perf: 记忆整合批量化 — 将 N 次 LLM 压缩为 1 次批量调用

- 重构 consolidator.js 为 4 阶段架构:
  Phase 0: 收集有效新节点
  Phase 1: 批量 Embed (直连 1 次 embedBatch / 后端逐次)
  Phase 2: 各节点查近邻 (直连本地 cosine / 后端逐次 query)
  Phase 3: 单次 LLM 批量判定 (统一 prompt 含所有新节点)
  Phase 4: 逐个处理结果

- 更新 panel.js DEFAULT_PROMPTS.consolidation 为 results 数组格式
- 直连模式: N embed + N LLM -> 1 embedBatch + 1 LLM (-67~75%)
- 后端模式: N query + N LLM -> N query + 1 LLM (-33~50%)
- 总体效果: 原始 6 LLM -> 2 LLM (2新节点场景)

consolidator.js

@@ -1,12 +1,14 @@
-// ST-BME: 统一记忆整合引擎
+// ST-BME: 统一记忆整合引擎（批量化版）
 // 合并 Mem0 精确对照 + A-MEM 记忆进化为单一阶段
-// 每个新节点只需 1 次 embed + 1 次 LLM 调用
+// 批量 embed + 批量查近邻 + 单次 LLM 调用
 
+import { embedBatch, searchSimilar } from './embedding.js';
 import { addEdge, createEdge, getActiveNodes, getNode } from './graph.js';
 import { callLLMForJSON } from './llm.js';
 import {
     buildNodeVectorText,
     findSimilarNodesByText,
+    isDirectVectorConfig,
     validateVectorConfig,
 } from './vector-index.js';
 
@@ -27,8 +29,7 @@ function throwIfAborted(signal) {
 }
 
 /**
- * 统一记忆整合系统提示词
- * 同时完成 Mem0 冲突判定 + A-MEM 进化分析
+ * 统一记忆整合系统提示词（支持批量输出）
  */
 const CONSOLIDATION_SYSTEM_PROMPT = `你是一个记忆整合分析器。当新记忆加入知识图谱时，你需要同时完成两项任务：
 
@@ -45,24 +46,30 @@ const CONSOLIDATION_SYSTEM_PROMPT = `你是一个记忆整合分析器。当新
 
 输出严格 JSON：
 {
-  "action": "keep" | "merge" | "skip",
-  "merge_target_id": "仅 action=merge 时必填：要合并到的旧节点 ID",
-  "merged_fields": { "仅 action=merge 时可选：合并后的字段更新" },
-  "reason": "判定理由（简述）",
-  "evolution": {
-    "should_evolve": true/false,
-    "connections": ["需要建立链接的旧记忆 ID 列表"],
-    "neighbor_updates": [
-      {
-        "nodeId": "需更新的旧节点 ID",
-        "newContext": "基于新信息修正后的描述（不需修改则为 null）",
-        "newTags": ["更新后的分类标签，不需修改则为 null"]
+  "results": [
+    {
+      "node_id": "新记忆的节点 ID",
+      "action": "keep" | "merge" | "skip",
+      "merge_target_id": "仅 action=merge 时必填：要合并到的旧节点 ID",
+      "merged_fields": { "仅 action=merge 时可选：合并后的字段更新" },
+      "reason": "判定理由（简述）",
+      "evolution": {
+        "should_evolve": true/false,
+        "connections": ["需要建立链接的旧记忆 ID 列表"],
+        "neighbor_updates": [
+          {
+            "nodeId": "需更新的旧节点 ID",
+            "newContext": "基于新信息修正后的描述（不需修改则为 null）",
+            "newTags": ["更新后的分类标签，不需修改则为 null"]
+          }
+        ]
       }
-    ]
-  }
+    }
+  ]
 }
 
 整合规则：
+- 必须对每条新记忆都给出一个 result 条目
 - 当 action=skip 时，evolution 可省略或设 should_evolve=false
 - 当 action=merge 时，evolution 可省略或设 should_evolve=false
 - 仅当 action=keep 且新信息确实改变了对旧记忆的理解时，才设 should_evolve=true
@@ -72,19 +79,23 @@ const CONSOLIDATION_SYSTEM_PROMPT = `你是一个记忆整合分析器。当新
 - neighbor_updates 中每条必须有实际意义的修改`;
 
