Add async test for file search tool and add test recordings

sdk/ai/azure-ai-projects/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ai/azure-ai-projects",
-  "Tag": "python/ai/azure-ai-projects_670570275d"
+  "Tag": "python/ai/azure-ai-projects_3268c5ebe1"
 }

sdk/ai/azure-ai-projects/tests/agents/tools/test_agent_file_search.py

@@ -9,17 +9,16 @@
 import pytest
 from io import BytesIO
 from test_base import TestBase, servicePreparer
-from devtools_testutils import is_live_and_not_recording
+from devtools_testutils import recorded_by_proxy, RecordedTransport
 from azure.ai.projects.models import PromptAgentDefinition, FileSearchTool
 
 
 class TestAgentFileSearch(TestBase):
 
+    # To only run this test:
+    # pytest tests/agents/tools/test_agent_file_search.py::TestAgentFileSearch::test_agent_file_search -s
     @servicePreparer()
-    @pytest.mark.skipif(
-        condition=(not is_live_and_not_recording()),
-        reason="Skipped because we cannot record network calls with OpenAI client",
-    )
+    @recorded_by_proxy(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
     def test_agent_file_search(self, **kwargs):
         """
         Test agent with File Search tool for document Q&A.
@@ -63,7 +62,7 @@ def test_agent_file_search(self, **kwargs):
             # Create vector store for file search
             vector_store = openai_client.vector_stores.create(name="ProductInfoStore")
             print(f"Vector store created (id: {vector_store.id})")
-            assert vector_store.id is not None
+            assert vector_store.id
 
             # Upload file to vector store
             with open(asset_file_path, "rb") as f:
@@ -73,23 +72,21 @@ def test_agent_file_search(self, **kwargs):
                 )
 
             print(f"File uploaded (id: {file.id}, status: {file.status})")
-            assert file.id is not None
+            assert file.id
             assert file.status == "completed", f"Expected file status 'completed', got '{file.status}'"
 
             # Create agent with file search tool
+            agent_name = "file-search-agent"
             agent = project_client.agents.create_version(
-                agent_name="file-search-agent",
+                agent_name=agent_name,
                 definition=PromptAgentDefinition(
                     model=model,
                     instructions="You are a helpful assistant that can search through uploaded documents to answer questions.",
                     tools=[FileSearchTool(vector_store_ids=[vector_store.id])],
                 ),
                 description="Agent for testing file search capabilities.",
             )
-            print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
-            assert agent.id is not None
-            assert agent.name == "file-search-agent"
-            assert agent.version is not None
+            self._validate_agent_version(agent, expected_name=agent_name)
 
             # Ask a question about the uploaded document
             print("\nAsking agent about the product information...")
@@ -100,7 +97,7 @@ def test_agent_file_search(self, **kwargs):
             )
 
             print(f"Response completed (id: {response.id})")
-            assert response.id is not None
+            assert response.id
             assert response.output is not None
             assert len(response.output) > 0
 
@@ -125,10 +122,7 @@ def test_agent_file_search(self, **kwargs):
             print("Vector store deleted")
 
     @servicePreparer()
-    @pytest.mark.skipif(
-        condition=(not is_live_and_not_recording()),
-        reason="Skipped because we cannot record network calls with OpenAI client",
-    )
+    @recorded_by_proxy(RecordedTransport.HTTPX)
     def test_agent_file_search_unsupported_file_type(self, **kwargs):
         """
         Negative test: Verify that unsupported file types are rejected with clear error messages.
@@ -200,10 +194,7 @@ def test_agent_file_search_unsupported_file_type(self, **kwargs):
             print("\nVector store deleted")
 
     @servicePreparer()
-    @pytest.mark.skipif(
-        condition=(not is_live_and_not_recording()),
-        reason="Skipped because we cannot record network calls with OpenAI client",
-    )
+    @recorded_by_proxy(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
     def test_agent_file_search_multi_turn_conversation(self, **kwargs):
         """
         Test multi-turn conversation with File Search.

