-
Notifications
You must be signed in to change notification settings - Fork 32
Description
I encountered an issue where the JSON output gets truncated partway when running sensemaking on a file that contains long Japanese text. Here is a snippet of the truncated output:
[
{
"id": "1870821933711040512",
"topics": [
{
"name": "M1 Grand Prix",
"subtopics": [
{
"name": "General Appreciation"
}
]
}
]
},
{
"id": "1870821933648163328",
"topics": [
{
"name": "M1 Grand Prix",
"subtopics": [
{
"name": "Performance Analysis"
}
]
}
]
},
...(many rows)..
{
"id": "1870821922046710016",
"topics": [
{
"name":
It appears that categorizationBatchSize is currently fixed at 100, which might be causing the model to exceed its output token limit, especially for languages like Japanese that consume more tokens or for comments that are very long.
Proposed Solution
It would be helpful if categorizationBatchSize could be passed as a parameter upon invocation, so users can adjust it according to their language or the size of their dataset. This way, we can avoid hitting the model’s output token limit and prevent truncated JSON outputs.
Would it be possible to make categorizationBatchSize configurable? If you have any suggestions or alternative approaches, I'd be happy to hear them. Thank you in advance!