mirror of
https://github.com/Youzini-afk/ST-Bionic-Memory-Ecology.git
synced 2026-05-15 22:30:38 +08:00
1838 lines
53 KiB
JavaScript
1838 lines
53 KiB
JavaScript
// ST-BME: 三层混合检索编排
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// 融合向量预筛（PeroCore）+ 图扩散（PeroCore PEDSA）+ 可选 LLM 精确召回
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// v2: + 认知边界过滤(RoleRAG) + 双记忆交叉检索(AriGraph) + 概率触发
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import { debugLog } from "./debug-logging.js";
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import { diffuseAndRank } from "./diffusion.js";
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import { hybridScore, reinforceAccessBatch } from "./dynamics.js";
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import {
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buildTemporalAdjacencyMap,
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getActiveNodes,
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getNode,
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getNodeEdges,
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} from "./graph.js";
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import { callLLMForJSON } from "./llm.js";
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import {
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buildTaskExecutionDebugContext,
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buildTaskLlmPayload,
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buildTaskPrompt,
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} from "./prompt-builder.js";
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import {
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applyCooccurrenceBoost,
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applyDiversitySampling,
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collectSupplementalAnchorNodeIds,
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createCooccurrenceIndex,
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isEligibleAnchorNode,
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mergeVectorResults,
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runResidualRecall,
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splitIntentSegments,
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} from "./retrieval-enhancer.js";
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import {
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MEMORY_SCOPE_BUCKETS,
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classifyNodeScopeBucket,
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describeMemoryScope,
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describeScopeBucket,
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getScopeRegionKey,
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normalizeMemoryScope,
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resolveScopeBucketWeight,
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} from "./memory-scope.js";
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import { applyTaskRegex } from "./task-regex.js";
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import { getSTContextForPrompt } from "./st-context.js";
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import { findSimilarNodesByText, validateVectorConfig } from "./vector-index.js";
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function createAbortError(message = "操作已终止") {
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const error = new Error(message);
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error.name = "AbortError";
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return error;
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}
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function createTaskLlmDebugContext(promptBuild, regexInput) {
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return typeof buildTaskExecutionDebugContext === "function"
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? buildTaskExecutionDebugContext(promptBuild, { regexInput })
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: null;
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}
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function resolveTaskPromptPayload(promptBuild, fallbackUserPrompt = "") {
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if (typeof buildTaskLlmPayload === "function") {
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return buildTaskLlmPayload(promptBuild, fallbackUserPrompt);
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}
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return {
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systemPrompt: String(promptBuild?.systemPrompt || ""),
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userPrompt: String(fallbackUserPrompt || ""),
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promptMessages: [],
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additionalMessages: Array.isArray(promptBuild?.privateTaskMessages)
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? promptBuild.privateTaskMessages
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: [],
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};
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}
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function resolveTaskLlmSystemPrompt(promptPayload, fallbackSystemPrompt = "") {
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const hasPromptMessages =
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Array.isArray(promptPayload?.promptMessages) &&
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promptPayload.promptMessages.length > 0;
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if (hasPromptMessages) {
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return String(promptPayload?.systemPrompt || "");
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}
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return String(promptPayload?.systemPrompt || fallbackSystemPrompt || "");
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}
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function buildRecallFallbackReason(llmResult) {
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const failureType = String(llmResult?.errorType || "").trim();
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const failureReason = String(llmResult?.failureReason || "").trim();
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switch (failureType) {
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case "timeout":
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return "LLM 精排请求超时，已回退到评分排序";
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case "empty-response":
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return "LLM 精排返回空响应，已回退到评分排序";
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case "truncated-json":
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return "LLM 精排输出被截断，已回退到评分排序";
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case "invalid-json":
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return "LLM 精排未返回有效 JSON，已回退到评分排序";
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case "provider-error":
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return failureReason
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? `LLM 精排调用失败（${failureReason}），已回退到评分排序`
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: "LLM 精排调用失败，已回退到评分排序";
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default:
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return failureReason || "LLM 精排未返回可用结果，已回退到评分排序";
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}
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}
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function isAbortError(error) {
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return error?.name === "AbortError";
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}
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function throwIfAborted(signal) {
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if (signal?.aborted) {
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throw signal.reason instanceof Error
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? signal.reason
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: createAbortError();
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}
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}
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function nowMs() {
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return typeof performance !== "undefined" && performance?.now
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? performance.now()
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: Date.now();
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}
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function roundMs(value) {
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return Math.round((Number(value) || 0) * 10) / 10;
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}
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function pushSkipReason(meta, reason) {
