import asyncio
import logging
import math
import warnings
from multiprocessing import get_all_start_methods
from typing import (
Any,
Awaitable,
Callable,
Dict,
Iterable,
List,
Mapping,
Optional,
Sequence,
Tuple,
Type,
Union,
get_args,
)
import httpx
import numpy as np
from grpc import Compression
from urllib3.util import Url, parse_url
from qdrant_client import grpc as grpc
from qdrant_client._pydantic_compat import construct
from qdrant_client.auth import BearerAuth
from qdrant_client.client_base import QdrantBase
from qdrant_client.connection import get_async_channel, get_channel
from qdrant_client.conversions import common_types as types
from qdrant_client.conversions.common_types import get_args_subscribed
from qdrant_client.conversions.conversion import (
GrpcToRest,
RestToGrpc,
grpc_payload_schema_to_field_type,
)
from qdrant_client.http import ApiClient, SyncApis, models
from qdrant_client.parallel_processor import ParallelWorkerPool
from qdrant_client.uploader.grpc_uploader import GrpcBatchUploader
from qdrant_client.uploader.rest_uploader import RestBatchUploader
from qdrant_client.uploader.uploader import BaseUploader
[docs]class QdrantRemote(QdrantBase):
def __init__(
self,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
grpc_options: Optional[Dict[str, Any]] = None,
auth_token_provider: Optional[
Union[Callable[[], str], Callable[[], Awaitable[str]]]
] = None,
**kwargs: Any,
):
super().__init__(**kwargs)
self._prefer_grpc = prefer_grpc
self._grpc_port = grpc_port
self._grpc_options = grpc_options
self._https = https if https is not None else api_key is not None
self._scheme = "https" if self._https else "http"
self._prefix = prefix or ""
if len(self._prefix) > 0 and self._prefix[0] != "/":
self._prefix = f"/{self._prefix}"
if url is not None and host is not None:
raise ValueError(f"Only one of (url, host) can be set. url is {url}, host is {host}")
if host is not None and (host.startswith("http://") or host.startswith("https://")):
raise ValueError(
f"`host` param is not expected to contain protocol (http:// or https://). "
f"Try to use `url` parameter instead."
)
elif url:
if url.startswith("localhost"):
# Handle for a special case when url is localhost:port
# Which is not parsed correctly by urllib
url = f"//{url}"
parsed_url: Url = parse_url(url)
self._host, self._port = parsed_url.host, parsed_url.port
if parsed_url.scheme:
self._https = parsed_url.scheme == "https"
self._scheme = parsed_url.scheme
self._port = self._port if self._port else port
if self._prefix and parsed_url.path:
raise ValueError(
"Prefix can be set either in `url` or in `prefix`. "
f"url is {url}, prefix is {parsed_url.path}"
)
if self._scheme not in ("http", "https"):
raise ValueError(f"Unknown scheme: {self._scheme}")
else:
self._host = host or "localhost"
self._port = port
self._timeout = (
math.ceil(timeout) if timeout is not None else None
) # it has been changed from float to int.
# convert it to the closest greater or equal int value (e.g. 0.5 -> 1)
self._api_key = api_key
self._auth_token_provider = auth_token_provider
limits = kwargs.pop("limits", None)
if limits is None:
if self._host in ["localhost", "127.0.0.1"]:
# Disable keep-alive for local connections
# Cause in some cases, it may cause extra delays
limits = httpx.Limits(max_connections=None, max_keepalive_connections=0)
http2 = kwargs.pop("http2", False)
self._grpc_headers = []
self._rest_headers = kwargs.pop("metadata", {})
if api_key is not None:
if self._scheme == "http":
warnings.warn("Api key is used with an insecure connection.")
# http2 = True
self._rest_headers["api-key"] = api_key
self._grpc_headers.append(("api-key", api_key))
# GRPC Channel-Level Compression
grpc_compression: Optional[Compression] = kwargs.pop("grpc_compression", None)
if grpc_compression is not None and not isinstance(grpc_compression, Compression):
raise TypeError(
f"Expected 'grpc_compression' to be of type "
f"grpc.Compression or None, but got {type(grpc_compression)}"
)
if grpc_compression == Compression.Deflate:
raise ValueError(
"grpc.Compression.Deflate is not supported. Try grpc.Compression.Gzip or grpc.Compression.NoCompression"
)
self._grpc_compression = grpc_compression
address = f"{self._host}:{self._port}" if self._port is not None else self._host
self.rest_uri = f"{self._scheme}://{address}{self._prefix}"
self._rest_args = {"headers": self._rest_headers, "http2": http2, **kwargs}
if limits is not None:
self._rest_args["limits"] = limits
if self._timeout is not None:
self._rest_args["timeout"] = self._timeout
if self._auth_token_provider is not None:
if self._scheme == "http":
warnings.warn("Auth token provider is used with an insecure connection.")
bearer_auth = BearerAuth(self._auth_token_provider)
self._rest_args["auth"] = bearer_auth
self.openapi_client: SyncApis[ApiClient] = SyncApis(
host=self.rest_uri,
**self._rest_args,
)
self._grpc_channel = None
self._grpc_points_client: Optional[grpc.PointsStub] = None
self._grpc_collections_client: Optional[grpc.CollectionsStub] = None
self._grpc_snapshots_client: Optional[grpc.SnapshotsStub] = None
self._grpc_root_client: Optional[grpc.QdrantStub] = None
self._aio_grpc_channel = None
self._aio_grpc_points_client: Optional[grpc.PointsStub] = None
self._aio_grpc_collections_client: Optional[grpc.CollectionsStub] = None
self._aio_grpc_snapshots_client: Optional[grpc.SnapshotsStub] = None
self._aio_grpc_root_client: Optional[grpc.QdrantStub] = None
self._closed: bool = False
@property
def closed(self) -> bool:
return self._closed
[docs] def close(self, grpc_grace: Optional[float] = None, **kwargs: Any) -> None:
if hasattr(self, "_grpc_channel") and self._grpc_channel is not None:
try:
self._grpc_channel.close()
except AttributeError:
logging.warning(
"Unable to close grpc_channel. Connection was interrupted on the server side"
)
if hasattr(self, "_aio_grpc_channel") and self._aio_grpc_channel is not None:
try:
loop = asyncio.get_running_loop()
loop.create_task(self._aio_grpc_channel.close(grace=grpc_grace))
except AttributeError:
logging.warning(
"Unable to close aio_grpc_channel. Connection was interrupted on the server side"
)
except RuntimeError:
pass
try:
self.openapi_client.close()
except Exception:
logging.warning(
"Unable to close http connection. Connection was interrupted on the server side"
)
self._closed = True
@staticmethod
def _parse_url(url: str) -> Tuple[Optional[str], str, Optional[int], Optional[str]]:
parse_result: Url = parse_url(url)
scheme, host, port, prefix = (
parse_result.scheme,
parse_result.host,
parse_result.port,
parse_result.path,
)
return scheme, host, port, prefix
def _init_grpc_channel(self) -> None:
if self._closed:
raise RuntimeError("Client was closed. Please create a new QdrantClient instance.")
if self._grpc_channel is None:
self._grpc_channel = get_channel(
host=self._host,
port=self._grpc_port,
ssl=self._https,
metadata=self._grpc_headers,
options=self._grpc_options,
compression=self._grpc_compression,
# sync get_channel does not accept coroutine functions,
# but we can't check type here, since it'll get into async client as well
auth_token_provider=self._auth_token_provider, # type: ignore
)
def _init_async_grpc_channel(self) -> None:
if self._closed:
raise RuntimeError("Client was closed. Please create a new QdrantClient instance.")
if self._aio_grpc_channel is None:
self._aio_grpc_channel = get_async_channel(
host=self._host,
port=self._grpc_port,
ssl=self._https,
metadata=self._grpc_headers,
options=self._grpc_options,
compression=self._grpc_compression,
auth_token_provider=self._auth_token_provider,
)
def _init_grpc_points_client(self) -> None:
self._init_grpc_channel()
self._grpc_points_client = grpc.PointsStub(self._grpc_channel)
def _init_grpc_collections_client(self) -> None:
self._init_grpc_channel()
self._grpc_collections_client = grpc.CollectionsStub(self._grpc_channel)
def _init_grpc_snapshots_client(self) -> None:
self._init_grpc_channel()
self._grpc_snapshots_client = grpc.SnapshotsStub(self._grpc_channel)
def _init_grpc_root_client(self) -> None:
self._init_grpc_channel()
self._grpc_root_client = grpc.QdrantStub(self._grpc_channel)
def _init_async_grpc_points_client(self) -> None:
self._init_async_grpc_channel()
self._aio_grpc_points_client = grpc.PointsStub(self._aio_grpc_channel)
def _init_async_grpc_collections_client(self) -> None:
self._init_async_grpc_channel()
self._aio_grpc_collections_client = grpc.CollectionsStub(self._aio_grpc_channel)
def _init_async_grpc_snapshots_client(self) -> None:
self._init_async_grpc_channel()
self._aio_grpc_snapshots_client = grpc.SnapshotsStub(self._aio_grpc_channel)
def _init_async_grpc_root_client(self) -> None:
self._init_async_grpc_channel()
self._aio_grpc_root_client = grpc.QdrantStub(self._aio_grpc_channel)
@property
def async_grpc_collections(self) -> grpc.CollectionsStub:
"""gRPC client for collections methods
Returns:
An instance of raw gRPC client, generated from Protobuf
"""
if self._aio_grpc_collections_client is None:
self._init_async_grpc_collections_client()
return self._aio_grpc_collections_client
@property
def async_grpc_points(self) -> grpc.PointsStub:
"""gRPC client for points methods
Returns:
An instance of raw gRPC client, generated from Protobuf
"""
if self._aio_grpc_points_client is None:
self._init_async_grpc_points_client()
return self._aio_grpc_points_client
@property
def async_grpc_snapshots(self) -> grpc.SnapshotsStub:
"""gRPC client for snapshots methods
Returns:
An instance of raw gRPC client, generated from Protobuf
"""
warnings.warn(
"async_grpc_snapshots is deprecated and will be removed in a future release. Use `AsyncQdrantRemote.grpc_snapshots` instead.",
DeprecationWarning,
stacklevel=2,
)
if self._aio_grpc_snapshots_client is None:
self._init_async_grpc_snapshots_client()
return self._aio_grpc_snapshots_client
@property
def async_grpc_root(self) -> grpc.QdrantStub:
"""gRPC client for info methods
Returns:
An instance of raw gRPC client, generated from Protobuf
"""
warnings.warn(
"async_grpc_root is deprecated and will be removed in a future release. Use `AsyncQdrantRemote.grpc_root` instead.",
DeprecationWarning,
stacklevel=2,
)
if self._aio_grpc_root_client is None:
self._init_async_grpc_root_client()
return self._aio_grpc_root_client
@property
def grpc_collections(self) -> grpc.CollectionsStub:
"""gRPC client for collections methods
Returns:
An instance of raw gRPC client, generated from Protobuf
"""
if self._grpc_collections_client is None:
self._init_grpc_collections_client()
return self._grpc_collections_client
@property
def grpc_points(self) -> grpc.PointsStub:
"""gRPC client for points methods
Returns:
An instance of raw gRPC client, generated from Protobuf
"""
if self._grpc_points_client is None:
self._init_grpc_points_client()
return self._grpc_points_client
@property
def grpc_snapshots(self) -> grpc.SnapshotsStub:
"""gRPC client for snapshots methods
