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>
This commit is contained in:
Utkarsh
co-authored by
utkarsh240799
Claude Opus 4.6
utkarsh240799
Claude Opus 4.6
parent
9eb5b9ed29
commit
336fbce60a
@@ -161,4 +161,370 @@ describe("ConfigManager", () => {
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expect(config.llm.config.url).toBe("http://10.0.0.100:11434");
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});
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});
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// ─────────────────────────────────────────────────────────────────────
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// LM Studio snake_case normalization
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// ─────────────────────────────────────────────────────────────────────
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describe("mergeConfig - LM Studio embedder config", () => {
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const baseLlm = { provider: "openai", config: { apiKey: "k" } };
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it("normalizes lmstudio_base_url to baseURL for embedder", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: {
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provider: "lmstudio",
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config: {
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model: "nomic-embed-text-v1.5",
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lmstudio_base_url: "http://192.168.1.1:1234/v1",
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} as any,
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},
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vectorStore: { provider: "memory", config: {} },
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llm: baseLlm,
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});
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expect(cfg.embedder.provider).toBe("lmstudio");
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expect(cfg.embedder.config.baseURL).toBe("http://192.168.1.1:1234/v1");
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expect(cfg.embedder.config.model).toBe("nomic-embed-text-v1.5");
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});
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it("normalizes embedding_dims to embeddingDims for embedder", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: {
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provider: "lmstudio",
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config: { model: "nomic-embed-text-v1.5", embedding_dims: 768 } as any,
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},
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vectorStore: { provider: "memory", config: {} },
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llm: baseLlm,
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});
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expect(cfg.embedder.config.embeddingDims).toBe(768);
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});
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it("prefers camelCase baseURL over snake_case lmstudio_base_url", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: {
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provider: "lmstudio",
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config: {
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model: "test",
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baseURL: "http://camel:1234/v1",
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lmstudio_base_url: "http://snake:1234/v1",
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} as any,
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},
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vectorStore: { provider: "memory", config: {} },
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llm: baseLlm,
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});
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expect(cfg.embedder.config.baseURL).toBe("http://camel:1234/v1");
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});
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it("prefers camelCase embeddingDims over snake_case embedding_dims", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: {
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provider: "lmstudio",
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config: {
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model: "test",
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embeddingDims: 1536,
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embedding_dims: 768,
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} as any,
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},
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vectorStore: { provider: "memory", config: {} },
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llm: baseLlm,
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});
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expect(cfg.embedder.config.embeddingDims).toBe(1536);
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});
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it("passes through camelCase config without issues", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: {
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provider: "lmstudio",
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config: {
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model: "nomic-embed-text-v1.5",
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baseURL: "http://localhost:1234/v1",
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embeddingDims: 768,
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},
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},
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vectorStore: { provider: "memory", config: {} },
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llm: baseLlm,
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});
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expect(cfg.embedder.config.baseURL).toBe("http://localhost:1234/v1");
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expect(cfg.embedder.config.embeddingDims).toBe(768);
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});
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});
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describe("mergeConfig - LM Studio LLM config", () => {
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const baseEmbedder = { provider: "openai", config: { apiKey: "k" } };
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it("normalizes lmstudio_base_url to baseURL for LLM", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: { provider: "memory", config: {} },
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llm: {
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provider: "lmstudio",
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config: {
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model: "meta-llama-3.1",
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lmstudio_base_url: "http://192.168.1.1:1234/v1",
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} as any,
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},
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});
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expect(cfg.llm.provider).toBe("lmstudio");
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expect(cfg.llm.config.baseURL).toBe("http://192.168.1.1:1234/v1");
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expect(cfg.llm.config.model).toBe("meta-llama-3.1");
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});
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it("prefers camelCase baseURL over lmstudio_base_url for LLM", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: { provider: "memory", config: {} },
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llm: {
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provider: "lmstudio",
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config: {
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baseURL: "http://camel:1234/v1",
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lmstudio_base_url: "http://snake:1234/v1",
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} as any,
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},
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});
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expect(cfg.llm.config.baseURL).toBe("http://camel:1234/v1");
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});
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it("falls back to default baseURL when neither is provided for LLM", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: { provider: "memory", config: {} },
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llm: { provider: "lmstudio", config: { model: "test-model" } },
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});
