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refactor(semantle): switch embedding model from bge-small-en-v1.5 to bge-m3

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Aligns semantle with doantu so both modules share one Workers AI model.
bge-m3 is multilingual and cheaper (1075 N/M input tokens vs 1841 N/M)
and produces 1024-dim vectors. Updates the api-client default, test
fake-vector dimensions, README, index.js doc comment, and the
wrangler.toml [ai] binding comment (Neurons/day budget recomputed).
This commit is contained in:
tiennm99
2026-04-23 00:22:28 +07:00
parent 9b331fc24d
commit 4f7f6896c5
5 changed files with 27 additions and 23 deletions
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tests/modules/semantle/api-client.test.js
+6 -6
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@@ -2,10 +2,10 @@ import { describe, expect, it, vi } from "vitest";
import { UpstreamError, createClient } from "../../../src/modules/semantle/api-client.js";
/**
* Build a deterministic 384-dim vector (bge-small output size) from a seed
* so cosine scores are reproducible without hardcoding 384 floats.
* Build a deterministic 1024-dim vector (bge-m3 output size) from a seed
* so cosine scores are reproducible without hardcoding 1024 floats.
*/
function fakeVector(seed, dim = 384) {
function fakeVector(seed, dim = 1024) {
const out = new Array(dim);
for (let i = 0; i < dim; i++) out[i] = Math.sin(seed * (i + 1));
return out;
@@ -45,14 +45,14 @@ describe("semantle/api-client", () => {
it("similarity batches target + guess in a single run() call", async () => {
const ai = fakeAi(async (_model, { text }) => ({
shape: [text.length, 384],
shape: [text.length, 1024],
data: text.map((_, i) => fakeVector(i + 1)),
}));
const client = createClient(ai);
await client.similarity("apple", "orange");
expect(ai.run).toHaveBeenCalledTimes(1);
const [model, input] = ai.run.mock.calls[0];
expect(model).toBe("@cf/baai/bge-small-en-v1.5");
expect(model).toBe("@cf/baai/bge-m3");
expect(input).toEqual({ text: ["apple", "orange"] });
});
@@ -107,7 +107,7 @@ describe("semantle/api-client", () => {
});
it("similarity returns null score when a vector norm is zero", async () => {
const zero = new Array(384).fill(0);
const zero = new Array(1024).fill(0);
const ai = fakeAi(async () => ({ data: [zero, fakeVector(1)] }));
const client = createClient(ai);
const res = await client.similarity("apple", "orange");