Returns:
An instance of raw gRPC client, generated from Protobuf
"""
if self._grpc_snapshots_client is None:
self._init_grpc_snapshots_client()
return self._grpc_snapshots_client
@property
def grpc_root(self) -> grpc.QdrantStub:
"""gRPC client for info methods
Returns:
An instance of raw gRPC client, generated from Protobuf
"""
if self._grpc_root_client is None:
self._init_grpc_root_client()
return self._grpc_root_client
@property
def rest(self) -> SyncApis[ApiClient]:
"""REST Client
Returns:
An instance of raw REST API client, generated from OpenAPI schema
"""
return self.openapi_client
@property
def http(self) -> SyncApis[ApiClient]:
"""REST Client
Returns:
An instance of raw REST API client, generated from OpenAPI schema
"""
return self.openapi_client
[docs] def search_batch(
self,
collection_name: str,
requests: Sequence[types.SearchRequest],
consistency: Optional[types.ReadConsistency] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[List[types.ScoredPoint]]:
if self._prefer_grpc:
requests = [
(
RestToGrpc.convert_search_request(r, collection_name)
if isinstance(r, models.SearchRequest)
else r
)
for r in requests
]
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
grpc_res: grpc.SearchBatchResponse = self.grpc_points.SearchBatch(
grpc.SearchBatchPoints(
collection_name=collection_name,
search_points=requests,
read_consistency=consistency,
timeout=timeout,
),
timeout=timeout if timeout is not None else self._timeout,
)
return [
[GrpcToRest.convert_scored_point(hit) for hit in r.result] for r in grpc_res.result
]
else:
requests = [
(GrpcToRest.convert_search_points(r) if isinstance(r, grpc.SearchPoints) else r)
for r in requests
]
http_res: Optional[List[List[models.ScoredPoint]]] = (
self.http.points_api.search_batch_points(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
search_request_batch=models.SearchRequestBatch(searches=requests),
).result
)
assert http_res is not None, "Search batch returned None"
return http_res
[docs] def search(
self,
collection_name: str,
query_vector: Union[
Sequence[float],
Tuple[str, List[float]],
types.NamedVector,
types.NamedSparseVector,
types.NumpyArray,
],
query_filter: Optional[types.Filter] = None,
search_params: Optional[types.SearchParams] = None,
limit: int = 10,
offset: Optional[int] = None,
with_payload: Union[bool, Sequence[str], types.PayloadSelector] = True,
with_vectors: Union[bool, Sequence[str]] = False,
score_threshold: Optional[float] = None,
append_payload: bool = True,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[types.ScoredPoint]:
if not append_payload:
logging.warning(
"Usage of `append_payload` is deprecated. Please consider using `with_payload` instead"
)
with_payload = append_payload
if isinstance(query_vector, np.ndarray):
query_vector = query_vector.tolist()
if self._prefer_grpc:
vector_name = None
sparse_indices = None
if isinstance(query_vector, types.NamedVector):
vector = query_vector.vector
vector_name = query_vector.name
elif isinstance(query_vector, types.NamedSparseVector):
vector_name = query_vector.name
sparse_indices = grpc.SparseIndices(data=query_vector.vector.indices)
vector = query_vector.vector.values
elif isinstance(query_vector, tuple):
vector_name = query_vector[0]
vector = query_vector[1]
else:
vector = list(query_vector)
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(search_params, models.SearchParams):
search_params = RestToGrpc.convert_search_params(search_params)
if isinstance(with_payload, get_args_subscribed(models.WithPayloadInterface)):
with_payload = RestToGrpc.convert_with_payload_interface(with_payload)
if isinstance(with_vectors, get_args_subscribed(models.WithVector)):
with_vectors = RestToGrpc.convert_with_vectors(with_vectors)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
res: grpc.SearchResponse = self.grpc_points.Search(
grpc.SearchPoints(
collection_name=collection_name,
vector=vector,
vector_name=vector_name,
filter=query_filter,
limit=limit,
offset=offset,
with_vectors=with_vectors,
with_payload=with_payload,
params=search_params,
score_threshold=score_threshold,
read_consistency=consistency,
timeout=timeout,
sparse_indices=sparse_indices,
shard_key_selector=shard_key_selector,
),
timeout=timeout if timeout is None else self._timeout,
)
return [GrpcToRest.convert_scored_point(hit) for hit in res.result]
else:
if isinstance(query_vector, tuple):
query_vector = types.NamedVector(name=query_vector[0], vector=query_vector[1])
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
if isinstance(search_params, grpc.SearchParams):
search_params = GrpcToRest.convert_search_params(search_params)
if isinstance(with_payload, grpc.WithPayloadSelector):
with_payload = GrpcToRest.convert_with_payload_selector(with_payload)
search_result = self.http.points_api.search_points(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
search_request=models.SearchRequest(
vector=query_vector,
filter=query_filter,
limit=limit,
offset=offset,
params=search_params,
with_vector=with_vectors,
with_payload=with_payload,
score_threshold=score_threshold,
shard_key=shard_key_selector,
),
)
result: Optional[List[types.ScoredPoint]] = search_result.result
assert result is not None, "Search returned None"
return result
[docs] def query_points(
self,
collection_name: str,
query: Optional[types.Query] = None,
using: Optional[str] = None,
prefetch: Union[types.Prefetch, List[types.Prefetch], None] = None,
query_filter: Optional[types.Filter] = None,
search_params: Optional[types.SearchParams] = None,
limit: int = 10,
offset: Optional[int] = None,
with_payload: Union[bool, Sequence[str], types.PayloadSelector] = True,
with_vectors: Union[bool, Sequence[str]] = False,
score_threshold: Optional[float] = None,
lookup_from: Optional[types.LookupLocation] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> types.QueryResponse:
if self._prefer_grpc:
if isinstance(query, get_args(models.Query)):
query = RestToGrpc.convert_query(query)
if isinstance(prefetch, models.Prefetch):
prefetch = [RestToGrpc.convert_prefetch_query(prefetch)]
if isinstance(prefetch, list):
prefetch = [
RestToGrpc.convert_prefetch_query(p) if isinstance(p, models.Prefetch) else p
for p in prefetch
]
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(search_params, models.SearchParams):
search_params = RestToGrpc.convert_search_params(search_params)
if isinstance(with_payload, get_args_subscribed(models.WithPayloadInterface)):
with_payload = RestToGrpc.convert_with_payload_interface(with_payload)
if isinstance(with_vectors, get_args_subscribed(models.WithVector)):
with_vectors = RestToGrpc.convert_with_vectors(with_vectors)
if isinstance(lookup_from, models.LookupLocation):
lookup_from = RestToGrpc.convert_lookup_location(lookup_from)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
res: grpc.QueryResponse = self.grpc_points.Query(
grpc.QueryPoints(
collection_name=collection_name,
query=query,
prefetch=prefetch,
filter=query_filter,
limit=limit,
offset=offset,
with_vectors=with_vectors,
with_payload=with_payload,
params=search_params,
score_threshold=score_threshold,
using=using,
lookup_from=lookup_from,
timeout=timeout,
shard_key_selector=shard_key_selector,
read_consistency=consistency,
),
timeout=timeout if timeout is None else self._timeout,
)
scored_points = [GrpcToRest.convert_scored_point(hit) for hit in res.result]
return models.QueryResponse(points=scored_points)
else:
if isinstance(query, grpc.Query):
query = GrpcToRest.convert_query(query)
if isinstance(prefetch, grpc.PrefetchQuery):
prefetch = GrpcToRest.convert_prefetch_query(prefetch)
if isinstance(prefetch, list):
prefetch = [
GrpcToRest.convert_prefetch_query(p)
if isinstance(p, grpc.PrefetchQuery)
else p
for p in prefetch
]
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
if isinstance(search_params, grpc.SearchParams):
search_params = GrpcToRest.convert_search_params(search_params)
if isinstance(with_payload, grpc.WithPayloadSelector):
with_payload = GrpcToRest.convert_with_payload_selector(with_payload)
if isinstance(lookup_from, grpc.LookupLocation):
lookup_from = GrpcToRest.convert_lookup_location(lookup_from)
query_request = models.QueryRequest(
shard_key=shard_key_selector,
prefetch=prefetch,
query=query,
using=using,
filter=query_filter,
params=search_params,
score_threshold=score_threshold,
limit=limit,
offset=offset,
with_vector=with_vectors,
with_payload=with_payload,
lookup_from=lookup_from,
)
query_result = self.http.points_api.query_points(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
query_request=query_request,
)
result: Optional[models.QueryResponse] = query_result.result
assert result is not None, "Search returned None"
return result
[docs] def query_batch_points(
self,
collection_name: str,
requests: Sequence[types.QueryRequest],
consistency: Optional[types.ReadConsistency] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[types.QueryResponse]:
if self._prefer_grpc:
requests = [
(
RestToGrpc.convert_query_request(r, collection_name)
if isinstance(r, models.QueryRequest)
else r
)
for r in requests
]
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
grpc_res: grpc.QueryBatchResponse = self.grpc_points.QueryBatch(
grpc.QueryBatchPoints(
collection_name=collection_name,
query_points=requests,
read_consistency=consistency,
timeout=timeout,
),
timeout=timeout if timeout is not None else self._timeout,
)
return [
models.QueryResponse(
points=[GrpcToRest.convert_scored_point(hit) for hit in r.result]
)
for r in grpc_res.result
]
else:
requests = [
(GrpcToRest.convert_query_points(r) if isinstance(r, grpc.QueryPoints) else r)
for r in requests
]
http_res: Optional[List[models.QueryResponse]] = (
self.http.points_api.query_batch_points(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
query_request_batch=models.QueryRequestBatch(searches=requests),
).result
)
assert http_res is not None, "Query batch returned None"
return http_res
[docs] def query_points_groups(
self,
collection_name: str,
group_by: str,
query: Union[
types.PointId,
List[float],
List[List[float]],
types.SparseVector,
types.Query,
types.NumpyArray,
types.Document,
None,
] = None,
using: Optional[str] = None,
prefetch: Union[types.Prefetch, List[types.Prefetch], None] = None,
query_filter: Optional[types.Filter] = None,
search_params: Optional[types.SearchParams] = None,
limit: int = 10,
group_size: int = 3,
with_payload: Union[bool, Sequence[str], types.PayloadSelector] = True,
with_vectors: Union[bool, Sequence[str]] = False,
score_threshold: Optional[float] = None,
with_lookup: Optional[types.WithLookupInterface] = None,
lookup_from: Optional[types.LookupLocation] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> types.GroupsResult:
if self._prefer_grpc:
if isinstance(query, get_args(models.Query)):
query = RestToGrpc.convert_query(query)
if isinstance(prefetch, models.Prefetch):
prefetch = [RestToGrpc.convert_prefetch_query(prefetch)]
