search(
"foo",
k=1,
index_name=index_name,
search_type="similarity",
metadata_keys=[ID_KEY],
)
assert actual == expected
def test_search_nonblocking(self, aerospike: Aerospike, admin_client: Any) -> None:
"""Test end to end construction and search."""
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
wait_for_index=True,
) # blocking
aerospike.add_texts(
["bay"], index_name=index_name, set_name=set_name, wait_for_index=False
)
expected = [
Document(page_content="foo", metadata={ID_KEY: "1"}),
Document(page_content="bar", metadata={ID_KEY: "2"}),
Document(page_content="baz", metadata={ID_KEY: "3"}),
]
actual = aerospike.search(
"foo",
k=4,
index_name=index_name,
search_type="similarity",
metadata_keys=[ID_KEY],
)
# "bay"
assert actual == expected
def test_similarity_search_with_score(
self, aerospike: Aerospike, admin_client: Any
) -> None:
"""Test end to end construction and search."""
expected = [(Document(page_content="foo", metadata={ID_KEY: "1"}), 0.0)]
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
)
actual = aerospike.similarity_search_with_score(
"foo", k=1, index_name=index_name, metadata_keys=[ID_KEY]
)
assert actual == expected
def test_similarity_search_by_vector_with_score(
self,
aerospike: Aerospike,
admin_client: Any,
embedder: ConsistentFakeEmbeddings,
) -> None:
"""Test end to end construction and search."""
expected = [
(Document(page_content="foo", metadata={"a": "b", ID_KEY: "1"}), 0.0)
]
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
metadatas=[{"a": "b", "1": "2"}, {"a": "c"}, {"a": "d"}],
)
actual = aerospike.similarity_search_by_vector_with_score(
embedder.embed_query("foo"),
k=1,
index_name=index_name,
metadata_keys=["a", ID_KEY],
)
assert actual == expected
def test_similarity_search_by_vector(
self,
aerospike: Aerospike,
admin_client: Any,
embedder: ConsistentFakeEmbeddings,
) -> None:
"""Test end to end construction and search."""
expected = [
Document(page_content="foo", metadata={"a": "b", ID_KEY: "1"}),
Document(page_content="bar", metadata={"a": "c", ID_KEY: "2"}),
]
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
metadatas=[{"a": "b", "1": "2"}, {"a": "c"}, {"a": "d"}],
)
actual = aerospike.similarity_search_by_vector(
embedder.embed_query("foo"),
k=2,
index_name=index_name,
metadata_keys=["a", ID_KEY],
)
assert actual == expected
def test_similarity_search(self, aerospike: Aerospike, admin_client: Any) -> None:
"""Test end to end construction and search."""
expected = [
Document(page_content="foo", metadata={ID_KEY: "1"}),
Document(page_content="bar", metadata={ID_KEY: "2"}),
Document(page_content="baz", metadata={ID_KEY: "3"}),
]
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
) # blocking
actual = aerospike.similarity_search(
"foo", k=3, index_name=index_name, metadata_keys=[ID_KEY]
)
assert actual == expected
def test_max_marginal_relevance_search_by_vector(
self,
client: Any,
admin_client: Any,
embedder: ConsistentFakeEmbeddings,
) -> None:
"""Test max marginal relevance search."""
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike = Aerospike.from_texts(
["foo", "bar", "baz", "bay", "bax", "baw", "bav"],
embedder,
client=client,
namespace=TEST_NAMESPACE,
index_name=index_name,
ids=["1", "2", "3", "4", "5", "6", "7"],
set_name=set_name,
)
mmr_output = aerospike.max_marginal_relevance_search_by_vector(
embedder.embed_query("foo"), index_name=index_name, k=3, fetch_k=3
)
sim_output = aerospike.similarity_search("foo", index_name=index_name, k=3)
assert len(mmr_output) == 3
assert mmr_output == sim_output
mmr_output = aerospike.max_marginal_relevance_search_by_vector(
embedder.embed_query("foo"), index_name=index_name, k=2, fetch_k=3
)
assert len(mmr_output) == 2
assert mmr_output[0].page_content == "foo"
assert mmr_output[1].page_content == "bar"
mmr_output = aerospike.max_marginal_relevance_search_by_vector(
embedder.embed_query("foo"),
index_name=index_name,
k=2,
fetch_k=3,
lambda_mult=0.1, # more diversity
)
assert len(mmr_output) == 2
assert mmr_output[0].page_content == "foo"
assert mmr_output[1].page_content == "baz"
# if fetch_k < k, then the output will be less than k
mmr_output = aerospike.max_marginal_relevance_search_by_vector(
embedder.embed_query("foo"), index_name=index_name, k=3, fetch_k=2
)
assert len(mmr_output) == 2
def test_max_marginal_relevance_search(
self, aerospike: Aerospike, admin_client: Any
) -> None:
"""Test max marginal relevance search."""
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "bar", "baz", "bay", "bax", "baw", "bav"],
ids=["1", "2", "3", "4", "5", "6", "7"],
index_name=index_name,
set_name=set_name,
)
mmr_output = aerospike.max_marginal_relevance_search(
"foo", index_name=index_name, k=3, fetch_k=3
)
sim_output = aerospike.similarity_search("foo", index_name=index_name, k=3)
assert len(mmr_output) == 3
assert mmr_output == sim_output
mmr_output = aerospike.max_marginal_relevance_search(
"foo", index_name=index_name, k=2, fetch_k=3
)
assert len(mmr_output) == 2
assert mmr_output[0].page_content == "foo"
assert mmr_output[1].page_content == "bar"
mmr_output = aerospike.max_marginal_relevance_search(
"foo",
index_name=index_name,
k=2,
fetch_k=3,
lambda_mult=0.1, # more diversity
)
assert len(mmr_output) == 2
assert mmr_output[0].page_content == "foo"
assert mmr_output[1].page_content == "baz"
# if fetch_k < k, then the output will be less than k
mmr_output = aerospike.max_marginal_relevance_search(
"foo", index_name=index_name, k=3, fetch_k=2
)
assert len(mmr_output) == 2
def test_cosine_distance(self, aerospike: Aerospike, admin_client: Any) -> None:
"""Test cosine distance."""
from aerospike_vector_search import types
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
vector_distance_metric=types.VectorDistanceMetric.COSINE,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
) # blocking
"""
foo vector = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]
far vector = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0]
cosine similarity ~= 0.71
cosine distance ~= 1 - cosine similarity = 0.29
"""
expected = pytest.approx(0.292, abs=0.002)
output = aerospike.similarity_search_with_score(
"far", index_name=index_name, k=3
)
_, actual_score = output[2]
assert actual_score == expected
def test_dot_product_distance(
self, aerospike: Aerospike, admin_client: Any
) -> None:
"""Test dot product distance."""
from aerospike_vector_search import types
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
vector_distance_metric=types.VectorDistanceMetric.DOT_PRODUCT,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
) # blocking
"""
foo vector = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]
far vector = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0]
dot product = 9.0
dot product distance = dot product * -1 = -9.0
"""
expected = -9.0
output = aerospike.similarity_search_with_score(
"far", index_name=index_name, k=3
)
_, actual_score = output[2]
assert actual_score == expected
def test_euclidean_distance(self, aerospike: Aerospike, admin_client: Any) -> None:
"""Test dot product distance."""
from aerospike_vector_search import types
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
vector_distance_metric=types.VectorDistanceMetric.SQUARED_EUCLIDEAN,
)
aerospike.add_texts(
["foo", "bar", "baz"],
ids=["1", "2", "3"],
index_name=index_name,
set_name=set_name,
) # blocking
"""
foo vector = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]
far vector = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0]
euclidean distance = 9.0
"""
expected = 9.0
output = aerospike.similarity_search_with_score(
"far", index_name=index_name, k=3
)
_, actual_score = output[2]
assert actual_score == expected
def test_as_retriever(self, aerospike: Aerospike, admin_client: Any) -> None:
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
)
aerospike.add_texts(
["foo", "foo", "foo", "foo", "bar"],
ids=["1", "2", "3", "4", "5"],
index_name=index_name,
set_name=set_name,
) # blocking
aerospike._index_name = index_name
retriever = aerospike.as_retriever(
search_type="similarity", search_kwargs={"k": 3}
)
results = retriever.invoke("foo")
assert len(results) == 3
assert all([d.page_content == "foo" for d in results])
def test_as_retriever_distance_threshold(
self, aerospike: Aerospike, admin_client: Any
) -> None:
from aerospike_vector_search import types
aerospike._distance_strategy = DistanceStrategy.COSINE
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
vector_distance_metric=types.VectorDistanceMetric.COSINE,
)
aerospike.add_texts(
["foo1", "foo2", "foo3", "bar4", "bar5", "bar6", "bar7", "bar8"],
ids=["1", "2", "3", "4", "5", "6", "7", "8"],
index_name=index_name,
set_name=set_name,
) # blocking
aerospike._index_name = index_name
retriever = aerospike.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 9, "score_threshold": 0.90},
)
results = retriever.invoke("foo1")
assert all([d.page_content.startswith("foo") for d in results])
assert len(results) == 3
def test_as_retriever_add_documents(
self, aerospike: Aerospike, admin_client: Any
) -> None:
from aerospike_vector_search import types
aerospike._distance_strategy = DistanceStrategy.COSINE
index_name = set_name = get_func_name()
admin_client.index_create(
namespace=TEST_NAMESPACE,
sets=set_name,
name=index_name,
vector_field=VECTOR_KEY,
dimensions=10,
vector_distance_metric=types.VectorDistanceMetric.COSINE,
)
retriever = aerospike.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 9, "score_threshold": 0.90},
)
documents = [
Document(
page_content="foo1",
metadata={
"a": 1,
},
),
Document(
page_content="foo2",
metadata={
"a": 2,
},
),
Document(
page_content="foo3",
metadata={
"a": 3,
},
),
Document(
page_content="bar4",
metadata={
"a": 4,
},
),
Document(
page_content="bar5",
metadata={
"a": 5,
},
),
Document(
page_content="bar6",
metadata={
"a": 6,
},
),
Document(
page_content="bar7",
metadata={
"a": 7,
},
),
]
retriever.add_documents(
documents,
ids=["1", "2", "3", "4", "5", "6", "7", "8"],
index_name=index_name,
set_name=set_name,
wait_for_index=True,
)
aerospike._index_name = index_name
results = retriever.invoke("foo1")
assert all([d.page_content.startswith("foo") for d in results])
assert len(results) == 3
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_llm_rails.py | from langchain_community.vectorstores.llm_rails import LLMRails
#
# For this test to run properly, please setup as follows:
# 1. Create a LLMRails account: sign up at https://console.llmrails.com/signup
# 2. Create an API_KEY for this corpus with permissions for query and indexing
# 3. Create a datastorea and get its id from datastore setting
# 3. Setup environment variable:
# LLM_RAILS_API_KEY, LLM_RAILS_DATASTORE_ID
#
def test_llm_rails_add_documents() -> None:
"""Test end to end construction and search."""
# create a new Vectara instance
docsearch: LLMRails = LLMRails()
# start with some initial texts, added with add_texts
texts1 = ["large language model", "information retrieval", "question answering"]
docsearch.add_texts(texts1)
# test without filter
output1 = docsearch.similarity_search("large language model", k=1)
print(output1) # noqa: T201
assert len(output1) == 1
assert output1[0].page_content == "large language model"
# test without filter but with similarity score
output2 = docsearch.similarity_search_with_score("large language model", k=1)
assert len(output2) == 1
assert output2[0][0].page_content == "large language model"
assert output2[0][1] > 0
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_clickhouse.py | """Test ClickHouse functionality."""
from langchain_core.documents import Document
from langchain_community.vectorstores import Clickhouse, ClickhouseSettings
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
def test_clickhouse() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
config = ClickhouseSettings()
config.table = "test_clickhouse"
docsearch = Clickhouse.from_texts(texts, FakeEmbeddings(), config=config)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
docsearch.drop()
async def test_clickhouse_async() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
config = ClickhouseSettings()
config.table = "test_clickhouse_async"
docsearch = Clickhouse.from_texts(
texts=texts, embedding=FakeEmbeddings(), config=config
)
output = await docsearch.asimilarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
docsearch.drop()
def test_clickhouse_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
config = ClickhouseSettings()
config.table = "test_clickhouse_with_metadatas"
docsearch = Clickhouse.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
config=config,
metadatas=metadatas,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
docsearch.drop()
def test_clickhouse_with_metadatas_with_relevance_scores() -> None:
"""Test end to end construction and scored search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
config = ClickhouseSettings()
config.table = "test_clickhouse_with_metadatas_with_relevance_scores"
docsearch = Clickhouse.from_texts(
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas, config=config
)
output = docsearch.similarity_search_with_relevance_scores("foo", k=1)
assert output[0][0] == Document(page_content="foo", metadata={"page": "0"})
docsearch.drop()
def test_clickhouse_search_filter() -> None:
"""Test end to end construction and search with metadata filtering."""
texts = ["far", "bar", "baz"]
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
config = ClickhouseSettings()
config.table = "test_clickhouse_search_filter"
docsearch = Clickhouse.from_texts(
texts=texts, embedding=FakeEmbeddings(), metadatas=metadatas, config=config
)
output = docsearch.similarity_search(
"far", k=1, where_str=f"{docsearch.metadata_column}.first_letter='f'"
)
assert output == [Document(page_content="far", metadata={"first_letter": "f"})]
output = docsearch.similarity_search(
"bar", k=1, where_str=f"{docsearch.metadata_column}.first_letter='b'"
)
assert output == [Document(page_content="bar", metadata={"first_letter": "b"})]
docsearch.drop()
def test_clickhouse_with_persistence() -> None:
"""Test end to end construction and search, with persistence."""
config = ClickhouseSettings()
config.table = "test_clickhouse_with_persistence"
texts = [
"foo",
"bar",
"baz",
]
docsearch = Clickhouse.from_texts(
texts=texts, embedding=FakeEmbeddings(), config=config
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"_dummy": 0})]
# Get a new VectorStore with same config
# it will reuse the table spontaneously
# unless you drop it
docsearch = Clickhouse(embedding=FakeEmbeddings(), config=config)
output = docsearch.similarity_search("foo", k=1)
# Clean up
docsearch.drop()
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_redis.py | """Test Redis functionality."""
import os
from typing import Any, Dict, List, Optional
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores.redis import (
Redis,
RedisFilter,
RedisNum,
RedisText,
)
from langchain_community.vectorstores.redis.filters import RedisFilterExpression
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
FakeEmbeddings,
)
TEST_INDEX_NAME = "test"
TEST_REDIS_URL = "redis://localhost:6379"
TEST_SINGLE_RESULT = [Document(page_content="foo")]
TEST_SINGLE_WITH_METADATA = {"a": "b"}
TEST_RESULT = [Document(page_content="foo"), Document(page_content="foo")]
RANGE_SCORE = pytest.approx(0.0513, abs=0.002)
COSINE_SCORE = pytest.approx(0.05, abs=0.002)
IP_SCORE = -8.0
EUCLIDEAN_SCORE = 1.0
def drop(index_name: str) -> bool:
return Redis.drop_index(
index_name=index_name, delete_documents=True, redis_url=TEST_REDIS_URL
)
def convert_bytes(data: Any) -> Any:
if isinstance(data, bytes):
return data.decode("ascii")
if isinstance(data, dict):
return dict(map(convert_bytes, data.items()))
if isinstance(data, list):
return list(map(convert_bytes, data))
if isinstance(data, tuple):
return map(convert_bytes, data)
return data
def make_dict(values: List[Any]) -> dict:
i = 0
di = {}
while i < len(values) - 1:
di[values[i]] = values[i + 1]
i += 2
return di
@pytest.fixture
def texts() -> List[str]:
return ["foo", "bar", "baz"]
def test_redis(texts: List[str]) -> None:
"""Test end to end construction and search."""
docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
output = docsearch.similarity_search("foo", k=1, return_metadata=False)
assert output == TEST_SINGLE_RESULT
assert drop(docsearch.index_name)
def test_redis_new_vector(texts: List[str]) -> None:
"""Test adding a new document"""
docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
docsearch.add_texts(["foo"])
output = docsearch.similarity_search("foo", k=2, return_metadata=False)
assert output == TEST_RESULT
assert drop(docsearch.index_name)
def test_redis_from_existing(texts: List[str]) -> None:
"""Test adding a new document"""
docsearch = Redis.from_texts(
texts, FakeEmbeddings(), index_name=TEST_INDEX_NAME, redis_url=TEST_REDIS_URL
)
schema: Dict = docsearch.schema
# write schema for the next test
docsearch.write_schema("test_schema.yml")
# Test creating from an existing
docsearch2 = Redis.from_existing_index(
FakeEmbeddings(),
index_name=TEST_INDEX_NAME,
redis_url=TEST_REDIS_URL,
schema=schema,
)
output = docsearch2.similarity_search("foo", k=1, return_metadata=False)
assert output == TEST_SINGLE_RESULT
def test_redis_add_texts_to_existing() -> None:
"""Test adding a new document"""
# Test creating from an existing with yaml from file
docsearch = Redis.from_existing_index(
FakeEmbeddings(),
index_name=TEST_INDEX_NAME,
redis_url=TEST_REDIS_URL,
schema="test_schema.yml",
)
docsearch.add_texts(["foo"])
output = docsearch.similarity_search("foo", k=2, return_metadata=False)
assert output == TEST_RESULT
assert drop(TEST_INDEX_NAME)
# remove the test_schema.yml file
os.remove("test_schema.yml")
def test_redis_from_texts_return_keys(texts: List[str]) -> None:
"""Test from_texts_return_keys constructor."""
