LegközelebbKövetkező útmutató
Befolyásolási függvények a képzési adatok hozzárendeléséhez
Műszaki
Műszaki ÚTMUTATÓ
Parallel Function Calling
Parallel function calling is when a language model requests several tool calls in a single response, so the application can run them at the same time and return all the results together.
Ezen az oldalon3 perc olvasás
Áttekintés
It matters because it cuts round trips and latency for independent lookups, but it is only safe when the calls do not depend on each other and when the application handles ordering, side effects and partial failures correctly.
Mély merülés
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.
Stratégiai hatás
Költség és költségvetés
Az építészeti döntések évekig növelik a teljesítményt és a működési költségeket.
Tisztább döntések
A technikai oktatás segít a csapatoknak a megfelelő verem kiválasztásában, nem csak a legújabb készletben.
Minőségellenőrzés
A jobb mérnöki döntések csökkentik a termelés megbízhatósági incidenseit.
The Future of Parallel Function Calling
As agents take on larger tasks, parallel tool use is becoming a routine way to reduce latency, and models are being trained to recognise which calls are independent. Harnesses are also adding features such as dependency-aware scheduling and resource locking so that batched requests can be executed safely. The model's judgement about dependencies will likely improve, but the application will remain responsible for enforcing safety on side effects, timeouts and retries, since those depend on real systems the model cannot see.
Valós megvalósítás
A travel assistant asked about weather in Paris, Rome and Madrid issues three weather lookups in one turn, and the app runs them concurrently.
A sales dashboard agent fetches a customer's orders, open support tickets and account notes in one parallel batch before writing a summary.
An agent asked to create a folder and then write a file into it must not issue both calls in parallel, because the write depends on the folder existing.
A research agent sends five searches in parallel; two time out, and the app returns three results plus two clear error messages so the model can retry only the failed ones.
Kockázatok és védőkorlátok
Egy benchmark optimalizálása elrejtheti a rendszer általános hiányosságait.
Az infrastrukturális és karbantartási költségeket gyakran alábecsülik.
A biztonsági és megfigyelhetőségi hiányosságok a rendszerek bonyolultabbá válásával nőhetnek.
Végrehajtási ütemterv
Határozza meg a késleltetési, minőségi és költségcélokat a megvalósítás előtt.
Benchmark reális terhelési és adatviszonyok mellett.
Műszerfigyelés a hibák, az eltolódás és a felhasználói hatások szempontjából.
A méretezés előtt készítse elő a visszagörgetési és az incidensre adott válaszútvonalakat.
Folytassa a felfedezést
Free newsletter
Get the daily AI briefing
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the Parallel Function Calling quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Gyakran ismételt kérdések
What is Parallel Function Calling?
Parallel function calling is when a language model requests several tool calls in a single response, so the application can run them at the same time and return all the results together. It matters because it cuts round trips and latency for independent lookups, but it is only safe when the calls do not depend on each other and when the application handles ordering, side effects and partial failures correctly.
In a parallel function call, what does the model return in one response?
The model emits multiple tool call requests in one turn, which the app can run together.
Who actually executes the tools concurrently?
The model only requests calls; concurrency, timeouts and retries are the application's job.
Which pair of calls should NOT be issued in parallel?
The file write depends on the folder existing, so the calls must run in sequence.
How should results be matched to their tool calls?
Concurrent calls finish in unpredictable order, so the identifier is the reliable link.
Two of five parallel searches time out. What is the best response?
Returning each result individually, including errors, lets the model continue and retry selectively.
Tanulj tovább
Kapcsolódó útmutatók
További útmutatók készültek ehhez a témához