mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-07 17:10:42 +00:00
535a281eec
* Read both HF_API_TOKEN and HF_TOKEN env vars in all HF related components * Add reno note * Test fixes * More test updates * More test updates
276 lines
11 KiB
Python
276 lines
11 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
|
|
#
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
from unittest.mock import MagicMock, patch
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from haystack import Document
|
|
from haystack.components.embedders.sentence_transformers_document_embedder import SentenceTransformersDocumentEmbedder
|
|
from haystack.utils import ComponentDevice, Secret
|
|
|
|
|
|
class TestSentenceTransformersDocumentEmbedder:
|
|
def test_init_default(self):
|
|
embedder = SentenceTransformersDocumentEmbedder(model="model")
|
|
assert embedder.model == "model"
|
|
assert embedder.device == ComponentDevice.resolve_device(None)
|
|
assert embedder.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
|
|
assert embedder.prefix == ""
|
|
assert embedder.suffix == ""
|
|
assert embedder.batch_size == 32
|
|
assert embedder.progress_bar is True
|
|
assert embedder.normalize_embeddings is False
|
|
assert embedder.meta_fields_to_embed == []
|
|
assert embedder.embedding_separator == "\n"
|
|
assert embedder.trust_remote_code is False
|
|
|
|
def test_init_with_parameters(self):
|
|
embedder = SentenceTransformersDocumentEmbedder(
|
|
model="model",
|
|
device=ComponentDevice.from_str("cuda:0"),
|
|
token=Secret.from_token("fake-api-token"),
|
|
prefix="prefix",
|
|
suffix="suffix",
|
|
batch_size=64,
|
|
progress_bar=False,
|
|
normalize_embeddings=True,
|
|
meta_fields_to_embed=["test_field"],
|
|
embedding_separator=" | ",
|
|
trust_remote_code=True,
|
|
)
|
|
assert embedder.model == "model"
|
|
assert embedder.device == ComponentDevice.from_str("cuda:0")
|
|
assert embedder.token == Secret.from_token("fake-api-token")
|
|
assert embedder.prefix == "prefix"
|
|
assert embedder.suffix == "suffix"
|
|
assert embedder.batch_size == 64
|
|
assert embedder.progress_bar is False
|
|
assert embedder.normalize_embeddings is True
|
|
assert embedder.meta_fields_to_embed == ["test_field"]
|
|
assert embedder.embedding_separator == " | "
|
|
assert embedder.trust_remote_code
|
|
|
|
def test_to_dict(self):
|
|
component = SentenceTransformersDocumentEmbedder(model="model", device=ComponentDevice.from_str("cpu"))
|
|
data = component.to_dict()
|
|
assert data == {
|
|
"type": "haystack.components.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder",
|
|
"init_parameters": {
|
|
"model": "model",
|
|
"device": ComponentDevice.from_str("cpu").to_dict(),
|
|
"token": {"env_vars": ["HF_API_TOKEN", "HF_TOKEN"], "strict": False, "type": "env_var"},
|
|
"prefix": "",
|
|
"suffix": "",
|
|
"batch_size": 32,
|
|
"progress_bar": True,
|
|
"normalize_embeddings": False,
|
|
"embedding_separator": "\n",
|
|
"meta_fields_to_embed": [],
|
|
"trust_remote_code": False,
|
|
},
|
|
}
|
|
|
|
def test_to_dict_with_custom_init_parameters(self):
|
|
component = SentenceTransformersDocumentEmbedder(
|
|
model="model",
|
|
device=ComponentDevice.from_str("cuda:0"),
|
|
token=Secret.from_env_var("ENV_VAR", strict=False),
|
|
prefix="prefix",
|
|
suffix="suffix",
|
|
batch_size=64,
|
|
progress_bar=False,
|
|
normalize_embeddings=True,
|
|
meta_fields_to_embed=["meta_field"],
|
|