if isinstance(prefetch, list):
prefetch = [
RestToGrpc.convert_prefetch_query(p) if isinstance(p, models.Prefetch) else p
for p in prefetch
]
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(search_params, models.SearchParams):
search_params = RestToGrpc.convert_search_params(search_params)
if isinstance(with_payload, get_args_subscribed(models.WithPayloadInterface)):
with_payload = RestToGrpc.convert_with_payload_interface(with_payload)
if isinstance(with_vectors, get_args_subscribed(models.WithVector)):
with_vectors = RestToGrpc.convert_with_vectors(with_vectors)
if isinstance(with_lookup, models.WithLookup):
with_lookup = RestToGrpc.convert_with_lookup(with_lookup)
if isinstance(with_lookup, str):
with_lookup = grpc.WithLookup(collection=with_lookup)
if isinstance(lookup_from, models.LookupLocation):
lookup_from = RestToGrpc.convert_lookup_location(lookup_from)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
result: grpc.QueryGroupsResponse = self.grpc_points.QueryGroups(
grpc.QueryPointGroups(
collection_name=collection_name,
query=query,
prefetch=prefetch,
filter=query_filter,
limit=limit,
with_vectors=with_vectors,
with_payload=with_payload,
params=search_params,
score_threshold=score_threshold,
using=using,
group_by=group_by,
group_size=group_size,
with_lookup=with_lookup,
lookup_from=lookup_from,
timeout=timeout,
shard_key_selector=shard_key_selector,
read_consistency=consistency,
),
timeout=timeout if timeout is None else self._timeout,
).result
return GrpcToRest.convert_groups_result(result)
else:
if isinstance(query, grpc.Query):
query = GrpcToRest.convert_query(query)
if isinstance(prefetch, grpc.PrefetchQuery):
prefetch = GrpcToRest.convert_prefetch_query(prefetch)
if isinstance(prefetch, list):
prefetch = [
GrpcToRest.convert_prefetch_query(p)
if isinstance(p, grpc.PrefetchQuery)
else p
for p in prefetch
]
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
if isinstance(search_params, grpc.SearchParams):
search_params = GrpcToRest.convert_search_params(search_params)
if isinstance(with_payload, grpc.WithPayloadSelector):
with_payload = GrpcToRest.convert_with_payload_selector(with_payload)
if isinstance(with_lookup, grpc.WithLookup):
with_lookup = GrpcToRest.convert_with_lookup(with_lookup)
if isinstance(lookup_from, grpc.LookupLocation):
lookup_from = GrpcToRest.convert_lookup_location(lookup_from)
query_request = models.QueryGroupsRequest(
shard_key=shard_key_selector,
prefetch=prefetch,
query=query,
using=using,
filter=query_filter,
params=search_params,
score_threshold=score_threshold,
limit=limit,
group_by=group_by,
group_size=group_size,
with_vector=with_vectors,
with_payload=with_payload,
with_lookup=with_lookup,
lookup_from=lookup_from,
)
query_result = self.http.points_api.query_points_groups(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
query_groups_request=query_request,
)
assert query_result is not None, "Query points groups API returned None"
return query_result.result
[docs] def search_groups(
self,
collection_name: str,
query_vector: Union[
Sequence[float],
Tuple[str, List[float]],
types.NamedVector,
types.NamedSparseVector,
types.NumpyArray,
],
group_by: str,
query_filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
limit: int = 10,
group_size: int = 1,
with_payload: Union[bool, Sequence[str], models.PayloadSelector] = True,
with_vectors: Union[bool, Sequence[str]] = False,
score_threshold: Optional[float] = None,
with_lookup: Optional[types.WithLookupInterface] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> types.GroupsResult:
if self._prefer_grpc:
vector_name = None
sparse_indices = None
if isinstance(with_lookup, models.WithLookup):
with_lookup = RestToGrpc.convert_with_lookup(with_lookup)
if isinstance(with_lookup, str):
with_lookup = grpc.WithLookup(collection=with_lookup)
if isinstance(query_vector, types.NamedVector):
vector = query_vector.vector
vector_name = query_vector.name
elif isinstance(query_vector, types.NamedSparseVector):
vector_name = query_vector.name
sparse_indices = grpc.SparseIndices(data=query_vector.vector.indices)
vector = query_vector.vector.values
elif isinstance(query_vector, tuple):
vector_name = query_vector[0]
vector = query_vector[1]
else:
vector = list(query_vector)
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(search_params, models.SearchParams):
search_params = RestToGrpc.convert_search_params(search_params)
if isinstance(with_payload, get_args_subscribed(models.WithPayloadInterface)):
with_payload = RestToGrpc.convert_with_payload_interface(with_payload)
if isinstance(with_vectors, get_args_subscribed(models.WithVector)):
with_vectors = RestToGrpc.convert_with_vectors(with_vectors)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
result: grpc.GroupsResult = self.grpc_points.SearchGroups(
grpc.SearchPointGroups(
collection_name=collection_name,
vector=vector,
vector_name=vector_name,
filter=query_filter,
limit=limit,
group_size=group_size,
with_vectors=with_vectors,
with_payload=with_payload,
params=search_params,
score_threshold=score_threshold,
group_by=group_by,
read_consistency=consistency,
with_lookup=with_lookup,
timeout=timeout,
sparse_indices=sparse_indices,
shard_key_selector=shard_key_selector,
),
timeout=timeout if timeout is not None else self._timeout,
).result
return GrpcToRest.convert_groups_result(result)
else:
if isinstance(with_lookup, grpc.WithLookup):
with_lookup = GrpcToRest.convert_with_lookup(with_lookup)
if isinstance(query_vector, tuple):
query_vector = construct(
models.NamedVector,
name=query_vector[0],
vector=query_vector[1],
)
if isinstance(query_vector, np.ndarray):
query_vector = query_vector.tolist()
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
if isinstance(search_params, grpc.SearchParams):
search_params = GrpcToRest.convert_search_params(search_params)
if isinstance(with_payload, grpc.WithPayloadSelector):
with_payload = GrpcToRest.convert_with_payload_selector(with_payload)
search_groups_request = construct(
models.SearchGroupsRequest,
vector=query_vector,
filter=query_filter,
params=search_params,
with_payload=with_payload,
with_vector=with_vectors,
score_threshold=score_threshold,
group_by=group_by,
group_size=group_size,
limit=limit,
with_lookup=with_lookup,
shard_key=shard_key_selector,
)
return self.openapi_client.points_api.search_point_groups(
search_groups_request=search_groups_request,
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
).result
[docs] def search_matrix_pairs(
self,
collection_name: str,
query_filter: Optional[types.Filter] = None,
limit: int = 3,
sample: int = 10,
using: Optional[str] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> types.SearchMatrixPairsResponse:
if self._prefer_grpc:
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
response = self.grpc_points.SearchMatrixPairs(
grpc.SearchMatrixPoints(
collection_name=collection_name,
filter=query_filter,
sample=sample,
limit=limit,
using=using,
timeout=timeout,
read_consistency=consistency,
shard_key_selector=shard_key_selector,
),
timeout=timeout if timeout is not None else self._timeout,
)
return GrpcToRest.convert_search_matrix_pairs(response.result)
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
search_matrix_result = self.openapi_client.points_api.search_matrix_pairs(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
search_matrix_request=models.SearchMatrixRequest(
shard_key=shard_key_selector,
limit=limit,
sample=sample,
using=using,
filter=query_filter,
),
).result
assert search_matrix_result is not None, "Search matrix pairs returned None result"
return search_matrix_result
[docs] def search_matrix_offsets(
self,
collection_name: str,
query_filter: Optional[types.Filter] = None,
limit: int = 3,
sample: int = 10,
using: Optional[str] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> types.SearchMatrixOffsetsResponse:
if self._prefer_grpc:
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
response = self.grpc_points.SearchMatrixOffsets(
grpc.SearchMatrixPoints(
collection_name=collection_name,
filter=query_filter,
sample=sample,
limit=limit,
using=using,
timeout=timeout,
read_consistency=consistency,
shard_key_selector=shard_key_selector,
),
timeout=timeout if timeout is not None else self._timeout,
)
return GrpcToRest.convert_search_matrix_offsets(response.result)
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
search_matrix_result = self.openapi_client.points_api.search_matrix_offsets(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
search_matrix_request=models.SearchMatrixRequest(
shard_key=shard_key_selector,
limit=limit,
sample=sample,
using=using,
filter=query_filter,
),
).result
assert search_matrix_result is not None, "Search matrix offsets returned None result"
return search_matrix_result
[docs] def recommend_batch(
self,
collection_name: str,
requests: Sequence[types.RecommendRequest],
consistency: Optional[types.ReadConsistency] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[List[types.ScoredPoint]]:
if self._prefer_grpc:
requests = [
(
RestToGrpc.convert_recommend_request(r, collection_name)
if isinstance(r, models.RecommendRequest)
else r
)
for r in requests
]
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
grpc_res: grpc.SearchBatchResponse = self.grpc_points.RecommendBatch(
grpc.RecommendBatchPoints(
collection_name=collection_name,
recommend_points=requests,
read_consistency=consistency,
timeout=timeout,
),
timeout=timeout if timeout is not None else self._timeout,
)
return [
[GrpcToRest.convert_scored_point(hit) for hit in r.result] for r in grpc_res.result
]
else:
requests = [
(
GrpcToRest.convert_recommend_points(r)
if isinstance(r, grpc.RecommendPoints)
else r
)
for r in requests
]
http_res: List[List[models.ScoredPoint]] = self.http.points_api.recommend_batch_points(
collection_name=collection_name,
consistency=consistency,
recommend_request_batch=models.RecommendRequestBatch(searches=requests),
).result
return http_res
[docs] def recommend(
self,
collection_name: str,
positive: Optional[Sequence[types.RecommendExample]] = None,
negative: Optional[Sequence[types.RecommendExample]] = None,
query_filter: Optional[types.Filter] = None,
search_params: Optional[types.SearchParams] = None,
limit: int = 10,
offset: int = 0,
with_payload: Union[bool, List[str], types.PayloadSelector] = True,
with_vectors: Union[bool, List[str]] = False,
score_threshold: Optional[float] = None,
using: Optional[str] = None,
lookup_from: Optional[types.LookupLocation] = None,
strategy: Optional[types.RecommendStrategy] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[types.ScoredPoint]:
if positive is None:
positive = []
if negative is None:
negative = []
if self._prefer_grpc:
positive_ids = RestToGrpc.convert_recommend_examples_to_ids(positive)
positive_vectors = RestToGrpc.convert_recommend_examples_to_vectors(positive)
negative_ids = RestToGrpc.convert_recommend_examples_to_ids(negative)
