From 54570e397d21d0e6fe5d20fc1356f2cd69cff339 Mon Sep 17 00:00:00 2001 From: Amna Mubashar Date: Mon, 28 Oct 2024 11:06:01 +0100 Subject: [PATCH] Fix linting --- integrations/azure_ai_search/example/embedding_retrieval.py | 5 +++-- integrations/azure_ai_search/tests/conftest.py | 1 - 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/integrations/azure_ai_search/example/embedding_retrieval.py b/integrations/azure_ai_search/example/embedding_retrieval.py index e323c33e3..088b08653 100644 --- a/integrations/azure_ai_search/example/embedding_retrieval.py +++ b/integrations/azure_ai_search/example/embedding_retrieval.py @@ -7,8 +7,9 @@ from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore """ -This example demonstrates how to use the AzureAISearchEmbeddingRetriever to retrieve documents using embeddings based on a query. -To run this example, you'll need an Azure Search service endpoint and API key, which can either be +This example demonstrates how to use the AzureAISearchEmbeddingRetriever to retrieve documents +using embeddings based on a query. To run this example, you'll need an Azure Search service endpoint +and API key, which can either be set as environment variables (AZURE_SEARCH_SERVICE_ENDPOINT and AZURE_SEARCH_API_KEY) or provided directly to AzureAISearchDocumentStore(as params "api_key", "azure_endpoint"). Otherwise you can use DefaultAzureCredential to authenticate with Azure services. diff --git a/integrations/azure_ai_search/tests/conftest.py b/integrations/azure_ai_search/tests/conftest.py index 48549d244..3017c79c2 100644 --- a/integrations/azure_ai_search/tests/conftest.py +++ b/integrations/azure_ai_search/tests/conftest.py @@ -26,7 +26,6 @@ def document_store(request): an instance of this document store so the base class can use it. """ index_name = f"haystack_test_{uuid.uuid4().hex}" - print (index_name) metadata_fields = getattr(request, "param", {}).get("metadata_fields", None) azure_endpoint = os.environ["AZURE_SEARCH_SERVICE_ENDPOINT"]