 /**
- * 统一记忆整合主函数
+ * 统一记忆整合主函数（批量化版）
  *
- * 合并了原先的 mem0ConflictCheck（精确对照）和 evolveMemories（进化），
- * 实现"1 次 embed + 1 次 LLM"完成冲突检测 + 进化分析。
+ * 4 阶段架构：
+ *   Phase 0: 收集有效新节点
+ *   Phase 1: 批量 Embed（直连 1 次 embedBatch / 后端逐次）
+ *   Phase 2: 各节点查近邻（直连本地 cosine / 后端逐次 query）
+ *   Phase 3: 单次 LLM 批量判定
+ *   Phase 4: 逐个处理结果
  *
  * @param {object} params
  * @param {object} params.graph - 当前图状态
  * @param {string[]} params.newNodeIds - 本次新创建的节点 ID 列表
  * @param {object} params.embeddingConfig - Embedding API 配置
  * @param {object} [params.options]
- * @param {number} [params.options.neighborCount=5]  - 近邻搜索数量
- * @param {number} [params.options.conflictThreshold=0.85] - 冲突判定阈值（低于此值跳过冲突检测）
- * @param {string} [params.customPrompt] - 自定义提示词
+ * @param {number} [params.options.neighborCount=5]
+ * @param {number} [params.options.conflictThreshold=0.85]
+ * @param {string} [params.customPrompt]
  * @param {AbortSignal} [params.signal]
  * @returns {Promise<{merged: number, skipped: number, kept: number, evolved: number, connections: number, updates: number}>}
  */
@@ -113,218 +124,200 @@ export async function consolidateMemories({
         return stats;
     }
 
+    // ══════════════════════════════════════════════
+    // Phase 0: 收集有效新节点
+    // ══════════════════════════════════════════════
+    const newEntries = [];
+    for (const id of newNodeIds) {
+        const node = getNode(graph, id);
+        if (!node || node.archived) continue;
+        const text = buildNodeVectorText(node);
+        if (!text) continue;
+        newEntries.push({ id, node, text });
+    }
+
+    if (newEntries.length === 0) return stats;
+
     const activeNodes = getActiveNodes(graph).filter(n => {
         const text = buildNodeVectorText(n);
         return typeof text === 'string' && text.length > 0;
     });
 
-    if (activeNodes.length < 2) return stats;
+    if (activeNodes.length < 2) {
+        // 图中节点不够，全部 keep
+        stats.kept = newEntries.length;
+        return stats;
+    }
 
-    for (const newId of newNodeIds) {
-        throwIfAborted(signal);
-        const newNode = getNode(graph, newId);
-        if (!newNode || newNode.archived) continue;
+    throwIfAborted(signal);
+    console.log(`[ST-BME] 记忆整合开始: ${newEntries.length} 个新节点`);
 
-        const queryText = buildNodeVectorText(newNode);
-        if (!queryText) continue;
+    // ══════════════════════════════════════════════
+    // Phase 1 + 2: 批量 Embed + 查近邻
+    // ══════════════════════════════════════════════
+    /** @type {Map<string, Array<{nodeId: string, score: number}>>} */
+    const neighborsMap = new Map();
 
-        // 排除自身的候选池
-        const candidates = activeNodes.filter(n => n.id !== newId);
-        if (candidates.length === 0) {
-            stats.kept++;
-            continue;
-        }
+    if (isDirectVectorConfig(embeddingConfig)) {
+        // ── 直连模式: 1 次 embedBatch + N 次本地 cosine ──
+        const texts = newEntries.map(e => e.text);
+        let queryVectors;
 
         try {
-            // ── 1次 Embed：查近邻 ──
-            const neighbors = await findSimilarNodesByText(
-                graph,
-                queryText,
-                embeddingConfig,
-                neighborCount,
-                candidates,
-                signal,
-            );
+            queryVectors = await embedBatch(texts, embeddingConfig, { signal });
+        } catch (e) {
+            if (isAbortError(e)) throw e;
+            console.warn('[ST-BME] 批量 embed 失败，回退到逐条:', e.message);
+            queryVectors = null;
+        }
 
