mirror of
https://github.com/Youzini-afk/ST-Bionic-Memory-Ecology.git
synced 2026-05-15 22:30:38 +08:00
2163 lines
66 KiB
JavaScript
2163 lines
66 KiB
JavaScript
// ST-BME: LLM 记忆提取管线（写入路径）
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// 分析对话 → 提取节点和关系 → 更新图谱
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// v2: 融合 Mem0 精确对照 + Graphiti 时序边 + MemoRAG 全局概要
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import { embedBatch } from "../vector/embedding.js";
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import { debugLog, debugWarn } from "../runtime/debug-logging.js";
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import {
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addEdge,
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addNode,
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createEdge,
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createNode,
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findLatestNode,
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getActiveNodes,
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getNode,
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invalidateEdge,
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updateNode,
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} from "../graph/graph.js";
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import { callLLMForJSON } from "../llm/llm.js";
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import { ensureEventTitle, getNodeDisplayName } from "../graph/node-labels.js";
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import {
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normalizeMemoryScope,
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isObjectiveScope,
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} from "../graph/memory-scope.js";
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import {
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applyCognitionUpdates,
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applyRegionUpdates,
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resolveKnowledgeOwner,
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} from "../graph/knowledge-state.js";
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import {
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applyBatchStoryTime,
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createSpanFromStoryTime,
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deriveStoryTimeSpanFromNodes,
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describeNodeStoryTime,
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normalizeStoryTime,
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resolveActiveStoryContext,
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upsertTimelineSegment,
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} from "../graph/story-timeline.js";
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import { getActiveSummaryEntries } from "../graph/summary-state.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 "../prompting/prompt-builder.js";
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import { RELATION_TYPES } from "../graph/schema.js";
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import { applyTaskRegex } from "../prompting/task-regex.js";
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import { getSTContextForPrompt, getSTContextSnapshot } from "../host/st-context.js";
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import { buildExtractionInputContext } from "./extraction-context.js";
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import {
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aliasSetMatchesValue,
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buildUserPovAliasNormalizedSet,
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getHostUserAliasHints,
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} from "../runtime/user-alias-utils.js";
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import { buildNodeVectorText, isDirectVectorConfig } from "../vector/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 createExtractTaskLlmDebugContext(promptBuild, regexInput, inputContext = null) {
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const debugContext = createTaskLlmDebugContext(promptBuild, regexInput);
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if (!inputContext || typeof inputContext !== "object") {
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return debugContext;
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}
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return {
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...debugContext,
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inputContext,
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};
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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 buildActiveSummariesText(graph) {
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const entries = getActiveSummaryEntries(graph);
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if (!Array.isArray(entries) || entries.length === 0) return "";
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return entries
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.map((entry, index) => {
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const rangeLabel = Array.isArray(entry.messageRange) && entry.messageRange.length >= 2
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&& entry.messageRange[0] >= 0 && entry.messageRange[1] >= 0
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? `楼${entry.messageRange[0]}~${entry.messageRange[1]}`
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: "";
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const levelLabel = entry.level ? `L${entry.level}` : "";
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const prefix = [rangeLabel, levelLabel].filter(Boolean).join(" ");
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return `[${index + 1}]${prefix ? ` (${prefix})` : ""} ${String(entry.text || entry.summary || "").trim()}`;
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})
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.filter((line) => line.trim())
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.join("\n");
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}
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function buildStoryTimeContextText(graph) {
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const storyCtx = resolveActiveStoryContext(graph);
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if (!storyCtx?.resolved) return "";
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const parts = [];
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if (storyCtx.activeStoryTimeLabel) {
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parts.push(`当前活跃剧情时间：${storyCtx.activeStoryTimeLabel}`);
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}
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if (storyCtx.source) {
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parts.push(`来源：${storyCtx.source}`);
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}
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const seg = storyCtx.segment;
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if (seg?.tense && seg.tense !== "unknown") {
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parts.push(`时态：${seg.tense}`);
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}
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return parts.join(" | ");
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}
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function applyRecentMessageCap(messages, cap = 0) {
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if (!Array.isArray(messages) || messages.length === 0) return messages;
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const numericCap = Number(cap);
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if (!Number.isFinite(numericCap) || numericCap <= 0) return messages;
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if (messages.length <= numericCap) return messages;
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return messages.slice(-numericCap);
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}
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function resolveExtractPromptStructuredMode(settings) {
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const mode = String(settings?.extractPromptStructuredMode || "both").trim().toLowerCase();
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if (["transcript", "structured", "both"].includes(mode)) return mode;
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return "both";
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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 ? signal.reason : createAbortError();
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}
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}
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const EXTRACTION_RESULT_CONTAINER_KEYS = [
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"operations",
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"nodes",
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"items",
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"entries",
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"memories",
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"results",
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"data",
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"memory_operations",
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"actions",
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"output",
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"extracted",
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"extractions",
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"memory_nodes",
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];
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const EXTRACTION_OPERATION_META_KEYS = new Set([
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"action",
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"op",
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"operation",
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"type",
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"fields",
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"nodeId",
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"node_id",
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"targetNodeId",
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"target_node_id",
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"sourceNodeId",
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"source_node_id",
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"ref",
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"reference",
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"id",
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"links",
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"relations",
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"edges",
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"importance",
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"clusters",
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"scope",
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"storyTime",
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"seq",
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"temporalStrength",
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"temporal_strength",
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]);
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function isPlainObject(value) {
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return Boolean(value) && typeof value === "object" && !Array.isArray(value);
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}
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/**
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* 判断一个对象是否像一个 extraction 操作
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* (包含 action/op/operation/type 中的至少一个)
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*/
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function looksLikeSingleOperation(obj) {
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if (!isPlainObject(obj)) return false;
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return (
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typeof obj.action === "string" ||
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typeof obj.op === "string" ||
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typeof obj.operation === "string" ||
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typeof obj.type === "string"
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);
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}
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function extractOperationsPayload(result) {
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// 直接是数组 → 直接返回
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if (Array.isArray(result)) {
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return result;
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}
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if (!isPlainObject(result)) {
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return null;
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}
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// 1. 优先匹配已知容器键
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for (const key of EXTRACTION_RESULT_CONTAINER_KEYS) {
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if (Array.isArray(result[key])) {
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return result[key];
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}
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}
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// 2. 智能探测：扫描对象中第一个值为非空数组且元素看起来像操作的键
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for (const [key, value] of Object.entries(result)) {
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if (
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Array.isArray(value) &&
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value.length > 0 &&
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value.some((item) => looksLikeSingleOperation(item))
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) {
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debugLog(
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`[ST-BME] 自动探测到非标准容器键: "${key}" (${value.length} 项)`,
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);
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return value;
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}
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}
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// 3. 单个操作对象兜底：如果整个结果看起来像一条操作，包装成数组
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if (looksLikeSingleOperation(result)) {
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debugLog(
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"[ST-BME] LLM 返回了单个操作对象，自动包装为数组",
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);
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return [result];
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}
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return null;
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}
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function resolveExtractionAction(rawOp) {
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const explicitAction = rawOp?.action ?? rawOp?.op ?? rawOp?.operation;
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if (typeof explicitAction === "string" && explicitAction.trim()) {
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return explicitAction.trim().toLowerCase();
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}
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if (rawOp?.type) {
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if (rawOp?.nodeId || rawOp?.node_id) {
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return "update";
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}
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return "create";
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}
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return "";
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}
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function resolveExtractionTypeDef(schema, type) {
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if (!Array.isArray(schema) || !type) {
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return null;
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}
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return schema.find((entry) => entry?.id === type) || null;
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}
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function resolveExtractionFieldNames(typeDef) {
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return new Set(
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Array.isArray(typeDef?.columns)
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? typeDef.columns
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.map((column) => String(column?.name || "").trim())
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.filter(Boolean)
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: [],
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);
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}
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function resolveExtractionNodeId(rawOp) {
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const nodeId =
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rawOp?.nodeId ??
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rawOp?.node_id ??
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rawOp?.targetNodeId ??
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rawOp?.target_node_id ??
