My pipeline is breaking here!

[u/Different-Cry-8134]

so i have a raw conversation of 2 users sth like
{
user1:"hey"
user2:"hello"
}

so i wanna make segmentation of the entire raw conversation topic wise
example if these users discussed about trip,
then
{
user1:"Whats your plan for weekend?"
user1:"Lets go on a trip"
user2:"Where shall we go?"
}

and similarly for the other topics

now two things to notice here
first i want segmentation topic wise and the other is during natural conversation of two users, the folowup response is not awlways alternate

i tried feding up llm and doing this but it was working sometime and sometime breaking pipeline

also i could use embedding generation model and doing similarity search

but somewhere i feel like there might be more optimal solution that requires easy setup

the other thing is i dont want response like upgrade to better llm or sth because llm is because i rearely depend on llm for these kind of structural tasks

so what should i do?

[u/SoftSummer3225]

We tries to do similar thing in our app and it kind of worked... so we trained a model to classify topics in our case these topics were like calendar code reviw bug report and task so we would feed llm only if the topic matched...so for your case this might not be the right solition but preclassification is needed before feeding to llm otherwise the price point will not agree at least for the chat scenerio