negative_vectors = RestToGrpc.convert_recommend_examples_to_vectors(negative)
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(search_params, models.SearchParams):
search_params = RestToGrpc.convert_search_params(search_params)
if isinstance(with_payload, get_args_subscribed(models.WithPayloadInterface)):
with_payload = RestToGrpc.convert_with_payload_interface(with_payload)
if isinstance(with_vectors, get_args_subscribed(models.WithVector)):
with_vectors = RestToGrpc.convert_with_vectors(with_vectors)
if isinstance(lookup_from, models.LookupLocation):
lookup_from = RestToGrpc.convert_lookup_location(lookup_from)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
if isinstance(strategy, (str, models.RecommendStrategy)):
strategy = RestToGrpc.convert_recommend_strategy(strategy)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
res: grpc.SearchResponse = self.grpc_points.Recommend(
grpc.RecommendPoints(
collection_name=collection_name,
positive=positive_ids,
negative=negative_ids,
filter=query_filter,
limit=limit,
offset=offset,
with_vectors=with_vectors,
with_payload=with_payload,
params=search_params,
score_threshold=score_threshold,
using=using,
lookup_from=lookup_from,
read_consistency=consistency,
strategy=strategy,
positive_vectors=positive_vectors,
negative_vectors=negative_vectors,
shard_key_selector=shard_key_selector,
timeout=timeout,
),
timeout=timeout if timeout is not None else self._timeout,
)
return [GrpcToRest.convert_scored_point(hit) for hit in res.result]
else:
positive = [
(
GrpcToRest.convert_point_id(example)
if isinstance(example, grpc.PointId)
else example
)
for example in positive
]
negative = [
(
GrpcToRest.convert_point_id(example)
if isinstance(example, grpc.PointId)
else example
)
for example in negative
]
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
if isinstance(search_params, grpc.SearchParams):
search_params = GrpcToRest.convert_search_params(search_params)
if isinstance(with_payload, grpc.WithPayloadSelector):
with_payload = GrpcToRest.convert_with_payload_selector(with_payload)
if isinstance(lookup_from, grpc.LookupLocation):
lookup_from = GrpcToRest.convert_lookup_location(lookup_from)
result = self.openapi_client.points_api.recommend_points(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
recommend_request=models.RecommendRequest(
filter=query_filter,
positive=positive,
negative=negative,
params=search_params,
limit=limit,
offset=offset,
with_payload=with_payload,
with_vector=with_vectors,
score_threshold=score_threshold,
lookup_from=lookup_from,
using=using,
strategy=strategy,
shard_key=shard_key_selector,
),
).result
assert result is not None, "Recommend points API returned None"
return result
[docs] def recommend_groups(
self,
collection_name: str,
group_by: str,
positive: Optional[Sequence[Union[types.PointId, List[float]]]] = None,
negative: Optional[Sequence[Union[types.PointId, List[float]]]] = None,
query_filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
limit: int = 10,
group_size: int = 1,
score_threshold: Optional[float] = None,
with_payload: Union[bool, Sequence[str], models.PayloadSelector] = True,
with_vectors: Union[bool, Sequence[str]] = False,
using: Optional[str] = None,
lookup_from: Optional[models.LookupLocation] = None,
with_lookup: Optional[types.WithLookupInterface] = None,
strategy: Optional[types.RecommendStrategy] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> types.GroupsResult:
positive = positive if positive is not None else []
negative = negative if negative is not None else []
if self._prefer_grpc:
if isinstance(with_lookup, models.WithLookup):
with_lookup = RestToGrpc.convert_with_lookup(with_lookup)
if isinstance(with_lookup, str):
with_lookup = grpc.WithLookup(collection=with_lookup)
positive_ids = RestToGrpc.convert_recommend_examples_to_ids(positive)
positive_vectors = RestToGrpc.convert_recommend_examples_to_vectors(positive)
negative_ids = RestToGrpc.convert_recommend_examples_to_ids(negative)
negative_vectors = RestToGrpc.convert_recommend_examples_to_vectors(negative)
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(search_params, models.SearchParams):
search_params = RestToGrpc.convert_search_params(search_params)
if isinstance(with_payload, get_args_subscribed(models.WithPayloadInterface)):
with_payload = RestToGrpc.convert_with_payload_interface(with_payload)
if isinstance(with_vectors, get_args_subscribed(models.WithVector)):
with_vectors = RestToGrpc.convert_with_vectors(with_vectors)
if isinstance(lookup_from, models.LookupLocation):
lookup_from = RestToGrpc.convert_lookup_location(lookup_from)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
if isinstance(strategy, (str, models.RecommendStrategy)):
strategy = RestToGrpc.convert_recommend_strategy(strategy)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
res: grpc.GroupsResult = self.grpc_points.RecommendGroups(
grpc.RecommendPointGroups(
collection_name=collection_name,
positive=positive_ids,
negative=negative_ids,
filter=query_filter,
group_by=group_by,
limit=limit,
group_size=group_size,
with_vectors=with_vectors,
with_payload=with_payload,
params=search_params,
score_threshold=score_threshold,
using=using,
lookup_from=lookup_from,
read_consistency=consistency,
with_lookup=with_lookup,
strategy=strategy,
positive_vectors=positive_vectors,
negative_vectors=negative_vectors,
shard_key_selector=shard_key_selector,
timeout=timeout,
),
timeout=timeout if timeout is not None else self._timeout,
).result
assert res is not None, "Recommend groups API returned None"
return GrpcToRest.convert_groups_result(res)
else:
if isinstance(with_lookup, grpc.WithLookup):
with_lookup = GrpcToRest.convert_with_lookup(with_lookup)
positive = [
(
GrpcToRest.convert_point_id(point_id)
if isinstance(point_id, grpc.PointId)
else point_id
)
for point_id in positive
]
negative = [
(
GrpcToRest.convert_point_id(point_id)
if isinstance(point_id, grpc.PointId)
else point_id
)
for point_id in negative
]
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
if isinstance(search_params, grpc.SearchParams):
search_params = GrpcToRest.convert_search_params(search_params)
if isinstance(with_payload, grpc.WithPayloadSelector):
with_payload = GrpcToRest.convert_with_payload_selector(with_payload)
if isinstance(lookup_from, grpc.LookupLocation):
lookup_from = GrpcToRest.convert_lookup_location(lookup_from)
result = self.openapi_client.points_api.recommend_point_groups(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
recommend_groups_request=construct(
models.RecommendGroupsRequest,
positive=positive,
negative=negative,
filter=query_filter,
group_by=group_by,
limit=limit,
group_size=group_size,
params=search_params,
with_payload=with_payload,
with_vector=with_vectors,
score_threshold=score_threshold,
lookup_from=lookup_from,
using=using,
with_lookup=with_lookup,
strategy=strategy,
shard_key=shard_key_selector,
),
).result
assert result is not None, "Recommend points API returned None"
return result
[docs] def discover(
self,
collection_name: str,
target: Optional[types.TargetVector] = None,
context: Optional[Sequence[types.ContextExamplePair]] = None,
query_filter: Optional[types.Filter] = None,
search_params: Optional[types.SearchParams] = None,
limit: int = 10,
offset: int = 0,
with_payload: Union[bool, List[str], types.PayloadSelector] = True,
with_vectors: Union[bool, List[str]] = False,
using: Optional[str] = None,
lookup_from: Optional[types.LookupLocation] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[types.ScoredPoint]:
if context is None:
context = []
if self._prefer_grpc:
target = (
RestToGrpc.convert_target_vector(target)
if target is not None
and isinstance(target, get_args_subscribed(models.RecommendExample))
else target
)
context = [
(
RestToGrpc.convert_context_example_pair(pair)
if isinstance(pair, models.ContextExamplePair)
else pair
)
for pair in context
]
if isinstance(query_filter, models.Filter):
query_filter = RestToGrpc.convert_filter(model=query_filter)
if isinstance(search_params, models.SearchParams):
search_params = RestToGrpc.convert_search_params(search_params)
if isinstance(with_payload, get_args_subscribed(models.WithPayloadInterface)):
with_payload = RestToGrpc.convert_with_payload_interface(with_payload)
if isinstance(with_vectors, get_args_subscribed(models.WithVector)):
with_vectors = RestToGrpc.convert_with_vectors(with_vectors)
if isinstance(lookup_from, models.LookupLocation):
lookup_from = RestToGrpc.convert_lookup_location(lookup_from)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
res: grpc.SearchResponse = self.grpc_points.Discover(
grpc.DiscoverPoints(
collection_name=collection_name,
target=target,
context=context,
filter=query_filter,
limit=limit,
offset=offset,
with_vectors=with_vectors,
with_payload=with_payload,
params=search_params,
using=using,
lookup_from=lookup_from,
read_consistency=consistency,
shard_key_selector=shard_key_selector,
timeout=timeout,
),
timeout=timeout if timeout is not None else self._timeout,
)
return [GrpcToRest.convert_scored_point(hit) for hit in res.result]
else:
target = (
GrpcToRest.convert_target_vector(target)
if target is not None and isinstance(target, grpc.TargetVector)
else target
)
context = [
(
GrpcToRest.convert_context_example_pair(pair)
if isinstance(pair, grpc.ContextExamplePair)
else pair
)
for pair in context
]
if isinstance(query_filter, grpc.Filter):
query_filter = GrpcToRest.convert_filter(model=query_filter)
if isinstance(search_params, grpc.SearchParams):
search_params = GrpcToRest.convert_search_params(search_params)
if isinstance(with_payload, grpc.WithPayloadSelector):
with_payload = GrpcToRest.convert_with_payload_selector(with_payload)
if isinstance(lookup_from, grpc.LookupLocation):
lookup_from = GrpcToRest.convert_lookup_location(lookup_from)
result = self.openapi_client.points_api.discover_points(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
discover_request=models.DiscoverRequest(
target=target,
context=context,
filter=query_filter,
params=search_params,
limit=limit,
offset=offset,
with_payload=with_payload,
with_vector=with_vectors,
lookup_from=lookup_from,
using=using,
shard_key=shard_key_selector,
),
).result
assert result is not None, "Discover points API returned None"
return result
[docs] def discover_batch(
self,
collection_name: str,
requests: Sequence[types.DiscoverRequest],
consistency: Optional[types.ReadConsistency] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[List[types.ScoredPoint]]:
if self._prefer_grpc:
requests = [
(
RestToGrpc.convert_discover_request(r, collection_name)
if isinstance(r, models.DiscoverRequest)
else r
)
for r in requests
]
grpc_res: grpc.SearchBatchResponse = self.grpc_points.DiscoverBatch(
grpc.DiscoverBatchPoints(
collection_name=collection_name,
discover_points=requests,
read_consistency=consistency,
timeout=timeout,
),
