1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
|
# Licensed to Elasticsearch B.V. under one or more contributor
# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you 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 elastic_transport import Transport
from elasticsearch.helpers.vectorstore import EmbeddingService
class RequestSavingTransport(Transport):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.requests: list = []
def perform_request(self, *args, **kwargs):
self.requests.append(kwargs)
return super().perform_request(*args, **kwargs)
class FakeEmbeddings(EmbeddingService):
"""Fake embeddings functionality for testing."""
def __init__(self, dimensionality: int = 10) -> None:
self.dimensionality = dimensionality
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return simple embeddings. Embeddings encode each text as its index."""
return [
[float(1.0)] * (self.dimensionality - 1) + [float(i)]
for i in range(len(texts))
]
def embed_query(self, text: str) -> List[float]:
"""Return constant query embeddings.
Embeddings are identical to embed_documents(texts)[0].
Distance to each text will be that text's index,
as it was passed to embed_documents.
"""
return [float(1.0)] * (self.dimensionality - 1) + [float(0.0)]
class ConsistentFakeEmbeddings(FakeEmbeddings):
"""Fake embeddings which remember all the texts seen so far to return consistent
vectors for the same texts."""
def __init__(self, dimensionality: int = 10) -> None:
self.known_texts: List[str] = []
self.dimensionality = dimensionality
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return consistent embeddings for each text seen so far."""
out_vectors = []
for text in texts:
if text not in self.known_texts:
self.known_texts.append(text)
vector = [float(1.0)] * (self.dimensionality - 1) + [
float(self.known_texts.index(text) + 1)
]
out_vectors.append(vector)
return out_vectors
def embed_query(self, text: str) -> List[float]:
"""Return consistent embeddings for the text, if seen before, or a constant
one if the text is unknown."""
result = self.embed_documents([text])
return result[0]
|
