Why does the LLM sometimes lost context when it ge...
# ask-ai
l
Why does the LLM sometimes lost context when it gets interrupted?
t
Thanks for reaching out, Computer. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
When an LLM loses context during interruptions, it’s usually because the system trims down the conversation history to keep things accurate. Basically, when there’s an interruption, the system only keeps the part of the response it actually heard before the interruption happened. This helps keep the conversation history precise, but it can mean the LLM loses track of the full response it was working on. Sometimes, timing issues between the audio playback and text processing can cause the system to cut off at the wrong spot, which adds to the context getting lost. If you’d like to tweak how interruptions work, you can play around with settings like: •
allow_interruptions
: Switch this to
False
if you want to prevent interruptions altogether. •
interrupt_speech_duration
: Set the minimum length of speech before an interruption counts as intentional. •
interrupt_min_words
: Choose the minimum number of transcribed words needed to treat an interruption as intentional. Feel free to reach out if you want a hand setting these up or if you have any other questions—I’m here to help! Sources: livekit-agents/livekit/agents/voice/agent_activity.py | VoicePipelineAgent | LiveKit Docs | Turn detection and interruptions | LiveKit Docs | Turn detection and interruptions | LiveKit Docs
l
The LLM asked me a question and when I answered, it though the answer was for the previous question and asked the question again. It's almost like the LLM output I interrupted was not added to the context before generating the next reply
t
Hey there! The behavior you're noticing—where the LLM repeats questions after interruptions—happens because sometimes the interrupted output doesn’t get added to the conversation context. Specifically, if the interruption happens before the agent starts speaking, the system doesn’t save that interrupted response. That’s why the LLM loses track of what it was about to say and ends up treating your next input like it’s responding to the previous question. Here’s a quick rundown of how interruptions are handled: • If the agent is interrupted while it’s speaking, the system cuts off the response and adds it to the context with an
interrupted=True
flag. • If the interruption happens before speech begins, no message gets added to the context, which leads to the hiccup you’re seeing. To help fix this, you can: • Try to interrupt only after the agent has started speaking so the partial context is saved. • Adjust the timing of interruptions or tweak settings like
interrupt_speech_duration
to better control when interruptions happen. If you want, I’m here to help you set these up or troubleshoot further—just let me know! Sources: livekit-plugins/livekit-plugins-aws/livekit/plugins/aws/experimental/realti...
l
But I interrupted after the agent had already spoken a full sentence. But it still lost context
t
Hey there! Just a quick heads-up: even if the agent finishes a full sentence before being interrupted, the system only keeps the part of the response the user actually heard up to that interruption. So, if the interruption happens mid-playback, only that portion stays in the conversation history, and the rest gets dropped. This way, the chat history truly reflects what both sides experienced. We designed it this way to keep things accurate, but if interruptions happen a lot or at tricky moments, some context might get lost. To help with that, you can: • Tweak settings like
interrupt_speech_duration
or
interrupt_min_words
to better manage when interruptions kick in. • Add your own custom logic to save more of the response during interruptions, if that fits your needs. If you'd like, I’m here to help you set these up or brainstorm some custom solutions together! Sources: Turn detection and interruptions | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_activity.py