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update import paths for beta5 #232

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Jan 18, 2024
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Merged

update import paths for beta5 #232

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13 changes: 0 additions & 13 deletions integrations/gradient/tests/test_gradient_document_embedder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@


class TestGradientDocumentEmbedder:
@pytest.mark.unit
def test_init_from_env(self, monkeypatch):
monkeypatch.setenv("GRADIENT_ACCESS_TOKEN", access_token)
monkeypatch.setenv("GRADIENT_WORKSPACE_ID", workspace_id)
Expand All @@ -23,28 +22,24 @@ def test_init_from_env(self, monkeypatch):
assert embedder._gradient.workspace_id == workspace_id
assert embedder._gradient._api_client.configuration.access_token == access_token

@pytest.mark.unit
def test_init_without_access_token(self, monkeypatch):
monkeypatch.delenv("GRADIENT_ACCESS_TOKEN", raising=False)

with pytest.raises(ValueError):
GradientDocumentEmbedder(workspace_id=workspace_id)

@pytest.mark.unit
def test_init_without_workspace(self, monkeypatch):
monkeypatch.delenv("GRADIENT_WORKSPACE_ID", raising=False)

with pytest.raises(ValueError):
GradientDocumentEmbedder(access_token=access_token)

@pytest.mark.unit
def test_init_from_params(self):
embedder = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id)
assert embedder is not None
assert embedder._gradient.workspace_id == workspace_id
assert embedder._gradient._api_client.configuration.access_token == access_token

@pytest.mark.unit
def test_init_from_params_precedence(self, monkeypatch):
monkeypatch.setenv("GRADIENT_ACCESS_TOKEN", "env_access_token")
monkeypatch.setenv("GRADIENT_WORKSPACE_ID", "env_workspace_id")
Expand All @@ -54,7 +49,6 @@ def test_init_from_params_precedence(self, monkeypatch):
assert embedder._gradient.workspace_id == workspace_id
assert embedder._gradient._api_client.configuration.access_token == access_token

@pytest.mark.unit
def test_to_dict(self):
component = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id)
data = component.to_dict()
Expand All @@ -63,29 +57,25 @@ def test_to_dict(self):
"init_parameters": {"workspace_id": workspace_id, "model_name": "bge-large"},
}

@pytest.mark.unit
def test_warmup(self):
embedder = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._gradient.get_embeddings_model = MagicMock()
embedder.warm_up()
embedder._gradient.get_embeddings_model.assert_called_once_with(slug="bge-large")

@pytest.mark.unit
def test_warmup_doesnt_reload(self):
embedder = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._gradient.get_embeddings_model = MagicMock(default_return_value="fake model")
embedder.warm_up()
embedder.warm_up()
embedder._gradient.get_embeddings_model.assert_called_once_with(slug="bge-large")

@pytest.mark.unit
def test_run_fail_if_not_warmed_up(self):
embedder = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id)

with pytest.raises(RuntimeError, match="warm_up()"):
embedder.run(documents=[Document(content=f"document number {i}") for i in range(5)])

@pytest.mark.unit
def test_run(self):
embedder = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._embedding_model = NonCallableMagicMock()
Expand All @@ -105,7 +95,6 @@ def test_run(self):
assert isinstance(doc.embedding, list)
assert isinstance(doc.embedding[0], float)

@pytest.mark.unit
def test_run_batch(self):
embedder = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._embedding_model = NonCallableMagicMock()
Expand All @@ -126,7 +115,6 @@ def test_run_batch(self):
assert isinstance(doc.embedding, list)
assert isinstance(doc.embedding[0], float)

@pytest.mark.unit
def test_run_custom_batch(self):
embedder = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id, batch_size=20)
embedder._embedding_model = NonCallableMagicMock()
Expand All @@ -148,7 +136,6 @@ def test_run_custom_batch(self):
assert isinstance(doc.embedding, list)
assert isinstance(doc.embedding[0], float)

