How to use the SharePoint tool

Prerequisites

• Your Microsoft Foundry Project endpoint.

  You can find your endpoint in the overview for your project in the Microsoft Foundry portal, under Libraries > Foundry.

  

  Save this endpoint to an environment variable named PROJECT_ENDPOINT.

• The name of your SharePoint connection name. You can find it in the Foundry portal by selecting Management center from the left navigation menu. Then selecting Connected resources.

  

  Save this endpoint to an environment variable named SHAREPOINT_RESOURCE_NAME

• The names of your model's deployment name. You can find it in Models + Endpoints in the left navigation menu.

  

  Save the name of your model deployment name as an environment variable named MODEL_DEPLOYMENT_NAME.

Create a project client

Create a client object, which will contain the connection string for connecting to your AI project and other resources.

import os
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
from azure.ai.agents.models import SharepointTool

# Retrieve the endpoint and credentials
project_endpoint = os.environ["PROJECT_ENDPOINT"]  # Ensure the PROJECT_ENDPOINT environment variable is set

# Initialize the AIProjectClient
project_client = AIProjectClient(
    endpoint=os.environ["PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential(),
)

Create an agent with the Sharepoint tool enabled

To make the Microsoft Fabric tool available to your agent, use a connection to initialize the tool and attach it to the agent. You can find your connection in the connected resources section of your project in the Foundry portal.

conn_id = project_client.connections.get(name=os.environ["SHAREPOINT_RESOURCE_NAME"]).id

# Initialize Sharepoint tool with connection id
sharepoint = SharepointTool(connection_id=conn_id)

# Create an agent with the Fabric tool
# Create an Agent with the Fabric tool and process an Agent run
with project_client:
    agents_client = project_client.agents

    agent = agents_client.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],
        name="my-agent",
        instructions="You are a helpful agent",
        tools=sharepoint.definitions,
    )
    print(f"Created agent, ID: {agent.id}")

Create a thread

# Create thread for communication
thread = agents_client.threads.create()
print(f"Created thread, ID: {thread.id}")

# Create message to thread
message = agents_client.messages.create(
    thread_id=thread.id,
    role="user",
    content="Hello, summarize the key points of the <sharepoint_resource_document>",
)
print(f"Created message, ID: {message.id}")

Create a run and check the output

# Create and process agent run in thread with tools
run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
print(f"Run finished with status: {run.status}")

if run.status == "failed":
    print(f"Run failed: {run.last_error}")

# Uncomment the following lines to delete the agent when done
#agents_client.delete_agent(agent.id)
#print("Deleted agent")

# Fetch and log all messages
messages = agents_client.messages.list(thread_id=thread.id)
for msg in messages:
    if msg.text_messages:
        last_text = msg.text_messages[-1]
        print(f"{msg.role}: {last_text.text.value}")

Create an agent

Follow the REST API Quickstart to set the right values for the environment variables AGENT_TOKEN, AZURE_AI_FOUNDRY_PROJECT_ENDPOINT and API_VERSION.

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/assistants?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
        "instructions": "You are a helpful agent.",
        "name": "my-agent",
        "model": "gpt-4o",
        "tools": [
          {
            "type": "sharepoint_grounding",
            "sharepoint_grounding": {
                "connections": [
                    {
                        "connection_id": "/subscriptions/<sub-id>/resourceGroups/<your-rg-name>/providers/Microsoft.CognitiveServices/accounts/<your-ai-services-name>/projects/<your-project-name>/connections/<your-sharepoint-connection-name>"
                    }
                ]
            }
          }
        ]
      }'

Create a thread

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d ''

Add a user question to the thread

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/messages?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
      "role": "user",
      "content": "What is the weather in Seattle?"
    }'

Run the thread

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/runs?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "assistant_id": "asst_abc123",
  }'

Retrieve the status of the run

curl --request GET \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/runs/run_abc123?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN"

Retrieve the agent response

curl --request GET \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/messages?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN"