feat(agent): Add functions to create AI agent connected to Microsoft …

Labfiles/01-build-agent-portal-and-vscode/Python/agent_with_functions.py

@@ -1 +1,111 @@
-
+from dotenv import load_dotenv
+import os
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+import base64
+from pathlib import Path
+
+
+def save_image(image_data, filename):
+    """Save base64 image data to a file."""
+    output_dir = Path("agent_outputs")
+    output_dir.mkdir(exist_ok=True)
+
+    filepath = output_dir / filename
+
+    # Decode and save the image
+    image_bytes = base64.b64decode(image_data)
+    with open(filepath, 'wb') as f:
+        f.write(image_bytes)
+
+    return str(filepath)
+
+
+def main():
+    # Initialize the project client
+    load_dotenv()
+    project_endpoint = os.getenv("PROJECT_ENDPOINT")
+    agent_name = os.environ.get("AGENT_NAME", "it-support-agent")
+
+    if not project_endpoint:
+        print("Error: PROJECT_ENDPOINT environment variable not set")
+        print("Please set it in your .env file or environment")
+        return
+
+    print("Connecting to Microsoft Foundry project...")
+    credential = DefaultAzureCredential()
+    project_client = AIProjectClient(
+        credential=credential,
+        endpoint=project_endpoint
+    )
+
+    # Get the OpenAI client for Responses API
+    openai_client = project_client.get_openai_client()
+
+    # Get the agent created in the portal
+    print(f"Loading agent: {agent_name}")
+    agent = project_client.agents.get_agent(agent_id=agent_name)
+    print(f"Connected to agent: {agent.name} (id: {agent.id})")
+
+    # Create a conversation
+    conversation = openai_client.conversations.create(items=[])
+    print(f"Conversation created (id: {conversation.id})")
+
+    # Chat loop
+    print("\n" + "="*60)
+    print("IT Support Agent Ready!")
+    print("Ask questions, request data analysis, or get help.")
+    print("Type 'exit' to quit.")
+    print("="*60 + "\n")
+
+    while True:
+        user_input = input("You: ").strip()
+
+        if user_input.lower() in ['exit', 'quit', 'bye']:
+            print("Goodbye!")
+            break
+
+        if not user_input:
+            continue
+
+        # Add user message to conversation
+        openai_client.conversations.items.create(
+            conversation_id=conversation.id,
+            items=[{"type": "message", "role": "user", "content": user_input}]
+        )
+
+        # Get response from agent
+        print("\n[Agent is thinking...]")
+        response = openai_client.responses.create(
+            conversation=conversation.id,
+            extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
+            input=""
+        )
+
+        # Display response
+        if hasattr(response, 'output_text') and response.output_text:
+            print(f"\nAgent: {response.output_text}\n")
+        elif hasattr(response, 'output') and response.output:
+            # Extract text from output items
+            image_count = 0
+            for item in response.output:
+                if hasattr(item, 'text') and item.text:
+                    print(f"\nAgent: {item.text}\n")
+                elif hasattr(item, 'type'):
+                    # Handle other output types like images from code interpreter
+                    if item.type == 'image':
+                        image_count += 1
+                        filename = f"chart_{image_count}.png"
+
+                        # Download and save the image
+                        if hasattr(item, 'image') and hasattr(item.image, 'data'):
+                            filepath = save_image(item.image.data, filename)
+                            print(f"\n[Agent generated a chart - saved to: {filepath}]")
+                        else:
+                            print(f"\n[Agent generated an image]")
+                    elif item.type == 'file':
+                        print(f"\n[Agent created a file]")
+
+
+if __name__ == "__main__":
+    main()