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2ecddff284
* Add revision argument to sentence transformer components * Use revision as last argument in sentence transformer Add release notes
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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from unittest.mock import patch
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from haystack.components.embedders.backends.sentence_transformers_backend import (
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_SentenceTransformersEmbeddingBackendFactory,
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)
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from haystack.utils.auth import Secret
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@patch("haystack.components.embedders.backends.sentence_transformers_backend.SentenceTransformer")
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def test_factory_behavior(mock_sentence_transformer):
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embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
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model="my_model", device="cpu"
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)
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same_embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
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model="my_model", device="cpu"
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)
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another_embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
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model="another_model", device="cpu"
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)
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yet_another_embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
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model="my_model", device="cpu", trust_remote_code=True
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)
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assert same_embedding_backend is embedding_backend
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assert another_embedding_backend is not embedding_backend
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assert yet_another_embedding_backend is not embedding_backend
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@patch("haystack.components.embedders.backends.sentence_transformers_backend.SentenceTransformer")
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def test_model_initialization(mock_sentence_transformer):
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_SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
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model="model",
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device="cpu",
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auth_token=Secret.from_token("fake-api-token"),
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trust_remote_code=True,
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local_files_only=True,
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truncate_dim=256,
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backend="torch",
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)
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mock_sentence_transformer.assert_called_once_with(
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model_name_or_path="model",
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device="cpu",
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token="fake-api-token",
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trust_remote_code=True,
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revision=None,
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local_files_only=True,
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truncate_dim=256,
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model_kwargs=None,
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tokenizer_kwargs=None,
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config_kwargs=None,
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backend="torch",
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)
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@patch("haystack.components.embedders.backends.sentence_transformers_backend.SentenceTransformer")
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def test_embedding_function_with_kwargs(mock_sentence_transformer):
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embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(model="model")
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data = ["sentence1", "sentence2"]
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embedding_backend.embed(data=data, normalize_embeddings=True)
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embedding_backend.model.encode.assert_called_once_with(data, normalize_embeddings=True)
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