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if (!reason) return;
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if (!Array.isArray(meta.skipReasons)) {
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meta.skipReasons = [];
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}
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if (!meta.skipReasons.includes(reason)) {
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meta.skipReasons.push(reason);
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}
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}
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function createRetrievalMeta(enableLLMRecall) {
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return {
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vectorHits: 0,
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diffusionHits: 0,
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scoredCandidates: 0,
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segmentsUsed: [],
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queryBlendActive: false,
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queryBlendParts: [],
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queryBlendWeights: {},
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vectorMergedHits: 0,
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seedCount: 0,
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temporalSyntheticEdgeCount: 0,
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teleportAlpha: 0,
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lexicalBoostedNodes: 0,
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lexicalTopHits: [],
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cooccurrenceBoostedNodes: 0,
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candidatePoolBeforeDpp: 0,
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candidatePoolAfterDpp: 0,
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diversityApplied: false,
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residualTriggered: false,
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residualHits: 0,
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scopeBuckets: {},
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activeRegion: "",
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activeCharacterPovOwner: "",
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activeUserPovOwner: "",
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bucketWeights: {},
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selectedByBucket: {},
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skipReasons: [],
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timings: {},
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llm: {
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enabled: enableLLMRecall,
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status: enableLLMRecall ? "pending" : "disabled",
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reason: enableLLMRecall ? "" : "LLM 精排已关闭",
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candidatePool: 0,
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selectedSeedCount: 0,
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},
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};
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}
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function clampPositiveInt(value, fallback, min = 1) {
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const parsed = Math.floor(Number(value));
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return Number.isFinite(parsed) && parsed >= min ? parsed : fallback;
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}
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function clampRange(value, fallback, min = 0, max = 1) {
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const parsed = Number(value);
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if (!Number.isFinite(parsed)) return fallback;
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return Math.max(min, Math.min(max, parsed));
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}
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function normalizeQueryText(value, maxLength = 400) {
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const normalized = String(value ?? "")
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.replace(/\r\n/g, "\n")
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.replace(/\s+/g, " ")
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.trim();
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if (!normalized) return "";
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return normalized.slice(0, Math.max(1, maxLength));
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}
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function createTextPreview(text, maxLength = 120) {
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const normalized = normalizeQueryText(text, maxLength + 4);
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if (!normalized) return "";
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return normalized.length > maxLength
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? `${normalized.slice(0, maxLength)}...`
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: normalized;
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}
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function roundBlendWeight(value) {
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return Math.round((Number(value) || 0) * 1000) / 1000;
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}
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function uniqueStrings(values = [], maxLength = 400) {
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const result = [];
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const seen = new Set();
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for (const value of values) {
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const text = normalizeQueryText(value, maxLength);
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const key = text.toLowerCase();
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if (!text || seen.has(key)) continue;
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seen.add(key);
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result.push(text);
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}
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return result;
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}
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function parseRecallContextLine(line = "") {
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const raw = String(line ?? "").trim();
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if (!raw) return null;
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const bracketMatch = raw.match(/^\[(user|assistant)\]\s*:\s*([\s\S]*)$/i);
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if (bracketMatch) {
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const role = String(bracketMatch[1] || "").toLowerCase();
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const text = normalizeQueryText(bracketMatch[2] || "");
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return text ? { role, text } : null;
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}
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const plainMatch = raw.match(
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/^(user|assistant|用户|助手|ai)\s*[：:]\s*([\s\S]*)$/i,
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);
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if (!plainMatch) return null;
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const roleToken = String(plainMatch[1] || "").toLowerCase();
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const role =
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roleToken === "assistant" || roleToken === "助手" || roleToken === "ai"
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? "assistant"
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: "user";
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const text = normalizeQueryText(plainMatch[2] || "");
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return text ? { role, text } : null;
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}
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function buildContextQueryBlend(
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userMessage,
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recentMessages = [],
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{
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enabled = true,
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assistantWeight = 0.2,
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previousUserWeight = 0.1,
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maxTextLength = 400,
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} = {},
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) {
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const currentText = normalizeQueryText(userMessage, maxTextLength);