docsearch, keys = Redis.from_texts_return_keys(
texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL
)
output = docsearch.similarity_search("foo", k=1, return_metadata=False)
assert output == TEST_SINGLE_RESULT
assert len(keys) == len(texts)
assert drop(docsearch.index_name)
def test_redis_from_documents(texts: List[str]) -> None:
"""Test from_documents constructor."""
docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
docsearch = Redis.from_documents(docs, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
output = docsearch.similarity_search("foo", k=1, return_metadata=True)
assert "a" in output[0].metadata.keys()
assert "b" in output[0].metadata.values()
assert drop(docsearch.index_name)
def test_custom_keys(texts: List[str]) -> None:
keys_in = ["test_key_1", "test_key_2", "test_key_3"]
docsearch, keys_out = Redis.from_texts_return_keys(
texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, keys=keys_in
)
# it will append the index key prefix to all keys
assert keys_out == [docsearch.key_prefix + ":" + key for key in keys_in]
assert drop(docsearch.index_name)
def test_custom_keys_from_docs(texts: List[str]) -> None:
keys_in = ["test_key_1", "test_key_2", "test_key_3"]
docs = [Document(page_content=t, metadata={"a": "b"}) for t in texts]
docsearch = Redis.from_documents(
docs, FakeEmbeddings(), redis_url=TEST_REDIS_URL, keys=keys_in
)
client = docsearch.client
# test keys are correct
assert client.hget(docsearch.key_prefix + ":" + "test_key_1", "content")
# test metadata is stored
assert client.hget(docsearch.key_prefix + ":" + "test_key_1", "a") == bytes(
"b", "utf-8"
)
# test all keys are stored
assert client.hget(docsearch.key_prefix + ":" + "test_key_2", "content")
assert drop(docsearch.index_name)
# -- test filters -- #
@pytest.mark.parametrize(
"filter_expr, expected_length, expected_nums",
[
(RedisText("text") == "foo", 1, None),
(RedisFilter.text("text") == "foo", 1, None),
(RedisText("text") % "ba*", 2, ["bar", "baz"]),
(RedisNum("num") > 2, 1, [3]),
(RedisNum("num") < 2, 1, [1]),
(RedisNum("num") >= 2, 2, [2, 3]),
(RedisNum("num") <= 2, 2, [1, 2]),
(RedisNum("num") != 2, 2, [1, 3]),
(RedisFilter.num("num") != 2, 2, [1, 3]),
(RedisFilter.tag("category") == "a", 3, None),
(RedisFilter.tag("category") == "b", 2, None),
(RedisFilter.tag("category") == "c", 2, None),
(RedisFilter.tag("category") == ["b", "c"], 3, None),
],
ids=[
"text-filter-equals-foo",
"alternative-text-equals-foo",
"text-filter-fuzzy-match-ba",
"number-filter-greater-than-2",
"number-filter-less-than-2",
"number-filter-greater-equals-2",
"number-filter-less-equals-2",
"number-filter-not-equals-2",
"alternative-number-not-equals-2",
"tag-filter-equals-a",
"tag-filter-equals-b",
"tag-filter-equals-c",
"tag-filter-equals-b-or-c",
],
)
def test_redis_filters_1(
filter_expr: RedisFilterExpression,
expected_length: int,
expected_nums: Optional[list],
) -> None:
metadata = [
{"name": "joe", "num": 1, "text": "foo", "category": ["a", "b"]},
{"name": "john", "num": 2, "text": "bar", "category": ["a", "c"]},
{"name": "jane", "num": 3, "text": "baz", "category": ["b", "c", "a"]},
]
documents = [Document(page_content="foo", metadata=m) for m in metadata]
docsearch = Redis.from_documents(
documents, FakeEmbeddings(), redis_url=TEST_REDIS_URL
)
sim_output = docsearch.similarity_search("foo", k=3, filter=filter_expr)
mmr_output = docsearch.max_marginal_relevance_search(
"foo", k=3, fetch_k=5, filter=filter_expr
)
assert len(sim_output) == expected_length
assert len(mmr_output) == expected_length
if expected_nums is not None:
for out in sim_output:
assert (
out.metadata["text"] in expected_nums
or int(out.metadata["num"]) in expected_nums
)
for out in mmr_output:
assert (
out.metadata["text"] in expected_nums
or int(out.metadata["num"]) in expected_nums
)
assert drop(docsearch.index_name)
# -- test index specification -- #
def test_index_specification_generation() -> None:
index_schema = {
"text": [{"name": "job"}, {"name": "title"}],
"numeric": [{"name": "salary"}],
}
text = ["foo"]
meta = {"job": "engineer", "title": "principal engineer", "salary": 100000}
docs = [Document(page_content=t, metadata=meta) for t in text]
r = Redis.from_documents(
docs, FakeEmbeddings(), redis_url=TEST_REDIS_URL, index_schema=index_schema
)
output = r.similarity_search("foo", k=1, return_metadata=True)
assert output[0].metadata["job"] == "engineer"
assert output[0].metadata["title"] == "principal engineer"
assert int(output[0].metadata["salary"]) == 100000
info = convert_bytes(r.client.ft(r.index_name).info())
attributes = info["attributes"]
assert len(attributes) == 5
for attr in attributes:
d = make_dict(attr)
if d["identifier"] == "job":
assert d["type"] == "TEXT"
elif d["identifier"] == "title":
assert d["type"] == "TEXT"
elif d["identifier"] == "salary":
assert d["type"] == "NUMERIC"
elif d["identifier"] == "content":
assert d["type"] == "TEXT"
elif d["identifier"] == "content_vector":
assert d["type"] == "VECTOR"
else:
raise ValueError("Unexpected attribute in index schema")
assert drop(r.index_name)
# -- test distance metrics -- #
cosine_schema: Dict = {"distance_metric": "cosine"}
ip_schema: Dict = {"distance_metric": "IP"}
l2_schema: Dict = {"distance_metric": "L2"}
def test_cosine(texts: List[str]) -> None:
"""Test cosine distance."""
docsearch = Redis.from_texts(
texts,
FakeEmbeddings(),
redis_url=TEST_REDIS_URL,
vector_schema=cosine_schema,
)
output = docsearch.similarity_search_with_score("far", k=2)
_, score = output[1]
assert score == COSINE_SCORE
assert drop(docsearch.index_name)
def test_l2(texts: List[str]) -> None:
"""Test Flat L2 distance."""
docsearch = Redis.from_texts(
texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, vector_schema=l2_schema
)
output = docsearch.similarity_search_with_score("far", k=2)
_, score = output[1]
assert score == EUCLIDEAN_SCORE
assert drop(docsearch.index_name)
def test_ip(texts: List[str]) -> None:
"""Test inner product distance."""
docsearch = Redis.from_texts(
texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, vector_schema=ip_schema
)
output = docsearch.similarity_search_with_score("far", k=2)
_, score = output[1]
assert score == IP_SCORE
assert drop(docsearch.index_name)
def test_similarity_search_limit_distance(texts: List[str]) -> None:
"""Test similarity search limit score."""
docsearch = Redis.from_texts(
texts,
FakeEmbeddings(),
redis_url=TEST_REDIS_URL,
)
output = docsearch.similarity_search(texts[0], k=3, distance_threshold=0.1)
# can't check score but length of output should be 2
assert len(output) == 2
assert drop(docsearch.index_name)
def test_similarity_search_with_score_with_limit_distance(texts: List[str]) -> None:
"""Test similarity search with score with limit score."""
docsearch = Redis.from_texts(
texts, ConsistentFakeEmbeddings(), redis_url=TEST_REDIS_URL
)
output = docsearch.similarity_search_with_score(
texts[0], k=3, distance_threshold=0.1, return_metadata=True
)
assert len(output) == 2
for out, score in output:
if out.page_content == texts[1]:
score == COSINE_SCORE
assert drop(docsearch.index_name)
def test_max_marginal_relevance_search(texts: List[str]) -> None:
"""Test max marginal relevance search."""
docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
mmr_output = docsearch.max_marginal_relevance_search(texts[0], k=3, fetch_k=3)
sim_output = docsearch.similarity_search(texts[0], k=3)
assert mmr_output == sim_output
mmr_output = docsearch.max_marginal_relevance_search(texts[0], k=2, fetch_k=3)
assert len(mmr_output) == 2
assert mmr_output[0].page_content == texts[0]
assert mmr_output[1].page_content == texts[1]
mmr_output = docsearch.max_marginal_relevance_search(
texts[0],
k=2,
fetch_k=3,
lambda_mult=0.1, # more diversity
)
assert len(mmr_output) == 2
assert mmr_output[0].page_content == texts[0]
assert mmr_output[1].page_content == texts[2]
# if fetch_k < k, then the output will be less than k
mmr_output = docsearch.max_marginal_relevance_search(texts[0], k=3, fetch_k=2)
assert len(mmr_output) == 2
assert drop(docsearch.index_name)
def test_delete(texts: List[str]) -> None:
"""Test deleting a new document"""
docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
ids = docsearch.add_texts(["foo"])
got = docsearch.delete(ids=ids, redis_url=TEST_REDIS_URL)
assert got
assert drop(docsearch.index_name)
def test_redis_as_retriever() -> None:
texts = ["foo", "foo", "foo", "foo", "bar"]
docsearch = Redis.from_texts(
texts, ConsistentFakeEmbeddings(), redis_url=TEST_REDIS_URL
)
retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
results = retriever.invoke("foo")
assert len(results) == 3
assert all([d.page_content == "foo" for d in results])
assert drop(docsearch.index_name)
def test_redis_retriever_distance_threshold() -> None:
texts = ["foo", "bar", "baz"]
docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
retriever = docsearch.as_retriever(
search_type="similarity_distance_threshold",
search_kwargs={"k": 3, "distance_threshold": 0.1},
)
results = retriever.invoke("foo")
assert len(results) == 2
assert drop(docsearch.index_name)
def test_redis_retriever_score_threshold() -> None:
texts = ["foo", "bar", "baz"]
docsearch = Redis.from_texts(texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL)
retriever = docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 3, "score_threshold": 0.91},
)
results = retriever.invoke("foo")
assert len(results) == 2
assert drop(docsearch.index_name)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_clarifai.py | """Test Clarifai vector store functionality."""
import time
from langchain_core.documents import Document
from langchain_community.vectorstores import Clarifai
def test_clarifai_with_from_texts() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
USER_ID = "minhajul"
APP_ID = "test-lang-2"
NUMBER_OF_DOCS = 1
docsearch = Clarifai.from_texts(
user_id=USER_ID,
app_id=APP_ID,
texts=texts,
pat=None,
number_of_docs=NUMBER_OF_DOCS,
)
time.sleep(2.5)
output = docsearch.similarity_search("foo")
assert output == [Document(page_content="foo")]
def test_clarifai_with_from_documents() -> None:
"""Test end to end construction and search."""
# Initial document content and id
initial_content = "foo"
# Create an instance of Document with initial content and metadata
original_doc = Document(page_content=initial_content, metadata={"page": "0"})
USER_ID = "minhajul"
APP_ID = "test-lang-2"
NUMBER_OF_DOCS = 1
docsearch = Clarifai.from_documents(
user_id=USER_ID,
app_id=APP_ID,
documents=[original_doc],
pat=None,
number_of_docs=NUMBER_OF_DOCS,
)
time.sleep(2.5)
output = docsearch.similarity_search("foo")
assert output == [Document(page_content=initial_content, metadata={"page": "0"})]
def test_clarifai_with_metadatas() -> None:
"""Test end to end construction and search with metadata."""
texts = ["oof", "rab", "zab"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
USER_ID = "minhajul"
APP_ID = "test-lang-2"
NUMBER_OF_DOCS = 1
docsearch = Clarifai.from_texts(
user_id=USER_ID,
app_id=APP_ID,
texts=texts,
pat=None,
number_of_docs=NUMBER_OF_DOCS,
metadatas=metadatas,
)
time.sleep(2.5)
output = docsearch.similarity_search("oof", k=1)
assert output == [Document(page_content="oof", metadata={"page": "0"})]
def test_clarifai_with_metadatas_with_scores() -> None:
"""Test end to end construction and scored search."""
texts = ["oof", "rab", "zab"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
USER_ID = "minhajul"
APP_ID = "test-lang-2"
NUMBER_OF_DOCS = 1
docsearch = Clarifai.from_texts(
user_id=USER_ID,
app_id=APP_ID,
texts=texts,
pat=None,
number_of_docs=NUMBER_OF_DOCS,
metadatas=metadatas,
)
time.sleep(2.5)
output = docsearch.similarity_search_with_score("oof", k=1)
assert output[0][0] == Document(page_content="oof", metadata={"page": "0"})
assert abs(output[0][1] - 1.0) < 0.001
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_azure_cosmos_db.py | """Test AzureCosmosDBVectorSearch functionality."""
import logging
import os
from time import sleep
from typing import Any, Generator, Optional, Union
import pytest
from langchain_core.documents import Document
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores.azure_cosmos_db import (
AzureCosmosDBVectorSearch,
CosmosDBSimilarityType,
CosmosDBVectorSearchType,
)
logging.basicConfig(level=logging.DEBUG)
model_deployment = os.getenv(
"OPENAI_EMBEDDINGS_DEPLOYMENT", "smart-agent-embedding-ada"
)
model_name = os.getenv("OPENAI_EMBEDDINGS_MODEL_NAME", "text-embedding-ada-002")
INDEX_NAME = "langchain-test-index"
INDEX_NAME_VECTOR_HNSW = "langchain-test-index-hnsw"
NAMESPACE = "langchain_test_db.langchain_test_collection"
CONNECTION_STRING: str = os.environ.get("MONGODB_VCORE_URI", "")
DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")
num_lists = 3
dimensions = 1536
similarity_algorithm = CosmosDBSimilarityType.COS
kind = CosmosDBVectorSearchType.VECTOR_IVF
m = 16
ef_construction = 64
ef_search = 40
score_threshold = 0.1
application_name = "LANGCHAIN_PYTHON"
def prepare_collection() -> Any:
from pymongo import MongoClient
test_client: MongoClient = MongoClient(CONNECTION_STRING)
return test_client[DB_NAME][COLLECTION_NAME]
@pytest.fixture()
def collection() -> Any:
return prepare_collection()
@pytest.fixture()
def azure_openai_embeddings() -> Any:
openai_embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
deployment=model_deployment, model=model_name, chunk_size=1
)
return openai_embeddings
"""
This is how to run the integration tests:
cd libs/langchain
pytest tests/integration_tests/vectorstores/test_azure_cosmos_db.py
"""
class TestAzureCosmosDBVectorSearch:
@classmethod
def setup_class(cls) -> None:
if not os.getenv("OPENAI_API_KEY"):
raise ValueError("OPENAI_API_KEY environment variable is not set")
# insure the test collection is empty
collection = prepare_collection()
assert collection.count_documents({}) == 0 # type: ignore[index]
@classmethod
def teardown_class(cls) -> None:
collection = prepare_collection()
# delete all the documents in the collection
collection.delete_many({}) # type: ignore[index]
@pytest.fixture(autouse=True)
def setup(self) -> None:
collection = prepare_collection()
# delete all the documents in the collection
collection.delete_many({}) # type: ignore[index]
@pytest.fixture(scope="class", autouse=True)
def cosmos_db_url(self) -> Union[str, Generator[str, None, None]]:
"""Return the cosmos db url."""
return "805.555.1212"
def test_from_documents_cosine_distance(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
"""Test end to end construction and search."""
documents = [
Document(page_content="Dogs are tough.", metadata={"a": 1}),
Document(page_content="Cats have fluff.", metadata={"b": 1}),
Document(page_content="What is a sandwich?", metadata={"c": 1}),
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
]
vectorstore = AzureCosmosDBVectorSearch.from_documents(
documents,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME,
application_name=application_name,
)
sleep(1) # waits for Cosmos DB to save contents to the collection
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, similarity_algorithm, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_from_documents_inner_product(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
"""Test end to end construction and search."""
documents = [
Document(page_content="Dogs are tough.", metadata={"a": 1}),
Document(page_content="Cats have fluff.", metadata={"b": 1}),
Document(page_content="What is a sandwich?", metadata={"c": 1}),
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
]
vectorstore = AzureCosmosDBVectorSearch.from_documents(
documents,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME,
)
sleep(1) # waits for Cosmos DB to save contents to the collection
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, CosmosDBSimilarityType.IP, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_from_texts_cosine_distance(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"That fence is purple.",
]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, CosmosDBSimilarityType.IP, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output[0].page_content == "What is a sandwich?"
vectorstore.delete_index()
def test_from_texts_with_metadatas_cosine_distance(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, similarity_algorithm, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_from_texts_with_metadatas_delete_one(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, similarity_algorithm, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
first_document_id_object = output[0].metadata["_id"]
first_document_id = str(first_document_id_object)
vectorstore.delete_document_by_id(first_document_id)
sleep(2) # waits for the index to be updated
output2 = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output2
assert output2[0].page_content != "What is a sandwich?"
