How Does GPT Decide When to Call External APIs in Assistants and Threads?

Below is an explanation in markdown:

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How Does External API/Function Calling Work?

When using external APIs (for example, one of your own) with GPT models—whether with the classic function calling approach or with assistants/threads—the model itself does not actually perform the API call. Instead, the model will decide if an external API call is needed and then returns the parameters required to make that call. It is up to the developer to take those parameters, call the external API (or invoke a local function), and then pass any results back to the model as needed.

With Classic Function Calling

• The GPT model inspects the conversation and may decide that a function call is appropriate.
• It returns a JSON payload containing the parameters for that function.
• You (the developer) then use those parameters to manually call your API (or run a local function).
• The result from your API can then be sent back to the model to continue the conversation.

With Assistants/Threads

The behavior is similar when using assistants or threads. When you set up an assistant (for example, a math tutor assistant with a code interpreter tool), you specify instructions, the model to use, and the available tools (external APIs). Once the assistant is running:

• The GPT model will determine whether your external API should be called based on the user’s prompt.
• If it decides that an API call is needed (say for a text-to-SQL conversion), it will return the necessary parameters.
• It is then your responsibility to call your external API with those parameters.
• After you receive a response from your API call, you can pass this result back into the conversation thread for further processing or as part of the answer.

This approach follows the same general pattern as classic function calling: the model suggests the call by outputting the parameters, and your integration layer decides when and how to actually call the external system.

Summary with a Concrete Example (Text-to-SQL)

1. User Query: The user asks a question that requires converting text to an SQL query.
2. Model Decision: Your assistant (set up with instructions for text-to-SQL, for instance) decides that a function/API call is necessary.
3. Return of Parameters: GPT outputs a JSON payload with the parameters needed for the text-to-SQL conversion.
4. Developer Action: Your backend parses these parameters and calls your external text-to-SQL API.
5. Incorporate the Result: Once your API returns the SQL result, you pass it back into the conversation on your assistant/thread.

Example Pseudocode

PYTHONCopied
# Example pseudocode for handling a function call from GPT

# Step 1: Receive GPT output with parameters for the text-to-SQL conversion.
gpt_output = {
    "function_call": "text_to_sql",
    "parameters": {"text": "Show me all orders in the last month"}
}

# Step 2: Extract parameters and call your external API.
params = gpt_output["parameters"]
sql_result = call_external_text_to_sql_api(params)

# Step 3: Return the API result to continue the conversation.
send_to_gpt({
    "role": "system",
    "content": f"The SQL query result is: {sql_result}"
})

Final Points

• Function-Style Behavior: In both classic and assistant/thread setups, GPT returns parameters much like a function call. The model does not automatically invoke your external API.
• Developer Responsibility: It is your responsibility to catch the returned parameters, perform the external API call, and then feed back the results.
• Integration Patterns: This pattern allows you to integrate any external API (text-to-SQL in your case) seamlessly with GPT’s decision-making and conversation flow.

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This process ensures that you have full control over when and how your external APIs are called, even as GPT guides the overall interaction by suggesting the necessary calls.