sdk/ai/azure-ai-projects/tests/agents/tools/test_agent_file_search_async.py

@@ -0,0 +1,222 @@
+# pylint: disable=too-many-lines,line-too-long,useless-suppression
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+# cSpell:disable
+
+import os
+from io import BytesIO
+from test_base import TestBase, servicePreparer
+from devtools_testutils.aio import recorded_by_proxy_async
+from devtools_testutils import RecordedTransport
+from azure.ai.projects.models import PromptAgentDefinition, FileSearchTool
+
+
+class TestAgentFileSearchAsync(TestBase):
+
+    @servicePreparer()
+    @recorded_by_proxy_async(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
+    async def test_agent_file_search_async(self, **kwargs):
+
+        model = self.test_agents_params["model_deployment_name"]
+
+        async with (
+            self.create_async_client(operation_group="agents", **kwargs) as project_client,
+            project_client.get_openai_client() as openai_client,
+        ):
+            # Get the path to the test file
+            asset_file_path = os.path.abspath(
+                os.path.join(os.path.dirname(__file__), "../../../samples/agents/assets/product_info.md")
+            )
+
+            assert os.path.exists(asset_file_path), f"Test file not found at: {asset_file_path}"
+            print(f"Using test file: {asset_file_path}")
+
+            # Create vector store for file search
+            vector_store = await openai_client.vector_stores.create(name="ProductInfoStore")
+            print(f"Vector store created (id: {vector_store.id})")
+            assert vector_store.id
+
+            # Upload file to vector store
+            with open(asset_file_path, "rb") as f:
+                file = await openai_client.vector_stores.files.upload_and_poll(
+                    vector_store_id=vector_store.id,
+                    file=f,
+                )
+
+            print(f"File uploaded (id: {file.id}, status: {file.status})")
+            assert file.id
+            assert file.status == "completed", f"Expected file status 'completed', got '{file.status}'"
+
+            # Create agent with file search tool
+            agent_name = "file-search-agent"
+            agent = await project_client.agents.create_version(
+                agent_name=agent_name,
+                definition=PromptAgentDefinition(
+                    model=model,
+                    instructions="You are a helpful assistant that can search through uploaded documents to answer questions.",
+                    tools=[FileSearchTool(vector_store_ids=[vector_store.id])],
+                ),
+                description="Agent for testing file search capabilities.",
+            )
+            self._validate_agent_version(agent, expected_name=agent_name)
+
+            # Ask a question about the uploaded document
+            print("\nAsking agent about the product information...")
+
+            response = await openai_client.responses.create(
+                input="What products are mentioned in the document?",
+                extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+            )
+
+            print(f"Response completed (id: {response.id})")
+            assert response.id
+            assert response.output is not None
+            assert len(response.output) > 0
+
+            # Get the response text
+            response_text = response.output_text
+            print(f"\nAgent's response: {response_text[:300]}...")
+
+            # Verify we got a meaningful response
+            assert len(response_text) > 50, "Expected a substantial response from the agent"
+
+            # The response should mention finding information (indicating file search was used)
+            # We can't assert exact product names without knowing the file content,
+            # but we can verify the agent provided an answer
+            print("\n✓ Agent successfully used file search tool to answer question from uploaded document")
+
+            # Teardown
+            print("\nCleaning up...")
+            await project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
+            print("Agent deleted")
+
+            await openai_client.vector_stores.delete(vector_store.id)
+            print("Vector store deleted")
+
+    @servicePreparer()
+    @recorded_by_proxy_async(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
+    async def test_agent_file_search_multi_turn_conversation_async(self, **kwargs):
+        """
+        Test multi-turn conversation with File Search (async version).
+
+        This test verifies that an agent can maintain context across multiple turns
+        while using File Search to answer follow-up questions.
+        """
+
+        model = self.test_agents_params["model_deployment_name"]
+
+        async with (
+            self.create_async_client(operation_group="agents", **kwargs) as project_client,
+            project_client.get_openai_client() as openai_client,
+        ):
+            # Create a document with information about products
+            product_info = """Product Catalog:
+
+Widget A:
+- Price: $150
+- Category: Electronics
+- Stock: 50 units
+- Rating: 4.5/5 stars
+
+Widget B:
+- Price: $220
+- Category: Electronics  
+- Stock: 30 units
+- Rating: 4.8/5 stars
+
+Widget C:
+- Price: $95
+- Category: Home & Garden
+- Stock: 100 units
+- Rating: 4.2/5 stars
+"""
+
+            # Create vector store and upload document
+            vector_store = await openai_client.vector_stores.create(name="ProductCatalog")
+            print(f"Vector store created: {vector_store.id}")
+
+            product_file = BytesIO(product_info.encode("utf-8"))
+            product_file.name = "products.txt"
+
+            file = await openai_client.vector_stores.files.upload_and_poll(
+                vector_store_id=vector_store.id,
+                file=product_file,
+            )
+            print(f"Product catalog uploaded: {file.id}")
+
+            # Create agent with File Search
+            agent = await project_client.agents.create_version(
+                agent_name="product-catalog-agent",
+                definition=PromptAgentDefinition(
+                    model=model,
+                    instructions="You are a product information assistant. Use file search to answer questions about products.",
+                    tools=[FileSearchTool(vector_store_ids=[vector_store.id])],
+                ),
+                description="Agent for multi-turn product queries.",
+            )
+            print(f"Agent created: {agent.id}")
+
+            # Turn 1: Ask about price
+            print("\n--- Turn 1: Initial query ---")
+            response_1 = await openai_client.responses.create(
+                input="What is the price of Widget B?",
+                extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+            )
+
+            response_1_text = response_1.output_text
+            print(f"Response 1: {response_1_text[:200]}...")
+            assert "$220" in response_1_text or "220" in response_1_text, "Response should mention Widget B's price"
+
+            # Turn 2: Follow-up question (requires context from turn 1)
+            print("\n--- Turn 2: Follow-up query (testing context retention) ---")
+            response_2 = await openai_client.responses.create(
+                input="What about its stock level?",
+                previous_response_id=response_1.id,
+                extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+            )
+
+            response_2_text = response_2.output_text
+            print(f"Response 2: {response_2_text[:200]}...")
+            assert (
+                "30" in response_2_text or "thirty" in response_2_text.lower()
+            ), "Response should mention Widget B's stock (30 units)"
+
+            # Turn 3: Another follow-up (compare with different product)
+            print("\n--- Turn 3: Comparison query ---")
+            response_3 = await openai_client.responses.create(
+                input="How does that compare to Widget A's stock?",
+                previous_response_id=response_2.id,
+                extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+            )
+
+            response_3_text = response_3.output_text
+            print(f"Response 3: {response_3_text[:200]}...")
+            assert (
+                "50" in response_3_text or "fifty" in response_3_text.lower()
+            ), "Response should mention Widget A's stock (50 units)"
+
+            # Turn 4: New topic (testing topic switching)
+            print("\n--- Turn 4: Topic switch ---")
+            response_4 = await openai_client.responses.create(
+                input="Which widget has the highest rating?",
+                previous_response_id=response_3.id,
+                extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+            )
+
+            response_4_text = response_4.output_text
+            print(f"Response 4: {response_4_text[:200]}...")
+            assert (
+                "widget b" in response_4_text.lower() or "4.8" in response_4_text
+            ), "Response should identify Widget B as highest rated (4.8/5)"
+
+            print("\n✓ Multi-turn conversation successful!")
+            print("  - Context maintained across turns")
+            print("  - Follow-up questions handled correctly")
+            print("  - Topic switching works")
+
+            # Cleanup
+            await project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
+            await openai_client.vector_stores.delete(vector_store.id)
+            print("Cleanup completed")

