06c25eb00b
Co-authored-by: utkarsh240799 <utkarsh240799@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
670 lines
22 KiB
TypeScript
670 lines
22 KiB
TypeScript
/// <reference types="jest" />
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import { ConfigManager } from "../src/config/manager";
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describe("ConfigManager", () => {
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describe("mergeConfig - dimension handling", () => {
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const baseLlm = {
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provider: "openai",
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config: { apiKey: "test-key" },
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};
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it("should leave dimension undefined when no explicit dimension or embeddingDims provided", () => {
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const config = ConfigManager.mergeConfig({
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embedder: { provider: "openai", config: { apiKey: "test-key" } },
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vectorStore: { provider: "memory", config: { collectionName: "test" } },
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llm: baseLlm,
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});
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// Dimension should be undefined so Memory._autoInitialize() will
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// auto-detect it via a probe embedding at runtime.
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expect(config.vectorStore.config.dimension).toBeUndefined();
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});
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it("should use embeddingDims from embedder config when provided", () => {
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const config = ConfigManager.mergeConfig({
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embedder: {
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provider: "ollama",
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config: { model: "nomic-embed-text", embeddingDims: 768 },
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},
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vectorStore: { provider: "qdrant", config: { collectionName: "test" } },
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llm: baseLlm,
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});
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expect(config.vectorStore.config.dimension).toBe(768);
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});
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it("should prefer explicit vector store dimension over embedder dims", () => {
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const config = ConfigManager.mergeConfig({
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embedder: {
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provider: "ollama",
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config: { model: "nomic-embed-text", embeddingDims: 768 },
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},
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vectorStore: {
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provider: "qdrant",
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config: { collectionName: "test", dimension: 1024 },
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},
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llm: baseLlm,
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});
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expect(config.vectorStore.config.dimension).toBe(1024);
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});
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it("should leave dimension undefined when using a custom client without explicit dims", () => {
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const mockClient = { someMethod: () => {} };
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const config = ConfigManager.mergeConfig({
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embedder: {
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provider: "ollama",
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config: { model: "nomic-embed-text" },
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},
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vectorStore: {
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provider: "qdrant",
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config: { collectionName: "test", client: mockClient },
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},
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llm: baseLlm,
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});
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// No embeddingDims and no explicit dimension → should be undefined
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// for auto-detection at runtime.
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expect(config.vectorStore.config.dimension).toBeUndefined();
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});
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it("should use embeddingDims when using a custom client", () => {
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const mockClient = { someMethod: () => {} };
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const config = ConfigManager.mergeConfig({
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embedder: {
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provider: "ollama",
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config: { model: "nomic-embed-text", embeddingDims: 768 },
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},
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vectorStore: {
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provider: "qdrant",
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config: { collectionName: "test", client: mockClient },
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},
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llm: baseLlm,
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});
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expect(config.vectorStore.config.dimension).toBe(768);
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});
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});
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describe("mergeConfig - LLM url passthrough for Ollama", () => {
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const baseEmbedder = {
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provider: "openai",
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config: { apiKey: "test-key" },
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};
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const baseVectorStore = {
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provider: "memory",
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config: { collectionName: "test" },
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};
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it("should preserve url in LLM config when provided", () => {
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const config = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: baseVectorStore,
