mirror of
https://github.com/Youzini-afk/ST-Bionic-Memory-Ecology.git
synced 2026-06-14 02:40:45 +08:00
396 lines
11 KiB
JavaScript
396 lines
11 KiB
JavaScript
import assert from "node:assert/strict";
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import {
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installResolveHooks,
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toDataModuleUrl,
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} from "./helpers/register-hooks-compat.mjs";
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const extensionsShimSource = [
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"export const extension_settings = {};",
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"export function getContext() {",
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" return globalThis.__stBmeTestContext || {",
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" chat: [],",
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" chatMetadata: {},",
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" extensionSettings: {},",
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" powerUserSettings: {},",
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" characters: {},",
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" characterId: null,",
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" name1: '玩家',",
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" name2: '艾琳',",
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" chatId: 'test-chat',",
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" };",
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"}",
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].join("\n");
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const scriptShimSource = [
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"export function getRequestHeaders() {",
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" return {};",
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"}",
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"export function substituteParamsExtended(value) {",
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" return String(value ?? '');",
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"}",
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].join("\n");
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const openAiShimSource = [
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"export const chat_completion_sources = {};",
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"export async function sendOpenAIRequest() {",
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" throw new Error('sendOpenAIRequest should not be called in extractor-split-pipeline test');",
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"}",
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].join("\n");
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installResolveHooks([
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{
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specifiers: [
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"../../../extensions.js",
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"../../../../extensions.js",
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"../../../../../extensions.js",
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],
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url: toDataModuleUrl(extensionsShimSource),
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},
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{
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specifiers: [
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"../../../../script.js",
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"../../../../../script.js",
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],
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url: toDataModuleUrl(scriptShimSource),
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},
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{
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specifiers: [
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"../../../../openai.js",
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"../../../../../openai.js",
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],
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url: toDataModuleUrl(openAiShimSource),
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},
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]);
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const { createEmptyGraph, createNode, addNode } = await import("../graph/graph.js");
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const { DEFAULT_NODE_SCHEMA } = await import("../graph/schema.js");
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const { extractMemories } = await import("../maintenance/extractor.js");
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const { defaultSettings } = await import("../runtime/settings-defaults.js");
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function setTestOverrides(overrides = {}) {
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globalThis.__stBmeTestOverrides = overrides;
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return () => {
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delete globalThis.__stBmeTestOverrides;
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};
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}
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globalThis.__stBmeTestContext = {
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chat: [],
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chatMetadata: {},
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extensionSettings: {},
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powerUserSettings: {},
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characters: {},
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characterId: null,
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name1: "玩家",
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name2: "艾琳",
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chatId: "test-chat",
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};
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function createGraphWithCharacter() {
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const graph = createEmptyGraph();
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addNode(
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graph,
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createNode({
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type: "character",
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fields: { name: "艾琳" },
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seq: 1,
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}),
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);
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return graph;
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}
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const baseExtractParams = {
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messages: [
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{ seq: 20, role: "user", content: "钟楼里传来第二次钟声。", name: "玩家", speaker: "玩家" },
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{ seq: 21, role: "assistant", content: "艾琳记下钟声，怀疑暗道就在附近。", name: "艾琳", speaker: "艾琳" },
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],
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startSeq: 20,
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endSeq: 21,
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schema: DEFAULT_NODE_SCHEMA,
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embeddingConfig: null,
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};
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function objectivePayload() {
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return {
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operations: [
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{
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action: "create",
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type: "event",
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ref: "evt-clock",
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fields: {
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title: "钟楼钟声",
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summary: "钟楼传来第二次钟声，暗示暗道线索仍在附近。",
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participants: "玩家,艾琳",
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status: "ongoing",
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},
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scope: { layer: "objective" },
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},
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],
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cognitionUpdates: [
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{
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ownerType: "character",
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ownerName: "艾琳",
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knownRefs: ["evt-clock"],
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},
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],
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regionUpdates: {},
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};
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}
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function subjectivePayload() {
