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Merge pull request #52 from umangsehgal/main
Foundry ProjectSDK Samples - umangsehgal
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‎doc-samples/agents/python/project-client/README.md‎

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@@ -8,14 +8,14 @@ This table tracks the current status of code samples for each supported tool in
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| Sample | Sample Description | Status | Notes / Known Issues |
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|--------------------|--------------------------------------------------|--------------------------|--------------------------------------------|
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| **Bing** | Using Bing in an agent | ⚠️ Doesn't work | |
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| **File Search** | Uploading files | ❌ Doesn't exist | |
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| | Using blob storage (project data assets) | ❌ Doesn't exist | |
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| **Bing** | Using Bing in an agent | ✅ Exists and works | |
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| **File Search** | Uploading files | ✅ Exists and works | |
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| | Using blob storage (project data assets) | ✅ Exists and works | |
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| | Managing files | ❌ Doesn't exist | |
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| **Azure AI Search**| Using a knowledge store | ❌ Doesn't exist | |
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| **Fabric** | Grounding with Fabric data | ❌ Doesn't exist | |
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| **SharePoint** | Grounding with SharePoint files | ❌ Doesn't exist | |
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| **TripAdvisor** | Using licensed TripAdvisor data | ❌ Doesn't exist | |
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| **Azure AI Search**| Using a knowledge store | ✅ Exists and works | |
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| **Fabric** | Grounding with Fabric data | ✅ Exists and works | |
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| **SharePoint** | Grounding with SharePoint files | ⚠️ Exists and yet to work | |
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| **TripAdvisor** | Using licensed TripAdvisor data | ⚠️ Exists and doesn't work | |
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| **Function Calling**| Calling local functions | ❌ Doesn't exist | |
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| **Azure Functions**| Calling durable Azure Functions | ❌ Doesn't exist | |
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| **Logic Apps** | Calling Logic Apps workflows | ❌ Doesn't exist | |