-            if (neighbors.length === 0) {
-                stats.kept++;
-                continue;
+        // 构建候选池（含 embedding 的活跃节点）
+        const candidatePool = activeNodes
+            .filter(n => Array.isArray(n.embedding) && n.embedding.length > 0)
+            .map(n => ({ nodeId: n.id, embedding: n.embedding }));
+
+        for (let i = 0; i < newEntries.length; i++) {
+            throwIfAborted(signal);
+            const entry = newEntries[i];
+            const candidates = candidatePool.filter(c => c.nodeId !== entry.id);
+
+            if (queryVectors?.[i] && candidates.length > 0) {
+                // 本地 cosine 搜索（0 API 调用）
+                const neighbors = searchSimilar(queryVectors[i], candidates, neighborCount);
+                neighborsMap.set(entry.id, neighbors);
+            } else {
+                // fallback: 逐条 embed
+                try {
+                    const neighbors = await findSimilarNodesByText(
+                        graph, entry.text, embeddingConfig, neighborCount,
+                        activeNodes.filter(n => n.id !== entry.id), signal,
+                    );
+                    neighborsMap.set(entry.id, neighbors);
+                } catch (e) {
+                    if (isAbortError(e)) throw e;
+                    console.warn(`[ST-BME] 近邻查询失败 (${entry.id}):`, e.message);
+                    neighborsMap.set(entry.id, []);
+                }
             }
+        }
+    } else {
+        // ── 后端模式: 逐条 /api/vector/query ──
+        for (let i = 0; i < newEntries.length; i++) {
+            throwIfAborted(signal);
+            const entry = newEntries[i];
+            try {
+                const neighbors = await findSimilarNodesByText(
+                    graph, entry.text, embeddingConfig, neighborCount,
+                    activeNodes.filter(n => n.id !== entry.id), signal,
+                );
+                neighborsMap.set(entry.id, neighbors);
+            } catch (e) {
+                if (isAbortError(e)) throw e;
+                console.warn(`[ST-BME] 近邻查询失败 (${entry.id}):`, e.message);
+                neighborsMap.set(entry.id, []);
+            }
+        }
+    }
 
