@millions-hair-24956 {"message": "Running <Handle _ProactorBaseWritePipeTransport._loop_writing(<_Ove rlappedF...shed result=4>)> took too long: 3.39 seconds", "level": "WARNING", "n ame": "livekit.agents", "timestamp": "2025-02-14T231759.666939+00:00"} {"message": "Running <Handle _ProactorBaseWritePipeTransport._loop_writing(<_Ove rlappedF...shed result=4>)> took too long: 3.39 seconds", "level": "WARNING", "n ame": "livekit.agents", "timestamp": "2025-02-14T231759.670928+00:00"} {"message": "Running <Handle BaseProactorEventLoop._loop_self_reading(<_Overlapp edF...esult=b'\\x00'>)> took too long: 3.39 seconds", "level": "WARNING", "name" : "livekit.agents", "timestamp": "2025-02-14T231759.677870+00:00"} {"message": "Running <Task pending name='ipc_read' coro=<_ProcClient._monitor_ta sk.<locals>._read_ipc_task() running at E:\\new_update\\voiceai-outboundcalls\\v env\\Lib\\site-packages\\livekit\\agents\\utils\\log.py:16> wait_for=<Future pen ding cb=[Task.task_wakeup()]> cb=[_ProcClient._monitor_task.<locals>._done_cb() at E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\livekit\\age nts\\ipc\\proc_client.py:145]> took too long: 3.41 seconds", "level": "WARNING", "name": "livekit.agents", "timestamp": "2025-02-14T231759.681859+00:00"} explain me the error that is occuring from livekit

The warnings you're seeing indicate that certain asynchronous tasks or event loop operations in LiveKit are taking longer than expected to complete. Specifically, the messages suggest that operations related to the Proactor event loop, which is used for handling asynchronous I/O operations on Windows, are experiencing delays. This can happen due to high system load, blocking operations, or network latency. To address this, you might want to check for any blocking code in your application that could be affecting the event loop's performance. Additionally, ensure that your system resources (CPU, memory) are not being maxed out, as this can also lead to such delays. Would you like more detailed guidance on how to troubleshoot or optimize your LiveKit setup? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1733239980.044189 - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1732121600.386179 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

.08

It seems likely that the LiveKit Server and the

agent.py

file serve different roles in your setup. The LiveKit Server acts as the core component of the LiveKit platform, handling signaling, media, and other real-time communication tasks. It is responsible for managing rooms, participants, and tracks, and can be deployed either on your infrastructure (self-hosted) or using LiveKit Cloud. On the other hand, the

