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chore: end to end test coverage for ts sdk (#4357)

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This commit is contained in:
Kartik
2026-03-17 21:13:52 +05:30
committed by GitHub
parent 336fbce60a
commit 3cdcb6564c
36 changed files with 4583 additions and 778 deletions
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mem0-ts/src/oss/tests/config-manager.test.ts
+92 -25
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@@ -190,7 +190,10 @@ describe("ConfigManager", () => {
const cfg = ConfigManager.mergeConfig({
embedder: {
provider: "lmstudio",
config: { model: "nomic-embed-text-v1.5", embedding_dims: 768 } as any,
config: {
model: "nomic-embed-text-v1.5",
embedding_dims: 768,
} as any,
},
vectorStore: { provider: "memory", config: {} },
llm: baseLlm,
@@ -313,7 +316,11 @@ describe("ConfigManager", () => {
},
vectorStore: {
provider: "qdrant",
config: { host: "192.168.200.12", port: 6333, checkCompatibility: false },
config: {
host: "192.168.200.12",
port: 6333,
checkCompatibility: false,
},
},
llm: {
provider: "lmstudio",
@@ -326,7 +333,9 @@ describe("ConfigManager", () => {
expect(cfg.embedder.provider).toBe("lmstudio");
expect(cfg.embedder.config.baseURL).toBe("http://192.168.200.83:1234/v1");
expect(cfg.embedder.config.model).toBe("text-embedding-gte-qwen2-1.5b-instruct");
expect(cfg.embedder.config.model).toBe(
"text-embedding-gte-qwen2-1.5b-instruct",
);
expect(cfg.embedder.config.embeddingDims).toBe(1536);
expect(cfg.llm.provider).toBe("lmstudio");
@@ -409,12 +418,18 @@ describe("Memory – LM Studio end-to-end flow", () => {
const mem = new MemoryClass({
embedder: {
provider: "lmstudio",
config: { model: "nomic-embed-text-v1.5", baseURL: "http://localhost:1234/v1" },
config: {
model: "nomic-embed-text-v1.5",
baseURL: "http://localhost:1234/v1",
},
},
vectorStore: { provider: "memory", config: { collectionName: "test" } },
llm: {
provider: "lmstudio",
config: { model: "meta-llama-3.1-70b", baseURL: "http://localhost:1234/v1" },
config: {
model: "meta-llama-3.1-70b",
baseURL: "http://localhost:1234/v1",
},
},
disableHistory: true,
});
@@ -423,11 +438,17 @@ describe("Memory – LM Studio end-to-end flow", () => {
expect(mockEmbedderFactory.create).toHaveBeenCalledWith(
"lmstudio",
expect.objectContaining({ model: "nomic-embed-text-v1.5", baseURL: "http://localhost:1234/v1" }),
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" }),
expect.objectContaining({
model: "meta-llama-3.1-70b",
baseURL: "http://localhost:1234/v1",
}),
);
});
@@ -435,10 +456,16 @@ describe("Memory – LM Studio end-to-end flow", () => {
const mem = new MemoryClass({
embedder: {
provider: "lmstudio",
config: { model: "nomic-embed-text-v1.5", baseURL: "http://localhost:1234/v1" },
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" } },
llm: {
provider: "lmstudio",
config: { baseURL: "http://localhost:1234/v1" },
},
disableHistory: true,
});
@@ -462,7 +489,10 @@ describe("Memory – LM Studio end-to-end flow", () => {
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,
config: {
model: "openai/gpt-oss-20b",
lmstudio_base_url: "http://192.168.200.83:1234/v1",
} as any,
},
disableHistory: true,
});
@@ -471,32 +501,57 @@ describe("Memory – LM Studio end-to-end flow", () => {
expect(mockEmbedderFactory.create).toHaveBeenCalledWith(
"lmstudio",
expect.objectContaining({ model: "text-embedding-gte-qwen2-1.5b-instruct", baseURL: "http://192.168.200.83:1234/v1" }),
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" }),
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,
}]);
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 },
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" },
},
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" });
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();
@@ -505,19 +560,31 @@ describe("Memory – LM Studio end-to-end flow", () => {
});
it("add flow works with lmstudio LLM for fact extraction", async () => {
mockLlm.generateResponse.mockResolvedValueOnce('{"facts":["User loves sushi"]}');
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 },
config: {
model: "nomic-embed-text-v1.5",
baseURL: "http://localhost:1234/v1",
embeddingDims: 768,
},
},
vectorStore: {
provider: "memory",
config: { collectionName: "test", dimension: 768 },
},
vectorStore: { provider: "memory", config: { collectionName: "test", dimension: 768 } },
llm: {
provider: "lmstudio",
config: { model: "meta-llama-3.1-70b", baseURL: "http://localhost:1234/v1" },
config: {
model: "meta-llama-3.1-70b",
baseURL: "http://localhost:1234/v1",
},
},
disableHistory: true,
});