embedding_separator=" - ",
|
|
trust_remote_code=True,
|
|
)
|
|
data = component.to_dict()
|
|
|
|
assert data == {
|
|
"type": "haystack.components.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder",
|
|
"init_parameters": {
|
|
"model": "model",
|
|
"device": ComponentDevice.from_str("cuda:0").to_dict(),
|
|
"token": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"},
|
|
"prefix": "prefix",
|
|
"suffix": "suffix",
|
|
"batch_size": 64,
|
|
"progress_bar": False,
|
|
"normalize_embeddings": True,
|
|
"embedding_separator": " - ",
|
|
"trust_remote_code": True,
|
|
"meta_fields_to_embed": ["meta_field"],
|
|
},
|
|
}
|
|
|
|
def test_from_dict(self):
|
|
init_parameters = {
|
|
"model": "model",
|
|
"device": ComponentDevice.from_str("cuda:0").to_dict(),
|
|
"token": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"},
|
|
"prefix": "prefix",
|
|
"suffix": "suffix",
|
|
"batch_size": 64,
|
|
"progress_bar": False,
|
|
"normalize_embeddings": True,
|
|
"embedding_separator": " - ",
|
|
"meta_fields_to_embed": ["meta_field"],
|
|
"trust_remote_code": True,
|
|
}
|
|
component = SentenceTransformersDocumentEmbedder.from_dict(
|
|
{
|
|
"type": "haystack.components.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder",
|
|
"init_parameters": init_parameters,
|
|
}
|
|
)
|
|
assert component.model == "model"
|
|
assert component.device == ComponentDevice.from_str("cuda:0")
|
|
assert component.token == Secret.from_env_var("ENV_VAR", strict=False)
|
|
assert component.prefix == "prefix"
|
|
assert component.suffix == "suffix"
|
|
assert component.batch_size == 64
|
|
assert component.progress_bar is False
|
|
assert component.normalize_embeddings is True
|
|
assert component.embedding_separator == " - "
|
|
assert component.trust_remote_code
|
|
assert component.meta_fields_to_embed == ["meta_field"]
|
|
|
|
def test_from_dict_none_device(self):
|
|
init_parameters = {
|
|
"model": "model",
|
|
"device": None,
|
|
"token": {"env_vars": ["ENV_VAR"], "strict": False, "type": "env_var"},
|
|
"prefix": "prefix",
|
|
"suffix": "suffix",
|
|
"batch_size": 64,
|
|
"progress_bar": False,
|
|
"normalize_embeddings": True,
|
|
"embedding_separator": " - ",
|
|
"meta_fields_to_embed": ["meta_field"],
|
|
"trust_remote_code": True,
|
|
}
|
|
component = SentenceTransformersDocumentEmbedder.from_dict(
|
|
{
|
|
"type": "haystack.components.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder",
|
|
"init_parameters": init_parameters,
|
|
}
|
|
)
|
|
assert component.model == "model"
|
|
assert component.device == ComponentDevice.resolve_device(None)
|
|
assert component.token == Secret.from_env_var("ENV_VAR", strict=False)
|
|
assert component.prefix == "prefix"
|
|
assert component.suffix == "suffix"
|
|
assert component.batch_size == 64
|
|
assert component.progress_bar is False
|
|
assert component.normalize_embeddings is True
|
|
assert component.embedding_separator == " - "
|
|
assert component.trust_remote_code
|
|
assert component.meta_fields_to_embed == ["meta_field"]
|
|
|
|
@patch(
|
|
"haystack.components.embedders.sentence_transformers_document_embedder._SentenceTransformersEmbeddingBackendFactory"
|
|
)
|
|
def test_warmup(self, mocked_factory):
|
|
embedder = SentenceTransformersDocumentEmbedder(
|
|
model="model", token=None, device=ComponentDevice.from_str("cpu")
|
|
)
|
|
mocked_factory.get_embedding_backend.assert_not_called()
|
|
