mirror of
https://github.com/qdrant/qdrant-client.git
synced 2026-07-29 22:21:10 -05:00
63f69540a6
* new: update models, add conversions, add conversion tests
* new: add list shard keys, add get_optimizations
* new: add weights to rrf
* new: add cluster telemetry
* new: add update_mode usage
* fix: fix mypy
* fix: populate inspection cache
* use RRF ranking as in the core
* timeouts propagation
* gen async clients
* implement score threshold for formular
* new: add tests for update mode
* fix: fix test skip comment
* Relevance feedback local mode (#1152)
* AI: implement local computation
prompt: Considering the implementation in the server, make the local
python implementation of the same scores calculation in this file:
[@distances.py](...), consider that the local mode calculates for all
points at once, while the server does it point by point.
Server impl:
```
...
```
* use constant for MARGIN
* relevance feedback integration and test
* do not exclude relevance context from result
* Revert "do not exclude relevance context from result"
This reverts commit 9c2a5cedc0.
---------
Co-authored-by: generall <andrey@vasnetsov.com>
* fix: fix conversion test
---------
Co-authored-by: generall <andrey@vasnetsov.com>
Co-authored-by: Luis Cossío <luis.cossio@qdrant.com>
394 lines
12 KiB
Python
394 lines
12 KiB
Python
# ****** WARNING: THIS FILE IS AUTOGENERATED ******
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#
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# This file is autogenerated. Do not edit it manually.
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# To regenerate this file, use
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#
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# ```
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# bash -x tools/generate_async_client.sh
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# ```
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#
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# ****** WARNING: THIS FILE IS AUTOGENERATED ******
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from typing import Any, Iterable, Mapping, Sequence
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from qdrant_client.conversions import common_types as types
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class AsyncQdrantBase:
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def __init__(self, **kwargs: Any):
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pass
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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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query_filter: types.Filter | None = None,
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limit: int = 3,
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sample: int = 10,
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using: str | None = None,
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**kwargs: Any,
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) -> types.SearchMatrixOffsetsResponse:
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raise NotImplementedError()
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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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query_filter: types.Filter | None = None,
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limit: int = 3,
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sample: int = 10,
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using: str | None = None,
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**kwargs: Any,
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) -> types.SearchMatrixPairsResponse:
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raise NotImplementedError()
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async def query_batch_points(
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self, collection_name: str, requests: Sequence[types.QueryRequest], **kwargs: Any
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) -> list[types.QueryResponse]:
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raise NotImplementedError()
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async def query_points(
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self,
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collection_name: str,
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query: types.PointId
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| list[float]
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| list[list[float]]
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| types.SparseVector
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| types.Query
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| types.NumpyArray
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| types.Document
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| types.Image
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| types.InferenceObject
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| None = None,
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using: str | None = None,
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prefetch: types.Prefetch | list[types.Prefetch] | None = None,
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query_filter: types.Filter | None = None,
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search_params: types.SearchParams | None = None,
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limit: int = 10,
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offset: int | None = None,
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with_payload: bool | Sequence[str] | types.PayloadSelector = True,
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with_vectors: bool | Sequence[str] = False,
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score_threshold: float | None = None,
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lookup_from: types.LookupLocation | None = None,
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**kwargs: Any,
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) -> types.QueryResponse:
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raise NotImplementedError()
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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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group_by: str,
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query: types.PointId
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| list[float]
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| list[list[float]]
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| types.SparseVector
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| types.Query
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| types.NumpyArray
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| types.Document
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| types.Image
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| types.InferenceObject
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| None = None,
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using: str | None = None,
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prefetch: types.Prefetch | list[types.Prefetch] | None = None,
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query_filter: types.Filter | None = None,
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search_params: types.SearchParams | None = None,
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limit: int = 10,
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group_size: int = 3,
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with_payload: bool | Sequence[str] | types.PayloadSelector = True,
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with_vectors: bool | Sequence[str] = False,
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score_threshold: float | None = None,
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with_lookup: types.WithLookupInterface | None = None,
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lookup_from: types.LookupLocation | None = None,
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**kwargs: Any,
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) -> types.GroupsResult:
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raise NotImplementedError()
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async def scroll(
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self,
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collection_name: str,
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scroll_filter: types.Filter | None = None,
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limit: int = 10,
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order_by: types.OrderBy | None = None,
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offset: types.PointId | None = None,
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with_payload: bool | Sequence[str] | types.PayloadSelector = True,
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with_vectors: bool | Sequence[str] = False,
