mirror of
https://github.com/qdrant/qdrant-client.git
synced 2026-08-01 15:40:54 -05:00
399449a6d9
* new: 1.19.0 updates * fix: fix search params as a dict in local mode * fix: update qdrant backward compatibility version * fix: add version check to the test * fix: add version check to the test
398 lines
13 KiB
Python
398 lines
13 KiB
Python
# flake8: noqa E501
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from typing import TYPE_CHECKING, Any, Dict, Set, TypeVar, Union
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from pydantic import BaseModel
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from pydantic.main import BaseModel
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from pydantic.version import VERSION as PYDANTIC_VERSION
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from qdrant_client.http.models import *
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from qdrant_client.http.models import models as m
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PYDANTIC_V2 = PYDANTIC_VERSION.startswith("2.")
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Model = TypeVar("Model", bound="BaseModel")
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SetIntStr = Set[Union[int, str]]
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DictIntStrAny = Dict[Union[int, str], Any]
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file = None
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def to_json(model: BaseModel, *args: Any, **kwargs: Any) -> str:
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if PYDANTIC_V2:
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return model.model_dump_json(*args, **kwargs)
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else:
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return model.json(*args, **kwargs)
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def jsonable_encoder(
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obj: Any,
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include: Union[SetIntStr, DictIntStrAny] = None,
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exclude=None,
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by_alias: bool = True,
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skip_defaults: bool = None,
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exclude_unset: bool = True,
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exclude_none: bool = True,
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):
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if hasattr(obj, "json") or hasattr(obj, "model_dump_json"):
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return to_json(
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obj,
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include=include,
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exclude=exclude,
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by_alias=by_alias,
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exclude_unset=bool(exclude_unset or skip_defaults),
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exclude_none=exclude_none,
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)
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return obj
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if TYPE_CHECKING:
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from qdrant_client.http.api_client import ApiClient
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class _SearchApi:
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def __init__(self, api_client: "Union[ApiClient, AsyncApiClient]"):
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self.api_client = api_client
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def _build_for_query_batch_points(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_request_batch: m.QueryRequestBatch = None,
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):
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"""
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Universally query points in batch. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.
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"""
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path_params = {
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"collection_name": str(collection_name),
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}
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query_params = {}
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if consistency is not None:
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query_params["consistency"] = str(consistency)
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if timeout is not None:
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query_params["timeout"] = str(timeout)
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headers = {}
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body = jsonable_encoder(query_request_batch)
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if "Content-Type" not in headers:
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headers["Content-Type"] = "application/json"
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return self.api_client.request(
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type_=m.InlineResponse20022,
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method="POST",
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url="/collections/{collection_name}/points/query/batch",
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headers=headers if headers else None,
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path_params=path_params,
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params=query_params,
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content=body,
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)
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def _build_for_query_points(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_request: m.QueryRequest = None,
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):
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"""
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Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.
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"""
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path_params = {
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"collection_name": str(collection_name),
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}
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query_params = {}
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if consistency is not None:
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query_params["consistency"] = str(consistency)
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if timeout is not None:
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query_params["timeout"] = str(timeout)
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headers = {}
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body = jsonable_encoder(query_request)
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if "Content-Type" not in headers:
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headers["Content-Type"] = "application/json"
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return self.api_client.request(
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type_=m.InlineResponse20021,
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method="POST",
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url="/collections/{collection_name}/points/query",
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headers=headers if headers else None,
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path_params=path_params,
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params=query_params,
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content=body,
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)
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def _build_for_query_points_groups(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_groups_request: m.QueryGroupsRequest = None,
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):
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"""
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Universally query points, grouped by a given payload field
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"""
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path_params = {
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"collection_name": str(collection_name),
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}
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query_params = {}
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if consistency is not None:
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query_params["consistency"] = str(consistency)
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if timeout is not None:
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query_params["timeout"] = str(timeout)
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headers = {}
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body = jsonable_encoder(query_groups_request)
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if "Content-Type" not in headers:
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headers["Content-Type"] = "application/json"
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return self.api_client.request(
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type_=m.InlineResponse20023,
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method="POST",
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url="/collections/{collection_name}/points/query/groups",
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headers=headers if headers else None,
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path_params=path_params,
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params=query_params,
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content=body,
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)
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def _build_for_search_matrix_offsets(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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search_matrix_request: m.SearchMatrixRequest = None,
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):
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"""
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Compute distance matrix for sampled points with an offset based output format
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"""
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path_params = {
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"collection_name": str(collection_name),
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}
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query_params = {}
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if consistency is not None:
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query_params["consistency"] = str(consistency)
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if timeout is not None:
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query_params["timeout"] = str(timeout)
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headers = {}
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body = jsonable_encoder(search_matrix_request)
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if "Content-Type" not in headers:
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headers["Content-Type"] = "application/json"
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return self.api_client.request(
