Files
Moltbot/src/memory/manager.embedding-batches.test.ts
2026-02-17 15:50:07 +09:00

122 lines
4.3 KiB
TypeScript

import fs from "node:fs/promises";
import path from "node:path";
import { describe, expect, it, vi } from "vitest";
import { installEmbeddingManagerFixture } from "./embedding-manager.test-harness.js";
const fx = installEmbeddingManagerFixture({
fixturePrefix: "openclaw-mem-",
largeTokens: 1250,
smallTokens: 200,
createCfg: ({ workspaceDir, indexPath, tokens }) => ({
agents: {
defaults: {
workspace: workspaceDir,
memorySearch: {
provider: "openai",
model: "mock-embed",
store: { path: indexPath, vector: { enabled: false } },
chunking: { tokens, overlap: 0 },
sync: { watch: false, onSessionStart: false, onSearch: false },
query: { minScore: 0, hybrid: { enabled: false } },
},
},
list: [{ id: "main", default: true }],
},
}),
});
const { embedBatch } = fx;
describe("memory embedding batches", () => {
it("splits large files across multiple embedding batches", async () => {
const memoryDir = fx.getMemoryDir();
const managerLarge = fx.getManagerLarge();
// Keep this small but above the embedding batch byte threshold (8k) so we
// exercise multi-batch behavior without generating lots of chunks/DB rows.
const line = "a".repeat(4200);
const content = [line, line].join("\n");
await fs.writeFile(path.join(memoryDir, "2026-01-03.md"), content);
const updates: Array<{ completed: number; total: number; label?: string }> = [];
await managerLarge.sync({
progress: (update) => {
updates.push(update);
},
});
const status = managerLarge.status();
const totalTexts = embedBatch.mock.calls.reduce(
(sum: number, call: unknown[]) => sum + ((call[0] as string[] | undefined)?.length ?? 0),
0,
);
expect(totalTexts).toBe(status.chunks);
expect(embedBatch.mock.calls.length).toBeGreaterThan(1);
expect(updates.length).toBeGreaterThan(0);
expect(updates.some((update) => update.label?.includes("/"))).toBe(true);
const last = updates[updates.length - 1];
expect(last?.total).toBeGreaterThan(0);
expect(last?.completed).toBe(last?.total);
});
it("keeps small files in a single embedding batch", async () => {
const memoryDir = fx.getMemoryDir();
const managerSmall = fx.getManagerSmall();
const line = "b".repeat(120);
const content = Array.from({ length: 4 }, () => line).join("\n");
await fs.writeFile(path.join(memoryDir, "2026-01-04.md"), content);
await managerSmall.sync({ reason: "test" });
expect(embedBatch.mock.calls.length).toBe(1);
});
it("retries embeddings on transient rate limit and 5xx errors", async () => {
const memoryDir = fx.getMemoryDir();
const managerSmall = fx.getManagerSmall();
const line = "d".repeat(120);
const content = Array.from({ length: 4 }, () => line).join("\n");
await fs.writeFile(path.join(memoryDir, "2026-01-06.md"), content);
const transientErrors = [
"openai embeddings failed: 429 rate limit",
"openai embeddings failed: 502 Bad Gateway (cloudflare)",
];
let calls = 0;
embedBatch.mockImplementation(async (texts: string[]) => {
calls += 1;
const transient = transientErrors[calls - 1];
if (transient) {
throw new Error(transient);
}
return texts.map(() => [0, 1, 0]);
});
const realSetTimeout = setTimeout;
const setTimeoutSpy = vi.spyOn(global, "setTimeout").mockImplementation(((
handler: TimerHandler,
timeout?: number,
...args: unknown[]
) => {
const delay = typeof timeout === "number" ? timeout : 0;
if (delay > 0 && delay <= 2000) {
return realSetTimeout(handler, 0, ...args);
}
return realSetTimeout(handler, delay, ...args);
}) as typeof setTimeout);
try {
await managerSmall.sync({ reason: "test" });
} finally {
setTimeoutSpy.mockRestore();
}
expect(calls).toBe(3);
}, 10000);
it("skips empty chunks so embeddings input stays valid", async () => {
const memoryDir = fx.getMemoryDir();
const managerSmall = fx.getManagerSmall();
await fs.writeFile(path.join(memoryDir, "2026-01-07.md"), "\n\n\n");
await managerSmall.sync({ reason: "test" });
const inputs = embedBatch.mock.calls.flatMap((call: unknown[]) => (call[0] as string[]) ?? []);
expect(inputs).not.toContain("");
});
});