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return {
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operations: [
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{
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action: "create",
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type: "pov_memory",
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fields: {
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summary: "艾琳把第二次钟声记成暗道仍在呼唤她的证据。",
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belief: "暗道就在钟楼附近",
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emotion: "警觉",
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certainty: "unsure",
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about: "evt-clock",
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},
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scope: {
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layer: "pov",
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ownerType: "character",
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ownerName: "艾琳",
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ownerId: "艾琳",
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},
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},
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],
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cognitionUpdates: [
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{
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ownerType: "character",
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ownerName: "艾琳",
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knownRefs: ["evt-clock"],
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},
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],
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regionUpdates: {},
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};
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}
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function activeNodes(graph, type) {
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return graph.nodes.filter((node) => node.type === type && node.archived !== true);
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}
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function hasActiveEdgeBetween(graph, leftId, rightId) {
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return graph.edges.some((edge) => {
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if (edge.invalidAt || edge.expiredAt) return false;
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return (
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(edge.fromId === leftId && edge.toId === rightId) ||
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(edge.fromId === rightId && edge.toId === leftId)
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);
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});
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}
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function characterKnowledgeEntries(graph) {
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return Object.values(graph.knowledgeState?.owners || {}).filter(
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(entry) =>
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String(entry?.ownerType || "") === "character" &&
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String(entry?.ownerName || "") === "艾琳",
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);
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}
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async function captureTaskTypesForExtract(settings, options = {}) {
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const graph = createGraphWithCharacter();
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const capturedTaskTypes = [];
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const capturedPayloads = [];
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const restore = setTestOverrides({
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llm: {
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async callLLMForJSON(payload = {}) {
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capturedTaskTypes.push(payload.taskType);
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capturedPayloads.push(payload);
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if (payload.taskType === "extract_objective") return objectivePayload();
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if (payload.taskType === "extract_subjective") return subjectivePayload();
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return { operations: [], cognitionUpdates: [], regionUpdates: {} };
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},
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},
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});
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try {
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const params = {
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graph,
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...baseExtractParams,
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};
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if (options.includeSettings !== false) {
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params.settings = settings;
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}
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const result = await extractMemories(params);
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return { graph, result, capturedTaskTypes, capturedPayloads };
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} finally {
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restore();
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}
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}
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// Phase 4 default switch: omitting settings should use the split pipeline by default.
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{
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const { result, capturedTaskTypes } = await captureTaskTypesForExtract(undefined, {
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includeSettings: false,
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});
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assert.equal(result.success, true);
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assert.deepEqual(
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capturedTaskTypes,
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["extract_objective", "extract_subjective"],
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"extractMemories without explicit settings should default to split objective+subjective extraction",
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);
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}
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// The default settings object should always use objective+subjective split extraction.
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{
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const { result, capturedTaskTypes, capturedPayloads } = await captureTaskTypesForExtract({
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...defaultSettings,
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});
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assert.equal(result.success, true);
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assert.deepEqual(
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capturedTaskTypes,
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["extract_objective", "extract_subjective"],
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"defaultSettings should call split objective+subjective extraction",
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);
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const subjectivePayloadText = JSON.stringify(
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capturedPayloads.find((payload) => payload.taskType === "extract_subjective") || {},
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);
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const subjectivePayload = capturedPayloads.find(
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(payload) => payload.taskType === "extract_subjective",
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);
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const objectiveRefMapBlock = subjectivePayload?.promptMessages?.find(
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(message) => message.sourceKey === "objectiveRefMap",
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);
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assert.match(
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subjectivePayloadText,
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/evt-clock/,
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"subjective extraction prompt should receive objective draft/ref context",
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);
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assert.match(
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String(objectiveRefMapBlock?.content || ""),
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/evt-clock/,
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"subjective extraction prompt should render objectiveRefMap with objective refs",
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);
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}
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// Removed legacy knobs are ignored and must not revive the old single extract task.