@pytest.mark.unit
def test_run_empty(self):
embedder = GradientDocumentEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._embedding_model = NonCallableMagicMock()
Expand Down
4 changes: 2 additions & 2 deletions integrations/gradient/tests/test_gradient_rag_pipelines.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,9 @@
from haystack import Document, Pipeline
from haystack.components.builders.answer_builder import AnswerBuilder
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.retrievers import InMemoryEmbeddingRetriever
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.components.writers import DocumentWriter
from haystack.document_stores import InMemoryDocumentStore
from haystack.document_stores.in_memory import InMemoryDocumentStore

from gradient_haystack.embedders.gradient_document_embedder import GradientDocumentEmbedder
from gradient_haystack.embedders.gradient_text_embedder import GradientTextEmbedder
Expand Down
12 changes: 0 additions & 12 deletions integrations/gradient/tests/test_gradient_text_embedder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,6 @@


class TestGradientTextEmbedder:
@pytest.mark.unit
def test_init_from_env(self, monkeypatch):
monkeypatch.setenv("GRADIENT_ACCESS_TOKEN", access_token)
monkeypatch.setenv("GRADIENT_WORKSPACE_ID", workspace_id)
Expand All @@ -22,28 +21,24 @@ def test_init_from_env(self, monkeypatch):
assert embedder._gradient.workspace_id == workspace_id
assert embedder._gradient._api_client.configuration.access_token == access_token

@pytest.mark.unit
def test_init_without_access_token(self, monkeypatch):
monkeypatch.delenv("GRADIENT_ACCESS_TOKEN", raising=False)

with pytest.raises(ValueError):
GradientTextEmbedder(workspace_id=workspace_id)

@pytest.mark.unit
def test_init_without_workspace(self, monkeypatch):
monkeypatch.delenv("GRADIENT_WORKSPACE_ID", raising=False)

with pytest.raises(ValueError):
GradientTextEmbedder(access_token=access_token)

@pytest.mark.unit
def test_init_from_params(self):
embedder = GradientTextEmbedder(access_token=access_token, workspace_id=workspace_id)
assert embedder is not None
assert embedder._gradient.workspace_id == workspace_id
assert embedder._gradient._api_client.configuration.access_token == access_token

@pytest.mark.unit
def test_init_from_params_precedence(self, monkeypatch):
monkeypatch.setenv("GRADIENT_ACCESS_TOKEN", "env_access_token")
monkeypatch.setenv("GRADIENT_WORKSPACE_ID", "env_workspace_id")
Expand All @@ -53,7 +48,6 @@ def test_init_from_params_precedence(self, monkeypatch):
assert embedder._gradient.workspace_id == workspace_id
assert embedder._gradient._api_client.configuration.access_token == access_token

@pytest.mark.unit
def test_to_dict(self):
component = GradientTextEmbedder(access_token=access_token, workspace_id=workspace_id)
data = component.to_dict()
Expand All @@ -62,29 +56,25 @@ def test_to_dict(self):
"init_parameters": {"workspace_id": workspace_id, "model_name": "bge-large"},
}

@pytest.mark.unit
def test_warmup(self):
embedder = GradientTextEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._gradient.get_embeddings_model = MagicMock()
embedder.warm_up()
embedder._gradient.get_embeddings_model.assert_called_once_with(slug="bge-large")

@pytest.mark.unit
def test_warmup_doesnt_reload(self):
embedder = GradientTextEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._gradient.get_embeddings_model = MagicMock(default_return_value="fake model")
embedder.warm_up()
embedder.warm_up()
embedder._gradient.get_embeddings_model.assert_called_once_with(slug="bge-large")

@pytest.mark.unit
def test_run_fail_if_not_warmed_up(self):
embedder = GradientTextEmbedder(access_token=access_token, workspace_id=workspace_id)

with pytest.raises(RuntimeError, match="warm_up()"):
embedder.run(text="The food was delicious")

@pytest.mark.unit
def test_run_fail_when_no_embeddings_returned(self):
embedder = GradientTextEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._embedding_model = NonCallableMagicMock()
Expand All @@ -94,7 +84,6 @@ def test_run_fail_when_no_embeddings_returned(self):
_result = embedder.run(text="The food was delicious")
embedder._embedding_model.embed.assert_called_once_with(inputs=[{"input": "The food was delicious"}])

@pytest.mark.unit
def test_run_empty_string(self):
embedder = GradientTextEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._embedding_model = NonCallableMagicMock()
Expand All @@ -108,7 +97,6 @@ def test_run_empty_string(self):
assert len(result["embedding"]) == 1024 # 1024 is the bge-large embedding size
assert all(isinstance(x, float) for x in result["embedding"])

@pytest.mark.unit
def test_run(self):
embedder = GradientTextEmbedder(access_token=access_token, workspace_id=workspace_id)
embedder._embedding_model = NonCallableMagicMock()
Expand Down