timeout=timeout if timeout is not None else self._timeout,
)
return [
[GrpcToRest.convert_scored_point(hit) for hit in r.result] for r in grpc_res.result
]
else:
requests = [
(
GrpcToRest.convert_discover_points(r)
if isinstance(r, grpc.DiscoverPoints)
else r
)
for r in requests
]
http_res: List[List[models.ScoredPoint]] = self.http.points_api.discover_batch_points(
collection_name=collection_name,
discover_request_batch=models.DiscoverRequestBatch(searches=requests),
consistency=consistency,
timeout=timeout,
).result
return http_res
[docs] def count(
self,
collection_name: str,
count_filter: Optional[types.Filter] = None,
exact: bool = True,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> types.CountResult:
if self._prefer_grpc:
if isinstance(count_filter, models.Filter):
count_filter = RestToGrpc.convert_filter(model=count_filter)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
response = self.grpc_points.Count(
grpc.CountPoints(
collection_name=collection_name,
filter=count_filter,
exact=exact,
shard_key_selector=shard_key_selector,
timeout=timeout,
),
timeout=timeout if timeout is None else self._timeout,
).result
return GrpcToRest.convert_count_result(response)
if isinstance(count_filter, grpc.Filter):
count_filter = GrpcToRest.convert_filter(model=count_filter)
count_result = self.openapi_client.points_api.count_points(
collection_name=collection_name,
count_request=models.CountRequest(
filter=count_filter,
exact=exact,
shard_key=shard_key_selector,
),
timeout=timeout,
).result
assert count_result is not None, "Count points returned None result"
return count_result
[docs] def facet(
self,
collection_name: str,
key: str,
facet_filter: Optional[types.Filter] = None,
limit: int = 10,
exact: bool = False,
timeout: Optional[int] = None,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.FacetResponse:
if self._prefer_grpc:
if isinstance(facet_filter, models.Filter):
facet_filter = RestToGrpc.convert_filter(model=facet_filter)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
response = self.grpc_points.Facet(
grpc.FacetCounts(
collection_name=collection_name,
key=key,
filter=facet_filter,
limit=limit,
exact=exact,
timeout=timeout,
read_consistency=consistency,
shard_key_selector=shard_key_selector,
),
timeout=timeout if timeout is not None else self._timeout,
)
return types.FacetResponse(
hits=[GrpcToRest.convert_facet_value_hit(hit) for hit in response.hits]
)
if isinstance(facet_filter, grpc.Filter):
facet_filter = GrpcToRest.convert_filter(model=facet_filter)
facet_result = self.openapi_client.points_api.facet(
collection_name=collection_name,
consistency=consistency,
timeout=timeout,
facet_request=models.FacetRequest(
shard_key=shard_key_selector,
key=key,
limit=limit,
filter=facet_filter,
exact=exact,
),
).result
assert facet_result is not None, "Facet points returned None result"
return facet_result
[docs] def upsert(
self,
collection_name: str,
points: types.Points,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
if isinstance(points, models.Batch):
vectors_batch: List[grpc.Vectors] = RestToGrpc.convert_batch_vector_struct(
points.vectors, len(points.ids)
)
points = [
grpc.PointStruct(
id=RestToGrpc.convert_extended_point_id(points.ids[idx]),
vectors=vectors_batch[idx],
payload=(
RestToGrpc.convert_payload(points.payloads[idx])
if points.payloads is not None
else None
),
)
for idx in range(len(points.ids))
]
if isinstance(points, list):
points = [
(
RestToGrpc.convert_point_struct(point)
if isinstance(point, models.PointStruct)
else point
)
for point in points
]
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
grpc_result = self.grpc_points.Upsert(
grpc.UpsertPoints(
collection_name=collection_name,
wait=wait,
points=points,
ordering=ordering,
shard_key_selector=shard_key_selector,
),
timeout=self._timeout,
).result
assert grpc_result is not None, "Upsert returned None result"
return GrpcToRest.convert_update_result(grpc_result)
else:
if isinstance(points, list):
points = [
(
GrpcToRest.convert_point_struct(point)
if isinstance(point, grpc.PointStruct)
else point
)
for point in points
]
points = models.PointsList(points=points, shard_key=shard_key_selector)
if isinstance(points, models.Batch):
points = models.PointsBatch(batch=points, shard_key=shard_key_selector)
http_result = self.openapi_client.points_api.upsert_points(
collection_name=collection_name,
wait=wait,
point_insert_operations=points,
ordering=ordering,
).result
assert http_result is not None, "Upsert returned None result"
return http_result
[docs] def update_vectors(
self,
collection_name: str,
points: Sequence[types.PointVectors],
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
points = [RestToGrpc.convert_point_vectors(point) for point in points]
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
grpc_result = self.grpc_points.UpdateVectors(
grpc.UpdatePointVectors(
collection_name=collection_name,
wait=wait,
points=points,
ordering=ordering,
shard_key_selector=shard_key_selector,
)
).result
assert grpc_result is not None, "Upsert returned None result"
return GrpcToRest.convert_update_result(grpc_result)
else:
return self.openapi_client.points_api.update_vectors(
collection_name=collection_name,
wait=wait,
update_vectors=models.UpdateVectors(
points=points,
shard_key=shard_key_selector,
),
ordering=ordering,
).result
[docs] def delete_vectors(
self,
collection_name: str,
vectors: Sequence[str],
points: types.PointsSelector,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
points_selector, opt_shard_key_selector = self._try_argument_to_grpc_selector(points)
shard_key_selector = shard_key_selector or opt_shard_key_selector
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
grpc_result = self.grpc_points.DeleteVectors(
grpc.DeletePointVectors(
collection_name=collection_name,
wait=wait,
vectors=grpc.VectorsSelector(
names=vectors,
),
points_selector=points_selector,
ordering=ordering,
shard_key_selector=shard_key_selector,
)
).result
assert grpc_result is not None, "Delete vectors returned None result"
return GrpcToRest.convert_update_result(grpc_result)
else:
_points, _filter = self._try_argument_to_rest_points_and_filter(points)
return self.openapi_client.points_api.delete_vectors(
collection_name=collection_name,
wait=wait,
ordering=ordering,
delete_vectors=construct(
models.DeleteVectors,
vector=vectors,
points=_points,
filter=_filter,
shard_key=shard_key_selector,
),
).result
[docs] def retrieve(
self,
collection_name: str,
ids: Sequence[types.PointId],
with_payload: Union[bool, Sequence[str], types.PayloadSelector] = True,
with_vectors: Union[bool, Sequence[str]] = False,
consistency: Optional[types.ReadConsistency] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[types.Record]:
if self._prefer_grpc:
if isinstance(with_payload, get_args_subscribed(models.WithPayloadInterface)):
with_payload = RestToGrpc.convert_with_payload_interface(with_payload)
ids = [
(
RestToGrpc.convert_extended_point_id(idx)
if isinstance(idx, get_args_subscribed(models.ExtendedPointId))
else idx
)
for idx in ids
]
with_vectors = RestToGrpc.convert_with_vectors(with_vectors)
if isinstance(consistency, get_args_subscribed(models.ReadConsistency)):
consistency = RestToGrpc.convert_read_consistency(consistency)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
result = self.grpc_points.Get(
grpc.GetPoints(
collection_name=collection_name,
ids=ids,
with_payload=with_payload,
with_vectors=with_vectors,
read_consistency=consistency,
shard_key_selector=shard_key_selector,
timeout=timeout,
),
timeout=timeout if timeout is None else self._timeout,
).result
assert result is not None, "Retrieve returned None result"
return [GrpcToRest.convert_retrieved_point(record) for record in result]
else:
if isinstance(with_payload, grpc.WithPayloadSelector):
with_payload = GrpcToRest.convert_with_payload_selector(with_payload)
ids = [
(GrpcToRest.convert_point_id(idx) if isinstance(idx, grpc.PointId) else idx)
for idx in ids
]
http_result = self.openapi_client.points_api.get_points(
collection_name=collection_name,
consistency=consistency,
point_request=models.PointRequest(
ids=ids,
with_payload=with_payload,
with_vector=with_vectors,
shard_key=shard_key_selector,
),
timeout=timeout,
).result
assert http_result is not None, "Retrieve API returned None result"
return http_result
@classmethod
def _try_argument_to_grpc_selector(
cls, points: types.PointsSelector
) -> Tuple[grpc.PointsSelector, Optional[grpc.ShardKeySelector]]:
shard_key_selector = None
if isinstance(points, list):
points_selector = grpc.PointsSelector(
points=grpc.PointsIdsList(
ids=[
(
RestToGrpc.convert_extended_point_id(idx)
if isinstance(idx, get_args_subscribed(models.ExtendedPointId))
else idx
)
for idx in points
]
)
)
elif isinstance(points, grpc.PointsSelector):
points_selector = points
elif isinstance(points, get_args(models.PointsSelector)):
if points.shard_key is not None:
shard_key_selector = RestToGrpc.convert_shard_key_selector(points.shard_key)
points_selector = RestToGrpc.convert_points_selector(points)
elif isinstance(points, models.Filter):
points_selector = RestToGrpc.convert_points_selector(
construct(models.FilterSelector, filter=points)
)
elif isinstance(points, grpc.Filter):
points_selector = grpc.PointsSelector(filter=points)
else:
raise ValueError(f"Unsupported points selector type: {type(points)}")
return points_selector, shard_key_selector
@classmethod
def _try_argument_to_rest_selector(
cls,
points: types.PointsSelector,
shard_key_selector: Optional[types.ShardKeySelector],
) -> models.PointsSelector:
if isinstance(points, list):
_points = [
(GrpcToRest.convert_point_id(idx) if isinstance(idx, grpc.PointId) else idx)
for idx in points
]
points_selector = construct(
models.PointIdsList,
points=_points,
shard_key=shard_key_selector,
)
elif isinstance(points, grpc.PointsSelector):
points_selector = GrpcToRest.convert_points_selector(points)
points_selector.shard_key = shard_key_selector
elif isinstance(points, get_args(models.PointsSelector)):
points_selector = points
points_selector.shard_key = shard_key_selector
elif isinstance(points, models.Filter):
points_selector = construct(
models.FilterSelector, filter=points, shard_key=shard_key_selector
)
elif isinstance(points, grpc.Filter):
points_selector = construct(
models.FilterSelector,
filter=GrpcToRest.convert_filter(points),
shard_key=shard_key_selector,
)
else:
raise ValueError(f"Unsupported points selector type: {type(points)}")
return points_selector
@classmethod
def _points_selector_to_points_list(
cls, points_selector: grpc.PointsSelector
) -> List[grpc.PointId]:
name = points_selector.WhichOneof("points_selector_one_of")
if name is None:
return []
val = getattr(points_selector, name)
if name == "points":
return list(val.ids)
return []
@classmethod
def _try_argument_to_rest_points_and_filter(
cls, points: types.PointsSelector
