Implement vector recovery and refresh docs

tests/vector-config.mjs

@@ -0,0 +1,71 @@
+import assert from "node:assert/strict";
+import fs from "node:fs/promises";
+import path from "node:path";
+import { fileURLToPath } from "node:url";
+import vm from "node:vm";
+
+async function loadVectorHelpers() {
+  const __dirname = path.dirname(fileURLToPath(import.meta.url));
+  const sourcePath = path.resolve(__dirname, "../vector-index.js");
+  const source = await fs.readFile(sourcePath, "utf8");
+
+  const pieces = [
+    source.match(/export const BACKEND_VECTOR_SOURCES = \[[\s\S]*?\];/m)?.[0],
+    source.match(/export const BACKEND_DEFAULT_MODELS = \{[\s\S]*?\};/m)?.[0],
+    source.match(/const BACKEND_SOURCES_REQUIRING_API_URL = new Set\([\s\S]*?\);/m)?.[0],
+    source.match(/export function normalizeOpenAICompatibleBaseUrl\(value, autoSuffix = true\) \{[\s\S]*?^\}/m)?.[0],
+    source.match(/export function getVectorConfigFromSettings\(settings = \{\}\) \{[\s\S]*?^\}/m)?.[0],
+    source.match(/export function isBackendVectorConfig\(config\) \{[\s\S]*?^\}/m)?.[0],
+    source.match(/export function isDirectVectorConfig\(config\) \{[\s\S]*?^\}/m)?.[0],
+    source.match(/export function validateVectorConfig\(config\) \{[\s\S]*?^\}/m)?.[0],
+  ].filter(Boolean);
+
+  if (pieces.length < 8) {
+    throw new Error("无法从 vector-index.js 提取向量配置辅助函数");
+  }
+
+  const context = vm.createContext({});
+  const script = new vm.Script(`
+${pieces.join("\n\n").replaceAll("export ", "")}
+this.getVectorConfigFromSettings = getVectorConfigFromSettings;
+this.validateVectorConfig = validateVectorConfig;
+  `);
+  script.runInContext(context);
+  return {
+    getVectorConfigFromSettings: context.getVectorConfigFromSettings,
+    validateVectorConfig: context.validateVectorConfig,
+  };
+}
+
+const { getVectorConfigFromSettings, validateVectorConfig } =
+  await loadVectorHelpers();
+
+const backendConfig = getVectorConfigFromSettings({
+  embeddingTransportMode: "backend",
+  embeddingBackendSource: "openai",
+  embeddingBackendModel: "",
+});
+assert.equal(backendConfig.mode, "backend");
+assert.equal(backendConfig.source, "openai");
+assert.equal(backendConfig.model, "text-embedding-3-small");
+assert.equal(validateVectorConfig(backendConfig).valid, true);
+
+const directConfig = getVectorConfigFromSettings({
+  embeddingTransportMode: "direct",
+  embeddingApiUrl: "https://example.com/v1/embeddings",
+  embeddingApiKey: "sk-test",
+  embeddingModel: "text-embedding-3-small",
+});
+assert.equal(directConfig.mode, "direct");
+assert.equal(directConfig.apiUrl, "https://example.com/v1");
+assert.equal(validateVectorConfig(directConfig).valid, true);
+
+const invalidBackendConfig = getVectorConfigFromSettings({
+  embeddingTransportMode: "backend",
+  embeddingBackendSource: "vllm",
+  embeddingBackendApiUrl: "",
+  embeddingBackendModel: "BAAI/bge-m3",
+});
+assert.equal(validateVectorConfig(invalidBackendConfig).valid, false);
+
+console.log("vector-config tests passed");