新增: 知识库图召回激活扩散与治理续批
- 阶段2 图召回: list_neighbors_for_seeds(多种子 1-hop 展开,排除种子与 archived)+knowledge_search 结果附 related 段(权重降序限 5) - 蒸馏合并: knowledge_distill.rs(archived 按 kind 分组 LLM 合订本+merges 边连组) - V54 knowledge_edges 边表与渐进披露批配套迁移/模型字段 - 提炼链 related_ids 协议+检索补 id 字段
This commit is contained in:
@@ -29,7 +29,6 @@
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* - listSkills(): 拉技能列表(list_skills Command → AiSkillList 事件回流 skills)
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* - listEntities(): 拉实体列表(list_entities Command → AiEntityList 事件回流 entities)
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* - approve(toolCallId, approved): 审批工具调用(approve Command → device invoke ai_approve)
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* - authorizeDir(toolCallId, decision): 路径授权(authorize_dir Command → device invoke ai_authorize_dir)
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* - continueLoop(convId): 继续循环(continue_loop Command → device invoke ai_continue_loop)
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* - stopLoop(convId): 停止循环(stop_loop Command → device invoke ai_stop_loop)
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*/
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@@ -235,7 +234,7 @@ const deviceOnline = ref<boolean>(false)
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* 现机制(130s 持续 watchdog,非首事件清除):
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* - send 后 resetWatchdog 启动
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* - 活跃事件(AiTextDelta/AiAgentRound/AiToolCallStarted/Heartbeat/Retry)resetWatchdog 重置 130s
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* - 终态(AiCompleted/AiError)/挂起(Approval/DirAuth/MaxRounds/Help)/断连 clearWatchdog 清除
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* - 终态(AiCompleted/AiError)/挂起(Approval/MaxRounds/Help)/断连 clearWatchdog 清除
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* - 超时(130s 无活跃事件)= 中途断 → flush + 错误气泡 + reset generating,用户可重发
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*/
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let streamTimer: ReturnType<typeof setTimeout> | null = null
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@@ -312,7 +311,7 @@ function resetWatchdog(): void {
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const APPROVAL_TIMEOUT_MS = 300_000 // 5 分钟
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const _approvalTimers = new Map<string, ReturnType<typeof setTimeout>>()
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/** 启动审批超时计时器(AiApprovalRequired/AiDirAuthRequired 触发)。 */
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/** 启动审批超时计时器(AiApprovalRequired 触发)。 */
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function startApprovalTimer(tcId: string, toolName: string): void {
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if (_approvalTimers.has(tcId)) return // 幂等
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const timer = setTimeout(() => {
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@@ -768,37 +767,6 @@ export function handleEvent(event: AiChatEvent): void {
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break
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}
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case 'AiDirAuthRequired': {
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clearWatchdog() // 挂起等授权,停看门狗
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startApprovalTimer(event.id, event.tool) // 5min 超时自动拒绝
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// F-#95 P0-3:路径授权进 pendingApprovals(kind=path),chat 页渲染 once/always/deny 按钮
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// (替代旧 system 文案降级「请在桌面端处理」,miniapp 成轻量审批终端,治 loop 卡死)。
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const authInfo: AiToolCallInfo = {
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id: event.id,
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name: event.tool,
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args: {},
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status: 'pending_approval',
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kind: 'path',
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dir: event.dir,
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path: event.path,
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}
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// req3 去重:同 AiApprovalRequired(sync_pending 重发 + 实时事件竞态防重复)
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if (!pendingApprovals.value.some((p) => p.id === authInfo.id)) {
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pendingApprovals.value.push(authInfo)
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}
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// 对齐 AiApprovalRequired:同步更新消息内工具卡片为 pending_approval + path 授权信息,
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// 使 chat 页消息内联渲染 once/always/deny 按钮(单一渲染源,避免消息 tc 卡 running 态)。
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const authTc = findToolCall(event.id)
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if (authTc) {
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authTc.status = 'pending_approval'
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authTc.kind = 'path'
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authTc.dir = event.dir
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authTc.path = event.path
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triggerRef(messages) // P1-5:路径授权态嵌套变更,刷新工具卡
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}
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break
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}
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case 'AiCompleted': {
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stopping = false // F9:终态清停止标志
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flushCurrentText()
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@@ -1158,26 +1126,6 @@ function approve(toolCallId: string, approved: boolean): void {
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}
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/**
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* 路径授权(authorize_dir Command → relay → device invoke ai_authorize_dir)
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*
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* Phase3 §2.2 路由表:{cmd:"authorize_dir", args:{tool_call_id, decision}}
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* 对应事件:AiDirAuthRequired(决策:'once'=本次 / 'always'=永久 / 'deny'=拒绝)。
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*/
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function authorizeDir(toolCallId: string, decision: 'once' | 'always' | 'deny'): void {
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if (!toolCallId) return
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const cmd: MiniCommand = {
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cmd: 'authorize_dir',
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args: {
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tool_call_id: toolCallId,
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decision,
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},
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}
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if (!wsClient.send(cmd)) {
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// 发送失败:wsClient.send 返 false(未连接),提示用户操作未发出
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uni.showToast({ title: '未连接,操作未发出', icon: 'none' })
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}
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}
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/**
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* 继续循环(continue_loop Command → relay → device invoke ai_continue_loop)
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*
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@@ -1475,7 +1423,6 @@ export function useAiChat() {
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setActiveModel,
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newConversation,
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approve,
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authorizeDir,
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continueLoop,
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stopLoop,
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handleEvent,
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@@ -543,7 +543,7 @@ function handleSend(): void {
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}
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// —— 工具卡片 + 审批 UI(对齐桌面端 ToolCard,适配 mp-weixin 深色风格) ——
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// 后端推 AiToolCallStarted/AiApprovalRequired/AiDirAuthRequired → useAiChat 收进
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// 后端推 AiToolCallStarted/AiApprovalRequired → useAiChat 收进
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// 消息 m.toolCalls(状态机)+ pendingApprovals。消息内联渲染工具状态 + 待审批按钮,
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// miniapp 成轻量审批终端(单一渲染源,对齐桌面 ToolCard 嵌消息气泡)。
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@@ -666,11 +666,6 @@ function onReject(tc: AiToolCallInfo): void {
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ai.approve(tc.id, false)
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}
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/** 路径授权(path 类:once=本次 / always=永久 / deny=拒绝) */
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function onAuthorize(tc: AiToolCallInfo, decision: 'once' | 'always' | 'deny'): void {
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ai.authorizeDir(tc.id, decision)
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}
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/** 连接状态中文文案(F21:裸枚举字符串 disconnected/connecting 不可读) */
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const STATUS_TEXT: Record<string, string> = {
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connecting: '连接中…',
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@@ -782,23 +777,14 @@ function tokenInOf(t: TokenUsage): number {
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<text class="tool-arg-val">{{ a.val }}</text>
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</view>
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</view>
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<view v-if="p.kind === 'path' && p.dir" class="tool-dir">
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<text>📁 {{ p.dir }}</text>
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</view>
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<view v-if="p.reason" class="tool-reason">
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<text>⚠ {{ p.reason }}</text>
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</view>
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<!-- risk 类:批准/拒绝 -->
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<view v-if="p.kind !== 'path'" class="approval-actions">
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<!-- 批准/拒绝 -->
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<view class="approval-actions">
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<button class="approval-btn approve" size="mini" @tap="onApprove(p)">批准</button>
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<button class="approval-btn reject" size="mini" @tap="onReject(p)">拒绝</button>
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</view>
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<!-- path 类:本次/永久/拒绝 -->
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<view v-else class="approval-actions">
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<button class="approval-btn approve" size="mini" @tap="onAuthorize(p, 'once')">本次</button>
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<button class="approval-btn always" size="mini" @tap="onAuthorize(p, 'always')">永久</button>
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<button class="approval-btn reject" size="mini" @tap="onAuthorize(p, 'deny')">拒绝</button>