vectorstore.delete_index()
def test_from_texts_with_metadatas_delete_multiple(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, similarity_algorithm, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=5,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
first_document_id = str(output[0].metadata["_id"])
second_document_id = str(output[1].metadata["_id"])
third_document_id = str(output[2].metadata["_id"])
document_ids = [first_document_id, second_document_id, third_document_id]
vectorstore.delete(document_ids)
sleep(2) # waits for the index to be updated
output_2 = vectorstore.similarity_search(
"Sandwich",
k=5,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output_2
assert len(output) == 4 # we should see all the four documents
assert (
len(output_2) == 1
) # we should see only one document left after three have been deleted
vectorstore.delete_index()
def test_from_texts_with_metadatas_inner_product(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, CosmosDBSimilarityType.IP, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_from_texts_with_metadatas_euclidean_distance(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, CosmosDBSimilarityType.L2, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=kind,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_max_marginal_relevance_cosine_distance(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = ["foo", "foo", "fou", "foy"]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, similarity_algorithm, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
query = "foo"
output = vectorstore.max_marginal_relevance_search(
query, k=10, kind=kind, lambda_mult=0.1, score_threshold=score_threshold
)
assert len(output) == len(texts)
assert output[0].page_content == "foo"
assert output[1].page_content != "foo"
vectorstore.delete_index()
def test_max_marginal_relevance_inner_product(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = ["foo", "foo", "fou", "foy"]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists, dimensions, CosmosDBSimilarityType.IP, kind, m, ef_construction
)
sleep(2) # waits for the index to be set up
query = "foo"
output = vectorstore.max_marginal_relevance_search(
query, k=10, kind=kind, lambda_mult=0.1, score_threshold=score_threshold
)
assert len(output) == len(texts)
assert output[0].page_content == "foo"
assert output[1].page_content != "foo"
vectorstore.delete_index()
"""
Test cases for the similarity algorithm using vector-hnsw
"""
def test_from_documents_cosine_distance_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
"""Test end to end construction and search."""
documents = [
Document(page_content="Dogs are tough.", metadata={"a": 1}),
Document(page_content="Cats have fluff.", metadata={"b": 1}),
Document(page_content="What is a sandwich?", metadata={"c": 1}),
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
]
vectorstore = AzureCosmosDBVectorSearch.from_documents(
documents,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
sleep(1) # waits for Cosmos DB to save contents to the collection
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_from_documents_inner_product_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
"""Test end to end construction and search."""
documents = [
Document(page_content="Dogs are tough.", metadata={"a": 1}),
Document(page_content="Cats have fluff.", metadata={"b": 1}),
Document(page_content="What is a sandwich?", metadata={"c": 1}),
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
]
vectorstore = AzureCosmosDBVectorSearch.from_documents(
documents,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
sleep(1) # waits for Cosmos DB to save contents to the collection
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_from_texts_cosine_distance_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"That fence is purple.",
]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output[0].page_content == "What is a sandwich?"
vectorstore.delete_index()
def test_from_texts_with_metadatas_cosine_distance_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_from_texts_with_metadatas_delete_one_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
first_document_id_object = output[0].metadata["_id"]
first_document_id = str(first_document_id_object)
vectorstore.delete_document_by_id(first_document_id)
sleep(2) # waits for the index to be updated
output2 = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output2
assert output2[0].page_content != "What is a sandwich?"
vectorstore.delete_index()
def test_from_texts_with_metadatas_delete_multiple_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=5,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
first_document_id = str(output[0].metadata["_id"])
second_document_id = str(output[1].metadata["_id"])
third_document_id = str(output[2].metadata["_id"])
document_ids = [first_document_id, second_document_id, third_document_id]
vectorstore.delete(document_ids)
sleep(2) # waits for the index to be updated
output_2 = vectorstore.similarity_search(
"Sandwich",
k=5,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output_2
assert len(output) == 4 # we should see all the four documents
assert (
len(output_2) == 1
) # we should see only one document left after three have been deleted
vectorstore.delete_index()
def test_from_texts_with_metadatas_inner_product_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"The fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas=metadatas,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
output = vectorstore.similarity_search(
"Sandwich",
k=1,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
ef_search=ef_search,
score_threshold=score_threshold,
)
assert output
assert output[0].page_content == "What is a sandwich?"
assert output[0].metadata["c"] == 1
vectorstore.delete_index()
def test_max_marginal_relevance_cosine_distance_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = ["foo", "foo", "fou", "foy"]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
query = "foo"
output = vectorstore.max_marginal_relevance_search(
query,
k=10,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
lambda_mult=0.1,
score_threshold=score_threshold,
)
assert len(output) == len(texts)
assert output[0].page_content == "foo"
assert output[1].page_content != "foo"
vectorstore.delete_index()
def test_max_marginal_relevance_inner_product_vector_hnsw(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
texts = ["foo", "foo", "fou", "foy"]
vectorstore = AzureCosmosDBVectorSearch.from_texts(
texts,
azure_openai_embeddings,
collection=collection,
index_name=INDEX_NAME_VECTOR_HNSW,
)
# Create the IVF index that will be leveraged later for vector search
vectorstore.create_index(
num_lists,
dimensions,
similarity_algorithm,
CosmosDBVectorSearchType.VECTOR_HNSW,
m,
ef_construction,
)
sleep(2) # waits for the index to be set up
query = "foo"
output = vectorstore.max_marginal_relevance_search(
query,
k=10,
kind=CosmosDBVectorSearchType.VECTOR_HNSW,
lambda_mult=0.1,
score_threshold=score_threshold,
)
assert len(output) == len(texts)
assert output[0].page_content == "foo"
assert output[1].page_content != "foo"
vectorstore.delete_index()
@staticmethod
def invoke_delete_with_no_args(
azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> Optional[bool]:
vectorstore: AzureCosmosDBVectorSearch = (
AzureCosmosDBVectorSearch.from_connection_string(
CONNECTION_STRING,
NAMESPACE,
azure_openai_embeddings,
index_name=INDEX_NAME,
application_name=application_name,
)
)
return vectorstore.delete()
@staticmethod
def invoke_delete_by_id_with_no_args(
azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
vectorstore: AzureCosmosDBVectorSearch = (
AzureCosmosDBVectorSearch.from_connection_string(
CONNECTION_STRING,
NAMESPACE,
azure_openai_embeddings,
index_name=INDEX_NAME,
application_name=application_name,
)
)
vectorstore.delete_document_by_id()
def test_invalid_arguments_to_delete(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
with pytest.raises(ValueError) as exception_info:
self.invoke_delete_with_no_args(azure_openai_embeddings, collection)
assert str(exception_info.value) == "No document ids provided to delete."
def test_no_arguments_to_delete_by_id(
self, azure_openai_embeddings: OpenAIEmbeddings, collection: Any
) -> None:
with pytest.raises(Exception) as exception_info:
self.invoke_delete_by_id_with_no_args(
azure_openai_embeddings=azure_openai_embeddings, collection=collection
)
assert str(exception_info.value) == "No document id provided to delete."
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_azure_cosmos_db_no_sql.py | """Test AzureCosmosDBNoSqlVectorSearch functionality."""
import logging
import os
from time import sleep
from typing import Any
import pytest
from langchain_core.documents import Document
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores.azure_cosmos_db_no_sql import (
AzureCosmosDBNoSqlVectorSearch,
)
logging.basicConfig(level=logging.DEBUG)
model_deployment = os.getenv(
"OPENAI_EMBEDDINGS_DEPLOYMENT", "smart-agent-embedding-ada"
)
model_name = os.getenv("OPENAI_EMBEDDINGS_MODEL_NAME", "text-embedding-ada-002")
# Host and Key for CosmosDB No SQl
HOST = os.environ.get("HOST")
KEY = os.environ.get("KEY")
database_name = "langchain_python_db"
container_name = "langchain_python_container"
@pytest.fixture()
def cosmos_client() -> Any:
from azure.cosmos import CosmosClient
return CosmosClient(HOST, KEY)
@pytest.fixture()
def partition_key() -> Any:
from azure.cosmos import PartitionKey
return PartitionKey(path="/id")
@pytest.fixture()
def azure_openai_embeddings() -> Any:
openai_embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
deployment=model_deployment, model=model_name, chunk_size=1
)
return openai_embeddings
def safe_delete_database(cosmos_client: Any) -> None:
cosmos_client.delete_database(database_name)
def get_vector_indexing_policy(embedding_type: str) -> dict:
return {
"indexingMode": "consistent",
"includedPaths": [{"path": "/*"}],
"excludedPaths": [{"path": '/"_etag"/?'}],
"vectorIndexes": [{"path": "/embedding", "type": embedding_type}],
}
def get_vector_embedding_policy(
distance_function: str, data_type: str, dimensions: int
) -> dict:
return {
"vectorEmbeddings": [
{
"path": "/embedding",
"dataType": data_type,
"dimensions": dimensions,
"distanceFunction": distance_function,
}
]
}
class TestAzureCosmosDBNoSqlVectorSearch:
def test_from_documents_cosine_distance(
self,
cosmos_client: Any,
partition_key: Any,
azure_openai_embeddings: OpenAIEmbeddings,
) -> None:
"""Test end to end construction and search."""
documents = [
Document(page_content="Dogs are tough.", metadata={"a": 1}),
Document(page_content="Cats have fluff.", metadata={"b": 1}),
Document(page_content="What is a sandwich?", metadata={"c": 1}),
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
]
store = AzureCosmosDBNoSqlVectorSearch.from_documents(
documents,
azure_openai_embeddings,
cosmos_client=cosmos_client,
database_name=database_name,
container_name=container_name,
vector_embedding_policy=get_vector_embedding_policy(
"cosine", "float32", 400
),
indexing_policy=get_vector_indexing_policy("flat"),
cosmos_container_properties={"partition_key": partition_key},
cosmos_database_properties={},
)
sleep(1) # waits for Cosmos DB to save contents to the collection
output = store.similarity_search("Dogs", k=2)
assert output
assert output[0].page_content == "Dogs are tough."
safe_delete_database(cosmos_client)
def test_from_texts_cosine_distance_delete_one(
self,
cosmos_client: Any,
partition_key: Any,
azure_openai_embeddings: OpenAIEmbeddings,
) -> None:
texts = [
"Dogs are tough.",
"Cats have fluff.",
"What is a sandwich?",
"That fence is purple.",
]
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
store = AzureCosmosDBNoSqlVectorSearch.from_texts(
texts,
azure_openai_embeddings,
metadatas,
cosmos_client=cosmos_client,
database_name=database_name,
container_name=container_name,
vector_embedding_policy=get_vector_embedding_policy(
"cosine", "float32", 400
),
indexing_policy=get_vector_indexing_policy("flat"),
cosmos_container_properties={"partition_key": partition_key},
cosmos_database_properties={},
)
sleep(1) # waits for Cosmos DB to save contents to the collection
output = store.similarity_search("Dogs", k=1)
assert output
assert output[0].page_content == "Dogs are tough."
# delete one document
store.delete_document_by_id(str(output[0].metadata["id"]))
sleep(2)
output2 = store.similarity_search("Dogs", k=1)
assert output2
assert output2[0].page_content != "Dogs are tough."
safe_delete_database(cosmos_client)
def test_from_documents_cosine_distance_with_filtering(
self,
cosmos_client: Any,
partition_key: Any,
azure_openai_embeddings: OpenAIEmbeddings,
) -> None:
"""Test end to end construction and search."""
documents = [
Document(page_content="Dogs are tough.", metadata={"a": 1}),
Document(page_content="Cats have fluff.", metadata={"a": 1}),
Document(page_content="What is a sandwich?", metadata={"c": 1}),
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
]
store = AzureCosmosDBNoSqlVectorSearch.from_documents(
documents,
azure_openai_embeddings,
cosmos_client=cosmos_client,
database_name=database_name,
container_name=container_name,
vector_embedding_policy=get_vector_embedding_policy(
"cosine", "float32", 400
),
indexing_policy=get_vector_indexing_policy("flat"),
cosmos_container_properties={"partition_key": partition_key},
cosmos_database_properties={},
)
sleep(1) # waits for Cosmos DB to save contents to the collection
output = store.similarity_search("Dogs", k=4)
assert len(output) == 4
assert output[0].page_content == "Dogs are tough."
assert output[0].metadata["a"] == 1
pre_filter = {
"where_clause": "WHERE c.metadata.a=1",
}
output = store.similarity_search(
"Dogs", k=4, pre_filter=pre_filter, with_embedding=True
)
assert len(output) == 2
assert output[0].page_content == "Dogs are tough."
assert output[0].metadata["a"] == 1
pre_filter = {
"where_clause": "WHERE c.metadata.a=1",
"limit_offset_clause": "OFFSET 0 LIMIT 1",
}
output = store.similarity_search("Dogs", k=4, pre_filter=pre_filter)
assert len(output) == 1
assert output[0].page_content == "Dogs are tough."
assert output[0].metadata["a"] == 1
safe_delete_database(cosmos_client)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_analyticdb.py | """Test PGVector functionality."""
import os
from typing import List
from langchain_core.documents import Document
from langchain_community.vectorstores.analyticdb import AnalyticDB
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
CONNECTION_STRING = AnalyticDB.connection_string_from_db_params(
driver=os.environ.get("PG_DRIVER", "psycopg2cffi"),
host=os.environ.get("PG_HOST", "localhost"),
port=int(os.environ.get("PG_PORT", "5432")),
database=os.environ.get("PG_DATABASE", "postgres"),
user=os.environ.get("PG_USER", "postgres"),
password=os.environ.get("PG_PASSWORD", "postgres"),
)
ADA_TOKEN_COUNT = 1536
class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
"""Fake embeddings functionality for testing."""
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return simple embeddings."""
return [
[float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
]
def embed_query(self, text: str) -> List[float]:
"""Return simple embeddings."""
return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
def test_analyticdb() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = AnalyticDB.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_analyticdb_with_engine_args() -> None:
engine_args = {"pool_recycle": 3600, "pool_size": 50}
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = AnalyticDB.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
engine_args=engine_args,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_analyticdb_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = AnalyticDB.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"page": "0"})]
def test_analyticdb_with_metadatas_with_scores() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = AnalyticDB.from_texts(
texts=texts,
collection_name="test_collection",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1)
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
def test_analyticdb_with_filter_match() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = AnalyticDB.from_texts(
texts=texts,
collection_name="test_collection_filter",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "0"})
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
def test_analyticdb_with_filter_distant_match() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = AnalyticDB.from_texts(
texts=texts,
collection_name="test_collection_filter",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
print(output) # noqa: T201
assert output == [(Document(page_content="baz", metadata={"page": "2"}), 4.0)]
def test_analyticdb_with_filter_no_match() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = AnalyticDB.from_texts(
texts=texts,
collection_name="test_collection_filter",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "5"})
assert output == []
def test_analyticdb_delete() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
ids = ["fooid", "barid", "bazid"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = AnalyticDB.from_texts(
texts=texts,
collection_name="test_collection_delete",
embedding=FakeEmbeddingsWithAdaDimension(),
metadatas=metadatas,
connection_string=CONNECTION_STRING,
ids=ids,
pre_delete_collection=True,
)
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
print(output) # noqa: T201
assert output == [(Document(page_content="baz", metadata={"page": "2"}), 4.0)]
docsearch.delete(ids=ids)
output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
assert output == []
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_xata.py | """Test Xata vector store functionality.
Before running this test, please create a Xata database by following
the instructions from:
https://python.langchain.com/docs/integrations/vectorstores/xata
"""
import os
from langchain_core.documents import Document
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores.xata import XataVectorStore
class TestXata:
@classmethod
def setup_class(cls) -> None:
assert os.getenv("XATA_API_KEY"), "XATA_API_KEY environment variable is not set"
assert os.getenv("XATA_DB_URL"), "XATA_DB_URL environment variable is not set"
def test_similarity_search_without_metadata(
self, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end constructions and search without metadata."""
texts = ["foo", "bar", "baz"]
docsearch = XataVectorStore.from_texts(
api_key=os.getenv("XATA_API_KEY"),
db_url=os.getenv("XATA_DB_URL"),
texts=texts,
embedding=embedding_openai,
)
docsearch.wait_for_indexing(ndocs=3)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
docsearch.delete(delete_all=True)
def test_similarity_search_with_metadata(
self, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search with a metadata filter.