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const normalizedAssistantWeight = clampRange(assistantWeight, 0.2, 0, 1);
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const normalizedPreviousUserWeight = clampRange(
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previousUserWeight,
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0.1,
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0,
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1,
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);
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const currentWeight = Math.max(
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0,
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1 - normalizedAssistantWeight - normalizedPreviousUserWeight,
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);
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let assistantText = "";
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let previousUserText = "";
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const parsedMessages = Array.isArray(recentMessages)
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? recentMessages.map((line) => parseRecallContextLine(line)).filter(Boolean)
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: [];
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for (let index = parsedMessages.length - 1; index >= 0; index--) {
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const item = parsedMessages[index];
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if (!assistantText && item.role === "assistant") {
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assistantText = normalizeQueryText(item.text, maxTextLength);
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}
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if (
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!previousUserText &&
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item.role === "user" &&
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normalizeQueryText(item.text, maxTextLength).toLowerCase() !==
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currentText.toLowerCase()
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) {
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previousUserText = normalizeQueryText(item.text, maxTextLength);
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}
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if (assistantText && previousUserText) break;
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}
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const rawParts = [
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{
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kind: "currentUser",
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label: "当前用户消息",
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text: currentText,
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weight: enabled ? currentWeight : 1,
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},
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];
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if (enabled && assistantText) {
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rawParts.push({
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kind: "assistantContext",
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label: "最近 assistant 回复",
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text: assistantText,
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weight: normalizedAssistantWeight,
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});
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}
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if (enabled && previousUserText) {
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rawParts.push({
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kind: "previousUser",
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label: "上一条 user 消息",
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text: previousUserText,
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weight: normalizedPreviousUserWeight,
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});
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}
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const dedupedParts = [];
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const seen = new Set();
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for (const part of rawParts) {
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const text = normalizeQueryText(part.text, maxTextLength);
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const key = text.toLowerCase();
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if (!text || seen.has(key)) continue;
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seen.add(key);
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dedupedParts.push({
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...part,
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text,
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});
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}
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if (dedupedParts.length === 0) {
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return {
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active: false,
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parts: [],
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currentText: "",
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assistantText: "",
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previousUserText: "",
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combinedText: "",
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};
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}
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const totalWeight = dedupedParts.reduce(
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(sum, part) => sum + Math.max(0, Number(part.weight) || 0),
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0,
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);
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const normalizedParts = dedupedParts.map((part) => ({
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...part,
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weight:
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totalWeight > 0
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? roundBlendWeight((Math.max(0, Number(part.weight) || 0) || 0) / totalWeight)
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: roundBlendWeight(1 / dedupedParts.length),
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}));
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const combinedText =
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normalizedParts.length <= 1
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? normalizedParts[0]?.text || ""
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: normalizedParts
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.map((part) => `${part.label}:\n${part.text}`)
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.join("\n\n");
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return {
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active: enabled && normalizedParts.length > 1,
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parts: normalizedParts,
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currentText: currentText || normalizedParts[0]?.text || "",
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assistantText,
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previousUserText,
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combinedText,
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};
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}
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function buildVectorQueryPlan(
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blendPlan,
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{ enableMultiIntent = true, maxSegments = 4 } = {},
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) {
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const plan = [];
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let currentSegments = [];
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for (const part of blendPlan?.parts || []) {
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let queries = [part.text];
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if (part.kind === "currentUser" && enableMultiIntent) {
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currentSegments = splitIntentSegments(part.text, { maxSegments });
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queries = uniqueStrings([
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part.text,
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...currentSegments.filter((item) => item !== part.text),
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]);
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} else {
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queries = uniqueStrings([part.text]);
