#758

Committed 04 Sep 2023 08:37PM UTC coverage: 0.0% (-78.3%) from 78.333%

Build # #758

Build Type

push

circle-ci

Committed by

hershd23

Commit Message

Increased underline length in at line 75 in text_summarization.rst
	modified:   docs/source/benchmarks/text_summarization.rst

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/evadb/third_party/vector_stores/qdrant.py

# coding=utf-8
# Copyright 2018-2023 EvaDB
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List

from evadb.third_party.vector_stores.types import (
    FeaturePayload,
    VectorIndexQuery,
    VectorIndexQueryResult,
    VectorStore,
)
from evadb.utils.generic_utils import try_to_import_qdrant_client

_qdrant_client_instance = None

required_params = ["index_db"]


def get_qdrant_client(path: str):
    global _qdrant_client_instance
    if _qdrant_client_instance is None:
        try_to_import_qdrant_client()
        import qdrant_client  # noqa: F401

        # creating a local mode client
        # modify to support server modes
        # https://github.com/qdrant/qdrant-client
        _qdrant_client_instance = qdrant_client.QdrantClient(path=path)
    return _qdrant_client_instance


class QdrantVectorStore(VectorStore):
    def __init__(self, index_name: str, index_db: str) -> None:
        self._client = get_qdrant_client(index_db)
        self._collection_name = index_name

    def create(self, vector_dim: int):
        from qdrant_client.models import Distance, VectorParams

        self._client.recreate_collection(
            collection_name=self._collection_name,
            vectors_config=VectorParams(size=vector_dim, distance=Distance.COSINE),
        )

    def add(self, payload: List[FeaturePayload]):
        from qdrant_client.models import Batch

        ids = [int(row.id) for row in payload]
        embeddings = [row.embedding.reshape(-1).tolist() for row in payload]
        self._client.upsert(
            collection_name=self._collection_name,
            points=Batch.construct(
                ids=ids,
                vectors=embeddings,
            ),
        )

    def delete(self) -> None:
        self._client.delete_collection(
            collection_name=self._collection_name,
        )

    def query(
        self,
        query: VectorIndexQuery,
    ) -> VectorIndexQueryResult:
        response = self._client.search(
            collection_name=self._collection_name,
            query_vector=query.embedding.reshape(-1).tolist(),
            limit=query.top_k,
        )

        distances, ids = [], []
        for point in response:
            distances.append(point.score)
            ids.append(int(point.id))

        return VectorIndexQueryResult(distances, ids)

1	# coding=utf-8
2	# Copyright 2018-2023 EvaDB
3	#
4	# Licensed under the Apache License, Version 2.0 (the "License");
5	# you may not use this file except in compliance with the License.
6	# You may obtain a copy of the License at
7	#
8	# http://www.apache.org/licenses/LICENSE-2.0
9	#
10	# Unless required by applicable law or agreed to in writing, software
11	# distributed under the License is distributed on an "AS IS" BASIS,
12	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13	# See the License for the specific language governing permissions and
14	# limitations under the License.
15	from typing import List	×
16
17	from evadb.third_party.vector_stores.types import (	×
18	FeaturePayload,
19	VectorIndexQuery,
20	VectorIndexQueryResult,
21	VectorStore,
22	)
23	from evadb.utils.generic_utils import try_to_import_qdrant_client	×
24
25	_qdrant_client_instance = None	×
26
27	required_params = ["index_db"]	×
28
29
30	def get_qdrant_client(path: str):	×
31	global _qdrant_client_instance
32	if _qdrant_client_instance is None:	×
33	try_to_import_qdrant_client()	×
34	import qdrant_client # noqa: F401	×
35
36	# creating a local mode client
37	# modify to support server modes
38	# https://github.com/qdrant/qdrant-client
39	_qdrant_client_instance = qdrant_client.QdrantClient(path=path)	×
40	return _qdrant_client_instance	×
41
42
43	class QdrantVectorStore(VectorStore):	×
44	def __init__(self, index_name: str, index_db: str) -> None:	×
45	self._client = get_qdrant_client(index_db)	×
46	self._collection_name = index_name	×
47
48	def create(self, vector_dim: int):	×
49	from qdrant_client.models import Distance, VectorParams	×
50
51	self._client.recreate_collection(	×
52	collection_name=self._collection_name,
53	vectors_config=VectorParams(size=vector_dim, distance=Distance.COSINE),
54	)
55
56	def add(self, payload: List[FeaturePayload]):	×
57	from qdrant_client.models import Batch	×
58
59	ids = [int(row.id) for row in payload]	×
60	embeddings = [row.embedding.reshape(-1).tolist() for row in payload]	×
61	self._client.upsert(	×
62	collection_name=self._collection_name,
63	points=Batch.construct(
64	ids=ids,
65	vectors=embeddings,
66	),
67	)
68
69	def delete(self) -> None:	×
70	self._client.delete_collection(	×
71	collection_name=self._collection_name,
72	)
73
74	def query(	×
75	self,
76	query: VectorIndexQuery,
77	) -> VectorIndexQueryResult:
78	response = self._client.search(	×
79	collection_name=self._collection_name,
80	query_vector=query.embedding.reshape(-1).tolist(),
81	limit=query.top_k,
82	)
83
84	distances, ids = [], []	×
85	for point in response:	×
86	distances.append(point.score)	×
87	ids.append(int(point.id))	×
88
89	return VectorIndexQueryResult(distances, ids)	×

georgia-tech-db / eva / #758

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