agent.py

file is likely part of the LiveKit Agents framework, which is used to create programmable, server-side participants (agents) that can join LiveKit rooms. These agents can perform tasks such as speech-to-text, text-to-speech, and other AI-driven functionalities. The agent runs as a separate process and connects to the LiveKit Server to participate in rooms as an AI participant. Would you like more detailed information on how to set up or use these components? Sources: - https://docs.livekit.io/agents/overview/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 {"message": "Failed to start egress: Enum VideoCodec has no value defined for name 'H264'", "level": "ERROR", "name": "outbound-caller", "pid": 214288, "job_id": "AJ_fayHwWMGu6fK", "timestamp": "2025-02-17T162359.645055+00:00"} {"message": "Stack trace: Traceback (most recent call last):\n File \"/home/faiq.khan/voiceai-outboundcalls/agent.py\", line 1342, in start_azure_egress\n video_codec=api.VideoCodec.H264,\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/faiq.khan/voiceai-outboundcalls/venv/lib/python3.12/site-packages/google/protobuf/internal/enum_type_wrapper.py\", line 102, in __getattr__\n raise AttributeError('Enum {} has no value defined for name {!r}'.format(\nAttributeError: Enum VideoCodec has no value defined for name 'H264'\n", "level": "ERROR", "name": "outbound-caller", "pid": 214288, "job_id": "AJ_fayHwWMGu6fK", "timestamp": "2025-02-17T162359.646607+00:00"} {"message": "Egress recording failed: Enum VideoCodec has no value defined for name 'H264'", "level": "ERROR", "name": "outbound-caller", "pid": 214288, "job_id": "AJ_fayHwWMGu6fK", "timestamp": "2025-02-17T162359.646856+00:00"} {"message": "Unclosed client session\nclient_session: <aiohttp.client.ClientSession object at 0x71dd60244770>", "level": "ERROR", "name": "asyncio", "pid": 214288, "job_id": "AJ_fayHwWMGu6fK", "timestamp": "2025-02-17T162359.647193+00:00"} {"message": "livekit_ffi:server134livekit ffi:server - initializing ffi server v0.12.10", "level": "INFO", "name": "livekit", "pid": 214288, "job_id": "AJ_fayHwWMGu6fK", "timestamp": "2025-02-17T162359.648679+00:00"} async def start_azure_egress(_ctx_: JobContext): """Start RoomComposite Egress to Azure Blob Storage""" _try_: # Initialize LiveKit API client lkapi = api.LiveKitAPI( os.getenv("LIVEKIT_HOST"), os.getenv("LIVEKIT_API_KEY"), os.getenv("LIVEKIT_API_SECRET") ) # Azure Blob Storage configuration azure_config = api.AzureBlobUpload( _account_name_=os.getenv("AZURE_STORAGE_ACCOUNT"), _account_key_=os.getenv("AZURE_STORAGE_KEY"), _container_name_=os.getenv("AZURE_CONTAINER_NAME"), ) # Generate unique filename with timestamp and phone number phone_number = ctx.job.metadata.replace('+', '') if ctx.job.metadata else 'unknown' filename = f"recordings/{datetime.utcnow().isoformat()}_{phone_number}.mp4" # Configure file output with Azure settings file_output = api.EncodedFileOutput( _file_type_=api.EncodedFileType.MP4, filepath=filename, azure=azure_config, # Audio-only configuration _audio_codec_=api.AudioCodec.AAC, _video_codec_=api.VideoCodec.H264, preset=api.EncodingPreset.H264_720P_30FPS_3_LAYERS, ) # Start RoomComposite Egress with auto-start configuration egress_request = api.RoomCompositeEgressRequest( _room_name_=ctx.room.name, layout="speaker-dark", # Audio-focused layout _audio_only_=True, # Force audio-only recording _file_outputs_=[file_output], options={ "auto_start": True, # Enable auto-start "wait_for_participants": True # Wait for participants before starting } ) egress_info = await lkapi.egress.start_room_composite_egress(egress_request) logger.info(f"Egress started: {egress_info.egress_id}") return egress_info except Exception as e: logger.error(f"Failed to start egress: {str(e)}") logger.error(f"Stack trace: {traceback.format_exc()}") raise whats the error ?

It seems possible that the error you're encountering is due to the

VideoCodec

enum not having a defined value for 'H264'. This suggests that the LiveKit API or the specific version of the library you're using does not support 'H264' as a valid video codec option. This could be due to a mismatch between the expected codec values in your code and those defined in the library. To resolve this, you might want to verify the available codec options in the LiveKit API documentation or the library version you are using. Ensure that 'H264' is indeed a supported codec, or consider using an alternative codec that is supported. Would you like more detailed guidance on how to check the available codec options or update your code accordingly? Sources: - https://livekit-users.slack.com/archives/C07FRP66XGE/p1730288245.597009 - https://livekit-users.slack.com/archives/C048FRL1N2C/p1729871093.168429 Note: I wasn’t able to find highly relevant data sources, but above are a few potentially relevant links. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 I am not using video only audio in my code

The error message indicates that the

VideoCodec

enum is being referenced in your code, specifically with the value 'H264'. Since you are only using audio, you should ensure that any video-related configurations are removed or set to