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}
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plan.push({
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kind: part.kind,
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label: part.label,
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weight: part.weight,
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queries,
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});
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}
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return {
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plan,
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currentSegments,
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};
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}
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function buildLexicalQuerySources(
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userMessage,
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{ enableMultiIntent = true, maxSegments = 4 } = {},
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) {
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const currentText = normalizeQueryText(userMessage, 400);
|
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const segments = enableMultiIntent
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? splitIntentSegments(currentText, { maxSegments })
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: [];
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return {
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sources: uniqueStrings([currentText, ...segments]),
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segments,
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};
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}
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function normalizeLexicalText(value = "") {
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return normalizeQueryText(value, 600).toLowerCase();
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}
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function buildLexicalUnits(text = "") {
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const normalized = normalizeLexicalText(text);
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if (!normalized) return [];
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const rawTokens = normalized.match(/[a-z0-9]+|[\u4e00-\u9fff]+/g) || [];
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const units = [];
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for (const token of rawTokens) {
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if (token.length >= 2) {
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units.push(token);
|
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}
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if (/[\u4e00-\u9fff]/.test(token) && token.length > 2) {
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for (let index = 0; index < token.length - 1; index++) {
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units.push(token.slice(index, index + 2));
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}
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}
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}
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return [...new Set(units)];
|
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}
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function computeTokenOverlapScore(sourceUnits = [], targetUnits = []) {
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if (!sourceUnits.length || !targetUnits.length) return 0;
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||
const targetSet = new Set(targetUnits);
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||
let overlap = 0;
|
||
for (const unit of sourceUnits) {
|
||
if (targetSet.has(unit)) {
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||
overlap += 1;
|
||
}
|
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}
|
||
return overlap / Math.max(1, sourceUnits.length);
|
||
}
|
||
|
||
function scoreFieldMatch(
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fieldText,
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||
querySources = [],
|
||
{ exact = 1, includes = 0.9, overlap = 0.6 } = {},
|
||
) {
|
||
const normalizedField = normalizeLexicalText(fieldText);
|
||
if (!normalizedField) return 0;
|
||
|
||
const fieldUnits = buildLexicalUnits(normalizedField);
|
||
let best = 0;
|
||
|
||
for (const sourceText of querySources) {
|
||
const normalizedSource = normalizeLexicalText(sourceText);
|
||
if (!normalizedSource) continue;
|
||
|
||
if (normalizedSource === normalizedField) {
|
||
best = Math.max(best, exact);
|
||
continue;
|
||
}
|
||
|
||
if (
|
||
Math.min(normalizedSource.length, normalizedField.length) >= 2 &&
|
||
(normalizedSource.includes(normalizedField) ||
|
||
normalizedField.includes(normalizedSource))
|
||
) {
|
||
best = Math.max(best, includes);
|
||
}
|
||
|
||
const overlapScore = computeTokenOverlapScore(
|
||
buildLexicalUnits(normalizedSource),
|
||
fieldUnits,
|
||
);
|
||
best = Math.max(best, overlapScore * overlap);
|
||
}
|
||
|
||
return Math.min(1, best);
|
||
}
|
||
|
||
function collectNodeLexicalTexts(node, fieldNames = []) {
|
||
const values = [];
|
||
for (const fieldName of fieldNames) {
|
||
const value = node?.fields?.[fieldName];
|
||
if (typeof value === "string" && value.trim()) {
|
||
values.push(value.trim());
|
||
} else if (Array.isArray(value)) {
|
||
for (const item of value) {
|
||
if (typeof item === "string" && item.trim()) {
|
||
values.push(item.trim());
|
||
}
|
||
}
|
||
}
|
||
}
|
||
return values;
|
||
}
|
||
|
||
function computeLexicalScore(node, querySources = []) {
|
||
if (!node || !Array.isArray(querySources) || querySources.length === 0) {
|
||
return 0;
|
||
}
|
||
|
||
const primaryTexts = collectNodeLexicalTexts(node, ["name", "title"]);
|
||
const secondaryTexts = collectNodeLexicalTexts(node, [
|
||
"summary",
|
||
"insight",
|
||
"state",
|
||
"traits",
|
||
"participants",
|
||
"status",
|
||
]);
|
||
const combinedText = [...primaryTexts, ...secondaryTexts].join(" ");
|
||
|
||
const primaryScore = primaryTexts.reduce(
|
||
(best, value) =>
|
||
Math.max(
|
||
best,
|
||
scoreFieldMatch(value, querySources, {
|
||
exact: 1,
|
||
includes: 0.92,
|
||
overlap: 0.72,
|
||
}),
|
||
),
|
||
0,
|
||
);
|
||
const secondaryScore = secondaryTexts.reduce(
|
||
(best, value) =>
|
||
Math.max(
|
||
best,
|
||
scoreFieldMatch(value, querySources, {
|
||
exact: 0.82,
|
||
includes: 0.68,
|
||
overlap: 0.52,
|
||
}),
|
||
),
|
||
0,
|
||
);
|
||
const tokenScore = scoreFieldMatch(combinedText, querySources, {
|
||
exact: 0.65,
|
||
includes: 0.55,
|
||
overlap: 0.45,
|
||
});
|
||
|
||
if (primaryScore <= 0 && secondaryScore <= 0 && tokenScore <= 0) {
|
||
return 0;
|
||
}
|
||
|
||
return Math.min(
|
||
1,
|
||
Math.max(
|
||
primaryScore,
|
||
secondaryScore * 0.82,
|
||
tokenScore * 0.7,
|
||
primaryScore * 0.75 + secondaryScore * 0.35 + tokenScore * 0.2,
|
||
),
|
||
);
|
||
}
|
||
|
||
function buildLexicalTopHits(scoredNodes = [], maxCount = 5) {
|
||
return scoredNodes
|
||
.filter((item) => (Number(item?.lexicalScore) || 0) > 0)
|
||
.sort((a, b) => {
|
||
const lexicalDelta =
|
||
(Number(b?.lexicalScore) || 0) - (Number(a?.lexicalScore) || 0);
|
||
if (lexicalDelta !== 0) return lexicalDelta;
|
||
return (Number(b?.finalScore) || 0) - (Number(a?.finalScore) || 0);
|
||
})
|
||
.slice(0, Math.max(1, maxCount))
|
||
.map((item) => ({
|
||
nodeId: item.nodeId,
|
||
type: item.node?.type || "",
|
||
label:
|
||
item.node?.fields?.name ||
|
||
item.node?.fields?.title ||
|
||
item.node?.fields?.summary ||
|
||
item.nodeId,
|
||
lexicalScore: Math.round((Number(item.lexicalScore) || 0) * 1000) / 1000,
|
||
finalScore: Math.round((Number(item.finalScore) || 0) * 1000) / 1000,
|
||
}));
|
||
}
|
||
|
||
function scaleVectorResults(results = [], weight = 1) {
|
||
return (Array.isArray(results) ? results : []).map((item) => ({
|
||
...item,
|
||
score: (Number(item?.score) || 0) * Math.max(0, Number(weight) || 0),
|
||
}));
|
||
}
|
||
|
||
function pickActiveRegion(graph, optionValue = "") {
|
||
const direct = String(optionValue || "").trim();
|
||
if (direct) return direct;
|
||
|
||
const historyRegion = String(
|
||
graph?.historyState?.activeRegion || graph?.historyState?.lastExtractedRegion || "",
|
||
).trim();
|
||
if (historyRegion) return historyRegion;
|
||
|
||
const fallback = getActiveNodes(graph)
|
||
.filter((node) => !node.archived)
|
||
.sort((a, b) => (b.seqRange?.[1] ?? b.seq ?? 0) - (a.seqRange?.[1] ?? a.seq ?? 0))
|
||
.find((node) => getScopeRegionKey(node?.scope));
|
||
|
||
return String(getScopeRegionKey(fallback?.scope) || "").trim();
|
||
}
|
||
|
||
function buildScopeBucketWeightMap(options = {}) {
|
||
return {
|
||
[MEMORY_SCOPE_BUCKETS.CHARACTER_POV]: Number(
|
||
options.recallCharacterPovWeight ?? 1.25,
|
||
),
|
||
[MEMORY_SCOPE_BUCKETS.USER_POV]: Number(options.recallUserPovWeight ?? 1.05),
|
||
[MEMORY_SCOPE_BUCKETS.OBJECTIVE_CURRENT_REGION]: Number(
|
||
options.recallObjectiveCurrentRegionWeight ?? 1.15,
|
||
),
|
||