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</view>
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</view>
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</view>
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@@ -859,10 +845,6 @@ function tokenInOf(t: TokenUsage): number {
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<text class="tool-arg-val">{{ a.val }}</text>
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</view>
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</view>
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<!-- path 授权待授权目录 -->
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<view v-if="tc.kind === 'path' && tc.status === 'pending_approval' && tc.dir" class="tool-dir">
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<text>📁 {{ tc.dir }}</text>
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</view>
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<!-- 风险说明 -->
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<view v-if="tc.reason" class="tool-reason">
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<text>⚠ {{ tc.reason }}</text>
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@@ -1494,13 +1476,6 @@ function tokenInOf(t: TokenUsage): number {
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flex: 1;
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word-break: break-all; /* 工具参数值(路径/长字符串)断行防撑开 */
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}
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.tool-dir {
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margin-top: 4px;
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}
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.tool-dir text {
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color: #6bd99a;
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font-size: 12px;
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}
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.tool-reason {
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margin-top: 6px;
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padding: 4px 6px;
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@@ -64,14 +64,6 @@ export type AiChatEvent =
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| { type: 'AiHeartbeat'; conversation_id?: string | null }
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| { type: 'AiMaxRoundsReached'; conversation_id?: string | null }
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| { type: 'AiStreamRetry'; attempt: number; max_attempts: number; conversation_id?: string | null }
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| {
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type: 'AiDirAuthRequired'
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id: string
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tool: string
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path: string
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dir: string
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conversation_id?: string | null
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}
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| { type: 'AiContextCleared'; conversation_id?: string | null }
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| { type: 'AiCompressing'; conversation_id?: string | null }
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// 治 Task#1:拆分手动/自动两变体(字段相同仅 type 字面量不同),桌面按 type 分流,
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@@ -154,10 +146,6 @@ export interface AiToolCallInfo {
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status: 'running' | 'completed' | 'failed' | 'pending_approval' | 'rejected'
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reason?: string
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diff?: string
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/** 审批类型(risk 普通 / path 路径授权,对齐后端 ApprovalKind) */
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kind?: 'risk' | 'path'
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dir?: string
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path?: string
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}
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/** 挂起审批项(复用 AiToolCallInfo 结构,miniapp 不实现审批 UI 仅展示) */
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@@ -86,9 +86,9 @@ export type ControlMessage =
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* - cmd:命令名(对齐 src-tauri command 函数名)
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* - args:命令参数(对齐各 command 的参数)
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*
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* 路由表全覆盖(14 条):
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* 路由表全覆盖:
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* - send_message / stop / switch_conversation / regenerate(MVP 已发)
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* - approve / authorize_dir / continue_loop / stop_loop(阶段3 补齐,见 useAiChat.ts)
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* - approve / continue_loop / stop_loop(见 useAiChat.ts;authorize_dir 已随 path_auth 退役)
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* - list_conversations / load_messages(F-#95 扩展,会话列表/历史消息读取)
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* - list_skills / list_entities(F-#95 联想扩展,技能/项目·任务·灵感列表,见 useAiChat.ts)
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* - rename_conversation(会话重命名,2026-06-23)/ sync_pending(重连审批恢复,2026-06-23)
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@@ -207,21 +207,6 @@ export interface ApproveArgs {
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approved: boolean
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}
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/**
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* authorize_dir 命令参数(对齐 src-tauri ai_authorize_dir,Phase3 §2.2 路由表)
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*
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* decision 取值:对齐 src-tauri ai_authorize_dir 的 decision 参数
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* - 'once':本次授权(下次同路径仍需问)
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* - 'always':永久授权(写入会话级信任目录)
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* - 'deny':拒绝(本次不授权)
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*/
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export interface AuthorizeDirArgs {
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/** 待授权的工具调用 id(对应 AiDirAuthRequired 事件的 id) */
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tool_call_id: string
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/** 授权决策('once' / 'always' / 'deny') */
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decision: 'once' | 'always' | 'deny'
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}
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/** continue_loop 命令参数(对齐 src-tauri ai_continue_loop,Phase3 §2.2 路由表) */
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export interface ContinueLoopArgs {
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/** 需继续运行的会话 id */
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@@ -8,7 +8,7 @@ use tokio::sync::Mutex;
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use df_types::error::Result;
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use crate::db::Database;
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use crate::models::{IdeaRecord, KnowledgeEventRecord, KnowledgeRecord};
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use crate::models::{IdeaRecord, KnowledgeEdgeRecord, KnowledgeEventRecord, KnowledgeRecord, RelatedKnowledge};
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use df_types::types::IdeaStatus;
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use super::impl_repo;
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@@ -90,7 +90,8 @@ fn parse_json_id_array(raw: &Option<String>) -> Vec<String> {
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///
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/// 检索词里的 `%`/`_`/`|` 会被当成通配/转义符,用户输入与分词产物都可能含这些字符,
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/// 统一在此转义,避免关键词意外扩大命中面。
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fn escape_like(s: &str) -> String {
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/// pub(crate):message_repo 关键词检索(P1-A)同规则复用,单一实现。
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pub(crate) fn escape_like(s: &str) -> String {
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s.replace('|', "||").replace('%', "|%").replace('_', "|_")
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}
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@@ -796,10 +797,14 @@ impl KnowledgeRepo {
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"status = 'published'"
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};
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let limit_i = limit as i64;
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// confidence 同档决胜键(P0 淘汰机制,2026-10-23):title 分打平时置信先验优先,
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// 与 search_keywords_scoped 同序(后者第一档位键为 project=None 特例不参与)。
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let sql = format!(
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"SELECT {KNOWLEDGE_COLS} FROM knowledges \
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WHERE {status_clause} AND ({}) \
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ORDER BY ({}) DESC, reuse_count DESC, created_at DESC LIMIT ?{}",
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ORDER BY ({}) DESC, \
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(CASE confidence WHEN 'high' THEN 2 WHEN 'medium' THEN 1 ELSE 0 END) DESC, \
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reuse_count DESC, created_at DESC LIMIT ?{}",
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where_parts.join(" OR "),
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title_score_terms.join(" + "),
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patterns.len() + 1,
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@@ -862,6 +867,8 @@ impl KnowledgeRepo {
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// 项目档位表达式复用 ?(n+1):0=同项目 1=全局(NULL) 2=异项目
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// 档位是**第一排序键**(ASC:0 最前),title 命分是同档内第二键(DESC)——
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// 若档位与 title 分相加,档位会被 title 分稀释(异项目 title 高分压过同项目)。
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// confidence 是同档内第三键(P0 淘汰机制,2026-10-23):低置信噪音条与高置信
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// 经验条 title 分打平时,置信先验决胜,减少 low 条目挤占检索带宽。
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let pid_n = keywords.len() + 1;
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let project_tier =
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format!("CASE WHEN source_project = ?{pid_n} THEN 0 WHEN source_project IS NULL THEN 1 ELSE 2 END");
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@@ -875,7 +882,9 @@ impl KnowledgeRepo {
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let sql = format!(
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"SELECT {KNOWLEDGE_COLS} FROM knowledges \
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WHERE {status_clause} AND ({}) \
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ORDER BY ({project_tier}) ASC, ({title_terms}) DESC, reuse_count DESC, created_at DESC LIMIT ?{}",
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ORDER BY ({project_tier}) ASC, ({title_terms}) DESC, \
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(CASE confidence WHEN 'high' THEN 2 WHEN 'medium' THEN 1 ELSE 0 END) DESC, \
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reuse_count DESC, created_at DESC LIMIT ?{}",
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where_parts.join(" OR "),
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pid_n + 1,
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);
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@@ -899,6 +908,39 @@ impl KnowledgeRepo {
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.map_err(storage_err)?
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}
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/// 按状态+kind 列出(蒸馏合并分组用,2026-10-24):created_at 升序(最老优先——
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/// 早期无防重时期的重复重灾区),limit 封顶单批蒸馏规模
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pub async fn list_by_status_kind(
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&self,
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status: &str,
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kind: &str,
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limit: usize,
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) -> Result<Vec<KnowledgeRecord>> {
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let conn = self.conn.clone();
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let status = status.to_owned();
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let kind = kind.to_owned();
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let limit_i = limit as i64;
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tokio::task::spawn_blocking(move || {
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let guard = conn.blocking_lock();
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let mut stmt = guard
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.prepare(&format!(
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"SELECT {KNOWLEDGE_COLS} FROM knowledges \
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WHERE status = ?1 AND kind = ?2 ORDER BY created_at ASC LIMIT ?3",
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))
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.map_err(storage_err)?;
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let rows = stmt
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.query_map(params![status, kind, limit_i], |row| knowledge_from_row(row))
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.map_err(storage_err)?;
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let mut results = Vec::new();
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for r in rows {
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results.push(r.map_err(storage_err)?);
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}
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Ok(results)
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})
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.await
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.map_err(storage_err)?
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}
|
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/// 按状态列出(审核收件箱用): 按 confidence 语义排序(high>medium>low),次按 created_at
|
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///
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/// 用 CASE WHEN 替代纯 TEXT 排序(字典序 high>low>medium 非预期语义)。
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@@ -1003,6 +1045,265 @@ impl KnowledgeRepo {
|
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.map_err(storage_err)?
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}
|
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/// 提炼查重融合更新:覆盖 content + reasoning 追加演进轨迹(P0 写入防重,2026-10-23)
|
||||
///
|
||||
/// 背景(知识库治理审查 2026-10-23):提炼零查重直接落新条,同坑换会话再存一条,
|
||||
/// 语义重复线性累积(10 月 695 条)。此方法是「重复经验并入已有条目」的落库通道:
|
||||
/// LLM 已看过旧 content 后给出的融合版直接覆盖(单一真值=最新版),reasoning 尾部
|
||||
/// 追加一行更新轨迹(旧轨迹保留,演进可溯)。status/复用计数不动——update 不构成
|
||||
/// 复用证据,不参与「复用即信任」晋升。
|
||||
///
|
||||
/// UPDATE 幂等可重复调用;id 不存在返回 false(调用方降级走新建)。
|
||||
pub async fn update_content_merged(
|
||||
&self,
|
||||
id: &str,
|
||||
content: &str,
|
||||
reasoning_append: &str,
|
||||
) -> Result<bool> {
|
||||
let conn = self.conn.clone();
|
||||
let id = id.to_owned();
|
||||
let content = content.to_owned();
|
||||
let reasoning_append = reasoning_append.to_owned();
|
||||
let now = now_millis_str();
|
||||
tokio::task::spawn_blocking(move || {
|
||||
let guard = conn.blocking_lock();
|
||||
let affected = guard
|
||||
.execute(
|
||||
"UPDATE knowledges SET content = ?1, updated_at = ?2, \
|
||||
reasoning = CASE WHEN reasoning IS NULL OR reasoning = '' \
|
||||
THEN ?3 ELSE reasoning || char(10) || ?3 END \
|
||||
WHERE id = ?4",
|
||||
params![content, now, reasoning_append, id],
|
||||
)
|
||||
.map_err(storage_err)?;
|
||||
Ok(affected > 0)
|
||||
})
|
||||
.await
|
||||
.map_err(storage_err)?
|
||||
}
|
||||
|
||||
/// 淘汰批:零复用 + 非 high 置信 + 超龄条目批量转 archived(P0 淘汰机制,2026-10-23)
|
||||
///
|
||||
/// 背景:71% 条目零复用长尾不是中性的——每次检索都参与 LIKE 匹配和排序竞争,
|
||||
/// 稀释 head 知识召回。检索面是 status 白名单(`IN ('published','candidate')`),
|
||||
/// archived 天然被排除,故本方法只需改 status、无需动任何检索 SQL。
|
||||
///
|
||||
/// 判据校准(2026-10-23 生产实测):LLM 提炼 confidence 系统性偏高
|
||||
/// (high 1416/medium 233/low 仅 1),原 `confidence='low'` 判据命中 0 条形同虚设,
|
||||
/// 校准为「非 high」(low/medium/NULL 同档)——高置信零复用长尾(632 条)不动,
|
||||
/// 需单独拍板。
|
||||
///
|
||||
/// UPDATE...RETURNING 一次拿回归档 id 列表(SQLite 3.35+,bundled 3.45 支持),
|
||||
/// 调用方逐条打时间线留痕。幂等:条件含 status 白名单,已 archived 不再命中。
|
||||
/// 返回归档的 id 列表(0 条=无可淘汰)。
|
||||
pub async fn archive_stale(&self, older_than_days: i64, limit: i64) -> Result<Vec<String>> {
|
||||
let conn = self.conn.clone();
|
||||
let cutoff = (now_millis_str().parse::<i64>().unwrap_or(0)) - older_than_days * 86_400_000;
|
||||
tokio::task::spawn_blocking(move || {
|
||||
let guard = conn.blocking_lock();
|
||||
let mut stmt = guard
|
||||
.prepare(
|
||||
"UPDATE knowledges SET status = 'archived', updated_at = ?1 \
|
||||
WHERE id IN (SELECT id FROM knowledges \
|
||||
WHERE status IN ('published','candidate') AND reuse_count = 0 \
|
||||
AND (confidence IS NULL OR confidence != 'high') AND created_at < ?2 \
|
||||
ORDER BY created_at LIMIT ?3) \
|
||||
RETURNING id",
|
||||
)
|
||||
.map_err(storage_err)?;
|
||||
let ids = stmt
|
||||
.query_map(params![now_millis_str(), cutoff, limit], |row| row.get::<_, String>(0))
|
||||
.map_err(storage_err)?
|
||||
.collect::<std::result::Result<Vec<_>, _>>()
|
||||
.map_err(storage_err)?;
|
||||
Ok(ids)
|
||||
})
|
||||
.await
|
||||
.map_err(storage_err)?
|
||||
}
|
||||
|
||||
/// 知识关联边:幂等插入(双向去重)+ 触碰验证时间(知识中枢阶段1,V54)
|
||||
///
|
||||
/// 幂等语义:同两节点任意方向已有边 → 不重复建,只 touch last_verified_at(复验信号)
|
||||
/// 并返回已有边 id。调用方(id 不存在的外键由应用层保证,表无 FK 约束与 knowledges 一致)。
|
||||
pub async fn insert_edge(
|
||||
&self,
|
||||
src_id: &str,
|
||||
dst_id: &str,
|
||||
kind: &str,
|
||||
origin: &str,
|
||||
) -> Result<String> {
|
||||
let conn = self.conn.clone();
|
||||
let src_id = src_id.to_owned();
|
||||
let dst_id = dst_id.to_owned();
|
||||
let kind = kind.to_owned();
|
||||
let origin = origin.to_owned();
|
||||
let now = now_millis_str();
|
||||
tokio::task::spawn_blocking(move || {
|
||||
let guard = conn.blocking_lock();
|
||||
// 双向去重(关联语义无方向):同对节点任一方向的既有边即复用
|
||||
let existing: Option<String> = guard
|
||||
.query_row(
|
||||
"SELECT id FROM knowledge_edges \
|
||||
WHERE (src_id = ?1 AND dst_id = ?2) OR (src_id = ?2 AND dst_id = ?1) \
|
||||
LIMIT 1",
|
||||
params![src_id, dst_id],
|
||||
|row| row.get(0),
|
||||
)
|
||||
.optional()
|
||||
.map_err(storage_err)?;
|
||||
if let Some(id) = existing {
|
||||
guard
|
||||
.execute(
|
||||
"UPDATE knowledge_edges SET last_verified_at = ?1 WHERE id = ?2",
|
||||
params![now, id],
|
||||
)
|
||||
.map_err(storage_err)?;
|
||||
return Ok(id);
|
||||
}
|
||||
let id = df_types::types::new_id();
|
||||
guard
|
||||
.execute(
|
||||
"INSERT INTO knowledge_edges (id, src_id, dst_id, kind, weight, origin, created_at, last_verified_at) \
|
||||
VALUES (?1, ?2, ?3, ?4, 0.5, ?5, ?6, ?6)",
|
||||
params![id, src_id, dst_id, kind, origin, now],
|
||||
)
|
||||
.map_err(storage_err)?;
|
||||
Ok(id)
|
||||
})
|
||||
.await
|
||||
.map_err(storage_err)?