-            // 构建近邻描述文本
-            const neighborsContext = neighbors.map(n => {
+    // ══════════════════════════════════════════════
+    // Phase 3: 单次 LLM 批量判定
+    // ══════════════════════════════════════════════
+    throwIfAborted(signal);
+
+    const userPromptSections = [];
+    userPromptSections.push(`本轮共新增 ${newEntries.length} 条记忆，请逐条分析：\n`);
+
+    for (let i = 0; i < newEntries.length; i++) {
+        const entry = newEntries[i];
+        const neighbors = neighborsMap.get(entry.id) || [];
+
+        const newNodeFieldsStr = Object.entries(entry.node.fields)
+            .map(([k, v]) => `${k}: ${v}`)
+            .join(', ');
+
+        // 构建近邻描述
+        let neighborText;
+        if (neighbors.length === 0) {
+            neighborText = '  (无近邻命中)';
+        } else {
+            neighborText = neighbors.map(n => {
                 const node = getNode(graph, n.nodeId);
                 if (!node) return null;
                 const fieldsStr = Object.entries(node.fields)
                     .map(([k, v]) => `${k}: ${v}`)
                     .join(', ');
-                return `[${node.id}] 类型=${node.type}, ${fieldsStr}, 相似度=${n.score.toFixed(3)}${
-                    (node.clusters || []).length > 0 ? `, 分类=${node.clusters.join('/')}` : ''
-                }`;
+                return `  - [${node.id}] 类型=${node.type}, ${fieldsStr} (相似度=${n.score.toFixed(3)})`;
             }).filter(Boolean).join('\n');
-
-            const newNodeFieldsStr = Object.entries(newNode.fields)
-                .map(([k, v]) => `${k}: ${v}`)
-                .join(', ');
-
-            // 检查是否有高相似度命中（决定是否启用冲突检测部分的提示）
-            const hasHighSimilarity = neighbors[0].score > conflictThreshold;
-
-            const userPrompt = [
-                '## 新加入的记忆',
-                `[${newNode.id}] 类型=${newNode.type}, ${newNodeFieldsStr}`,
-                '',
-                '## 最近邻的已有记忆',
-                neighborsContext,
-                '',
-                `共 ${neighbors.length} 条近邻记忆。`,
-                hasHighSimilarity
-                    ? `最高相似度 ${neighbors[0].score.toFixed(3)} 超过阈值 ${conflictThreshold}，请先判断是否冲突/重复，再分析进化关系。`
-                    : '相似度均较低，请重点分析新记忆是否揭示了关于旧记忆的新信息。',
-            ].join('\n');
-
-            // ── 1次 LLM：统一判定 ──
-            const decision = await callLLMForJSON({
-                systemPrompt: customPrompt || CONSOLIDATION_SYSTEM_PROMPT,
-                userPrompt,
-                maxRetries: 1,
-                signal,
-            });
-
-            if (!decision) {
-                stats.kept++;
-                continue;
-            }
-
-            // ── 处理 action ──
-            switch (decision.action) {
-                case 'skip': {
-                    console.log(`[ST-BME] 记忆整合: skip (重复) — ${newId}`);
-                    newNode.archived = true;
-                    stats.skipped++;
-                    break;
-                }
-
-                case 'merge': {
-                    const targetId = decision.merge_target_id;
-                    const targetNode = targetId ? getNode(graph, targetId) : null;
-
-                    if (targetNode && !targetNode.archived) {
-                        console.log(`[ST-BME] 记忆整合: merge ${newId} → ${targetId}`);
-
-                        // 合并字段到旧节点
-                        if (decision.merged_fields && typeof decision.merged_fields === 'object') {
-                            for (const [key, value] of Object.entries(decision.merged_fields)) {
-                                if (value != null && value !== '') {
-                                    targetNode.fields[key] = value;
-                                }
-                            }
-                        } else {
-                            // 如果没提供 merged_fields，将新节点的非空字段补充到旧节点
-                            for (const [key, value] of Object.entries(newNode.fields)) {
-                                if (value != null && value !== '' && !targetNode.fields[key]) {
-                                    targetNode.fields[key] = value;
-                                }
-                            }
-                        }
-
-                        // 更新旧节点的 seq 为更新的值
-                        if (Number.isFinite(newNode.seq) && newNode.seq > (targetNode.seq || 0)) {
-                            targetNode.seq = newNode.seq;
-                        }
-
-                        // 标记旧节点需要 re-embed
-                        targetNode.embedding = null;
-
-                        // 归档新节点
-                        newNode.archived = true;
-                        stats.merged++;
-                    } else {
-                        // merge target 无效，回退为 keep
-                        console.warn(`[ST-BME] 记忆整合: merge target ${targetId} 不存在，回退为 keep`);
-                        stats.kept++;
-                    }
-                    break;
-                }
-
-                case 'keep':
-                default: {
-                    stats.kept++;
-                    break;
-                }
-            }
-
-            // ── 处理 evolution（仅 keep 时有意义，但也容错处理其它 action） ──
-            const evolution = decision.evolution;
-            if (evolution?.should_evolve && !newNode.archived) {
-                stats.evolved++;
-                console.log(`[ST-BME] 记忆整合/进化触发: ${decision.reason || '(无理由)'}`);
-
-                // 建立关联边
-                if (Array.isArray(evolution.connections)) {
-                    for (const targetId of evolution.connections) {
-                        if (!getNode(graph, targetId)) continue;
-                        const edge = createEdge({
-                            fromId: newId,
-                            toId: targetId,
-                            relation: 'related',
-                            strength: 0.7,
-                        });
-                        if (addEdge(graph, edge)) {
-                            stats.connections++;
-                        }
-                    }
-                }
-
-                // 反向更新旧节点
-                if (Array.isArray(evolution.neighbor_updates)) {
-                    for (const update of evolution.neighbor_updates) {
-                        if (!update.nodeId) continue;
-                        const oldNode = getNode(graph, update.nodeId);
-                        if (!oldNode || oldNode.archived) continue;
-
-                        let changed = false;
-
-                        // 更新 context/state 字段
-                        if (update.newContext && typeof update.newContext === 'string') {
-                            if (oldNode.fields.state !== undefined) {
-                                oldNode.fields.state = update.newContext;
-                                changed = true;
-                            } else if (oldNode.fields.summary !== undefined) {
-                                oldNode.fields.summary = update.newContext;
-                                changed = true;
-                            } else if (oldNode.fields.core_note !== undefined) {
-                                oldNode.fields.core_note = update.newContext;
-                                changed = true;
-                            }
-                        }
-
-                        // 更新分类标签
-                        if (update.newTags && Array.isArray(update.newTags)) {
-                            oldNode.clusters = update.newTags;
-                            changed = true;
-                        }
-
-                        if (changed) {
-                            oldNode.embedding = null;
-                            if (!oldNode._evolutionHistory) oldNode._evolutionHistory = [];
-                            oldNode._evolutionHistory.push({
-                                triggeredBy: newId,
-                                timestamp: Date.now(),
-                                reason: decision.reason || '',
-                            });
-                            stats.updates++;
-                        }
-                    }
-                }
-            }
-        } catch (e) {
-            if (isAbortError(e)) throw e;
-            console.error(`[ST-BME] 记忆整合失败 (${newId}):`, e);
-            stats.kept++;
         }
+
+        // 检查高相似度
+        const hasHighSimilarity = neighbors.length > 0 && neighbors[0].score > conflictThreshold;
+        const hint = hasHighSimilarity
+            ? `  ⚠ 最高相似度 ${neighbors[0].score.toFixed(3)} 超过阈值 ${conflictThreshold}`
+            : '';
+
+        userPromptSections.push([
+            `### 新记忆 #${i + 1}`,
+            `[${entry.id}] 类型=${entry.node.type}, ${newNodeFieldsStr}`,
+            '近邻记忆:',
+            neighborText,
+            hint,
+        ].filter(Boolean).join('\n'));
     }
 
