my prompt became so big model have problem to process

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i am using gpt 4.1 mini so solve the that problem i use update instructon using tool but problem is updating instructon taking time i want update as soon as possible

Thanks — I hear you: you’re using GPT-4.1‑mini and updates via your tool’s

update_instructions

call are taking too long. I can help speed that up. A few quick questions so I can give targeted steps: 1. Is the tool implemented server-side (your backend) or run inside a client/agent runtime? 2. Can you share a short pseudo-code snippet of how you currently call

update_instructions

(no secrets)? 3. Do you observe the delay before the tool runs, during the tool, or between the tool returning and the model responding? Immediate actions you can apply now • Update first, do everything else later: Make the tool perform the instruction update as the very first line and return immediately with a short confirmation. Example flow: a. Receive tool call. b. Call

update_instructions(new_instructions)

immediately. c. Return "Instructions updated." without doing additional work in the same call. • Avoid blocking work after update: Do not perform long-running work (I/O, heavy processing, sleeps) in the same tool execution after the update. If you need follow-up tasks, schedule them asynchronously outside the update call. • Minimize preemptive generation interference: If your agent uses preemptive or background generation, try disabling it briefly while performing instruction updates so it doesn’t add extra prediction latency. • Keep confirmations tiny: Return a single short string rather than large payloads so the agent can continue the conversation quickly. If you want a concrete code example for your stack, tell me whether you use Python, Node, or another runtime and paste the current function body. I’ll rewrite it to ensure the update happens immediately and show how to offload follow-up work. Sources: Introducing LiveKit Inference: A unified model interface for voice AI | Live Conversations with AI using ChatGPT and WebRTC

@function_tool() async def workflow_calssification(self,workflow:str): """Classifi User Workflow and update Instruction """ try: logger.info(f"Customer Workflow: {workflow}") if workflow =="NO Referance number or Case Number": asyncio.create_task(self.update_instructions(instructions=CUSTOMER_DONT_HAVE_CASE_NO_OR_REFERANCE_NUMBER)) await self.session.say("Please allow me a moment.") await self.session.say("May I please have your name, email address to send the form?") elif workflow =="Referance number or Case Number": asyncio.create_task(self.update_instructions(instructions=CUSTOMER_HAVE_CASE_NO_OR_REFERANCE_NUMBER)) #self.session.say("Please tell me the last five digits of your reference number, or the last four digits of your case number") self.session.say("Please allow me a moment.") else: asyncio.create_task(self.update_instructions(instructions=CUSTOMER_LOST_HAVE_CASE_NO_OR_REFERANCE_NUMBER)) await self.session.generate_reply(instructions="Please allow me a moment.") except Exception as e: logger.exception(f"Error In Intent Classification: {e}")

Thanks — this is helpful. I reviewed your snippet and suggest a small refactor so the instruction update happens immediately (synchronously from the tool’s perspective) and any follow‑up work runs truly asynchronously afterward. Key changes: • Call

await self.update_instructions(...)

as the first action in the branch so the update happens before any session messages. • Return a short confirmation quickly (don’t await long work after the update). • Offload any non‑urgent follow‑ups to background tasks (use asyncio.create_task) that do not block returning to the model. Refactored example (Python):

@function_tool()
async def workflow_classification(self, workflow: str):
    """Classify user workflow and update instructions immediately."""
    try:
        <http://logger.info|logger.info>(f"Customer Workflow: {workflow}")

        if workflow == "NO Referance number or Case Number":
            # update instructions immediately
            await self.update_instructions(instructions=CUSTOMER_DONT_HAVE_CASE_NO_OR_REFERANCE_NUMBER)
            # return quickly so the agent can continue
            await self.session.say("Please allow me a moment.")
            await self.session.say("May I please have your name, email address to send the form?")
            # any extra work can run in background if needed
            # asyncio.create_task(do_background_logging(...))

        elif workflow == "Referance number or Case Number":
            await self.update_instructions(instructions=CUSTOMER_HAVE_CASE_NO_OR_REFERANCE_NUMBER)
            await self.session.say("Please allow me a moment.")
            # prompt for digits or schedule follow-up background task

        else:
            await self.update_instructions(instructions=CUSTOMER_LOST_HAVE_CASE_NO_OR_REFERANCE_NUMBER)
            await self.session.generate_reply(instructions="Please allow me a moment.")

        return "Instructions updated."

    except Exception as e:
        logger.exception(f"Error In Intent Classification: {e}")
        return "Failed to update instructions."

