mirror of
https://github.com/qdrant/qdrant-client.git
synced 2026-08-03 08:30:58 -05:00
d957ca7ba7
* bearer token authentication provider support * add tests and checks, move auth file to separate dir * fix error message * remove locks * rename var * refactoring: refactor exceptions, fix mypy * fix: regen async * tests: extend token tests to check token updates * new: add warning when auth token provider is used with an insecure connection * fix: propagate auth token to rest client even with prefer_grpc set --------- Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
2001 lines
61 KiB
Python
2001 lines
61 KiB
Python
import asyncio
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import os
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import uuid
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from pprint import pprint
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from tempfile import mkdtemp
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from time import sleep
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from typing import List
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import numpy as np
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import pytest
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from grpc import Compression, RpcError
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import qdrant_client.http.exceptions
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from qdrant_client import QdrantClient, models
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from qdrant_client._pydantic_compat import to_dict
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from qdrant_client.conversions.common_types import PointVectors, Record
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from qdrant_client.conversions.conversion import grpc_to_payload, json_to_value
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from qdrant_client.local.qdrant_local import QdrantLocal
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from qdrant_client.models import (
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Batch,
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CompressionRatio,
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CreateAlias,
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CreateAliasOperation,
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Distance,
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FieldCondition,
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Filter,
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HasIdCondition,
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HnswConfigDiff,
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MatchAny,
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MatchText,
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MatchValue,
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OptimizersConfigDiff,
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PayloadSchemaType,
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PointIdsList,
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PointStruct,
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ProductQuantization,
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ProductQuantizationConfig,
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QuantizationSearchParams,
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Range,
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RecommendRequest,
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ScalarQuantization,
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ScalarQuantizationConfig,
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ScalarType,
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SearchParams,
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SearchRequest,
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TextIndexParams,
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TokenizerType,
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VectorParams,
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VectorParamsDiff,
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)
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from qdrant_client.qdrant_remote import QdrantRemote
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from qdrant_client.uploader.grpc_uploader import payload_to_grpc
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from tests.congruence_tests.test_common import (
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generate_fixtures,
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init_client,
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init_remote,
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)
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from tests.fixtures.payload import (
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one_random_payload_please,
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random_payload,
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random_real_word,
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)
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DIM = 100
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NUM_VECTORS = 1_000
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COLLECTION_NAME = "client_test"
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COLLECTION_NAME_ALIAS = "client_test_alias"
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TIMEOUT = 60
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def create_random_vectors():
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vectors_path = os.path.join(mkdtemp(), "vectors.npy")
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fp = np.memmap(vectors_path, dtype="float32", mode="w+", shape=(NUM_VECTORS, DIM))
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data = np.random.rand(NUM_VECTORS, DIM).astype(np.float32)
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fp[:] = data[:]
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fp.flush()
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return vectors_path
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def test_client_init():
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import tempfile
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client = QdrantClient(":memory:")
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assert isinstance(client._client, QdrantLocal)
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assert client._client.location == ":memory:"
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with tempfile.TemporaryDirectory() as tmpdir:
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client = QdrantClient(path=tmpdir + "/test.db")
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assert isinstance(client._client, QdrantLocal)
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assert client._client.location == tmpdir + "/test.db"
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client = QdrantClient()
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://localhost:6333"
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client = QdrantClient(https=True)
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "https://localhost:6333"
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client = QdrantClient(https=True, port=7333)
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "https://localhost:7333"
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client = QdrantClient(host="hidden_port_addr.com", prefix="custom")
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://hidden_port_addr.com:6333/custom"
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client = QdrantClient(host="hidden_port_addr.com", port=None)
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://hidden_port_addr.com"
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client = QdrantClient(
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host="hidden_port_addr.com",
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port=None,
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prefix="custom",
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)
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://hidden_port_addr.com/custom"
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client = QdrantClient("http://hidden_port_addr.com", port=None)
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://hidden_port_addr.com"
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# url takes precedence over port, which has default value for a backward compatibility
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client = QdrantClient(url="http://localhost:6333", port=7333)
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://localhost:6333"
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client = QdrantClient(url="http://localhost:6333", prefix="custom")
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://localhost:6333/custom"
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client = QdrantClient("my-domain.com")
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://my-domain.com:6333"
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client = QdrantClient("my-domain.com:80")
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://my-domain.com:80"
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with pytest.raises(ValueError):
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QdrantClient(url="http://localhost:6333", host="localhost")
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with pytest.raises(ValueError):
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QdrantClient(url="http://localhost:6333/origin", prefix="custom")
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client = QdrantClient("127.0.0.1:6333")
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://127.0.0.1:6333"
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client = QdrantClient("localhost:6333")
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assert isinstance(client._client, QdrantRemote)
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assert client._client.rest_uri == "http://localhost:6333"
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client = QdrantClient(":memory:", not_exist_param="test")
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assert isinstance(client._client, QdrantLocal)
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grid_params = [
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{"location": ":memory:", "url": "http://localhost:6333"},
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{"location": ":memory:", "host": "localhost"},
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{"location": ":memory:", "path": "/tmp/test.db"},
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{"url": "http://localhost:6333", "host": "localhost"},
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{"url": "http://localhost:6333", "path": "/tmp/test.db"},
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{"host": "localhost", "path": "/tmp/test.db"},
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]
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for params in grid_params:
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with pytest.raises(
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ValueError,
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match="Only one of <location>, <url>, <host> or <path> should be specified.",
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):
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QdrantClient(**params)
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@pytest.mark.parametrize("prefer_grpc", [False, True])
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@pytest.mark.parametrize("parallel", [1, 2])
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def test_records_upload(prefer_grpc, parallel):
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import warnings
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warnings.simplefilter("ignore", category=DeprecationWarning)
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records = (
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Record(
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id=idx,
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vector=np.random.rand(DIM).tolist(),
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payload=one_random_payload_please(idx),
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)
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for idx in range(NUM_VECTORS)
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)
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client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
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timeout=TIMEOUT,
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)
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client.upload_records(collection_name=COLLECTION_NAME, records=records, parallel=parallel)
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# By default, Qdrant indexes data updates asynchronously, so client don't need to wait before sending next batch
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# Let's give it a second to actually add all points to a collection.
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# If you need to change this behaviour - simply enable synchronous processing by enabling `wait=true`
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sleep(1)
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collection_info = client.get_collection(collection_name=COLLECTION_NAME)
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assert collection_info.points_count == NUM_VECTORS
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result_count = client.count(
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COLLECTION_NAME,
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count_filter=Filter(
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must=[
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FieldCondition(
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key="rand_number", # Condition based on values of `rand_number` field.
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range=Range(gte=0.5), # Select only those results where `rand_number` >= 0.5
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)
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]
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),
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)
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assert result_count.count < 900
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assert result_count.count > 100
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records = (Record(id=idx, vector=np.random.rand(DIM).tolist()) for idx in range(NUM_VECTORS))
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
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timeout=TIMEOUT,
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)
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client.upload_records(
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collection_name=COLLECTION_NAME, records=records, parallel=parallel, wait=True
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)
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collection_info = client.get_collection(collection_name=COLLECTION_NAME)
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assert collection_info.points_count == NUM_VECTORS
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@pytest.mark.parametrize("prefer_grpc", [False, True])
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@pytest.mark.parametrize("parallel", [1, 2])
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def test_point_upload(prefer_grpc, parallel):
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points = (
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PointStruct(
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id=idx,
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vector=np.random.rand(DIM).tolist(),
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payload=one_random_payload_please(idx),
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)
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for idx in range(NUM_VECTORS)
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)
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client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
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timeout=TIMEOUT,
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)
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client.upload_points(collection_name=COLLECTION_NAME, points=points, parallel=parallel)
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# By default, Qdrant indexes data updates asynchronously, so client don't need to wait before sending next batch
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# Let's give it a second to actually add all points to a collection.
