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重构主控编排与运行时预热链路，统一工作区提示词/专家调度协议并补齐 wiki 记忆注入与写回闭环。

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同时收敛启动与运维脚本默认行为（含 wiki worker）、更新 Admin 可观测性与相关测试，降低首轮时延并提高运行稳定性。

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oliver 2026-04-26 08:34:33 +08:00
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191
openclaw/extensions/google/embedding-provider.test.ts Normal file
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import { afterEach, describe, expect, it, vi } from "vitest";
import {
buildGeminiEmbeddingRequest,
buildGeminiTextEmbeddingRequest,
createGeminiEmbeddingProvider,
DEFAULT_GEMINI_EMBEDDING_MODEL,
GEMINI_EMBEDDING_2_MODELS,
isGeminiEmbedding2Model,
normalizeGeminiModel,
resolveGeminiOutputDimensionality,
} from "./embedding-provider.js";
afterEach(() => {
vi.restoreAllMocks();
vi.unstubAllGlobals();
});
function installFetchMock(
handler: (input: RequestInfo | URL, init?: RequestInit) => unknown,
): ReturnType<typeof vi.fn> {
const fetchMock = vi.fn(async (input: RequestInfo | URL, init?: RequestInit) => {
return new Response(JSON.stringify(handler(input, init)), {
status: 200,
headers: { "Content-Type": "application/json" },
});
});
vi.stubGlobal("fetch", fetchMock);
return fetchMock;
}
function fetchJsonBody(fetchMock: ReturnType<typeof vi.fn>, index: number): unknown {
const init = fetchMock.mock.calls[index]?.[1] as RequestInit | undefined;
const body = init?.body;
if (typeof body !== "string") {
throw new Error("Expected JSON string request body.");
}
return JSON.parse(body) as unknown;
}
describe("Gemini embedding request helpers", () => {
it("builds requests and resolves model settings", () => {
expect(
buildGeminiTextEmbeddingRequest({
text: "hello",
taskType: "RETRIEVAL_DOCUMENT",
modelPath: "models/gemini-embedding-2-preview",
outputDimensionality: 1536,
}),
).toEqual({
model: "models/gemini-embedding-2-preview",
content: { parts: [{ text: "hello" }] },
taskType: "RETRIEVAL_DOCUMENT",
outputDimensionality: 1536,
});
expect(
buildGeminiEmbeddingRequest({
input: {
text: "Image file: diagram.png",
parts: [
{ type: "text", text: "Image file: diagram.png" },
{ type: "inline-data", mimeType: "image/png", data: "abc123" },
],
},
taskType: "RETRIEVAL_DOCUMENT",
modelPath: "models/gemini-embedding-2-preview",
outputDimensionality: 1536,
}),
).toEqual({
model: "models/gemini-embedding-2-preview",
content: {
parts: [
{ text: "Image file: diagram.png" },
{ inlineData: { mimeType: "image/png", data: "abc123" } },
],
},
taskType: "RETRIEVAL_DOCUMENT",
outputDimensionality: 1536,
});
expect(GEMINI_EMBEDDING_2_MODELS.has("gemini-embedding-2-preview")).toBe(true);
expect(isGeminiEmbedding2Model("gemini-embedding-2-preview")).toBe(true);
expect(isGeminiEmbedding2Model("gemini-embedding-001")).toBe(false);
expect(isGeminiEmbedding2Model("text-embedding-004")).toBe(false);
expect(resolveGeminiOutputDimensionality("gemini-embedding-001")).toBeUndefined();
expect(resolveGeminiOutputDimensionality("text-embedding-004")).toBeUndefined();
expect(resolveGeminiOutputDimensionality("gemini-embedding-2-preview")).toBe(3072);
expect(resolveGeminiOutputDimensionality("gemini-embedding-2-preview", 768)).toBe(768);
expect(resolveGeminiOutputDimensionality("gemini-embedding-2-preview", 1536)).toBe(1536);
expect(resolveGeminiOutputDimensionality("gemini-embedding-2-preview", 3072)).toBe(3072);
expect(() => resolveGeminiOutputDimensionality("gemini-embedding-2-preview", 512)).toThrow(
/Invalid outputDimensionality 512/,
);
expect(() => resolveGeminiOutputDimensionality("gemini-embedding-2-preview", 1024)).toThrow(
/Valid values: 768, 1536, 3072/,
);
expect(normalizeGeminiModel("models/gemini-embedding-2-preview")).toBe(
"gemini-embedding-2-preview",
);
expect(normalizeGeminiModel("gemini/gemini-embedding-2-preview")).toBe(
"gemini-embedding-2-preview",
);
expect(normalizeGeminiModel("google/gemini-embedding-2-preview")).toBe(
"gemini-embedding-2-preview",
);
expect(normalizeGeminiModel("")).toBe(DEFAULT_GEMINI_EMBEDDING_MODEL);
});
});
describe("Gemini embedding provider", () => {
it("handles legacy and v2 request/response behavior", async () => {
const fetchMock = installFetchMock((input) => {
const url = input instanceof URL ? input.href : typeof input === "string" ? input : input.url;
return url.endsWith(":batchEmbedContents")
? {
embeddings: Array.from({ length: 2 }, () => ({
values: [0, Number.POSITIVE_INFINITY, 5],
})),
}
: { embedding: { values: [3, 4, Number.NaN] } };
});
const { provider } = await createGeminiEmbeddingProvider({
config: {} as never,
provider: "gemini",
remote: { apiKey: "test-key" },
model: "gemini-embedding-2-preview",
outputDimensionality: 768,
taskType: "SEMANTIC_SIMILARITY",
fallback: "none",
});
await expect(provider.embedQuery(" ")).resolves.toEqual([]);
await expect(provider.embedBatch([])).resolves.toEqual([]);
await expect(provider.embedQuery("test query")).resolves.toEqual([0.6, 0.8, 0]);
const structuredBatch = await provider.embedBatchInputs?.([
{
text: "Image file: diagram.png",
parts: [
{ type: "text", text: "Image file: diagram.png" },
{ type: "inline-data", mimeType: "image/png", data: "img" },
],
},
{
text: "Audio file: note.wav",
parts: [
{ type: "text", text: "Audio file: note.wav" },
{ type: "inline-data", mimeType: "audio/wav", data: "aud" },
],
},
]);
expect(structuredBatch).toEqual([
[0, 0, 1],
[0, 0, 1],
]);
expect(fetchMock.mock.calls[0]?.[0]).toBe(
"https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2-preview:embedContent",
);
expect(fetchJsonBody(fetchMock, 0)).toMatchObject({
outputDimensionality: 768,
taskType: "SEMANTIC_SIMILARITY",
content: { parts: [{ text: "test query" }] },
});
expect(fetchJsonBody(fetchMock, 1)).toMatchObject({
requests: [
{
model: "models/gemini-embedding-2-preview",
content: {
parts: [
{ text: "Image file: diagram.png" },
{ inlineData: { mimeType: "image/png", data: "img" } },
],
},
taskType: "SEMANTIC_SIMILARITY",
outputDimensionality: 768,
},
{
model: "models/gemini-embedding-2-preview",
content: {
parts: [
{ text: "Audio file: note.wav" },
{ inlineData: { mimeType: "audio/wav", data: "aud" } },
],
},
taskType: "SEMANTIC_SIMILARITY",
outputDimensionality: 768,
},
],
});
});
});