This test requires a column named "a" of type integer to be present
in the Xata table."""
texts = ["foo", "foo", "foo"]
metadatas = [{"a": i} for i in range(len(texts))]
docsearch = XataVectorStore.from_texts(
api_key=os.getenv("XATA_API_KEY"),
db_url=os.getenv("XATA_DB_URL"),
texts=texts,
embedding=embedding_openai,
metadatas=metadatas,
)
docsearch.wait_for_indexing(ndocs=3)
output = docsearch.similarity_search("foo", k=1, filter={"a": 1})
assert output == [Document(page_content="foo", metadata={"a": 1})]
docsearch.delete(delete_all=True)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_dashvector.py | from time import sleep
from langchain_core.documents import Document
from langchain_community.vectorstores import DashVector
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
texts = ["foo", "bar", "baz"]
ids = ["1", "2", "3"]
def test_dashvector_from_texts() -> None:
dashvector = DashVector.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
ids=ids,
)
# the vector insert operation is async by design, we wait here a bit for the
# insertion to complete.
sleep(0.5)
output = dashvector.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
def test_dashvector_with_text_with_metadatas() -> None:
metadatas = [{"meta": i} for i in range(len(texts))]
dashvector = DashVector.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
ids=ids,
)
# the vector insert operation is async by design, we wait here a bit for the
# insertion to complete.
sleep(0.5)
output = dashvector.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", metadata={"meta": 0})]
def test_dashvector_search_with_filter() -> None:
metadatas = [{"meta": i} for i in range(len(texts))]
dashvector = DashVector.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
ids=ids,
)
# the vector insert operation is async by design, we wait here a bit for the
# insertion to complete.
sleep(0.5)
output = dashvector.similarity_search("foo", filter="meta=2")
assert output == [Document(page_content="baz", metadata={"meta": 2})]
def test_dashvector_search_with_scores() -> None:
dashvector = DashVector.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
ids=ids,
)
# the vector insert operation is async by design, we wait here a bit for the
# insertion to complete.
sleep(0.5)
output = dashvector.similarity_search_with_relevance_scores("foo")
docs, scores = zip(*output)
assert scores[0] < scores[1] < scores[2]
assert list(docs) == [
Document(page_content="foo"),
Document(page_content="bar"),
Document(page_content="baz"),
]
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/test_azuresearch.py | import os
import time
import pytest
from dotenv import load_dotenv
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores.azuresearch import AzureSearch
load_dotenv()
model = os.getenv("OPENAI_EMBEDDINGS_ENGINE_DOC", "text-embedding-ada-002")
# Vector store settings
vector_store_address: str = os.getenv("AZURE_SEARCH_ENDPOINT", "")
vector_store_password: str = os.getenv("AZURE_SEARCH_ADMIN_KEY", "")
access_token: str = os.getenv("AZURE_SEARCH_ACCESS_TOKEN", "")
index_name: str = "embeddings-vector-store-test"
@pytest.fixture
def similarity_search_test() -> None:
"""Test end to end construction and search."""
# Create Embeddings
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(model=model, chunk_size=1)
# Create Vector store
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
azure_ad_access_token=access_token,
index_name=index_name,
embedding_function=embeddings.embed_query,
)
# Add texts to vector store and perform a similarity search
vector_store.add_texts(
["Test 1", "Test 2", "Test 3"],
[
{"title": "Title 1", "any_metadata": "Metadata 1"},
{"title": "Title 2", "any_metadata": "Metadata 2"},
{"title": "Title 3", "any_metadata": "Metadata 3"},
],
)
time.sleep(1)
res = vector_store.similarity_search(query="Test 1", k=3)
assert len(res) == 3
def from_text_similarity_search_test() -> None:
"""Test end to end construction and search."""
# Create Embeddings
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(model=model, chunk_size=1)
# Create Vector store
vector_store: AzureSearch = AzureSearch.from_texts(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
texts=["Test 1", "Test 2", "Test 3"],
embedding=embeddings,
)
time.sleep(1)
# Perform a similarity search
res = vector_store.similarity_search(query="Test 1", k=3)
assert len(res) == 3
def test_semantic_hybrid_search() -> None:
"""Test end to end construction and search."""
# Create Embeddings
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(model=model, chunk_size=1)
# Create Vector store
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
azure_ad_access_token=access_token,
index_name=index_name,
embedding_function=embeddings.embed_query,
semantic_configuration_name="default",
)
# Add texts to vector store and perform a semantic hybrid search
vector_store.add_texts(
["Test 1", "Test 2", "Test 3"],
[
{"title": "Title 1", "any_metadata": "Metadata 1"},
{"title": "Title 2", "any_metadata": "Metadata 2"},
{"title": "Title 3", "any_metadata": "Metadata 3"},
],
)
time.sleep(1)
res = vector_store.semantic_hybrid_search(query="What's Azure Search?", k=3)
assert len(res) == 3
|
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0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docarray/test_hnsw.py | from pathlib import Path
from typing import List
import numpy as np
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores.docarray import DocArrayHnswSearch
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
@pytest.fixture
def texts() -> List[str]:
return ["foo", "bar", "baz"]
def test_from_texts(texts: List[str], tmp_path: Path) -> None:
"""Test end to end construction and simple similarity search."""
docsearch = DocArrayHnswSearch.from_texts(
texts,
FakeEmbeddings(),
work_dir=str(tmp_path),
n_dim=10,
)
assert docsearch.doc_index.num_docs() == 3
def test_add_texts(texts: List[str], tmp_path: Path) -> None:
"""Test end to end construction and simple similarity search."""
docsearch = DocArrayHnswSearch.from_params(
work_dir=str(tmp_path),
n_dim=10,
embedding=FakeEmbeddings(),
)
docsearch.add_texts(texts=texts)
assert docsearch.doc_index.num_docs() == 3
@pytest.mark.parametrize("metric", ["cosine", "l2"])
def test_sim_search(metric: str, texts: List[str], tmp_path: Path) -> None:
"""Test end to end construction and simple similarity search."""
hnsw_vec_store = DocArrayHnswSearch.from_texts(
texts,
FakeEmbeddings(),
work_dir=str(tmp_path),
n_dim=10,
dist_metric=metric,
index=True,
)
output = hnsw_vec_store.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
@pytest.mark.parametrize("metric", ["cosine", "l2"])
def test_sim_search_all_configurations(
metric: str, texts: List[str], tmp_path: Path
) -> None:
"""Test end to end construction and simple similarity search."""
hnsw_vec_store = DocArrayHnswSearch.from_texts(
texts,
FakeEmbeddings(),
work_dir=str(tmp_path),
dist_metric=metric,
n_dim=10,
max_elements=8,
ef_construction=300,
ef=20,
M=8,
allow_replace_deleted=False,
num_threads=2,
)
output = hnsw_vec_store.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
@pytest.mark.parametrize("metric", ["cosine", "l2"])
def test_sim_search_by_vector(metric: str, texts: List[str], tmp_path: Path) -> None:
"""Test end to end construction and similarity search by vector."""
hnsw_vec_store = DocArrayHnswSearch.from_texts(
texts,
FakeEmbeddings(),
work_dir=str(tmp_path),
n_dim=10,
dist_metric=metric,
)
embedding = [1.0] * 10
output = hnsw_vec_store.similarity_search_by_vector(embedding, k=1)
assert output == [Document(page_content="bar")]
@pytest.mark.parametrize("metric", ["cosine", "l2"])
def test_sim_search_with_score(metric: str, tmp_path: Path) -> None:
"""Test end to end construction and similarity search with score."""
texts = ["foo", "bar", "baz"]
hnsw_vec_store = DocArrayHnswSearch.from_texts(
texts,
FakeEmbeddings(),
work_dir=str(tmp_path),
n_dim=10,
dist_metric=metric,
)
output = hnsw_vec_store.similarity_search_with_score("foo", k=1)
assert len(output) == 1
out_doc, out_score = output[0]
assert out_doc == Document(page_content="foo")
assert np.isclose(out_score, 0.0, atol=1.0e-6)
def test_sim_search_with_score_for_ip_metric(texts: List[str], tmp_path: Path) -> None:
"""
Test end to end construction and similarity search with score for ip
(inner-product) metric.
"""
hnsw_vec_store = DocArrayHnswSearch.from_texts(
texts,
FakeEmbeddings(),
work_dir=str(tmp_path),
n_dim=10,
dist_metric="ip",
)
output = hnsw_vec_store.similarity_search_with_score("foo", k=3)
assert len(output) == 3
for result in output:
assert result[1] == -8.0
@pytest.mark.parametrize("metric", ["cosine", "l2"])
def test_max_marginal_relevance_search(
metric: str, texts: List[str], tmp_path: Path
) -> None:
"""Test MRR search."""
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = DocArrayHnswSearch.from_texts(
texts,
FakeEmbeddings(),
metadatas=metadatas,
dist_metric=metric,
work_dir=str(tmp_path),
n_dim=10,
)
output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3)
assert output == [
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="bar", metadata={"page": 1}),
]
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docarray/test_in_memory.py | from pathlib import Path
from typing import List
import numpy as np
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores.docarray import DocArrayInMemorySearch
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
@pytest.fixture
def texts() -> List[str]:
return ["foo", "bar", "baz"]
def test_from_texts(texts: List[str]) -> None:
"""Test end to end construction and simple similarity search."""
docsearch = DocArrayInMemorySearch.from_texts(
texts,
FakeEmbeddings(),
)
assert isinstance(docsearch, DocArrayInMemorySearch)
assert docsearch.doc_index.num_docs() == 3
def test_add_texts(texts: List[str], tmp_path: Path) -> None:
"""Test end to end construction and simple similarity search."""
docsearch = DocArrayInMemorySearch.from_params(FakeEmbeddings())
assert isinstance(docsearch, DocArrayInMemorySearch)
assert docsearch.doc_index.num_docs() == 0
docsearch.add_texts(texts=texts)
assert docsearch.doc_index.num_docs() == 3
@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
def test_sim_search(metric: str, texts: List[str]) -> None:
"""Test end to end construction and simple similarity search."""
texts = ["foo", "bar", "baz"]
in_memory_vec_store = DocArrayInMemorySearch.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
metric=metric,
)
output = in_memory_vec_store.similarity_search("foo", k=1)
assert output == [Document(page_content="foo")]
@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
def test_sim_search_with_score(metric: str, texts: List[str]) -> None:
"""Test end to end construction and similarity search with score."""
in_memory_vec_store = DocArrayInMemorySearch.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
metric=metric,
)
output = in_memory_vec_store.similarity_search_with_score("foo", k=1)
out_doc, out_score = output[0]
assert out_doc == Document(page_content="foo")
expected_score = 0.0 if "dist" in metric else 1.0
assert np.isclose(out_score, expected_score, atol=1.0e-6)
@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
def test_sim_search_by_vector(metric: str, texts: List[str]) -> None:
"""Test end to end construction and similarity search by vector."""
in_memory_vec_store = DocArrayInMemorySearch.from_texts(
texts=texts,
embedding=FakeEmbeddings(),
metric=metric,
)
embedding = [1.0] * 10
output = in_memory_vec_store.similarity_search_by_vector(embedding, k=1)
assert output == [Document(page_content="bar")]
@pytest.mark.parametrize("metric", ["cosine_sim", "euclidean_dist", "sqeuclidean_dist"])
def test_max_marginal_relevance_search(metric: str, texts: List[str]) -> None:
"""Test MRR search."""
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = DocArrayInMemorySearch.from_texts(
texts, FakeEmbeddings(), metadatas=metadatas, metric=metric
)
output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3)
assert output == [
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="bar", metadata={"page": 1}),
]
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/common.py | from typing import List
from langchain_core.documents import Document
def qdrant_is_not_running() -> bool:
"""Check if Qdrant is not running."""
import requests
try:
response = requests.get("http://localhost:6333", timeout=10.0)
response_json = response.json()
return response_json.get("title") != "qdrant - vector search engine"
except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
return True
def assert_documents_equals(actual: List[Document], expected: List[Document]): # type: ignore[no-untyped-def]
assert len(actual) == len(expected)
for actual_doc, expected_doc in zip(actual, expected):
assert actual_doc.page_content == expected_doc.page_content
assert "_id" in actual_doc.metadata
assert "_collection_name" in actual_doc.metadata
actual_doc.metadata.pop("_id")
actual_doc.metadata.pop("_collection_name")
assert actual_doc.metadata == expected_doc.metadata
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py | import tempfile
import uuid
from typing import Optional
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores import Qdrant
from langchain_community.vectorstores.qdrant import QdrantException
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.common import (
assert_documents_equals,
qdrant_is_not_running,
)
def test_qdrant_from_texts_stores_duplicated_texts() -> None:
"""Test end to end Qdrant.from_texts stores duplicated texts separately."""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["abc", "abc"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
path=str(tmpdir),
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_from_texts_stores_ids(
batch_size: int, vector_name: Optional[str]
) -> None:
"""Test end to end Qdrant.from_texts stores provided ids."""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
vec_store = Qdrant.from_texts(
["abc", "def"],
ConsistentFakeEmbeddings(),
ids=ids,
collection_name=collection_name,
path=str(tmpdir),
batch_size=batch_size,
vector_name=vector_name,
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_from_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
"""Test end to end Qdrant.from_texts stores named vectors if name is provided."""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
def test_qdrant_from_texts_reuses_same_collection(vector_name: Optional[str]) -> None:
"""Test if Qdrant.from_texts reuses the same collection"""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
embeddings = ConsistentFakeEmbeddings()
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
embeddings,
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
vec_store = Qdrant.from_texts(
["foo", "bar"],
embeddings,
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 7 == client.count(collection_name).count
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
def test_qdrant_from_texts_raises_error_on_different_dimensionality(
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.from_texts raises an exception if dimensionality does not match"""
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
with pytest.raises(QdrantException):
Qdrant.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
@pytest.mark.parametrize(
["first_vector_name", "second_vector_name"],
[
(None, "custom-vector"),
("custom-vector", None),
("my-first-vector", "my-second_vector"),
],
)
def test_qdrant_from_texts_raises_error_on_different_vector_name(
first_vector_name: Optional[str],
second_vector_name: Optional[str],
) -> None:
"""Test if Qdrant.from_texts raises an exception if vector name does not match"""
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
path=str(tmpdir),
vector_name=first_vector_name,
)
del vec_store
with pytest.raises(QdrantException):
Qdrant.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
path=str(tmpdir),
vector_name=second_vector_name,
)
def test_qdrant_from_texts_raises_error_on_different_distance() -> None:
"""Test if Qdrant.from_texts raises an exception if distance does not match"""
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
path=str(tmpdir),
distance_func="Cosine",
)
del vec_store
with pytest.raises(QdrantException) as excinfo:
Qdrant.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
path=str(tmpdir),
distance_func="Euclid",
)
expected_message = (
"configured for COSINE similarity, but requested EUCLID. Please set "
"`distance_func` parameter to `COSINE`"
)
assert expected_message in str(excinfo.value)
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
def test_qdrant_from_texts_recreates_collection_on_force_recreate(
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.from_texts recreates the collection even if config mismatches"""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
vec_store = Qdrant.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
force_recreate=True,
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
def test_qdrant_from_texts_stores_metadatas(
batch_size: int, content_payload_key: str, metadata_payload_key: str
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
)
output = docsearch.similarity_search("foo", k=1)
assert_documents_equals(
output, [Document(page_content="foo", metadata={"page": 0})]
)
@pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running")
def test_from_texts_passed_optimizers_config_and_on_disk_payload() -> None:
from qdrant_client import models
collection_name = uuid.uuid4().hex
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
optimizers_config = models.OptimizersConfigDiff(memmap_threshold=1000)
vec_store = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
optimizers_config=optimizers_config,
on_disk_payload=True,
on_disk=True,
collection_name=collection_name,
)
collection_info = vec_store.client.get_collection(collection_name)
assert collection_info.config.params.vectors.on_disk is True # type: ignore
assert collection_info.config.optimizer_config.memmap_threshold == 1000
assert collection_info.config.params.on_disk_payload is True
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py | import uuid
from typing import Optional
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_documents_extends_existing_collection(
batch_size: int, vector_name: Optional[str]
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch: Qdrant = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
batch_size=batch_size,
vector_name=vector_name,
)
new_texts = ["foobar", "foobaz"]
docsearch.add_documents(
[Document(page_content=content) for content in new_texts], batch_size=batch_size
)
output = docsearch.similarity_search("foobar", k=1)
# ConsistentFakeEmbeddings return the same query embedding as the first document
# embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the
# same as "foobar" embedding
assert_documents_equals(output, [Document(page_content="foobar")])
@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
"""Test end to end Qdrant.add_texts returns unique ids."""