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rawOp?.id;
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return nodeId == null || nodeId === "" ? "" : String(nodeId);
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}
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function resolveExtractionRef(rawOp) {
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const ref = rawOp?.ref ?? rawOp?.reference ?? rawOp?.id;
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return ref == null || ref === "" ? "" : String(ref);
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}
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function collectNormalizedOperationFields(rawOp, typeDef) {
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const fieldNames = resolveExtractionFieldNames(typeDef);
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const fields = isPlainObject(rawOp?.fields) ? { ...rawOp.fields } : {};
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for (const [key, value] of Object.entries(rawOp || {})) {
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if (key === "fields") {
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continue;
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}
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if (key === "scope") {
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if (!isPlainObject(value) && (fieldNames.has("scope") || !typeDef)) {
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fields.scope = value;
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}
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continue;
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}
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if (EXTRACTION_OPERATION_META_KEYS.has(key)) {
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continue;
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}
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if (!typeDef || fieldNames.has(key)) {
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fields[key] = value;
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}
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}
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return fields;
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}
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function normalizeExtractionOperation(rawOp, schema) {
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if (!isPlainObject(rawOp)) {
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return rawOp;
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}
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const action = resolveExtractionAction(rawOp);
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const type = rawOp?.type == null ? "" : String(rawOp.type).trim();
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const typeDef = resolveExtractionTypeDef(schema, type);
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const normalized = {
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...rawOp,
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...(action ? { action } : {}),
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...(type ? { type } : {}),
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};
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const nodeId = resolveExtractionNodeId(rawOp);
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const ref = resolveExtractionRef(rawOp);
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if (action === "create") {
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if (ref) {
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normalized.ref = ref;
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}
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delete normalized.nodeId;
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} else if ((action === "update" || action === "delete") && nodeId) {
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normalized.nodeId = nodeId;
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}
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if (Array.isArray(rawOp?.relations) && !Array.isArray(rawOp?.links)) {
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normalized.links = rawOp.relations;
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} else if (Array.isArray(rawOp?.edges) && !Array.isArray(rawOp?.links)) {
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normalized.links = rawOp.edges;
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}
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if (!Array.isArray(normalized.clusters) && normalized.clusters != null) {
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normalized.clusters = [normalized.clusters].filter(Boolean);
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}
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if (isPlainObject(rawOp?.scope)) {
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normalized.scope = rawOp.scope;
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} else if (action === "create" || action === "update") {
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delete normalized.scope;
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}
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if (isPlainObject(rawOp?.storyTime)) {
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normalized.storyTime = normalizeStoryTime(rawOp.storyTime, {
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source: "extract",
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});
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} else if (action === "create" || action === "update") {
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delete normalized.storyTime;
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}
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if (action === "create" || action === "update") {
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const fields = collectNormalizedOperationFields(rawOp, typeDef);
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if (Object.keys(fields).length > 0) {
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normalized.fields = fields;
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}
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}
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delete normalized.op;
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delete normalized.operation;
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delete normalized.node_id;
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delete normalized.target_node_id;
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delete normalized.source_node_id;
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delete normalized.reference;
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delete normalized.relations;
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delete normalized.edges;
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delete normalized.temporal_strength;
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return normalized;
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}
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function normalizeExtractionResultPayload(result, schema) {
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const operations = extractOperationsPayload(result);
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if (!Array.isArray(operations)) {
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return result;
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}
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const normalizedOperations = operations.map((op) =>
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normalizeExtractionOperation(op, schema),
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);
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const normalizedCognitionUpdates = Array.isArray(result?.cognitionUpdates)
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? result.cognitionUpdates
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.filter(isPlainObject)
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.map((entry) => ({
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ownerType: String(entry?.ownerType || "").trim(),
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ownerName: String(entry?.ownerName || "").trim(),
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ownerId: String(entry?.ownerId || "").trim(),
|
||
ownerNodeId: String(entry?.ownerNodeId || "").trim(),
|
||
knownRefs: Array.isArray(entry?.knownRefs)
|
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? entry.knownRefs
|
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: entry?.knownRefs != null
|
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? [entry.knownRefs]
|
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: [],
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mistakenRefs: Array.isArray(entry?.mistakenRefs)
|
||
? entry.mistakenRefs
|
||
: entry?.mistakenRefs != null
|
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? [entry.mistakenRefs]
|
||
: [],
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visibility: Array.isArray(entry?.visibility) ? entry.visibility : [],
|
||
}))
|
||
: [];
|
||
const normalizedRegionUpdates = isPlainObject(result?.regionUpdates)
|
||
? {
|
||
activeRegionHint: String(result.regionUpdates?.activeRegionHint || "").trim(),
|
||
adjacency: Array.isArray(result.regionUpdates?.adjacency)
|
||
? result.regionUpdates.adjacency
|
||
.filter(isPlainObject)
|
||
.map((entry) => ({
|
||
region: String(entry?.region || "").trim(),
|
||
adjacent: Array.isArray(entry?.adjacent)
|
||
? entry.adjacent
|
||
: entry?.adjacent != null
|
||
? [entry.adjacent]
|
||
: [],
|
||
source: String(entry?.source || "").trim(),
|
||
}))
|
||
: [],
|
||
}
|
||
: null;
|
||
const normalizedBatchStoryTime = isPlainObject(result?.batchStoryTime)
|
||
? {
|
||
...normalizeStoryTime(result.batchStoryTime, { source: "extract" }),
|
||
advancesActiveTimeline: result.batchStoryTime?.advancesActiveTimeline === true,
|
||
}
|
||
: null;
|
||
|
||
if (Array.isArray(result) || !isPlainObject(result)) {
|
||
return {
|
||
operations: normalizedOperations,
|
||
cognitionUpdates: normalizedCognitionUpdates,
|
||
regionUpdates: normalizedRegionUpdates,
|
||
batchStoryTime: normalizedBatchStoryTime,
|
||
};
|
||
}
|
||
|
||
return {
|
||
...result,
|
||
operations: normalizedOperations,
|
||
cognitionUpdates: normalizedCognitionUpdates,
|
||
regionUpdates: normalizedRegionUpdates,
|
||
batchStoryTime: normalizedBatchStoryTime,
|
||
};
|
||
}
|
||
|
||
function normalizeExtractionOwnerText(value) {
|
||
return String(value || "").trim();
|
||
}
|
||
|
||
function collectExtractorUserAliasHints(scopeRuntime = {}, extraHints = []) {
