Howie/samples 6

sdk/ai/azure-ai-projects/.env.template

@@ -40,7 +40,9 @@ AZURE_AI_PROJECTS_TESTS_CONTAINER_INGRESS_SUBDOMAIN_SUFFIX=
 # Used in tools
 BING_PROJECT_CONNECTION_ID=
 MCP_PROJECT_CONNECTION_ID=
-
+FABRIC_PROJECT_CONNECTION_ID=
+AI_SEARCH_PROJECT_CONNECTION_ID=
+AI_SEARCH_INDEX_NAME=
 
 
 

sdk/ai/azure-ai-projects/samples/agents/tools/sample_agent_ai_search.py

@@ -0,0 +1,112 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to create an AI agent with Azure AI Search capabilities
+    using the AzureAISearchAgentTool and synchronous Azure AI Projects client. The agent can search
+    indexed content and provide responses with citations from search results.
+
+USAGE:
+    python sample_agent_ai_search.py
+
+    Before running the sample:
+
+    pip install "azure-ai-projects>=2.0.0b1" azure-identity openai python-dotenv
+
+    Set these environment variables with your own values:
+    1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+       page of your Azure AI Foundry portal.
+    2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Azure AI Foundry project.
+    3) AI_SEARCH_PROJECT_CONNECTION_ID - The AI Search project connection ID,
+       as found in the "Connections" tab in your Azure AI Foundry project.
+    4) AI_SEARCH_INDEX_NAME - The name of the AI Search index to use for searching.
+"""
+
+import os
+from dotenv import load_dotenv
+from azure.identity import DefaultAzureCredential
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.models import (
+    AzureAISearchAgentTool,
+    PromptAgentDefinition,
+    AzureAISearchToolResource,
+    AISearchIndexResource,
+    AzureAISearchQueryType,
+)
+
+load_dotenv()
+
+project_client = AIProjectClient(
+    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+    credential=DefaultAzureCredential(),
+)
+
+openai_client = project_client.get_openai_client()
+
+with project_client:
+    agent = project_client.agents.create_version(
+        agent_name="MyAgent",
+        definition=PromptAgentDefinition(
+            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            instructions="""You are a helpful assistant. You must always provide citations for
+            answers using the tool and render them as: `[message_idx:search_idx†source]`.""",
+            tools=[
+                AzureAISearchAgentTool(
+                    azure_ai_search=AzureAISearchToolResource(
+                        indexes=[
+                            AISearchIndexResource(
+                                project_connection_id=os.environ["AI_SEARCH_PROJECT_CONNECTION_ID"],
+                                index_name=os.environ["AI_SEARCH_INDEX_NAME"],
+                                query_type=AzureAISearchQueryType.SIMPLE,
+                            ),
+                        ]
+                    )
+                )
+            ],
+        ),
+        description="You are a helpful agent.",
+    )
+    print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
+
+    user_input = input(
+        """Enter your question for the AI Search agent available in the index
+        (e.g., 'Tell me about the mental health services available from Premera'): \n"""
+    )
+
+    stream_response = openai_client.responses.create(
+        stream=True,
+        tool_choice="required",
+        input=user_input,
+        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+    )
+
+    for event in stream_response:
+        if event.type == "response.created":
+            print(f"Follow-up response created with ID: {event.response.id}")
+        elif event.type == "response.output_text.delta":
+            print(f"Delta: {event.delta}")
+        elif event.type == "response.text.done":
+            print(f"\nFollow-up response done!")
+        elif event.type == "response.output_item.done":
+            if event.item.type == "message":
+                item = event.item
+                if item.content[-1].type == "output_text":
+                    text_content = item.content[-1]
+                    for annotation in text_content.annotations:
+                        if annotation.type == "url_citation":
+                            print(
+                                f"URL Citation: {annotation.url}, "
+                                f"Start index: {annotation.start_index}, "
+                                f"End index: {annotation.end_index}"
+                            )
+        elif event.type == "response.completed":
+            print(f"\nFollow-up completed!")
+            print(f"Full response: {event.response.output_text}")
+
+    print("\nCleaning up...")
+    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
+    print("Agent deleted")

sdk/ai/azure-ai-projects/samples/agents/tools/sample_agent_fabric.py

@@ -0,0 +1,79 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to create an AI agent with Microsoft Fabric capabilities
+    using the MicrosoftFabricAgentTool and synchronous Azure AI Projects client. The agent can query
+    Fabric data sources and provide responses based on data analysis.
+
+USAGE:
+    python sample_agent_fabric.py
+
+    Before running the sample:
+
+    pip install "azure-ai-projects>=2.0.0b1" azure-identity openai python-dotenv
+
+    Set these environment variables with your own values:
+    1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+       page of your Azure AI Foundry portal.
+    2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
+       the "Models + endpoints" tab in your Azure AI Foundry project.
+    3) FABRIC_PROJECT_CONNECTION_ID - The Fabric project connection ID,
+       as found in the "Connections" tab in your Azure AI Foundry project.
+"""
+
+import os
+from dotenv import load_dotenv
+from azure.identity import DefaultAzureCredential
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.models import (
+    PromptAgentDefinition,
+    MicrosoftFabricAgentTool,
+    FabricDataAgentToolParameters,
+    ToolProjectConnection,
+)
+
+load_dotenv()
+
+project_client = AIProjectClient(
+    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+    credential=DefaultAzureCredential(),
+)
+
+openai_client = project_client.get_openai_client()
+
+with project_client:
+    agent = project_client.agents.create_version(
+        agent_name="MyAgent",
+        definition=PromptAgentDefinition(
+            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            instructions="You are a helpful assistant.",
+            tools=[
+                MicrosoftFabricAgentTool(
+                    fabric_dataagent_preview=FabricDataAgentToolParameters(
+                        project_connections=[
+                            ToolProjectConnection(project_connection_id=os.environ["FABRIC_PROJECT_CONNECTION_ID"])
+                        ]
+                    )
+                )
+            ],
+        ),
+    )
+    print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
+
+    user_input = input("Enter your question for Fabric (e.g., 'Tell me about sales records'): \n")
+
+    response = openai_client.responses.create(
+        tool_choice="required",
+        input=user_input,
+        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+    )
+
+    print(f"Response output: {response.output_text}")
+
+    print("\nCleaning up...")
+    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
+    print("Agent deleted")