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# If you need to change this behaviour - simply enable synchronous processing by enabling `wait=true`
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sleep(1)
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collection_info = client.get_collection(collection_name=COLLECTION_NAME)
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assert collection_info.points_count == NUM_VECTORS
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result_count = client.count(
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COLLECTION_NAME,
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count_filter=Filter(
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must=[
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FieldCondition(
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key="rand_number", # Condition based on values of `rand_number` field.
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range=Range(gte=0.5), # Select only those results where `rand_number` >= 0.5
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)
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]
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),
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)
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assert result_count.count < 900
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assert result_count.count > 100
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
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timeout=TIMEOUT,
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)
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points = (
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PointStruct(id=idx, vector=np.random.rand(DIM).tolist()) for idx in range(NUM_VECTORS)
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)
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client.upload_points(
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collection_name=COLLECTION_NAME, points=points, parallel=parallel, wait=True
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)
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collection_info = client.get_collection(collection_name=COLLECTION_NAME)
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assert collection_info.points_count == NUM_VECTORS
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@pytest.mark.parametrize("prefer_grpc", [False, True])
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@pytest.mark.parametrize("parallel", [1, 2])
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def test_upload_collection(prefer_grpc, parallel):
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size = 3
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batch_size = 2
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client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=VectorParams(size=size, distance=Distance.DOT),
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timeout=TIMEOUT,
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)
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vectors = [
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[1.0, 2.0, 3.0],
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[4.0, 5.0, 6.0],
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[7.0, 8.0, 9.0],
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[10.0, 11.0, 12.0],
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[13.0, 14.0, 15.0],
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]
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payload = [{"a": 2}, {"b": 3}, {"c": 4}, {"d": 5}, {"e": 6}]
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ids = [1, 2, 3, 4, 5]
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client.upload_collection(
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collection_name=COLLECTION_NAME,
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vectors=vectors,
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parallel=parallel,
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wait=True,
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batch_size=batch_size,
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)
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assert client.get_collection(collection_name=COLLECTION_NAME).points_count == 5
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=VectorParams(size=size, distance=Distance.DOT),
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timeout=TIMEOUT,
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)
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client.upload_collection(
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collection_name=COLLECTION_NAME,
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vectors=vectors,
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payload=payload,
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ids=ids,
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parallel=parallel,
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wait=True,
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batch_size=batch_size,
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)
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assert client.get_collection(collection_name=COLLECTION_NAME).points_count == 5
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@pytest.mark.parametrize("prefer_grpc", [False, True])
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def test_multiple_vectors(prefer_grpc):
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num_vectors = 100
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points = [
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PointStruct(
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id=idx,
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vector={
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"image": np.random.rand(DIM).tolist(),
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"text": np.random.rand(DIM * 2).tolist(),
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},
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payload=one_random_payload_please(idx),
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)
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for idx in range(num_vectors)
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]
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client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config={
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"image": VectorParams(size=DIM, distance=Distance.DOT),
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"text": VectorParams(size=DIM * 2, distance=Distance.COSINE),
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},
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timeout=TIMEOUT,
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)
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client.upload_points(collection_name=COLLECTION_NAME, points=points, parallel=1)
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query_vector = list(np.random.rand(DIM))
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hits = client.search(
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collection_name=COLLECTION_NAME,
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query_vector=("image", query_vector),
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with_vectors=True,
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limit=5, # Return 5 closest points
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)
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assert len(hits) == 5
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assert "image" in hits[0].vector
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assert "text" in hits[0].vector
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hits = client.search(
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collection_name=COLLECTION_NAME,
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query_vector=("text", query_vector * 2),
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with_vectors=True,
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limit=5, # Return 5 closest points
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)
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assert len(hits) == 5
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assert "image" in hits[0].vector
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assert "text" in hits[0].vector
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@pytest.mark.parametrize("prefer_grpc", [False, True])
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@pytest.mark.parametrize("numpy_upload", [False, True])
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@pytest.mark.parametrize("local_mode", [False, True])
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def test_qdrant_client_integration(prefer_grpc, numpy_upload, local_mode):
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version = os.getenv("QDRANT_VERSION")
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vectors_path = create_random_vectors()
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if numpy_upload:
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vectors = np.memmap(vectors_path, dtype="float32", mode="r", shape=(NUM_VECTORS, DIM))
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vectors_2 = vectors[2].tolist()
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else:
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vectors = [np.random.rand(DIM).tolist() for _ in range(NUM_VECTORS)]
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vectors_2 = vectors[2]
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payload = random_payload(NUM_VECTORS)
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if local_mode:
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client = QdrantClient(location=":memory:", prefer_grpc=prefer_grpc)
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else:
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client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
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client.recreate_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
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timeout=TIMEOUT,
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)
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if version is None or (version >= "v1.8.0" or version == "dev"):
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assert client.collection_exists(collection_name=COLLECTION_NAME)
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assert not client.collection_exists(collection_name="non_existing_collection")
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# Call Qdrant API to retrieve list of existing collections
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collections = client.get_collections().collections
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# Print all existing collections
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for collection in collections:
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print(to_dict(collection))
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# Retrieve detailed information about newly created collection
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test_collection = client.get_collection(COLLECTION_NAME)
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pprint(to_dict(test_collection))
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# Upload data to a new collection
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client.upload_collection(
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collection_name=COLLECTION_NAME,
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vectors=vectors,
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payload=payload,
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ids=range(len(vectors)),
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parallel=2,
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)
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# By default, Qdrant indexes data updates asynchronously, so client don't need to wait before sending next batch
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# Let's give it a second to actually add all points to a collection.
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# If you need to change this behaviour - simply enable synchronous processing by enabling `wait=true`
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sleep(1)
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result_count = client.count(
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COLLECTION_NAME,
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count_filter=Filter(
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must=[
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FieldCondition(
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key="rand_number", # Condition based on values of `rand_number` field.