|
||
const hints = [];
|
||
const pushHint = (value) => {
|
||
const normalized = normalizeExtractionOwnerText(value);
|
||
if (!normalized || hints.includes(normalized)) return;
|
||
hints.push(normalized);
|
||
};
|
||
const ingest = (value) => {
|
||
if (value == null) return;
|
||
if (typeof value === "string") {
|
||
pushHint(value);
|
||
return;
|
||
}
|
||
if (Array.isArray(value)) {
|
||
for (const item of value) ingest(item);
|
||
return;
|
||
}
|
||
if (typeof value === "object") {
|
||
pushHint(value.name1);
|
||
pushHint(value.userName);
|
||
pushHint(value.personaName);
|
||
pushHint(value.name);
|
||
ingest(value.aliases);
|
||
}
|
||
};
|
||
|
||
pushHint(scopeRuntime.activeUserOwner);
|
||
ingest(getHostUserAliasHints(extraHints));
|
||
return hints;
|
||
}
|
||
|
||
function buildExtractorUserAliasContext(scopeRuntime = {}, extraHints = []) {
|
||
const aliasHints = collectExtractorUserAliasHints(scopeRuntime, extraHints);
|
||
return {
|
||
aliasHints,
|
||
aliasSet: buildUserPovAliasNormalizedSet(aliasHints),
|
||
preferredName: aliasHints[0] || "",
|
||
};
|
||
}
|
||
|
||
function matchesExtractorUserAlias(ownerName = "", scopeRuntime = {}, extraHints = []) {
|
||
const aliasContext = buildExtractorUserAliasContext(scopeRuntime, extraHints);
|
||
return aliasSetMatchesValue(aliasContext.aliasSet, ownerName);
|
||
}
|
||
|
||
function resolveExtractorUserOwnerName(
|
||
scopeRuntime = {},
|
||
rawOwnerName = "",
|
||
extraHints = [],
|
||
) {
|
||
const aliasContext = buildExtractorUserAliasContext(scopeRuntime, [
|
||
rawOwnerName,
|
||
...(Array.isArray(extraHints) ? extraHints : [extraHints]),
|
||
]);
|
||
return aliasContext.preferredName || normalizeExtractionOwnerText(rawOwnerName);
|
||
}
|
||
|
||
function resolveCharacterOwnerCandidate(graph, ownerName = "", ownerNodeId = "") {
|
||
const resolved = resolveKnowledgeOwner(graph, {
|
||
ownerType: "character",
|
||
ownerName,
|
||
nodeId: ownerNodeId,
|
||
});
|
||
return resolved?.ownerType === "character" && resolved?.ownerKey ? resolved : null;
|
||
}
|
||
|
||
function deriveExtractionOwnerContext(
|
||
graph,
|
||
normalizedResult = {},
|
||
scopeRuntime = {},
|
||
) {
|
||
const ownerMap = new Map();
|
||
const registerCharacterOwner = (ownerName = "", ownerNodeId = "", source = "") => {
|
||
if (
|
||
normalizeExtractionOwnerText(ownerName) &&
|
||
!normalizeExtractionOwnerText(ownerNodeId) &&
|
||
matchesExtractorUserAlias(ownerName, scopeRuntime)
|
||
) {
|
||
return;
|
||
}
|
||
const resolved = resolveCharacterOwnerCandidate(graph, ownerName, ownerNodeId);
|
||
if (!resolved?.ownerKey) return;
|
||
const existing = ownerMap.get(resolved.ownerKey) || {
|
||
...resolved,
|
||
sources: [],
|
||
};
|
||
if (source && !existing.sources.includes(source)) {
|
||
existing.sources.push(source);
|
||
}
|
||
ownerMap.set(resolved.ownerKey, existing);
|
||
};
|
||
|
||
for (const op of Array.isArray(normalizedResult?.operations)
|
||
? normalizedResult.operations
|
||
: []) {
|
||
if (String(op?.type || "") === "pov_memory") {
|
||
registerCharacterOwner(
|
||
op?.scope?.ownerName || op?.scope?.ownerId,
|
||
"",
|
||
"pov-memory-scope",
|
||
);
|
||
}
|
||
if (
|
||
String(op?.type || "") === "character" &&
|
||
["create", "update"].includes(String(op?.action || ""))
|
||
) {
|
||
registerCharacterOwner(
|
||
op?.fields?.name || "",
|
||
String(op?.nodeId || ""),
|
||
"character-operation",
|
||
);
|
||
}
|
||
}
|
||
|
||
for (const entry of Array.isArray(normalizedResult?.cognitionUpdates)
|
||
? normalizedResult.cognitionUpdates
|
||
: []) {
|
||
if (String(entry?.ownerType || "") !== "character") continue;
|
||
registerCharacterOwner(
|
||
entry?.ownerName || entry?.ownerId,
|
||
entry?.ownerNodeId,
|
||
"cognition-update",
|
||
);
|
||
}
|
||
|
||
const runtimeOwner = resolveCharacterOwnerCandidate(
|
||
graph,
|
||
scopeRuntime.activeCharacterOwner,
|
||
"",
|
||
);
|
||
if (runtimeOwner?.ownerKey && runtimeOwner?.nodeId && ownerMap.size <= 1) {
|
||
registerCharacterOwner(runtimeOwner.ownerName, runtimeOwner.nodeId, "runtime-unique");
|
||
}
|
||
|
||
const ownerCandidates = [...ownerMap.values()];
|
||
return {
|
||
ownerCandidates,
|
||
soleCharacterOwner: ownerCandidates.length === 1 ? ownerCandidates[0] : null,
|
||
};
|
||
}
|
||
|
||
function normalizeCognitionUpdatesWithOwnerContext(
|
||
graph,
|
||
cognitionUpdates = [],
|
||
scopeRuntime = {},
|
||
ownerContext = {},
|
||
ownershipWarnings = [],
|
||
) {
|
||
const normalized = [];
|
||
|
||
for (const entry of Array.isArray(cognitionUpdates) ? cognitionUpdates : []) {
|
||
const ownerType = normalizeExtractionOwnerText(entry?.ownerType);
|
||
const rawOwnerName = normalizeExtractionOwnerText(
|
||
entry?.ownerName || entry?.ownerId,
|
||
);
|
||
const rawOwnerNodeId = normalizeExtractionOwnerText(entry?.ownerNodeId);
|
||
if (ownerType === "character") {
|
||
if (
|
||
rawOwnerName &&
|
||
!rawOwnerNodeId &&
|
||
matchesExtractorUserAlias(rawOwnerName, scopeRuntime)
|
||
) {
|
||
const resolvedUserName = resolveExtractorUserOwnerName(
|
||
scopeRuntime,
|
||
rawOwnerName,
|
||
);
|
||
if (!resolvedUserName) {
|
||
ownershipWarnings.push({
|
||
kind: "invalid-owner-scope",
|
||
source: "cognitionUpdate",
|
||
ownerType: "user",
|
||
});
|
||
continue;
|
||
}
|
||
normalized.push({
|
||
...entry,
|
||
ownerType: "user",
|
||
ownerName: resolvedUserName,
|
||
ownerId: resolvedUserName,
|
||
ownerNodeId: "",
|
||
});
|
||
continue;
|
||
}
|
||
const resolved =
|
||
resolveCharacterOwnerCandidate(
|
||
graph,
|
||
rawOwnerName,
|
||
rawOwnerNodeId,
|
||
) || ownerContext?.soleCharacterOwner || null;
|
||
if (!resolved?.ownerKey) {
|
||
ownershipWarnings.push({
|
||
kind: "invalid-owner-scope",
|
||
source: "cognitionUpdate",
|
||
ownerType,
|
||
});
|
||
continue;
|
||
}
|
||
normalized.push({
|
||
...entry,
|
||
ownerType: "character",
|
||
ownerName: resolved.ownerName,
|
||
ownerId: resolved.ownerName,
|
||
ownerNodeId: resolved.nodeId || normalizeExtractionOwnerText(entry?.ownerNodeId),
|
||
});
|
||
continue;
|
||
}
|
||
|
||
if (ownerType === "user") {
|
||
const resolvedUserName = resolveExtractorUserOwnerName(
|
||
scopeRuntime,
|
||
rawOwnerName,
|
||
);
|
||
if (!resolvedUserName) {
|
||
ownershipWarnings.push({
|
||
kind: "invalid-owner-scope",
|
||
source: "cognitionUpdate",
|
||
ownerType,
|
||
});
|
||
continue;
|
||
}
|
||
normalized.push({
|
||
...entry,
|
||
ownerType: "user",
|
||
ownerName: resolvedUserName,
|
||
ownerId: resolvedUserName,
|
||
});
|
||
continue;
|
||
}
|
||
|
||
if (ownerContext?.soleCharacterOwner) {
|
||
normalized.push({
|
||
...entry,
|
||
ownerType: "character",
|
||
ownerName: ownerContext.soleCharacterOwner.ownerName,
|
||
ownerId: ownerContext.soleCharacterOwner.ownerName,
|
||
ownerNodeId: ownerContext.soleCharacterOwner.nodeId || "",
|
||
});
|
||
continue;
|
||
}
|
||
|
||
ownershipWarnings.push({
|
||
kind: "invalid-owner-scope",
|
||
source: "cognitionUpdate",
|
||
ownerType,
|
||
});
|
||
}
|
||
|
||
return normalized;
|
||
}
|
||
|
||
function supportsPointStoryTime(type = "") {
|
||
return ["event", "pov_memory"].includes(String(type || ""));
|
||
}
|
||
|
||
function supportsSpanStoryTime(type = "") {
|
||
return ["thread", "synopsis", "reflection"].includes(String(type || ""));
|
||
}
|
||
|
||
function resolveOperationStoryTime(
|
||
graph,
|
||
op = {},
|
||
batchStoryTime = null,
|
||
{ source = "extract" } = {},
|
||
) {
|
||
const explicitStoryTime = normalizeStoryTime(op?.storyTime, { source });
|
||
const fallbackStoryTime = normalizeStoryTime(batchStoryTime, { source });
|
||
const candidate =
|
||
explicitStoryTime.segmentId || explicitStoryTime.label
|
||
? explicitStoryTime
|
||
: fallbackStoryTime.segmentId || fallbackStoryTime.label
|
||
? fallbackStoryTime
|
||
: null;
|
||
if (!candidate) {
|
||
return {
|
||
storyTime: normalizeStoryTime(),
|
||
storyTimeSpan: createSpanFromStoryTime(null, source),
|
||
timelineAdvanceApplied: false,
|
||
};
|
||
}
|
||
|
||
const activeReferenceSegmentId = String(
|
||
graph?.historyState?.activeStorySegmentId ||
|
||
graph?.historyState?.lastExtractedStorySegmentId ||
|
||
"",
|
||
).trim();
|
||
const upserted = upsertTimelineSegment(graph, candidate, {
|
||
referenceSegmentId: activeReferenceSegmentId,
|
||
source,
|
||
});
|
||
return {
|
||
storyTime: upserted.storyTime,
|
||
storyTimeSpan: createSpanFromStoryTime(upserted.storyTime, source),
|
||
timelineAdvanceApplied: false,
|
||
};
|
||
}
|
||
|
||
function applyOperationStoryTimeToNode(
|
||
graph,
|
||
node,
|
||
op = {},
|
||
batchStoryTime = null,
|
||
{ source = "extract" } = {},
|
||
) {
|
||
if (!node || typeof node !== "object") return;
|
||
const resolved = resolveOperationStoryTime(graph, op, batchStoryTime, { source });
|
||
if (supportsPointStoryTime(node.type)) {
|
||
node.storyTime = resolved.storyTime;
|
||
node.storyTimeSpan = createSpanFromStoryTime(null, source);
|
||
return;
|
||
}
|
||
if (supportsSpanStoryTime(node.type)) {
|
||
node.storyTime = normalizeStoryTime();
|
||
node.storyTimeSpan = resolved.storyTimeSpan;
|
||
return;
|
||
}
|
||
node.storyTime = normalizeStoryTime();
|
||
node.storyTimeSpan = createSpanFromStoryTime(null, source);
|
||
}
|
||
|
||
/**
|
||
* 对未处理的对话楼层执行记忆提取
|
||
*
|
||
* @param {object} params
|
||
* @param {object} params.graph - 当前图状态
|
||
* @param {Array<{seq?: number, role: string, content: string}>} params.messages - 要处理的对话消息
|
||
* @param {number} params.startSeq - 本批处理的首个 assistant 消息 chat 索引
|
||
* @param {number} params.endSeq - 本批处理的末个 assistant 消息 chat 索引
|
||
* @param {number} [params.lastProcessedSeq] - 上次处理到的 chat 索引
|
||
* @param {object[]} params.schema - 节点类型 Schema
|
||
* @param {object} params.embeddingConfig - Embedding API 配置
|
||
* @param {string} [params.extractPrompt] - 自定义提取提示词
|
||
* @param {object} [params.v2Options] - v2 增强选项
|
||
* @returns {Promise<{success: boolean, newNodes: number, updatedNodes: number, newEdges: number, newNodeIds: string[], processedRange: [number, number]}>}
|
||
*/
|
||
export async function extractMemories({
|
||
graph,
|
||
messages,
|
||
startSeq,
|
||
endSeq,
|
||
lastProcessedSeq = -1,
|
||
schema,
|
||
embeddingConfig,
|
||
extractPrompt,
|
||
signal = undefined,
|
||
settings = {},
|
||
onStreamProgress = null,
|
||
}) {
|
||
throwIfAborted(signal);
|
||
if (!messages || messages.length === 0) {
|
||
return {
|
||
success: true,
|
||
newNodes: 0,
|
||
updatedNodes: 0,
|
||
newEdges: 0,
|
||
newNodeIds: [],
|
||
processedRange: [lastProcessedSeq, lastProcessedSeq],
|
||
};
|
||
}
|
||
|
||
const effectiveStartSeq = Number.isFinite(startSeq)
|
||
? startSeq
|
||
: (messages.find((m) => Number.isFinite(m.seq))?.seq ??
|
||
lastProcessedSeq + 1);
|
||
const effectiveEndSeq = Number.isFinite(endSeq)
|
||
? endSeq
|
||
: ([...messages].reverse().find((m) => Number.isFinite(m.seq))?.seq ??