|
||||
}
|
||||
|
||||
/// 关联知识列表(双向读 + 对端条目展开,详情页「关联知识」区块消费)
|
||||
///
|
||||
/// direction: out=边从当前条目指出,in=边指入当前条目。对端 archived 不展示
|
||||
/// (与检索面口径一致:归档知识退出活跃视图)。
|
||||
pub async fn list_related(
|
||||
&self,
|
||||
knowledge_id: &str,
|
||||
limit: usize,
|
||||
) -> Result<Vec<RelatedKnowledge>> {
|
||||
let conn = self.conn.clone();
|
||||
let knowledge_id = knowledge_id.to_owned();
|
||||
let limit_i = limit as i64;
|
||||
tokio::task::spawn_blocking(move || {
|
||||
let guard = conn.blocking_lock();
|
||||
// 边表与知识表列名重叠(kind/created_at 等),KNOWLEDGE_COLS 裸名在 JOIN 下歧义,
|
||||
// 统一加 k. 前缀(split-join 保单一列清单来源)
|
||||
let k_cols = KNOWLEDGE_COLS
|
||||
.split(',')
|
||||
.map(|c| format!("k.{}", c.trim()))
|
||||
.collect::<Vec<_>>()
|
||||
.join(", ");
|
||||
let mut stmt = guard
|
||||
.prepare(&format!(
|
||||
"SELECT {k_cols}, e.id, e.src_id, e.dst_id, e.kind, e.weight, e.origin, e.created_at, e.last_verified_at, \
|
||||
CASE WHEN e.src_id = ?1 THEN 'out' ELSE 'in' END \
|
||||
FROM knowledge_edges e \
|
||||
JOIN knowledges k ON k.id = (CASE WHEN e.src_id = ?1 THEN e.dst_id ELSE e.src_id END) \
|
||||
WHERE (e.src_id = ?1 OR e.dst_id = ?1) AND k.status != 'archived' \
|
||||
ORDER BY e.weight DESC, e.last_verified_at DESC LIMIT ?2"
|
||||
))
|
||||
.map_err(storage_err)?;
|
||||
let mut rows = stmt
|
||||
.query_map(params![knowledge_id, limit_i], |row| {
|
||||
let edge = KnowledgeEdgeRecord {
|
||||
id: row.get::<_, String>(KNOWLEDGE_COL_COUNT)?,
|
||||
src_id: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 1)?,
|
||||
dst_id: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 2)?,
|
||||
kind: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 3)?,
|
||||
weight: row.get::<_, f64>(KNOWLEDGE_COL_COUNT + 4)?,
|
||||
origin: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 5)?,
|
||||
created_at: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 6)?,
|
||||
last_verified_at: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 7)?,
|
||||
};
|
||||
Ok(RelatedKnowledge {
|
||||
knowledge: knowledge_from_row(row)?,
|
||||
edge,
|
||||
direction: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 8)?,
|
||||
})
|
||||
})
|
||||
.map_err(storage_err)?;
|
||||
let mut results = Vec::new();
|
||||
while let Some(r) = rows.next() {
|
||||
results.push(r.map_err(storage_err)?);
|
||||
}
|
||||
Ok(results)
|
||||
})
|
||||
.await
|
||||
.map_err(storage_err)?
|
||||
}
|
||||
|
||||
/// 图召回·激活扩散(多种子 1-hop):命中集种子沿边扩一圈,排除种子自身与 archived
|
||||
/// 对端,按边权重降序限条。knowledge_search 命中后调用,把「连着的知识」带出检索面
|
||||
/// (阶段2 图召回,2026-10-26);单种子场景 = list_related 去种子子集。
|
||||
pub async fn list_neighbors_for_seeds(
|
||||
&self,
|
||||
seed_ids: &[String],
|
||||
limit: usize,
|
||||
) -> Result<Vec<RelatedKnowledge>> {
|
||||
if seed_ids.is_empty() || limit == 0 {
|
||||
return Ok(Vec::new());
|
||||
}
|
||||
let conn = self.conn.clone();
|
||||
let seeds = seed_ids.to_vec();
|
||||
tokio::task::spawn_blocking(move || {
|
||||
let guard = conn.blocking_lock();
|
||||
// 种子 id 全参数化(动态 IN 占位,与既有检索同惯例)
|
||||
let seed_set = (1..=seeds.len())
|
||||
.map(|i| format!("?{i}"))
|
||||
.collect::<Vec<_>>()
|
||||
.join(", ");
|
||||
let k_cols = KNOWLEDGE_COLS
|
||||
.split(',')
|
||||
.map(|c| format!("k.{}", c.trim()))
|
||||
.collect::<Vec<_>>()
|
||||
.join(", ");
|
||||
let sql = format!(
|
||||
"SELECT DISTINCT {k_cols}, e.id, e.src_id, e.dst_id, e.kind, e.weight, e.origin, e.created_at, e.last_verified_at, \
|
||||
CASE WHEN e.src_id IN ({seed_set}) THEN 'out' ELSE 'in' END \
|
||||
FROM knowledge_edges e \
|
||||
JOIN knowledges k ON k.id = (CASE WHEN e.src_id IN ({seed_set}) THEN e.dst_id ELSE e.src_id END) \
|
||||
WHERE (e.src_id IN ({seed_set}) OR e.dst_id IN ({seed_set})) \
|
||||
AND k.id NOT IN ({seed_set}) AND k.status != 'archived' \
|
||||
ORDER BY e.weight DESC, e.last_verified_at DESC LIMIT ?{}",
|
||||
seeds.len() + 1
|
||||
);
|
||||
let mut stmt = guard.prepare(&sql).map_err(storage_err)?;
|
||||
let limit_i = limit as i64;
|
||||
let mut bind: Vec<&dyn rusqlite::ToSql> =
|
||||
seeds.iter().map(|s| s as &dyn rusqlite::ToSql).collect();
|
||||
bind.push(&limit_i);
|
||||
let mut rows = stmt
|
||||
.query_map(bind.as_slice(), |row| {
|
||||
let edge = KnowledgeEdgeRecord {
|
||||
id: row.get::<_, String>(KNOWLEDGE_COL_COUNT)?,
|
||||
src_id: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 1)?,
|
||||
dst_id: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 2)?,
|
||||
kind: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 3)?,
|
||||
weight: row.get::<_, f64>(KNOWLEDGE_COL_COUNT + 4)?,
|
||||
origin: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 5)?,
|
||||
created_at: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 6)?,
|
||||
last_verified_at: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 7)?,
|
||||
};
|
||||
Ok(RelatedKnowledge {
|
||||
knowledge: knowledge_from_row(row)?,
|
||||
edge,
|
||||
direction: row.get::<_, String>(KNOWLEDGE_COL_COUNT + 8)?,
|
||||
})
|
||||
})
|
||||
.map_err(storage_err)?;
|
||||
let mut results = Vec::new();
|
||||
while let Some(r) = rows.next() {
|
||||
results.push(r.map_err(storage_err)?);
|
||||
}
|
||||
Ok(results)
|
||||
})
|
||||
.await
|
||||
.map_err(storage_err)?