[MEMORY_SCOPE_BUCKETS.OBJECTIVE_ADJACENT_REGION]: Number(
|
||
options.recallObjectiveAdjacentRegionWeight ?? 0.9,
|
||
),
|
||
[MEMORY_SCOPE_BUCKETS.OBJECTIVE_GLOBAL]: Number(
|
||
options.recallObjectiveGlobalWeight ?? 0.75,
|
||
),
|
||
[MEMORY_SCOPE_BUCKETS.OTHER_POV]: 0.6,
|
||
};
|
||
}
|
||
|
||
function createEmptyScopeBucketMap() {
|
||
return {
|
||
[MEMORY_SCOPE_BUCKETS.CHARACTER_POV]: [],
|
||
[MEMORY_SCOPE_BUCKETS.USER_POV]: [],
|
||
[MEMORY_SCOPE_BUCKETS.OBJECTIVE_CURRENT_REGION]: [],
|
||
[MEMORY_SCOPE_BUCKETS.OBJECTIVE_ADJACENT_REGION]: [],
|
||
[MEMORY_SCOPE_BUCKETS.OBJECTIVE_GLOBAL]: [],
|
||
};
|
||
}
|
||
|
||
function pushScopeBucketDebug(map, bucket, value) {
|
||
if (!Object.prototype.hasOwnProperty.call(map, bucket)) {
|
||
map[bucket] = [];
|
||
}
|
||
map[bucket].push(value);
|
||
}
|
||
|
||
function getScopeBucketPriority(bucket) {
|
||
switch (bucket) {
|
||
case MEMORY_SCOPE_BUCKETS.CHARACTER_POV:
|
||
return 5;
|
||
case MEMORY_SCOPE_BUCKETS.USER_POV:
|
||
return 4;
|
||
case MEMORY_SCOPE_BUCKETS.OBJECTIVE_CURRENT_REGION:
|
||
return 3;
|
||
case MEMORY_SCOPE_BUCKETS.OBJECTIVE_ADJACENT_REGION:
|
||
return 2;
|
||
case MEMORY_SCOPE_BUCKETS.OBJECTIVE_GLOBAL:
|
||
return 1;
|
||
default:
|
||
return 0;
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 三层混合检索管线
|
||
*
|
||
* @param {object} params
|
||
* @param {object} params.graph - 当前图状态
|
||
* @param {string} params.userMessage - 用户输入
|
||
* @param {string[]} params.recentMessages - 最近几轮对话内容
|
||
* @param {object} params.embeddingConfig - Embedding API 配置
|
||
* @param {object[]} params.schema - 节点类型 Schema
|
||
* @param {object} [params.options] - 检索选项
|
||
* @returns {Promise<RetrievalResult>}
|
||
*/
|
||
export async function retrieve({
|
||
graph,
|
||
userMessage,
|
||
recentMessages = [],
|
||
embeddingConfig,
|
||
schema,
|
||
signal = undefined,
|
||
options = {},
|
||
settings = {},
|
||
onStreamProgress = null,
|
||
}) {
|
||
throwIfAborted(signal);
|
||
const startedAt = nowMs();
|
||
const topK = clampPositiveInt(options.topK, 20);
|
||
const maxRecallNodes = clampPositiveInt(options.maxRecallNodes, 8);
|
||
const enableLLMRecall = options.enableLLMRecall ?? true;
|
||
const enableVectorPrefilter = options.enableVectorPrefilter ?? true;
|
||
const enableGraphDiffusion = options.enableGraphDiffusion ?? true;
|
||
const diffusionTopK = clampPositiveInt(options.diffusionTopK, 100);
|
||
const llmCandidatePool = clampPositiveInt(options.llmCandidatePool, 30);
|
||
const weights = options.weights ?? {};
|
||
const enableVisibility = options.enableVisibility ?? false;
|
||
const visibilityFilter = options.visibilityFilter ?? null;
|
||
const enableCrossRecall = options.enableCrossRecall ?? false;
|
||
const enableProbRecall = options.enableProbRecall ?? false;
|
||
const probRecallChance = options.probRecallChance ?? 0.15;
|
||
const enableMultiIntent = options.enableMultiIntent ?? true;
|
||
const multiIntentMaxSegments = clampPositiveInt(
|
||
options.multiIntentMaxSegments,
|
||
4,
|
||
);
|
||
const teleportAlpha = clampRange(options.teleportAlpha, 0.15);
|
||
const enableTemporalLinks = options.enableTemporalLinks ?? true;
|
||
const temporalLinkStrength = clampRange(
|
||
options.temporalLinkStrength,
|
||
0.2,
|
||
);
|
||
const enableDiversitySampling = options.enableDiversitySampling ?? true;
|
||
const dppCandidateMultiplier = clampPositiveInt(
|
||
options.dppCandidateMultiplier,
|
||
3,
|
||
);
|
||
const dppQualityWeight = clampRange(
|
||
options.dppQualityWeight,
|
||
1.0,
|
||
0,
|
||
10,
|
||
);
|
||
const enableCooccurrenceBoost = options.enableCooccurrenceBoost ?? false;
|
||
const cooccurrenceScale = clampRange(
|
||
options.cooccurrenceScale,
|
||
0.1,
|
||
0,
|
||
10,
|
||
);
|
||
const cooccurrenceMaxNeighbors = clampPositiveInt(
|
||
options.cooccurrenceMaxNeighbors,
|
||
10,
|
||
);
|
||
const enableResidualRecall = options.enableResidualRecall ?? false;
|
||
const residualBasisMaxNodes = clampPositiveInt(
|
||
options.residualBasisMaxNodes,
|
||
24,
|
||
2,
|
||
);
|
||
const residualNmfTopics = clampPositiveInt(options.residualNmfTopics, 15);
|
||
const residualNmfNoveltyThreshold = clampRange(
|
||
options.residualNmfNoveltyThreshold,
|
||
0.4,
|
||
);
|
||
const residualThreshold = clampRange(
|
||
options.residualThreshold,
|
||
0.3,
|
||
0,
|
||
10,
|
||
);
|
||
const residualTopK = clampPositiveInt(options.residualTopK, 5);
|
||
const enableContextQueryBlend = options.enableContextQueryBlend ?? true;
|
||
const contextAssistantWeight = clampRange(
|
||
options.contextAssistantWeight,
|
||
0.2,
|
||
0,
|
||
1,
|
||
);
|
||
const contextPreviousUserWeight = clampRange(
|
||
options.contextPreviousUserWeight,
|
||
0.1,
|
||
0,
|
||
1,
|
||
);
|
||
const enableLexicalBoost = options.enableLexicalBoost ?? true;
|
||
const lexicalWeight = clampRange(options.lexicalWeight, 0.18, 0, 10);
|
||
const enableScopedMemory = options.enableScopedMemory ?? true;
|
||
const enablePovMemory = options.enablePovMemory ?? true;
|
||
const enableRegionScopedObjective =
|
||
options.enableRegionScopedObjective ?? true;
|
||
const injectUserPovMemory = options.injectUserPovMemory ?? true;
|
||
const injectObjectiveGlobalMemory = options.injectObjectiveGlobalMemory ?? true;
|
||
const stPromptContext = getSTContextForPrompt();
|
||
const activeCharacterPovOwner = String(
|
||
options.activeCharacterPovOwner ||
|
||
graph?.historyState?.activeCharacterPovOwner ||
|
||
stPromptContext?.charName ||
|
||
"",
|
||
).trim();
|
||
const activeUserPovOwner = String(
|
||
options.activeUserPovOwner ||
|
||
graph?.historyState?.activeUserPovOwner ||
|
||
stPromptContext?.userName ||
|
||
"",
|
||
).trim();
|
||
const activeRegion = pickActiveRegion(graph, options.activeRegion);
|
||
const bucketWeights = buildScopeBucketWeightMap(options);
|
||
|
||
let activeNodes = getActiveNodes(graph).filter(
|
||
(node) =>
|
||
!node.archived &&
|
||
Array.isArray(node.seqRange) &&
|
||
Number.isFinite(node.seqRange[1]),
|
||
);
|
||
|
||
if (enableVisibility && visibilityFilter) {
|
||
activeNodes = filterByVisibility(activeNodes, visibilityFilter);
|
||
}
|
||
|
||
const nodeCount = activeNodes.length;
|
||
const normalizedTopK = Math.max(1, topK);
|
||
const normalizedMaxRecallNodes = Math.max(1, maxRecallNodes);
|
||
const normalizedDiffusionTopK = Math.max(1, diffusionTopK);
|
||
const normalizedLlmCandidatePool = Math.max(
|
||
normalizedMaxRecallNodes,
|
||
llmCandidatePool,
|
||
);
|
||
const vectorValidation = validateVectorConfig(embeddingConfig);
|
||
const retrievalMeta = createRetrievalMeta(enableLLMRecall);
|
||
retrievalMeta.activeRegion = activeRegion;
|
||
retrievalMeta.activeCharacterPovOwner = activeCharacterPovOwner;
|
||
retrievalMeta.activeUserPovOwner = activeUserPovOwner;
|
||
retrievalMeta.bucketWeights = { ...bucketWeights };
|
||
const contextQueryBlend = buildContextQueryBlend(userMessage, recentMessages, {
|
||
enabled: enableContextQueryBlend,
|
||
assistantWeight: contextAssistantWeight,
|
||
previousUserWeight: contextPreviousUserWeight,
|
||
});
|
||
retrievalMeta.queryBlendActive = contextQueryBlend.active;
|
||
retrievalMeta.queryBlendParts = (contextQueryBlend.parts || []).map((part) => ({
|
||
kind: part.kind,
|
||
label: part.label,
|
||
weight: part.weight,
|
||
text: createTextPreview(part.text),
|
||
length: part.text.length,
|
||
}));
|
||
retrievalMeta.queryBlendWeights = Object.fromEntries(
|
||
(contextQueryBlend.parts || []).map((part) => [part.kind, part.weight]),
|
||
);
|
||
const lexicalQuery = buildLexicalQuerySources(
|
||
contextQueryBlend.currentText || userMessage,
|
||
{
|
||
enableMultiIntent,
|
||
maxSegments: multiIntentMaxSegments,
|
||
},
|
||
);
|
||
debugLog(
|
||
`[ST-BME] 检索开始: ${nodeCount} 个活跃节点${enableVisibility ? " (认知边界已启用)" : ""}`,
|
||
);
|
||
|
||
let vectorResults = [];
|
||
let diffusionResults = [];
|
||
let llmMeta = { ...retrievalMeta.llm };
|
||
const exactEntityAnchors = [];
|
||
let supplementalAnchorNodeIds = [];
|
||
|
||
if (nodeCount === 0) {
|
||
return buildResult(graph, [], schema, {
|
||
retrieval: {
|
||
...retrievalMeta,
|
||
llm: {
|
||
...llmMeta,
|
||
status: enableLLMRecall ? "skipped" : "disabled",
|
||
reason: "当前没有可参与召回的活跃节点",
|
||
},
|
||
timings: {
|
||
total: roundMs(nowMs() - startedAt),
|
||
},
|
||
},
|
||
scopeContext: {
|
||
enableScopedMemory,
|
||
enablePovMemory,
|
||
enableRegionScopedObjective,
|
||
injectUserPovMemory,
|
||
injectObjectiveGlobalMemory,
|
||
activeRegion,
|
||
activeCharacterPovOwner,
|
||
activeUserPovOwner,
|
||
bucketWeights,
|
||
},
|
||
});
|
||
}
|
||
|
||
const vectorStartedAt = nowMs();
|
||
if (enableVectorPrefilter && vectorValidation.valid) {
|
||
debugLog("[ST-BME] 第1层: 向量预筛");
|
||
const queryPlan = buildVectorQueryPlan(contextQueryBlend, {
|
||
enableMultiIntent,
|
||
maxSegments: multiIntentMaxSegments,
|
||
});
|
||
const groups = [];
|
||
|
||
retrievalMeta.segmentsUsed = queryPlan.currentSegments;
|
||
for (const part of queryPlan.plan) {
|
||
for (const queryText of part.queries) {
|
||
const results = await vectorPreFilter(
|
||
graph,
|
||
queryText,
|
||
activeNodes,
|
||
embeddingConfig,
|
||
normalizedTopK,
|
||
signal,
|
||
);
|
||
groups.push(scaleVectorResults(results, part.weight || 1));
|
||
}
|
||
}
|
