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47f4db8698
* added truncate_dim to sentence transformers embedder * Update haystack/components/embedders/sentence_transformers_document_embedder.py Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com> * Update releasenotes/notes/release-note-2b603a123cd36214.yaml Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com> * fixed parameter description * added test for truncation to text embedder * fix format --------- Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>
54 lines
2.0 KiB
Python
54 lines
2.0 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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import pytest
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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("my_model", "cpu")
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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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assert same_embedding_backend is embedding_backend
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assert 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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truncate_dim=256,
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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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use_auth_token="fake-api-token",
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trust_remote_code=True,
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truncate_dim=256,
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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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