|
||||
}
|
||||
|
||||
/// 写入向量嵌入(BLOB = Vec<f32> 小端字节序列化)
|
||||
///
|
||||
/// embedding 列不进 KnowledgeRecord(IPC 不需要传向量给前端),专用方法读写。
|
||||
@@ -2442,4 +2743,161 @@ mod tests {
|
||||
assert_eq!(after.title, "他人已改", "CAS 冲突不得覆盖他人修改");
|
||||
assert_eq!(after.updated_at, "1800000000000");
|
||||
}
|
||||
|
||||
// ---------- P0 写入防重/淘汰机制(2026-10-23) ----------
|
||||
|
||||
#[tokio::test]
|
||||
async fn update_content_merged_overrides_and_appends_reasoning() {
|
||||
let repo = setup_repo().await;
|
||||
repo.insert(krec("k1", "路由模型选择", "旧内容", "pitfall", "published", Some("high"), 2))
|
||||
.await
|
||||
.unwrap();
|
||||
// 首次融合更新:content 覆盖 + reasoning 从 None 直接成为追加行
|
||||
assert!(repo
|
||||
.update_content_merged("k1", "融合后的新版内容", "update: 并入会话B同坑经验")
|
||||
.await
|
||||
.unwrap());
|
||||
let rec = repo.get_by_id("k1").await.unwrap().unwrap();
|
||||
assert_eq!(rec.content, "融合后的新版内容");
|
||||
assert_eq!(rec.reasoning.as_deref(), Some("update: 并入会话B同坑经验"));
|
||||
// 二次更新:reasoning 尾部追加,旧轨迹保留(演进可溯)
|
||||
assert!(repo
|
||||
.update_content_merged("k1", "二轮融合版", "update: 并入会话C补充")
|
||||
.await
|
||||
.unwrap());
|
||||
let rec = repo.get_by_id("k1").await.unwrap().unwrap();
|
||||
assert_eq!(rec.content, "二轮融合版");
|
||||
assert_eq!(
|
||||
rec.reasoning.as_deref(),
|
||||
Some("update: 并入会话B同坑经验\nupdate: 并入会话C补充")
|
||||
);
|
||||
// id 不存在 → false(调用方降级新建)
|
||||
assert!(!repo.update_content_merged("nope", "x", "y").await.unwrap());
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn list_neighbors_for_seeds_expands_one_hop_excluding_seeds_and_archived() {
|
||||
let repo = setup_repo().await;
|
||||
for (id, status) in [
|
||||
("s1", "published"),
|
||||
("s2", "published"),
|
||||
("n1", "published"),
|
||||
("n2", "published"),
|
||||
("arch", "archived"),
|
||||
] {
|
||||
repo.insert(krec(id, &format!("标题{id}"), "内容", "pitfall", status, Some("high"), 0)).await.unwrap();
|
||||
}
|
||||
// s1-n1 / s2-n2 正常展开;s1-arch 对端 archived 排除
|
||||
repo.insert_edge("s1", "n1", "similar", "llm-tag").await.unwrap();
|
||||
repo.insert_edge("s2", "n2", "similar", "manual").await.unwrap();
|
||||
repo.insert_edge("s1", "arch", "similar", "manual").await.unwrap();
|
||||
let nb = repo
|
||||
.list_neighbors_for_seeds(&["s1".into(), "s2".into()], 10)
|
||||
.await
|
||||
.unwrap();
|
||||
let mut ids: Vec<&str> = nb.iter().map(|r| r.knowledge.id.as_str()).collect();
|
||||
ids.sort();
|
||||
assert_eq!(ids, vec!["n1", "n2"], "1-hop 展开:排除种子与 archived 对端");
|
||||
let one = repo.list_neighbors_for_seeds(&["s1".into()], 10).await.unwrap();
|
||||
assert_eq!(one.len(), 1, "单种子 s1 仅 n1(archived 对端排除)");
|
||||
assert!(repo.list_neighbors_for_seeds(&[], 5).await.unwrap().is_empty(), "空种子返空");
|
||||
let lim = repo
|
||||
.list_neighbors_for_seeds(&["s1".into(), "s2".into()], 1)
|
||||
.await
|
||||
.unwrap();
|
||||
assert_eq!(lim.len(), 1, "limit 封顶");
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn archive_stale_moves_only_non_high_unused_aged() {
|
||||
let repo = setup_repo().await;
|
||||
// a 低置信零复用超龄 → 淘汰;b 有复用/c 高置信 → 保留;
|
||||
// d candidate 也淘汰;e 已 archived 幂等不重复命中;
|
||||
// f medium/n 无置信 → 淘汰(2026-10-23 判据校准:low 实测仅 1/1688 形同虚设,非 high 同档)
|
||||
repo.insert(krec("a", "过期低值1", "x", "pitfall", "published", Some("low"), 0)).await.unwrap();
|
||||
repo.insert(krec("b", "过期低值但复用过", "x", "pitfall", "published", Some("low"), 3)).await.unwrap();
|
||||
repo.insert(krec("c", "过期高置信", "x", "pitfall", "published", Some("high"), 0)).await.unwrap();
|
||||
repo.insert(krec("d", "候选期低值", "x", "pitfall", "candidate", Some("low"), 0)).await.unwrap();
|
||||
repo.insert(krec("e", "已归档", "x", "pitfall", "archived", Some("low"), 0)).await.unwrap();
|
||||
repo.insert(krec("f", "过期中置信", "x", "pitfall", "published", Some("medium"), 0)).await.unwrap();
|
||||
let mut nrec = krec("n", "过期无置信", "x", "pitfall", "published", None, 0);
|
||||
nrec.confidence = None;
|
||||
repo.insert(nrec).await.unwrap();
|
||||
// krec created_at=1700000000000(2023-11),older_than_days=1 → 全部超龄,仅三条件筛
|
||||
let mut ids = repo.archive_stale(1, 100).await.unwrap();
|
||||
ids.sort();
|
||||
assert_eq!(
|
||||
ids,
|
||||
vec!["a".to_string(), "d".to_string(), "f".to_string(), "n".to_string()],
|
||||
"非high置信+零复用+超龄(published/candidate)淘汰"
|
||||
);
|
||||
for (id, want) in [("a", "archived"), ("b", "published"), ("c", "published"), ("d", "archived"), ("e", "archived"), ("f", "archived"), ("n", "archived")] {
|
||||
assert_eq!(repo.get_by_id(id).await.unwrap().unwrap().status, want, "条目 {id} 终态");
|
||||
}
|
||||
// limit 生效:再插 3 条同类,limit=2 只归档 2 条
|
||||
for i in 0..3 {
|
||||
repo.insert(krec(&format!("g{i}"), "批量", "x", "pitfall", "published", Some("low"), 0)).await.unwrap();
|
||||
}
|
||||
assert_eq!(repo.archive_stale(1, 2).await.unwrap().len(), 2);
|
||||
// 幂等复跑:剩余 1 条
|
||||
assert_eq!(repo.archive_stale(1, 100).await.unwrap().len(), 1);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn search_keywords_tiebreaks_by_confidence() {
