Sure, let's break down steps 3 and 4:
Step 3: Handle Function Calls
When the assistant decides to call a function, it will include a tool_calls field in its response. This field is a list of function calls, where each function call includes the function's name and its arguments.
Here's an example of how you might handle this in Python:
response = client.chat.completions.create(
model="gpt-4-turbo",
messages=messages,
functions=[function_definition],
)
tool_calls = response['choices'][0]['message']['tool_calls']
if tool_calls:
for tool_call in tool_calls:
function_name = tool_call['function']['name']
function_args = json.loads(tool_call['function']['arguments'])
if function_name == 'send_email':
send_email(function_args['sender_id'], function_args['receiver_id'], function_args['message'])
In this example, send_email is a function you've defined elsewhere in your code that sends an email given a sender ID, receiver ID, and message.
Step 4: Test the Function
You can test the function in the OpenAI Playground by entering a message that would trigger the model to call the function. For example, you could enter a message like "Send an email to [receiver's email] from [sender's email] with the message [message]".
Remember to replace [receiver's email], [sender's email], and [message] with actual values. The model should then return a response that includes a tool_calls field with a call to the send_email function.
Please note that the OpenAI Playground does not actually execute the function calls. It only simulates them for testing purposes. To actually execute the function calls, you would need to run the code in your own environment.
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