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expect(cfg.llm.config.baseURL).toBe("https://api.openai.com/v1");
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});
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});
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describe("mergeConfig - full OpenClaw-style LM Studio config", () => {
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it("handles the exact config from issue #4235", () => {
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const cfg = ConfigManager.mergeConfig({
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embedder: {
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provider: "lmstudio",
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config: {
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model: "text-embedding-gte-qwen2-1.5b-instruct",
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embedding_dims: 1536,
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lmstudio_base_url: "http://192.168.200.83:1234/v1",
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} as any,
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},
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vectorStore: {
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provider: "qdrant",
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config: { host: "192.168.200.12", port: 6333, checkCompatibility: false },
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},
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llm: {
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provider: "lmstudio",
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config: {
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model: "openai/gpt-oss-20b",
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lmstudio_base_url: "http://192.168.200.83:1234/v1",
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} as any,
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},
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});
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expect(cfg.embedder.provider).toBe("lmstudio");
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expect(cfg.embedder.config.baseURL).toBe("http://192.168.200.83:1234/v1");
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expect(cfg.embedder.config.model).toBe("text-embedding-gte-qwen2-1.5b-instruct");
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expect(cfg.embedder.config.embeddingDims).toBe(1536);
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expect(cfg.llm.provider).toBe("lmstudio");
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expect(cfg.llm.config.baseURL).toBe("http://192.168.200.83:1234/v1");
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expect(cfg.llm.config.model).toBe("openai/gpt-oss-20b");
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expect(cfg.vectorStore.provider).toBe("qdrant");
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expect(cfg.vectorStore.config.host).toBe("192.168.200.12");
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expect(cfg.vectorStore.config.port).toBe(6333);
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});
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});
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});
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// ─────────────────────────────────────────────────────────────────────────
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// Memory class – LM Studio end-to-end flow (mocked factories)
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// ─────────────────────────────────────────────────────────────────────────
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describe("Memory – LM Studio end-to-end flow", () => {
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let MemoryClass: any;
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let mockEmbedderFactory: any;
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let mockVectorStoreFactory: any;
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let mockLlmFactory: any;
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let mockHistoryFactory: any;
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let mockEmbedder: any;
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let mockVStore: any;
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let mockLlm: any;
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beforeEach(() => {
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jest.resetModules();
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mockEmbedder = {
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embed: jest.fn().mockResolvedValue(new Array(768).fill(0.1)),
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embedBatch: jest.fn().mockResolvedValue([new Array(768).fill(0.1)]),
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};
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mockVStore = {
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insert: jest.fn().mockResolvedValue(undefined),
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search: jest.fn().mockResolvedValue([]),
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get: jest.fn().mockResolvedValue(null),
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update: jest.fn().mockResolvedValue(undefined),
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delete: jest.fn().mockResolvedValue(undefined),
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deleteCol: jest.fn().mockResolvedValue(undefined),
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list: jest.fn().mockResolvedValue([[], 0]),
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getUserId: jest.fn().mockResolvedValue("test-user-id"),
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setUserId: jest.fn().mockResolvedValue(undefined),
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initialize: jest.fn().mockResolvedValue(undefined),
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};
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mockLlm = {
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generateResponse: jest.fn().mockResolvedValue('{"facts":[]}'),
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};
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mockEmbedderFactory = { create: jest.fn().mockReturnValue(mockEmbedder) };
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mockVectorStoreFactory = { create: jest.fn().mockReturnValue(mockVStore) };
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mockLlmFactory = { create: jest.fn().mockReturnValue(mockLlm) };
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mockHistoryFactory = {
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create: jest.fn().mockReturnValue({
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addHistory: jest.fn().mockResolvedValue(undefined),
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getHistory: jest.fn().mockResolvedValue([]),
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reset: jest.fn().mockResolvedValue(undefined),
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}),
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};
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jest.doMock("../src/utils/factory", () => ({
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EmbedderFactory: mockEmbedderFactory,
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VectorStoreFactory: mockVectorStoreFactory,
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LLMFactory: mockLlmFactory,
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HistoryManagerFactory: mockHistoryFactory,
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}));
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jest.doMock("../src/utils/telemetry", () => ({
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captureClientEvent: jest.fn().mockResolvedValue(undefined),
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}));
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MemoryClass = require("../src/memory").Memory;
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});
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afterEach(() => {
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jest.restoreAllMocks();
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jest.resetModules();
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});
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it("creates Memory with lmstudio embedder and llm providers", async () => {
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const mem = new MemoryClass({
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embedder: {
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provider: "lmstudio",
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config: { model: "nomic-embed-text-v1.5", baseURL: "http://localhost:1234/v1" },
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},
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vectorStore: { provider: "memory", config: { collectionName: "test" } },