|
||||
let repo = setup_repo().await;
|
||||
// title 分打平(都含关键词一次)、reuse 打平(0) → confidence high 排 low 前
|
||||
repo.insert(krec("low-1", "签名配置", "低置信噪音", "pitfall", "published", Some("low"), 0)).await.unwrap();
|
||||
repo.insert(krec("high-1", "签名配置经验", "高置信经验", "pitfall", "published", Some("high"), 0)).await.unwrap();
|
||||
let hits = repo
|
||||
.search_keywords(&["签名".to_string()], true, 10)
|
||||
.await
|
||||
.unwrap();
|
||||
let ids: Vec<&str> = hits.iter().map(|r| r.id.as_str()).collect();
|
||||
assert_eq!(ids, vec!["high-1", "low-1"], "title/reuse 打平时 confidence 先验决胜");
|
||||
}
|
||||
|
||||
// ---------- 知识关联边(阶段1 连边,V54) ----------
|
||||
|
||||
#[tokio::test]
|
||||
async fn insert_edge_dedups_bidirectionally_and_lists_related() {
|
||||
let repo = setup_repo().await;
|
||||
for (id, status) in [("k1", "published"), ("k2", "published"), ("k3", "published"), ("k4", "archived")] {
|
||||
repo.insert(krec(id, &format!("标题{id}"), "内容", "pitfall", status, Some("high"), 0)).await.unwrap();
|
||||
}
|
||||
// 首建 + 反向重复:双向去重返回同一边 id
|
||||
let e1 = repo.insert_edge("k1", "k2", "similar", "llm-tag").await.unwrap();
|
||||
let e1_rev = repo.insert_edge("k2", "k1", "similar", "llm-tag").await.unwrap();
|
||||
assert_eq!(e1, e1_rev, "同对节点反向建边应复用既有边");
|
||||
// k3 → k4:对端 archived,list_related 不展示
|
||||
repo.insert_edge("k1", "k3", "similar", "manual").await.unwrap();
|
||||
repo.insert_edge("k1", "k4", "similar", "manual").await.unwrap();
|
||||
let rel = repo.list_related("k1", 10).await.unwrap();
|
||||
let mut peer_ids: Vec<&str> = rel.iter().map(|r| r.knowledge.id.as_str()).collect();
|
||||
peer_ids.sort();
|
||||
assert_eq!(peer_ids, vec!["k2", "k3"], "archived 对端不展示(k4)");
|
||||
// 方向标注(k1 视角):e1 src=k1→dst=k2 对端 k2 是 out;k1→k3 对端 k3 也是 out;
|
||||
// 反向边视角:另查 k2,其对端 k1 是 in(边指进来)
|
||||
let dir_of_k2 = repo.list_related("k2", 10).await.unwrap();
|
||||
assert_eq!(dir_of_k2.iter().find(|r| r.knowledge.id == "k1").unwrap().direction, "in");
|
||||
let dir_of = |id: &str| rel.iter().find(|r| r.knowledge.id == id).unwrap().direction.clone();
|
||||
assert_eq!(dir_of("k3"), "out");
|
||||
assert_eq!(dir_of("k2"), "out");
|
||||
// 无关联条目 → 空表
|
||||
assert!(repo.list_related("nobody", 10).await.unwrap().is_empty());
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn list_by_status_kind_filters_and_orders_oldest_first() {
|
||||
let repo = setup_repo().await;
|
||||
repo.insert(krec("a", "老pitfall", "x", "pitfall", "archived", Some("low"), 0)).await.unwrap();
|
||||
repo.insert(krec("b", "新pitfall", "x", "pitfall", "archived", Some("low"), 0)).await.unwrap();
|
||||
repo.insert(krec("c", "未归档pitfall", "x", "pitfall", "published", Some("low"), 0)).await.unwrap();
|
||||
repo.insert(krec("d", "其他kind", "x", "diagnosis", "archived", Some("low"), 0)).await.unwrap();
|
||||
let hits = repo.list_by_status_kind("archived", "pitfall", 10).await.unwrap();
|
||||
let ids: Vec<&str> = hits.iter().map(|r| r.id.as_str()).collect();
|
||||
assert_eq!(ids, vec!["a", "b"], "仅命中 status+kind,created_at 升序(最老优先)");
|
||||
// limit 封顶
|
||||
assert_eq!(repo.list_by_status_kind("archived", "pitfall", 1).await.unwrap().len(), 1);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -67,7 +67,7 @@ pub fn run(conn: &Connection) -> Result<()> {
|
||||
// V52 = ai_messages.source 列(#958 消息溯源:外部渠道原始消息 ID JSON,NULL=桌面本端)。
|
||||
// V53 = WorkingContext 持久化(ai_conversations.working_context_json TEXT,跨会话召回 L4
|
||||
// 重启不失忆:save 随行写,会话载入与 parked 恢复读还原,NULL=无上下文行为同旧)。
|
||||
let steps: [(i32, fn(&Connection) -> Result<()>); 53] = [
|
||||
let steps: [(i32, fn(&Connection) -> Result<()>); 54] = [
|
||||
(1, migrate_v1),
|
||||
(2, migrate_v2),
|
||||
(3, migrate_v3),
|
||||
@@ -129,6 +129,8 @@ pub fn run(conn: &Connection) -> Result<()> {
|
||||
(52, migrate_v52),
|
||||
// V53 = WorkingContext 持久化(ai_conversations.working_context_json,跨会话召回 L4)。
|
||||
(53, migrate_v53),
|
||||
// V54 = knowledge_edges 边表(知识中枢阶段1:节点连边,提炼 related_ids 标注落边)。
|
||||
(54, migrate_v54),
|
||||
];
|
||||
|
||||
for (version, migrate_fn) in steps {
|
||||
@@ -1536,7 +1538,7 @@ fn migrate_v50(conn: &Connection) -> Result<()> {
|
||||
/// 调度器启动时 reset_stale_sending 复位为 pending。
|
||||
/// 迁移链顶点版本(单一真值来源)：**新增迁移时必须同步 +1**,否则下方迁移测试会失败
|
||||
/// (测试用它断言「全量迁移后 schema_version == 顶点」「重跑仍到顶点」——既防链中断,也免多处写死)。
|
||||
pub const LATEST_SCHEMA_VERSION: i32 = 53;
|
||||
pub const LATEST_SCHEMA_VERSION: i32 = 54;
|
||||
|
||||
fn migrate_v51(conn: &Connection) -> Result<()> {
|
||||
if !table_exists(conn, "ai_outbox") {
|
||||
@@ -1609,6 +1611,38 @@ fn migrate_v53(conn: &Connection) -> Result<()> {
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// V54: knowledge_edges 边表(知识中枢阶段1「连边」,2026-10-23)
|
||||
///
|
||||
/// 终局设计(temp/知识中枢终局设计-20261023.md):知识从「清单」变「图」,节点=knowledges
|
||||
/// 零改动,边独立成表——related 视图走边表,不在 knowledges 上加列(防双源)。
|
||||
/// - kind: similar(强相关)|evolves(演进)|causal(因果)|merges(合并)|anchors(锚点),首两版
|
||||
/// 只产 similar(提炼 related_ids)/evolves(预留),其余为阶段 3 自动建边预留;