||
|
||
const merged = mergeVectorResults(
|
||
groups,
|
||
Math.max(normalizedTopK * 2, 24),
|
||
);
|
||
retrievalMeta.vectorHits = merged.rawHitCount;
|
||
retrievalMeta.vectorMergedHits = merged.results.length;
|
||
vectorResults = merged.results;
|
||
} else if (enableVectorPrefilter) {
|
||
pushSkipReason(retrievalMeta, "vector-config-invalid");
|
||
}
|
||
retrievalMeta.timings.vector = roundMs(nowMs() - vectorStartedAt);
|
||
|
||
exactEntityAnchors.push(
|
||
...extractEntityAnchors(
|
||
contextQueryBlend.currentText || userMessage,
|
||
activeNodes,
|
||
),
|
||
);
|
||
supplementalAnchorNodeIds = collectSupplementalAnchorNodeIds(
|
||
graph,
|
||
vectorResults,
|
||
exactEntityAnchors.map((item) => item.nodeId),
|
||
5,
|
||
);
|
||
|
||
let residualResult = {
|
||
triggered: false,
|
||
hits: [],
|
||
skipReason: "",
|
||
};
|
||
const residualStartedAt = nowMs();
|
||
if (enableResidualRecall) {
|
||
const basisNodes = buildResidualBasisNodes(
|
||
graph,
|
||
exactEntityAnchors,
|
||
vectorResults,
|
||
residualBasisMaxNodes,
|
||
);
|
||
residualResult = await runResidualRecall({
|
||
queryText: contextQueryBlend.combinedText || userMessage,
|
||
graph,
|
||
embeddingConfig,
|
||
basisNodes,
|
||
candidateNodes: activeNodes,
|
||
basisLimit: residualBasisMaxNodes,
|
||
nTopics: residualNmfTopics,
|
||
noveltyThreshold: residualNmfNoveltyThreshold,
|
||
residualThreshold,
|
||
residualTopK,
|
||
signal,
|
||
});
|
||
retrievalMeta.residualTriggered = Boolean(residualResult.triggered);
|
||
retrievalMeta.residualHits = residualResult.hits?.length || 0;
|
||
pushSkipReason(retrievalMeta, residualResult.skipReason);
|
||
}
|
||
retrievalMeta.timings.residual = roundMs(nowMs() - residualStartedAt);
|
||
|
||
const diffusionStartedAt = nowMs();
|
||
if (enableGraphDiffusion) {
|
||
debugLog("[ST-BME] 第2层: PEDSA 图扩散");
|
||
const seeds = [
|
||
...vectorResults.map((v) => ({ id: v.nodeId, energy: v.score })),
|
||
...exactEntityAnchors.map((item) => ({ id: item.nodeId, energy: 2.0 })),
|
||
...(residualResult.hits || []).map((item) => ({
|
||
id: item.nodeId,
|
||
energy: item.score,
|
||
})),
|
||
];
|
||
|
||
if (enableCrossRecall && exactEntityAnchors.length > 0) {
|
||
for (const anchor of exactEntityAnchors) {
|
||
const connectedEdges = getNodeEdges(graph, anchor.nodeId);
|
||
for (const edge of connectedEdges) {
|
||
if (edge.invalidAt) continue;
|
||
const neighborId =
|
||
edge.fromId === anchor.nodeId ? edge.toId : edge.fromId;
|
||
const neighbor = getNode(graph, neighborId);
|
||
if (neighbor && !neighbor.archived && neighbor.type === "event") {
|
||
seeds.push({ id: neighborId, energy: 1.5 * edge.strength });
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
const seedMap = new Map();
|
||
for (const s of seeds) {
|
||
const existing = seedMap.get(s.id) || 0;
|
||
if (s.energy > existing) seedMap.set(s.id, s.energy);
|
||
}
|
||
const uniqueSeeds = [...seedMap.entries()].map(([id, energy]) => ({
|
||
id,
|
||
energy,
|
||
}));
|
||
retrievalMeta.seedCount = uniqueSeeds.length;
|
||
|
||
if (uniqueSeeds.length > 0) {
|
||
const adjacencyMap = buildTemporalAdjacencyMap(graph, {
|
||
includeTemporalLinks: enableTemporalLinks,
|
||
temporalLinkStrength,
|
||
});
|
||
retrievalMeta.temporalSyntheticEdgeCount =
|
||
Number(adjacencyMap.syntheticEdgeCount) || 0;
|
||
retrievalMeta.teleportAlpha = teleportAlpha;
|
||
diffusionResults = diffuseAndRank(adjacencyMap, uniqueSeeds, {
|
||
maxSteps: 2,
|
||
decayFactor: 0.6,
|
||
topK: normalizedDiffusionTopK,
|
||
teleportAlpha,
|
||
}).filter((item) => {
|
||
const node = getNode(graph, item.nodeId);
|
||
return node && !node.archived;
|
||
});
|
||
}
|
||
}
|
||
retrievalMeta.diffusionHits = diffusionResults.length;
|
||
retrievalMeta.timings.diffusion = roundMs(nowMs() - diffusionStartedAt);
|
||
|
||
debugLog("[ST-BME] 第3层: 混合评分");
|
||
|
||
const scoreMap = new Map();
|
||
|
||
for (const v of vectorResults) {
|
||
const entry = scoreMap.get(v.nodeId) || { graphScore: 0, vectorScore: 0 };
|
||
entry.vectorScore = v.score;
|
||
scoreMap.set(v.nodeId, entry);
|
||
}
|
||
|
||
for (const d of diffusionResults) {
|
||
const entry = scoreMap.get(d.nodeId) || { graphScore: 0, vectorScore: 0 };
|
||
entry.graphScore = d.energy;
|
||
scoreMap.set(d.nodeId, entry);
|
||
}
|
||
|
||
if (scoreMap.size === 0) {
|
||
for (const node of activeNodes) {
|
||
if (!scoreMap.has(node.id)) {
|
||
scoreMap.set(node.id, { graphScore: 0, vectorScore: 0 });
|
||
}
|
||
}
|
||
}
|
||
|
||
const cooccurrenceStartedAt = nowMs();
|
||
if (enableCooccurrenceBoost) {
|
||
const anchorWeights = new Map();
|
||
for (const anchor of exactEntityAnchors) {
|
||
anchorWeights.set(anchor.nodeId, 2.0);
|
||
}
|
||
for (const nodeId of supplementalAnchorNodeIds) {
|
||
const fallbackWeight =
|
||
scoreMap.get(nodeId)?.vectorScore ||
|
||
scoreMap.get(nodeId)?.graphScore ||
|
||
0.5;
|
||
anchorWeights.set(
|
||
nodeId,
|
||
Math.max(anchorWeights.get(nodeId) || 0, fallbackWeight),
|
||
);
|
||
}
|
||
|
||
if (anchorWeights.size > 0) {
|
||
const cooccurrenceIndex = createCooccurrenceIndex(graph, {
|
||
maxAnchorsPerBatch: 10,
|
||
eligibleNodes: activeNodes.filter(isEligibleAnchorNode),
|
||
});
|
||
const graphScores = new Map(
|
||
[...scoreMap.entries()].map(([nodeId, value]) => [
|
||
nodeId,
|
||
value.graphScore || 0,
|
||
]),
|
||
);
|
||
const boosted = applyCooccurrenceBoost(
|
||
graphScores,
|
||
anchorWeights,
|
||
cooccurrenceIndex,
|
||
{
|
||
scale: cooccurrenceScale,
|
||
maxNeighbors: cooccurrenceMaxNeighbors,
|
||
},
|
||
);
|
||
retrievalMeta.cooccurrenceBoostedNodes = boosted.boostedNodes.length;
|
||
|
||
for (const [nodeId, boostedScore] of boosted.scores.entries()) {
|
||
const entry = scoreMap.get(nodeId) || { graphScore: 0, vectorScore: 0 };
|
||
entry.graphScore = boostedScore;
|
||
scoreMap.set(nodeId, entry);
|
||
}
|
||
if (boosted.boostedNodes.length === 0) {
|
||
pushSkipReason(retrievalMeta, "cooccurrence-no-neighbors");
|
||
}
|
||
} else {
|
||
pushSkipReason(retrievalMeta, "cooccurrence-no-anchor");
|
||
}
|
||
}
|
||
retrievalMeta.timings.cooccurrence = roundMs(nowMs() - cooccurrenceStartedAt);
|
||
|
||
const scoringStartedAt = nowMs();
|
||
const scoredNodes = [];
|
||
for (const [nodeId, scores] of scoreMap) {
|
||
const node = getNode(graph, nodeId);
|
||
if (!node || node.archived) continue;
|
||
const lexicalScore = enableLexicalBoost
|
||
? computeLexicalScore(node, lexicalQuery.sources)
|
||
: 0;
|
||
|
||
const finalScore = hybridScore(
|
||
{
|
||
graphScore: scores.graphScore,
|
||
vectorScore: scores.vectorScore,
|
||
lexicalScore,
|
||
importance: node.importance,
|
||
createdTime: node.createdTime,
|
||
},
|
||
{
|
||
...weights,
|
||
lexicalWeight: enableLexicalBoost ? lexicalWeight : 0,
|
||
},
|
||
);
|
||
const scopeBucket = enableScopedMemory
|
||
? classifyNodeScopeBucket(node, {
|
||
activeCharacterPovOwner,
|
||
activeUserPovOwner,
|
||
activeRegion,
|
||
enablePovMemory,
|
||
enableRegionScopedObjective,
|
||
})
|
||
: MEMORY_SCOPE_BUCKETS.OBJECTIVE_GLOBAL;
|
||
const scopeWeight = enableScopedMemory
|
||
? resolveScopeBucketWeight(scopeBucket, bucketWeights)
|
||
: 1;
|
||
const weightedScore = finalScore * scopeWeight;
|
||
|
||
scoredNodes.push({
|
||
nodeId,
|
||
node,
|
||
finalScore,
|
||
weightedScore,
|
||
lexicalScore,
|
||
scopeBucket,
|
||
scopeWeight,
|
||
...scores,
|
||
});
|
||
pushScopeBucketDebug(
|
||
retrievalMeta.scopeBuckets,
|
||
scopeBucket,
|
||
nodeId,
|
||
);
|
||
}
|
||
|
||
scoredNodes.sort((a, b) => {
|
||
const bucketDelta =
|
||
getScopeBucketPriority(b.scopeBucket) - getScopeBucketPriority(a.scopeBucket);
|
||
if (bucketDelta !== 0) return bucketDelta;
|
||
const weightedDelta =
|
||
(Number(b.weightedScore) || 0) - (Number(a.weightedScore) || 0);
|
||
if (weightedDelta !== 0) return weightedDelta;
|
||
return (Number(b.finalScore) || 0) - (Number(a.finalScore) || 0);
|
||
});
|
||
retrievalMeta.scoredCandidates = scoredNodes.length;
|
||
retrievalMeta.lexicalBoostedNodes = scoredNodes.filter(
|
||
(item) => (Number(item.lexicalScore) || 0) > 0,
|
||
).length;
|
||
retrievalMeta.lexicalTopHits = buildLexicalTopHits(scoredNodes);
|
||
retrievalMeta.timings.scoring = roundMs(nowMs() - scoringStartedAt);
|
||
|
||
let selectedNodeIds;
|
||
let llmCandidates = [];
|
||
const diversityStartedAt = nowMs();
|
||
let llmDurationMs = 0;
|
||
|
||
if (enableLLMRecall && nodeCount > 0) {
|
||
debugLog("[ST-BME] LLM 精确召回");
|
||
llmCandidates = resolveCandidatePool(
|
||
scoredNodes,
|
||
normalizedLlmCandidatePool,
|
||
dppCandidateMultiplier,
|
||
enableDiversitySampling,
|
||
dppQualityWeight,
|
||
retrievalMeta,
|
||
);
|
||
const llmStartedAt = nowMs();
|
||
const llmResult = await llmRecall(
|
||
userMessage,
|
||
recentMessages,
|
||
llmCandidates,
|
||
graph,
|
||
schema,
|
||
normalizedMaxRecallNodes,
|
||
options.recallPrompt,
|
||
settings,
|
||
signal,
|
||
onStreamProgress,
|
||
);
|
||
llmDurationMs = nowMs() - llmStartedAt;
|
||
selectedNodeIds = llmResult.selectedNodeIds;
|
||
llmMeta = {
|
||
enabled: true,
|
||
status: llmResult.status,
|
||
reason: llmResult.reason,
|
||
fallbackType: llmResult.fallbackType || "",
|
||
candidatePool: llmCandidates.length,
|
||
selectedSeedCount: llmResult.selectedNodeIds.length,
