Update the example based on review

.github/workflows/azure_ai_search.yml

@@ -30,7 +30,6 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
-      max-parallel: 1
       matrix:
         os: [ubuntu-latest]
         python-version: ["3.8", "3.9", "3.10", "3.11"]

integrations/azure_ai_search/example/embedding_retrieval.py

@@ -1,20 +1,21 @@
 from haystack import Document, Pipeline
 from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
+from haystack.components.writers import DocumentWriter
 from haystack.document_stores.types import DuplicatePolicy
 
 from haystack_integrations.components.retrievers.azure_ai_search import AzureAISearchEmbeddingRetriever
 from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore
 
 """
-This example demonstrates how to use the AzureAISearchEmbeddingRetriever to retrieve documents based on a query.
+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.
 See more details at https://learn.microsoft.com/en-us/azure/search/keyless-connections?tabs=python%2Cazure-cli
 """
 
-document_store = AzureAISearchDocumentStore()
+document_store = AzureAISearchDocumentStore(index_name="retrieval-example")
 
 model = "sentence-transformers/all-mpnet-base-v2"
 
@@ -32,8 +33,18 @@
 
 document_embedder = SentenceTransformersDocumentEmbedder(model=model)
 document_embedder.warm_up()
-documents_with_embeddings = document_embedder.run(documents)
-document_store.write_documents(documents_with_embeddings.get("documents"), policy=DuplicatePolicy.SKIP)
+
+# Indexing Pipeline
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component(instance=document_embedder, name="doc_embedder")
+indexing_pipeline.add_component(
+    instance=DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP), name="doc_writer"
+)
+indexing_pipeline.connect("doc_embedder", "doc_writer")
+
+indexing_pipeline.run({"doc_embedder": {"documents": documents}})
+
+# Query Pipeline
 query_pipeline = Pipeline()
 query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder(model=model))
 query_pipeline.add_component("retriever", AzureAISearchEmbeddingRetriever(document_store=document_store))

integrations/azure_ai_search/tests/conftest.py

@@ -1,5 +1,6 @@
 import os
 import time
+import uuid
 
 import pytest
 from azure.core.credentials import AzureKeyCredential
@@ -10,7 +11,7 @@
 from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore
 
 # This is the approximate time in seconds it takes for the documents to be available in Azure Search index
-SLEEP_TIME_IN_SECONDS = 10
+SLEEP_TIME_IN_SECONDS = 5
 
 
 @pytest.fixture()
@@ -24,7 +25,8 @@ def document_store(request):
     This is the most basic requirement for the child class: provide
     an instance of this document store so the base class can use it.
     """
-    index_name = "haystack_test_integration"
+    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"]