docsearch: Qdrant = Qdrant.from_texts(
["foobar"],
ConsistentFakeEmbeddings(),
location=":memory:",
batch_size=batch_size,
)
ids = docsearch.add_texts(["foo", "bar", "baz"])
assert 3 == len(ids)
assert 3 == len(set(ids))
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> None:
"""Test end to end Qdrant.add_texts stores duplicated texts separately."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
if vector_name is not None:
vectors_config = {vector_name: vectors_config} # type: ignore[assignment]
client.recreate_collection(collection_name, vectors_config=vectors_config)
vec_store = Qdrant(
client,
collection_name,
embeddings=ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
assert 2 == len(set(ids))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
"""Test end to end Qdrant.add_texts stores provided ids."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
client.recreate_collection(
collection_name,
vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
)
vec_store = Qdrant(client, collection_name, ConsistentFakeEmbeddings())
returned_ids = vec_store.add_texts(["abc", "def"], ids=ids, batch_size=batch_size)
assert all(first == second for first, second in zip(ids, returned_ids))
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
"""Test end to end Qdrant.add_texts stores named vectors if name is provided."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
collection_name = uuid.uuid4().hex
client = QdrantClient(":memory:")
client.recreate_collection(
collection_name,
vectors_config={
vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
},
)
vec_store = Qdrant(
client,
collection_name,
ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
vec_store.add_texts(["lorem", "ipsum", "dolor", "sit", "amet"])
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/test_delete.py | # TODO: implement tests for delete
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py | from typing import Optional
import numpy as np
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
output = docsearch.similarity_search("foo", k=1)
assert_documents_equals(actual=output, expected=[Document(page_content="foo")])
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_by_vector(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = docsearch.similarity_search_by_vector(embeddings, k=1)
assert_documents_equals(output, [Document(page_content="foo")])
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_score_by_vector(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = docsearch.similarity_search_with_score_by_vector(embeddings, k=1)
assert len(output) == 1
document, score = output[0]
assert_documents_equals(actual=[document], expected=[Document(page_content="foo")])
assert score >= 0
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_filters(
batch_size: int, vector_name: Optional[str]
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
batch_size=batch_size,
vector_name=vector_name,
)
output = docsearch.similarity_search(
"foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}}
)
assert_documents_equals(
actual=output,
expected=[
Document(
page_content="bar",
metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_relevance_score_no_threshold(
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
vector_name=vector_name,
)
output = docsearch.similarity_search_with_relevance_scores(
"foo", k=3, score_threshold=None
)
assert len(output) == 3
for i in range(len(output)):
assert round(output[i][1], 2) >= 0
assert round(output[i][1], 2) <= 1
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_relevance_score_with_threshold(
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
vector_name=vector_name,
)
score_threshold = 0.98
kwargs = {"score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_relevance_score_with_threshold_and_filter(
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
vector_name=vector_name,
)
score_threshold = 0.99 # for almost exact match
# test negative filter condition
negative_filter = {"page": 1, "metadata": {"page": 2, "pages": [3]}}
kwargs = {"filter": negative_filter, "score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 0
# test positive filter condition
positive_filter = {"page": 0, "metadata": {"page": 1, "pages": [2]}}
kwargs = {"filter": positive_filter, "score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_filters_with_qdrant_filters(
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
from qdrant_client.http import models as rest
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
vector_name=vector_name,
)
qdrant_filter = rest.Filter(
must=[
rest.FieldCondition(
key="metadata.page",
match=rest.MatchValue(value=1),
),
rest.FieldCondition(
key="metadata.details.page",
match=rest.MatchValue(value=2),
),
rest.FieldCondition(
key="metadata.details.pages",
match=rest.MatchAny(any=[3]),
),
]
)
output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
assert_documents_equals(
actual=output,
expected=[
Document(
page_content="bar",
metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_relevance_scores(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
assert all(
(1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py | from typing import Optional
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "test_content"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "test_metadata"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_max_marginal_relevance_search(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and MRR search."""
from qdrant_client import models
filter = models.Filter(
must=[
models.FieldCondition(
key=f"{metadata_payload_key}.page",
match=models.MatchValue(
value=2,
),
),
],
)
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
distance_func="EUCLID", # Euclid distance used to avoid normalization
)
output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0
)
assert_documents_equals(
output,
[
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="baz", metadata={"page": 2}),
],
)
output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter
)
assert_documents_equals(
output,
[Document(page_content="baz", metadata={"page": 2})],
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/test_embedding_interface.py | import uuid
from typing import Callable, Optional
import pytest
from langchain_core.embeddings import Embeddings
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
@pytest.mark.parametrize(
["embeddings", "embedding_function"],
[
(ConsistentFakeEmbeddings(), None),
(ConsistentFakeEmbeddings().embed_query, None),
(None, ConsistentFakeEmbeddings().embed_query),
],
)
def test_qdrant_embedding_interface(
embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
) -> None:
"""Test Qdrant may accept different types for embeddings."""
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
Qdrant(
client,
collection_name,
embeddings=embeddings,
embedding_function=embedding_function,
)
@pytest.mark.parametrize(
["embeddings", "embedding_function"],
[
(ConsistentFakeEmbeddings(), ConsistentFakeEmbeddings().embed_query),
(None, None),
],
)
def test_qdrant_embedding_interface_raises_value_error(
embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
) -> None:
"""Test Qdrant requires only one method for embeddings."""
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
with pytest.raises(ValueError):
Qdrant(
client,
collection_name,
embeddings=embeddings,
embedding_function=embedding_function,
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_existing_collection.py | import tempfile
import uuid
import pytest
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_from_existing_collection_uses_same_collection(vector_name: str) -> None:
"""Test if the Qdrant.from_existing_collection reuses the same collection."""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
docs = ["foo"]
qdrant = Qdrant.from_texts(
docs,
embedding=ConsistentFakeEmbeddings(),
path=str(tmpdir),
collection_name=collection_name,
vector_name=vector_name,
)
del qdrant
qdrant = Qdrant.from_existing_collection(
embedding=ConsistentFakeEmbeddings(),
path=str(tmpdir),
collection_name=collection_name,
vector_name=vector_name,
)
qdrant.add_texts(["baz", "bar"])
del qdrant
client = QdrantClient(path=str(tmpdir))
assert 3 == client.count(collection_name).count
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py | import uuid
from typing import Optional
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores import Qdrant
from langchain_community.vectorstores.qdrant import QdrantException
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import (
qdrant_locations,
)
from tests.integration_tests.vectorstores.qdrant.common import (
assert_documents_equals,
qdrant_is_not_running,
)
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_from_texts_stores_duplicated_texts(qdrant_location: str) -> None:
"""Test end to end Qdrant.afrom_texts stores duplicated texts separately."""
collection_name = uuid.uuid4().hex
vec_store = await Qdrant.afrom_texts(
["abc", "abc"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
location=qdrant_location,
)
client = vec_store.client
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_from_texts_stores_ids(
batch_size: int, vector_name: Optional[str], qdrant_location: str
) -> None:
"""Test end to end Qdrant.afrom_texts stores provided ids."""
collection_name = uuid.uuid4().hex
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
vec_store = await Qdrant.afrom_texts(
["abc", "def"],
ConsistentFakeEmbeddings(),
ids=ids,
collection_name=collection_name,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
client = vec_store.client
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_from_texts_stores_embeddings_as_named_vectors(
vector_name: str,
qdrant_location: str,
) -> None:
"""Test end to end Qdrant.afrom_texts stores named vectors if name is provided."""
collection_name = uuid.uuid4().hex
vec_store = await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
vector_name=vector_name,
location=qdrant_location,
)
client = vec_store.client
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
@pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running")
async def test_qdrant_from_texts_reuses_same_collection(
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.afrom_texts reuses the same collection"""
collection_name = uuid.uuid4().hex
embeddings = ConsistentFakeEmbeddings()
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
embeddings,
collection_name=collection_name,
vector_name=vector_name,
)
vec_store = await Qdrant.afrom_texts(
["foo", "bar"],
embeddings,
collection_name=collection_name,
vector_name=vector_name,
)
client = vec_store.client
assert 7 == client.count(collection_name).count
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
@pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running")
async def test_qdrant_from_texts_raises_error_on_different_dimensionality(
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.afrom_texts raises an exception if dimensionality does not
match"""
collection_name = uuid.uuid4().hex
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
vector_name=vector_name,
)
with pytest.raises(QdrantException):
await Qdrant.afrom_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
vector_name=vector_name,
)
@pytest.mark.parametrize(
["first_vector_name", "second_vector_name"],
[
(None, "custom-vector"),
("custom-vector", None),
("my-first-vector", "my-second_vector"),
],
)
@pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running")
async def test_qdrant_from_texts_raises_error_on_different_vector_name(
first_vector_name: Optional[str],
second_vector_name: Optional[str],
) -> None:
"""Test if Qdrant.afrom_texts raises an exception if vector name does not match"""
collection_name = uuid.uuid4().hex
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
vector_name=first_vector_name,
)
with pytest.raises(QdrantException):
await Qdrant.afrom_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
vector_name=second_vector_name,
)
@pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running")
async def test_qdrant_from_texts_raises_error_on_different_distance() -> None:
"""Test if Qdrant.afrom_texts raises an exception if distance does not match"""
collection_name = uuid.uuid4().hex
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
distance_func="Cosine",
)
with pytest.raises(QdrantException):
await Qdrant.afrom_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
distance_func="Euclid",
)
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
@pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running")
async def test_qdrant_from_texts_recreates_collection_on_force_recreate(
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.afrom_texts recreates the collection even if config mismatches"""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
vector_name=vector_name,
)
await Qdrant.afrom_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
vector_name=vector_name,
force_recreate=True,
)
client = QdrantClient()
assert 2 == client.count(collection_name).count
vector_params = client.get_collection(collection_name).config.params.vectors
if vector_name is not None:
vector_params = vector_params[vector_name] # type: ignore[index]
assert 5 == vector_params.size # type: ignore[union-attr]
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_from_texts_stores_metadatas(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = await Qdrant.afrom_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
location=qdrant_location,
)
output = await docsearch.asimilarity_search("foo", k=1)
assert_documents_equals(
output, [Document(page_content="foo", metadata={"page": 0})]
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_add_texts.py | import uuid
from typing import Optional
import pytest
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import (
qdrant_locations,
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_aadd_texts_returns_all_ids(
batch_size: int, qdrant_location: str
) -> None:
"""Test end to end Qdrant.aadd_texts returns unique ids."""
docsearch: Qdrant = Qdrant.from_texts(
["foobar"],
ConsistentFakeEmbeddings(),
batch_size=batch_size,
location=qdrant_location,
)
ids = await docsearch.aadd_texts(["foo", "bar", "baz"])
assert 3 == len(ids)
assert 3 == len(set(ids))
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_aadd_texts_stores_duplicated_texts(
vector_name: Optional[str], qdrant_location: str
) -> None:
"""Test end to end Qdrant.aadd_texts stores duplicated texts separately."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
client = QdrantClient(location=qdrant_location)
collection_name = uuid.uuid4().hex
vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
if vector_name is not None:
vectors_config = {vector_name: vectors_config} # type: ignore[assignment]
client.recreate_collection(collection_name, vectors_config=vectors_config)
vec_store = Qdrant(
client,
collection_name,
embeddings=ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
ids = await vec_store.aadd_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
assert 2 == len(set(ids))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_aadd_texts_stores_ids(
batch_size: int, qdrant_location: str
) -> None:
"""Test end to end Qdrant.aadd_texts stores provided ids."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
client = QdrantClient(location=qdrant_location)
collection_name = uuid.uuid4().hex
client.recreate_collection(
collection_name,
vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
)
vec_store = Qdrant(client, collection_name, ConsistentFakeEmbeddings())
returned_ids = await vec_store.aadd_texts(
["abc", "def"], ids=ids, batch_size=batch_size
)
assert all(first == second for first, second in zip(ids, returned_ids))
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_aadd_texts_stores_embeddings_as_named_vectors(
vector_name: str, qdrant_location: str
) -> None:
"""Test end to end Qdrant.aadd_texts stores named vectors if name is provided."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
collection_name = uuid.uuid4().hex
client = QdrantClient(location=qdrant_location)
client.recreate_collection(
collection_name,
vectors_config={
vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
},
)
vec_store = Qdrant(
client,
collection_name,
ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
await vec_store.aadd_texts(["lorem", "ipsum", "dolor", "sit", "amet"])
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py | from typing import Optional
import numpy as np
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import (
qdrant_locations,
)
from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
output = await docsearch.asimilarity_search("foo", k=1)
assert_documents_equals(output, [Document(page_content="foo")])
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_by_vector(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = await docsearch.asimilarity_search_by_vector(embeddings, k=1)
assert_documents_equals(output, [Document(page_content="foo")])
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_with_score_by_vector(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = await docsearch.asimilarity_search_with_score_by_vector(embeddings, k=1)
assert len(output) == 1
document, score = output[0]
assert_documents_equals([document], [Document(page_content="foo")])
assert score >= 0
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_filters(
batch_size: int, vector_name: Optional[str], qdrant_location: str
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
output = await docsearch.asimilarity_search(
"foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}}
)
assert_documents_equals(
output,
[
Document(
page_content="bar",
metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_with_relevance_score_no_threshold(
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
vector_name=vector_name,
location=qdrant_location,
)
output = await docsearch.asimilarity_search_with_relevance_scores(
"foo", k=3, score_threshold=None
)
assert len(output) == 3
for i in range(len(output)):
assert round(output[i][1], 2) >= 0
assert round(output[i][1], 2) <= 1
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_with_relevance_score_with_threshold(
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
vector_name=vector_name,
location=qdrant_location,
)
score_threshold = 0.98
kwargs = {"score_threshold": score_threshold}
output = await docsearch.asimilarity_search_with_relevance_scores(
"foo", k=3, **kwargs
)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_similarity_search_with_relevance_score_with_threshold_and_filter(
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
vector_name=vector_name,
location=qdrant_location,
)
score_threshold = 0.99 # for almost exact match
# test negative filter condition
negative_filter = {"page": 1, "metadata": {"page": 2, "pages": [3]}}
kwargs = {"filter": negative_filter, "score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 0
# test positive filter condition
positive_filter = {"page": 0, "metadata": {"page": 1, "pages": [2]}}
kwargs = {"filter": positive_filter, "score_threshold": score_threshold}
output = await docsearch.asimilarity_search_with_relevance_scores(
"foo", k=3, **kwargs
)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_filters_with_qdrant_filters(
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
from qdrant_client.http import models as rest
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
vector_name=vector_name,
location=qdrant_location,
)
qdrant_filter = rest.Filter(
must=[
rest.FieldCondition(
key="metadata.page",
match=rest.MatchValue(value=1),
),
rest.FieldCondition(
key="metadata.details.page",
match=rest.MatchValue(value=2),
),
rest.FieldCondition(
key="metadata.details.pages",
match=rest.MatchAny(any=[3]),
),
]
)
output = await docsearch.asimilarity_search("foo", k=1, filter=qdrant_filter)
assert_documents_equals(
output,
[
Document(
page_content="bar",
metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_with_relevance_scores(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: str,
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
output = await docsearch.asimilarity_search_with_relevance_scores("foo", k=3)
assert all(
(1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py | from typing import Optional
import pytest
from langchain_core.documents import Document
from langchain_community.vectorstores import Qdrant
from tests.integration_tests.vectorstores.fake_embeddings import (
ConsistentFakeEmbeddings,
)
from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import (
qdrant_locations,
)
from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "test_content"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "test_metadata"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_max_marginal_relevance_search(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and MRR search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
distance_func="EUCLID", # Euclid distance used to avoid normalization
)
output = await docsearch.amax_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0
)
assert_documents_equals(
output,
[
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="baz", metadata={"page": 2}),
],
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/fixtures.py | import logging
from typing import List
from tests.integration_tests.vectorstores.qdrant.common import qdrant_is_not_running
logger = logging.getLogger(__name__)
def qdrant_locations() -> List[str]:
if qdrant_is_not_running():
logger.warning("Running Qdrant async tests in memory mode only.")
return [":memory:"]
return ["http://localhost:6333", ":memory:"]
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/meilisearch.yaml | version: "3.8"
services:
meilisearch:
image: getmeili/meilisearch:latest
environment:
- MEILI_MASTER_KEY=${MEILI_MASTER_KEY:-masterKey}
- MEILI_NO_ANALYTICS=${MEILI_NO_ANALYTICS:-true}
- MEILI_ENV=${MEILI_ENV:-development}
ports:
- ${MEILI_PORT:-7700}:7700
restart: unless-stopped
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:7700"]
interval: 10s
timeout: 5s
retries: 5
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/elasticsearch.yml | version: "3"
services:
elasticsearch:
image: docker.elastic.co/elasticsearch/elasticsearch:8.9.0 # https://www.docker.elastic.co/r/elasticsearch/elasticsearch
environment:
- discovery.type=single-node
- xpack.security.enabled=false # security has been disabled, so no login or password is required.