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type_=m.InlineResponse20025,
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method="POST",
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url="/collections/{collection_name}/points/search/matrix/offsets",
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headers=headers if headers else None,
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path_params=path_params,
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params=query_params,
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content=body,
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)
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def _build_for_search_matrix_pairs(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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search_matrix_request: m.SearchMatrixRequest = None,
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):
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"""
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Compute distance matrix for sampled points with a pair based output format
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"""
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path_params = {
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"collection_name": str(collection_name),
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}
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query_params = {}
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if consistency is not None:
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query_params["consistency"] = str(consistency)
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if timeout is not None:
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query_params["timeout"] = str(timeout)
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headers = {}
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body = jsonable_encoder(search_matrix_request)
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if "Content-Type" not in headers:
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headers["Content-Type"] = "application/json"
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return self.api_client.request(
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type_=m.InlineResponse20024,
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method="POST",
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url="/collections/{collection_name}/points/search/matrix/pairs",
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headers=headers if headers else None,
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path_params=path_params,
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params=query_params,
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content=body,
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)
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class AsyncSearchApi(_SearchApi):
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async def query_batch_points(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_request_batch: m.QueryRequestBatch = None,
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) -> m.InlineResponse20022:
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"""
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Universally query points in batch. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.
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"""
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return await self._build_for_query_batch_points(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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query_request_batch=query_request_batch,
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)
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async def query_points(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_request: m.QueryRequest = None,
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) -> m.InlineResponse20021:
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"""
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Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.
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"""
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return await self._build_for_query_points(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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query_request=query_request,
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)
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async def query_points_groups(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_groups_request: m.QueryGroupsRequest = None,
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) -> m.InlineResponse20023:
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"""
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Universally query points, grouped by a given payload field
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"""
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return await self._build_for_query_points_groups(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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query_groups_request=query_groups_request,
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)
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async def search_matrix_offsets(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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search_matrix_request: m.SearchMatrixRequest = None,
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) -> m.InlineResponse20025:
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"""
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Compute distance matrix for sampled points with an offset based output format
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"""
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return await self._build_for_search_matrix_offsets(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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search_matrix_request=search_matrix_request,
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)
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async def search_matrix_pairs(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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search_matrix_request: m.SearchMatrixRequest = None,
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) -> m.InlineResponse20024:
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"""
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Compute distance matrix for sampled points with a pair based output format
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"""
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return await self._build_for_search_matrix_pairs(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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search_matrix_request=search_matrix_request,
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)
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class SyncSearchApi(_SearchApi):
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def query_batch_points(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_request_batch: m.QueryRequestBatch = None,
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) -> m.InlineResponse20022:
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"""
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Universally query points in batch. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.
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"""
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return self._build_for_query_batch_points(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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query_request_batch=query_request_batch,
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)
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def query_points(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_request: m.QueryRequest = None,
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) -> m.InlineResponse20021:
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"""
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Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.
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"""
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return self._build_for_query_points(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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query_request=query_request,
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)
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def query_points_groups(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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query_groups_request: m.QueryGroupsRequest = None,
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) -> m.InlineResponse20023:
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"""
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Universally query points, grouped by a given payload field
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"""
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return self._build_for_query_points_groups(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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query_groups_request=query_groups_request,
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)
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def search_matrix_offsets(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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search_matrix_request: m.SearchMatrixRequest = None,
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) -> m.InlineResponse20025:
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"""
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Compute distance matrix for sampled points with an offset based output format
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"""
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return self._build_for_search_matrix_offsets(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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search_matrix_request=search_matrix_request,
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)
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def search_matrix_pairs(
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self,
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collection_name: str,
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consistency: m.ReadConsistency = None,
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timeout: int = None,
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search_matrix_request: m.SearchMatrixRequest = None,
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) -> m.InlineResponse20024:
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"""
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Compute distance matrix for sampled points with a pair based output format
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"""
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return self._build_for_search_matrix_pairs(
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collection_name=collection_name,
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consistency=consistency,
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timeout=timeout,
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search_matrix_request=search_matrix_request,
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)
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