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range=Range(gte=0.5), # Select only those results where `rand_number` >= 0.5
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)
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]
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),
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)
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assert result_count.count < 900
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assert result_count.count > 100
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client.update_collection_aliases(
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change_aliases_operations=[
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CreateAliasOperation(
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create_alias=CreateAlias(
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collection_name=COLLECTION_NAME, alias_name=COLLECTION_NAME_ALIAS
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)
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)
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]
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)
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collection_aliases = client.get_collection_aliases(COLLECTION_NAME)
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assert collection_aliases.aliases[0].collection_name == COLLECTION_NAME
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assert collection_aliases.aliases[0].alias_name == COLLECTION_NAME_ALIAS
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all_aliases = client.get_aliases()
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assert all_aliases.aliases[0].collection_name == COLLECTION_NAME
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assert all_aliases.aliases[0].alias_name == COLLECTION_NAME_ALIAS
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|
|
# Create payload index for field `rand_number`
|
|
# If indexed field appear in filtering condition - search operation could be performed faster
|
|
index_create_result = client.create_payload_index(
|
|
COLLECTION_NAME, field_name="rand_number", field_schema=PayloadSchemaType.FLOAT
|
|
)
|
|
pprint(to_dict(index_create_result))
|
|
|
|
# Let's now check details about our new collection
|
|
test_collection = client.get_collection(COLLECTION_NAME_ALIAS)
|
|
pprint(to_dict(test_collection))
|
|
|
|
# Now we can actually search in the collection
|
|
# Let's create some random vector
|
|
query_vector = np.random.rand(DIM)
|
|
query_vector_1: List[float] = list(np.random.rand(DIM))
|
|
query_vector_2: List[float] = list(np.random.rand(DIM))
|
|
query_vector_3: List[float] = list(np.random.rand(DIM))
|
|
|
|
# and use it as a query
|
|
hits = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
query_filter=None, # Don't use any filters for now, search across all indexed points
|
|
with_payload=True, # Also return a stored payload for found points
|
|
limit=5, # Return 5 closest points
|
|
)
|
|
|
|
assert len(hits) == 5
|
|
|
|
# Print found results
|
|
print("Search result:")
|
|
for hit in hits:
|
|
print(hit)
|
|
|
|
client.create_payload_index(COLLECTION_NAME, "id_str", field_schema=PayloadSchemaType.KEYWORD)
|
|
# and use it as a query
|
|
hits = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
query_filter=Filter(must=[FieldCondition(key="id_str", match=MatchValue(value="11"))]),
|
|
with_payload=True,
|
|
limit=5,
|
|
)
|
|
|
|
assert "11" in hits[0].payload["id_str"]
|
|
|
|
hits_should = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
query_filter=Filter(
|
|
should=[
|
|
FieldCondition(key="id_str", match=MatchValue(value="10")),
|
|
FieldCondition(key="id_str", match=MatchValue(value="11")),
|
|
]
|
|
),
|
|
with_payload=True,
|
|
limit=5,
|
|
)
|
|
|
|
hits_match_any = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
query_filter=Filter(
|
|
must=[
|
|
FieldCondition(
|
|
key="id_str",
|
|
match=MatchAny(any=["10", "11"]),
|
|
)
|
|
]
|
|
),
|
|
with_payload=True,
|
|
limit=5,
|
|
)
|
|
assert hits_should == hits_match_any
|
|
|
|
if version is None or (version >= "v1.8.0" or version == "dev"):
|
|
hits_min_should = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
query_filter=Filter(
|
|
min_should=models.MinShould(
|
|
conditions=[
|
|
FieldCondition(key="id_str", match=MatchValue(value="11")),
|
|
FieldCondition(key="rand_digit", match=MatchAny(any=list(range(10)))),
|
|
FieldCondition(key="id", match=MatchAny(any=list(range(100, 150)))),
|
|
],
|
|
min_count=2,
|
|
)
|
|
),
|
|
with_payload=True,
|
|
limit=5,
|
|
)
|
|
assert len(hits_min_should) > 0
|
|
|
|
hits_min_should_empty = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
query_filter=Filter(
|
|
min_should=models.MinShould(
|
|
conditions=[
|
|
FieldCondition(key="id_str", match=MatchValue(value="11")),
|
|
],
|
|
min_count=2,
|
|
)
|
|
),
|
|
with_payload=True,
|
|
limit=5,
|
|
)
|
|
assert len(hits_min_should_empty) == 0
|
|
|
|
# Let's now query same vector with filter condition
|
|
hits = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
query_filter=Filter(
|
|
must=[ # These conditions are required for search results
|
|
FieldCondition(
|
|
key="rand_number", # Condition based on values of `rand_number` field.
|
|
range=Range(gte=0.5), # Select only those results where `rand_number` >= 0.5
|
|
)
|
|
]
|
|
),
|
|
append_payload=True, # Also return a stored payload for found points
|
|
limit=5, # Return 5 closest points
|
|
)
|
|
|
|
print("Filtered search result (`rand_number` >= 0.5):")
|
|
for hit in hits:
|
|
print(hit)
|
|
|
|
got_points = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[1, 2, 3],
|
|
with_payload=True,
|
|
with_vectors=True,
|
|
)
|
|
|
|
# ------------------ Test for full-text filtering ------------------
|
|
|
|
# Create index for full-text search
|
|
client.create_payload_index(
|
|
COLLECTION_NAME,
|
|
"words",
|
|
field_schema=TextIndexParams(
|
|
type="text",
|
|
tokenizer=TokenizerType.WORD,
|
|
min_token_len=2,
|
|
max_token_len=15,
|
|
lowercase=True,
|
|
),
|
|
)
|
|
|
|
for i in range(10):
|
|
query_word = random_real_word()
|
|
hits, _offset = client.scroll(
|
|
collection_name=COLLECTION_NAME,
|
|
scroll_filter=Filter(
|
|
must=[FieldCondition(key="words", match=MatchText(text=query_word))]
|
|
),
|
|
with_payload=True,
|
|
limit=10,
|
|
)
|
|
|
|
assert len(hits) > 0
|
|
|
|
for hit in hits:
|
|
assert query_word in hit.payload["words"]
|
|
|
|
# ------------------ Test for batch queries ------------------
|
|
filter_1 = Filter(must=[FieldCondition(key="rand_number", range=Range(gte=0.3))])
|
|
filter_2 = Filter(must=[FieldCondition(key="rand_number", range=Range(gte=0.5))])
|
|
filter_3 = Filter(must=[FieldCondition(key="rand_number", range=Range(gte=0.7))])
