Implement vector recovery and refresh docs

This commit is contained in:
Youzini-afk
2026-03-24 12:38:46 +08:00
parent 7b68eebb9e
commit 50ee8cc8ed
13 changed files with 2668 additions and 805 deletions

66
tests/runtime-history.mjs Normal file
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import assert from "node:assert/strict";
import {
appendBatchJournal,
cloneGraphSnapshot,
createBatchJournalEntry,
detectHistoryMutation,
findJournalRecoveryPoint,
snapshotProcessedMessageHashes,
} from "../runtime-state.js";
import { createEmptyGraph } from "../graph.js";
const chat = [
{ is_user: true, mes: "你好" },
{ is_user: false, mes: "我记住了。" },
{ is_user: true, mes: "继续" },
{ is_user: false, mes: "新的回复" },
];
const hashes = snapshotProcessedMessageHashes(chat, 3);
const cleanDetection = detectHistoryMutation(chat, {
lastProcessedAssistantFloor: 3,
processedMessageHashes: hashes,
});
assert.equal(cleanDetection.dirty, false);
const editedChat = structuredClone(chat);
editedChat[1].mes = "我改过内容了。";
const editedDetection = detectHistoryMutation(editedChat, {
lastProcessedAssistantFloor: 3,
processedMessageHashes: hashes,
});
assert.equal(editedDetection.dirty, true);
assert.equal(editedDetection.earliestAffectedFloor, 1);
const truncatedChat = chat.slice(0, 2);
const truncatedDetection = detectHistoryMutation(truncatedChat, {
lastProcessedAssistantFloor: 3,
processedMessageHashes: hashes,
});
assert.equal(truncatedDetection.dirty, true);
assert.equal(truncatedDetection.earliestAffectedFloor, 2);
const graph = createEmptyGraph();
graph.historyState.chatId = "chat-history-test";
const beforeSnapshot = cloneGraphSnapshot(graph);
graph.lastProcessedSeq = 3;
graph.historyState.lastProcessedAssistantFloor = 3;
const afterSnapshot = cloneGraphSnapshot(graph);
appendBatchJournal(
graph,
createBatchJournalEntry(beforeSnapshot, afterSnapshot, {
processedRange: [1, 3],
postProcessArtifacts: ["compression"],
vectorHashesInserted: [1234],
}),
);
const recoveryPoint = findJournalRecoveryPoint(graph, 2);
assert.ok(recoveryPoint);
assert.equal(recoveryPoint.journal.processedRange[1], 3);
assert.equal(
recoveryPoint.snapshotBefore.historyState.lastProcessedAssistantFloor,
-1,
);
console.log("runtime-history tests passed");

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tests/vector-config.mjs Normal file
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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");