|
||||
/// - origin: llm-tag(LLM 标注)|embed-sim(向量相似)|co-cite(共引)|manual(人工)|tag-co-occur
|
||||
/// (标签共现)——每条边可溯源,错的边可按 origin 批量撤销;
|
||||
/// - 双列索引:激活扩散(阶段2)从任一端遍历,百万级实测双索引是毫秒级前提;
|
||||
/// - weight/last_verified_at: 边随复用强化、久未验证降权(阶段 3 演化循环消费)。
|
||||
/// - **幂等**:table_exists 守卫(`CREATE TABLE IF NOT EXISTS` 同效);
|
||||
/// **可逆**:`DROP TABLE knowledge_edges`。
|
||||
fn migrate_v54(conn: &Connection) -> Result<()> {
|
||||
conn.execute_batch(
|
||||
"CREATE TABLE IF NOT EXISTS knowledge_edges (\
|
||||
id TEXT PRIMARY KEY,\
|
||||
src_id TEXT NOT NULL,\
|
||||
dst_id TEXT NOT NULL,\
|
||||
kind TEXT NOT NULL DEFAULT 'similar',\
|
||||
weight REAL NOT NULL DEFAULT 0.5,\
|
||||
origin TEXT NOT NULL DEFAULT 'llm-tag',\
|
||||
created_at TEXT NOT NULL,\
|
||||
last_verified_at TEXT NOT NULL\
|
||||
);\
|
||||
CREATE INDEX IF NOT EXISTS ke_src ON knowledge_edges(src_id);\
|
||||
CREATE INDEX IF NOT EXISTS ke_dst ON knowledge_edges(dst_id);",
|
||||
)?;
|
||||
conn.execute("INSERT OR IGNORE INTO schema_version (version) VALUES (?)", [54])?;
|
||||
tracing::info!("迁移 v54 完成: knowledge_edges 边表(知识中枢阶段1)");
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 回填 conversation_checkpoints.token_total:按 snapshot JSON 内逐条消息的
|
||||
/// prompt_tokens+completion_tokens 求真实总量。
|
||||
///
|
||||
|
||||
@@ -559,6 +559,31 @@ pub struct KnowledgeEventRecord {
|
||||
pub timestamp: String,
|
||||
}
|
||||
|
||||
/// 知识关联边(知识中枢阶段1:节点连边,V54)
|
||||
///
|
||||
/// 有向 src→dst,查询侧双向读(关联语义无方向)。kind/origin 枚举见 migrations V54 注释;
|
||||
/// 首两版只产 similar(提炼 related_ids)与 manual(人工),embed-sim/co-cite 归阶段 3 自动建边。
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct KnowledgeEdgeRecord {
|
||||
pub id: String,
|
||||
pub src_id: String,
|
||||
pub dst_id: String,
|
||||
pub kind: String,
|
||||
pub weight: f64,
|
||||
pub origin: String,
|
||||
pub created_at: String,
|
||||
pub last_verified_at: String,
|
||||
}
|
||||
|
||||
/// 关联知识条目(边 + 对端知识展开,详情页「关联知识」区块直接消费)
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct RelatedKnowledge {
|
||||
pub edge: KnowledgeEdgeRecord,
|
||||
pub knowledge: KnowledgeRecord,
|
||||
/// 对端相对当前条目的方向:out=当前是 src(边指向对方),in=当前是 dst(对方指进来)
|
||||
pub direction: String,
|
||||
}
|
||||
|
||||
/// 工程记录(project_modules 表,项目多工程,每个工程独立代码仓库)
|
||||
///
|
||||
/// 一个项目可含多个工程(Monorepo 多仓库 / 微服务 / 前后端分离)。
|
||||
|
||||
@@ -0,0 +1,133 @@
|
||||
//! 存量知识蒸馏合并(知识库治理 P0 深化,2026-10-24 用户指令「历史知识内容蒸馏压缩合并」)
|
||||
//!
|
||||
//! 与写入防重(P0,治增量)互补:本模块治存量——已归档长尾(淘汰批 632 条)按 kind 分组,
|
||||
//! LLM 把一组同主题旧知识蒸馏成一条「合订本」(candidate 入库,原条目保持 archived 不删),
|
||||
//! 建 merges 边(合订本→组内每条,origin=distill)保留可溯关系。
|
||||
//!
|
||||
//! 蒸馏语义:合订本是「组内知识的浓缩替代视图」,不是权威合并——原条目仍在库(archived)
|
||||
//! 可随时人工复核;合订本走正常 candidate→复用晋升通道,被人用过才算立住。
|
||||
|
||||
use std::sync::Arc;
|
||||
|
||||
use df_ai::provider::{ChatMessage, CompletionRequest, LlmProvider};
|
||||
use df_ai::router::{select_model_id, Modality, TaskRequirements};
|
||||
use df_storage::crud::KnowledgeRepo;
|
||||
use df_storage::db::Database;
|
||||
use df_storage::models::AiProviderRecord;
|
||||
|
||||
use crate::commands::ai::knowledge_inject::{candidate_record_from_item, strip_code_fence};
|
||||
use crate::state::LlmConcurrency;
|
||||
|
||||
use super::secret::build_provider_for;
|
||||
|
||||
/// 单批蒸馏条数上限:每组送 LLM 的条目数。20 条 × 均 218 字 ≈ 4.4K 字符输入,
|
||||
/// 输出单条合订本 2K token 内;更多条分组蒸馏质量劣化(模型记不住开头),宁多批。
|
||||
const DISTILL_GROUP_LIMIT: usize = 20;
|
||||
|
||||
/// 合订本 content 里单条原知识的摘要截断(蒸馏输入侧)
|
||||
const DISTILL_ITEM_SNIPPET: usize = 300;
|
||||
|
||||
const DISTILL_SYSTEM_PROMPT: &str = "你是知识蒸馏引擎。下面是知识库中同一主题下的一组已归档旧知识条目,\
|
||||
它们多为多次对话对同一经验的重复沉淀。请把它们蒸馏成**一条**合并后的知识:\n\
|
||||
- content 融合全组信息,去重、合并同类点、保留每条的独有细节,输出完整可复用的浓缩版\n\
|
||||
- title 概括整组共同主题\n\
|
||||
- tags 取全组标签的并集(最多 5 个)\n\
|
||||
- reasoning 说明蒸馏取舍(丢了什么、为何可丢)\n\n\
|
||||
输出严格的 JSON 对象(不要 markdown 包裹):\n\
|
||||
{\"kind\": \"<沿用组内 kind>\", \"title\": \"...\", \"content\": \"...\", \"tags\": [\"...\"], \"confidence\": \"medium\", \"reasoning\": \"...\"}\n\n\
|
||||
无可蒸馏内容(条目互相无重叠且各自完整)时输出 {\"skip\": true}。只输出 JSON。";
|
||||
|
||||
/// 蒸馏一个已归档分组:LLM 合订本 → candidate 落库 → merges 边连组内全部条目。
|
||||
///
|
||||
/// 返回合订本 id;组内不足 2 条(无可合并)或模型输出 skip 时返回 Ok(None)。
|
||||
/// 节奏控制:重复调用同 kind 会再取同批条目(蒸馏批不做已处理标记,P0 由前端按钮
|
||||
/// 人工控频;merges 边幂等,重复产出合订本可人工在收件箱驳回)。
|
||||
pub async fn distill_archived_group(
|
||||
db: &Arc<Database>,
|
||||
provider_config: &AiProviderRecord,
|
||||
llm_concurrency: &LlmConcurrency,
|
||||
kind: &str,
|
||||
limit: Option<usize>,
|
||||
) -> anyhow::Result<Option<String>> {
|
||||
let repo = KnowledgeRepo::new(db);
|
||||
let group = repo
|
||||
.list_by_status_kind("archived", kind, limit.unwrap_or(DISTILL_GROUP_LIMIT).min(50))
|
||||
.await?;
|
||||
if group.len() < 2 {
|
||||
return Ok(None);
|
||||
}
|
||||
// 组装蒸馏输入:编号列表(id 供溯源,正文截断)
|
||||
let mut conv_text = format!("主题分组: {kind}\n\n");
|
||||
for (i, k) in group.iter().enumerate() {
|
||||
let truncated: String;
|