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llm: {
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provider: "ollama",
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config: { model: "llama3.2:3b", url: "http://10.0.0.100:11434" },
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},
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});
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expect(config.llm.config.url).toBe("http://10.0.0.100:11434");
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});
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it("should prefer baseURL over url when both are provided", () => {
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const config = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: baseVectorStore,
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llm: {
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provider: "ollama",
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config: {
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model: "llama3.2:3b",
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baseURL: "http://custom:11434",
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url: "http://fallback:11434",
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},
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},
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});
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expect(config.llm.config.baseURL).toBe("http://custom:11434");
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expect(config.llm.config.url).toBe("http://fallback:11434");
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});
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it("should use default baseURL when no url or baseURL provided", () => {
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const config = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: baseVectorStore,
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llm: {
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provider: "ollama",
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config: { model: "llama3.2:3b" },
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},
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});
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expect(config.llm.config.url).toBeUndefined();
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expect(config.llm.config.baseURL).toBe("https://api.openai.com/v1");
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});
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it("should preserve url in embedder config (existing behavior)", () => {
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const config = ConfigManager.mergeConfig({
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embedder: {
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provider: "ollama",
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config: {
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model: "nomic-embed-text",
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url: "http://10.0.0.100:11434",
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},
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},
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vectorStore: baseVectorStore,
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llm: {
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provider: "ollama",
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config: { model: "llama3.2:3b", url: "http://10.0.0.100:11434" },
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},
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});
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expect(config.embedder.config.url).toBe("http://10.0.0.100:11434");
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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: {
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model: "nomic-embed-text-v1.5",
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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(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: {
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host: "192.168.200.12",
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port: 6333,
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checkCompatibility: false,
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},
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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(
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"text-embedding-gte-qwen2-1.5b-instruct",
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);
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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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// Graph store LLM config propagation (issue #3425)
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// ─────────────────────────────────────────────────────────────────────────
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describe("mergeConfig - graph store LLM config (issue #3425)", () => {
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const baseEmbedder = {
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provider: "openai",
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config: { apiKey: "test-key" },
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};
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const baseVectorStore = {
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provider: "memory",
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config: { collectionName: "test" },
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};
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const graphStoreNeo4j = {
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provider: "neo4j",
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config: {
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url: "neo4j://localhost:7687",
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username: "neo4j",
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password: "password",
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},
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};
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it("should NOT have a default graphStore.llm — root llm should be the fallback", () => {
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const config = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: baseVectorStore,
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llm: {
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provider: "anthropic",
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config: { model: "claude-sonnet-4-20250514" },
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},
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graphStore: graphStoreNeo4j,
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});
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// graphStore should NOT have its own llm after merge
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expect(config.graphStore?.llm).toBeUndefined();
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// root llm should be anthropic
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expect(config.llm.provider).toBe("anthropic");
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expect(config.llm.config.model).toBe("claude-sonnet-4-20250514");