|
||
effectiveStartSeq);
|
||
const currentSeq = effectiveEndSeq;
|
||
const stContext = getSTContextSnapshot();
|
||
const scopeRuntime = {
|
||
activeCharacterOwner: stContext?.prompt?.charName || "",
|
||
activeUserOwner: stContext?.prompt?.userName || "",
|
||
};
|
||
|
||
debugLog(
|
||
`[ST-BME] 提取开始: chat[${effectiveStartSeq}..${effectiveEndSeq}], ${messages.length} 条消息`,
|
||
);
|
||
|
||
const extractionInput = buildExtractionInputContext(messages, {
|
||
settings,
|
||
userName: stContext?.prompt?.userName || "",
|
||
charName: stContext?.prompt?.charName || "",
|
||
});
|
||
const allStructuredMessages = Array.isArray(extractionInput?.filteredMessages)
|
||
? extractionInput.filteredMessages.map((message) => ({
|
||
seq: message?.seq,
|
||
role: message?.role,
|
||
content: message?.content,
|
||
speaker: message?.speaker,
|
||
name: message?.name,
|
||
}))
|
||
: [];
|
||
|
||
// Phase 3: apply recent message cap
|
||
const structuredMessages = applyRecentMessageCap(
|
||
allStructuredMessages,
|
||
settings?.extractRecentMessageCap,
|
||
);
|
||
const cappedMessageCount = allStructuredMessages.length - structuredMessages.length;
|
||
if (cappedMessageCount > 0) {
|
||
debugLog(
|
||
`[ST-BME][extract-p3] extractRecentMessageCap=${settings?.extractRecentMessageCap}, ` +
|
||
`capped ${cappedMessageCount} messages (${allStructuredMessages.length} -> ${structuredMessages.length})`,
|
||
);
|
||
}
|
||
|
||
// Phase 3: structured mode determines what goes into recentMessages/dialogueText
|
||
const structuredMode = resolveExtractPromptStructuredMode(settings);
|
||
const dialogueText = structuredMode === "structured"
|
||
? ""
|
||
: String(extractionInput?.filteredTranscript || "");
|
||
const promptRecentMessages = structuredMode === "transcript"
|
||
? dialogueText
|
||
: structuredMessages;
|
||
|
||
// 构建当前图概览（让 LLM 知道已有哪些节点，避免重复）
|
||
const graphOverview = buildGraphOverview(graph, schema);
|
||
|
||
// 构建 Schema 描述
|
||
const schemaDescription = buildSchemaDescription(schema);
|
||
const currentRange =
|
||
messages.length > 0
|
||
? `${messages[0]?.seq ?? "?"} ~ ${messages[messages.length - 1]?.seq ?? "?"}`
|
||
: "";
|
||
|
||
// Phase 3: layered context — active summaries and story time
|
||
const activeSummaries = settings?.extractIncludeSummaries !== false
|
||
? buildActiveSummariesText(graph)
|
||
: "";
|
||
const storyTimeContext = settings?.extractIncludeStoryTime !== false
|
||
? buildStoryTimeContextText(graph)
|
||
: "";
|
||
|
||
debugLog(
|
||
`[ST-BME][extract-p3] structuredMode=${structuredMode}, ` +
|
||
`activeSummaries=${activeSummaries ? activeSummaries.split("\n").length + " entries" : "none"}, ` +
|
||
`storyTimeContext=${storyTimeContext ? "present" : "none"}, ` +
|
||
`worldbookMode=${String(settings?.extractWorldbookMode || "active")}`,
|
||
);
|
||
|
||
const extractWorldbookMode = String(settings?.extractWorldbookMode || "active").trim().toLowerCase();
|
||
const promptBuild = await buildTaskPrompt(settings, "extract", {
|
||
taskName: "extract",
|
||
schema: schemaDescription,
|
||
schemaDescription,
|
||
recentMessages: promptRecentMessages,
|
||
chatMessages: structuredMessages,
|
||
dialogueText,
|
||
graphStats: graphOverview,
|
||
graphOverview,
|
||
currentRange,
|
||
activeSummaries,
|
||
storyTimeContext,
|
||
taskInputDebug: extractionInput?.debug || null,
|
||
__skipWorldInfo: extractWorldbookMode === "none",
|
||
...getSTContextForPrompt(),
|
||
});
|
||
|
||
// 系统提示词
|
||
const extractRegexInput = { entries: [] };
|
||
const systemPrompt = applyTaskRegex(
|
||
settings,
|
||
"extract",
|
||
"finalPrompt",
|
||
promptBuild.systemPrompt ||
|
||
extractPrompt ||
|
||
buildDefaultExtractPrompt(schema),
|
||
extractRegexInput,
|
||
"system",
|
||
);
|
||
|
||
// 用户提示词 — Phase 3 分层信息结构
|
||
const userPromptSections = [];
|
||
|
||
// Layer 1: 当前对话切片
|
||
if (dialogueText) {
|
||
userPromptSections.push("## 当前对话内容（需提取记忆）", dialogueText, "");
|
||
} else if (structuredMode === "structured" && structuredMessages.length > 0) {
|
||
userPromptSections.push(
|
||
"## 当前对话内容（结构化消息，需提取记忆）",
|
||
"(结构化消息已通过 profile blocks 注入，请参考上方 recentMessages 块。)",
|
||
"",
|
||
);
|
||
}
|
||
|
||
// Layer 2: 当前图谱状态
|
||
userPromptSections.push(
|
||
"## 当前图谱状态",
|
||
graphOverview || "(空图谱，尚无节点)",
|
||
"",
|
||
);
|
||
|
||
// Layer 3: 已有总结快照（帮助避免重复提取）
|
||
if (activeSummaries) {
|
||
userPromptSections.push(
|
||
"## 近期局面总结（已有覆盖，避免重复）",
|
||
activeSummaries,
|
||
"",
|
||
);
|
||
}
|
||
|
||
// Layer 4: 故事时间线位置
|
||
if (storyTimeContext) {
|
||
userPromptSections.push(
|
||
"## 当前故事时间",
|
||
storyTimeContext,
|
||
"",
|
||
);
|
||
}
|
||
|
||
// Layer 5: 节点类型定义
|
||
userPromptSections.push("## 节点类型定义", schemaDescription, "");
|
||
|
||
userPromptSections.push("请分析对话，按 JSON 格式输出操作列表。");
|
||
const userPrompt = userPromptSections.join("\n");
|
||
const promptPayload = resolveTaskPromptPayload(promptBuild, userPrompt);
|
||
const extractionAugmentPrompt = buildCognitiveExtractAugmentPrompt();
|
||
const promptPayloadAdditionalMessages = Array.isArray(
|
||
promptPayload.additionalMessages,
|
||
)
|
||
? [
|
||
...promptPayload.additionalMessages,
|
||
{
|
||
role: "system",
|
||
content: extractionAugmentPrompt,
|
||
},
|
||
]
|
||
: [
|
||
{
|
||
role: "system",
|
||
content: extractionAugmentPrompt,
|
||
},
|
||
];
|
||
const llmSystemPrompt = resolveTaskLlmSystemPrompt(
|
||
promptPayload,
|
||
systemPrompt,
|
||
);
|
||
|
||
// 诊断：追踪 promptPayload
|
||
{
|
||
const pm = Array.isArray(promptPayload.promptMessages) ? promptPayload.promptMessages : [];
|
||
const pmUser = pm.filter((m) => m?.role === "user");
|
||
const am = Array.isArray(promptPayload.additionalMessages) ? promptPayload.additionalMessages : [];
|
||
debugLog(
|
||
`[ST-BME][prompt-diag] resolveTaskPromptPayload: ` +
|
||
`promptMessages=${pm.length} (user=${pmUser.length}), ` +
|
||
`additionalMessages=${am.length}, ` +
|
||
`userPrompt length=${String(promptPayload.userPrompt || "").length}, ` +
|
||
`systemPrompt length=${String(promptPayload.systemPrompt || "").length}, ` +
|
||
`llmSystemPrompt length=${String(llmSystemPrompt || "").length}`,
|
||
);
|
||
if (pmUser.length > 0) {
|
||
for (const m of pmUser) {
|
||
debugLog(
|
||
`[ST-BME][prompt-diag] user msg: contentLen=${String(m.content || "").length}, ` +
|
||
`blockName="${m.blockName || ""}", preview="${String(m.content || "").slice(0, 60)}..."`,
|
||
);
|
||
}
|
||
} else {
|
||
debugWarn(
|
||
`[ST-BME][prompt-diag] NO user messages in promptMessages! Fallback userPrompt will be used.`,
|
||
);
|
||
}
|
||
if (extractionInput?.debug) {
|
||
debugLog(
|
||
`[ST-BME][extract-input] raw=${Number(extractionInput.debug.rawMessageCount || 0)}, ` +
|
||
`filtered=${Number(extractionInput.debug.filteredMessageCount || 0)}, ` +
|
||
`assistantChanged=${Number(extractionInput.debug.changedAssistantMessageCount || 0)}, ` +
|
||
`assistantDropped=${Number(extractionInput.debug.droppedAssistantMessageCount || 0)}, ` +
|
||
`extractRules=${Number(extractionInput.debug.assistantBoundaryConfig?.extractRuleCount || 0)}, ` +
|
||
`excludeRules=${Number(extractionInput.debug.assistantBoundaryConfig?.excludeRuleCount || 0)}`,
|
||
);
|
||
}
|
||
}
|
||
|
||
// 调用 LLM
|
||
const result = await callLLMForJSON({
|
||
systemPrompt: llmSystemPrompt,
|
||
userPrompt: promptPayload.userPrompt,
|
||
maxRetries: 2,
|
||
signal,
|
||
taskType: "extract",
|
||
debugContext: createExtractTaskLlmDebugContext(
|
||
promptBuild,
|
||
extractRegexInput,
|
||
extractionInput?.debug || null,
|
||
),
|
||
promptMessages: promptPayload.promptMessages,
|
||
additionalMessages: promptPayloadAdditionalMessages,
|
||
onStreamProgress,
|
||
});
|
||
throwIfAborted(signal);
|
||
const normalizedResult = normalizeExtractionResultPayload(result, schema);
|
||
const ownershipWarnings = [];
|
||
const extractionOwnerContext = deriveExtractionOwnerContext(
|
||
graph,
|
||
normalizedResult,
|
||