+    const userPrompt = userPromptSections.join('\n\n');
+
+    let decision;
+    try {
+        decision = await callLLMForJSON({
+            systemPrompt: customPrompt || CONSOLIDATION_SYSTEM_PROMPT,
+            userPrompt,
+            maxRetries: 1,
+            signal,
+        });
+    } catch (e) {
+        if (isAbortError(e)) throw e;
+        console.error('[ST-BME] 记忆整合 LLM 调用失败:', e);
+        stats.kept = newEntries.length;
+        return stats;
+    }
+
+    // ══════════════════════════════════════════════
+    // Phase 4: 逐个处理结果
+    // ══════════════════════════════════════════════
+
+    // 解析 LLM 返回——兼容单条和批量格式
+    let results;
+    if (Array.isArray(decision?.results)) {
+        results = decision.results;
+    } else if (decision?.action) {
+        // 单条返回格式（LLM 可能忽略 results 包装）
+        results = [{ ...decision, node_id: newEntries[0]?.id }];
+    } else {
+        console.warn('[ST-BME] 记忆整合: LLM 返回格式异常，全部 keep');
+        stats.kept = newEntries.length;
+        return stats;
+    }
+
+    // 建立 node_id → result 的映射
+    const resultMap = new Map();
+    for (const r of results) {
+        if (r.node_id) resultMap.set(r.node_id, r);
+    }
+
+    // 处理每个新节点
+    for (const entry of newEntries) {
+        const result = resultMap.get(entry.id);
+        if (!result) {
+            // LLM 未返回此节点的结果，fallback 为 keep
+            stats.kept++;
+            continue;
+        }
+
+        processOneResult(graph, entry, result, stats);
+    }
+
+    // 日志
     const actionSummary = [];
     if (stats.merged > 0) actionSummary.push(`合并 ${stats.merged}`);
     if (stats.skipped > 0) actionSummary.push(`跳过 ${stats.skipped}`);
@@ -339,3 +332,122 @@ export async function consolidateMemories({
 
