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https://github.com/deepset-ai/haystack.git
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d2348ad462
* feat: SentenceTransformersDocumentEmbedder and SentenceTransformersTextEmbedder can accept and pass any arguments to SentenceTransformer.encode * refactor: encode_kwargs parameter of SentenceTransformersDocumentEmbedder and SentenceTransformersTextEmbedder mae to be the last positional parameter for backward compatibility reasons * docs: added explanation for encode_kwargs in SentenceTransformersTextEmbedder and SentenceTransformersDocumentEmbedder * test: added tests for encode_kwargs in SentenceTransformersTextEmbedder and SentenceTransformersDocumentEmbedder * doc: removed empty lines from docstrings of SentenceTransformersTextEmbedder and SentenceTransformersDocumentEmbedder * refactor: encode_kwargs parameter of SentenceTransformersDocumentEmbedder and SentenceTransformersTextEmbedder mae to be the last positional parameter for backward compatibility (part II.)
317 lines
13 KiB
Python
317 lines
13 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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import random
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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from haystack.components.embedders.sentence_transformers_text_embedder import SentenceTransformersTextEmbedder
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from haystack.utils import ComponentDevice, Secret
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class TestSentenceTransformersTextEmbedder:
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def test_init_default(self):
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embedder = SentenceTransformersTextEmbedder(model="model")
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assert embedder.model == "model"
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assert embedder.device == ComponentDevice.resolve_device(None)
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assert embedder.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
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assert embedder.prefix == ""
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assert embedder.suffix == ""
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assert embedder.batch_size == 32
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assert embedder.progress_bar is True
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assert embedder.normalize_embeddings is False
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assert embedder.trust_remote_code is False
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assert embedder.truncate_dim is None
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assert embedder.precision == "float32"
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def test_init_with_parameters(self):
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embedder = SentenceTransformersTextEmbedder(
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model="model",
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device=ComponentDevice.from_str("cuda:0"),
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token=Secret.from_token("fake-api-token"),
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prefix="prefix",
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suffix="suffix",
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batch_size=64,
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progress_bar=False,
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normalize_embeddings=True,
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trust_remote_code=True,
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truncate_dim=256,
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precision="int8",
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)
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assert embedder.model == "model"
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assert embedder.device == ComponentDevice.from_str("cuda:0")
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assert embedder.token == Secret.from_token("fake-api-token")
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assert embedder.prefix == "prefix"
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assert embedder.suffix == "suffix"
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assert embedder.batch_size == 64
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assert embedder.progress_bar is False
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assert embedder.normalize_embeddings is True
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assert embedder.trust_remote_code is True
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assert embedder.truncate_dim == 256
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assert embedder.precision == "int8"
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def test_to_dict(self):
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component = SentenceTransformersTextEmbedder(model="model", device=ComponentDevice.from_str("cpu"))
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder",
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"init_parameters": {
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"token": {"env_vars": ["HF_API_TOKEN", "HF_TOKEN"], "strict": False, "type": "env_var"},
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"model": "model",
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"device": ComponentDevice.from_str("cpu").to_dict(),
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"prefix": "",
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"suffix": "",
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"batch_size": 32,
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"progress_bar": True,
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"normalize_embeddings": False,
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"trust_remote_code": False,
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"truncate_dim": None,
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"model_kwargs": None,
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"tokenizer_kwargs": None,
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"encode_kwargs": None,
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"config_kwargs": None,
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"precision": "float32",
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},
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}
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def test_to_dict_with_custom_init_parameters(self):
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component = SentenceTransformersTextEmbedder(
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model="model",
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device=ComponentDevice.from_str("cuda:0"),
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token=Secret.from_env_var("ENV_VAR", strict=False),
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prefix="prefix",
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suffix="suffix",
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batch_size=64,
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progress_bar=False,
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normalize_embeddings=True,
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trust_remote_code=True,
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truncate_dim=256,
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model_kwargs={"torch_dtype": torch.float32},
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tokenizer_kwargs={"model_max_length": 512},
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config_kwargs={"use_memory_efficient_attention": False},
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precision="int8",
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encode_kwargs={"task": "clustering"},
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)
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data = component.to_dict()
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assert data == {
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"type": "haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder",
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"init_parameters": {
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"token": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"},
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"model": "model",
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"device": ComponentDevice.from_str("cuda:0").to_dict(),