If you must not await update_instructions (for architectural reasons), call it first and return immediately while using

asyncio.create_task(self.update_instructions(...))

, but that can introduce a small race where the model continues before instructions are applied. Awaiting the update ensures the model sees the new instructions as soon as possible. Quick question: do you have any long-running work immediately after the update that must run before the model responds (e.g., heavy I/O or external API calls)? If yes, tell me which so I can show how to move it to a true background task.

long-running work immediately yes main conversation start from here and i am changing instruction meddile of conversation

Thanks — I understand: your main conversation flow starts immediately after classification and you need instruction updates to take effect right away without blocking that flow. That’s doable by awaiting the instruction update first, then offloading the long-running follow-up work to background tasks so the model can continue. Suggested approach (summary): • Await

update_instructions

immediately so instructions are applied before the model continues. • Return/continue the conversation right after that awaited call. • Move any heavy I/O or processing into an independent background task using

asyncio.create_task

. • If background tasks must send messages into the session, use a non-blocking queue or session-safe async method so they don’t block the main request. Refactored pattern (Python — concise):

@function_tool()
async def workflow_classification(self, workflow: str):
    try:
        <http://logger.info|logger.info>(f"Customer Workflow: {workflow}")

        # apply new instructions immediately (await so update is in effect)
        if workflow == "NO Referance number or Case Number":
            await self.update_instructions(instructions=CUSTOMER_DONT_HAVE_CASE_NO_OR_REFERANCE_NUMBER)
            # continue conversation immediately
            await self.session.say("Please allow me a moment.")
            await self.session.say("May I please have your name and email to send the form?")
            # offload heavy work (e.g., DB/API calls, form generation) to background
            asyncio.create_task(self._handle_no_ref_followups(workflow))

        elif workflow == "Referance number or Case Number":
            await self.update_instructions(instructions=CUSTOMER_HAVE_CASE_NO_OR_REFERANCE_NUMBER)
            await self.session.say("Please allow me a moment.")
            asyncio.create_task(self._handle_with_ref_followups(workflow))

        else:
            await self.update_instructions(instructions=CUSTOMER_LOST_HAVE_CASE_NO_OR_REFERANCE_NUMBER)
            await self.session.generate_reply(instructions="Please allow me a moment.")
            asyncio.create_task(self._handle_other_followups(workflow))

        return "Instructions updated."

    except Exception as e:
        logger.exception(f"Error In Intent Classification: {e}")
        return "Failed to update instructions."

# Example background handler
async def _handle_no_ref_followups(self, workflow):
    try:
        # heavy work: DB/API calls, email sending, form generation, etc.
        # do NOT await these inside the main function
        result = await some_heavy_io_call()   # runs in background
        # if you must send messages back to user from background:
        await self.session.say_nonblocking("Follow-up completed.")  # use a non-blocking session API if available
    except Exception:
        logger.exception("Background follow-up failed")

Notes and practical tips: • Awaiting

update_instructions

removes the race where the model continues before instructions take effect. • If your runtime lacks a safe non-blocking

session.say

for background tasks, use a message queue (Redis, SQS) that a worker consumes to send messages, keeping the main request path fast. • If you cannot await

update_instructions

(due to architecture), call it first synchronously and still return quickly — this risks a short race if the update hasn’t fully propagated. • Keep the confirmation messages minimal so the conversation isn't delayed by large responses. One quick question to fine-tune this: what exactly does the long-running work do (external API calls, DB writes, sending emails, large file processing)? I’ll show a tailored example for safely sending any follow-up messages to the user.

can i use tool background parallel

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!