|
||
};
|
||
} else {
|
||
const selectedCandidates = resolveCandidatePool(
|
||
scoredNodes,
|
||
normalizedTopK,
|
||
dppCandidateMultiplier,
|
||
enableDiversitySampling,
|
||
dppQualityWeight,
|
||
retrievalMeta,
|
||
);
|
||
selectedNodeIds = selectedCandidates.map((item) => item.nodeId);
|
||
llmMeta = {
|
||
enabled: false,
|
||
status: "disabled",
|
||
reason: "LLM 精排已关闭，直接采用评分排序",
|
||
candidatePool: 0,
|
||
selectedSeedCount: selectedNodeIds.length,
|
||
};
|
||
}
|
||
retrievalMeta.timings.diversity = roundMs(nowMs() - diversityStartedAt);
|
||
retrievalMeta.timings.llm = roundMs(llmDurationMs);
|
||
|
||
selectedNodeIds = reconstructSceneNodeIds(
|
||
graph,
|
||
selectedNodeIds,
|
||
normalizedMaxRecallNodes,
|
||
);
|
||
|
||
// 访问强化
|
||
const selectedNodes = selectedNodeIds
|
||
.map((id) => getNode(graph, id))
|
||
.filter(Boolean);
|
||
retrievalMeta.selectedByBucket = selectedNodes.reduce((acc, node) => {
|
||
const bucket = enableScopedMemory
|
||
? classifyNodeScopeBucket(node, {
|
||
activeCharacterPovOwner,
|
||
activeUserPovOwner,
|
||
activeRegion,
|
||
enablePovMemory,
|
||
enableRegionScopedObjective,
|
||
})
|
||
: MEMORY_SCOPE_BUCKETS.OBJECTIVE_GLOBAL;
|
||
pushScopeBucketDebug(acc, bucket, node.id);
|
||
return acc;
|
||
}, createEmptyScopeBucketMap());
|
||
|
||
reinforceAccessBatch(selectedNodes);
|
||
|
||
debugLog(`[ST-BME] 检索完成: 选中 ${selectedNodeIds.length} 个节点`);
|
||
|
||
if (enableProbRecall && probRecallChance > 0) {
|
||
const selectedSet = new Set(selectedNodeIds);
|
||
const probability = Math.max(0.01, Math.min(0.5, probRecallChance));
|
||
const candidates = activeNodes
|
||
.filter(
|
||
(n) =>
|
||
!selectedSet.has(n.id) &&
|
||
n.importance >= 6 &&
|
||
n.type !== "synopsis" &&
|
||
n.type !== "rule",
|
||
)
|
||
.sort((a, b) => (b.importance || 0) - (a.importance || 0))
|
||
.slice(0, 3);
|
||
for (const c of candidates) {
|
||
if (Math.random() < probability) {
|
||
selectedNodeIds.push(c.id);
|
||
debugLog(
|
||
`[ST-BME] 概率触发: ${c.fields?.name || c.fields?.summary || c.id}`,
|
||
);
|
||
}
|
||
}
|
||
}
|
||
|
||
selectedNodeIds = uniqueNodeIds(selectedNodeIds).slice(
|
||
0,
|
||
normalizedMaxRecallNodes,
|
||
);
|
||
retrievalMeta.llm = llmMeta;
|
||
retrievalMeta.timings.total = roundMs(nowMs() - startedAt);
|
||
|
||
return buildResult(graph, selectedNodeIds, schema, {
|
||
retrieval: retrievalMeta,
|
||
scopeContext: {
|
||
enableScopedMemory,
|
||
enablePovMemory,
|
||
enableRegionScopedObjective,
|
||
injectUserPovMemory,
|
||
injectObjectiveGlobalMemory,
|
||
activeRegion,
|
||
activeCharacterPovOwner,
|
||
activeUserPovOwner,
|
||
bucketWeights,
|
||
},
|
||
});
|
||
}
|
||
|
||
/**
|
||
* 向量预筛选
|
||
*/
|
||
async function vectorPreFilter(
|
||
graph,
|
||
userMessage,
|
||
activeNodes,
|
||
embeddingConfig,
|
||
topK,
|
||
signal,
|
||
) {
|
||
try {
|
||
return await findSimilarNodesByText(
|
||
graph,
|
||
userMessage,
|
||
embeddingConfig,
|
||
topK,
|
||
activeNodes,
|
||
signal,
|
||
);
|
||
} catch (e) {
|
||
if (isAbortError(e)) {
|
||
throw e;
|
||
}
|
||
console.error("[ST-BME] 向量预筛失败:", e);
|
||
return [];
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 实体锚点提取
|
||
* 从用户消息中提取名词/实体，匹配图中的节点名称
|
||
*/
|
||
function extractEntityAnchors(userMessage, activeNodes) {
|
||
const anchors = [];
|
||
const seen = new Set();
|
||
|
||
for (const node of activeNodes) {
|
||
const candidates = [node.fields?.name, node.fields?.title]
|
||
.filter((value) => typeof value === "string")
|
||
.map((value) => value.trim())
|
||
.filter((value) => value.length >= 2);
|
||
|
||
for (const candidate of candidates) {
|
||
if (!userMessage.includes(candidate)) continue;
|
||
const key = `${node.id}:${candidate}`;
|
||
if (seen.has(key)) continue;
|
||
seen.add(key);
|
||
anchors.push({ nodeId: node.id, entity: candidate });
|
||
break;
|
||
}
|
||
}
|
||
|
||
return anchors;
|
||
}
|
||
|
||
function buildResidualBasisNodes(
|
||
graph,
|
||
exactEntityAnchors,
|
||
vectorResults,
|
||
maxNodes = 24,
|
||
) {
|
||
const basis = [];
|
||
const seen = new Set();
|
||
|
||
for (const anchor of exactEntityAnchors || []) {
|
||
const node = getNode(graph, anchor?.nodeId);
|
||
if (
|
||
!node ||
|
||
seen.has(node.id) ||
|
||
!Array.isArray(node.embedding) ||
|
||
node.embedding.length === 0
|
||
) {
|
||
continue;
|
||
}
|
||
seen.add(node.id);
|
||
basis.push(node);
|
||
if (basis.length >= maxNodes) return basis;
|
||
}
|
||
|
||
for (const result of vectorResults || []) {
|
||
const node = getNode(graph, result?.nodeId);
|
||
if (
|
||
!isEligibleAnchorNode(node) ||
|
||
seen.has(node?.id) ||
|
||
!Array.isArray(node?.embedding) ||
|
||
node.embedding.length === 0
|
||
) {
|
||
continue;
|
||
}
|
||
seen.add(node.id);
|
||
basis.push(node);
|
||
if (basis.length >= maxNodes) break;
|
||
}
|
||
|
||
return basis;
|
||
}
|
||
|
||
function resolveCandidatePool(
|
||
scoredNodes,
|
||
targetCount,
|
||
multiplier,
|
||
enableDiversitySampling,
|
||
qualityWeight,
|
||
retrievalMeta,
|
||
) {
|
||
const safeTarget = Math.max(1, targetCount);
|
||
const fallback = scoredNodes.slice(0, Math.min(safeTarget, scoredNodes.length));
|
||
retrievalMeta.candidatePoolBeforeDpp = fallback.length;
|
||
retrievalMeta.candidatePoolAfterDpp = fallback.length;
|
||
retrievalMeta.diversityApplied = false;
|
||
|
||
if (!enableDiversitySampling) {
|
||
return fallback;
|
||
}
|
||
|
||
const poolLimit = Math.min(
|
||
scoredNodes.length,
|
||
Math.max(safeTarget, safeTarget * Math.max(1, multiplier)),
|
||
);
|
||
const pool = scoredNodes.slice(0, poolLimit);
|
||
retrievalMeta.candidatePoolBeforeDpp = pool.length;
|
||
|
||
const diversity = applyDiversitySampling(pool, {
|
||
k: safeTarget,
|
||
qualityWeight,
|
||
});
|
||
retrievalMeta.candidatePoolAfterDpp = diversity.afterCount;
|
||
retrievalMeta.diversityApplied = diversity.applied;
|
||
pushSkipReason(retrievalMeta, diversity.reason);
|
||
|
||
return diversity.applied ? diversity.selected : fallback;
|
||
}
|
||
|
||
/**
|
||
* LLM 精确召回
|
||
*/
|
||
async function llmRecall(
|
||
userMessage,
|
||
recentMessages,
|
||
candidates,
|
||
graph,
|
||
schema,
|
||
maxNodes,
|
||
customPrompt,
|
||
settings = {},
|
||
signal,
|
||
onStreamProgress = null,
|
||
) {
|
||
throwIfAborted(signal);
|
||
const contextStr = recentMessages.join("\n---\n");
|
||
const candidateDescriptions = candidates
|
||
.map((c) => {
|
||
const node = c.node;
|
||
const typeDef = schema.find((s) => s.id === node.type);
|
||
const typeLabel = typeDef?.label || node.type;
|
||
const fieldsStr = Object.entries(node.fields)
|
||
.map(([k, v]) => `${k}: ${v}`)
|
||
.join(", ");
|
||
return `[${node.id}] 类型=${typeLabel}, 作用域=${describeMemoryScope(node.scope)}, 召回桶=${describeScopeBucket(c.scopeBucket)}, ${fieldsStr} (评分=${(c.weightedScore ?? c.finalScore).toFixed(3)})`;
|
||
})
|
||
.join("\n");
|
||
|
||
const recallPromptBuild = await buildTaskPrompt(settings, "recall", {
|
||
taskName: "recall",
|
||
recentMessages: contextStr || "(无)",
|
||
userMessage,
|
||
candidateNodes: candidateDescriptions,
|
||
candidateText: candidateDescriptions,
|
||
graphStats: `candidate_count=${candidates.length}`,
|
||
...getSTContextForPrompt(),
|
||
});
|
||
const recallRegexInput = { entries: [] };
|
||
const systemPrompt = applyTaskRegex(
|
||
settings,
|
||
"recall",
|
||
"finalPrompt",
|
||
recallPromptBuild.systemPrompt || customPrompt || [
|
||
"你是一个记忆召回分析器。",
|
||
"根据用户最新输入和对话上下文，从候选记忆节点中选择最相关的节点。",
|
||
"优先选择：(1) 直接相关的当前场景节点, (2) 因果关系连续性节点, (3) 有潜在影响的背景节点。",
|
||
`最多选择 ${maxNodes} 个节点。`,
|
||
"输出严格的 JSON 格式：",
|
||
'{"selected_ids": ["id1", "id2", ...], "reason": "简要说明选择理由"}',
|
||
].join("\n"),
|
||
recallRegexInput,
|
||
"system",
|
||
);
|
||
|
||
const userPrompt = [
|
||
"## 最近对话上下文",
|
||
contextStr || "(无)",
|
||
"",
|
||
"## 用户最新输入",
|
||
userMessage,
|
||
"",
|
||
"## 候选记忆节点",
|
||
candidateDescriptions,
|
||
"",
|
||
"请选择最相关的节点并输出 JSON。",
|
||
].join("\n");
|
||
const promptPayload = resolveTaskPromptPayload(recallPromptBuild, userPrompt);
|
||
|
||
const llmResult = await callLLMForJSON({
|
||
systemPrompt: resolveTaskLlmSystemPrompt(promptPayload, systemPrompt),
|
||
userPrompt: promptPayload.userPrompt,
|
||
maxRetries: 2,
|
||
signal,
|
||
taskType: "recall",
|
||
debugContext: createTaskLlmDebugContext(
|
||
recallPromptBuild,
|
||
recallRegexInput,
|
||
),
|
||
promptMessages: promptPayload.promptMessages,
|
||
additionalMessages: promptPayload.additionalMessages,
|
||
onStreamProgress,
|
||
maxCompletionTokens: Math.max(512, maxNodes * 160),
|
||
returnFailureDetails: true,
|
||
});
|
||
const result = llmResult?.ok ? llmResult.data : null;
|
||
|
||
if (result?.selected_ids && Array.isArray(result.selected_ids)) {
|
||
// 校验 ID 有效性
|
||
const validIds = uniqueNodeIds(
|
||
result.selected_ids.filter((id) =>
|
||
candidates.some((c) => c.nodeId === id),
|
||
),
|
||
).slice(0, maxNodes);
|
||
|
||
if (validIds.length > 0 || result.selected_ids.length === 0) {
|
||
return {
|
||
selectedNodeIds: validIds,
|
||
status: "llm",
|
||
reason:
|
||
validIds.length < result.selected_ids.length
|
||
? "LLM 返回了部分无效或超限 ID，已自动裁剪"
|
||
: "LLM 精排完成",
|
||
};
|
||
}
|
||
}
|
||
|
||
// LLM 失败时回退到纯评分排序
|
||
const fallbackReason = llmResult?.ok
|