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**kwargs: Any,
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) -> tuple[list[types.Record], types.PointId | None]:
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raise NotImplementedError()
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async def count(
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self,
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collection_name: str,
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count_filter: types.Filter | None = None,
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exact: bool = True,
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**kwargs: Any,
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) -> types.CountResult:
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raise NotImplementedError()
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async def facet(
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self,
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collection_name: str,
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key: str,
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facet_filter: types.Filter | None = None,
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limit: int = 10,
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exact: bool = False,
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**kwargs: Any,
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) -> types.FacetResponse:
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raise NotImplementedError()
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async def upsert(
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self, collection_name: str, points: types.Points, **kwargs: Any
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def update_vectors(
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self, collection_name: str, points: Sequence[types.PointVectors], **kwargs: Any
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def delete_vectors(
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self,
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collection_name: str,
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vectors: Sequence[str],
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points: types.PointsSelector,
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**kwargs: Any,
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def retrieve(
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self,
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collection_name: str,
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ids: Sequence[types.PointId],
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with_payload: bool | Sequence[str] | types.PayloadSelector = True,
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with_vectors: bool | Sequence[str] = False,
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**kwargs: Any,
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) -> list[types.Record]:
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raise NotImplementedError()
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async def delete(
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self, collection_name: str, points_selector: types.PointsSelector, **kwargs: Any
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def set_payload(
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self,
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collection_name: str,
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payload: types.Payload,
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points: types.PointsSelector,
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key: str | None = None,
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**kwargs: Any,
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def overwrite_payload(
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self,
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collection_name: str,
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payload: types.Payload,
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points: types.PointsSelector,
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**kwargs: Any,
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def delete_payload(
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self,
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collection_name: str,
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keys: Sequence[str],
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points: types.PointsSelector,
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**kwargs: Any,
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def clear_payload(
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self, collection_name: str, points_selector: types.PointsSelector, **kwargs: Any
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def batch_update_points(
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self,
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collection_name: str,
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update_operations: Sequence[types.UpdateOperation],
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**kwargs: Any,
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) -> list[types.UpdateResult]:
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raise NotImplementedError()
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async def update_collection_aliases(
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self, change_aliases_operations: Sequence[types.AliasOperations], **kwargs: Any
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) -> bool:
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raise NotImplementedError()
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async def get_collection_aliases(
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self, collection_name: str, **kwargs: Any
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) -> types.CollectionsAliasesResponse:
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raise NotImplementedError()
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async def get_aliases(self, **kwargs: Any) -> types.CollectionsAliasesResponse:
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raise NotImplementedError()
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async def get_collections(self, **kwargs: Any) -> types.CollectionsResponse:
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raise NotImplementedError()
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async def get_collection(self, collection_name: str, **kwargs: Any) -> types.CollectionInfo:
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raise NotImplementedError()
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async def collection_exists(self, collection_name: str, **kwargs: Any) -> bool:
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raise NotImplementedError()
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async def update_collection(self, collection_name: str, **kwargs: Any) -> bool:
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raise NotImplementedError()
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async def delete_collection(self, collection_name: str, **kwargs: Any) -> bool:
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raise NotImplementedError()
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async def create_collection(
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self,
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collection_name: str,
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vectors_config: types.VectorParams | Mapping[str, types.VectorParams],
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**kwargs: Any,
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) -> bool:
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raise NotImplementedError()
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async def recreate_collection(
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self,
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collection_name: str,
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vectors_config: types.VectorParams | Mapping[str, types.VectorParams],
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**kwargs: Any,
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) -> bool:
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raise NotImplementedError()
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def upload_points(
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self, collection_name: str, points: Iterable[types.PointStruct], **kwargs: Any
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) -> None:
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raise NotImplementedError()
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def upload_collection(
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self,
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collection_name: str,
