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288ed150c9
* rename model parameter in the openai doc embedder * fix tests for openai doc embedder * rename model parameter in the openai text embedder * fix tests for openai text embedder * rename model parameter in the st doc embedder * fix tests for st doc embedder * rename model parameter in the st backend * fix tests for st backend * rename model parameter in the st text embedder * fix tests for st text embedder * fix docstring * fix pipeline utils * fix e2e * reno * fix the indexing pipeline _create_embedder function * fix e2e eval rag pipeline * pytest
40 lines
1.7 KiB
Python
40 lines
1.7 KiB
Python
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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@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", device="cpu", use_auth_token="my_token"
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)
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mock_sentence_transformer.assert_called_once_with(
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model_name_or_path="model", device="cpu", use_auth_token="my_token"
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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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