|
|
|
|
search_queries = [
|
|
SearchRequest(
|
|
vector=query_vector_1,
|
|
filter=filter_1,
|
|
limit=5,
|
|
with_payload=True,
|
|
),
|
|
SearchRequest(
|
|
vector=query_vector_2,
|
|
filter=filter_2,
|
|
limit=5,
|
|
with_payload=True,
|
|
),
|
|
SearchRequest(
|
|
vector=query_vector_3,
|
|
filter=filter_3,
|
|
limit=5,
|
|
with_payload=True,
|
|
),
|
|
]
|
|
single_search_result_1 = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector_1,
|
|
query_filter=filter_1,
|
|
limit=5,
|
|
)
|
|
single_search_result_2 = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector_2,
|
|
query_filter=filter_2,
|
|
limit=5,
|
|
)
|
|
single_search_result_3 = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector_3,
|
|
query_filter=filter_3,
|
|
limit=5,
|
|
)
|
|
|
|
batch_search_result = client.search_batch(
|
|
collection_name=COLLECTION_NAME, requests=search_queries
|
|
)
|
|
|
|
assert len(batch_search_result) == 3
|
|
assert batch_search_result[0] == single_search_result_1
|
|
assert batch_search_result[1] == single_search_result_2
|
|
assert batch_search_result[2] == single_search_result_3
|
|
|
|
recommend_queries = [
|
|
RecommendRequest(
|
|
positive=[1],
|
|
negative=[],
|
|
filter=filter_1,
|
|
limit=5,
|
|
with_payload=True,
|
|
),
|
|
RecommendRequest(
|
|
positive=[2],
|
|
negative=[],
|
|
filter=filter_2,
|
|
limit=5,
|
|
with_payload=True,
|
|
),
|
|
RecommendRequest(
|
|
positive=[3],
|
|
negative=[],
|
|
filter=filter_3,
|
|
limit=5,
|
|
with_payload=True,
|
|
),
|
|
]
|
|
reco_result_1 = client.recommend(
|
|
collection_name=COLLECTION_NAME, positive=[1], query_filter=filter_1, limit=5
|
|
)
|
|
reco_result_2 = client.recommend(
|
|
collection_name=COLLECTION_NAME, positive=[2], query_filter=filter_2, limit=5
|
|
)
|
|
reco_result_3 = client.recommend(
|
|
collection_name=COLLECTION_NAME, positive=[3], query_filter=filter_3, limit=5
|
|
)
|
|
|
|
batch_reco_result = client.recommend_batch(
|
|
collection_name=COLLECTION_NAME, requests=recommend_queries
|
|
)
|
|
|
|
assert len(batch_reco_result) == 3
|
|
assert batch_reco_result[0] == reco_result_1
|
|
assert batch_reco_result[1] == reco_result_2
|
|
assert batch_reco_result[2] == reco_result_3
|
|
|
|
# ------------------ End of batch queries test ----------------
|
|
|
|
assert len(got_points) == 3
|
|
|
|
client.delete(
|
|
collection_name=COLLECTION_NAME,
|
|
wait=True,
|
|
points_selector=PointIdsList(points=[2, 3]),
|
|
)
|
|
|
|
got_points = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[1, 2, 3],
|
|
with_payload=True,
|
|
with_vectors=True,
|
|
)
|
|
|
|
assert len(got_points) == 1
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
wait=True,
|
|
points=[PointStruct(id=2, payload={"hello": "world"}, vector=vectors_2)],
|
|
)
|
|
|
|
got_points = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[1, 2, 3],
|
|
with_payload=True,
|
|
with_vectors=True,
|
|
)
|
|
|
|
assert len(got_points) == 2
|
|
|
|
client.set_payload(
|
|
collection_name=COLLECTION_NAME,
|
|
payload={"new_key": 123},
|
|
points=[1, 2],
|
|
wait=True,
|
|
)
|
|
|
|
got_points = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[1, 2],
|
|
with_payload=True,
|
|
with_vectors=True,
|
|
)
|
|
|
|
for point in got_points:
|
|
assert point.payload.get("new_key") == 123
|
|
|
|
client.delete_payload(
|
|
collection_name=COLLECTION_NAME,
|
|
keys=["new_key"],
|
|
points=[1],
|
|
)
|
|
|
|
got_points = client.retrieve(
|
|
collection_name=COLLECTION_NAME, ids=[1], with_payload=True, with_vectors=True
|
|
)
|
|
|
|
for point in got_points:
|
|
assert "new_key" not in point.payload
|
|
|
|
client.clear_payload(
|
|
collection_name=COLLECTION_NAME,
|
|
points_selector=PointIdsList(points=[1, 2]),
|
|
)
|
|
|
|
got_points = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[1, 2],
|
|
with_payload=True,
|
|
with_vectors=True,
|
|
)
|
|
|
|
for point in got_points:
|
|
assert not point.payload
|
|
|
|
positive = [1, 2, query_vector.tolist()]
|
|
negative = []
|
|
|
|
if version is not None and version < "v1.6.0":
|
|
positive = [1, 2]
|
|
negative = []
|
|
|
|
recommended_points = client.recommend(
|
|
collection_name=COLLECTION_NAME,
|
|
positive=positive,
|
|
negative=negative,
|
|
query_filter=Filter(
|
|
must=[ # These conditions are required for recommend results
|
|
FieldCondition(
|
|
key="rand_number", # Condition based on values of `rand_number` field.
|
|
range=Range(lte=0.5), # Select only those results where `rand_number` >= 0.5
|
|
)
|
|
]
|
|
),
|
|
limit=5,
|
|
with_payload=True,
|
|
with_vectors=False,
|
|
)
|
|
|
|
assert len(recommended_points) == 5
|
|
|
|
scrolled_points, next_page = client.scroll(
|
|
collection_name=COLLECTION_NAME,
|
|
scroll_filter=Filter(
|
|
must=[ # These conditions are required for scroll results
|
|
FieldCondition(
|
|
key="rand_number", # Condition based on values of `rand_number` field.
|
|
range=Range(lte=0.5), # Return only those results where `rand_number` <= 0.5
|
|
)
|
|
]
|
|
),
|
|
limit=5,
|
|
offset=None,
|
|
with_payload=True,
|
|
with_vectors=False,
|
|
)
|
|
|
|
assert isinstance(next_page, (int, str))
|
|
|
|
assert len(scrolled_points) == 5
|
|
|
|
_, next_page = client.scroll(
|
|
collection_name=COLLECTION_NAME,
|
|
scroll_filter=Filter(
|
|
must=[ # These conditions are required for scroll results
|
|
FieldCondition(
|
|
key="rand_number", # Condition based on values of `rand_number` field.
|
|
range=Range(lte=0.5), # Return only those results where `rand_number` <= 0.5
|
|
)
|
|
]
|
|
),
|
|
limit=1000,
|
|
offset=None,
|
|
with_payload=True,
|
|
with_vectors=False,
|
|
)
|
|
|
|
assert next_page is None
|
|
|
|
if version is None or (version >= "v1.5.0" or version == "dev"):
|
|
client.batch_update_points(
|
|
collection_name=COLLECTION_NAME,
|
|
ordering=models.WriteOrdering.STRONG,
|
|
update_operations=[
|
|
models.UpsertOperation(
|
|
upsert=models.PointsList(
|
|
points=[
|
|
models.PointStruct(
|
|
id=1,
|
|
payload={"new_key": 123},
|
|
vector=vectors_2,
|
|
),
|
|
models.PointStruct(
|
|
id=2,
|
|
payload={"new_key": 321},
|
|
vector=vectors_2,
|
|
),
|
|
]
|
|
)
|
|
),
|
|
models.DeleteOperation(delete=models.PointIdsList(points=[2])),
|
|
models.SetPayloadOperation(
|
|
set_payload=models.SetPayload(payload={"new_key2": 321}, points=[1])
|
|
),
|
|
models.OverwritePayloadOperation(
|
|
overwrite_payload=models.SetPayload(
|
|
payload={
|
|
"new_key3": 321,
|
|
"new_key4": 321,
|
|
},
|
|
points=[1],
|
|
)
|
|
),
|
|
models.DeletePayloadOperation(
|
|
delete_payload=models.DeletePayload(keys=["new_key3"], points=[1])
|
|
),
|
|
models.ClearPayloadOperation(clear_payload=models.PointIdsList(points=[1])),
|
|
models.UpdateVectorsOperation(
|
|
update_vectors=models.UpdateVectors(
|
|
points=[
|
|
models.PointVectors(
|
|
id=1,
|
|
vector=vectors_2,
|
|
)
|
|
]
|
|
)