None

if not needed. The error might be occurring because the code is attempting to set a video codec when it should be focusing solely on audio. To resolve this, you can modify your

start_azure_egress

function to exclude any video codec settings. Ensure that the

RoomCompositeEgressRequest

and

EncodedFileOutput

configurations are set for audio-only operations. Here's a simplified version of your code focusing on audio:

async def start_azure_egress(_ctx_: JobContext):
    """Start RoomComposite Egress to Azure Blob Storage"""
    try:
        # Initialize LiveKit API client
        lkapi = api.LiveKitAPI(
            os.getenv("LIVEKIT_HOST"),
            os.getenv("LIVEKIT_API_KEY"),
            os.getenv("LIVEKIT_API_SECRET")
        )

        # Azure Blob Storage configuration
        azure_config = api.AzureBlobUpload(
            account_name=os.getenv("AZURE_STORAGE_ACCOUNT"),
            account_key=os.getenv("AZURE_STORAGE_KEY"),
            container_name=os.getenv("AZURE_CONTAINER_NAME"),
        )

        # Generate unique filename with timestamp and phone number
        phone_number = ctx.job.metadata.replace('+', '') if ctx.job.metadata else 'unknown'
        filename = f"recordings/{datetime.utcnow().isoformat()}_{phone_number}.mp4"

        # Configure file output with Azure settings
        file_output = api.EncodedFileOutput(
            file_type=api.EncodedFileType.MP4,
            filepath=filename,
            azure=azure_config,
            audio_codec=api.AudioCodec.AAC,
            # Remove or set video_codec to None
            video_codec=None,
            preset=None,  # Remove video preset
        )

        # Start RoomComposite Egress with auto-start configuration
        egress_request = api.RoomCompositeEgressRequest(
            room_name=ctx.room.name,
            layout="speaker-dark",  # Audio-focused layout
            audio_only=True,  # Force audio-only recording
            file_outputs=[file_output],
            options={
                "auto_start": True,  # Enable auto-start
                "wait_for_participants": True  # Wait for participants before starting
            }
        )

        egress_info = await lkapi.egress.start_room_composite_egress(egress_request)
        logger.info(f"Egress started: {egress_info.egress_id}")
        return egress_info

    except Exception as e:
        logger.error(f"Failed to start egress: {str(e)}")
        logger.error(f"Stack trace: {traceback.format_exc()}")
        raise

Would you like more detailed guidance on configuring audio-only egress? Sources: - Track Egress - Composite and Web Recordings 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 WHAT IS livekit_host here?

I'm not able to find any data sources to answer your question. You might want to specify the context or environment where

livekit_host

is being used, such as a configuration file or a specific part of your code. If this doesn't help, please reach out to the Livekit support team for further assistance. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 {"message": "Failed to start egress: Protocol message EncodedFileOutput has no \"audio_codec\" field.", "level": "ERROR", "name": "outbound-caller", "pid": 215668, "job_id": "AJ_mDwTa62rxMSm", "timestamp": "2025-02-17T163548.656171+00:00"} {"message": "Stack trace: Traceback (most recent call last):\n File \"/home/faiq.khan/voiceai-outboundcalls/agent.py\", line 1336, in start_azure_egress\n file_output = api.EncodedFileOutput(\n ^^^^^^^^^^^^^^^^^^^^^^\nValueError: Protocol message EncodedFileOutput has no \"audio_codec\" field.\n", "level": "ERROR", "name": "outbound-caller", "pid": 215668, "job_id": "AJ_mDwTa62rxMSm", "timestamp": "2025-02-17T163548.657266+00:00"} {"message": "Egress recording failed: Protocol message EncodedFileOutput has no \"audio_codec\" field.", "level": "ERROR", "name": "outbound-caller", "pid": 215668, "job_id": "AJ_mDwTa62rxMSm", "timestamp": "2025-02-17T163548.657567+00:00"}

It seems likely that the error you're encountering is due to the

EncodedFileOutput

object not having an

audio_codec

field. This suggests that the

audio_codec

parameter is not a valid attribute for the

EncodedFileOutput

class in the LiveKit API you are using. To resolve this, you should remove the

audio_codec

field from the

EncodedFileOutput

configuration. Instead, focus on configuring the output settings that are supported by the

