การเรียกฟังก์ชันแบบขนาน

In basic tool use, a model asks for one tool, the app runs it, returns the result, and the model continues. Each step costs a full model round trip. With parallel function calling, the model can emit multiple tool call requests in one response, each with its own identifier, name and arguments. The application executes them, often concurrently, and sends back one result per call, each linked to its identifier so the model knows which result belongs to which request. OpenAI added parallel function calling to its API in late 2023, and other major providers support multiple tool calls in one turn. Some APIs expose a setting to turn this behaviour off when an application needs strictly one call at a time. Parallel calls are safe when they are independent: reads that do not affect each other, such as looking up several records or running several searches. They are risky when one call needs another's output, or when calls have side effects that interact, such as two writes to the same record or a charge followed by a refund. The order in which the model lists calls is not a guaranteed execution order, and the application may finish them in any order. A common misconception is that the model runs the tools in parallel. It does not; it only requests them. Concurrency, timeouts and retries are the application's responsibility. Another misconception is that if one call fails, the whole batch fails. A well-built app returns each result individually, including errors, so the model can proceed with what succeeded. For dependent steps, the model should call the first tool, wait for its result, then call the next. Good tool descriptions and system instructions can help steer when to batch and when to sequence.