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for (const legacyPatch of [
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{ extractPrompt: "CUSTOM LEGACY EXTRACT PROMPT" },
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{ extractPipelineVersion: "legacy-single" },
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{
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taskProfiles: {
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...defaultSettings.taskProfiles,
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extract: {
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activeProfileId: "legacy-custom",
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profiles: [
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{
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id: "legacy-custom",
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taskType: "extract",
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builtin: false,
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blocks: [],
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},
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],
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},
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},
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},
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]) {
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const { result, capturedTaskTypes } = await captureTaskTypesForExtract({
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...defaultSettings,
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...legacyPatch,
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});
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assert.equal(result.success, true);
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assert.deepEqual(capturedTaskTypes, ["extract_objective", "extract_subjective"]);
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assert.equal(capturedTaskTypes.includes("extract"), false);
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}
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// split-v1 calls objective then subjective, merges both stage outputs, and commits once.
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{
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const graph = createGraphWithCharacter();
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const capturedTaskTypes = [];
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const restore = setTestOverrides({
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llm: {
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async callLLMForJSON(payload = {}) {
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capturedTaskTypes.push(payload.taskType);
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if (payload.taskType === "extract_objective") return objectivePayload();
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if (payload.taskType === "extract_subjective") return subjectivePayload();
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return { operations: [], cognitionUpdates: [], regionUpdates: {} };
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},
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},
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});
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try {
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const result = await extractMemories({
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graph,
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...baseExtractParams,
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settings: defaultSettings,
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});
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assert.deepEqual(
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capturedTaskTypes,
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["extract_objective", "extract_subjective"],
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"split-v1 should call the LLM once for objective extraction, then once for subjective extraction",
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);
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assert.equal(result.success, true);
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assert.equal(result.newNodes, 2, "objective event and subjective POV memory should be committed together");
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const [eventNode] = activeNodes(graph, "event");
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const [povNode] = activeNodes(graph, "pov_memory");
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assert.ok(eventNode, "objective event operation should be committed");
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assert.ok(povNode, "subjective pov_memory operation should be committed");
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assert.equal(povNode.scope?.ownerType, "character");
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assert.equal(povNode.scope?.ownerName, "艾琳");
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assert.equal(graph.lastProcessedSeq, 21);
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assert.ok(
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hasActiveEdgeBetween(graph, eventNode.id, povNode.id),
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"merged split stages should be committed as one batch so default batch edges see both nodes",
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);
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const knowledgeEntry = characterKnowledgeEntries(graph).find((entry) =>
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Array.isArray(entry.knownNodeIds) && entry.knownNodeIds.includes(eventNode.id),
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);
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assert.ok(
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knowledgeEntry,
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"subjective cognitionUpdates should apply through the merged ref map",
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);
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} finally {
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restore();
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}
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}
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// Invalid subjective output fails the split extraction before any objective-only commit mutates the graph.
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{
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const graph = createGraphWithCharacter();
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const initialNodeCount = graph.nodes.length;
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const initialEdgeCount = graph.edges.length;
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const capturedTaskTypes = [];
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const restore = setTestOverrides({
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llm: {
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async callLLMForJSON(payload = {}) {
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capturedTaskTypes.push(payload.taskType);
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if (payload.taskType === "extract_objective") return objectivePayload();
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if (payload.taskType === "extract_subjective") return { thought: "missing operations" };
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return { thought: "legacy path should not be used for split-v1" };
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},
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},
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});
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try {
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const result = await extractMemories({
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graph,
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...baseExtractParams,
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settings: defaultSettings,
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});
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assert.deepEqual(
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capturedTaskTypes,
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["extract_objective", "extract_subjective"],
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"split-v1 should validate both objective and subjective payloads before commit",
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);
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assert.equal(result.success, false);
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assert.equal(graph.nodes.length, initialNodeCount, "invalid subjective payload should not commit objective nodes");
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assert.equal(graph.edges.length, initialEdgeCount, "invalid subjective payload should not create edges");
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assert.equal(graph.lastProcessedSeq ?? -1, -1, "invalid split extraction should not advance extraction progress");
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} finally {
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restore();
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}
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}
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console.log("extractor-split-pipeline tests passed");
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