‎doc-samples/agents/python/project-client/agent_sharepoint.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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FILE: agent_sharepoint.py
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DESCRIPTION:
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This sample demonstrates how to use agent operations with the SharePoint tool from
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the Azure Agents service using a synchronous client.
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USAGE:
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python agent_sharepoint.py
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Before running the sample:
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pip install azure.ai.projects azure-identity
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Set these environment variables with your own values:
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PROJECT_ENDPOINT - the Azure AI Project endpoint, as found in your AI Studio Project.
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MODEL_DEPLOYMENT_NAME - the deployment name of the AI model.
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CONNECTION_NAME - the name of the SharePoint connection.
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"""
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import os
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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 import AIProjectClient
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from azure.ai.agents.models import SharepointTool
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from azure.identity import DefaultAzureCredential
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# Retrieve endpoint and model deployment name from environment variables
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endpoint = os.environ["PROJECT_ENDPOINT"], # Ensure the PROJECT_ENDPOINT environment variable is set
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model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"] # Ensure the MODEL_DEPLOYMENT_NAME environment variable is set
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# Initialize the AIProjectClient with the endpoint and credentials
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with AIProjectClient(
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endpoint=endpoint,
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credential=DefaultAzureCredential(exclude_interactive_browser_credential=False), # Use Azure Default Credential for authentication
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) as project_client:
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# Initialize SharePoint tool with connection ID
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sharepoint_connection = project_client.connections.get(
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name="CONNECTION_NAME", # Replace with your actual connection name
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)
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conn_id = sharepoint_connection.id # Retrieve the connection ID
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print(conn_id)
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sharepoint = SharepointTool(connection_id=conn_id) # Initialize the SharePoint tool with the connection ID
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# Access the agents client from the project client
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agents_client = project_client.agents
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# Create an agent with the specified model, name, instructions, and tools
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agent = agents_client.create_agent(
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model=model_deployment_name, # Model deployment name
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name="my-agent", # Name of the agent
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instructions="You are a helpful agent", # Instructions for the agent
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tools=sharepoint.definitions, # Tools available to the agent
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)
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print(f"Created agent, ID: {agent.id}")
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# Create a thread for communication with the agent
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thread = agents_client.create_thread()
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print(f"Created thread, ID: {thread.id}")
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# Send a message to the thread
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message = agents_client.create_message(
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thread_id=thread.id, # ID of the thread
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role="user", # Role of the message sender (e.g., user)
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content="Hello, summarize the key points of the <sharepoint_resource_document>", # Message content
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)
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print(f"Created message, ID: {message.id}")
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# Create and process an agent run in the thread using the tools
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run = agents_client.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
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print(f"Run finished with status: {run.status}")
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if run.status == "failed":
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# Log the error if the run fails
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print(f"Run failed: {run.last_error}")
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# Delete the agent when done to clean up resources
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agents_client.delete_agent(agent.id)
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print("Deleted agent")
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# Fetch and log all messages from the thread
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messages = agents_client.list_message(thread_id=thread.id)
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for msg in messages:
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if msg.text_messages: # Check if there are text messages
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last_text = msg.text_messages[-1] # Get the last text message
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print(f"{msg.role}: {last_text.text.value}") # Print the role and message content
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‎doc-samples/agents/python/project-client/azure_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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FILE: azure_ai_search.py
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DESCRIPTION:
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This sample demonstrates how to use agent operations with the Azure AI Search tool from
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the Azure Agents service using a synchronous client.
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USAGE:
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python azure_ai_search.py
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Before running the sample:
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pip install azure.ai.projects azure-identity
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Set these environment variables with your own values:
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PROJECT_ENDPOINT - the Azure AI Project endpoint, as found in your AI Studio Project.
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MODEL_DEPLOYMENT_NAME - the deployment name of the AI model.
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AZURE_AI_CONNECTION_ID - the connection ID for the Azure AI Search tool.
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"""
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# Import necessary libraries and modules
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import os
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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 import AIProjectClient
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from azure.identity import DefaultAzureCredential
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from azure.ai.agents.models import AzureAISearchQueryType, AzureAISearchTool, ListSortOrder, MessageRole
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from azure.identity import DefaultAzureCredential
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# Define the Azure AI Search connection ID (replace with your actual connection ID)
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azure_ai_conn_id = os.environ["AZURE_AI_CONNECTION_ID"]
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# Initialize the Azure AI Search tool with the required parameters
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ai_search = AzureAISearchTool(
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index_connection_id=azure_ai_conn_id, # Connection ID for the Azure AI Search index
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index_name="sample_index", # Name of the search index
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query_type=AzureAISearchQueryType.SIMPLE, # Query type (e.g., SIMPLE, FULL)
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top_k=3, # Number of top results to retrieve
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filter="" # Optional filter for search results
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)
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# Define the endpoint and model deployment name (replace with your actual values)
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endpoint = os.environ["PROJECT_ENDPOINT"],
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model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"]
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# Create an AIProjectClient instance to interact with the Azure AI service
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with AIProjectClient(
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endpoint=endpoint, # Azure AI service endpoint
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credential=DefaultAzureCredential(exclude_interactive_browser_credential=False), # Authentication credentials
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) as project_client:
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# Access the agents client from the project client
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agents_client = project_client.agents
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# Create an agent with the specified model, name, instructions, and tools
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agent = agents_client.create_agent(
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model=model_deployment_name, # Model deployment name
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name="my-agent", # Name of the agent
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instructions="You are a helpful agent", # Instructions for the agent
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tools=ai_search.definitions, # Tools available to the agent
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tool_resources=ai_search.resources, # Resources for the tools
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)
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# [END create_agent_with_azure_ai_search_tool]
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print(f"Created agent, ID: {agent.id}")
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# Create a thread for communication with the agent
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thread = agents_client.create_thread()
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print(f"Created thread, ID: {thread.id}")
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# Create a message in the thread to interact with the agent
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message = agents_client.create_message(
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thread_id=thread.id, # ID of the thread
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role="user", # Role of the message sender (e.g., user)
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content="What is the temperature rating of the cozynights sleeping bag?", # Message content
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)
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print(f"Created message, ID: {message.id}")
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# Create and process an agent run in the thread using the tools
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run = agents_client.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
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print(f"Run finished with status: {run.status}")
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# Check if the run failed and log the error if applicable
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if run.status == "failed":
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print(f"Run failed: {run.last_error}")
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# Fetch and log the details of the agent run steps
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run_steps = agents_client.list_run_steps(thread_id=thread.id, run_id=run.id)
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for step in run_steps.data:
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print(f"Step {step['id']} status: {step['status']}")
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step_details = step.get("step_details", {})
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tool_calls = step_details.get("tool_calls", [])
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# Log details of tool calls if available
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if tool_calls:
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print(" Tool calls:")
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for call in tool_calls:
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print(f" Tool Call ID: {call.get('id')}")
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print(f" Type: {call.get('type')}")
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azure_ai_search_details = call.get("azure_ai_search", {})
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if azure_ai_search_details:
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print(f" azure_ai_search input: {azure_ai_search_details.get('input')}")
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print(f" azure_ai_search output: {azure_ai_search_details.get('output')}")
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print() # Add an extra newline between steps
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# Delete the agent when done to clean up resources
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agents_client.delete_agent(agent.id)
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print("Deleted agent")
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# [START populate_references_agent_with_azure_ai_search_tool]
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# Fetch and log all messages in the thread
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messages = agents_client.list_messages(thread_id=thread.id, order=ListSortOrder.ASCENDING)
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for message in messages.data:
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# Log agent messages with URL citation annotations
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if message.role == MessageRole.AGENT and message.url_citation_annotations:
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placeholder_annotations = {
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annotation.text: f" [see {annotation.url_citation.title}] ({annotation.url_citation.url})"
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for annotation in message.url_citation_annotations
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}
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for message_text in message.text_messages:
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message_str = message_text.text.value
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for k, v in placeholder_annotations.items():
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message_str = message_str.replace(k, v)
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print(f"{message.role}: {message_str}")
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else:
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# Log other messages without annotations
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for message_text in message.text_messages:
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print(f"{message.role}: {message_text.text.value}")
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# [END populate_references_agent_with_azure_ai_search_tool]

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