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Howie/samples 6 (#43901)
* Samples for ai search and fabric * Resolved comments
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‎sdk/ai/azure-ai-projects/.env.template‎

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@@ -40,7 +40,9 @@ AZURE_AI_PROJECTS_TESTS_CONTAINER_INGRESS_SUBDOMAIN_SUFFIX=
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# Used in tools
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BING_PROJECT_CONNECTION_ID=
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MCP_PROJECT_CONNECTION_ID=
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FABRIC_PROJECT_CONNECTION_ID=
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AI_SEARCH_PROJECT_CONNECTION_ID=
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AI_SEARCH_INDEX_NAME=
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‎sdk/ai/azure-ai-projects/samples/agents/tools/sample_agent_ai_search.py‎

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

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

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

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