EncodedFileOutput

class, such as

file_type

,

filepath

, and any storage-specific configurations like

azure

. Here's a simplified version of your code without the

audio_codec

field:

async def start_azure_egress(_ctx_: JobContext):
    """Start RoomComposite Egress to Azure Blob Storage"""
    try:
        # Initialize LiveKit API client
        lkapi = api.LiveKitAPI(
            os.getenv("LIVEKIT_HOST"),
            os.getenv("LIVEKIT_API_KEY"),
            os.getenv("LIVEKIT_API_SECRET")
        )

        # Azure Blob Storage configuration
        azure_config = api.AzureBlobUpload(
            account_name=os.getenv("AZURE_STORAGE_ACCOUNT"),
            account_key=os.getenv("AZURE_STORAGE_KEY"),
            container_name=os.getenv("AZURE_CONTAINER_NAME"),
        )

        # Generate unique filename with timestamp and phone number
        phone_number = ctx.job.metadata.replace('+', '') if ctx.job.metadata else 'unknown'
        filename = f"recordings/{datetime.utcnow().isoformat()}_{phone_number}.mp4"

        # Configure file output with Azure settings
        file_output = api.EncodedFileOutput(
            file_type=api.EncodedFileType.MP4,
            filepath=filename,
            azure=azure_config
        )

        # Start RoomComposite Egress with auto-start configuration
        egress_request = api.RoomCompositeEgressRequest(
            room_name=ctx.room.name,
            layout="speaker-dark",  # Audio-focused layout
            audio_only=True,  # Force audio-only recording
            file_outputs=[file_output],
            options={
                "auto_start": True,  # Enable auto-start
                "wait_for_participants": True  # Wait for participants before starting
            }
        )

        egress_info = await lkapi.egress.start_room_composite_egress(egress_request)
        logger.info(f"Egress started: {egress_info.egress_id}")
        return egress_info

    except Exception as e:
        logger.error(f"Failed to start egress: {str(e)}")
        logger.error(f"Stack trace: {traceback.format_exc()}")
        raise

Would you like more detailed guidance on configuring egress for audio-only recordings? Sources: - https://docs.livekit.io/home/egress/api/ - https://docs.livekit.io/home/egress/room-composite/ Note: I wasn’t able to find highly relevant data sources, but above are a few potentially relevant links. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 Removing agent.py by disconnecting from room or muting when warm transfer happens

To handle a warm transfer in LiveKit, where you want to disconnect an agent or mute it when transferring a call, you can follow these steps: 1. Disconnect the Agent: You can disconnect the agent from the room by calling the

disconnect

method on the room object. This will remove the agent from the session. 2. Mute the Agent: If you prefer to keep the agent in the room but mute it, you can use the

mute_published_track

method to mute the agent's audio track. Here's a basic example of how you might implement these actions:

async def handle_warm_transfer(ctx: JobContext):
    # Option 1: Disconnect the agent
    await ctx.room.disconnect()
    logger.info("Agent disconnected from the room.")

    # Option 2: Mute the agent
    # Assuming you have access to the agent's track publication
    # await ctx.room.mute_published_track(track_sid, muted=True)
    # logger.info("Agent's audio track muted.")