) -> Tuple[Optional[List[models.ExtendedPointId]], Optional[models.Filter]]:
_points = None
_filter = None
if isinstance(points, list):
_points = [
(GrpcToRest.convert_point_id(idx) if isinstance(idx, grpc.PointId) else idx)
for idx in points
]
elif isinstance(points, grpc.PointsSelector):
selector = GrpcToRest.convert_points_selector(points)
if isinstance(selector, models.PointIdsList):
_points = selector.points
elif isinstance(selector, models.FilterSelector):
_filter = selector.filter
elif isinstance(points, models.PointIdsList):
_points = points.points
elif isinstance(points, models.FilterSelector):
_filter = points.filter
elif isinstance(points, models.Filter):
_filter = points
elif isinstance(points, grpc.Filter):
_filter = GrpcToRest.convert_filter(points)
else:
raise ValueError(f"Unsupported points selector type: {type(points)}")
return _points, _filter
[docs] def delete(
self,
collection_name: str,
points_selector: types.PointsSelector,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
points_selector, opt_shard_key_selector = self._try_argument_to_grpc_selector(
points_selector
)
shard_key_selector = shard_key_selector or opt_shard_key_selector
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
return GrpcToRest.convert_update_result(
self.grpc_points.Delete(
grpc.DeletePoints(
collection_name=collection_name,
wait=wait,
points=points_selector,
ordering=ordering,
shard_key_selector=shard_key_selector,
),
timeout=self._timeout,
).result
)
else:
points_selector = self._try_argument_to_rest_selector(
points_selector, shard_key_selector
)
result: Optional[types.UpdateResult] = self.openapi_client.points_api.delete_points(
collection_name=collection_name,
wait=wait,
points_selector=points_selector,
ordering=ordering,
).result
assert result is not None, "Delete points returned None"
return result
[docs] def set_payload(
self,
collection_name: str,
payload: types.Payload,
points: types.PointsSelector,
key: Optional[str] = None,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
points_selector, opt_shard_key_selector = self._try_argument_to_grpc_selector(points)
shard_key_selector = shard_key_selector or opt_shard_key_selector
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
return GrpcToRest.convert_update_result(
self.grpc_points.SetPayload(
grpc.SetPayloadPoints(
collection_name=collection_name,
wait=wait,
payload=RestToGrpc.convert_payload(payload),
points_selector=points_selector,
ordering=ordering,
shard_key_selector=shard_key_selector,
key=key,
),
timeout=self._timeout,
).result
)
else:
_points, _filter = self._try_argument_to_rest_points_and_filter(points)
result: Optional[types.UpdateResult] = self.openapi_client.points_api.set_payload(
collection_name=collection_name,
wait=wait,
ordering=ordering,
set_payload=models.SetPayload(
payload=payload,
points=_points,
filter=_filter,
shard_key=shard_key_selector,
key=key,
),
).result
assert result is not None, "Set payload returned None"
return result
[docs] def overwrite_payload(
self,
collection_name: str,
payload: types.Payload,
points: types.PointsSelector,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
points_selector, opt_shard_key_selector = self._try_argument_to_grpc_selector(points)
shard_key_selector = shard_key_selector or opt_shard_key_selector
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
return GrpcToRest.convert_update_result(
self.grpc_points.OverwritePayload(
grpc.SetPayloadPoints(
collection_name=collection_name,
wait=wait,
payload=RestToGrpc.convert_payload(payload),
points_selector=points_selector,
ordering=ordering,
shard_key_selector=shard_key_selector,
),
timeout=self._timeout,
).result
)
else:
_points, _filter = self._try_argument_to_rest_points_and_filter(points)
result: Optional[types.UpdateResult] = (
self.openapi_client.points_api.overwrite_payload(
collection_name=collection_name,
wait=wait,
ordering=ordering,
set_payload=models.SetPayload(
payload=payload,
points=_points,
filter=_filter,
shard_key=shard_key_selector,
),
).result
)
assert result is not None, "Overwrite payload returned None"
return result
[docs] def delete_payload(
self,
collection_name: str,
keys: Sequence[str],
points: types.PointsSelector,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
points_selector, opt_shard_key_selector = self._try_argument_to_grpc_selector(points)
shard_key_selector = shard_key_selector or opt_shard_key_selector
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
return GrpcToRest.convert_update_result(
self.grpc_points.DeletePayload(
grpc.DeletePayloadPoints(
collection_name=collection_name,
wait=wait,
keys=keys,
points_selector=points_selector,
ordering=ordering,
shard_key_selector=shard_key_selector,
),
timeout=self._timeout,
).result
)
else:
_points, _filter = self._try_argument_to_rest_points_and_filter(points)
result: Optional[types.UpdateResult] = self.openapi_client.points_api.delete_payload(
collection_name=collection_name,
wait=wait,
ordering=ordering,
delete_payload=models.DeletePayload(
keys=keys,
points=_points,
filter=_filter,
shard_key=shard_key_selector,
),
).result
assert result is not None, "Delete payload returned None"
return result
[docs] def clear_payload(
self,
collection_name: str,
points_selector: types.PointsSelector,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
points_selector, opt_shard_key_selector = self._try_argument_to_grpc_selector(
points_selector
)
shard_key_selector = shard_key_selector or opt_shard_key_selector
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
if isinstance(shard_key_selector, get_args_subscribed(models.ShardKeySelector)):
shard_key_selector = RestToGrpc.convert_shard_key_selector(shard_key_selector)
return GrpcToRest.convert_update_result(
self.grpc_points.ClearPayload(
grpc.ClearPayloadPoints(
collection_name=collection_name,
wait=wait,
points=points_selector,
ordering=ordering,
shard_key_selector=shard_key_selector,
),
timeout=self._timeout,
).result
)
else:
points_selector = self._try_argument_to_rest_selector(
points_selector, shard_key_selector
)
result: Optional[types.UpdateResult] = self.openapi_client.points_api.clear_payload(
collection_name=collection_name,
wait=wait,
ordering=ordering,
points_selector=points_selector,
).result
assert result is not None, "Clear payload returned None"
return result
[docs] def batch_update_points(
self,
collection_name: str,
update_operations: Sequence[types.UpdateOperation],
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
**kwargs: Any,
) -> List[types.UpdateResult]:
if self._prefer_grpc:
update_operations = [
RestToGrpc.convert_update_operation(operation) for operation in update_operations
]
if isinstance(ordering, models.WriteOrdering):
ordering = RestToGrpc.convert_write_ordering(ordering)
return [
GrpcToRest.convert_update_result(result)
for result in self.grpc_points.UpdateBatch(
grpc.UpdateBatchPoints(
collection_name=collection_name,
wait=wait,
operations=update_operations,
ordering=ordering,
),
timeout=self._timeout,
).result
]
else:
result: Optional[List[types.UpdateResult]] = (
self.openapi_client.points_api.batch_update(
collection_name=collection_name,
wait=wait,
ordering=ordering,
update_operations=models.UpdateOperations(operations=update_operations),
).result
)
assert result is not None, "Batch update points returned None"
return result
[docs] def update_collection_aliases(
self,
change_aliases_operations: Sequence[types.AliasOperations],
timeout: Optional[int] = None,
**kwargs: Any,
) -> bool:
if self._prefer_grpc:
change_aliases_operation = [
(
RestToGrpc.convert_alias_operations(operation)
if not isinstance(operation, grpc.AliasOperations)
else operation
)
for operation in change_aliases_operations
]
return self.grpc_collections.UpdateAliases(
grpc.ChangeAliases(
timeout=timeout,
actions=change_aliases_operation,
),
timeout=self._timeout,
).result
change_aliases_operation = [
(
GrpcToRest.convert_alias_operations(operation)
if isinstance(operation, grpc.AliasOperations)
else operation
)
for operation in change_aliases_operations
]
result: Optional[bool] = self.http.collections_api.update_aliases(
timeout=timeout,
change_aliases_operation=models.ChangeAliasesOperation(
actions=change_aliases_operation
),
).result
assert result is not None, "Update aliases returned None"
return result
[docs] def get_collection_aliases(
self, collection_name: str, **kwargs: Any
) -> types.CollectionsAliasesResponse:
if self._prefer_grpc:
response = self.grpc_collections.ListCollectionAliases(
grpc.ListCollectionAliasesRequest(collection_name=collection_name),
timeout=self._timeout,
).aliases
return types.CollectionsAliasesResponse(
aliases=[
GrpcToRest.convert_alias_description(description) for description in response
]
)
result: Optional[types.CollectionsAliasesResponse] = (
self.http.collections_api.get_collection_aliases(
collection_name=collection_name
).result
)
assert result is not None, "Get collection aliases returned None"
return result
[docs] def get_aliases(self, **kwargs: Any) -> types.CollectionsAliasesResponse:
if self._prefer_grpc:
response = self.grpc_collections.ListAliases(
grpc.ListAliasesRequest(), timeout=self._timeout
).aliases
return types.CollectionsAliasesResponse(
aliases=[
GrpcToRest.convert_alias_description(description) for description in response
]
)
result: Optional[types.CollectionsAliasesResponse] = (
self.http.collections_api.get_collections_aliases().result
)
assert result is not None, "Get aliases returned None"
return result
[docs] def get_collections(self, **kwargs: Any) -> types.CollectionsResponse:
if self._prefer_grpc:
response = self.grpc_collections.List(
grpc.ListCollectionsRequest(), timeout=self._timeout
).collections
return types.CollectionsResponse(
collections=[
GrpcToRest.convert_collection_description(description)
for description in response
]
)
result: Optional[types.CollectionsResponse] = (
self.http.collections_api.get_collections().result
)
assert result is not None, "Get collections returned None"
return result
[docs] def get_collection(self, collection_name: str, **kwargs: Any) -> types.CollectionInfo:
if self._prefer_grpc:
return GrpcToRest.convert_collection_info(
self.grpc_collections.Get(
grpc.GetCollectionInfoRequest(collection_name=collection_name),
timeout=self._timeout,
).result
)
result: Optional[types.CollectionInfo] = self.http.collections_api.get_collection(
collection_name=collection_name
).result
assert result is not None, "Get collection returned None"
return result
[docs] def collection_exists(self, collection_name: str, **kwargs: Any) -> bool:
if self._prefer_grpc:
return self.grpc_collections.CollectionExists(
grpc.CollectionExistsRequest(collection_name=collection_name),
timeout=self._timeout,
).result.exists
result: Optional[models.CollectionExistence] = self.http.collections_api.collection_exists(
collection_name=collection_name
).result
assert result is not None, "Collection exists returned None"
return result.exists