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});
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it("should preserve explicit graphStore.llm when user provides it", () => {
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const config = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: baseVectorStore,
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llm: {
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provider: "anthropic",
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config: { model: "claude-sonnet-4-20250514" },
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},
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graphStore: {
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...graphStoreNeo4j,
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llm: { provider: "openai", config: { model: "gpt-4o" } },
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},
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});
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// graphStore should have its own llm
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expect(config.graphStore?.llm?.provider).toBe("openai");
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expect(config.graphStore?.llm?.config).toEqual({ model: "gpt-4o" });
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// root llm should still be anthropic
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expect(config.llm.provider).toBe("anthropic");
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});
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it("should not have graphStore.llm when user does not provide one", () => {
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const config = ConfigManager.mergeConfig({
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embedder: baseEmbedder,
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vectorStore: baseVectorStore,
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llm: { provider: "openai", config: { model: "gpt-4o" } },
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});
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// Default graphStore should not have llm
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expect(config.graphStore?.llm).toBeUndefined();
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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: {
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model: "nomic-embed-text-v1.5",
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baseURL: "http://localhost:1234/v1",
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},
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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: {
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model: "meta-llama-3.1-70b",
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baseURL: "http://localhost:1234/v1",
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},
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},
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disableHistory: true,
|
||
});
|
||
|
||
await mem.getAll({ userId: "u1" });
|
||
|
||
expect(mockEmbedderFactory.create).toHaveBeenCalledWith(
|
||
"lmstudio",
|
||
expect.objectContaining({
|
||
model: "nomic-embed-text-v1.5",
|
||
baseURL: "http://localhost:1234/v1",
|
||
}),
|
||
);
|
||
expect(mockLlmFactory.create).toHaveBeenCalledWith(
|
||
"lmstudio",
|
||
expect.objectContaining({
|
||
model: "meta-llama-3.1-70b",
|
||
baseURL: "http://localhost:1234/v1",
|
||
}),
|
||
);
|
||
});
|
||
|
||
it("auto-detects embedding dimension via probe with lmstudio", async () => {
|
||
const mem = new MemoryClass({
|
||
embedder: {
|
||
provider: "lmstudio",
|
||
config: {
|
||
model: "nomic-embed-text-v1.5",
|
||
baseURL: "http://localhost:1234/v1",
|
||
},
|
||
},
|
||
vectorStore: { provider: "qdrant", config: { collectionName: "test" } },
|
||
llm: {
|
||
provider: "lmstudio",
|
||
config: { baseURL: "http://localhost:1234/v1" },
|
||
},
|
||
disableHistory: true,
|
||
});
|
||
|
||
await mem.getAll({ userId: "u1" });
|
||
|
||
expect(mockEmbedder.embed).toHaveBeenCalledWith("dimension probe");
|
||
const vsCall = mockVectorStoreFactory.create.mock.calls[0];
|
||
expect(vsCall[1].dimension).toBe(768);
|
||
});
|
||
|
||
it("handles snake_case OpenClaw config through full Memory stack", async () => {
|
||
const mem = new MemoryClass({
|
||
embedder: {
|
||
provider: "lmstudio",
|
||
config: {
|
||
model: "text-embedding-gte-qwen2-1.5b-instruct",
|
||
embedding_dims: 1536,
|
||
lmstudio_base_url: "http://192.168.200.83:1234/v1",
|
||
} as any,
|
||
},
|
||
vectorStore: { provider: "memory", config: { collectionName: "test" } },
|
||
llm: {
|
||
provider: "lmstudio",
|
||
config: {
|
||
model: "openai/gpt-oss-20b",
|
||
lmstudio_base_url: "http://192.168.200.83:1234/v1",
|
||
} as any,
|
||
},
|
||
disableHistory: true,
|
||
});
|
||
|
||
await mem.getAll({ userId: "u1" });
|
||
|
||
expect(mockEmbedderFactory.create).toHaveBeenCalledWith(
|
||
"lmstudio",
|
||
expect.objectContaining({
|
||
model: "text-embedding-gte-qwen2-1.5b-instruct",
|
||
baseURL: "http://192.168.200.83:1234/v1",
|
||
}),
|
||
);
|
||
expect(mockLlmFactory.create).toHaveBeenCalledWith(
|
||
"lmstudio",
|
||
expect.objectContaining({
|
||
model: "openai/gpt-oss-20b",
|
||
baseURL: "http://192.168.200.83:1234/v1",
|
||
}),
|
||
);
|
||
});
|
||
|
||
it("search flow works with lmstudio embedder", async () => {
|
||
mockVStore.search.mockResolvedValueOnce([
|
||
{
|
||
id: "mem-1",
|
||
payload: {
|
||
data: "User likes hiking",
|
||
user_id: "u1",
|
||
hash: "abc123",
|
||
created_at: "2026-01-01",
|
||
},
|
||
score: 0.95,
|
||
},
|
||
]);
|
||
|
||
const mem = new MemoryClass({
|
||
embedder: {
|
||
provider: "lmstudio",
|
||
config: {
|
||
model: "nomic-embed-text-v1.5",
|
||
baseURL: "http://localhost:1234/v1",
|
||
embeddingDims: 768,
|
||
},
|
||
},
|
||
vectorStore: {
|
||
provider: "memory",
|
||
config: { collectionName: "test", dimension: 768 },
|
||
},
|
||
llm: {
|
||
provider: "lmstudio",
|
||
config: { baseURL: "http://localhost:1234/v1" },
|
||
},
|
||
disableHistory: true,
|
||
});
|
||
|
||
const result = await mem.search("What does the user like?", {
|
||
userId: "u1",
|
||
});
|
||
|
||
expect(mockEmbedder.embed).toHaveBeenCalledWith("What does the user like?");
|
||
expect(mockVStore.search).toHaveBeenCalled();
|
||
expect(result.results).toHaveLength(1);
|
||
expect(result.results[0].memory).toBe("User likes hiking");
|
||
});
|
||
|
||
it("add flow works with lmstudio LLM for fact extraction", async () => {
|
||
mockLlm.generateResponse.mockResolvedValueOnce(
|
||
'{"facts":["User loves sushi"]}',
|
||
);
|
||
mockVStore.search.mockResolvedValue([]);
|
||
mockVStore.list.mockResolvedValue([[], 0]);
|
||
|
||
const mem = new MemoryClass({
|
||
embedder: {
|
||
provider: "lmstudio",
|
||
config: {
|
||
model: "nomic-embed-text-v1.5",
|
||
baseURL: "http://localhost:1234/v1",
|
||
embeddingDims: 768,
|
||
},
|
||
},
|
||
vectorStore: {
|
||
provider: "memory",
|
||
config: { collectionName: "test", dimension: 768 },
|
||
},
|
||
llm: {
|
||
provider: "lmstudio",
|
||
config: {
|
||
model: "meta-llama-3.1-70b",
|
||
baseURL: "http://localhost:1234/v1",
|
||
},
|
||
},
|
||
disableHistory: true,
|
||
});
|
||
|
||
await mem.add("I love sushi", { userId: "u1" });
|
||
|
||
expect(mockLlm.generateResponse).toHaveBeenCalled();
|
||
expect(mockEmbedder.embed).toHaveBeenCalled();
|
||
});
|
||
});
|