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vectors: dict[str, types.NumpyArray] | types.NumpyArray | Iterable[types.VectorStruct],
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payload: Iterable[dict[Any, Any]] | None = None,
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ids: Iterable[types.PointId] | None = None,
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**kwargs: Any,
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) -> None:
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raise NotImplementedError()
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async def create_payload_index(
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self,
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collection_name: str,
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field_name: str,
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field_schema: types.PayloadSchemaType | None = None,
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field_type: types.PayloadSchemaType | None = None,
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**kwargs: Any,
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def delete_payload_index(
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self, collection_name: str, field_name: str, **kwargs: Any
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) -> types.UpdateResult:
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raise NotImplementedError()
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async def list_snapshots(
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self, collection_name: str, **kwargs: Any
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) -> list[types.SnapshotDescription]:
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raise NotImplementedError()
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async def create_snapshot(
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self, collection_name: str, **kwargs: Any
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) -> types.SnapshotDescription | None:
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raise NotImplementedError()
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async def delete_snapshot(
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self, collection_name: str, snapshot_name: str, **kwargs: Any
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) -> bool | None:
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raise NotImplementedError()
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async def list_full_snapshots(self, **kwargs: Any) -> list[types.SnapshotDescription]:
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raise NotImplementedError()
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async def create_full_snapshot(self, **kwargs: Any) -> types.SnapshotDescription | None:
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raise NotImplementedError()
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async def delete_full_snapshot(self, snapshot_name: str, **kwargs: Any) -> bool | None:
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raise NotImplementedError()
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async def recover_snapshot(
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self, collection_name: str, location: str, **kwargs: Any
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) -> bool | None:
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raise NotImplementedError()
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async def list_shard_snapshots(
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self, collection_name: str, shard_id: int, **kwargs: Any
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) -> list[types.SnapshotDescription]:
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raise NotImplementedError()
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async def create_shard_snapshot(
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self, collection_name: str, shard_id: int, **kwargs: Any
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) -> types.SnapshotDescription | None:
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raise NotImplementedError()
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async def delete_shard_snapshot(
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self, collection_name: str, shard_id: int, snapshot_name: str, **kwargs: Any
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) -> bool | None:
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raise NotImplementedError()
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async def recover_shard_snapshot(
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self, collection_name: str, shard_id: int, location: str, **kwargs: Any
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) -> bool | None:
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raise NotImplementedError()
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async def close(self, **kwargs: Any) -> None:
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pass
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def migrate(
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self,
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dest_client: "AsyncQdrantBase",
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collection_names: list[str] | None = None,
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batch_size: int = 100,
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recreate_on_collision: bool = False,
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) -> None:
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raise NotImplementedError()
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async def create_shard_key(
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self,
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collection_name: str,
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shard_key: types.ShardKey,
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shards_number: int | None = None,
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replication_factor: int | None = None,
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placement: list[int] | None = None,
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**kwargs: Any,
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) -> bool:
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raise NotImplementedError()
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async def delete_shard_key(
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self, collection_name: str, shard_key: types.ShardKey, **kwargs: Any
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) -> bool:
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raise NotImplementedError()
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async def info(self) -> types.VersionInfo:
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raise NotImplementedError()
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async def cluster_collection_update(
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self, collection_name: str, cluster_operation: types.ClusterOperations, **kwargs: Any
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) -> bool:
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raise NotImplementedError()
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async def collection_cluster_info(self, collection_name: str) -> types.CollectionClusterInfo:
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raise NotImplementedError()
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async def cluster_status(self) -> types.ClusterStatus:
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raise NotImplementedError()
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async def recover_current_peer(self) -> bool:
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raise NotImplementedError()
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async def remove_peer(self, peer_id: int, **kwargs: Any) -> bool:
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raise NotImplementedError()
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async def get_optimizations(
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self, collection_name: str, **kwargs: Any
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) -> types.OptimizationsResponse:
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raise NotImplementedError()
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async def list_shard_keys(
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self, collection_name: str, **kwargs: Any
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) -> types.ShardKeysResponse:
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raise NotImplementedError()
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async def cluster_telemetry(self, **kwargs: Any) -> types.DistributedTelemetryData:
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raise NotImplementedError()
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