     return stats;
 }
+
+/**
+ * 处理单个节点的整合结果
+ */
+function processOneResult(graph, entry, result, stats) {
+    const { id: newId, node: newNode } = entry;
+
+    // ── 处理 action ──
+    switch (result.action) {
+        case 'skip': {
+            console.log(`[ST-BME] 记忆整合: skip (重复) — ${newId}`);
+            newNode.archived = true;
+            stats.skipped++;
+            break;
+        }
+
+        case 'merge': {
+            const targetId = result.merge_target_id;
+            const targetNode = targetId ? getNode(graph, targetId) : null;
+
+            if (targetNode && !targetNode.archived) {
+                console.log(`[ST-BME] 记忆整合: merge ${newId} → ${targetId}`);
+
+                if (result.merged_fields && typeof result.merged_fields === 'object') {
+                    for (const [key, value] of Object.entries(result.merged_fields)) {
+                        if (value != null && value !== '') {
+                            targetNode.fields[key] = value;
+                        }
+                    }
+                } else {
+                    for (const [key, value] of Object.entries(newNode.fields)) {
+                        if (value != null && value !== '' && !targetNode.fields[key]) {
+                            targetNode.fields[key] = value;
+                        }
+                    }
+                }
+
+                if (Number.isFinite(newNode.seq) && newNode.seq > (targetNode.seq || 0)) {
+                    targetNode.seq = newNode.seq;
+                }
+
+                targetNode.embedding = null;
+                newNode.archived = true;
+                stats.merged++;
+            } else {
+                console.warn(`[ST-BME] 记忆整合: merge target ${targetId} 不存在，回退为 keep`);
+                stats.kept++;
+            }
+            break;
+        }
+
+        case 'keep':
+        default: {
+            stats.kept++;
+            break;
+        }
+    }
+
+    // ── 处理 evolution ──
+    const evolution = result.evolution;
+    if (evolution?.should_evolve && !newNode.archived) {
+        stats.evolved++;
+        console.log(`[ST-BME] 记忆整合/进化触发: ${result.reason || '(无理由)'}`);
+
+        if (Array.isArray(evolution.connections)) {
+            for (const targetId of evolution.connections) {
+                if (!getNode(graph, targetId)) continue;
+                const edge = createEdge({
+                    fromId: newId,
+                    toId: targetId,
+                    relation: 'related',
+                    strength: 0.7,
+                });
+                if (addEdge(graph, edge)) {
+                    stats.connections++;
+                }
+            }
+        }
+
+        if (Array.isArray(evolution.neighbor_updates)) {
+            for (const update of evolution.neighbor_updates) {
+                if (!update.nodeId) continue;
+                const oldNode = getNode(graph, update.nodeId);
+                if (!oldNode || oldNode.archived) continue;
+
+                let changed = false;
+
+                if (update.newContext && typeof update.newContext === 'string') {
+                    if (oldNode.fields.state !== undefined) {
+                        oldNode.fields.state = update.newContext;
+                        changed = true;
+                    } else if (oldNode.fields.summary !== undefined) {
+                        oldNode.fields.summary = update.newContext;
+                        changed = true;
+                    } else if (oldNode.fields.core_note !== undefined) {
+                        oldNode.fields.core_note = update.newContext;
+                        changed = true;
+                    }
+                }
+
+                if (update.newTags && Array.isArray(update.newTags)) {
+                    oldNode.clusters = update.newTags;
+                    changed = true;
+                }
+
+                if (changed) {
+                    oldNode.embedding = null;
+                    if (!oldNode._evolutionHistory) oldNode._evolutionHistory = [];
+                    oldNode._evolutionHistory.push({
+                        triggeredBy: newId,
+                        timestamp: Date.now(),
+                        reason: result.reason || '',
+                    });
+                    stats.updates++;
+                }
+            }
+        }
+    }
+}

panel.js

@@ -67,17 +67,14 @@ const DEFAULT_PROMPTS = {
         "- 反向更新旧记忆",
         "",
         "输出严格 JSON：",
-        "{",
-        '  "action": "keep"|"merge"|"skip",',
-        '  "merge_target_id": "旧节点ID",',
-        '  "merged_fields": {},',
-        '  "reason": "理由",',
-        '  "evolution": {',
-        '    "should_evolve": true/false,',
-        '    "connections": ["旧记忆ID"],',
-        '    "neighbor_updates": [{"nodeId": "旧节点ID", "newContext": "修正描述", "newTags": ["标签"]}]',
+        '{ "results": [',
+        '  { "node_id": "新记忆节点ID",',
+        '    "action": "keep"|"merge"|"skip",',
+        '    "merge_target_id": "旧节点ID (仅merge)",',
+        '    "reason": "理由",',
+        '    "evolution": { "should_evolve": true/false, "connections": ["旧记忆ID"], "neighbor_updates": [...] }',
         "  }",
-        "}",
+        "] }",
     ].join("\n"),
 
     compress: [