Would you like more detailed guidance on implementing these actions in your specific setup? Sources: - Managing Participants 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@llm.ai_callable() async def warm_transfer_with_fallback(self): """Initiate warm transfer to human agent with 20s timeout and fallback contact collection""" local_participant = None _try_: agent_number = "+161473" # Get local participant with null check local_participant = self.room.local_participant if not local_participant: logger.error("No local participant found") return "TRANSFER_FAILED" # Mute agent's audio track if local_participant.track_publications: for publication in local_participant.track_publications.values(): if publication and publication.kind == rtc.TrackKind.KIND_AUDIO and publication.track: publication.track.mute() logger.info("Muted agent's audio track") # Deafen agent by unsubscribing from remote audio if self.room.remote_participants: for participant in self.room.remote_participants.values(): if participant and participant.track_publications: for publication in participant.track_publications.values(): if publication and publication.kind == rtc.TrackKind.KIND_AUDIO: _try_: publication.set_subscribed(False) logger.info(f"Unsubscribed from {participant.identity}'s audio track") except Exception as e: logger.error(f"Error unsubscribing from track: {e}") # Announce transfer using TTS await self.agent.say("Connecting you to a specialist...") # Create SIP participant with initial DTMF sip_response = await self.api.sip.create_sip_participant( api.CreateSIPParticipantRequest( _room_name_=self.room.name, _sip_trunk_id_=os.getenv("SIP_OUTBOUND_TRUNK_ID"), _sip_call_to_=agent_number, _participant_identity_=f"agent_{agent_number.replace('+', '')}", _participant_name_="Human Agent", _play_dialtone_=True, dtmf="ww0" # Initial DTMF sequence ) )`12 ` # Wait for human agent human_joined = await asyncio.wait_for( self._wait_for_human_agent(agent_number), timeout=20 ) if not human_joined: raise asyncio.TimeoutError("Human agent did not join in time") return "TRANSFER_COMPLETE" except (asyncio.TimeoutError, Exception) as e: logger.error(f"Transfer failed: {str(e)}") await self._restore_audio_state(local_participant) await self.agent.say( "I'm sorry, we're having trouble connecting you. " "Please provide your contact details so we can reach you." ) return "TRANSFER_FAILED" except Exception as e: logger.error(f"Transfer error: {e}") if local_participant: await self._restore_audio_state(local_participant) return "TRANSFER_FAILED" async def _restore_audio_state(self, _local_participant_): """Helper method to restore audio state after failed transfer""" _try_: # Unmute local participant if local_participant and local_participant.track_publications: for publication in local_participant.track_publications.values(): if publication and publication.kind == rtc.TrackKind.KIND_AUDIO and publication.track: publication.track.unmute() # Resubscribe to remote tracks if self.room.remote_participants: for participant in self.room.remote_participants.values(): if participant and participant.track_publications: for publication in participant.track_publications.values(): if publication and publication.kind == rtc.TrackKind.KIND_AUDIO: _try_: publication.set_subscribed(True) except Exception as e: logger.error(f"Error resubscribing to track: {e}") except Exception as e: logger.error(f"Error restoring audio state: {e}") async def _wait_for_human_agent(self, _agent_number_: str) -> bool: """Monitor room for human agent connection""" _try_: human_agent_id = f"agent_{agent_number.replace('+', '')}" for _ in range(30): # Check every 0.5s for 15s total if self.room.remote_participants and human_agent_id in self.room.remote_participants: return True await asyncio.sleep(0.5) return False except Exception as e: logger.error(f"Error waiting for human agent: {e}") return False @llm.ai_callable() async def deafen_participant(self, _participant_id_: str): """Deafen a participant by muting their audio""" _try_: participant = self.room.remote_participants.get(participant_id) if not participant: return "PARTICIPANT_NOT_FOUND" for pub in participant.tracks.values(): if pub.kind == rtc.TrackKind.KIND_AUDIO: await pub.set_subscribed(False) return "DEAFENED_SUCCESSFULLY" except Exception as e: logger.error(f"Deafen failed: {e}") return "DEAFEN_ERROR" @millions-hair-24956 I want to just remove disconnect the agent totally

To completely disconnect the agent from the room during a warm transfer, you can use the

disconnect

method on the room object. This will remove the agent from the session entirely. Here's how you can modify your

warm_transfer_with_fallback

function to achieve this:

@llm.ai_callable()
async def warm_transfer_with_fallback(self):
    """Initiate warm transfer to human agent with 20s timeout and fallback contact collection"""
    try:
        agent_number = "<tel:+16147453|+161473>"

