Summarize a transcript using LeMUR

POST

Custom Summary allows you to distill a piece of audio into a few impactful sentences. You can give the model context to obtain more targeted results while outputting the results in a variety of formats described in human language.

Request

This endpoint expects an object.
answer_formatstringOptional

How you want the summary to be returned. This can be any text. Examples: “TLDR”, “bullet points”

contextstring or map from strings to anyOptional

Context to provide the model. This can be a string or a free-form JSON value.

final_modelenumOptional

The model that is used for the final prompt after compression is performed.

input_textstringOptional

Custom formatted transcript data. Maximum size is the context limit of the selected model, which defaults to 100000. Use either transcript_ids or input_text as input into LeMUR.

max_output_sizeintegerOptionalDefaults to 2000

Max output size in tokens, up to 4000

temperaturedoubleOptional

The temperature to use for the model. Higher values result in answers that are more creative, lower values are more conservative. Can be any value between 0.0 and 1.0 inclusive.

transcript_idslist of stringsOptional

A list of completed transcripts with text. Up to a maximum of 100 files or 100 hours, whichever is lower. Use either transcript_ids or input_text as input into LeMUR.

Response

This endpoint returns an object.
request_idstring

The ID of the LeMUR request

responsestring

The response generated by LeMUR.

usageobject

The usage numbers for the LeMUR request

Errors