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"prefix": "prefix",
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"suffix": "suffix",
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"batch_size": 64,
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"progress_bar": False,
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"normalize_embeddings": True,
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"trust_remote_code": True,
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"truncate_dim": 256,
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"model_kwargs": {"torch_dtype": "torch.float32"},
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"tokenizer_kwargs": {"model_max_length": 512},
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"config_kwargs": {"use_memory_efficient_attention": False},
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"precision": "int8",
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"encode_kwargs": {"task": "clustering"},
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},
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}
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def test_to_dict_not_serialize_token(self):
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component = SentenceTransformersTextEmbedder(model="model", token=Secret.from_token("fake-api-token"))
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with pytest.raises(ValueError, match="Cannot serialize token-based secret"):
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component.to_dict()
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def test_from_dict(self):
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data = {
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"type": "haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder",
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"init_parameters": {
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"token": {"env_vars": ["HF_API_TOKEN", "HF_TOKEN"], "strict": False, "type": "env_var"},
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"model": "model",
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"device": ComponentDevice.from_str("cpu").to_dict(),
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"prefix": "",
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"suffix": "",
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"batch_size": 32,
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"progress_bar": True,
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"normalize_embeddings": False,
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"trust_remote_code": False,
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"truncate_dim": None,
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"model_kwargs": {"torch_dtype": "torch.float32"},
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"tokenizer_kwargs": {"model_max_length": 512},
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"config_kwargs": {"use_memory_efficient_attention": False},
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"precision": "float32",
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},
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}
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component = SentenceTransformersTextEmbedder.from_dict(data)
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assert component.model == "model"
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assert component.device == ComponentDevice.from_str("cpu")
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assert component.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
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assert component.prefix == ""
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assert component.suffix == ""
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assert component.batch_size == 32
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assert component.progress_bar is True
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assert component.normalize_embeddings is False
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assert component.trust_remote_code is False
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assert component.truncate_dim is None
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assert component.model_kwargs == {"torch_dtype": torch.float32}
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assert component.tokenizer_kwargs == {"model_max_length": 512}
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assert component.config_kwargs == {"use_memory_efficient_attention": False}
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assert component.precision == "float32"
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def test_from_dict_no_default_parameters(self):
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data = {
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"type": "haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder",
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"init_parameters": {},
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}
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component = SentenceTransformersTextEmbedder.from_dict(data)
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assert component.model == "sentence-transformers/all-mpnet-base-v2"
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assert component.device == ComponentDevice.resolve_device(None)
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assert component.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
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assert component.prefix == ""
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assert component.suffix == ""
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assert component.batch_size == 32
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assert component.progress_bar is True
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assert component.normalize_embeddings is False
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assert component.trust_remote_code is False
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assert component.truncate_dim is None
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assert component.precision == "float32"
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def test_from_dict_none_device(self):
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data = {
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"type": "haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder",
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"init_parameters": {
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"token": {"env_vars": ["HF_API_TOKEN", "HF_TOKEN"], "strict": False, "type": "env_var"},
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"model": "model",
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"device": None,
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"prefix": "",
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"suffix": "",
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"batch_size": 32,
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"progress_bar": True,
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"normalize_embeddings": False,
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"trust_remote_code": False,
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"truncate_dim": 256,
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"precision": "int8",
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},
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}
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component = SentenceTransformersTextEmbedder.from_dict(data)
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assert component.model == "model"
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assert component.device == ComponentDevice.resolve_device(None)
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assert component.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
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assert component.prefix == ""
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assert component.suffix == ""
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assert component.batch_size == 32
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assert component.progress_bar is True
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assert component.normalize_embeddings is False
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assert component.trust_remote_code is False