|
|
),
|
|
models.DeleteVectorsOperation(
|
|
delete_vectors=models.DeleteVectors(points=[1], vector=[""])
|
|
),
|
|
],
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_qdrant_client_integration_update_collection(prefer_grpc):
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config={
|
|
"text": VectorParams(size=DIM, distance=Distance.DOT),
|
|
},
|
|
timeout=TIMEOUT,
|
|
)
|
|
|
|
client.update_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config={
|
|
"text": VectorParamsDiff(
|
|
hnsw_config=HnswConfigDiff(
|
|
m=32,
|
|
ef_construct=123,
|
|
),
|
|
quantization_config=ProductQuantization(
|
|
product=ProductQuantizationConfig(
|
|
compression=CompressionRatio.X32,
|
|
always_ram=True,
|
|
),
|
|
),
|
|
on_disk=True,
|
|
),
|
|
},
|
|
hnsw_config=HnswConfigDiff(
|
|
ef_construct=123,
|
|
),
|
|
quantization_config=ScalarQuantization(
|
|
scalar=ScalarQuantizationConfig(
|
|
type=ScalarType.INT8,
|
|
quantile=0.8,
|
|
always_ram=False,
|
|
),
|
|
),
|
|
optimizers_config=OptimizersConfigDiff(max_segment_size=10000),
|
|
)
|
|
|
|
collection_info = client.get_collection(COLLECTION_NAME)
|
|
|
|
# Many collection update parameters are available since v1.4.0
|
|
version = os.getenv("QDRANT_VERSION")
|
|
if version is None or (version >= "v1.4.0" or version == "dev"):
|
|
assert collection_info.config.params.vectors["text"].hnsw_config.m == 32
|
|
assert collection_info.config.params.vectors["text"].hnsw_config.ef_construct == 123
|
|
assert (
|
|
collection_info.config.params.vectors["text"].quantization_config.product.compression
|
|
== CompressionRatio.X32
|
|
)
|
|
assert collection_info.config.params.vectors["text"].quantization_config.product.always_ram
|
|
assert collection_info.config.params.vectors["text"].on_disk
|
|
assert collection_info.config.hnsw_config.ef_construct == 123
|
|
assert collection_info.config.quantization_config.scalar.type == ScalarType.INT8
|
|
assert 0.7999 < collection_info.config.quantization_config.scalar.quantile < 0.8001
|
|
assert not collection_info.config.quantization_config.scalar.always_ram
|
|
|
|
assert collection_info.config.optimizer_config.max_segment_size == 10000
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_points_crud(prefer_grpc):
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
|
|
timeout=TIMEOUT,
|
|
)
|
|
|
|
# Create a single point
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointStruct(id=123, payload={"test": "value"}, vector=np.random.rand(DIM).tolist())
|
|
],
|
|
wait=True,
|
|
)
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=Batch(
|
|
ids=[3, 4],
|
|
vectors=[np.random.rand(DIM).tolist(), np.random.rand(DIM).tolist()],
|
|
payloads=[
|
|
{"test": "value", "test2": "value2"},
|
|
{"test": "value", "test2": {"haha": "???"}},
|
|
],
|
|
),
|
|
)
|
|
|
|
# Read a single point
|
|
|
|
points = client.retrieve(COLLECTION_NAME, ids=[123])
|
|
|
|
print("read a single point", points)
|
|
|
|
# Update a single point
|
|
|
|
client.set_payload(
|
|
collection_name=COLLECTION_NAME,
|
|
payload={"test2": ["value2", "value3"]},
|
|
points=[123],
|
|
)
|
|
|
|
# Delete a single point
|
|
client.delete(collection_name=COLLECTION_NAME, points_selector=PointIdsList(points=[123]))
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_quantization_config(prefer_grpc):
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
|
|
quantization_config=ScalarQuantization(
|
|
scalar=ScalarQuantizationConfig(
|
|
type=ScalarType.INT8,
|
|
quantile=1.0,
|
|
always_ram=True,
|
|
),
|
|
),
|
|
timeout=TIMEOUT,
|
|
)
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointStruct(id=2001, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=2002, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=2003, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=2004, vector=np.random.rand(DIM).tolist()),
|
|
],
|
|
wait=True,
|
|
)
|
|
|
|
collection_info = client.get_collection(COLLECTION_NAME)
|
|
|
|
quantization_config = collection_info.config.quantization_config
|
|
|
|
assert isinstance(quantization_config, ScalarQuantization)
|
|
assert quantization_config.scalar.type == ScalarType.INT8
|
|
assert quantization_config.scalar.quantile == 1.0
|
|
assert quantization_config.scalar.always_ram is True
|
|
|
|
_res = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=np.random.rand(DIM),
|
|
search_params=SearchParams(
|
|
quantization=QuantizationSearchParams(
|
|
rescore=True,
|
|
)
|
|
),
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_custom_sharding(prefer_grpc):
|
|
version = os.getenv("QDRANT_VERSION")
|
|
if version is not None and version < "v1.7.0":
|
|
pytest.skip("Custom sharding is supported since v1.7.0")
|
|
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
def init_collection():
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
|
|
sharding_method=models.ShardingMethod.CUSTOM,
|
|
)
|
|
|
|
client.create_shard_key(collection_name=COLLECTION_NAME, shard_key=cats_shard_key)
|
|
client.create_shard_key(collection_name=COLLECTION_NAME, shard_key=dogs_shard_key)
|
|
|
|
cat_ids = [1, 2, 3]
|
|
cat_vectors = [np.random.rand(DIM).tolist() for _ in range(len(cat_ids))]
|
|
cat_payload = [{"name": "Barsik"}, {"name": "Murzik"}, {"name": "Chubais"}]
|
|
cats_shard_key = "cats"
|
|
|
|
dog_ids = [4, 5, 6]
|
|
dog_vectors = [np.random.rand(DIM).tolist() for _ in range(len(dog_ids))]
|
|
dog_payload = [{"name": "Sharik"}, {"name": "Tuzik"}, {"name": "Bobik"}]
|
|
dogs_shard_key = "dogs"
|
|
|
|
cat_points = [
|
|
PointStruct(id=id_, vector=vector, payload=payload)
|
|
for id_, vector, payload in zip(cat_ids, cat_vectors, cat_payload)
|
|
]
|
|
dog_points = [
|
|
PointStruct(id=id_, vector=vector, payload=payload)
|
|
for id_, vector, payload in zip(dog_ids, dog_vectors, dog_payload)
|
|
]
|
|
|
|
# region upsert
|
|
init_collection()
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=cat_points,
|
|
shard_key_selector=cats_shard_key,
|
|
)
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=dog_points,
|
|
shard_key_selector=dogs_shard_key,
|
|
)
|
|
|
|
res = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=np.random.rand(DIM),
|
|
shard_key_selector=cats_shard_key,
|
|
)
|
|
|
|
assert len(res) == 3
|
|
for record in res:
|
|
assert record.shard_key == cats_shard_key
|
|
|
|
res = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=np.random.rand(DIM),
|
|
shard_key_selector=[cats_shard_key, dogs_shard_key],
|
|
)
|
|
|
|
assert len(res) == 6
|
|
|
|
res = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=np.random.rand(DIM),
|
|
)
|
|
|
|
assert len(res) == 6
|
|
|
|
# endregion
|
|
|
|
# region upload_collection
|
|
init_collection()
|
|
|
|
client.upload_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors=cat_vectors,
|
|
ids=cat_ids,
|
|
payload=cat_payload,
|
|
