56 lines
1.6 KiB
TypeScript
56 lines
1.6 KiB
TypeScript
import type { ResponseItem } from "openai/resources/responses/responses.mjs";
|
||
|
||
/**
|
||
* Roughly estimate the number of language‑model tokens represented by a list
|
||
* of OpenAI `ResponseItem`s.
|
||
*
|
||
* A full tokenizer would be more accurate, but would add a heavyweight
|
||
* dependency for only marginal benefit. Empirically, assuming ~4 characters
|
||
* per token offers a good enough signal for displaying context‑window usage
|
||
* to the user.
|
||
*
|
||
* The algorithm counts characters from the different content types we may
|
||
* encounter and then converts that char count to tokens by dividing by four
|
||
* and rounding up.
|
||
*/
|
||
export function approximateTokensUsed(items: Array<ResponseItem>): number {
|
||
let charCount = 0;
|
||
|
||
for (const item of items) {
|
||
switch (item.type) {
|
||
case "message": {
|
||
if (item.role !== "user" && item.role !== "assistant") {
|
||
continue;
|
||
}
|
||
|
||
for (const c of item.content) {
|
||
if (c.type === "input_text" || c.type === "output_text") {
|
||
charCount += c.text.length;
|
||
} else if (c.type === "refusal") {
|
||
charCount += c.refusal.length;
|
||
} else if (c.type === "input_file") {
|
||
charCount += c.filename?.length ?? 0;
|
||
}
|
||
// images and other content types are ignored (0 chars)
|
||
}
|
||
break;
|
||
}
|
||
|
||
case "function_call": {
|
||
charCount += (item.name?.length || 0) + (item.arguments?.length || 0);
|
||
break;
|
||
}
|
||
|
||
case "function_call_output": {
|
||
charCount += item.output.length;
|
||
break;
|
||
}
|
||
|
||
default:
|
||
break;
|
||
}
|
||
}
|
||
|
||
return Math.ceil(charCount / 4);
|
||
}
|