Spaces:
Runtime error
Runtime error
File size: 5,577 Bytes
6b8dc99 | 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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | import json
from pathlib import Path
from unittest.mock import Mock, patch
from openai.types.create_embedding_response import CreateEmbeddingResponse
from kotaemon.base import Document, DocumentWithEmbedding
from kotaemon.embeddings import (
AzureOpenAIEmbeddings,
FastEmbedEmbeddings,
LCCohereEmbeddings,
LCHuggingFaceEmbeddings,
OpenAIEmbeddings,
VoyageAIEmbeddings,
)
from .conftest import (
skip_when_cohere_not_installed,
skip_when_fastembed_not_installed,
skip_when_sentence_bert_not_installed,
skip_when_voyageai_not_installed,
)
with open(Path(__file__).parent / "resources" / "embedding_openai_batch.json") as f:
openai_embedding_batch = CreateEmbeddingResponse.model_validate(json.load(f))
with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
openai_embedding = CreateEmbeddingResponse.model_validate(json.load(f))
def assert_embedding_result(output):
assert isinstance(output, list)
assert isinstance(output[0], Document)
assert isinstance(output[0].embedding, list)
assert isinstance(output[0].embedding[0], float)
@patch(
"openai.resources.embeddings.Embeddings.create",
side_effect=lambda *args, **kwargs: openai_embedding,
)
def test_azureopenai_embeddings_raw(openai_embedding_call):
model = AzureOpenAIEmbeddings(
azure_deployment="embedding-deployment",
azure_endpoint="https://test.openai.azure.com/",
api_key="some-key",
api_version="version",
)
output = model("Hello world")
assert_embedding_result(output)
openai_embedding_call.assert_called()
@patch(
"openai.resources.embeddings.Embeddings.create",
side_effect=lambda *args, **kwargs: openai_embedding_batch,
)
def test_lcazureopenai_embeddings_batch_raw(openai_embedding_call):
model = AzureOpenAIEmbeddings(
azure_deployment="embedding-deployment",
azure_endpoint="https://test.openai.azure.com/",
api_key="some-key",
api_version="version",
)
output = model(["Hello world", "Goodbye world"])
assert_embedding_result(output)
openai_embedding_call.assert_called()
@patch(
"openai.resources.embeddings.Embeddings.create",
side_effect=lambda *args, **kwargs: openai_embedding_batch,
)
def test_azureopenai_embeddings_batch_raw(openai_embedding_call):
model = AzureOpenAIEmbeddings(
azure_deployment="text-embedding-ada-002",
azure_endpoint="https://test.openai.azure.com/",
api_key="some-key",
api_version="version",
)
output = model(["Hello world", "Goodbye world"])
assert_embedding_result(output)
openai_embedding_call.assert_called()
@patch(
"openai.resources.embeddings.Embeddings.create",
side_effect=lambda *args, **kwargs: openai_embedding,
)
def test_openai_embeddings_raw(openai_embedding_call):
model = OpenAIEmbeddings(
api_key="some-key",
model="text-embedding-ada-002",
)
output = model("Hello world")
assert_embedding_result(output)
openai_embedding_call.assert_called()
@patch(
"openai.resources.embeddings.Embeddings.create",
side_effect=lambda *args, **kwargs: openai_embedding_batch,
)
def test_openai_embeddings_batch_raw(openai_embedding_call):
model = OpenAIEmbeddings(
api_key="some-key",
model="text-embedding-ada-002",
)
output = model(["Hello world", "Goodbye world"])
assert_embedding_result(output)
openai_embedding_call.assert_called()
@skip_when_sentence_bert_not_installed
@patch(
"sentence_transformers.SentenceTransformer",
side_effect=lambda *args, **kwargs: None,
)
@patch(
"langchain.embeddings.huggingface.HuggingFaceBgeEmbeddings.embed_documents",
side_effect=lambda *args, **kwargs: [[1.0, 2.1, 3.2]],
)
def test_lchuggingface_embeddings(
langchain_huggingface_embedding_call, sentence_transformers_init
):
model = LCHuggingFaceEmbeddings(
model_name="intfloat/multilingual-e5-large",
model_kwargs={"device": "cpu"},
encode_kwargs={"normalize_embeddings": False},
)
output = model("Hello World")
assert_embedding_result(output)
sentence_transformers_init.assert_called()
langchain_huggingface_embedding_call.assert_called()
@skip_when_cohere_not_installed
@patch(
"langchain_cohere.CohereEmbeddings.embed_documents",
side_effect=lambda *args, **kwargs: [[1.0, 2.1, 3.2]],
)
def test_lccohere_embeddings(langchain_cohere_embedding_call):
model = LCCohereEmbeddings(
model="embed-english-light-v2.0",
cohere_api_key="my-api-key",
user_agent="test",
)
output = model("Hello World")
assert_embedding_result(output)
langchain_cohere_embedding_call.assert_called()
@skip_when_fastembed_not_installed
def test_fastembed_embeddings():
model = FastEmbedEmbeddings()
output = model("Hello World")
assert_embedding_result(output)
voyage_output_mock = Mock()
voyage_output_mock.embeddings = [[1.0, 2.1, 3.2]]
@skip_when_voyageai_not_installed
@patch("voyageai.Client.embed", return_value=voyage_output_mock)
@patch("voyageai.AsyncClient.embed", return_value=voyage_output_mock)
def test_voyageai_embeddings(sync_call, async_call):
model = VoyageAIEmbeddings(api_key="test")
output = model("Hello, world!")
assert all(isinstance(doc, DocumentWithEmbedding) for doc in output)
|