||
? Array.isArray(result?.selected_ids)
|
||
? "LLM 返回的候选 ID 无效，已回退到评分排序"
|
||
: "LLM 返回了无法识别的 JSON 结构，已回退到评分排序"
|
||
: buildRecallFallbackReason(llmResult);
|
||
return {
|
||
selectedNodeIds: candidates.slice(0, maxNodes).map((c) => c.nodeId),
|
||
status: "fallback",
|
||
reason: fallbackReason,
|
||
fallbackType: llmResult?.ok ? "invalid-candidate" : llmResult?.errorType || "unknown",
|
||
};
|
||
}
|
||
|
||
// ==================== v2 辅助函数 ====================
|
||
|
||
/**
|
||
* ⑥ 认知边界过滤（RoleRAG 启发）
|
||
* 过滤掉设置了 visibility 但不包含当前角色的节点
|
||
* @param {object[]} nodes
|
||
* @param {string} characterName - 当前视角角色名
|
||
* @returns {object[]}
|
||
*/
|
||
function filterByVisibility(nodes, characterName) {
|
||
if (!characterName || typeof characterName !== "string") return nodes;
|
||
return nodes.filter((node) => {
|
||
if (!node.fields?.visibility) return true;
|
||
if (Array.isArray(node.fields.visibility)) {
|
||
return (
|
||
node.fields.visibility.includes(characterName) ||
|
||
node.fields.visibility.includes("*")
|
||
);
|
||
}
|
||
if (typeof node.fields.visibility === "string") {
|
||
const visibleTo = node.fields.visibility
|
||
.split(",")
|
||
.map((s) => s.trim())
|
||
.filter(Boolean);
|
||
return visibleTo.includes(characterName) || visibleTo.includes("*");
|
||
}
|
||
return true;
|
||
});
|
||
}
|
||
|
||
/**
|
||
* 构建最终检索结果
|
||
* 分离常驻注入（Core）和召回注入（Recall）
|
||
*/
|
||
function buildResult(graph, selectedNodeIds, schema, meta = {}) {
|
||
const coreNodes = [];
|
||
const recallNodes = [];
|
||
const selectedSet = new Set(uniqueNodeIds(selectedNodeIds));
|
||
const scopeContext = meta.scopeContext || {};
|
||
|
||
// 常驻注入节点（alwaysInject=true 的类型）
|
||
const alwaysInjectTypes = new Set(
|
||
schema.filter((s) => s.alwaysInject).map((s) => s.id),
|
||
);
|
||
|
||
const activeNodes = getActiveNodes(graph).filter((node) => !node.archived);
|
||
|
||
for (const node of activeNodes) {
|
||
if (alwaysInjectTypes.has(node.type)) {
|
||
coreNodes.push(node);
|
||
}
|
||
}
|
||
|
||
for (const nodeId of selectedSet) {
|
||
const node = getNode(graph, nodeId);
|
||
if (!node || node.archived) continue;
|
||
if (!alwaysInjectTypes.has(node.type)) {
|
||
recallNodes.push(node);
|
||
}
|
||
}
|
||
|
||
coreNodes.sort(compareNodeRecallOrder);
|
||
recallNodes.sort(compareNodeRecallOrder);
|
||
const groupedRecallNodes = groupRecallNodes(recallNodes);
|
||
const selectedNodes = [...selectedSet]
|
||
.map((nodeId) => getNode(graph, nodeId))
|
||
.filter((node) => node && !node.archived)
|
||
.sort(compareNodeRecallOrder);
|
||
const scopeBuckets = buildScopedInjectionBuckets(
|
||
coreNodes,
|
||
selectedNodes,
|
||
scopeContext,
|
||
);
|
||
|
||
return {
|
||
coreNodes,
|
||
recallNodes,
|
||
groupedRecallNodes,
|
||
scopeBuckets,
|
||
selectedNodeIds: [...selectedSet],
|
||
meta,
|
||
stats: {
|
||
totalActive: activeNodes.length,
|
||
coreCount: coreNodes.length,
|
||
recallCount: recallNodes.length,
|
||
characterPovCount: scopeBuckets.characterPov.length,
|
||
userPovCount: scopeBuckets.userPov.length,
|
||
objectiveCurrentRegionCount: scopeBuckets.objectiveCurrentRegion.length,
|
||
objectiveGlobalCount: scopeBuckets.objectiveGlobal.length,
|
||
episodicCount: groupedRecallNodes.episodic.length,
|
||
stateCount: groupedRecallNodes.state.length,
|
||
reflectiveCount: groupedRecallNodes.reflective.length,
|
||
ruleCount: groupedRecallNodes.rule.length,
|
||
},
|
||
};
|
||
}
|
||
|
||
function buildScopedInjectionBuckets(coreNodes, selectedNodes, scopeContext = {}) {
|
||
const buckets = {
|
||
characterPov: [],
|
||
userPov: [],
|
||
objectiveCurrentRegion: [],
|
||
objectiveGlobal: [],
|
||
};
|
||
const combinedNodes = [
|
||
...selectedNodes,
|
||
...coreNodes,
|
||
];
|
||
const seen = new Set();
|
||
const globalCandidates = [];
|
||
|
||
for (const node of combinedNodes) {
|
||
if (!node?.id || seen.has(node.id)) continue;
|
||
seen.add(node.id);
|
||
const bucket = classifyNodeScopeBucket(node, {
|
||
activeCharacterPovOwner: scopeContext.activeCharacterPovOwner,
|
||
activeUserPovOwner: scopeContext.activeUserPovOwner,
|
||
activeRegion: scopeContext.activeRegion,
|
||
enablePovMemory: scopeContext.enablePovMemory !== false,
|
||
enableRegionScopedObjective:
|
||
scopeContext.enableRegionScopedObjective !== false,
|
||
});
|
||
|
||
if (bucket === MEMORY_SCOPE_BUCKETS.CHARACTER_POV) {
|
||
buckets.characterPov.push(node);
|
||
continue;
|
||
}
|
||
if (bucket === MEMORY_SCOPE_BUCKETS.USER_POV) {
|
||
if (scopeContext.injectUserPovMemory !== false) {
|
||
buckets.userPov.push(node);
|
||
}
|
||
continue;
|
||
}
|
||
if (bucket === MEMORY_SCOPE_BUCKETS.OBJECTIVE_CURRENT_REGION) {
|
||
buckets.objectiveCurrentRegion.push(node);
|
||
continue;
|
||
}
|
||
if (
|
||
bucket === MEMORY_SCOPE_BUCKETS.OBJECTIVE_ADJACENT_REGION ||
|
||
bucket === MEMORY_SCOPE_BUCKETS.OBJECTIVE_GLOBAL
|
||
) {
|
||
globalCandidates.push(node);
|
||
}
|
||
}
|
||
|
||
buckets.characterPov.sort(compareNodeRecallOrder);
|
||
buckets.userPov.sort(compareNodeRecallOrder);
|
||
buckets.objectiveCurrentRegion.sort(compareNodeRecallOrder);
|
||
const cappedGlobal = (scopeContext.injectObjectiveGlobalMemory === false
|
||
? []
|
||
: globalCandidates.sort(compareNodeRecallOrder).slice(0, 6));
|
||
buckets.objectiveGlobal = cappedGlobal;
|
||
|
||
return buckets;
|
||
}
|
||
|
||
function reconstructSceneNodeIds(graph, seedNodeIds, limit = 16) {
|
||
const selected = [];
|
||
const seen = new Set();
|
||
|
||
function push(nodeId) {
|
||
if (!nodeId || seen.has(nodeId) || selected.length >= limit) return;
|
||
const node = getNode(graph, nodeId);
|
||
if (!node || node.archived) return;
|
||
seen.add(nodeId);
|
||
selected.push(nodeId);
|
||
}
|
||
|
||
for (const nodeId of uniqueNodeIds(seedNodeIds)) {
|
||
if (selected.length >= limit) break;
|
||
push(nodeId);
|
||
const node = getNode(graph, nodeId);
|
||
if (!node) continue;
|
||
|
||
if (node.type === "event") {
|
||
expandEventScene(graph, node, push);
|
||
} else if (node.type === "pov_memory") {
|
||
const relatedNodes = getNodeEdges(graph, node.id)
|
||
.filter(isUsableSceneEdge)
|
||
.map((e) => (e.fromId === node.id ? e.toId : e.fromId))
|
||
.map((id) => getNode(graph, id))
|
||
.filter(Boolean)
|
||
.sort(compareNodeRecallOrder)
|
||
.slice(0, 2);
|
||
for (const relatedNode of relatedNodes) {
|
||
push(relatedNode.id);
|
||
if (relatedNode.type === "event") {
|
||
expandEventScene(graph, relatedNode, push);
|
||
}
|
||
}
|
||
} else if (node.type === "character" || node.type === "location") {
|
||
const relatedEvents = getNodeEdges(graph, node.id)
|
||
.filter(isUsableSceneEdge)
|
||
.map((e) => (e.fromId === node.id ? e.toId : e.fromId))
|
||
.map((id) => getNode(graph, id))
|
||
.filter((n) => n && !n.archived && n.type === "event")
|
||
.sort(compareNodeRecallOrder)
|
||
.slice(0, 2);
|
||
for (const eventNode of relatedEvents) {
|
||
push(eventNode.id);
|
||
expandEventScene(graph, eventNode, push);
|
||
}
|
||
}
|
||
}
|
||
|
||
return selected.slice(0, limit);
|
||
}
|
||
|
||
function expandEventScene(graph, eventNode, push) {
|
||
const edges = getNodeEdges(graph, eventNode.id).filter(isUsableSceneEdge);
|
||
for (const edge of edges) {
|
||
const neighborId = edge.fromId === eventNode.id ? edge.toId : edge.fromId;
|
||
const neighbor = getNode(graph, neighborId);
|
||
if (!neighbor || neighbor.archived) continue;
|
||
if (
|
||
neighbor.type === "character" ||
|
||
neighbor.type === "location" ||
|
||
neighbor.type === "thread" ||
|
||
neighbor.type === "reflection" ||
|
||
neighbor.type === "pov_memory"
|
||
) {
|
||
push(neighbor.id);
|
||
}
|
||
}
|
||
|
||
const adjacentEvents = getTemporalNeighborEvents(
|
||
graph,
|
||
eventNode.seq,
|
||
eventNode.id,
|
||
);
|
||
for (const neighborEvent of adjacentEvents) {
|
||
push(neighborEvent.id);
|
||
}
|
||
}
|
||
|
||
function getTemporalNeighborEvents(graph, seq, excludeId) {
|
||
return getActiveNodes(graph, "event")
|
||
.filter((n) => n.id !== excludeId && !n.archived)
|
||
.sort((a, b) => {
|
||
const distance =
|
||
Math.abs((a.seq || 0) - seq) - Math.abs((b.seq || 0) - seq);
|
||
if (distance !== 0) return distance;
|
||
return (b.seq || 0) - (a.seq || 0);
|
||
})
|
||
.slice(0, 2);
|
||
}
|
||
|
||
function isUsableSceneEdge(edge) {
|
||
return edge && !edge.invalidAt && !edge.expiredAt;
|
||
}
|
||
|
||
function compareNodeRecallOrder(a, b) {
|
||
const aSeq = a?.seqRange?.[1] ?? a?.seq ?? 0;
|
||
const bSeq = b?.seqRange?.[1] ?? b?.seq ?? 0;
|
||
if (bSeq !== aSeq) return bSeq - aSeq;
|
||
return (b.importance || 0) - (a.importance || 0);
|
||
}
|
||
|
||
function groupRecallNodes(nodes) {
|
||
return {
|
||
state: nodes.filter((n) => n.type === "character" || n.type === "location"),
|
||
episodic: nodes.filter((n) => n.type === "event" || n.type === "thread"),
|
||
reflective: nodes.filter(
|
||
(n) => n.type === "reflection" || n.type === "synopsis",
|
||
),
|
||
rule: nodes.filter((n) => n.type === "rule"),
|
||
other: nodes.filter(
|
||
(n) =>
|
||
![
|
||
"character",
|
||
"location",
|
||
"event",
|
||
"thread",
|
||
"reflection",
|
||
"synopsis",
|
||
"rule",
|
||
].includes(n.type),
|
||
),
|
||
};
|
||
}
|
||
|
||
function uniqueNodeIds(nodeIds) {
|
||
return [...new Set(nodeIds)];
|
||
}
|