- xpack.security.http.ssl.enabled=false
- xpack.license.self_generated.type=trial
ports:
- "9200:9200"
healthcheck:
test:
[
"CMD-SHELL",
"curl --silent --fail http://localhost:9200/_cluster/health || exit 1",
]
interval: 10s
retries: 60
kibana:
image: docker.elastic.co/kibana/kibana:8.9.0
environment:
- ELASTICSEARCH_URL=http://elasticsearch:9200
ports:
- "5601:5601"
healthcheck:
test:
[
"CMD-SHELL",
"curl --silent --fail http://localhost:5601/login || exit 1",
]
interval: 10s
retries: 60
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/pgvector.yml | version: "3.8"
services:
pgvector:
image: ankane/pgvector:latest
environment:
POSTGRES_DB: ${PGVECTOR_DB:-postgres}
POSTGRES_USER: ${PGVECTOR_USER:-postgres}
POSTGRES_PASSWORD: ${PGVECTOR_PASSWORD:-postgres}
ports:
- ${PGVECTOR_PORT:-5432}:5432
restart: unless-stopped
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:5432"]
interval: 10s
timeout: 5s
retries: 5 |
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/neo4j.yml | version: "3.8"
services:
neo4j:
image: neo4j:5.11.0
restart: on-failure:0
hostname: neo4j-test
container_name: neo4j-test
ports:
- 7474:7474
- 7687:7687
environment:
- NEO4J_AUTH=neo4j/pleaseletmein
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/weaviate.yml | version: '3.4'
services:
weaviate:
command:
- --host
- 0.0.0.0
- --port
- '8080'
- --scheme
- http
image: semitechnologies/weaviate:1.18.2
ports:
- 8080:8080
restart: on-failure:0
environment:
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'text2vec-openai'
ENABLE_MODULES: 'text2vec-openai'
OPENAI_APIKEY: '${OPENAI_API_KEY}'
CLUSTER_HOSTNAME: 'node1'
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/opensearch.sh | #/bin/sh
# references:
# https://github.com/opensearch-project/documentation-website/blob/2.10/assets/examples/docker-compose.yml
# https://opensearch.org/docs/latest/security/configuration/disable/
cd opensearch
docker build --tag=opensearch-dashboards-no-security -f opensearch-dashboards-no-security.Dockerfile .
docker compose -f opensearch.yml up |
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan.sh | #/bin/sh
cd infinispan
docker compose up
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/aperturedb.yml | services:
aperturedb:
image: aperturedata/aperturedb-standalone:latest
restart: on-failure:0
container_name: aperturedb
ports:
- 55555:55555
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/docker-compose.yaml | version: "3.7"
services:
infinispan:
image: quay.io/infinispan/server:15.0
ports:
- '11222:11222'
- '11232:11232'
- '11242:11242'
deploy:
resources:
limits:
memory: 25Gb
volumes:
- ./conf:/user-config
command: -c /user-config/infinispan.xml
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/infinispan.xml | <infinispan
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="urn:infinispan:config:15.0 https://infinispan.org/schemas/infinispan-config-15.0.xsd
urn:infinispan:server:15.0 https://infinispan.org/schemas/infinispan-server-15.0.xsd"
xmlns="urn:infinispan:config:15.0"
xmlns:server="urn:infinispan:server:15.0">
<cache-container name="default" statistics="true">
<transport cluster="${infinispan.cluster.name:cluster}" stack="${infinispan.cluster.stack:tcp}" node-name="${infinispan.node.name:}"/>
</cache-container>
<server xmlns="urn:infinispan:server:15.0">
<interfaces>
<interface name="public">
<inet-address value="${infinispan.bind.address:127.0.0.1}"/>
</interface>
</interfaces>
<socket-bindings default-interface="public" port-offset="${infinispan.socket.binding.port-offset:0}">
<socket-binding name="default" port="${infinispan.bind.port:11222}"/>
<socket-binding name="authenticated" port="11232"/>
<socket-binding name="auth-tls" port="11242"/>
</socket-bindings>
<security>
<credential-stores>
<credential-store name="credentials" path="credentials.pfx">
<clear-text-credential clear-text="secret"/>
</credential-store>
</credential-stores>
<security-realms>
<security-realm name="default">
<properties-realm groups-attribute="Roles">
<user-properties path="/user-config/users.properties"/>
<group-properties path="/user-config/groups.properties"/>
</properties-realm>
</security-realm>
<security-realm name="tls">
<!-- Uncomment to enable TLS on the realm -->
<server-identities>
<ssl>
<keystore path="application.keystore"
password="password" alias="server"
generate-self-signed-certificate-host="localhost"/>
</ssl>
</server-identities>
<properties-realm groups-attribute="Roles">
<user-properties path="/user-config/users.properties"/>
<group-properties path="/user-config/groups.properties"/>
</properties-realm>
</security-realm>
</security-realms>
</security>
<endpoints>
<endpoint socket-binding="default"/>
<endpoint socket-binding="authenticated" security-realm="default"/>
<endpoint socket-binding="auth-tls" security-realm="tls"/>
</endpoints>
</server>
</infinispan>
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/groups.properties | #Fri May 03 10:19:58 CEST 2024
user=ADMIN,admin
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/infinispan/conf/users.properties | #$REALM_NAME=default$
#$ALGORITHM=encrypted$
#Fri May 03 10:19:58 CEST 2024
user=scram-sha-1\:BYGcIAws2gznU/kpezoSb1VQNVd+YMX9r+9SAINFoZtPHaHTAQ\=\=;scram-sha-256\:BYGcIAwRiWiD+8f7dyQEs1Wsum/64MOcjGJ2UcmZFQB6DZJqwRDJ4NrvII4NttmxlA\=\=;scram-sha-384\:BYGcIAz+Eud65N8GWK4TMwhSCZpeE5EFSdynywdryQj3ZwBEgv+KF8hRUuGxiq3EyRxsby6w7DHK3CICGZLsPrM\=;scram-sha-512\:BYGcIAwWxVY9DHn42kHydivyU3s9LSPmyfPPJkIFYyt/XsMASFHGoy5rzk4ahX4HjpJgb+NjdCwhGfi33CY0azUIrn439s62Yg5mq9i+ISto;digest-md5\:AgR1c2VyB2RlZmF1bHSYYyzPjRDR7MhrsdFSK03P;digest-sha\:AgR1c2VyB2RlZmF1bHTga5gDNnNYh7/2HqhBVOdUHjBzhw\=\=;digest-sha-256\:AgR1c2VyB2RlZmF1bHTig5qZQIxqtJBTUp3EMh5UIFoS4qOhz9Uk5aOW9ZKCfw\=\=;digest-sha-384\:AgR1c2VyB2RlZmF1bHT01pAN/pRMLS5afm4Q9S0kuLlA0NokuP8F0AISTwXCb1E8RMsFHlBVPOa5rC6Nyso\=;digest-sha-512\:AgR1c2VyB2RlZmF1bHTi+cHn1Ez2Ze41CvPXb9eP/7JmRys7m1f5qPMQWhAmDOuuUXNWEG4yKSI9k2EZgQvMKTd5hDbR24ul1BsYP8X5;
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/opensearch/opensearch.yml | version: '3'
services:
opensearch-node1: # This is also the hostname of the container within the Docker network (i.e. http://opensearch-node1/)
image: opensearchproject/opensearch:2.10.0
container_name: opensearch-node1
environment:
- node.name=opensearch-node1 # Name the node that will run in this container
- plugins.security.disabled=true # security has been disabled, so no login or password is required.
- discovery.type=single-node
- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m" # Set min and max JVM heap sizes to at least 50% of system RAM
volumes:
- opensearch-data1:/usr/share/opensearch/data # Creates volume called opensearch-data1 and mounts it to the container
ports:
- 9200:9200 # REST API
- 9600:9600 # Performance Analyzer
networks:
- opensearch-net # All of the containers will join the same Docker bridge network
# opensearch-dashboards does not work if OpenSearch cluster is not secure.
# to use dashboards, build opensearch-dashboards-no-security first by running
#
opensearch-dashboards:
image: opensearch-dashboards-no-security
container_name: opensearch-dashboards
ports:
- 5601:5601 # Map host port 5601 to container port 5601
expose:
- "5601" # Expose port 5601 for web access to OpenSearch Dashboards
environment:
OPENSEARCH_HOSTS: '["http://opensearch-node1:9200"]' # Define the OpenSearch nodes that OpenSearch Dashboards will query
networks:
- opensearch-net
volumes:
opensearch-data1:
networks:
opensearch-net:
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/opensearch/opensearch_dashboards.yml | server.name: opensearch-dashboards
server.host: "0.0.0.0"
opensearch.hosts: http://localhost:9200 |
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/opensearch/opensearch-dashboards-no-security.Dockerfile | FROM opensearchproject/opensearch-dashboards:2.10.0
RUN /usr/share/opensearch-dashboards/bin/opensearch-dashboards-plugin remove securityDashboards
COPY --chown=opensearch-dashboards:opensearch-dashboards opensearch_dashboards.yml /usr/share/opensearch-dashboards/config/ |
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/aerospike/aerospike.conf | # Aerospike database configuration file for use with systemd.
service {
cluster-name quote-demo
proto-fd-max 15000
}
logging {
file /var/log/aerospike/aerospike.log {
context any info
}
# Send log messages to stdout
console {
context any info
context query critical
}
}
network {
service {
address any
port 3000
}
heartbeat {
mode multicast
multicast-group 239.1.99.222
port 9918
interval 150
timeout 10
}
fabric {
port 3001
}
info {
port 3003
}
}
namespace test {
replication-factor 1
nsup-period 60
storage-engine device {
file /opt/aerospike/data/test.dat
filesize 1G
}
}
namespace proximus-meta {
replication-factor 1
nsup-period 100
storage-engine memory {
data-size 1G
}
}
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/aerospike/docker-compose.yml | services:
aerospike:
image: aerospike/aerospike-server-enterprise:7.0.0.2
ports:
- "3000:3000"
networks:
- aerospike-test
volumes:
- .:/opt/aerospike/etc/aerospike
command:
- "--config-file"
- "/opt/aerospike/etc/aerospike/aerospike.conf"
proximus:
image: aerospike/aerospike-proximus:0.4.0
ports:
- "5002:5002"
networks:
- aerospike-test
volumes:
- .:/etc/aerospike-proximus
networks:
aerospike-test: {}
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/docker-compose/aerospike/aerospike-proximus.yml | cluster:
# Unique identifier for this cluster.
cluster-name: aerospike-vector
# The Proximus service listening ports, TLS and network interface.
service:
ports:
5002: {}
# Uncomment for local debugging
advertised-listeners:
default:
address: 127.0.0.1
port: 5002
# Management API listening ports, TLS and network interface.
manage:
ports:
5040: {}
# Intra cluster interconnect listening ports, TLS and network interface.
interconnect:
ports:
5001: {}
# Target Aerospike cluster
aerospike:
seeds:
- aerospike:
port: 3000
# The logging properties.
logging:
enable-console-logging: true
levels:
metrics-ticker: off
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/fixtures/filtering_test_cases.py | """Module contains test cases for testing filtering of documents in vector stores."""
from langchain_core.documents import Document
metadatas = [
{
"name": "adam",
"date": "2021-01-01",
"count": 1,
"is_active": True,
"tags": ["a", "b"],
"location": [1.0, 2.0],
"id": 1,
"height": 10.0, # Float column
"happiness": 0.9, # Float column
"sadness": 0.1, # Float column
},
{
"name": "bob",
"date": "2021-01-02",
"count": 2,
"is_active": False,
"tags": ["b", "c"],
"location": [2.0, 3.0],
"id": 2,
"height": 5.7, # Float column
"happiness": 0.8, # Float column
"sadness": 0.1, # Float column
},
{
"name": "jane",
"date": "2021-01-01",
"count": 3,
"is_active": True,
"tags": ["b", "d"],
"location": [3.0, 4.0],
"id": 3,
"height": 2.4, # Float column
"happiness": None,
# Sadness missing intentionally
},
]
texts = ["id {id}".format(id=metadata["id"]) for metadata in metadatas]
DOCUMENTS = [
Document(page_content=text, metadata=metadata)
for text, metadata in zip(texts, metadatas)
]
TYPE_1_FILTERING_TEST_CASES = [
# These tests only involve equality checks
(
{"id": 1},
[1],
),
# String field
(
# check name
{"name": "adam"},
[1],
),
# Boolean fields
(
{"is_active": True},
[1, 3],
),
(
{"is_active": False},
[2],
),
# And semantics for top level filtering
(
{"id": 1, "is_active": True},
[1],
),
(
{"id": 1, "is_active": False},
[],
),
]
TYPE_2_FILTERING_TEST_CASES = [
# These involve equality checks and other operators
# like $ne, $gt, $gte, $lt, $lte, $not
(
{"id": 1},
[1],
),
(
{"id": {"$ne": 1}},
[2, 3],
),
(
{"id": {"$gt": 1}},
[2, 3],
),
(
{"id": {"$gte": 1}},
[1, 2, 3],
),
(
{"id": {"$lt": 1}},
[],
),
(
{"id": {"$lte": 1}},
[1],
),
# Repeat all the same tests with name (string column)
(
{"name": "adam"},
[1],
),
(
{"name": "bob"},
[2],
),
(
{"name": {"$eq": "adam"}},
[1],
),
(
{"name": {"$ne": "adam"}},
[2, 3],
),
# And also gt, gte, lt, lte relying on lexicographical ordering
(
{"name": {"$gt": "jane"}},
[],
),
(
{"name": {"$gte": "jane"}},
[3],
),
(
{"name": {"$lt": "jane"}},
[1, 2],
),
(
{"name": {"$lte": "jane"}},
[1, 2, 3],
),
(
{"is_active": {"$eq": True}},
[1, 3],
),
(
{"is_active": {"$ne": True}},
[2],
),
# Test float column.
(
{"height": {"$gt": 5.0}},
[1, 2],
),
(
{"height": {"$gte": 5.0}},
[1, 2],
),
(
{"height": {"$lt": 5.0}},
[3],
),
(
{"height": {"$lte": 5.8}},
[2, 3],
),
]
TYPE_3_FILTERING_TEST_CASES = [
# These involve usage of AND and OR operators
(
{"$or": [{"id": 1}, {"id": 2}]},
[1, 2],
),
(
{"$or": [{"id": 1}, {"name": "bob"}]},
[1, 2],
),
(
{"$and": [{"id": 1}, {"id": 2}]},
[],
),
(
{"$or": [{"id": 1}, {"id": 2}, {"id": 3}]},
[1, 2, 3],
),
]
TYPE_4_FILTERING_TEST_CASES = [
# These involve special operators like $in, $nin, $between
# Test between
(
{"id": {"$between": (1, 2)}},
[1, 2],
),
(
{"id": {"$between": (1, 1)}},
[1],
),
(
{"name": {"$in": ["adam", "bob"]}},
[1, 2],
),
]
TYPE_5_FILTERING_TEST_CASES = [
# These involve special operators like $like, $ilike that
# may be specified to certain databases.
(
{"name": {"$like": "a%"}},
[1],
),
(
{"name": {"$like": "%a%"}}, # adam and jane
[1, 3],
),
]
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores | lc_public_repos/langchain/libs/community/tests/integration_tests/vectorstores/fixtures/sharks.txt | Sharks are a group of elasmobranch fish characterized by a cartilaginous skeleton, five to seven gill slits on the sides of the head, and pectoral fins that are not fused to the head. Modern sharks are classified within the clade Selachimorpha (or Selachii) and are the sister group to the Batoidea (rays and kin). Some sources extend the term "shark" as an informal category including extinct members of Chondrichthyes (cartilaginous fish) with a shark-like morphology, such as hybodonts and xenacanths. Shark-like chondrichthyans such as Cladoselache and Doliodus first appeared in the Devonian Period (419-359 Ma), though some fossilized chondrichthyan-like scales are as old as the Late Ordovician (458-444 Ma). The oldest modern sharks (selachians) are known from the Early Jurassic, about 200 Ma.
Sharks range in size from the small dwarf lanternshark (Etmopterus perryi), a deep sea species that is only 17 centimetres (6.7 in) in length, to the whale shark (Rhincodon typus), the largest fish in the world, which reaches approximately 12 metres (40 ft) in length. They are found in all seas and are common to depths up to 2,000 metres (6,600 ft). They generally do not live in freshwater, although there are a few known exceptions, such as the bull shark and the river shark, which can be found in both seawater and freshwater.[3] Sharks have a covering of dermal denticles that protects their skin from damage and parasites in addition to improving their fluid dynamics. They have numerous sets of replaceable teeth.
Several species are apex predators, which are organisms that are at the top of their food chain. Select examples include the tiger shark, blue shark, great white shark, mako shark, thresher shark, and hammerhead shark.
Sharks are caught by humans for shark meat or shark fin soup. Many shark populations are threatened by human activities. Since 1970, shark populations have been reduced by 71%, mostly from overfishing. |
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/document_compressors/test_rankllm_rerank.py | """Test rankllm reranker."""
from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank
def test_rankllm_reranker_init() -> None:
"""Test the RankLLM reranker initializes correctly."""
RankLLMRerank()
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/document_compressors/test_volcengine_rerank.py | from langchain_core.documents import Document
from langchain_community.document_compressors.volcengine_rerank import (
VolcengineRerank,
)
def test_rerank() -> None:
reranker = VolcengineRerank()
docs = [
Document(page_content="量子计算是计算科学的一个前沿领域"),
Document(page_content="预训练语言模型的发展给文本排序模型带来了新的进展"),
Document(
page_content="文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序"
),
Document(page_content="random text for nothing"),
]
compressed = reranker.compress_documents(
query="什么是文本排序模型",
documents=docs,
)
assert len(compressed) == 3, "default top_n is 3"
assert compressed[0].page_content == docs[2].page_content, "rerank works"
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/document_compressors/test_infinity_rerank.py | from langchain_core.documents import Document
from langchain_community.document_compressors.infinity_rerank import (
InfinityRerank,
)
def test_rerank() -> None:
reranker = InfinityRerank()
docs = [
Document(
page_content=(
"This is a document not related to the python package infinity_emb, "
"hence..."
)
),
Document(page_content="Paris is in France!"),
Document(
page_content=(
"infinity_emb is a package for sentence embeddings and rerankings using"
" transformer models in Python!"
)
),
Document(page_content="random text for nothing"),
]
compressed = reranker.compress_documents(
query="What is the python package infinity_emb?",
documents=docs,
)
assert len(compressed) == 3, "default top_n is 3"
assert compressed[0].page_content == docs[2].page_content, "rerank works"
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/document_compressors/__init__.py | """Test document compressor integrations."""