scopeRuntime,
|
||
);
|
||
const normalizedCognitionUpdates = normalizeCognitionUpdatesWithOwnerContext(
|
||
graph,
|
||
normalizedResult?.cognitionUpdates,
|
||
scopeRuntime,
|
||
extractionOwnerContext,
|
||
ownershipWarnings,
|
||
);
|
||
|
||
if (!normalizedResult || !Array.isArray(normalizedResult.operations)) {
|
||
const diagType = result === null
|
||
? "null"
|
||
: Array.isArray(result)
|
||
? `array(len=${result.length})`
|
||
: typeof result;
|
||
const diagKeys = isPlainObject(result)
|
||
? Object.keys(result).slice(0, 10).join(", ")
|
||
: "";
|
||
const diagPreview = typeof result === "string"
|
||
? result.slice(0, 120)
|
||
: "";
|
||
console.warn(
|
||
`[ST-BME] 提取 LLM 未返回有效操作 ` +
|
||
`[type=${diagType}]` +
|
||
(diagKeys ? ` [keys=${diagKeys}]` : "") +
|
||
(diagPreview ? ` [preview=${diagPreview}]` : ""),
|
||
);
|
||
return {
|
||
success: false,
|
||
error: "提取 LLM 未返回有效操作",
|
||
newNodes: 0,
|
||
updatedNodes: 0,
|
||
newEdges: 0,
|
||
newNodeIds: [],
|
||
processedRange: [lastProcessedSeq, lastProcessedSeq],
|
||
};
|
||
}
|
||
|
||
// 执行操作
|
||
const stats = { newNodes: 0, updatedNodes: 0, newEdges: 0 };
|
||
const newNodeIds = []; // v2: 收集新建节点 ID（用于进化引擎）
|
||
const updatedNodeIds = [];
|
||
const refMap = new Map();
|
||
const operationErrors = [];
|
||
const normalizedBatchStoryTime = normalizedResult?.batchStoryTime || null;
|
||
|
||
for (const op of normalizedResult.operations) {
|
||
try {
|
||
switch (op.action) {
|
||
case "create": {
|
||
const createdId = handleCreate(
|
||
graph,
|
||
op,
|
||
currentSeq,
|
||
schema,
|
||
refMap,
|
||
stats,
|
||
scopeRuntime,
|
||
extractionOwnerContext,
|
||
ownershipWarnings,
|
||
normalizedBatchStoryTime,
|
||
);
|
||
if (createdId) newNodeIds.push(createdId);
|
||
break;
|
||
}
|
||
case "update":
|
||
{
|
||
const updatedNodeId = handleUpdate(
|
||
graph,
|
||
op,
|
||
currentSeq,
|
||
stats,
|
||
scopeRuntime,
|
||
extractionOwnerContext,
|
||
ownershipWarnings,
|
||
normalizedBatchStoryTime,
|
||
);
|
||
if (updatedNodeId) updatedNodeIds.push(updatedNodeId);
|
||
}
|
||
break;
|
||
case "delete":
|
||
handleDelete(graph, op, stats);
|
||
break;
|
||
case "_skip":
|
||
// Mem0 对照判定为重复，跳过
|
||
break;
|
||
default: {
|
||
const message = `[ST-BME] 未知操作类型: ${op?.action ?? "<missing>"}`;
|
||
console.warn(message, op);
|
||
operationErrors.push(message);
|
||
break;
|
||
}
|
||
}
|
||
} catch (e) {
|
||
console.error(`[ST-BME] 操作执行失败:`, op, e);
|
||
operationErrors.push(e?.message || String(e));
|
||
}
|
||
}
|
||
|
||
if (operationErrors.length > 0) {
|
||
return {
|
||
success: false,
|
||
error: operationErrors.join(" | "),
|
||
...stats,
|
||
newNodeIds,
|
||
processedRange: [effectiveStartSeq, effectiveEndSeq],
|
||
};
|
||
}
|
||
|
||
// 为新建节点生成 embedding。失败不应回滚整批图谱写入。
|
||
try {
|
||
await generateNodeEmbeddings(graph, embeddingConfig, signal);
|
||
} catch (error) {
|
||
if (isAbortError(error)) {
|
||
throw error;
|
||
}
|
||
console.error("[ST-BME] 节点 embedding 生成失败，保留图谱写入:", error);
|
||
}
|
||
|
||
// 更新处理进度：统一记录为已处理到的末个 chat 索引
|
||
graph.lastProcessedSeq = Math.max(
|
||
graph.lastProcessedSeq ?? -1,
|
||
effectiveEndSeq,
|
||
);
|
||
const changedNodeIds = [...new Set([...newNodeIds, ...updatedNodeIds])];
|
||
if (ownershipWarnings.length > 0) {
|
||
debugWarn(
|
||
`[ST-BME] 已跳过 ${ownershipWarnings.length} 条缺少具体人物 owner 的主观记忆或认知更新`,
|
||
);
|
||
}
|
||
applyCognitionUpdates(graph, normalizedCognitionUpdates, {
|
||
refMap,
|
||
changedNodeIds,
|
||
scopeRuntime,
|
||
source: "extract",
|
||
});
|
||
applyRegionUpdates(graph, normalizedResult.regionUpdates, {
|
||
changedNodeIds,
|
||
source: "extract",
|
||
});
|
||
const batchStoryTimeResult = applyBatchStoryTime(
|
||
graph,
|
||
normalizedBatchStoryTime,
|
||
"extract",
|
||
);
|
||
updateRuntimeScopeState(graph, newNodeIds, scopeRuntime, extractionOwnerContext);
|
||
|
||
debugLog(
|
||
`[ST-BME] 提取完成: 新建 ${stats.newNodes}, 更新 ${stats.updatedNodes}, 新边 ${stats.newEdges}, lastProcessedSeq=${graph.lastProcessedSeq}`,
|
||
);
|
||
|
||
return {
|
||
success: true,
|
||
error: "",
|
||
...stats,
|
||
newNodeIds,
|
||
changedNodeIds,
|
||
ownerWarnings: ownershipWarnings,
|
||
batchStoryTime: normalizedBatchStoryTime,
|
||
batchStoryTimeResult,
|
||
processedRange: [effectiveStartSeq, effectiveEndSeq],
|
||
};
|
||
}
|
||
|
||
/**
|
||
* 处理 create 操作
|
||
*/
|
||
function handleCreate(
|
||
graph,
|
||
op,
|
||
seq,
|
||
schema,
|
||
refMap,
|
||
stats,
|
||
scopeRuntime = {},
|
||
ownerContext = {},
|
||
ownershipWarnings = [],
|
||
batchStoryTime = null,
|
||
) {
|
||
const normalizedFields =
|
||
op.type === "event" ? ensureEventTitle(op.fields || {}) : op.fields || {};
|
||
const typeDef = schema.find((s) => s.id === op.type);
|
||
if (!typeDef) {
|
||
console.warn(`[ST-BME] 未知节点类型: ${op.type}`);
|
||
return null;
|
||
}
|
||
const scopeDecision = resolveOperationScope(
|
||
graph,
|
||
op,
|
||
scopeRuntime,
|
||
ownerContext,
|
||
);
|
||
if (scopeDecision.invalidReason) {
|
||
ownershipWarnings.push({
|
||
kind: scopeDecision.invalidReason,
|
||
source: "operation",
|
||
action: String(op?.action || ""),
|
||
type: String(op?.type || ""),
|
||
});
|
||
return null;
|
||
}
|
||
const nodeScope = scopeDecision.scope;
|
||
|
||
// latestOnly 类型：检查是否已存在同名节点
|
||
if (typeDef.latestOnly && op.fields?.name) {
|
||
const existing = findLatestNode(
|
||
graph,
|
||
op.type,
|
||
op.fields.name,
|
||
"name",
|
||
nodeScope,
|
||
);
|
||
if (existing) {
|
||
// 转为更新操作
|
||
updateNode(graph, existing.id, { fields: op.fields, seq, scope: nodeScope });
|
||
applyOperationStoryTimeToNode(graph, existing, op, batchStoryTime);
|
||
stats.updatedNodes++;
|
||
|
||
if (op.ref) refMap.set(op.ref, existing.id);
|
||
|
||
// 处理关联边
|
||
if (op.links) {
|
||
handleLinks(graph, existing.id, op.links, refMap, stats);
|
||
}
|
||
return null;
|
||
}
|
||
}
|
||
|
||
// 创建新节点
|
||
const node = createNode({
|
||
type: op.type,
|
||
fields: normalizedFields,
|
||
seq,
|
||
importance: op.importance ?? 5.0,
|
||
clusters: op.clusters || [],
|
||
scope: nodeScope,
|
||
});
|
||
applyOperationStoryTimeToNode(graph, node, op, batchStoryTime);
|
||
|
||
addNode(graph, node);
|
||
stats.newNodes++;
|
||
|
||
// 保存 ref 用于同批次引用
|
||
if (op.ref) {
|
||
refMap.set(op.ref, node.id);
|
||
}
|
||
|
||
// 处理关联边
|
||
if (op.links) {
|
||
handleLinks(graph, node.id, op.links, refMap, stats);
|
||
}
|
||
|
||
return node.id;
|
||
}
|
||
|
||
/**
|
||
* 处理 update 操作
|
||
*/
|
||
function handleUpdate(
|
||
graph,
|
||
op,
|
||
currentSeq,
|
||
stats,
|
||
scopeRuntime = {},
|
||
ownerContext = {},
|
||
ownershipWarnings = [],
|
||
batchStoryTime = null,
|
||
) {
|
||
if (!op.nodeId) {
|
||
console.warn("[ST-BME] update 操作缺少 nodeId");
|
||
return "";
|
||
}
|
||
|
||
const previousNode = getNode(graph, op.nodeId);
|
||
if (!previousNode) {
|
||
console.warn(`[ST-BME] update 目标节点不存在: ${op.nodeId}`);
|
||
return "";
|
||
}
|
||
|
||
const previousFields = { ...(previousNode.fields || {}) };
|
||
const nextFields =
|
||
previousNode.type === "event"
|
||
? ensureEventTitle({ ...previousFields, ...(op.fields || {}) })
|
||
: { ...previousFields, ...(op.fields || {}) };
|
||
const changeSummary = buildFieldChangeSummary(previousFields, nextFields);
|
||
const scopeDecision = resolveOperationScope(
|
||
graph,
|
||
op,
|
||
scopeRuntime,
|
||
ownerContext,
|
||
{ existingScope: previousNode.scope },
|
||
);
|
||
if (scopeDecision.invalidReason && previousNode.type === "pov_memory") {
|
||
ownershipWarnings.push({
|
||
kind: scopeDecision.invalidReason,
|
||
source: "operation",
|
||
action: String(op?.action || ""),
|
||
type: String(op?.type || ""),
|
||
nodeId: previousNode.id,
|
||
});
|
||
}
|
||
const resolvedScope = scopeDecision.scope;
|
||
|
||
const updateSeq = Number.isFinite(op.seq) ? op.seq : currentSeq;
|
||
const updated = updateNode(graph, op.nodeId, {
|
||
fields: op.fields || {},
|
||