sdk/ai/azure-ai-projects/tests/agents/tools/test_agent_file_search_stream.py

@@ -6,19 +6,15 @@
 # cSpell:disable
 
 import os
-import pytest
 from test_base import TestBase, servicePreparer
-from devtools_testutils import is_live_and_not_recording
+from devtools_testutils import recorded_by_proxy, RecordedTransport
 from azure.ai.projects.models import PromptAgentDefinition, FileSearchTool
 
 
 class TestAgentFileSearchStream(TestBase):
 
     @servicePreparer()
-    @pytest.mark.skipif(
-        condition=(not is_live_and_not_recording()),
-        reason="Skipped because we cannot record network calls with OpenAI client",
-    )
+    @recorded_by_proxy(RecordedTransport.AZURE_CORE, RecordedTransport.HTTPX)
     def test_agent_file_search_stream(self, **kwargs):
         """
         Test agent with File Search tool using streaming responses.
@@ -62,7 +58,7 @@ def test_agent_file_search_stream(self, **kwargs):
             # Create vector store for file search
             vector_store = openai_client.vector_stores.create(name="ProductInfoStoreStream")
             print(f"Vector store created (id: {vector_store.id})")
-            assert vector_store.id is not None
+            assert vector_store.id
 
             # Upload file to vector store
             with open(asset_file_path, "rb") as f:
@@ -72,23 +68,21 @@ def test_agent_file_search_stream(self, **kwargs):
                 )
 
             print(f"File uploaded (id: {file.id}, status: {file.status})")
-            assert file.id is not None
+            assert file.id
             assert file.status == "completed", f"Expected file status 'completed', got '{file.status}'"
 
             # Create agent with file search tool
+            agent_name = "file-search-stream-agent"
             agent = project_client.agents.create_version(
-                agent_name="file-search-stream-agent",
+                agent_name=agent_name,
                 definition=PromptAgentDefinition(
                     model=model,
                     instructions="You are a helpful assistant that can search through uploaded documents to answer questions.",
                     tools=[FileSearchTool(vector_store_ids=[vector_store.id])],
                 ),
                 description="Agent for testing file search with streaming.",
             )
-            print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
-            assert agent.id is not None
-            assert agent.name == "file-search-stream-agent"
-            assert agent.version is not None
+            self._validate_agent_version(agent, expected_name=agent_name)
 
             # Ask a question with streaming enabled
             print("\nAsking agent about the product information (streaming)...")