shard_key_selector=cats_shard_key,
|
|
)
|
|
|
|
res = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=np.random.rand(DIM),
|
|
shard_key_selector=cats_shard_key,
|
|
)
|
|
|
|
assert len(res) == 3
|
|
for record in res:
|
|
assert record.shard_key == cats_shard_key
|
|
# endregion
|
|
|
|
# region upload_points
|
|
init_collection()
|
|
|
|
cat_points = [
|
|
PointStruct(id=id_, vector=vector, payload=payload)
|
|
for id_, vector, payload in zip(cat_ids, cat_vectors, cat_payload)
|
|
]
|
|
|
|
client.upload_points(
|
|
collection_name=COLLECTION_NAME,
|
|
points=cat_points,
|
|
shard_key_selector=cats_shard_key,
|
|
)
|
|
|
|
res = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=np.random.rand(DIM),
|
|
shard_key_selector=cats_shard_key,
|
|
)
|
|
|
|
assert len(res) == 3
|
|
|
|
res = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=np.random.rand(DIM),
|
|
shard_key_selector=dogs_shard_key,
|
|
)
|
|
|
|
assert len(res) == 0
|
|
|
|
# endregion
|
|
|
|
client.delete_shard_key(collection_name=COLLECTION_NAME, shard_key=dogs_shard_key)
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_sparse_vectors(prefer_grpc):
|
|
version = os.getenv("QDRANT_VERSION")
|
|
if version is not None and version < "v1.7.0":
|
|
pytest.skip("Sparse vectors are supported since v1.7.0")
|
|
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config={},
|
|
sparse_vectors_config={
|
|
"text": models.SparseVectorParams(
|
|
index=models.SparseIndexParams(
|
|
on_disk=False,
|
|
full_scan_threshold=100,
|
|
)
|
|
)
|
|
},
|
|
)
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
models.PointStruct(
|
|
id=1,
|
|
vector={
|
|
"text": models.SparseVector(
|
|
indices=[1, 2, 3],
|
|
values=[1.0, 2.0, 3.0],
|
|
)
|
|
},
|
|
),
|
|
models.PointStruct(
|
|
id=2,
|
|
vector={
|
|
"text": models.SparseVector(
|
|
indices=[3, 4, 5],
|
|
values=[1.0, 2.0, 3.0],
|
|
)
|
|
},
|
|
),
|
|
models.PointStruct(
|
|
id=3,
|
|
vector={
|
|
"text": models.SparseVector(
|
|
indices=[5, 6, 7],
|
|
values=[1.0, 2.0, 3.0],
|
|
)
|
|
},
|
|
),
|
|
],
|
|
)
|
|
|
|
result = client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=models.NamedSparseVector(
|
|
name="text",
|
|
vector=models.SparseVector(
|
|
indices=[1, 7],
|
|
values=[2.0, 1.0],
|
|
),
|
|
),
|
|
with_vectors=["text"],
|
|
)
|
|
|
|
assert len(result) == 2
|
|
assert result[0].id == 3
|
|
assert result[1].id == 1
|
|
|
|
assert result[0].score == 3.0
|
|
assert result[1].score == 2.0
|
|
|
|
assert result[0].vector["text"].indices == [5, 6, 7]
|
|
assert result[0].vector["text"].values == [1.0, 2.0, 3.0]
|
|
assert result[1].vector["text"].indices == [1, 2, 3]
|
|
assert result[1].vector["text"].values == [1.0, 2.0, 3.0]
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_sparse_vectors_batch(prefer_grpc):
|
|
version = os.getenv("QDRANT_VERSION")
|
|
if version is not None and version < "v1.7.0":
|
|
pytest.skip("Sparse vectors are supported since v1.7.0")
|
|
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config={},
|
|
sparse_vectors_config={
|
|
"text": models.SparseVectorParams(
|
|
index=models.SparseIndexParams(
|
|
on_disk=False,
|
|
full_scan_threshold=100,
|
|
)
|
|
)
|
|
},
|
|
)
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
models.PointStruct(
|
|
id=1,
|
|
vector={
|
|
"text": models.SparseVector(
|
|
indices=[1, 2, 3],
|
|
values=[1.0, 2.0, 3.0],
|
|
)
|
|
},
|
|
),
|
|
models.PointStruct(
|
|
id=2,
|
|
vector={
|
|
"text": models.SparseVector(
|
|
indices=[3, 4, 5],
|
|
values=[1.0, 2.0, 3.0],
|
|
)
|
|
},
|
|
),
|
|
models.PointStruct(
|
|
id=3,
|
|
vector={
|
|
"text": models.SparseVector(
|
|
indices=[5, 6, 7],
|
|
values=[1.0, 2.0, 3.0],
|
|
)
|
|
},
|
|
),
|
|
],
|
|
)
|
|
|
|
request = models.SearchRequest(
|
|
vector=models.NamedSparseVector(
|
|
name="text",
|
|
vector=models.SparseVector(
|
|
indices=[1, 7],
|
|
values=[2.0, 1.0],
|
|
),
|
|
),
|
|
limit=3,
|
|
with_vector=["text"],
|
|
)
|
|
|
|
results = client.search_batch(
|
|
collection_name=COLLECTION_NAME,
|
|
requests=[request],
|
|
)
|
|
|
|
result = results[0]
|
|
|
|
assert len(result) == 2
|
|
assert result[0].id == 3
|
|
assert result[1].id == 1
|
|
|
|
assert result[0].score == 3.0
|
|
assert result[1].score == 2.0
|
|
|
|
assert result[0].vector["text"].indices == [5, 6, 7]
|
|
assert result[0].vector["text"].values == [1.0, 2.0, 3.0]
|
|
assert result[1].vector["text"].indices == [1, 2, 3]
|
|
assert result[1].vector["text"].values == [1.0, 2.0, 3.0]
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_vector_update(prefer_grpc):
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
|
|
timeout=TIMEOUT,
|
|
)
|
|
|
|
uuid1 = str(uuid.uuid4())
|
|
uuid2 = str(uuid.uuid4())
|
|
uuid3 = str(uuid.uuid4())
|
|
uuid4 = str(uuid.uuid4())
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointStruct(id=uuid1, payload={"a": 1}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=uuid2, payload={"a": 2}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=uuid3, payload={"b": 1}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=uuid4, payload={"b": 2}, vector=np.random.rand(DIM).tolist()),
|
|
],
|
|
wait=True,
|
|
)
|
|
|
|
client.update_vectors(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointVectors(
|
|
id=uuid2,
|
|
vector=[1.0] * DIM,
|
|
)
|
|
],
|
|
)
|
|
|
|
result = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[uuid2],
|
|
with_vectors=True,
|
|
)[0]
|
|
|
|
assert result.vector == [1] * DIM
|
|
|
|
client.delete_vectors(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors=[""],
|
|
points=Filter(must=[FieldCondition(key="b", range=Range(gte=1))]),
|
|
)
|
|
|
|
result = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[uuid4],
|
|
with_vectors=True,
|
|
)[0]
|
|
|
|
assert result.vector == {}
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_conditional_payload_update(prefer_grpc):
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
|
|
timeout=TIMEOUT,
|
|
)
|
|
|
|
uuid1 = str(uuid.uuid4())
|
|
uuid2 = str(uuid.uuid4())
|
|
uuid3 = str(uuid.uuid4())
|
|
uuid4 = str(uuid.uuid4())
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointStruct(id=uuid1, payload={"a": 1}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=uuid2, payload={"a": 2}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=uuid3, payload={"b": 1}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=uuid4, payload={"b": 2}, vector=np.random.rand(DIM).tolist()),
|
|
],
|
|
wait=True,
|
|
)
|
|
|
|
res = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[uuid1, uuid2, uuid4],
|
|
)
|
|
|
|
assert len(res) == 3
|
|
retrieved_ids = [uuid.UUID(point.id) for point in res]
|
|
|
|
assert uuid.UUID(uuid1) in retrieved_ids
|
|
assert uuid.UUID(uuid2) in retrieved_ids
|
|
assert uuid.UUID(uuid4) in retrieved_ids
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_conditional_payload_update_2(prefer_grpc):