seq: Math.max(previousNode.seq || 0, updateSeq),
|
||
scope: resolvedScope,
|
||
});
|
||
|
||
if (updated) {
|
||
stats.updatedNodes++;
|
||
const node = getNode(graph, op.nodeId);
|
||
if (node) {
|
||
applyOperationStoryTimeToNode(graph, node, op, batchStoryTime);
|
||
node.embedding = null;
|
||
node.seq = Math.max(node.seq || 0, updateSeq);
|
||
node.seqRange = [
|
||
Math.min(node.seqRange?.[0] ?? node.seq, updateSeq),
|
||
Math.max(node.seqRange?.[1] ?? node.seq, updateSeq),
|
||
];
|
||
}
|
||
|
||
// v2 Graphiti: 标记旧的 updates/temporal_update 边为失效
|
||
const oldEdges = graph.edges.filter(
|
||
(e) =>
|
||
!e.invalidAt &&
|
||
((e.relation === "updates" && e.toId === op.nodeId) ||
|
||
(e.relation === "temporal_update" &&
|
||
e.toId === op.nodeId &&
|
||
op.sourceNodeId &&
|
||
e.fromId === op.sourceNodeId)),
|
||
);
|
||
for (const e of oldEdges) {
|
||
invalidateEdge(e);
|
||
}
|
||
|
||
if (op.sourceNodeId && op.sourceNodeId !== op.nodeId) {
|
||
const temporalEdge = createEdge({
|
||
fromId: op.sourceNodeId,
|
||
toId: op.nodeId,
|
||
relation: "temporal_update",
|
||
strength: op.temporalStrength ?? 0.95,
|
||
edgeType: 0,
|
||
});
|
||
if (addEdge(graph, temporalEdge)) {
|
||
stats.newEdges++;
|
||
}
|
||
}
|
||
|
||
if (changeSummary) {
|
||
const updateEventNode = createNode({
|
||
type: "event",
|
||
fields: {
|
||
title: `${previousNode.fields?.name || previousNode.fields?.title || previousNode.type} 状态更新`,
|
||
summary: `${previousNode.type} 状态更新：${changeSummary}`,
|
||
participants:
|
||
previousNode.fields?.name ||
|
||
previousNode.fields?.title ||
|
||
previousNode.id,
|
||
status: "resolved",
|
||
},
|
||
seq: updateSeq,
|
||
importance: Math.max(
|
||
4,
|
||
Math.min(8, op.importance ?? previousNode.importance ?? 5),
|
||
),
|
||
scope: isObjectiveScope(previousNode.scope)
|
||
? normalizeMemoryScope(previousNode.scope)
|
||
: normalizeMemoryScope({
|
||
layer: "objective",
|
||
regionPrimary: resolvedScope.regionPrimary,
|
||
regionPath: resolvedScope.regionPath,
|
||
regionSecondary: resolvedScope.regionSecondary,
|
||
}),
|
||
});
|
||
addNode(graph, updateEventNode);
|
||
stats.newNodes++;
|
||
|
||
const updateEdge = createEdge({
|
||
fromId: updateEventNode.id,
|
||
toId: op.nodeId,
|
||
relation: "updates",
|
||
strength: 0.9,
|
||
edgeType: 0,
|
||
scope: updateEventNode.scope,
|
||
});
|
||
if (addEdge(graph, updateEdge)) {
|
||
stats.newEdges++;
|
||
}
|
||
}
|
||
}
|
||
return updated ? op.nodeId : "";
|
||
}
|
||
|
||
function buildFieldChangeSummary(previousFields = {}, nextFields = {}) {
|
||
const changes = [];
|
||
const keys = new Set([
|
||
...Object.keys(previousFields),
|
||
...Object.keys(nextFields),
|
||
]);
|
||
|
||
for (const key of keys) {
|
||
const before = previousFields[key];
|
||
const after = nextFields[key];
|
||
if (before === after) continue;
|
||
|
||
const beforeText = before == null || before === "" ? "空" : String(before);
|
||
const afterText = after == null || after === "" ? "空" : String(after);
|
||
changes.push(`${key}: ${beforeText} -> ${afterText}`);
|
||
}
|
||
|
||
return changes.slice(0, 3).join("；");
|
||
}
|
||
|
||
/**
|
||
* 处理 delete 操作
|
||
*/
|
||
function handleDelete(graph, op, stats) {
|
||
if (!op.nodeId) return;
|
||
const node = graph.nodes.find((n) => n.id === op.nodeId);
|
||
if (node) {
|
||
node.archived = true; // 软删除
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 处理关联边
|
||
*/
|
||
function handleLinks(graph, sourceId, links, refMap, stats) {
|
||
const sourceNode = getNode(graph, sourceId);
|
||
const sourceScope = normalizeMemoryScope(sourceNode?.scope);
|
||
for (const link of links) {
|
||
let targetId = link.targetNodeId || null;
|
||
|
||
// 通过 ref 解析目标节点
|
||
if (!targetId && link.targetRef) {
|
||
targetId = refMap.get(link.targetRef);
|
||
}
|
||
|
||
if (!targetId) continue;
|
||
|
||
// 验证关系类型
|
||
const relation = RELATION_TYPES.includes(link.relation)
|
||
? link.relation
|
||
: "related";
|
||
|
||
const edgeType = relation === "contradicts" ? 255 : 0;
|
||
|
||
const edge = createEdge({
|
||
fromId: sourceId,
|
||
toId: targetId,
|
||
relation,
|
||
strength: link.strength ?? 0.8,
|
||
edgeType,
|
||
scope: link.scope || sourceScope,
|
||
});
|
||
|
||
if (addEdge(graph, edge)) {
|
||
stats.newEdges++;
|
||
}
|
||
}
|
||
}
|
||
|
||
function resolveOperationScope(
|
||
graph,
|
||
op,
|
||
scopeRuntime = {},
|
||
ownerContext = {},
|
||
{ existingScope = null } = {},
|
||
) {
|
||
const fallbackScope = normalizeMemoryScope(
|
||
existingScope || { layer: op?.type === "pov_memory" ? "pov" : "objective" },
|
||
);
|
||
|
||
if (op?.type !== "pov_memory") {
|
||
return {
|
||
scope: op?.scope
|
||
? normalizeMemoryScope(op.scope, existingScope || {})
|
||
: fallbackScope.layer === "objective"
|
||
? fallbackScope
|
||
: normalizeMemoryScope({ layer: "objective" }),
|
||
invalidReason: "",
|
||
};
|
||
}
|
||
|
||
if (!op?.scope && existingScope) {
|
||
return {
|
||
scope: normalizeMemoryScope(existingScope),
|
||
invalidReason: "",
|
||
};
|
||
}
|
||
|
||
const rawScope = op?.scope ? normalizeMemoryScope(op.scope) : null;
|
||
const ownerType = String(rawScope?.ownerType || "").trim();
|
||
const explicitOwnerName = normalizeExtractionOwnerText(
|
||
rawScope?.ownerName || rawScope?.ownerId,
|
||
);
|
||
const explicitOwnerNodeId = normalizeExtractionOwnerText(
|
||
op?.scope?.ownerNodeId || op?.scope?.owner_node_id,
|
||
);
|
||
|
||
if (ownerType === "user") {
|
||
const userName = resolveExtractorUserOwnerName(
|
||
scopeRuntime,
|
||
explicitOwnerName,
|
||
);
|
||
if (!userName) {
|
||
return {
|
||
scope: fallbackScope,
|
||
invalidReason: "invalid-owner-scope",
|
||
};
|
||
}
|
||
return {
|
||
scope: normalizeMemoryScope({
|
||
...(rawScope || {}),
|
||
layer: "pov",
|
||
ownerType: "user",
|
||
ownerId: userName,
|
||
ownerName: userName,
|
||
}),
|
||
invalidReason: "",
|
||
};
|
||
}
|
||
|
||
if (
|
||
ownerType === "character" &&
|
||
explicitOwnerName &&
|
||
!explicitOwnerNodeId &&
|
||
matchesExtractorUserAlias(explicitOwnerName, scopeRuntime)
|
||
) {
|
||
const userName = resolveExtractorUserOwnerName(
|
||
scopeRuntime,
|
||
explicitOwnerName,
|
||
);
|
||
if (!userName) {
|
||
return {
|
||
scope: fallbackScope,
|
||
invalidReason: "invalid-owner-scope",
|
||
};
|
||
}
|
||
return {
|
||
scope: normalizeMemoryScope({
|
||
...(rawScope || {}),
|
||
layer: "pov",
|
||
ownerType: "user",
|
||
ownerId: userName,
|
||
ownerName: userName,
|
||
}),
|
||
invalidReason: "",
|
||
};
|
||
}
|
||
|
||
const resolvedCharacterOwner =
|
||
resolveCharacterOwnerCandidate(graph, explicitOwnerName, explicitOwnerNodeId) ||
|
||
ownerContext?.soleCharacterOwner ||
|
||
null;
|
||
if (!resolvedCharacterOwner?.ownerKey) {
|
||
return {
|
||
scope: fallbackScope,
|
||
invalidReason: "invalid-owner-scope",
|
||
};
|
||
}
|
||
|
||
return {
|
||
scope: normalizeMemoryScope({
|
||
...(rawScope || {}),
|
||
layer: "pov",
|
||
ownerType: "character",
|
||
ownerId: resolvedCharacterOwner.ownerName,
|
||
ownerName: resolvedCharacterOwner.ownerName,
|
||
}),
|
||
invalidReason: "",
|
||
};
|
||
}
|
||
|
||
function updateRuntimeScopeState(
|
||
graph,
|
||
newNodeIds = [],
|
||
scopeRuntime = {},
|
||
ownerContext = {},
|
||
) {
|
||
if (!graph?.historyState || typeof graph.historyState !== "object") {
|
||
return;
|
||
}
|
||
|
||
graph.historyState.activeCharacterPovOwner =
|
||
String(ownerContext?.soleCharacterOwner?.ownerName || "");
|
||
graph.historyState.activeUserPovOwner =
|
||
String(scopeRuntime.activeUserOwner || "");
|
||
|
||
const objectiveCandidates = (Array.isArray(newNodeIds) ? newNodeIds : [])
|
||
.map((nodeId) => getNode(graph, nodeId))
|
||
.filter((node) => node && !node.archived && isObjectiveScope(node.scope))
|
||
.sort((a, b) => (b.seqRange?.[1] ?? b.seq ?? 0) - (a.seqRange?.[1] ?? a.seq ?? 0));
|
||
|
||
const regionNode =
|
||
objectiveCandidates.find((node) => node.scope?.regionPrimary) ||
