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255 lines
12 KiB
Python
255 lines
12 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 os
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from openai import APIError
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from haystack.utils.auth import Secret
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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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from haystack.utils.azure import default_azure_ad_token_provider
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from unittest.mock import Mock, patch
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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.model == "text-embedding-ada-002"
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assert embedder.dimensions is None
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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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assert embedder.default_headers == {}
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assert embedder.azure_ad_token_provider is None
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assert embedder.http_client_kwargs is None
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def test_init_with_0_max_retries(self, monkeypatch):
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"""Tests that the max_retries init param is set correctly if equal 0"""
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monkeypatch.setenv("AZURE_OPENAI_API_KEY", "fake-api-key")
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embedder = AzureOpenAIDocumentEmbedder(
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azure_endpoint="https://example-resource.azure.openai.com/", max_retries=0
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)
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assert embedder.azure_deployment == "text-embedding-ada-002"
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assert embedder.model == "text-embedding-ada-002"
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assert embedder.dimensions is None
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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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assert embedder.default_headers == {}
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assert embedder.azure_ad_token_provider is None
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assert embedder.max_retries == 0
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def test_to_dict(self, monkeypatch):
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monkeypatch.setenv("AZURE_OPENAI_API_KEY", "fake-api-key")
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component = AzureOpenAIDocumentEmbedder(azure_endpoint="https://example-resource.azure.openai.com/")
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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_key": {"env_vars": ["AZURE_OPENAI_API_KEY"], "strict": False, "type": "env_var"},
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"azure_ad_token": {"env_vars": ["AZURE_OPENAI_AD_TOKEN"], "strict": False, "type": "env_var"},
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"api_version": "2023-05-15",
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"azure_deployment": "text-embedding-ada-002",
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"dimensions": None,
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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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"max_retries": 5,
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"timeout": 30.0,
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"default_headers": {},
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"azure_ad_token_provider": None,
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"http_client_kwargs": None,
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},
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}
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def test_to_dict_with_parameters(self, monkeypatch):
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monkeypatch.setenv("AZURE_OPENAI_API_KEY", "fake-api-key")
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component = AzureOpenAIDocumentEmbedder(
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azure_endpoint="https://example-resource.azure.openai.com/",
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azure_deployment="text-embedding-ada-002",
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dimensions=768,
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organization="HaystackCI",
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timeout=60.0,
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max_retries=10,
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prefix="prefix ",
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suffix=" suffix",
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default_headers={"x-custom-header": "custom-value"},
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azure_ad_token_provider=default_azure_ad_token_provider,
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http_client_kwargs={"proxy": "http://example.com:3128", "verify": False},
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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_key": {"env_vars": ["AZURE_OPENAI_API_KEY"], "strict": False, "type": "env_var"},
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"azure_ad_token": {"env_vars": ["AZURE_OPENAI_AD_TOKEN"], "strict": False, "type": "env_var"},
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"api_version": "2023-05-15",
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"azure_deployment": "text-embedding-ada-002",
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"dimensions": 768,
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"azure_endpoint": "https://example-resource.azure.openai.com/",
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"organization": "HaystackCI",
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"prefix": "prefix ",
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"suffix": " 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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"max_retries": 10,
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"timeout": 60.0,
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"default_headers": {"x-custom-header": "custom-value"},
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"azure_ad_token_provider": "haystack.utils.azure.default_azure_ad_token_provider",
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"http_client_kwargs": {"proxy": "http://example.com:3128", "verify": False},
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},
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}
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def test_from_dict(self, monkeypatch):
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monkeypatch.setenv("AZURE_OPENAI_API_KEY", "fake-api-key")
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data = {
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"type": "haystack.components.embedders.azure_document_embedder.AzureOpenAIDocumentEmbedder",
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"init_parameters": {
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"api_key": {"env_vars": ["AZURE_OPENAI_API_KEY"], "strict": False, "type": "env_var"},
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"azure_ad_token": {"env_vars": ["AZURE_OPENAI_AD_TOKEN"], "strict": False, "type": "env_var"},
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"api_version": "2023-05-15",
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"azure_deployment": "text-embedding-ada-002",
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"dimensions": None,
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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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"max_retries": 5,
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"timeout": 30.0,
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"default_headers": {},
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"azure_ad_token_provider": None,
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"http_client_kwargs": None,
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},
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}
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component = AzureOpenAIDocumentEmbedder.from_dict(data)
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assert component.azure_deployment == "text-embedding-ada-002"
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assert component.azure_endpoint == "https://example-resource.azure.openai.com/"
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assert component.api_version == "2023-05-15"
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assert component.max_retries == 5
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assert component.timeout == 30.0
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assert component.prefix == ""
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assert component.suffix == ""
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assert component.default_headers == {}
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assert component.azure_ad_token_provider is None
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assert component.http_client_kwargs is None
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def test_from_dict_with_parameters(self, monkeypatch):
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monkeypatch.setenv("AZURE_OPENAI_API_KEY", "fake-api-key")
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data = {
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"type": "haystack.components.embedders.azure_document_embedder.AzureOpenAIDocumentEmbedder",
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"init_parameters": {
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"api_key": {"env_vars": ["AZURE_OPENAI_API_KEY"], "strict": False, "type": "env_var"},
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"azure_ad_token": {"env_vars": ["AZURE_OPENAI_AD_TOKEN"], "strict": False, "type": "env_var"},
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"api_version": "2023-05-15",
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"azure_deployment": "text-embedding-ada-002",
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"dimensions": 768,
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"azure_endpoint": "https://example-resource.azure.openai.com/",
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"organization": "HaystackCI",
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"prefix": "prefix ",
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"suffix": " 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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"max_retries": 10,
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"timeout": 60.0,
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"default_headers": {"x-custom-header": "custom-value"},
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"azure_ad_token_provider": "haystack.utils.azure.default_azure_ad_token_provider",
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"http_client_kwargs": {"proxy": "http://example.com:3128", "verify": False},
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},
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}
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component = AzureOpenAIDocumentEmbedder.from_dict(data)
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assert component.azure_deployment == "text-embedding-ada-002"
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assert component.azure_endpoint == "https://example-resource.azure.openai.com/"
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assert component.api_version == "2023-05-15"
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assert component.max_retries == 10
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assert component.timeout == 60.0
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assert component.prefix == "prefix "
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assert component.suffix == " suffix"
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assert component.default_headers == {"x-custom-header": "custom-value"}
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assert component.azure_ad_token_provider is not None
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assert component.http_client_kwargs == {"proxy": "http://example.com:3128", "verify": False}
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def test_embed_batch_handles_exceptions_gracefully(self, caplog):
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embedder = AzureOpenAIDocumentEmbedder(
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azure_endpoint="https://test.openai.azure.com",
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api_key=Secret.from_token("fake-api-key"),
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azure_deployment="text-embedding-ada-002",
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embedding_separator=" | ",
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)
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fake_texts_to_embed = {"1": "text1", "2": "text2"}
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with patch.object(
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embedder.client.embeddings,
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"create",
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side_effect=APIError(message="Mocked error", request=Mock(), body=None),
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):
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embedder._embed_batch(texts_to_embed=fake_texts_to_embed, batch_size=32)
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assert len(caplog.records) == 1
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assert "Failed embedding of documents 1, 2 caused by Mocked error" in caplog.text
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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["usage"]["prompt_tokens"] == 15
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assert metadata["usage"]["total_tokens"] == 15
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assert "ada" in metadata["model"]
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