Reorder: tool use is now 05, neural networks is 06

The LLM arc completes at section 05 (agentic systems), with
neural networks as a standalone ML deep-dive in section 06.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

05-tool-use/tool_demo.py

@@ -0,0 +1,65 @@
+# tool_demo.py
+#
+# Minimal example of Ollama tool calling.
+# The model requests a function call; our code executes it.
+#
+# CHEG 667-013
+# E. M. Furst
+
+from ollama import chat
+
+# Define a tool as a regular Python function.
+# The type hints and docstring tell the model what the function does.
+
+def add(a: int, b: int) -> int:
+    """
+    Add two numbers together.
+
+    Args:
+        a: The first number
+        b: The second number
+
+    Returns:
+        The sum of the two numbers
+    """
+    return a + b
+
+# A lookup table so we can dispatch by name
+tools = {
+    'add': add,
+}
+
+# Send a message with the tool available
+messages = [{'role': 'user', 'content': 'What is 247 plus 863?'}]
+
+response = chat(
+    'llama3.1:8b',
+    messages=messages,
+    tools=[add],
+)
+
+# The model responds with a tool call, not text
+if response.message.tool_calls:
+    for tool_call in response.message.tool_calls:
+        name = tool_call.function.name
+        args = tool_call.function.arguments
+        print(f'Model wants to call: {name}({args})')
+
+        # Execute the function
+        result = tools[name](**args)
+        print(f'Result: {result}')
+
+        # Send the result back to the model
+        messages.append(response.message)
+        messages.append({
+            'role': 'tool',
+            'content': str(result),
+            'tool_name': name,
+        })
+
+    # Get the final natural-language response
+    final = chat('llama3.1:8b', messages=messages)
+    print(f'\nModel: {final.message.content}')
+else:
+    # Model chose not to use a tool
+    print(f'Model: {response.message.content}')