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
|
|
timeout=TIMEOUT,
|
|
)
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointStruct(id=1001, payload={"a": 1}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=1002, payload={"a": 2}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=1003, payload={"b": 1}, vector=np.random.rand(DIM).tolist()),
|
|
PointStruct(id=1004, payload={"b": 2}, vector=np.random.rand(DIM).tolist()),
|
|
],
|
|
wait=True,
|
|
)
|
|
|
|
client.set_payload(
|
|
collection_name=COLLECTION_NAME,
|
|
payload={"c": 1},
|
|
points=Filter(must=[FieldCondition(key="a", range=Range(gte=1))]),
|
|
wait=True,
|
|
)
|
|
|
|
points = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[1001, 1002, 1003, 1004],
|
|
with_payload=True,
|
|
with_vectors=False,
|
|
)
|
|
|
|
points = sorted(points, key=lambda p: p.id)
|
|
|
|
assert points[0].payload.get("c") == 1
|
|
assert points[1].payload.get("c") == 1
|
|
assert points[2].payload.get("c") is None
|
|
assert points[3].payload.get("c") is None
|
|
|
|
client.overwrite_payload(
|
|
collection_name=COLLECTION_NAME,
|
|
payload={"c": 2},
|
|
points=Filter(must=[FieldCondition(key="b", range=Range(lt=10))]),
|
|
)
|
|
|
|
points = client.retrieve(
|
|
collection_name=COLLECTION_NAME,
|
|
ids=[1001, 1002, 1003, 1004],
|
|
with_payload=True,
|
|
with_vectors=False,
|
|
)
|
|
points = sorted(points, key=lambda p: p.id)
|
|
|
|
assert points[0].payload.get("c") == 1
|
|
assert points[1].payload.get("c") == 1
|
|
assert points[2].payload == {"c": 2}
|
|
assert points[3].payload == {"c": 2}
|
|
|
|
|
|
def test_has_id_condition():
|
|
query = to_dict(
|
|
Filter(
|
|
must=[
|
|
HasIdCondition(has_id=[42, 43]),
|
|
FieldCondition(key="field_name", match=MatchValue(value="field_value_42")),
|
|
]
|
|
)
|
|
)
|
|
|
|
assert query["must"][0]["has_id"] == [42, 43]
|
|
|
|
|
|
def test_insert_float():
|
|
point = PointStruct(id=123, payload={"value": 0.123}, vector=np.random.rand(DIM).tolist())
|
|
|
|
assert isinstance(point.payload["value"], float)
|
|
|
|
|
|
def test_locks():
|
|
client = QdrantClient(timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config=VectorParams(size=DIM, distance=Distance.DOT),
|
|
timeout=TIMEOUT,
|
|
)
|
|
|
|
client.lock_storage(reason="testing reason")
|
|
|
|
try:
|
|
# Create a single point
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointStruct(
|
|
id=123,
|
|
payload={"test": "value"},
|
|
vector=np.random.rand(DIM).tolist(),
|
|
)
|
|
],
|
|
wait=True,
|
|
)
|
|
assert False, "Should not be able to insert a point when storage is locked"
|
|
except Exception as e:
|
|
assert "testing reason" in str(e)
|
|
pass
|
|
|
|
lock_options = client.get_locks()
|
|
assert lock_options.write is True
|
|
assert lock_options.error_message == "testing reason"
|
|
|
|
client.unlock_storage()
|
|
|
|
# should be fine now
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointStruct(id=123, payload={"test": "value"}, vector=np.random.rand(DIM).tolist())
|
|
],
|
|
wait=True,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [False, True])
|
|
def test_empty_vector(prefer_grpc):
|
|
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
|
|
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config={},
|
|
timeout=TIMEOUT,
|
|
)
|
|
|
|
client.upsert(
|
|
collection_name=COLLECTION_NAME,
|
|
points=[
|
|
PointStruct(id=123, payload={"test": "value"}, vector={}),
|
|
],
|
|
)
|
|
|
|
|
|
def test_legacy_imports():
|
|
try:
|
|
from qdrant_openapi_client.api.points_api import SyncPointsApi
|
|
from qdrant_openapi_client.exceptions import UnexpectedResponse
|
|
from qdrant_openapi_client.models.models import FieldCondition, Filter
|
|
except ImportError:
|
|
assert False # can't import, fail
|
|
|
|
|
|
def test_value_serialization():
|
|
v = json_to_value(123)
|
|
print(v)
|
|
|
|
|
|
def test_serialization():
|
|
from qdrant_client.grpc import PointId as PointIdGrpc
|
|
from qdrant_client.grpc import PointStruct as PointStructGrpc
|
|
from qdrant_client.grpc import Vector, Vectors
|
|
|
|
point = PointStructGrpc(
|
|
id=PointIdGrpc(num=1),
|
|
vectors=Vectors(vector=Vector(data=[1.0, 2.0, 3.0, 4.0])),
|
|
payload=payload_to_grpc(
|
|
{
|
|
"a": 123,
|
|
"b": "text",
|
|
"c": [1, 2, 3],
|
|
"d": {
|
|
"val1": "val2",
|
|
"val2": [1, 2, 3],
|
|
"val3": [],
|
|
"val4": {},
|
|
},
|
|
"e": True,
|
|
"f": None,
|
|
}
|
|
),
|
|
)
|
|
print("\n")
|
|
print(point.payload)
|
|
data = point.SerializeToString()
|
|
res = PointStructGrpc()
|
|
res.ParseFromString(data)
|
|
print(res.payload)
|
|
print(grpc_to_payload(res.payload))
|
|
|
|
|
|
def test_client_close():
|
|
import tempfile
|
|
|
|
from qdrant_client.http import exceptions as qdrant_exceptions
|
|
|
|
# region http
|
|
client_http = QdrantClient(timeout=TIMEOUT)
|
|
client_http.recreate_collection(
|
|
"test", vectors_config=VectorParams(size=100, distance=Distance.COSINE)
|
|
)
|
|
client_http.close()
|
|
with pytest.raises(qdrant_exceptions.ResponseHandlingException):
|
|
client_http.recreate_collection(
|
|
"test", vectors_config=VectorParams(size=100, distance=Distance.COSINE)
|
|
)
|
|
# endregion
|
|
|
|
# region grpc
|
|
client_grpc = QdrantClient(prefer_grpc=True, timeout=TIMEOUT)
|
|
client_grpc.recreate_collection(
|
|
"test", vectors_config=VectorParams(size=100, distance=Distance.COSINE)
|
|
)
|
|
client_grpc.close()
|
|
with pytest.raises(ValueError):
|
|
client_grpc.get_collection("test")
|
|
with pytest.raises(
|
|
RuntimeError
|
|
): # prevent reinitializing grpc connection, since http connection is closed
|
|
client_grpc._client._init_grpc_channel()
|
|
|
|
client_grpc_do_nothing = QdrantClient(
|
|
prefer_grpc=True, timeout=TIMEOUT
|
|
) # do not establish a connection
|
|
client_grpc_do_nothing.close()
|
|
with pytest.raises(
|
|
RuntimeError
|
|
): # prevent initializing grpc connection, since http connection is closed
|
|
_ = client_grpc_do_nothing.get_collection("test")
|
|
|
|
client_aio_grpc = QdrantClient(prefer_grpc=True, timeout=TIMEOUT)
|
|
_ = client_aio_grpc.async_grpc_collections
|
|
client_aio_grpc.close()
|
|
|
|
client_aio_grpc = QdrantClient(prefer_grpc=True, timeout=TIMEOUT)
|
|
_ = client_aio_grpc.async_grpc_collections
|
|
client_aio_grpc.close(grace=2.0)
|
|
with pytest.raises(RuntimeError):
|
|
client_aio_grpc._client._init_async_grpc_channel() # prevent reinitializing grpc connection, since
|
|
# http connection is closed
|
|
|
|
client_aio_grpc_do_nothing = QdrantClient(prefer_grpc=True, timeout=TIMEOUT)
|
|
client_aio_grpc_do_nothing.close()
|
|
with pytest.raises(
|
|
RuntimeError
|
|
): # prevent initializing grpc connection, since http connection is closed
|
|
_ = client_aio_grpc_do_nothing.async_grpc_collections
|
|
# endregion grpc
|
|
|
|
# region local
|