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assert component.truncate_dim == 256
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assert component.precision == "int8"
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@patch(
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"haystack.components.embedders.sentence_transformers_text_embedder._SentenceTransformersEmbeddingBackendFactory"
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)
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def test_warmup(self, mocked_factory):
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embedder = SentenceTransformersTextEmbedder(
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model="model",
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token=None,
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device=ComponentDevice.from_str("cpu"),
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tokenizer_kwargs={"model_max_length": 512},
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)
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mocked_factory.get_embedding_backend.assert_not_called()
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embedder.warm_up()
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embedder.embedding_backend.model.max_seq_length = 512
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mocked_factory.get_embedding_backend.assert_called_once_with(
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model="model",
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device="cpu",
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auth_token=None,
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trust_remote_code=False,
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truncate_dim=None,
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model_kwargs=None,
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tokenizer_kwargs={"model_max_length": 512},
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config_kwargs=None,
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)
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@patch(
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"haystack.components.embedders.sentence_transformers_text_embedder._SentenceTransformersEmbeddingBackendFactory"
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)
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def test_warmup_doesnt_reload(self, mocked_factory):
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embedder = SentenceTransformersTextEmbedder(model="model")
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mocked_factory.get_embedding_backend.assert_not_called()
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embedder.warm_up()
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embedder.warm_up()
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mocked_factory.get_embedding_backend.assert_called_once()
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def test_run(self):
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embedder = SentenceTransformersTextEmbedder(model="model")
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embedder.embedding_backend = MagicMock()
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embedder.embedding_backend.embed = lambda x, **kwargs: [
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[random.random() for _ in range(16)] for _ in range(len(x))
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]
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text = "a nice text to embed"
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result = embedder.run(text=text)
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embedding = result["embedding"]
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assert isinstance(embedding, list)
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assert all(isinstance(el, float) for el in embedding)
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def test_run_wrong_input_format(self):
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embedder = SentenceTransformersTextEmbedder(model="model")
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embedder.embedding_backend = MagicMock()
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list_integers_input = [1, 2, 3]
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with pytest.raises(TypeError, match="SentenceTransformersTextEmbedder expects a string as input"):
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embedder.run(text=list_integers_input)
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@pytest.mark.integration
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def test_run_trunc(self, monkeypatch):
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"""
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sentence-transformers/paraphrase-albert-small-v2 maps sentences & paragraphs to a 768 dimensional dense vector space
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"""
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monkeypatch.delenv("HF_API_TOKEN", raising=False) # https://github.com/deepset-ai/haystack/issues/8811
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checkpoint = "sentence-transformers/paraphrase-albert-small-v2"
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text = "a nice text to embed"
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embedder_def = SentenceTransformersTextEmbedder(model=checkpoint)
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embedder_def.warm_up()
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result_def = embedder_def.run(text=text)
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embedding_def = result_def["embedding"]
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embedder_trunc = SentenceTransformersTextEmbedder(model=checkpoint, truncate_dim=128)
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embedder_trunc.warm_up()
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result_trunc = embedder_trunc.run(text=text)
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embedding_trunc = result_trunc["embedding"]
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assert len(embedding_def) == 768
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assert len(embedding_trunc) == 128
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@pytest.mark.integration
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def test_run_quantization(self):
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"""
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sentence-transformers/paraphrase-albert-small-v2 maps sentences & paragraphs to a 768 dimensional dense vector space
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"""
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checkpoint = "sentence-transformers/paraphrase-albert-small-v2"
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text = "a nice text to embed"
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embedder_def = SentenceTransformersTextEmbedder(model=checkpoint, precision="int8")
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embedder_def.warm_up()
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result_def = embedder_def.run(text=text)
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embedding_def = result_def["embedding"]
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assert len(embedding_def) == 768
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assert all(isinstance(el, int) for el in embedding_def)
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def test_embed_encode_kwargs(self):
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embedder = SentenceTransformersTextEmbedder(model="model", encode_kwargs={"task": "retrieval.query"})
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embedder.embedding_backend = MagicMock()
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text = "a nice text to embed"
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embedder.run(text=text)
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embedder.embedding_backend.embed.assert_called_once_with(
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[text],
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batch_size=32,
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show_progress_bar=True,
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normalize_embeddings=False,
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precision="float32",
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task="retrieval.query",
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)
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