        # Get local participant with null check
        local_participant = self.room.local_participant
        if not local_participant:
            logger.error("No local participant found")
            return "TRANSFER_FAILED"

        # Announce transfer using TTS
        await self.agent.say("Connecting you to a specialist...")

        # Create SIP participant with initial DTMF
        sip_response = await self.api.sip.create_sip_participant(
            api.CreateSIPParticipantRequest(
                room_name=self.room.name,
                sip_trunk_id=os.getenv("SIP_OUTBOUND_TRUNK_ID"),
                sip_call_to=agent_number,
                participant_identity=f"agent_{agent_number.replace('+', '')}",
                participant_name="Human Agent",
                play_dialtone=True,
                dtmf="ww0"  # Initial DTMF sequence
            )
        )

        # Wait for human agent
        human_joined = await asyncio.wait_for(
            self._wait_for_human_agent(agent_number),
            timeout=20
        )

        if not human_joined:
            raise asyncio.TimeoutError("Human agent did not join in time")

        # Disconnect the agent from the room
        await self.room.disconnect()
        logger.info("Agent disconnected from the room.")

        return "TRANSFER_COMPLETE"

    except (asyncio.TimeoutError, Exception) as e:
        logger.error(f"Transfer failed: {str(e)}")
        await self.agent.say(
            "I'm sorry, we're having trouble connecting you. "
            "Please provide your contact details so we can reach you."
        )
        return "TRANSFER_FAILED"

    except Exception as e:
        logger.error(f"Transfer error: {e}")
        return "TRANSFER_FAILED"

This code will disconnect the agent from the room once the SIP participant is created and the human agent joins. If the transfer fails, it will inform the user and not attempt to restore the agent's audio state since the agent is disconnected. Would you like more detailed guidance on implementing this in your setup? Sources: - Room.disconnect 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

more detailed guidance on Implementing this

To implement the complete disconnection of an agent during a warm transfer in LiveKit, follow these steps: 1. Initialize the LiveKit API Client: Ensure you have the necessary environment variables set up for connecting to the LiveKit server. This includes

LIVEKIT_HOST

,

LIVEKIT_API_KEY

, and

LIVEKIT_API_SECRET

. 2. Create the SIP Participant: Use the

create_sip_participant

method to initiate a call to the human agent. This involves specifying the room name, SIP trunk ID, and the target phone number. 3. Wait for the Human Agent: Implement a mechanism to wait for the human agent to join the room. This can be done using a loop that checks for the presence of the human agent's participant ID in the room's remote participants. 4. Disconnect the Agent: Once the human agent joins, call the

disconnect

method on the room object to remove the agent from the session. This ensures that the agent is no longer part of the conversation. 5. Handle Transfer Failures: Implement error handling to manage scenarios where the transfer fails, such as timeouts or exceptions. Inform the user and provide a fallback mechanism, like collecting contact details. Here's a detailed implementation:

