[Azure AI Agents] Sample changes to use dataset with file search (#41732)

* Sample changes to use dataset

Signed-off-by: trangevi <trangevi@microsoft.com>

* Add check for run status, and update message to be more applicable.

Signed-off-by: trangevi <trangevi@microsoft.com>

* Update snippet code

Signed-off-by: trangevi <trangevi@microsoft.com>

---------

Signed-off-by: trangevi <trangevi@microsoft.com>

Author: trangevi

sdk/ai/azure-ai-agents/README.md

@@ -279,10 +279,17 @@ We can upload file to Azure as it is shown in the example, or use the existing A
 <!-- SNIPPET:sample_agents_enterprise_file_search.upload_file_and_create_agent_with_file_search -->
 
 ```python
-# We will upload the local file to Azure and will use it for vector store creation.
-asset_uri = os.environ["AZURE_BLOB_URI"]
-
-# Create a vector store with no file and wait for it to be processed
+# If provided, we will upload the local file to Azure and will use it for vector store creation.
+# Otherwise, we'll use a previously created dataset reference
+if "AZURE_BLOB_URI" in os.environ:
+    asset_uri = os.environ["AZURE_BLOB_URI"]
+else:
+    dataset_name = os.environ["AZURE_DATASET_NAME"]
+    dataset_version = os.environ["AZURE_DATASET_VERSION"]
+    dataset = project_client.datasets.get(name=dataset_name, version=dataset_version)
+    asset_uri = dataset.id
+
+# Create a vector store and wait for it to be processed
 ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
 vector_store = agents_client.vector_stores.create_and_poll(data_sources=[ds], name="sample_vector_store")
 print(f"Created vector store, vector store ID: {vector_store.id}")

sdk/ai/azure-ai-agents/samples/agents_tools/sample_agents_enterprise_file_search.py

@@ -25,7 +25,8 @@
 
 import os
 from azure.ai.projects import AIProjectClient
-from azure.ai.agents.models import FileSearchTool, ListSortOrder, VectorStoreDataSource, VectorStoreDataSourceAssetType
+from azure.ai.agents.models import FileSearchTool, ListSortOrder, VectorStoreDataSource, VectorStoreDataSourceAssetType, \
+    RunStatus
 from azure.identity import DefaultAzureCredential
 
 project_client = AIProjectClient(
@@ -37,10 +38,17 @@
     agents_client = project_client.agents
 
     # [START upload_file_and_create_agent_with_file_search]
-    # We will upload the local file to Azure and will use it for vector store creation.
-    asset_uri = os.environ["AZURE_BLOB_URI"]
-
-    # Create a vector store with no file and wait for it to be processed
+    # If provided, we will upload the local file to Azure and will use it for vector store creation.
+    # Otherwise, we'll use a previously created dataset reference
+    if "AZURE_BLOB_URI" in os.environ:
+        asset_uri = os.environ["AZURE_BLOB_URI"]
+    else:
+        dataset_name = os.environ["AZURE_DATASET_NAME"]
+        dataset_version = os.environ["AZURE_DATASET_VERSION"]
+        dataset = project_client.datasets.get(name=dataset_name, version=dataset_version)
+        asset_uri = dataset.id
+
+    # Create a vector store and wait for it to be processed
     ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
     vector_store = agents_client.vector_stores.create_and_poll(data_sources=[ds], name="sample_vector_store")
     print(f"Created vector store, vector store ID: {vector_store.id}")
@@ -63,13 +71,16 @@
     print(f"Created thread, thread ID: {thread.id}")
 
     message = agents_client.messages.create(
-        thread_id=thread.id, role="user", content="What feature does Smart Eyewear offer?"
+        thread_id=thread.id, role="user", content="What is the content of the files you have access to?"
     )
     print(f"Created message, message ID: {message.id}")
 
     run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
     print(f"Created run, run ID: {run.id}")
 
+    if run.status == RunStatus.FAILED:
+        print(f"Run failed with: |{run.last_error}|")
+
     agents_client.vector_stores.delete(vector_store.id)
     print("Deleted vector store")