Spracherkennung für: .ts vermutete Sprache: Unknown {[0] [0] [0]} [Methode: Schwerpunktbildung, einfache Gewichte, sechs Dimensionen]
import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
import { createLocalEmbeddingProvider, DEFAULT_LOCAL_MODEL } from "./embeddings.js";
import * as nodeLlamaModule from "./node-llama.js";
beforeEach(() => {
vi.spyOn(nodeLlamaModule, "importNodeLlamaCpp");
});
afterEach(() => {
vi.resetAllMocks();
});
function mockLocalEmbeddingRuntime(vector = new Float32Array([2.35, 3.45, 0.63, 4.3])) {
const getEmbeddingFor = vi.fn().mockResolvedValue({ vector });
const createEmbeddingContext = vi.fn().mockResolvedValue({ getEmbeddingFor });
const loadModel = vi.fn().mockResolvedValue({ createEmbeddingContext });
const resolveModelFile = vi.fn(async (modelPath: string) => `/resolved/${modelPath}`);
vi.mocked(nodeLlamaModule.importNodeLlamaCpp).mockResolvedValue({
getLlama: async () => ({ loadModel }),
resolveModelFile,
LlamaLogLevel: { error: 0 },
} as never);
return { createEmbeddingContext, getEmbeddingFor, loadModel, resolveModelFile };
}
describe("local embedding provider", () => {
it("normalizes local embeddings and resolves the default local model", async () => {
const runtime = mockLocalEmbeddingRuntime();
const provider = await createLocalEmbeddingProvider({
config: {} as never,
provider: "local",
model: "",
fallback: "none",
});
const embedding = await provider.embedQuery("test query");
const magnitude = Math.sqrt(embedding.reduce((sum, value) => sum + value * value, 0));
expect(magnitude).toBeCloseTo(1, 5);
expect(runtime.resolveModelFile).toHaveBeenCalledWith(DEFAULT_LOCAL_MODEL, undefined);
expect(runtime.getEmbeddingFor).toHaveBeenCalledWith("test query");
});
it("passes default contextSize (4096) to createEmbeddingContext when not configured", async () => {
const runtime = mockLocalEmbeddingRuntime();
const provider = await createLocalEmbeddingProvider({
config: {} as never,
provider: "local",
model: "",
fallback: "none",
});
await provider.embedQuery("context size default test");
expect(runtime.createEmbeddingContext).toHaveBeenCalledWith({ contextSize: 4096 });
});
it("passes configured contextSize to createEmbeddingContext", async () => {
const runtime = mockLocalEmbeddingRuntime();
const provider = await createLocalEmbeddingProvider({
config: {} as never,
provider: "local",
model: "",
fallback: "none",
local: { contextSize: 2048 },
});
await provider.embedQuery("context size custom test");
expect(runtime.createEmbeddingContext).toHaveBeenCalledWith({ contextSize: 2048 });
});
it('passes "auto" contextSize to createEmbeddingContext when explicitly set', async () => {
const runtime = mockLocalEmbeddingRuntime();
const provider = await createLocalEmbeddingProvider({
config: {} as never,
provider: "local",
model: "",
fallback: "none",
local: { contextSize: "auto" },
});
await provider.embedQuery("context size auto test");
expect(runtime.createEmbeddingContext).toHaveBeenCalledWith({ contextSize: "auto" });
});
it("trims explicit local model paths and cache directories", async () => {
const runtime = mockLocalEmbeddingRuntime(new Float32Array([1, 0]));
const provider = await createLocalEmbeddingProvider({
config: {} as never,
provider: "local",
model: "",
fallback: "none",
local: {
modelPath: " /models/embed.gguf ",
modelCacheDir: " /cache/models ",
},
});
await provider.embedBatch(["a", "b"]);
expect(provider.model).toBe("/models/embed.gguf");
expect(runtime.resolveModelFile).toHaveBeenCalledWith("/models/embed.gguf", "/cache/models");
expect(runtime.getEmbeddingFor).toHaveBeenCalledTimes(2);
});
});
¤ Dauer der Verarbeitung: 0.17 Sekunden
(vorverarbeitet am 2026-04-27)
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