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/document_compressors/test_dashscope_rerank.py | from langchain_core.documents import Document
from langchain_community.document_compressors.dashscope_rerank import (
DashScopeRerank,
)
def test_rerank() -> None:
reranker = DashScopeRerank(api_key=None)
docs = [
Document(page_content="量子计算是计算科学的一个前沿领域"),
Document(page_content="预训练语言模型的发展给文本排序模型带来了新的进展"),
Document(
page_content="文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序"
),
Document(page_content="random text for nothing"),
]
compressed = reranker.compress_documents(
query="什么是文本排序模型",
documents=docs,
)
assert len(compressed) == 3, "default top_n is 3"
assert compressed[0].page_content == docs[2].page_content, "rerank works"
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/storage/test_sql.py | """Implement integration tests for Redis storage."""
import pytest
from sqlalchemy import Engine, create_engine, text
from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
from langchain_community.storage import SQLStore
pytest.importorskip("sqlalchemy")
@pytest.fixture
def sql_engine() -> Engine:
"""Yield redis client."""
return create_engine(url="sqlite://", echo=True)
@pytest.fixture
def sql_aengine() -> AsyncEngine:
"""Yield redis client."""
return create_async_engine(url="sqlite+aiosqlite:///:memory:", echo=True)
def test_mget(sql_engine: Engine) -> None:
"""Test mget method."""
store = SQLStore(engine=sql_engine, namespace="test")
store.create_schema()
keys = ["key1", "key2"]
with sql_engine.connect() as session:
session.execute(
text(
"insert into langchain_key_value_stores ('namespace', 'key', 'value') "
"values('test','key1',:value)"
).bindparams(value=b"value1"),
)
session.execute(
text(
"insert into langchain_key_value_stores ('namespace', 'key', 'value') "
"values('test','key2',:value)"
).bindparams(value=b"value2"),
)
session.commit()
result = store.mget(keys)
assert result == [b"value1", b"value2"]
async def test_amget(sql_aengine: AsyncEngine) -> None:
"""Test mget method."""
store = SQLStore(engine=sql_aengine, namespace="test")
await store.acreate_schema()
keys = ["key1", "key2"]
async with sql_aengine.connect() as session:
await session.execute(
text(
"insert into langchain_key_value_stores ('namespace', 'key', 'value') "
"values('test','key1',:value)"
).bindparams(value=b"value1"),
)
await session.execute(
text(
"insert into langchain_key_value_stores ('namespace', 'key', 'value') "
"values('test','key2',:value)"
).bindparams(value=b"value2"),
)
await session.commit()
result = await store.amget(keys)
assert result == [b"value1", b"value2"]
def test_mset(sql_engine: Engine) -> None:
"""Test that multiple keys can be set."""
store = SQLStore(engine=sql_engine, namespace="test")
store.create_schema()
key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
store.mset(key_value_pairs)
with sql_engine.connect() as session:
result = session.exec_driver_sql("select * from langchain_key_value_stores")
assert result.keys() == ["namespace", "key", "value"]
data = [(row[0], row[1]) for row in result]
assert data == [("test", "key1"), ("test", "key2")]
session.commit()
async def test_amset(sql_aengine: AsyncEngine) -> None:
"""Test that multiple keys can be set."""
store = SQLStore(engine=sql_aengine, namespace="test")
await store.acreate_schema()
key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
await store.amset(key_value_pairs)
async with sql_aengine.connect() as session:
result = await session.exec_driver_sql(
"select * from langchain_key_value_stores"
)
assert result.keys() == ["namespace", "key", "value"]
data = [(row[0], row[1]) for row in result]
assert data == [("test", "key1"), ("test", "key2")]
await session.commit()
def test_mdelete(sql_engine: Engine) -> None:
"""Test that deletion works as expected."""
store = SQLStore(engine=sql_engine, namespace="test")
store.create_schema()
keys = ["key1", "key2"]
with sql_engine.connect() as session:
session.execute(
text(
"insert into langchain_key_value_stores ('namespace', 'key', 'value') "
"values('test','key1',:value)"
).bindparams(value=b"value1"),
)
session.execute(
text(
"insert into langchain_key_value_stores ('namespace', 'key', 'value') "
"values('test','key2',:value)"
).bindparams(value=b"value2"),
)
session.commit()
store.mdelete(keys)
with sql_engine.connect() as session:
result = session.exec_driver_sql("select * from langchain_key_value_stores")
assert result.keys() == ["namespace", "key", "value"]
data = [row for row in result]
assert data == []
session.commit()
async def test_amdelete(sql_aengine: AsyncEngine) -> None:
"""Test that deletion works as expected."""
store = SQLStore(engine=sql_aengine, namespace="test")
await store.acreate_schema()
keys = ["key1", "key2"]
async with sql_aengine.connect() as session:
await session.execute(
text(
"insert into langchain_key_value_stores ('namespace', 'key', 'value') "
"values('test','key1',:value)"
).bindparams(value=b"value1"),
)
await session.execute(
text(
"insert into langchain_key_value_stores ('namespace', 'key', 'value') "
"values('test','key2',:value)"
).bindparams(value=b"value2"),
)
await session.commit()
await store.amdelete(keys)
async with sql_aengine.connect() as session:
result = await session.exec_driver_sql(
"select * from langchain_key_value_stores"
)
assert result.keys() == ["namespace", "key", "value"]
data = [row for row in result]
assert data == []
await session.commit()
def test_yield_keys(sql_engine: Engine) -> None:
store = SQLStore(engine=sql_engine, namespace="test")
store.create_schema()
key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
store.mset(key_value_pairs)
assert sorted(store.yield_keys()) == ["key1", "key2"]
assert sorted(store.yield_keys(prefix="key")) == ["key1", "key2"]
assert sorted(store.yield_keys(prefix="lang")) == []
async def test_ayield_keys(sql_aengine: AsyncEngine) -> None:
store = SQLStore(engine=sql_aengine, namespace="test")
await store.acreate_schema()
key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
await store.amset(key_value_pairs)
assert sorted([k async for k in store.ayield_keys()]) == ["key1", "key2"]
assert sorted([k async for k in store.ayield_keys(prefix="key")]) == [
"key1",
"key2",
]
assert sorted([k async for k in store.ayield_keys(prefix="lang")]) == []
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/storage/test_mongodb.py | from typing import Generator, Tuple
import pytest
from langchain_core.documents import Document
from langchain_tests.integration_tests.base_store import BaseStoreSyncTests
from langchain_community.storage.mongodb import MongoDBByteStore, MongoDBStore
pytest.importorskip("pymongo")
@pytest.fixture
def mongo_store() -> Generator:
import mongomock
# mongomock creates a mock MongoDB instance for testing purposes
with mongomock.patch(servers=(("localhost", 27017),)):
yield MongoDBStore("mongodb://localhost:27017/", "test_db", "test_collection")
def test_mset_and_mget(mongo_store: MongoDBStore) -> None:
doc1 = Document(page_content="doc1")
doc2 = Document(page_content="doc2")
# Set documents in the store
mongo_store.mset([("key1", doc1), ("key2", doc2)])
# Get documents from the store
retrieved_docs = mongo_store.mget(["key1", "key2"])
assert retrieved_docs[0] and retrieved_docs[0].page_content == "doc1"
assert retrieved_docs[1] and retrieved_docs[1].page_content == "doc2"
def test_yield_keys(mongo_store: MongoDBStore) -> None:
mongo_store.mset(
[
("key1", Document(page_content="doc1")),
("key2", Document(page_content="doc2")),
("another_key", Document(page_content="other")),
]
)
# Test without prefix
keys = list(mongo_store.yield_keys())
assert set(keys) == {"key1", "key2", "another_key"}
# Test with prefix
keys_with_prefix = list(mongo_store.yield_keys(prefix="key"))
assert set(keys_with_prefix) == {"key1", "key2"}
def test_mdelete(mongo_store: MongoDBStore) -> None:
mongo_store.mset(
[
("key1", Document(page_content="doc1")),
("key2", Document(page_content="doc2")),
]
)
# Delete single document
mongo_store.mdelete(["key1"])
remaining_docs = list(mongo_store.yield_keys())
assert "key1" not in remaining_docs
assert "key2" in remaining_docs
# Delete multiple documents
mongo_store.mdelete(["key2"])
remaining_docs = list(mongo_store.yield_keys())
assert len(remaining_docs) == 0
def test_init_errors() -> None:
with pytest.raises(ValueError):
MongoDBStore("", "", "")
class TestMongoDBStore(BaseStoreSyncTests):
@pytest.fixture
def three_values(self) -> Tuple[bytes, bytes, bytes]: # <-- Provide 3
return b"foo", b"bar", b"buzz"
@pytest.fixture
def kv_store(self) -> MongoDBByteStore:
import mongomock
# mongomock creates a mock MongoDB instance for testing purposes
with mongomock.patch(servers=(("localhost", 27017),)):
return MongoDBByteStore(
"mongodb://localhost:27017/", "test_db", "test_collection"
)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/storage/test_astradb.py | """Implement integration tests for AstraDB storage."""
from __future__ import annotations
import os
from typing import TYPE_CHECKING
import pytest
from langchain_community.storage.astradb import AstraDBByteStore, AstraDBStore
from langchain_community.utilities.astradb import SetupMode
if TYPE_CHECKING:
from astrapy.db import AstraDB, AsyncAstraDB
def _has_env_vars() -> bool:
return all(
[
"ASTRA_DB_APPLICATION_TOKEN" in os.environ,
"ASTRA_DB_API_ENDPOINT" in os.environ,
]
)
@pytest.fixture
def astra_db() -> AstraDB:
from astrapy.db import AstraDB
return AstraDB(
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
namespace=os.environ.get("ASTRA_DB_KEYSPACE"),
)
@pytest.fixture
def async_astra_db() -> AsyncAstraDB:
from astrapy.db import AsyncAstraDB
return AsyncAstraDB(
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
namespace=os.environ.get("ASTRA_DB_KEYSPACE"),
)
def init_store(astra_db: AstraDB, collection_name: str) -> AstraDBStore:
store = AstraDBStore(collection_name=collection_name, astra_db_client=astra_db)
store.mset([("key1", [0.1, 0.2]), ("key2", "value2")])
return store
def init_bytestore(astra_db: AstraDB, collection_name: str) -> AstraDBByteStore:
store = AstraDBByteStore(collection_name=collection_name, astra_db_client=astra_db)
store.mset([("key1", b"value1"), ("key2", b"value2")])
return store
async def init_async_store(
async_astra_db: AsyncAstraDB, collection_name: str
) -> AstraDBStore:
store = AstraDBStore(
collection_name=collection_name,
async_astra_db_client=async_astra_db,
setup_mode=SetupMode.ASYNC,
)
await store.amset([("key1", [0.1, 0.2]), ("key2", "value2")])
return store
@pytest.mark.requires("astrapy")
@pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars")
class TestAstraDBStore:
def test_mget(self, astra_db: AstraDB) -> None:
"""Test AstraDBStore mget method."""
collection_name = "lc_test_store_mget"
try:
store = init_store(astra_db, collection_name)
assert store.mget(["key1", "key2"]) == [[0.1, 0.2], "value2"]
finally:
astra_db.delete_collection(collection_name)
async def test_amget(self, async_astra_db: AsyncAstraDB) -> None:
"""Test AstraDBStore amget method."""
collection_name = "lc_test_store_mget"
try:
store = await init_async_store(async_astra_db, collection_name)
assert await store.amget(["key1", "key2"]) == [[0.1, 0.2], "value2"]
finally:
await async_astra_db.delete_collection(collection_name)
def test_mset(self, astra_db: AstraDB) -> None:
"""Test that multiple keys can be set with AstraDBStore."""
collection_name = "lc_test_store_mset"
try:
store = init_store(astra_db, collection_name)
result = store.collection.find_one({"_id": "key1"})
assert result["data"]["document"]["value"] == [0.1, 0.2]
result = store.collection.find_one({"_id": "key2"})
assert result["data"]["document"]["value"] == "value2"
finally:
astra_db.delete_collection(collection_name)
async def test_amset(self, async_astra_db: AsyncAstraDB) -> None:
"""Test that multiple keys can be set with AstraDBStore."""
collection_name = "lc_test_store_mset"
try:
store = await init_async_store(async_astra_db, collection_name)
result = await store.async_collection.find_one({"_id": "key1"})
assert result["data"]["document"]["value"] == [0.1, 0.2]
result = await store.async_collection.find_one({"_id": "key2"})
assert result["data"]["document"]["value"] == "value2"
finally:
await async_astra_db.delete_collection(collection_name)
def test_mdelete(self, astra_db: AstraDB) -> None:
"""Test that deletion works as expected."""
collection_name = "lc_test_store_mdelete"
try:
store = init_store(astra_db, collection_name)
store.mdelete(["key1", "key2"])
result = store.mget(["key1", "key2"])
assert result == [None, None]
finally:
astra_db.delete_collection(collection_name)
async def test_amdelete(self, async_astra_db: AsyncAstraDB) -> None:
"""Test that deletion works as expected."""
collection_name = "lc_test_store_mdelete"
try:
store = await init_async_store(async_astra_db, collection_name)
await store.amdelete(["key1", "key2"])
result = await store.amget(["key1", "key2"])
assert result == [None, None]
finally:
await async_astra_db.delete_collection(collection_name)
def test_yield_keys(self, astra_db: AstraDB) -> None:
collection_name = "lc_test_store_yield_keys"
try:
store = init_store(astra_db, collection_name)
assert set(store.yield_keys()) == {"key1", "key2"}
assert set(store.yield_keys(prefix="key")) == {"key1", "key2"}
assert set(store.yield_keys(prefix="lang")) == set()
finally:
astra_db.delete_collection(collection_name)
async def test_ayield_keys(self, async_astra_db: AsyncAstraDB) -> None:
collection_name = "lc_test_store_yield_keys"
try:
store = await init_async_store(async_astra_db, collection_name)
assert {key async for key in store.ayield_keys()} == {"key1", "key2"}
assert {key async for key in store.ayield_keys(prefix="key")} == {
"key1",
"key2",
}
assert {key async for key in store.ayield_keys(prefix="lang")} == set()
finally:
await async_astra_db.delete_collection(collection_name)
def test_bytestore_mget(self, astra_db: AstraDB) -> None:
"""Test AstraDBByteStore mget method."""
collection_name = "lc_test_bytestore_mget"
try:
store = init_bytestore(astra_db, collection_name)
assert store.mget(["key1", "key2"]) == [b"value1", b"value2"]
finally:
astra_db.delete_collection(collection_name)
def test_bytestore_mset(self, astra_db: AstraDB) -> None:
"""Test that multiple keys can be set with AstraDBByteStore."""
collection_name = "lc_test_bytestore_mset"
try:
store = init_bytestore(astra_db, collection_name)
result = store.collection.find_one({"_id": "key1"})
assert result["data"]["document"]["value"] == "dmFsdWUx"
result = store.collection.find_one({"_id": "key2"})
assert result["data"]["document"]["value"] == "dmFsdWUy"
finally:
astra_db.delete_collection(collection_name)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/storage/test_cassandra.py | """Implement integration tests for Cassandra storage."""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from langchain_community.storage.cassandra import CassandraByteStore
from langchain_community.utilities.cassandra import SetupMode
if TYPE_CHECKING:
from cassandra.cluster import Session
KEYSPACE = "storage_test_keyspace"
@pytest.fixture(scope="session")
def session() -> Session:
from cassandra.cluster import Cluster
cluster = Cluster()
session = cluster.connect()
session.execute(
(
f"CREATE KEYSPACE IF NOT EXISTS {KEYSPACE} "
f"WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': 1}}"
)
)
return session
def init_store(table_name: str, session: Session) -> CassandraByteStore:
store = CassandraByteStore(table=table_name, keyspace=KEYSPACE, session=session)
store.mset([("key1", b"value1"), ("key2", b"value2")])
return store
async def init_async_store(table_name: str, session: Session) -> CassandraByteStore:
store = CassandraByteStore(
table=table_name, keyspace=KEYSPACE, session=session, setup_mode=SetupMode.ASYNC
)
await store.amset([("key1", b"value1"), ("key2", b"value2")])
return store
def drop_table(table_name: str, session: Session) -> None:
session.execute(f"DROP TABLE {KEYSPACE}.{table_name}")
async def test_mget(session: Session) -> None:
"""Test CassandraByteStore mget method."""
table_name = "lc_test_store_mget"
try:
store = init_store(table_name, session)
assert store.mget(["key1", "key2"]) == [b"value1", b"value2"]
assert await store.amget(["key1", "key2"]) == [b"value1", b"value2"]
finally:
drop_table(table_name, session)
async def test_amget(session: Session) -> None:
"""Test CassandraByteStore amget method."""
table_name = "lc_test_store_amget"
try:
store = await init_async_store(table_name, session)
assert await store.amget(["key1", "key2"]) == [b"value1", b"value2"]
finally:
drop_table(table_name, session)
def test_mset(session: Session) -> None:
"""Test that multiple keys can be set with CassandraByteStore."""
table_name = "lc_test_store_mset"
try:
init_store(table_name, session)
result = session.execute(
"SELECT row_id, body_blob FROM storage_test_keyspace.lc_test_store_mset "
"WHERE row_id = 'key1';"
).one()
assert result.body_blob == b"value1"
result = session.execute(
"SELECT row_id, body_blob FROM storage_test_keyspace.lc_test_store_mset "
"WHERE row_id = 'key2';"
).one()
assert result.body_blob == b"value2"
finally:
drop_table(table_name, session)
async def test_amset(session: Session) -> None:
"""Test that multiple keys can be set with CassandraByteStore."""