embedder.warm_up()
|
|
mocked_factory.get_embedding_backend.assert_called_once_with(
|
|
model="model", device="cpu", auth_token=None, trust_remote_code=False
|
|
)
|
|
|
|
@patch(
|
|
"haystack.components.embedders.sentence_transformers_document_embedder._SentenceTransformersEmbeddingBackendFactory"
|
|
)
|
|
def test_warmup_doesnt_reload(self, mocked_factory):
|
|
embedder = SentenceTransformersDocumentEmbedder(model="model")
|
|
mocked_factory.get_embedding_backend.assert_not_called()
|
|
embedder.warm_up()
|
|
embedder.warm_up()
|
|
mocked_factory.get_embedding_backend.assert_called_once()
|
|
|
|
def test_run(self):
|
|
embedder = SentenceTransformersDocumentEmbedder(model="model")
|
|
embedder.embedding_backend = MagicMock()
|
|
embedder.embedding_backend.embed = lambda x, **kwargs: np.random.rand(len(x), 16).tolist()
|
|
|
|
documents = [Document(content=f"document number {i}") for i in range(5)]
|
|
|
|
result = embedder.run(documents=documents)
|
|
|
|
assert isinstance(result["documents"], list)
|
|
assert len(result["documents"]) == len(documents)
|
|
for doc in result["documents"]:
|
|
assert isinstance(doc, Document)
|
|
assert isinstance(doc.embedding, list)
|
|
assert isinstance(doc.embedding[0], float)
|
|
|
|
def test_run_wrong_input_format(self):
|
|
embedder = SentenceTransformersDocumentEmbedder(model="model")
|
|
|
|
string_input = "text"
|
|
list_integers_input = [1, 2, 3]
|
|
|
|
with pytest.raises(
|
|
TypeError, match="SentenceTransformersDocumentEmbedder expects a list of Documents as input"
|
|
):
|
|
embedder.run(documents=string_input)
|
|
|
|
with pytest.raises(
|
|
TypeError, match="SentenceTransformersDocumentEmbedder expects a list of Documents as input"
|
|
):
|
|
embedder.run(documents=list_integers_input)
|
|
|
|
def test_embed_metadata(self):
|
|
embedder = SentenceTransformersDocumentEmbedder(
|
|
model="model", meta_fields_to_embed=["meta_field"], embedding_separator="\n"
|
|
)
|
|
embedder.embedding_backend = MagicMock()
|
|
|
|
documents = [Document(content=f"document number {i}", meta={"meta_field": f"meta_value {i}"}) for i in range(5)]
|
|
|
|
embedder.run(documents=documents)
|
|
|
|
embedder.embedding_backend.embed.assert_called_once_with(
|
|
[
|
|
"meta_value 0\ndocument number 0",
|
|
"meta_value 1\ndocument number 1",
|
|
"meta_value 2\ndocument number 2",
|
|
"meta_value 3\ndocument number 3",
|
|
"meta_value 4\ndocument number 4",
|
|
],
|
|
batch_size=32,
|
|
show_progress_bar=True,
|
|
normalize_embeddings=False,
|
|
)
|
|
|
|
def test_prefix_suffix(self):
|
|
embedder = SentenceTransformersDocumentEmbedder(
|
|
model="model",
|
|
prefix="my_prefix ",
|
|
suffix=" my_suffix",
|
|
meta_fields_to_embed=["meta_field"],
|
|
embedding_separator="\n",
|
|
)
|
|
embedder.embedding_backend = MagicMock()
|
|
|
|
documents = [Document(content=f"document number {i}", meta={"meta_field": f"meta_value {i}"}) for i in range(5)]
|
|
|
|
embedder.run(documents=documents)
|
|
|
|
embedder.embedding_backend.embed.assert_called_once_with(
|
|
[
|
|
"my_prefix meta_value 0\ndocument number 0 my_suffix",
|
|
"my_prefix meta_value 1\ndocument number 1 my_suffix",
|
|
"my_prefix meta_value 2\ndocument number 2 my_suffix",
|
|
"my_prefix meta_value 3\ndocument number 3 my_suffix",
|
|
"my_prefix meta_value 4\ndocument number 4 my_suffix",
|
|
],
|
|
batch_size=32,
|
|
show_progress_bar=True,
|
|
normalize_embeddings=False,
|
|
)
|