@millions-hair-24956 {"message": "failed to generate LLM completion, retrying in 0.1s", "level": "WARNING", "name": "livekit.agents", "exc_info": "Traceback (most recent call last):\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpx\\_transports\\default.py\", line 101, in map_httpcore_exceptions\n yield\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpx\\_transports\\default.py\", line 394, in handle_async_request\n resp = await self._pool.handle_async_request(req)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpcore\\_async\\connection_pool.py\", line 256, in handle_async_request\n raise exc from None\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpcore\\_async\\connection_pool.py\", line 236, in handle_async_request\n response = await connection.handle_async_request(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpcore\\_async\\connection.py\", line 101, in handle_async_request\n raise exc\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpcore\\_async\\connection.py\", line 78, in handle_async_request\n stream = await self._connect(request)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpcore\\_async\\connection.py\", line 124, in _connect\n stream = await self._network_backend.connect_tcp(**kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpcore\\_backends\\auto.py\", line 31, in connect_tcp\n return await self._backend.connect_tcp(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpcore\\_backends\\anyio.py\", line 113, in connect_tcp\n with map_exceptions(exc_map):\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\Lenovo\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 158, in __exit__\n self.gen.throw(value)\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpcore\\_exceptions.py\", line 14, in map_exceptions\n raise to_exc(exc) from exc\nhttpcore.ConnectTimeout\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\openai\\_base_client.py\", line 1582, in _request\n response = await self._client.send(\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpx\\_client.py\", line 1629, in send\n response = await self._send_handling_auth(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpx\\_client.py\", line 1657, in _send_handling_auth\n response = await self._send_handling_redirects(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpx\\_client.py\", line 1694, in _send_handling_redirects\n response = await self._send_single_request(request)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpx\\_client.py\", line 1730, in _send_single_request\n response = await transport.handle_async_request(request)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpx\\_transports\\default.py\", line 393, in handle_async_request\n with map_httpcore_exceptions():\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Users\\Lenovo\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 158, in __exit__\n self.gen.throw(value)\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\httpx\\_transports\\default.py\", line 118, in map_httpcore_exceptions\n raise mapped_exc(message) from exc\nhttpx.ConnectTimeout\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\livekit\\plugins\\openai\\llm.py\", line 743, in _run\n stream = await self._client.chat.completions.create(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\openai\\resources\\chat\\completions.py\", line 1720, in create\n return await self._post(\n ^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\openai\\_base_client.py\", line 1849, in post\n return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\openai\\_base_client.py\", line 1543, in request\n return await self._request(\n ^^^^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\openai\\_base_client.py\", line 1601, in _request\n raise APITimeoutError(request=request) from err\nopenai.APITimeoutError: Request timed out.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\livekit\\agents\\llm\\llm.py\", line 149, in _main_task\n return await self._run()\n ^^^^^^^^^^^^^^^^^\n File \"E:\\new_update\\voiceai-outboundcalls\\venv\\Lib\\site-packages\\livekit\\plugins\\openai\\llm.py\", line 776, in _run\n raise APITimeoutError(retryable=retryable)\nlivekit.agents._exceptions.APITimeoutError: Request timed out.", "llm": "livekit.plugins.openai.llm.LLM", "attempt": 1, "timestamp": "2025-02-18T190402.984621+00:00"} whats the problem from livekit ?

The error message indicates that there is a timeout issue when trying to generate a completion using the OpenAI API through LiveKit. Specifically, the

APITimeoutError

suggests that the request to OpenAI's API is taking too long to complete, resulting in a timeout. Possible Causes: 1. Network Latency: The connection to OpenAI's API might be experiencing high latency, causing the request to exceed the timeout limit. 2. API Rate Limiting: If you're making too many requests in a short period, you might be hitting rate limits, which can lead to delays and timeouts. 3. Server Load: The OpenAI servers might be under heavy load, causing delays in processing requests. Suggested Solutions: - Increase Timeout: If possible, increase the timeout setting for the API request to allow more time for the response. - Retry Logic: Implement a retry mechanism with exponential backoff to handle transient network issues or server load. - Check Network: Ensure that your network connection is stable and has low latency to the OpenAI servers. - Monitor API Usage: Check your API usage to ensure you are not hitting rate limits. Here's a basic example of how you might implement a retry mechanism:

import asyncio
import openai

async def generate_completion_with_retry(prompt, max_retries=3):
    attempt = 0
    while attempt < max_retries:
        try:
            response = await openai.Completion.create(
                engine="davinci",
                prompt=prompt,
                max_tokens=150
            )
            return response
        except openai.error.Timeout as e:
            attempt += 1
            if attempt >= max_retries:
                raise e
            await asyncio.sleep(2 ** attempt)  # Exponential backoff

# Usage
try:
    result = await generate_completion_with_retry("Hello, world!")
    print(result)
except openai.error.Timeout:
    print("Failed to generate completion after multiple attempts.")

Would you like more detailed guidance on implementing these solutions? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1732345136.594869 - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1732682471.692099 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.