[docs] def update_collection(
self,
collection_name: str,
optimizers_config: Optional[types.OptimizersConfigDiff] = None,
collection_params: Optional[types.CollectionParamsDiff] = None,
vectors_config: Optional[types.VectorsConfigDiff] = None,
hnsw_config: Optional[types.HnswConfigDiff] = None,
quantization_config: Optional[types.QuantizationConfigDiff] = None,
timeout: Optional[int] = None,
sparse_vectors_config: Optional[Mapping[str, types.SparseVectorParams]] = None,
**kwargs: Any,
) -> bool:
if self._prefer_grpc:
if isinstance(optimizers_config, models.OptimizersConfigDiff):
optimizers_config = RestToGrpc.convert_optimizers_config_diff(optimizers_config)
if isinstance(collection_params, models.CollectionParamsDiff):
collection_params = RestToGrpc.convert_collection_params_diff(collection_params)
if isinstance(vectors_config, dict):
vectors_config = RestToGrpc.convert_vectors_config_diff(vectors_config)
if isinstance(hnsw_config, models.HnswConfigDiff):
hnsw_config = RestToGrpc.convert_hnsw_config_diff(hnsw_config)
if isinstance(quantization_config, get_args(models.QuantizationConfigDiff)):
quantization_config = RestToGrpc.convert_quantization_config_diff(
quantization_config
)
if isinstance(sparse_vectors_config, dict):
sparse_vectors_config = RestToGrpc.convert_sparse_vector_config(
sparse_vectors_config
)
return self.grpc_collections.Update(
grpc.UpdateCollection(
collection_name=collection_name,
optimizers_config=optimizers_config,
params=collection_params,
vectors_config=vectors_config,
hnsw_config=hnsw_config,
quantization_config=quantization_config,
sparse_vectors_config=sparse_vectors_config,
),
timeout=self._timeout,
).result
if isinstance(optimizers_config, grpc.OptimizersConfigDiff):
optimizers_config = GrpcToRest.convert_optimizers_config_diff(optimizers_config)
if isinstance(collection_params, grpc.CollectionParamsDiff):
collection_params = GrpcToRest.convert_collection_params_diff(collection_params)
if isinstance(vectors_config, grpc.VectorsConfigDiff):
vectors_config = GrpcToRest.convert_vectors_config_diff(vectors_config)
if isinstance(hnsw_config, grpc.HnswConfigDiff):
hnsw_config = GrpcToRest.convert_hnsw_config_diff(hnsw_config)
if isinstance(quantization_config, grpc.QuantizationConfigDiff):
quantization_config = GrpcToRest.convert_quantization_config_diff(quantization_config)
result: Optional[bool] = self.http.collections_api.update_collection(
collection_name,
update_collection=models.UpdateCollection(
optimizers_config=optimizers_config,
params=collection_params,
vectors=vectors_config,
hnsw_config=hnsw_config,
quantization_config=quantization_config,
sparse_vectors=sparse_vectors_config,
),
timeout=timeout,
).result
assert result is not None, "Update collection returned None"
return result
[docs] def delete_collection(
self, collection_name: str, timeout: Optional[int] = None, **kwargs: Any
) -> bool:
if self._prefer_grpc:
return self.grpc_collections.Delete(
grpc.DeleteCollection(collection_name=collection_name),
timeout=self._timeout,
).result
result: Optional[bool] = self.http.collections_api.delete_collection(
collection_name, timeout=timeout
).result
assert result is not None, "Delete collection returned None"
return result
[docs] def create_collection(
self,
collection_name: str,
vectors_config: Union[types.VectorParams, Mapping[str, types.VectorParams]],
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[types.HnswConfigDiff] = None,
optimizers_config: Optional[types.OptimizersConfigDiff] = None,
wal_config: Optional[types.WalConfigDiff] = None,
quantization_config: Optional[types.QuantizationConfig] = None,
init_from: Optional[types.InitFrom] = None,
timeout: Optional[int] = None,
sparse_vectors_config: Optional[Mapping[str, types.SparseVectorParams]] = None,
sharding_method: Optional[types.ShardingMethod] = None,
**kwargs: Any,
) -> bool:
if init_from is not None:
logging.warning("init_from is deprecated")
if self._prefer_grpc:
if isinstance(vectors_config, (models.VectorParams, dict)):
vectors_config = RestToGrpc.convert_vectors_config(vectors_config)
if isinstance(hnsw_config, models.HnswConfigDiff):
hnsw_config = RestToGrpc.convert_hnsw_config_diff(hnsw_config)
if isinstance(optimizers_config, models.OptimizersConfigDiff):
optimizers_config = RestToGrpc.convert_optimizers_config_diff(optimizers_config)
if isinstance(wal_config, models.WalConfigDiff):
wal_config = RestToGrpc.convert_wal_config_diff(wal_config)
if isinstance(
quantization_config,
get_args(models.QuantizationConfig),
):
quantization_config = RestToGrpc.convert_quantization_config(quantization_config)
if isinstance(init_from, models.InitFrom):
init_from = RestToGrpc.convert_init_from(init_from)
if isinstance(sparse_vectors_config, dict):
sparse_vectors_config = RestToGrpc.convert_sparse_vector_config(
sparse_vectors_config
)
if isinstance(sharding_method, models.ShardingMethod):
sharding_method = RestToGrpc.convert_sharding_method(sharding_method)
create_collection = grpc.CreateCollection(
collection_name=collection_name,
hnsw_config=hnsw_config,
wal_config=wal_config,
optimizers_config=optimizers_config,
shard_number=shard_number,
on_disk_payload=on_disk_payload,
timeout=timeout,
vectors_config=vectors_config,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
init_from_collection=init_from,
quantization_config=quantization_config,
sparse_vectors_config=sparse_vectors_config,
sharding_method=sharding_method,
)
return self.grpc_collections.Create(create_collection).result
if isinstance(hnsw_config, grpc.HnswConfigDiff):
hnsw_config = GrpcToRest.convert_hnsw_config_diff(hnsw_config)
if isinstance(optimizers_config, grpc.OptimizersConfigDiff):
optimizers_config = GrpcToRest.convert_optimizers_config_diff(optimizers_config)
if isinstance(wal_config, grpc.WalConfigDiff):
wal_config = GrpcToRest.convert_wal_config_diff(wal_config)
if isinstance(quantization_config, grpc.QuantizationConfig):
quantization_config = GrpcToRest.convert_quantization_config(quantization_config)
if isinstance(init_from, str):
init_from = GrpcToRest.convert_init_from(init_from)
create_collection_request = models.CreateCollection(
vectors=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
sparse_vectors=sparse_vectors_config,
sharding_method=sharding_method,
)
result: Optional[bool] = self.http.collections_api.create_collection(
collection_name=collection_name,
create_collection=create_collection_request,
timeout=timeout,
).result
assert result is not None, "Create collection returned None"
return result
[docs] def recreate_collection(
self,
collection_name: str,
vectors_config: Union[types.VectorParams, Mapping[str, types.VectorParams]],
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[types.HnswConfigDiff] = None,
optimizers_config: Optional[types.OptimizersConfigDiff] = None,
wal_config: Optional[types.WalConfigDiff] = None,
quantization_config: Optional[types.QuantizationConfig] = None,
init_from: Optional[types.InitFrom] = None,
timeout: Optional[int] = None,
sparse_vectors_config: Optional[Mapping[str, types.SparseVectorParams]] = None,
sharding_method: Optional[types.ShardingMethod] = None,
**kwargs: Any,
) -> bool:
self.delete_collection(collection_name, timeout=timeout)
return self.create_collection(
collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout,
sparse_vectors_config=sparse_vectors_config,
sharding_method=sharding_method,
)
@property
def _updater_class(self) -> Type[BaseUploader]:
if self._prefer_grpc:
return GrpcBatchUploader
else:
return RestBatchUploader
def _upload_collection(
self,
batches_iterator: Iterable,
collection_name: str,
max_retries: int,
parallel: int = 1,
method: Optional[str] = None,
wait: bool = False,
shard_key_selector: Optional[types.ShardKeySelector] = None,
) -> None:
if method is not None:
if method in get_all_start_methods():
start_method = method
else:
raise ValueError(
f"Start methods {method} is not available, available methods: {get_all_start_methods()}"
)
else:
start_method = "forkserver" if "forkserver" in get_all_start_methods() else "spawn"
if self._prefer_grpc:
updater_kwargs = {
"collection_name": collection_name,
"host": self._host,
"port": self._grpc_port,
"max_retries": max_retries,
"ssl": self._https,
"metadata": self._grpc_headers,
"wait": wait,
"shard_key_selector": shard_key_selector,
}
else:
updater_kwargs = {
"collection_name": collection_name,
"uri": self.rest_uri,
"max_retries": max_retries,
"wait": wait,
"shard_key_selector": shard_key_selector,
**self._rest_args,
}
if parallel == 1:
updater = self._updater_class.start(**updater_kwargs)
for _ in updater.process(batches_iterator):
pass
else:
pool = ParallelWorkerPool(parallel, self._updater_class, start_method=start_method)
for _ in pool.unordered_map(batches_iterator, **updater_kwargs):
pass
[docs] def upload_records(
self,
collection_name: str,
records: Iterable[types.Record],
batch_size: int = 64,
parallel: int = 1,
method: Optional[str] = None,
max_retries: int = 3,
wait: bool = False,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> None:
batches_iterator = self._updater_class.iterate_records_batches(
records=records, batch_size=batch_size
)
self._upload_collection(
batches_iterator=batches_iterator,
collection_name=collection_name,
max_retries=max_retries,
parallel=parallel,
method=method,
shard_key_selector=shard_key_selector,
wait=wait,
)
[docs] def upload_points(
self,
collection_name: str,
points: Iterable[types.PointStruct],
batch_size: int = 64,
parallel: int = 1,
method: Optional[str] = None,
max_retries: int = 3,
wait: bool = False,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> None:
batches_iterator = self._updater_class.iterate_records_batches(
records=points, batch_size=batch_size
)
self._upload_collection(
batches_iterator=batches_iterator,
collection_name=collection_name,
max_retries=max_retries,
parallel=parallel,
method=method,
wait=wait,
shard_key_selector=shard_key_selector,
)
[docs] def upload_collection(
self,
collection_name: str,
vectors: Union[
Dict[str, types.NumpyArray], types.NumpyArray, Iterable[types.VectorStruct]
],
payload: Optional[Iterable[Dict[Any, Any]]] = None,
ids: Optional[Iterable[types.PointId]] = None,
batch_size: int = 64,
parallel: int = 1,
method: Optional[str] = None,
max_retries: int = 3,
wait: bool = False,
shard_key_selector: Optional[types.ShardKeySelector] = None,
**kwargs: Any,
) -> None:
batches_iterator = self._updater_class.iterate_batches(
vectors=vectors,
payload=payload,
ids=ids,
batch_size=batch_size,
)
self._upload_collection(
batches_iterator=batches_iterator,
collection_name=collection_name,
max_retries=max_retries,
parallel=parallel,
method=method,
wait=wait,
shard_key_selector=shard_key_selector,
)
[docs] def create_payload_index(
self,
collection_name: str,
field_name: str,
field_schema: Optional[types.PayloadSchemaType] = None,
field_type: Optional[types.PayloadSchemaType] = None,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
