feat(mem0-ts): add LM Studio embedder and LLM support (#4354)
Co-authored-by: utkarsh240799 <utkarsh240799@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Utkarsh
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utkarsh240799
Claude Opus 4.6
utkarsh240799
Claude Opus 4.6
parent
9eb5b9ed29
commit
336fbce60a
@@ -0,0 +1,170 @@
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/// <reference types="jest" />
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/**
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* LM Studio integration tests against a real local server.
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* Skipped by default. Enable with: LMSTUDIO_INTEGRATION=1
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*
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* Prerequisites:
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* 1. LM Studio installed with `lms` CLI
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* 2. Server running: lms server start
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* 3. Embedding model loaded: lms load text-embedding-nomic-embed-text-v1.5
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* 4. (Optional) Chat model loaded for LLM tests
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*/
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import { LMStudioEmbedder } from "../src/embeddings/lmstudio";
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import { LMStudioLLM } from "../src/llms/lmstudio";
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const LMSTUDIO_BASE_URL =
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process.env.LMSTUDIO_BASE_URL || "http://localhost:1234/v1";
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const RUN_INTEGRATION = process.env.LMSTUDIO_INTEGRATION === "1";
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const describeIf = RUN_INTEGRATION ? describe : describe.skip;
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jest.setTimeout(120_000);
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async function listModels(): Promise<{ embedding: string | null; chat: string | null }> {
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const res = await fetch(`${LMSTUDIO_BASE_URL}/models`);
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const body = await res.json();
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const models: any[] = body.data || [];
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const embedding = models.find((m) => m.id.includes("embed") || m.id.includes("nomic"));
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const chat = models.find((m) => !m.id.includes("embed") && !m.id.includes("nomic"));
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return { embedding: embedding?.id ?? null, chat: chat?.id ?? null };
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}
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function cosineSim(a: number[], b: number[]): number {
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let dot = 0, normA = 0, normB = 0;
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for (let i = 0; i < a.length; i++) {
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dot += a[i] * b[i];
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normA += a[i] * a[i];
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normB += b[i] * b[i];
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}
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const denom = Math.sqrt(normA) * Math.sqrt(normB);
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return denom === 0 ? 0 : dot / denom;
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}
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describeIf("LM Studio Integration", () => {
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it("server is reachable and lists models", async () => {
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const res = await fetch(`${LMSTUDIO_BASE_URL}/models`);
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expect(res.ok).toBe(true);
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const body = await res.json();
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expect(body.data).toBeDefined();
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console.log("Loaded models:", body.data.map((m: any) => m.id));
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});
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// ─── Embedder ────────────────────────────────────────────────────────
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describe("LMStudioEmbedder (real server)", () => {
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let embedder: LMStudioEmbedder;
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let modelId: string;
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beforeAll(async () => {
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const models = await listModels();
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if (!models.embedding) throw new Error("No embedding model loaded");
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modelId = models.embedding;
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embedder = new LMStudioEmbedder({ baseURL: LMSTUDIO_BASE_URL, model: modelId });
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});
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it("embed() returns a numeric vector", async () => {
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const vector = await embedder.embed("Hello world");
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expect(Array.isArray(vector)).toBe(true);
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expect(vector.length).toBeGreaterThan(0);
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vector.forEach((v) => expect(typeof v).toBe("number"));
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console.log(` Model: ${modelId}, dimension: ${vector.length}`);
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});
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it("embed() produces identical output for newline-normalized text", async () => {
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const v1 = await embedder.embed("hello world");
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const v2 = await embedder.embed("hello\nworld");
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expect(v1.length).toBe(v2.length);
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const totalDiff = v1.reduce((s, val, i) => s + Math.abs(val - v2[i]), 0);
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expect(totalDiff).toBeLessThan(0.001);
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});
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it("embedBatch() returns correct number of vectors", async () => {
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const vectors = await embedder.embedBatch(["first", "second", "third"]);
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expect(vectors).toHaveLength(3);
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vectors.forEach((v) => {
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expect(v.length).toBe(vectors[0].length);
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v.forEach((val) => expect(typeof val).toBe("number"));
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});
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});
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it("semantically similar texts have higher cosine similarity", async () => {
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const [v1, v2, v3] = await Promise.all([
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embedder.embed("I love hiking in the mountains"),
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embedder.embed("I enjoy trekking through mountain trails"),
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embedder.embed("The stock market crashed yesterday"),
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]);
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const simSimilar = cosineSim(v1, v2);
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const simDifferent = cosineSim(v1, v3);
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console.log(` Similar: ${simSimilar.toFixed(4)}, Different: ${simDifferent.toFixed(4)}`);
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expect(Number.isFinite(simSimilar)).toBe(true);
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expect(Number.isFinite(simDifferent)).toBe(true);
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expect(simSimilar).toBeGreaterThan(simDifferent);
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});
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it("embed() handles empty string", async () => {
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const vector = await embedder.embed("");
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expect(Array.isArray(vector)).toBe(true);
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expect(vector.length).toBeGreaterThan(0);
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});
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it("embed() handles long text", async () => {
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const longText = "This is a test sentence. ".repeat(200);
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const vector = await embedder.embed(longText);
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expect(Array.isArray(vector)).toBe(true);
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expect(vector.length).toBeGreaterThan(0);
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});
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});
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// ─── LLM ─────────────────────────────────────────────────────────────
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describe("LMStudioLLM (real server)", () => {
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let llm: LMStudioLLM;
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let chatModelId: string | null;
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beforeAll(async () => {
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const models = await listModels();
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chatModelId = models.chat;
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if (!chatModelId) {
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console.warn("No chat model loaded — LLM tests will be skipped");
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return;
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}
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llm = new LMStudioLLM({ baseURL: LMSTUDIO_BASE_URL, model: chatModelId });
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});
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it("generateResponse() returns a response", async () => {
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if (!chatModelId) return;
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const result = await llm.generateResponse([
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{ role: "user", content: "Say hello in exactly 3 words." },
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]);
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if (typeof result === "string") {
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expect(result.length).toBeGreaterThan(0);
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console.log(` Response (string): ${result.slice(0, 100)}`);
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} else {
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expect(result).toHaveProperty("content");
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expect(result.content.length).toBeGreaterThan(0);
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console.log(` Response (object): ${result.content.slice(0, 100)}`);
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}
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});
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it("generateChat() returns LLMResponse with content and role", async () => {
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if (!chatModelId) return;
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const result = await llm.generateChat([
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{ role: "user", content: "What is 2+2?" },
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]);
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expect(result).toHaveProperty("content");
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expect(result).toHaveProperty("role");
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expect(result.role).toBe("assistant");
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expect(result.content.length).toBeGreaterThan(0);
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console.log(` Chat: ${result.content.slice(0, 100)}`);
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});
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it("generateChat() handles multi-turn conversation", async () => {
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if (!chatModelId) return;
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const result = await llm.generateChat([
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{ role: "user", content: "My name is Alice." },
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{ role: "assistant", content: "Hello Alice!" },
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{ role: "user", content: "What is my name?" },
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]);
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expect(result.content.length).toBeGreaterThan(0);
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console.log(` Multi-turn: ${result.content.slice(0, 100)}`);
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});
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});
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});
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