|
||
getActiveNodes(graph)
|
||
.filter((node) => !node.archived && isObjectiveScope(node.scope))
|
||
.sort((a, b) => (b.seqRange?.[1] ?? b.seq ?? 0) - (a.seqRange?.[1] ?? a.seq ?? 0))
|
||
.find((node) => node.scope?.regionPrimary);
|
||
|
||
if (regionNode?.scope?.regionPrimary) {
|
||
graph.historyState.lastExtractedRegion = String(
|
||
regionNode.scope.regionPrimary || "",
|
||
);
|
||
if (!String(graph?.regionState?.manualActiveRegion || "").trim()) {
|
||
graph.historyState.activeRegion = String(regionNode.scope.regionPrimary || "");
|
||
graph.historyState.activeRegionSource = "extract";
|
||
}
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 为缺少 embedding 的节点生成向量
|
||
*/
|
||
async function generateNodeEmbeddings(graph, embeddingConfig, signal) {
|
||
if (!isDirectVectorConfig(embeddingConfig)) return;
|
||
throwIfAborted(signal);
|
||
|
||
const needsEmbedding = graph.nodes.filter(
|
||
(n) =>
|
||
!n.archived && (!Array.isArray(n.embedding) || n.embedding.length === 0),
|
||
);
|
||
|
||
if (needsEmbedding.length === 0) return;
|
||
|
||
const texts = needsEmbedding.map(
|
||
(node) => buildNodeVectorText(node) || node.type,
|
||
);
|
||
|
||
debugLog(`[ST-BME] 为 ${texts.length} 个节点生成 embedding`);
|
||
|
||
const embeddings = await embedBatch(texts, embeddingConfig, { signal });
|
||
|
||
for (let i = 0; i < needsEmbedding.length; i++) {
|
||
if (embeddings[i]) {
|
||
needsEmbedding[i].embedding = Array.from(embeddings[i]);
|
||
}
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 构建图谱概览文本（给 LLM 看）
|
||
*/
|
||
function buildGraphOverview(graph, schema) {
|
||
const activeNodes = graph.nodes
|
||
.filter((n) => !n.archived)
|
||
.sort((a, b) => (a.seq || 0) - (b.seq || 0));
|
||
if (activeNodes.length === 0) return "";
|
||
|
||
const lines = [];
|
||
for (const typeDef of schema) {
|
||
const nodesOfType = activeNodes.filter((n) => n.type === typeDef.id);
|
||
if (nodesOfType.length === 0) continue;
|
||
|
||
lines.push(`### ${typeDef.label} (${nodesOfType.length} 个节点)`);
|
||
for (const node of nodesOfType.slice(-10)) {
|
||
// 只展示最近 10 个
|
||
lines.push(` - [${node.id}] ${getNodeDisplayName(node)}`);
|
||
}
|
||
}
|
||
|
||
return lines.join("\n");
|
||
}
|
||
|
||
/**
|
||
* 构建 Schema 描述文本
|
||
*/
|
||
function buildSchemaDescription(schema) {
|
||
return schema
|
||
.map((t) => {
|
||
const cols = t.columns
|
||
.map((c) => `${c.name}${c.required ? "(必填)" : ""}: ${c.hint}`)
|
||
.join("\n ");
|
||
return `类型 "${t.id}" (${t.label}):\n ${cols}`;
|
||
})
|
||
.join("\n\n");
|
||
}
|
||
|
||
/**
|
||
* 构建默认提取提示词
|
||
*/
|
||
function buildDefaultExtractPrompt(schema) {
|
||
const typeNames = schema.map((s) => `${s.id}(${s.label})`).join(", ");
|
||
|
||
return [
|
||
"你是一个记忆提取分析器。从对话中提取结构化记忆节点并存入知识图谱。",
|
||
"",
|
||
`支持的节点类型：${typeNames}`,
|
||
"",
|
||
"这轮必须同时考虑三层信息：",
|
||
"- 客观事实：继续写入 event / character / location / thread / rule / synopsis / reflection",
|
||
'- 主观记忆：统一写入 pov_memory，使用 scope.layer = "pov"',
|
||
"- 地区归属：能判断时写入 scope.regionPrimary / regionPath / regionSecondary，判断不出来就留空",
|
||
"",
|
||
"输出格式为严格 JSON：",
|
||
"{",
|
||
' "thought": "你对本段对话的分析（事件/角色变化/新信息/谁如何理解）",',
|
||
' "batchStoryTime": {"label": "第二天清晨", "tense": "ongoing", "relation": "after", "anchorLabel": "昨夜冲突之后", "confidence": "high", "advancesActiveTimeline": true},',
|
||
' "operations": [',
|
||
" {",
|
||
' "action": "create",',
|
||
' "type": "event",',
|
||
' "fields": {"title": "简短事件名", "summary": "...", "participants": "...", "status": "ongoing"},',
|
||
' "scope": {"layer": "objective", "regionPrimary": "主地区", "regionPath": ["上级地区", "主地区"], "regionSecondary": ["次级地区"]},',
|
||
' "storyTime": {"label": "第二天清晨", "tense": "ongoing", "relation": "same", "confidence": "high"},',
|
||
' "importance": 6,',
|
||
' "ref": "evt1",',
|
||
' "links": [',
|
||
' {"targetNodeId": "existing-id", "relation": "involved_in", "strength": 0.9},',
|
||
' {"targetRef": "char1", "relation": "occurred_at", "strength": 0.8}',
|
||
" ]",
|
||
" },",
|
||
" {",
|
||
' "action": "create",',
|
||
' "type": "pov_memory",',
|
||
' "fields": {"summary": "角色怎么记住这件事", "belief": "她认为发生了什么", "emotion": "情绪", "attitude": "态度", "certainty": "unsure", "about": "evt1"},',
|
||
' "scope": {"layer": "pov", "ownerType": "character", "ownerId": "角色名", "ownerName": "角色名", "regionPrimary": "主地区", "regionPath": ["上级地区", "主地区"]}',
|
||
" },",
|
||
" {",
|
||
' "action": "create",',
|
||
' "type": "pov_memory",',
|
||
' "fields": {"summary": "用户怎么记住这件事", "belief": "用户视角判断", "emotion": "情绪", "attitude": "态度", "certainty": "certain", "about": "evt1"},',
|
||
' "scope": {"layer": "pov", "ownerType": "user", "ownerId": "用户名", "ownerName": "用户名"}',
|
||
" }",
|
||
" ],",
|
||
' "cognitionUpdates": [',
|
||
" {",
|
||
' "ownerType": "character",',
|
||
' "ownerName": "艾琳",',
|
||
' "ownerNodeId": "char-1",',
|
||
' "knownRefs": ["evt1", "char2"],',
|
||
' "mistakenRefs": ["evt2"],',
|
||
' "visibility": [',
|
||
' {"ref": "evt1", "score": 1.0, "reason": "direct witness"},',
|
||
' {"ref": "thread-1", "score": 0.55, "reason": "heard nearby"}',
|
||
" ]",
|
||
" }",
|
||
" ],",
|
||
' "regionUpdates": {',
|
||
' "activeRegionHint": "钟楼",',
|
||
' "adjacency": [',
|
||
' {"region": "钟楼", "adjacent": ["旧城区", "内廷"]}',
|
||
" ]",
|
||
" }",
|
||
"}",
|
||
"",
|
||
"规则：",
|
||
"- 每批对话最多创建 1 个事件节点，多个子事件合并为一条",
|
||
"- batchStoryTime 表示这批对话主叙事所处的剧情时间；普通当前场景尽量填写，推不出来就留空",
|
||
"- operations[].storyTime 用于节点自己的剧情时间；不写时系统会继承 batchStoryTime",
|
||
"- 必须区分聊天顺序和剧情顺序，不要把“后说到”误当成“后发生”",
|
||
"- flashback / hypothetical / future 可以写 storyTime，但通常不要把 advancesActiveTimeline 设为 true",
|
||
"- 涉及到的角色都尽量尝试生成对应 POV 记忆和 cognitionUpdates；不必强行覆盖全图所有角色",
|
||
"- cognitionUpdates 用来表达谁确定知道、谁误解了什么、谁只是模糊可见",
|
||
"- 多角色场景里，pov_memory 和 cognitionUpdates 必须写清具体人物；不要把角色卡名当作 POV owner",
|
||
"- 只有在这一批明显只涉及一个具体角色实体时，才允许省略 character POV 的 owner 并让系统安全归属",
|
||
"- knownRefs / mistakenRefs 优先引用同批 ref；没有 ref 再引用现有 nodeId",
|
||
"- regionUpdates 只有在对话里明确出现地区线索时才写；不确定就留空",
|
||
"- 角色/地点节点：如果图中已有同名同作用域节点，用 update 而非 create",
|
||
`- 关系类型限定：${RELATION_TYPES.join(", ")}`,
|
||
"- contradicts 关系用于矛盾/冲突信息",
|
||
"- evolves 关系用于新信息揭示旧记忆需修正的情况",
|
||
"- temporal_update 关系用于实体状态的时序变化",
|
||
"- 不要虚构内容，只提取对话中有证据支持的信息",
|
||
"- 用户 POV 不等于角色已知事实，不要把用户想法伪装成客观事实",
|
||
"- pov_memory 只能用于主观记忆，不要拿 character/location/event 去伪装第一视角记忆",
|
||
"- 地区不确定就留空，不要硬编",
|
||
"- importance 范围 1-10，普通事件 5，关键转折 8+",
|
||
"- event.fields.title 需要是简短事件名，建议 6-18 字，只用于图谱和列表显示",
|
||
"- summary 应该是摘要抽象，不要复制原文",
|
||
].join("\n");
|
||
}
|
||
|
||
function buildCognitiveExtractAugmentPrompt() {
|
||
return [
|
||
"增强要求：这一轮提取除了 operations，还要尽量补 cognitionUpdates 与 regionUpdates。",
|
||
"- cognitionUpdates 表达谁明确知道哪些客观节点、谁产生了误解、谁只是低置信可见。",
|
||
"- 本批涉及到的角色都尽量尝试生成 POV 和记忆认知更新，不必覆盖全图全部角色。",
|
||
"- ownerType 只能是 character 或 user；ownerName 必须写清楚角色名或用户名。",
|
||
"- 不要把角色卡名、旁白身份或群像统称当成 POV owner；多角色时一定写具体人物。",
|
||
"- knownRefs / mistakenRefs 优先引用同批 ref，没有 ref 再用现有 nodeId。",
|
||
"- visibility.score 取 0..1，1 表示亲历或明确得知，0.5 左右表示间接听闻。",
|
||
"- regionUpdates.activeRegionHint 只在这批对话明确落到某个地区时填写。",
|
||
"- regionUpdates.adjacency 只在文本里明确出现邻接关系时填写，不要猜。",
|
||
"- batchStoryTime.label 尽量写成可复用的剧情时间标签，例如“第二天清晨”“昨夜之后”“回忆里的童年时期”。",
|
||
"- advancesActiveTimeline 只有在这批确实推动当前主叙事时间线时才写 true。",
|
||
"- 若没有认知或空间变化，可返回空数组或空对象，但不要返回无效结构。",
|
||
].join("\n");
|
||
}
|
||
|
||
// ==================== v2 增强功能 ====================
|
||
|
||
/**
|
||
* 全局故事概要生成（MemoRAG 启发）
|
||
* 基于图中事件/角色/主线自动生成/更新 synopsis 节点
|
||
*
|
||
* @param {object} params
|
||
* @param {object} params.graph
|
||
* @param {object[]} params.schema
|
||
* @param {number} params.currentSeq
|
||
* @returns {Promise<void>}
|
||
*/
|
||
export async function generateSynopsis({
|
||
graph,
|
||
schema,
|
||
currentSeq,
|
||
customPrompt,
|
||
signal,
|
||
settings = {},
|
||
}) {
|
||
const eventNodes = getActiveNodes(graph, "event").sort(
|
||
(a, b) => a.seq - b.seq,
|
||
);
|
||
|
||
