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77 lines
3.1 KiB
Python
77 lines
3.1 KiB
Python
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import os
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import pytest
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from haystack import Document
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from haystack.components.embedders import AzureOpenAIDocumentEmbedder
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class TestAzureOpenAIDocumentEmbedder:
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def test_init_default(self, monkeypatch):
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monkeypatch.setenv("AZURE_OPENAI_API_KEY", "fake-api-key")
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embedder = AzureOpenAIDocumentEmbedder(azure_endpoint="https://example-resource.azure.openai.com/")
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assert embedder.azure_deployment == "text-embedding-ada-002"
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assert embedder.organization is None
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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.meta_fields_to_embed == []
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assert embedder.embedding_separator == "\n"
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def test_to_dict(self):
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component = AzureOpenAIDocumentEmbedder(
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api_key="fake-api-key", azure_endpoint="https://example-resource.azure.openai.com/"
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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.azure_document_embedder.AzureOpenAIDocumentEmbedder",
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"init_parameters": {
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"api_version": "2023-05-15",
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"azure_deployment": "text-embedding-ada-002",
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"azure_endpoint": "https://example-resource.azure.openai.com/",
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"organization": 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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"meta_fields_to_embed": [],
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"embedding_separator": "\n",
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},
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}
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@pytest.mark.integration
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@pytest.mark.skipif(
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not os.environ.get("AZURE_OPENAI_API_KEY", None) and not os.environ.get("AZURE_OPENAI_ENDPOINT", None),
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reason=(
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"Please export env variables called AZURE_OPENAI_API_KEY containing "
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"the Azure OpenAI key, AZURE_OPENAI_ENDPOINT containing "
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"the Azure OpenAI endpoint URL to run this test."
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),
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)
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def test_run(self):
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docs = [
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Document(content="I love cheese", meta={"topic": "Cuisine"}),
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Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
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]
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# the default model is text-embedding-ada-002 even if we don't specify it, but let's be explicit
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embedder = AzureOpenAIDocumentEmbedder(
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azure_deployment="text-embedding-ada-002",
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meta_fields_to_embed=["topic"],
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embedding_separator=" | ",
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organization="HaystackCI",
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)
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result = embedder.run(documents=docs)
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documents_with_embeddings = result["documents"]
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metadata = result["meta"]
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assert isinstance(documents_with_embeddings, list)
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assert len(documents_with_embeddings) == len(docs)
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for doc in documents_with_embeddings:
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assert isinstance(doc, Document)
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assert isinstance(doc.embedding, list)
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assert len(doc.embedding) == 1536
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assert all(isinstance(x, float) for x in doc.embedding)
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assert metadata == {"model": "ada", "usage": {"prompt_tokens": 15, "total_tokens": 15}}
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