**kwargs: Any,
) -> types.UpdateResult:
if field_type is not None:
warnings.warn("field_type is deprecated, use field_schema instead", DeprecationWarning)
field_schema = field_type
if self._prefer_grpc:
field_index_params = None
if isinstance(field_schema, models.PayloadSchemaType):
field_schema = RestToGrpc.convert_payload_schema_type(field_schema)
if isinstance(field_schema, int):
# There are no means to distinguish grpc.PayloadSchemaType and grpc.FieldType,
# as both of them are just ints
# method signature assumes that grpc.PayloadSchemaType is passed,
# otherwise the value will be corrupted
field_schema = grpc_payload_schema_to_field_type(field_schema)
if isinstance(field_schema, get_args(models.PayloadSchemaParams)):
field_schema = RestToGrpc.convert_payload_schema_params(field_schema)
if isinstance(field_schema, grpc.PayloadIndexParams):
field_index_params = field_schema
name = field_index_params.WhichOneof("index_params")
index_params = getattr(field_index_params, name)
if isinstance(index_params, grpc.TextIndexParams):
field_schema = grpc.FieldType.FieldTypeText
if isinstance(index_params, grpc.IntegerIndexParams):
field_schema = grpc.FieldType.FieldTypeInteger
if isinstance(index_params, grpc.KeywordIndexParams):
field_schema = grpc.FieldType.FieldTypeKeyword
if isinstance(index_params, grpc.FloatIndexParams):
field_schema = grpc.FieldType.FieldTypeFloat
if isinstance(index_params, grpc.GeoIndexParams):
field_schema = grpc.FieldType.FieldTypeGeo
if isinstance(index_params, grpc.BoolIndexParams):
field_schema = grpc.FieldType.FieldTypeBool
if isinstance(index_params, grpc.DatetimeIndexParams):
field_schema = grpc.FieldType.FieldTypeDatetime
if isinstance(index_params, grpc.UuidIndexParams):
field_schema = grpc.FieldType.FieldTypeUuid
request = grpc.CreateFieldIndexCollection(
collection_name=collection_name,
field_name=field_name,
field_type=field_schema,
field_index_params=field_index_params,
wait=wait,
ordering=ordering,
)
return GrpcToRest.convert_update_result(
self.grpc_points.CreateFieldIndex(request).result
)
if isinstance(field_schema, int): # type(grpc.PayloadSchemaType) == int
field_schema = GrpcToRest.convert_payload_schema_type(field_schema)
if isinstance(field_schema, grpc.PayloadIndexParams):
field_schema = GrpcToRest.convert_payload_schema_params(field_schema)
result: Optional[types.UpdateResult] = (
self.openapi_client.collections_api.create_field_index(
collection_name=collection_name,
create_field_index=models.CreateFieldIndex(
field_name=field_name, field_schema=field_schema
),
wait=wait,
ordering=ordering,
).result
)
assert result is not None, "Create field index returned None"
return result
[docs] def delete_payload_index(
self,
collection_name: str,
field_name: str,
wait: bool = True,
ordering: Optional[types.WriteOrdering] = None,
**kwargs: Any,
) -> types.UpdateResult:
if self._prefer_grpc:
request = grpc.DeleteFieldIndexCollection(
collection_name=collection_name,
field_name=field_name,
wait=wait,
ordering=ordering,
)
return GrpcToRest.convert_update_result(
self.grpc_points.DeleteFieldIndex(request).result
)
result: Optional[types.UpdateResult] = (
self.openapi_client.collections_api.delete_field_index(
collection_name=collection_name,
field_name=field_name,
wait=wait,
ordering=ordering,
).result
)
assert result is not None, "Delete field index returned None"
return result
[docs] def list_snapshots(
self, collection_name: str, **kwargs: Any
) -> List[types.SnapshotDescription]:
if self._prefer_grpc:
snapshots = self.grpc_snapshots.List(
grpc.ListSnapshotsRequest(collection_name=collection_name)
).snapshot_descriptions
return [GrpcToRest.convert_snapshot_description(snapshot) for snapshot in snapshots]
snapshots = self.openapi_client.collections_api.list_snapshots(
collection_name=collection_name
).result
assert snapshots is not None, "List snapshots API returned None result"
return snapshots
[docs] def create_snapshot(
self, collection_name: str, wait: bool = True, **kwargs: Any
) -> Optional[types.SnapshotDescription]:
if self._prefer_grpc:
snapshot = self.grpc_snapshots.Create(
grpc.CreateSnapshotRequest(collection_name=collection_name)
).snapshot_description
return GrpcToRest.convert_snapshot_description(snapshot)
return self.openapi_client.collections_api.create_snapshot(
collection_name=collection_name, wait=wait
).result
[docs] def delete_snapshot(
self, collection_name: str, snapshot_name: str, wait: bool = True, **kwargs: Any
) -> Optional[bool]:
if self._prefer_grpc:
self.grpc_snapshots.Delete(
grpc.DeleteSnapshotRequest(
collection_name=collection_name, snapshot_name=snapshot_name
)
)
return True
return self.openapi_client.collections_api.delete_snapshot(
collection_name=collection_name,
snapshot_name=snapshot_name,
wait=wait,
).result
[docs] def list_full_snapshots(self, **kwargs: Any) -> List[types.SnapshotDescription]:
if self._prefer_grpc:
snapshots = self.grpc_snapshots.ListFull(
grpc.ListFullSnapshotsRequest()
).snapshot_descriptions
return [GrpcToRest.convert_snapshot_description(snapshot) for snapshot in snapshots]
snapshots = self.openapi_client.snapshots_api.list_full_snapshots().result
assert snapshots is not None, "List full snapshots API returned None result"
return snapshots
[docs] def create_full_snapshot(self, wait: bool = True, **kwargs: Any) -> types.SnapshotDescription:
if self._prefer_grpc:
snapshot_description = self.grpc_snapshots.CreateFull(
grpc.CreateFullSnapshotRequest()
).snapshot_description
return GrpcToRest.convert_snapshot_description(snapshot_description)
return self.openapi_client.snapshots_api.create_full_snapshot(wait=wait).result
[docs] def delete_full_snapshot(
self, snapshot_name: str, wait: bool = True, **kwargs: Any
) -> Optional[bool]:
if self._prefer_grpc:
self.grpc_snapshots.DeleteFull(
grpc.DeleteFullSnapshotRequest(snapshot_name=snapshot_name)
)
return True
return self.openapi_client.snapshots_api.delete_full_snapshot(
snapshot_name=snapshot_name, wait=wait
).result
[docs] def recover_snapshot(
self,
collection_name: str,
location: str,
api_key: Optional[str] = None,
checksum: Optional[str] = None,
priority: Optional[types.SnapshotPriority] = None,
wait: bool = True,
**kwargs: Any,
) -> Optional[bool]:
return self.openapi_client.snapshots_api.recover_from_snapshot(
collection_name=collection_name,
wait=wait,
snapshot_recover=models.SnapshotRecover(
location=location,
priority=priority,
checksum=checksum,
api_key=api_key,
),
).result
[docs] def list_shard_snapshots(
self, collection_name: str, shard_id: int, **kwargs: Any
) -> List[types.SnapshotDescription]:
snapshots = self.openapi_client.snapshots_api.list_shard_snapshots(
collection_name=collection_name,
shard_id=shard_id,
).result
assert snapshots is not None, "List snapshots API returned None result"
return snapshots
[docs] def create_shard_snapshot(
self, collection_name: str, shard_id: int, wait: bool = True, **kwargs: Any
) -> Optional[types.SnapshotDescription]:
return self.openapi_client.snapshots_api.create_shard_snapshot(
collection_name=collection_name,
shard_id=shard_id,
wait=wait,
).result
[docs] def delete_shard_snapshot(
self,
collection_name: str,
shard_id: int,
snapshot_name: str,
wait: bool = True,
**kwargs: Any,
) -> Optional[bool]:
return self.openapi_client.snapshots_api.delete_shard_snapshot(
collection_name=collection_name,
shard_id=shard_id,
snapshot_name=snapshot_name,
wait=wait,
).result
[docs] def recover_shard_snapshot(
self,
collection_name: str,
shard_id: int,
location: str,
api_key: Optional[str] = None,
checksum: Optional[str] = None,
priority: Optional[types.SnapshotPriority] = None,
wait: bool = True,
**kwargs: Any,
) -> Optional[bool]:
return self.openapi_client.snapshots_api.recover_shard_from_snapshot(
collection_name=collection_name,
shard_id=shard_id,
wait=wait,
shard_snapshot_recover=models.ShardSnapshotRecover(
location=location,
priority=priority,
checksum=checksum,
api_key=api_key,
),
).result
[docs] def lock_storage(self, reason: str, **kwargs: Any) -> types.LocksOption:
result: Optional[types.LocksOption] = self.openapi_client.service_api.post_locks(
models.LocksOption(error_message=reason, write=True)
).result
assert result is not None, "Lock storage returned None"
return result
[docs] def unlock_storage(self, **kwargs: Any) -> types.LocksOption:
result: Optional[types.LocksOption] = self.openapi_client.service_api.post_locks(
models.LocksOption(write=False)
).result
assert result is not None, "Post locks returned None"
return result
[docs] def get_locks(self, **kwargs: Any) -> types.LocksOption:
result: Optional[types.LocksOption] = self.openapi_client.service_api.get_locks().result
assert result is not None, "Get locks returned None"
return result
[docs] def create_shard_key(
self,
collection_name: str,
shard_key: types.ShardKey,
shards_number: Optional[int] = None,
replication_factor: Optional[int] = None,
placement: Optional[List[int]] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> bool:
if self._prefer_grpc:
if isinstance(shard_key, get_args_subscribed(models.ShardKey)):
shard_key = RestToGrpc.convert_shard_key(shard_key)
return self.grpc_collections.CreateShardKey(
grpc.CreateShardKeyRequest(
collection_name=collection_name,
timeout=timeout,
request=grpc.CreateShardKey(
shard_key=shard_key,
shards_number=shards_number,
replication_factor=replication_factor,
placement=placement or [],
),
),
timeout=self._timeout,
).result
else:
result = self.openapi_client.cluster_api.create_shard_key(
collection_name=collection_name,
timeout=timeout,
create_sharding_key=models.CreateShardingKey(
shard_key=shard_key,
shards_number=shards_number,
replication_factor=replication_factor,
placement=placement,
),
).result
assert result is not None, "Create shard key returned None"
return result
[docs] def delete_shard_key(
self,
collection_name: str,
shard_key: types.ShardKey,
timeout: Optional[int] = None,
**kwargs: Any,
) -> bool:
if self._prefer_grpc:
if isinstance(shard_key, get_args_subscribed(models.ShardKey)):
shard_key = RestToGrpc.convert_shard_key(shard_key)
return self.grpc_collections.DeleteShardKey(
grpc.DeleteShardKeyRequest(
collection_name=collection_name,
timeout=timeout,
request=grpc.DeleteShardKey(
shard_key=shard_key,
),
),
timeout=self._timeout,
).result
else:
result = self.openapi_client.cluster_api.delete_shard_key(
collection_name=collection_name,
timeout=timeout,
drop_sharding_key=models.DropShardingKey(
shard_key=shard_key,
),
).result
assert result is not None, "Delete shard key returned None"
return result
[docs] def info(self) -> types.VersionInfo:
if self._prefer_grpc:
version_info = self.grpc_root.HealthCheck(grpc.HealthCheckRequest())
return GrpcToRest.convert_health_check_reply(version_info)
version_info = self.rest.service_api.root()
assert version_info is not None, "Healthcheck returned None"
return version_info