|
local_client_in_mem = QdrantClient(":memory:")
|
|
local_client_in_mem.recreate_collection(
|
|
"test", vectors_config=VectorParams(size=100, distance=Distance.COSINE)
|
|
)
|
|
local_client_in_mem.close()
|
|
assert local_client_in_mem._client.closed is True
|
|
|
|
with pytest.raises(RuntimeError):
|
|
local_client_in_mem.upsert(
|
|
"test", [PointStruct(id=1, vector=np.random.rand(100).tolist())]
|
|
)
|
|
|
|
with pytest.raises(RuntimeError):
|
|
local_client_in_mem.create_collection(
|
|
"test", vectors_config=VectorParams(size=100, distance=Distance.COSINE)
|
|
)
|
|
|
|
with pytest.raises(RuntimeError):
|
|
local_client_in_mem.delete_collection("test")
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
path = tmpdir + "/test.db"
|
|
|
|
local_client_persist_1 = QdrantClient(path=path)
|
|
local_client_persist_1.recreate_collection(
|
|
"test", vectors_config=VectorParams(size=100, distance=Distance.COSINE)
|
|
)
|
|
local_client_persist_1.close()
|
|
|
|
local_client_persist_2 = QdrantClient(path=path)
|
|
local_client_persist_2.recreate_collection(
|
|
"test", vectors_config=VectorParams(size=100, distance=Distance.COSINE)
|
|
)
|
|
local_client_persist_2.close()
|
|
# endregion local
|
|
|
|
|
|
def test_timeout_propagation():
|
|
import time
|
|
|
|
from httpx import Timeout
|
|
|
|
client = QdrantClient()
|
|
vectors_config = models.VectorParams(size=2, distance=models.Distance.COSINE)
|
|
with pytest.raises(
|
|
qdrant_client.http.exceptions.ResponseHandlingException, match=r"timed out"
|
|
):
|
|
# timeout is Optional[int]
|
|
# if we set it to 0 - recreate_collection raises operation is in progress instead of timed out
|
|
client.http.client._client._timeout = Timeout(0.01)
|
|
client.recreate_collection(collection_name=COLLECTION_NAME, vectors_config=vectors_config)
|
|
time.sleep(0.5)
|
|
client.recreate_collection(
|
|
collection_name=COLLECTION_NAME, vectors_config=vectors_config, timeout=10
|
|
)
|
|
|
|
|
|
def test_grpc_options():
|
|
client = QdrantClient(prefer_grpc=True)
|
|
assert client._client._grpc_options is None
|
|
|
|
client = QdrantClient(prefer_grpc=True, grpc_options={"grpc.max_send_message_length": 3})
|
|
assert client._client._grpc_options == {"grpc.max_send_message_length": 3}
|
|
|
|
with pytest.raises(RpcError):
|
|
client.create_collection(
|
|
"grpc_collection",
|
|
vectors_config=models.VectorParams(size=100, distance=models.Distance.COSINE),
|
|
)
|
|
|
|
|
|
def test_grpc_compression():
|
|
client = QdrantClient(prefer_grpc=True, grpc_compression=Compression.Gzip)
|
|
client.get_collections()
|
|
|
|
client = QdrantClient(prefer_grpc=True, grpc_compression=Compression.NoCompression)
|
|
client.get_collections()
|
|
|
|
with pytest.raises(ValueError):
|
|
# creates a grpc client with not supported Compression type
|
|
QdrantClient(prefer_grpc=True, grpc_compression=Compression.Deflate)
|
|
|
|
with pytest.raises(TypeError):
|
|
QdrantClient(prefer_grpc=True, grpc_compression="gzip")
|
|
|
|
|
|
def test_auth_token_provider():
|
|
"""Check that the token provided is called for both http and grpc clients."""
|
|
token = ""
|
|
call_num = 0
|
|
|
|
def auth_token_provider():
|
|
nonlocal token
|
|
nonlocal call_num
|
|
|
|
token = f"token_{call_num}"
|
|
call_num += 1
|
|
return token
|
|
|
|
client = QdrantClient(auth_token_provider=auth_token_provider)
|
|
client.get_collections()
|
|
assert token == "token_0"
|
|
client.get_collections()
|
|
assert token == "token_1"
|
|
|
|
token = ""
|
|
call_num = 0
|
|
|
|
client = QdrantClient(prefer_grpc=True, auth_token_provider=auth_token_provider)
|
|
client.get_collections()
|
|
assert token == "token_0"
|
|
client.get_collections()
|
|
assert token == "token_1"
|
|
|
|
client.unlock_storage()
|
|
assert token == "token_2"
|
|
|
|
|
|
def test_async_auth_token_provider():
|
|
"""Check that initialization fails if async auth_token_provider is provided to sync client."""
|
|
token = ""
|
|
|
|
async def auth_token_provider():
|
|
nonlocal token
|
|
await asyncio.sleep(0.1)
|
|
token = "test_token"
|
|
return token
|
|
|
|
client = QdrantClient(auth_token_provider=auth_token_provider)
|
|
|
|
with pytest.raises(
|
|
qdrant_client.http.exceptions.ResponseHandlingException,
|
|
match="Synchronous token provider is not set.",
|
|
):
|
|
client.get_collections()
|
|
|
|
assert token == ""
|
|
|
|
client = QdrantClient(auth_token_provider=auth_token_provider, prefer_grpc=True)
|
|
with pytest.raises(
|
|
ValueError, match="Synchronous channel requires synchronous auth token provider."
|
|
):
|
|
client.get_collections()
|
|
|
|
assert token == ""
|
|
|
|
|
|
@pytest.mark.parametrize("prefer_grpc", [True, False])
|
|
def test_read_consistency(prefer_grpc):
|
|
fixture_points = generate_fixtures(vectors_sizes=DIM, num=NUM_VECTORS)
|
|
client = init_remote(prefer_grpc=prefer_grpc)
|
|
init_client(
|
|
client,
|
|
fixture_points,
|
|
collection_name=COLLECTION_NAME,
|
|
vectors_config=models.VectorParams(size=DIM, distance=models.Distance.DOT),
|
|
)
|
|
|
|
query_vector = fixture_points[0].vector
|
|
|
|
client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
limit=5, # Return 5 closest points
|
|
consistency=models.ReadConsistencyType.MAJORITY,
|
|
)
|
|
|
|
client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
limit=5, # Return 5 closest points
|
|
consistency=models.ReadConsistencyType.MAJORITY,
|
|
)
|
|
|
|
client.search(
|
|
collection_name=COLLECTION_NAME,
|
|
query_vector=query_vector,
|
|
limit=5, # Return 5 closest points
|
|
consistency=2,
|
|
)
|
|
|
|
search_requests = [models.SearchRequest(vector=query_vector, limit=5)]
|
|
client.search_batch(
|
|
collection_name=COLLECTION_NAME,
|
|
requests=search_requests,
|
|
)
|
|
|
|
client.search_batch(
|
|
collection_name=COLLECTION_NAME,
|
|
requests=search_requests,
|
|
consistency=models.ReadConsistencyType.MAJORITY,
|
|
)
|
|
|
|
client.search_batch(collection_name=COLLECTION_NAME, requests=search_requests, consistency=2)
|
|
|
|
client.search_groups(
|
|
collection_name=COLLECTION_NAME,
|
|
group_by="word",
|
|
query_vector=query_vector,
|
|
limit=5, # Return 5 closest points
|
|
consistency=models.ReadConsistencyType.MAJORITY,
|
|
)
|
|
|
|
client.search_groups(
|
|
collection_name=COLLECTION_NAME,
|
|
group_by="word",
|
|
query_vector=query_vector,
|
|
limit=5, # Return 5 closest points
|
|
consistency=models.ReadConsistencyType.MAJORITY,
|
|
)
|
|
|
|
client.search_groups(
|
|
collection_name=COLLECTION_NAME,
|
|
group_by="word",
|
|
query_vector=query_vector,
|
|
limit=5, # Return 5 closest points
|
|
consistency=models.ReadConsistencyType.MAJORITY,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_qdrant_client_integration()
|
|
test_points_crud()
|
|
test_has_id_condition()
|
|
test_insert_float()
|
|
test_legacy_imports()
|