if (eventNodes.length < 3) return;
|
||
|
||
const eventSummaries = eventNodes
|
||
.map((n) => {
|
||
const storyLabel = describeNodeStoryTime(n);
|
||
return `[楼${n.seq}]${storyLabel ? ` [${storyLabel}]` : ""} ${n.fields.summary || "(无)"}`;
|
||
})
|
||
.join("\n");
|
||
|
||
const characterNodes = getActiveNodes(graph, "character");
|
||
const charSummary = characterNodes
|
||
.map((n) => `${n.fields.name}: ${n.fields.state || "(无状态)"}`)
|
||
.join("; ");
|
||
|
||
const threadNodes = getActiveNodes(graph, "thread");
|
||
const threadSummary = threadNodes
|
||
.map((n) => {
|
||
const storyLabel = describeNodeStoryTime(n);
|
||
return `${n.fields.title}: ${n.fields.status || "active"}${storyLabel ? `（${storyLabel}）` : ""}`;
|
||
})
|
||
.join("; ");
|
||
const synopsisStoryTimeSpan = deriveStoryTimeSpanFromNodes(
|
||
graph,
|
||
[...eventNodes, ...threadNodes],
|
||
"derived",
|
||
);
|
||
|
||
const synopsisPromptBuild = await buildTaskPrompt(settings, "synopsis", {
|
||
taskName: "synopsis",
|
||
eventSummary: eventSummaries,
|
||
characterSummary: charSummary || "(无)",
|
||
threadSummary: threadSummary || "(无)",
|
||
graphStats: `event=${eventNodes.length}, character=${characterNodes.length}, thread=${threadNodes.length}`,
|
||
...getSTContextForPrompt(),
|
||
});
|
||
const synopsisRegexInput = { entries: [] };
|
||
const synopsisSystemPrompt = applyTaskRegex(
|
||
settings,
|
||
"synopsis",
|
||
"finalPrompt",
|
||
synopsisPromptBuild.systemPrompt ||
|
||
customPrompt ||
|
||
[
|
||
"你是故事概要生成器。根据事件线、角色和主线生成简洁的前情提要。",
|
||
'输出 JSON：{"summary": "前情提要文本（200字以内）"}',
|
||
"要求：涵盖核心冲突、关键转折、主要角色当前状态。",
|
||
].join("\n"),
|
||
synopsisRegexInput,
|
||
"system",
|
||
);
|
||
|
||
const synopsisUserPrompt = [
|
||
"## 事件时间线",
|
||
eventSummaries,
|
||
"",
|
||
"## 角色状态",
|
||
charSummary || "(无)",
|
||
"",
|
||
"## 活跃主线",
|
||
threadSummary || "(无)",
|
||
].join("\n");
|
||
const synopsisPromptPayload = resolveTaskPromptPayload(
|
||
synopsisPromptBuild,
|
||
synopsisUserPrompt,
|
||
);
|
||
|
||
const result = await callLLMForJSON({
|
||
systemPrompt: resolveTaskLlmSystemPrompt(
|
||
synopsisPromptPayload,
|
||
synopsisSystemPrompt,
|
||
),
|
||
userPrompt: synopsisPromptPayload.userPrompt,
|
||
maxRetries: 1,
|
||
signal,
|
||
taskType: "synopsis",
|
||
debugContext: createTaskLlmDebugContext(
|
||
synopsisPromptBuild,
|
||
synopsisRegexInput,
|
||
),
|
||
promptMessages: synopsisPromptPayload.promptMessages,
|
||
additionalMessages: synopsisPromptPayload.additionalMessages,
|
||
});
|
||
|
||
if (!result?.summary) return;
|
||
|
||
const existingSynopsis = graph.nodes.find(
|
||
(n) => n.type === "synopsis" && !n.archived,
|
||
);
|
||
|
||
if (existingSynopsis) {
|
||
updateNode(graph, existingSynopsis.id, {
|
||
fields: { summary: result.summary, scope: `楼 1 ~ ${currentSeq}` },
|
||
seq: Math.max(existingSynopsis.seq || 0, currentSeq),
|
||
storyTimeSpan: synopsisStoryTimeSpan,
|
||
});
|
||
existingSynopsis.seqRange = [
|
||
Math.min(existingSynopsis.seqRange?.[0] ?? currentSeq, currentSeq),
|
||
Math.max(existingSynopsis.seqRange?.[1] ?? currentSeq, currentSeq),
|
||
];
|
||
existingSynopsis.embedding = null;
|
||
debugLog("[ST-BME] 全局概要已更新");
|
||
} else {
|
||
const node = createNode({
|
||
type: "synopsis",
|
||
fields: { summary: result.summary, scope: `楼 1 ~ ${currentSeq}` },
|
||
seq: currentSeq,
|
||
importance: 9.0,
|
||
});
|
||
node.storyTimeSpan = synopsisStoryTimeSpan;
|
||
addNode(graph, node);
|
||
debugLog("[ST-BME] 全局概要已创建");
|
||
}
|
||
}
|
||
|
||
export async function generateReflection({
|
||
graph,
|
||
currentSeq,
|
||
customPrompt,
|
||
signal,
|
||
settings = {},
|
||
}) {
|
||
const recentEvents = getActiveNodes(graph, "event")
|
||
.sort((a, b) => b.seq - a.seq)
|
||
.slice(0, 6)
|
||
.reverse();
|
||
|
||
if (recentEvents.length < 2) return null;
|
||
|
||
const recentCharacters = getActiveNodes(graph, "character")
|
||
.sort((a, b) => b.seq - a.seq)
|
||
.slice(0, 5);
|
||
|
||
const recentThreads = getActiveNodes(graph, "thread")
|
||
.sort((a, b) => b.seq - a.seq)
|
||
.slice(0, 4);
|
||
|
||
const contradictEdges = graph.edges
|
||
.filter((e) => e.relation === "contradicts" && !e.invalidAt)
|
||
.slice(-5);
|
||
|
||
const eventSummary = recentEvents
|
||
.map((n) => {
|
||
const storyLabel = describeNodeStoryTime(n);
|
||
return `[楼${n.seq}]${storyLabel ? ` [${storyLabel}]` : ""} ${n.fields.summary || "(无)"}`;
|
||
})
|
||
.join("\n");
|
||
const characterSummary = recentCharacters
|
||
.map(
|
||
(n) =>
|
||
`${n.fields.name || n.fields.title || n.id}: ${n.fields.state || n.fields.summary || "(无)"}`,
|
||
)
|
||
.join("\n");
|
||
const threadSummary = recentThreads
|
||
.map(
|
||
(n) =>
|
||
`${n.fields.title || n.fields.name || n.id}: ${n.fields.status || n.fields.summary || "active"}${describeNodeStoryTime(n) ? `（${describeNodeStoryTime(n)}）` : ""}`,
|
||
)
|
||
.join("\n");
|
||
const reflectionStoryTimeSpan = deriveStoryTimeSpanFromNodes(
|
||
graph,
|
||
[...recentEvents, ...recentThreads],
|
||
"derived",
|
||
);
|
||
const contradictionSummary = contradictEdges
|
||
.map((e) => `${e.fromId} -> ${e.toId} (${e.relation})`)
|
||
.join("\n");
|
||
|
||
const reflectionPromptBuild = await buildTaskPrompt(settings, "reflection", {
|
||
taskName: "reflection",
|
||
eventSummary,
|
||
characterSummary: characterSummary || "(无)",
|
||
threadSummary: threadSummary || "(无)",
|
||
contradictionSummary: contradictionSummary || "(无)",
|
||
graphStats: `event=${recentEvents.length}, character=${recentCharacters.length}, thread=${recentThreads.length}`,
|
||
...getSTContextForPrompt(),
|
||
});
|
||
const reflectionRegexInput = { entries: [] };
|
||
const reflectionSystemPrompt = applyTaskRegex(
|
||
settings,
|
||
"reflection",
|
||
"finalPrompt",
|
||
reflectionPromptBuild.systemPrompt ||
|
||
customPrompt ||
|
||
[
|
||
"你是 RP 长期记忆系统的反思生成器。",
|
||
'输出严格 JSON：{"insight":"...","trigger":"...","suggestion":"...","importance":1-10}',
|
||
"insight 应总结最近情节中最值得长期保留的变化、关系趋势或潜在线索。",
|
||
"trigger 说明触发这条反思的关键事件或矛盾。",
|
||
"suggestion 给出后续检索或叙事上值得关注的提示。",
|
||
"不要复述全部事件，要提炼高层结论。",
|
||
].join("\n"),
|
||
reflectionRegexInput,
|
||
"system",
|
||
);
|
||
|
||
const reflectionUserPrompt = [
|
||
"## 最近事件",
|
||
eventSummary,
|
||
"",
|
||
"## 近期角色状态",
|
||
characterSummary || "(无)",
|
||
"",
|
||
"## 当前主线",
|
||
threadSummary || "(无)",
|
||
"",
|
||
"## 已知矛盾",
|
||
contradictionSummary || "(无)",
|
||
].join("\n");
|
||
const reflectionPromptPayload = resolveTaskPromptPayload(
|
||
reflectionPromptBuild,
|
||
reflectionUserPrompt,
|
||
);
|
||
|
||
const result = await callLLMForJSON({
|
||
systemPrompt: resolveTaskLlmSystemPrompt(
|
||
reflectionPromptPayload,
|
||
reflectionSystemPrompt,
|
||
),
|
||
userPrompt: reflectionPromptPayload.userPrompt,
|
||
maxRetries: 1,
|
||
signal,
|
||
taskType: "reflection",
|
||
debugContext: createTaskLlmDebugContext(
|
||
reflectionPromptBuild,
|
||
reflectionRegexInput,
|
||
),
|
||
promptMessages: reflectionPromptPayload.promptMessages,
|
||
additionalMessages: reflectionPromptPayload.additionalMessages,
|
||
});
|
||
|
||
if (!result?.insight) return null;
|
||
|
||
const reflectionNode = createNode({
|
||
type: "reflection",
|
||
fields: {
|
||
insight: result.insight,
|
||
trigger:
|
||
result.trigger ||
|
||
recentEvents[recentEvents.length - 1]?.fields?.summary ||
|
||
"",
|
||
suggestion: result.suggestion || "",
|
||
},
|
||
seq: currentSeq,
|
||
importance: Math.max(5, Math.min(10, result.importance ?? 7)),
|
||
});
|
||
reflectionNode.storyTimeSpan = reflectionStoryTimeSpan;
|
||
addNode(graph, reflectionNode);
|
||
|
||
for (const eventNode of recentEvents.slice(-3)) {
|
||
const edge = createEdge({
|
||
fromId: reflectionNode.id,
|
||
toId: eventNode.id,
|
||
relation: "evolves",
|
||
strength: 0.75,
|
||
edgeType: 0,
|
||
});
|
||
addEdge(graph, edge);
|
||
}
|
||
|
||
debugLog("[ST-BME] 反思条目已生成");
|
||
return reflectionNode.id;
|
||
}
|