table_name = "lc_test_store_amset"
try:
await init_async_store(table_name, session)
result = session.execute(
"SELECT row_id, body_blob FROM storage_test_keyspace.lc_test_store_amset "
"WHERE row_id = 'key1';"
).one()
assert result.body_blob == b"value1"
result = session.execute(
"SELECT row_id, body_blob FROM storage_test_keyspace.lc_test_store_amset "
"WHERE row_id = 'key2';"
).one()
assert result.body_blob == b"value2"
finally:
drop_table(table_name, session)
def test_mdelete(session: Session) -> None:
"""Test that deletion works as expected."""
table_name = "lc_test_store_mdelete"
try:
store = init_store(table_name, session)
store.mdelete(["key1", "key2"])
result = store.mget(["key1", "key2"])
assert result == [None, None]
finally:
drop_table(table_name, session)
async def test_amdelete(session: Session) -> None:
"""Test that deletion works as expected."""
table_name = "lc_test_store_amdelete"
try:
store = await init_async_store(table_name, session)
await store.amdelete(["key1", "key2"])
result = await store.amget(["key1", "key2"])
assert result == [None, None]
finally:
drop_table(table_name, session)
def test_yield_keys(session: Session) -> None:
table_name = "lc_test_store_yield_keys"
try:
store = init_store(table_name, session)
assert set(store.yield_keys()) == {"key1", "key2"}
assert set(store.yield_keys(prefix="key")) == {"key1", "key2"}
assert set(store.yield_keys(prefix="lang")) == set()
finally:
drop_table(table_name, session)
async def test_ayield_keys(session: Session) -> None:
table_name = "lc_test_store_ayield_keys"
try:
store = await init_async_store(table_name, session)
assert {key async for key in store.ayield_keys()} == {"key1", "key2"}
assert {key async for key in store.ayield_keys(prefix="key")} == {
"key1",
"key2",
}
assert {key async for key in store.ayield_keys(prefix="lang")} == set()
finally:
drop_table(table_name, session)
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/storage/test_redis.py | """Implement integration tests for Redis storage."""
import os
import typing
import uuid
import pytest
from langchain_community.storage.redis import RedisStore
if typing.TYPE_CHECKING:
from redis import Redis
pytest.importorskip("redis")
@pytest.fixture
def redis_client() -> Redis:
"""Yield redis client."""
import redis
# Using standard port, but protecting against accidental data loss
# by requiring a password.
# This fixture flushes the database!
# The only role of the password is to prevent users from accidentally
# deleting their data.
# The password should establish the identity of the server being.
port = 6379
password = os.environ.get("REDIS_PASSWORD") or str(uuid.uuid4())
client = redis.Redis(host="localhost", port=port, password=password, db=0)
try:
client.ping()
except redis.exceptions.ConnectionError:
pytest.skip(
"Redis server is not running or is not accessible. "
"Verify that credentials are correct. "
)
# ATTENTION: This will delete all keys in the database!
client.flushdb()
return client
def test_mget(redis_client: Redis) -> None:
"""Test mget method."""
store = RedisStore(client=redis_client, ttl=None)
keys = ["key1", "key2"]
redis_client.mset({"key1": b"value1", "key2": b"value2"})
result = store.mget(keys)
assert result == [b"value1", b"value2"]
def test_mset(redis_client: Redis) -> None:
"""Test that multiple keys can be set."""
store = RedisStore(client=redis_client, ttl=None)
key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
store.mset(key_value_pairs)
result = redis_client.mget(["key1", "key2"])
assert result == [b"value1", b"value2"]
def test_mdelete(redis_client: Redis) -> None:
"""Test that deletion works as expected."""
store = RedisStore(client=redis_client, ttl=None)
keys = ["key1", "key2"]
redis_client.mset({"key1": b"value1", "key2": b"value2"})
store.mdelete(keys)
result = redis_client.mget(keys)
assert result == [None, None]
def test_yield_keys(redis_client: Redis) -> None:
store = RedisStore(client=redis_client, ttl=None)
redis_client.mset({"key1": b"value1", "key2": b"value2"})
assert sorted(store.yield_keys()) == ["key1", "key2"]
assert sorted(store.yield_keys(prefix="key*")) == ["key1", "key2"]
assert sorted(store.yield_keys(prefix="lang*")) == []
def test_namespace(redis_client: Redis) -> None:
"""Test that a namespace is prepended to all keys properly."""
store = RedisStore(client=redis_client, ttl=None, namespace="meow")
key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
store.mset(key_value_pairs)
assert sorted(redis_client.scan_iter("*")) == [
b"meow/key1",
b"meow/key2",
]
store.mdelete(["key1"])
assert sorted(redis_client.scan_iter("*")) == [
b"meow/key2",
]
assert list(store.yield_keys()) == ["key2"]
assert list(store.yield_keys(prefix="key*")) == ["key2"]
assert list(store.yield_keys(prefix="key1")) == []
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/storage/test_upstash_redis.py | """Implement integration tests for Redis storage."""
from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from langchain_community.storage.upstash_redis import UpstashRedisByteStore
if TYPE_CHECKING:
from upstash_redis import Redis
pytest.importorskip("upstash_redis")
URL = "<UPSTASH_REDIS_REST_URL>"
TOKEN = "<UPSTASH_REDIS_REST_TOKEN>"
@pytest.fixture
def redis_client() -> Redis:
"""Yield redis client."""
from upstash_redis import Redis
# This fixture flushes the database!
client = Redis(url=URL, token=TOKEN)
try:
client.ping()
except Exception:
pytest.skip("Ping request failed. Verify that credentials are correct.")
client.flushdb()
return client
def test_mget(redis_client: Redis) -> None:
store = UpstashRedisByteStore(client=redis_client, ttl=None)
keys = ["key1", "key2"]
redis_client.mset({"key1": "value1", "key2": "value2"})
result = store.mget(keys)
assert result == [b"value1", b"value2"]
def test_mset(redis_client: Redis) -> None:
store = UpstashRedisByteStore(client=redis_client, ttl=None)
key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
store.mset(key_value_pairs)
result = redis_client.mget("key1", "key2")
assert result == ["value1", "value2"]
def test_mdelete(redis_client: Redis) -> None:
"""Test that deletion works as expected."""
store = UpstashRedisByteStore(client=redis_client, ttl=None)
keys = ["key1", "key2"]
redis_client.mset({"key1": "value1", "key2": "value2"})
store.mdelete(keys)
result = redis_client.mget(*keys)
assert result == [None, None]
def test_yield_keys(redis_client: Redis) -> None:
store = UpstashRedisByteStore(client=redis_client, ttl=None)
redis_client.mset({"key1": "value2", "key2": "value2"})
assert sorted(store.yield_keys()) == ["key1", "key2"]
assert sorted(store.yield_keys(prefix="key*")) == ["key1", "key2"]
assert sorted(store.yield_keys(prefix="lang*")) == []
def test_namespace(redis_client: Redis) -> None:
store = UpstashRedisByteStore(client=redis_client, ttl=None, namespace="meow")
key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
store.mset(key_value_pairs)
cursor, all_keys = redis_client.scan(0)
while cursor != 0:
cursor, keys = redis_client.scan(cursor)
if len(keys) != 0:
all_keys.extend(keys)
assert sorted(all_keys) == [
"meow/key1",
"meow/key2",
]
store.mdelete(["key1"])
cursor, all_keys = redis_client.scan(0, match="*")
while cursor != 0:
cursor, keys = redis_client.scan(cursor, match="*")
if len(keys) != 0:
all_keys.extend(keys)
assert sorted(all_keys) == [
"meow/key2",
]
assert list(store.yield_keys()) == ["key2"]
assert list(store.yield_keys(prefix="key*")) == ["key2"]
assert list(store.yield_keys(prefix="key1")) == []
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/indexes/test_document_manager.py | from datetime import datetime
from unittest.mock import patch
import pytest
import pytest_asyncio
from langchain_community.indexes._document_manager import MongoDocumentManager
@pytest.fixture
@pytest.mark.requires("pymongo")
def manager() -> MongoDocumentManager:
"""Initialize the test MongoDB and yield the DocumentManager instance."""
document_manager = MongoDocumentManager(
namespace="kittens",
mongodb_url="mongodb://langchain:langchain@localhost:6022/",
db_name="test_db",
collection_name="test_collection",
)
return document_manager
@pytest_asyncio.fixture
@pytest.mark.requires("motor")
async def amanager() -> MongoDocumentManager:
"""Initialize the test MongoDB and yield the DocumentManager instance."""
document_manager = MongoDocumentManager(
namespace="kittens",
mongodb_url="mongodb://langchain:langchain@localhost:6022/",
db_name="test_db",
collection_name="test_collection",
)
return document_manager
@pytest.mark.requires("pymongo")
def test_update(manager: MongoDocumentManager) -> None:
"""Test updating records in the MongoDB."""
read_keys = manager.list_keys()
updated_keys = ["update_key1", "update_key2", "update_key3"]
manager.update(updated_keys)
all_keys = manager.list_keys()
assert sorted(all_keys) == sorted(read_keys + updated_keys)
@pytest.mark.requires("motor")
async def test_aupdate(amanager: MongoDocumentManager) -> None:
"""Test updating records in the MongoDB."""
read_keys = await amanager.alist_keys()
aupdated_keys = ["aupdate_key1", "aupdate_key2", "aupdate_key3"]
await amanager.aupdate(aupdated_keys)
all_keys = await amanager.alist_keys()
assert sorted(all_keys) == sorted(read_keys + aupdated_keys)
@pytest.mark.requires("pymongo")
def test_update_timestamp(manager: MongoDocumentManager) -> None:
"""Test updating records with timestamps in MongoDB."""
with patch.object(
manager, "get_time", return_value=datetime(2024, 2, 23).timestamp()
):
manager.update(["key1"])
records = list(
manager.sync_collection.find({"namespace": manager.namespace, "key": "key1"})
)
assert [
{
"key": record["key"],
"namespace": record["namespace"],
"updated_at": record["updated_at"],
"group_id": record.get("group_id"),
}
for record in records
] == [
{
"group_id": None,
"key": "key1",
"namespace": "kittens",
"updated_at": datetime(2024, 2, 23).timestamp(),
}
]
@pytest.mark.requires("motor")
async def test_aupdate_timestamp(amanager: MongoDocumentManager) -> None:
"""Test asynchronously updating records with timestamps in MongoDB."""
with patch.object(
amanager, "aget_time", return_value=datetime(2024, 2, 23).timestamp()
):
await amanager.aupdate(["key1"])
records = [
doc
async for doc in amanager.async_collection.find(
{"namespace": amanager.namespace, "key": "key1"}
)
]
assert [
{
"key": record["key"],
"namespace": record["namespace"],
"updated_at": record["updated_at"],
"group_id": record.get("group_id"),
}
for record in records
] == [
{
"group_id": None,
"key": "key1",
"namespace": "kittens",
"updated_at": datetime(2024, 2, 23).timestamp(),
}
]
@pytest.mark.requires("pymongo")
def test_exists(manager: MongoDocumentManager) -> None:
"""Test checking if keys exist in MongoDB."""
keys = ["key1", "key2", "key3"]
manager.update(keys)
exists = manager.exists(keys)
assert len(exists) == len(keys)
assert all(exists)
exists = manager.exists(["key1", "key4"])
assert len(exists) == 2
assert exists == [True, False]
@pytest.mark.requires("motor")
async def test_aexists(amanager: MongoDocumentManager) -> None:
"""Test asynchronously checking if keys exist in MongoDB."""
keys = ["key1", "key2", "key3"]
await amanager.aupdate(keys)
exists = await amanager.aexists(keys)
assert len(exists) == len(keys)
assert all(exists)
exists = await amanager.aexists(["key1", "key4"])
assert len(exists) == 2
assert exists == [True, False]
@pytest.mark.requires("pymongo")
def test_list_keys(manager: MongoDocumentManager) -> None:
"""Test listing keys in MongoDB."""
manager.delete_keys(manager.list_keys())
with patch.object(
manager, "get_time", return_value=datetime(2021, 1, 1).timestamp()
):
manager.update(["key1"])
with patch.object(
manager, "get_time", return_value=datetime(2022, 1, 1).timestamp()
):
manager.update(["key2"])
with patch.object(
manager, "get_time", return_value=datetime(2023, 1, 1).timestamp()
):
manager.update(["key3"])
with patch.object(
manager, "get_time", return_value=datetime(2024, 1, 1).timestamp()
):
manager.update(["key4"], group_ids=["group1"])
assert sorted(manager.list_keys()) == sorted(["key1", "key2", "key3", "key4"])
assert sorted(manager.list_keys(after=datetime(2022, 2, 1).timestamp())) == sorted(
["key3", "key4"]
)
assert sorted(manager.list_keys(group_ids=["group1", "group2"])) == sorted(["key4"])
@pytest.mark.requires("motor")
async def test_alist_keys(amanager: MongoDocumentManager) -> None:
"""Test asynchronously listing keys in MongoDB."""
await amanager.adelete_keys(await amanager.alist_keys())
with patch.object(
amanager, "aget_time", return_value=datetime(2021, 1, 1).timestamp()
):
await amanager.aupdate(["key1"])
with patch.object(
amanager, "aget_time", return_value=datetime(2022, 1, 1).timestamp()
):
await amanager.aupdate(["key2"])
with patch.object(
amanager, "aget_time", return_value=datetime(2023, 1, 1).timestamp()
):
await amanager.aupdate(["key3"])
with patch.object(
amanager, "aget_time", return_value=datetime(2024, 1, 1).timestamp()
):
await amanager.aupdate(["key4"], group_ids=["group1"])
assert sorted(await amanager.alist_keys()) == sorted(
["key1", "key2", "key3", "key4"]
)
assert sorted(
await amanager.alist_keys(after=datetime(2022, 2, 1).timestamp())
) == sorted(["key3", "key4"])
assert sorted(await amanager.alist_keys(group_ids=["group1", "group2"])) == sorted(
["key4"]
)
@pytest.mark.requires("pymongo")
def test_namespace_is_used(manager: MongoDocumentManager) -> None:
"""Verify that namespace is taken into account for all operations in MongoDB."""
manager.delete_keys(manager.list_keys())
manager.update(["key1", "key2"], group_ids=["group1", "group2"])
manager.sync_collection.insert_many(
[
{"key": "key1", "namespace": "puppies", "group_id": None},
{"key": "key3", "namespace": "puppies", "group_id": None},
]
)
assert sorted(manager.list_keys()) == sorted(["key1", "key2"])
manager.delete_keys(["key1"])
assert sorted(manager.list_keys()) == sorted(["key2"])
manager.update(["key3"], group_ids=["group3"])
assert (
manager.sync_collection.find_one({"key": "key3", "namespace": "kittens"})[
"group_id"
]
== "group3"
)
@pytest.mark.requires("motor")
async def test_anamespace_is_used(amanager: MongoDocumentManager) -> None:
"""
Verify that namespace is taken into account for all operations
in MongoDB asynchronously.
"""
await amanager.adelete_keys(await amanager.alist_keys())
await amanager.aupdate(["key1", "key2"], group_ids=["group1", "group2"])
await amanager.async_collection.insert_many(
[
{"key": "key1", "namespace": "puppies", "group_id": None},
{"key": "key3", "namespace": "puppies", "group_id": None},
]
)
assert sorted(await amanager.alist_keys()) == sorted(["key1", "key2"])
await amanager.adelete_keys(["key1"])
assert sorted(await amanager.alist_keys()) == sorted(["key2"])
await amanager.aupdate(["key3"], group_ids=["group3"])
assert (
await amanager.async_collection.find_one(
{"key": "key3", "namespace": "kittens"}
)
)["group_id"] == "group3"
@pytest.mark.requires("pymongo")
def test_delete_keys(manager: MongoDocumentManager) -> None:
"""Test deleting keys from MongoDB."""
manager.update(["key1", "key2", "key3"])
manager.delete_keys(["key1", "key2"])
remaining_keys = manager.list_keys()
assert sorted(remaining_keys) == sorted(["key3"])
@pytest.mark.requires("motor")
async def test_adelete_keys(amanager: MongoDocumentManager) -> None:
"""Test asynchronously deleting keys from MongoDB."""
await amanager.aupdate(["key1", "key2", "key3"])
await amanager.adelete_keys(["key1", "key2"])
remaining_keys = await amanager.alist_keys()
assert sorted(remaining_keys) == sorted(["key3"])
|
0 | lc_public_repos/langchain/libs/community/tests/integration_tests | lc_public_repos/langchain/libs/community/tests/integration_tests/cross_encoders/test_huggingface.py | """Test huggingface cross encoders."""
from langchain_community.cross_encoders import HuggingFaceCrossEncoder
def _assert(encoder: HuggingFaceCrossEncoder) -> None:
query = "I love you"
texts = ["I love you", "I like you", "I don't like you", "I hate you"]
output = encoder.score([(query, text) for text in texts])
for i in range(len(texts) - 1):
assert output[i] > output[i + 1]
def test_huggingface_cross_encoder() -> None:
encoder = HuggingFaceCrossEncoder()
_assert(encoder)
def test_huggingface_cross_encoder_with_designated_model_name() -> None:
encoder = HuggingFaceCrossEncoder(model_name="cross-encoder/ms-marco-MiniLM-L-6-v2")
_assert(encoder)
|
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