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llm: {
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provider: "lmstudio",
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config: { model: "meta-llama-3.1-70b", baseURL: "http://localhost:1234/v1" },
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},
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disableHistory: true,
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});
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await mem.getAll({ userId: "u1" });
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expect(mockEmbedderFactory.create).toHaveBeenCalledWith(
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"lmstudio",
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expect.objectContaining({ model: "nomic-embed-text-v1.5", baseURL: "http://localhost:1234/v1" }),
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);
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expect(mockLlmFactory.create).toHaveBeenCalledWith(
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"lmstudio",
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expect.objectContaining({ model: "meta-llama-3.1-70b", baseURL: "http://localhost:1234/v1" }),
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);
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});
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it("auto-detects embedding dimension via probe with lmstudio", async () => {
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const mem = new MemoryClass({
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embedder: {
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provider: "lmstudio",
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config: { model: "nomic-embed-text-v1.5", baseURL: "http://localhost:1234/v1" },
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},
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vectorStore: { provider: "qdrant", config: { collectionName: "test" } },
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llm: { provider: "lmstudio", config: { baseURL: "http://localhost:1234/v1" } },
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disableHistory: true,
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});
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await mem.getAll({ userId: "u1" });
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expect(mockEmbedder.embed).toHaveBeenCalledWith("dimension probe");
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const vsCall = mockVectorStoreFactory.create.mock.calls[0];
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expect(vsCall[1].dimension).toBe(768);
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});
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it("handles snake_case OpenClaw config through full Memory stack", async () => {
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const mem = new MemoryClass({
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embedder: {
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provider: "lmstudio",
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config: {
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model: "text-embedding-gte-qwen2-1.5b-instruct",
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embedding_dims: 1536,
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lmstudio_base_url: "http://192.168.200.83:1234/v1",
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} as any,
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},
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vectorStore: { provider: "memory", config: { collectionName: "test" } },
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llm: {
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provider: "lmstudio",
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config: { model: "openai/gpt-oss-20b", lmstudio_base_url: "http://192.168.200.83:1234/v1" } as any,
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},
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disableHistory: true,
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});
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await mem.getAll({ userId: "u1" });
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expect(mockEmbedderFactory.create).toHaveBeenCalledWith(
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"lmstudio",
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expect.objectContaining({ model: "text-embedding-gte-qwen2-1.5b-instruct", baseURL: "http://192.168.200.83:1234/v1" }),
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);
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expect(mockLlmFactory.create).toHaveBeenCalledWith(
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"lmstudio",
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expect.objectContaining({ model: "openai/gpt-oss-20b", baseURL: "http://192.168.200.83:1234/v1" }),
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);
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});
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it("search flow works with lmstudio embedder", async () => {
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mockVStore.search.mockResolvedValueOnce([{
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id: "mem-1",
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payload: { data: "User likes hiking", user_id: "u1", hash: "abc123", created_at: "2026-01-01" },
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score: 0.95,
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}]);
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const mem = new MemoryClass({
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embedder: {
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provider: "lmstudio",
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config: { model: "nomic-embed-text-v1.5", baseURL: "http://localhost:1234/v1", embeddingDims: 768 },
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},
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vectorStore: { provider: "memory", config: { collectionName: "test", dimension: 768 } },
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llm: { provider: "lmstudio", config: { baseURL: "http://localhost:1234/v1" } },
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disableHistory: true,
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});
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const result = await mem.search("What does the user like?", { userId: "u1" });
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expect(mockEmbedder.embed).toHaveBeenCalledWith("What does the user like?");
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expect(mockVStore.search).toHaveBeenCalled();
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expect(result.results).toHaveLength(1);
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expect(result.results[0].memory).toBe("User likes hiking");
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});
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it("add flow works with lmstudio LLM for fact extraction", async () => {
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mockLlm.generateResponse.mockResolvedValueOnce('{"facts":["User loves sushi"]}');
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mockVStore.search.mockResolvedValue([]);
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mockVStore.list.mockResolvedValue([[], 0]);
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const mem = new MemoryClass({
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embedder: {
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provider: "lmstudio",
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config: { model: "nomic-embed-text-v1.5", baseURL: "http://localhost:1234/v1", embeddingDims: 768 },
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},
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vectorStore: { provider: "memory", config: { collectionName: "test", dimension: 768 } },
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llm: {
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provider: "lmstudio",
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config: { model: "meta-llama-3.1-70b", baseURL: "http://localhost:1234/v1" },
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},
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disableHistory: true,
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});
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await mem.add("I love sushi", { userId: "u1" });
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expect(mockLlm.generateResponse).toHaveBeenCalled();
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expect(mockEmbedder.embed).toHaveBeenCalled();
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});
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});
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