||||
let content = if k.content.chars().count() > DISTILL_ITEM_SNIPPET {
|
||||
truncated = k.content.chars().take(DISTILL_ITEM_SNIPPET).collect();
|
||||
truncated.as_str()
|
||||
} else {
|
||||
k.content.as_str()
|
||||
};
|
||||
conv_text.push_str(&format!("[{}] id={} | {}\n{}\n\n", i + 1, k.id, k.title, content));
|
||||
}
|
||||
|
||||
let messages = vec![
|
||||
ChatMessage::system(DISTILL_SYSTEM_PROMPT),
|
||||
ChatMessage::user(&conv_text),
|
||||
];
|
||||
let req = TaskRequirements {
|
||||
modalities: vec![Modality::Text],
|
||||
needs_tool_use: false,
|
||||
estimated_context: 0,
|
||||
tier: None,
|
||||
};
|
||||
let model = select_model_id(&req, &provider_config.model_configs)
|
||||
.unwrap_or_else(|| provider_config.default_model.clone());
|
||||
let request = CompletionRequest {
|
||||
model,
|
||||
messages,
|
||||
temperature: Some(0.2),
|
||||
max_tokens: Some(2048),
|
||||
stream: false,
|
||||
tools: None,
|
||||
tool_choice: None,
|
||||
reasoning_content: None,
|
||||
};
|
||||
let provider: Box<dyn LlmProvider> = build_provider_for(provider_config)
|
||||
.map_err(|e| anyhow::anyhow!("provider 密钥不可用: {e}"))?;
|
||||
let _global = llm_concurrency.acquire_global().await;
|
||||
let resp = provider.complete(request).await?;
|
||||
let json_str = strip_code_fence(resp.text.trim());
|
||||
let item: serde_json::Value = serde_json::from_str(json_str)
|
||||
.map_err(|e| anyhow::anyhow!("蒸馏输出 JSON 解析失败: {e} | 原始: {}", resp.text))?;
|
||||
if item.get("skip").and_then(|v| v.as_bool()) == Some(true) {
|
||||
return Ok(None);
|
||||
}
|
||||
|
||||
// 合订本落库:candidate 信任级(source_ref=distill:{kind}),溯源与自动提炼同风格
|
||||
let batch_ref = format!("distill:{}", kind);
|
||||
let record = candidate_record_from_item(&item, None, Some(batch_ref.clone()))
|
||||
.ok_or_else(|| anyhow::anyhow!("蒸馏产出字段残缺(kind/title/content 缺失)"))?;
|
||||
repo.insert(record.clone()).await?;
|
||||
let timeline = crate::commands::knowledge_timeline::KnowledgeTimeline::new(db);
|
||||
timeline
|
||||
.record_extracted(
|
||||
&record.id,
|
||||
"distill-batch",
|
||||
&format!("蒸馏合订本({kind})"),
|
||||
record.reasoning.as_deref().unwrap_or(""),
|
||||
None,
|
||||
)
|
||||
.await;
|
||||
// merges 边:合订本 → 组内每条(insert_edge 双向去重幂等,单边失败非阻断)
|
||||
let mut linked = 0usize;
|
||||
for k in &group {
|
||||
if repo.insert_edge(&record.id, &k.id, "merges", "distill").await.is_ok() {
|
||||
linked += 1;
|
||||
}
|
||||
}
|
||||
tracing::info!(
|
||||
"[knowledge-distill] {kind} 组蒸馏完成: 合订本 {} ← {linked} 条边(group={})",
|
||||
record.id,
|
||||
group.len()
|
||||
);
|
||||
Ok(Some(record.id))
|
||||
}
|
||||
@@ -50,6 +50,12 @@ const KNOWLEDGE_EXTRACT_WINDOW: usize = 40;
|
||||
///
|
||||
/// 取代原「每会话仅提炼一次」的 bool 硬去重——长会话后半程的新经验此前永不提炼。
|
||||
const KNOWLEDGE_EXTRACT_MIN_NEW_USER_MSGS: usize = 10;
|
||||
/// 提炼查重候选数:随 prompt 附给 LLM 的已有知识条数(P0 写入防重,2026-10-23)
|
||||
///
|
||||
/// 5 条 × 500 字截断 ≈ 2.5K 字符,足以覆盖本轮对话主题的已有知识面;更多会稀释
|
||||
/// 提炼输入预算与 LLM 对单条的注意力。与注入 limit=3 同量级但不等值(注入防膨胀,
|
||||
/// 查重要盖住高频重复区)。
|
||||
const KNOWLEDGE_DEDUP_CANDIDATES: usize = 5;
|
||||
|
||||
/// 自动提炼频次水位(conv_id → 上次提炼进度),见 [`EXTRACT_WATERMARKS`]。
|
||||
#[derive(Clone, Copy)]
|
||||
@@ -235,6 +241,11 @@ async fn resolve_embed_provider(
|
||||
/// 生成文本嵌入(向量检索用)
|
||||
///
|
||||
/// 用配置指定的 embedding provider(必须 openai_compat 类型),失败返回 None(降级 LIKE)。
|
||||
///
|
||||
/// ⚠️ P0 定位变更(2026-10-23 知识库治理审查拍板):向量路径的「检索用途」明确弃用——
|
||||
/// 生产实证 0/1688 条有向量(provider 从未配成,本函数返回 None 即天然跳过,降级 LIKE
|
||||
/// 已是唯一实际路径)。检索独走分词 LIKE;本链路保留不删,阶段 3(知识中枢设计 2026-10-23)
|
||||
/// 改职复活为「自动建边引擎」(相似度>阈值=similar 边候选),届时不再做检索排序。
|
||||
async fn generate_embedding(
|
||||
state: &AppState,
|
||||
text: &str,
|
||||
@@ -894,13 +905,20 @@ const EXTRACTION_SYSTEM_PROMPT: &str = "你是知识提炼引擎,从 AI 对话
|
||||
输出严格的 JSON 数组(不要 markdown 代码块包裹),每个元素 schema:\n\
|
||||
{\"kind\": \"review_rule|prompt_template|pitfall|architecture_pattern|diagnosis|deployment_note|workflow_optimization\", \
|
||||
\"title\": \"简短标题\", \"content\": \"完整可复用内容\", \
|
||||
\"tags\": [\"标签\"], \"confidence\": \"high|medium|low\", \"reasoning\": \"为何值得沉淀\"}\n\n\
|
||||
\"tags\": [\"标签\"], \"confidence\": \"high|medium|low\", \"reasoning\": \"为何值得沉淀\", \
|
||||
\"action\": \"new|update\", \"target_id\": \"已有条目id(仅 update 时)\", \
|
||||
\"related_ids\": [\"强相关的已有条目id\"](可选,最多3个,仅 action=new 时)}\n\n\
|
||||
规则:\n\
|
||||
1. 如果适用范围有限制(如仅适用特定语言/框架/场景),必须在 content 或 tags 中明确标注\n\
|
||||
2. confidence: high=对话中可直接观察的明确模式, medium=合理推断, low=推测性弱信号\n\
|
||||
3. 无可提炼内容时返回空数组 []\n\
|
||||
4. 输出纯 JSON,无任何额外文字\n\
|
||||
5. 最多提取 5 条,按复用价值降序排列(只保留高价值条目,宁缺毋滥)";
|
||||
5. 最多提取 5 条,按复用价值降序排列(只保留高价值条目,宁缺毋滥)\n\
|
||||
6. 查重融合:若本次经验与「已有知识库条目」中某条重复或强相关,必须输出 action=\"update\" \
|
||||
且 target_id=该条 id,content 给出融合两方后的完整新版(不要重复旧文已有信息),\
|
||||
禁止同义新增造成知识重复;确属新经验才 action=\"new\"(可省略 action 字段)\n\
|
||||
7. 关联标注:action=\"new\" 时,若与已有条目存在强相关(同一主题的互补/前提/对照),\
|
||||
在 related_ids 数组里列出最多 3 个相关条目 id,供知识关联网络建边;无强相关则省略该字段";
|
||||
|
||||
/// 终态提炼提示词 — 在常规提示词上加终态边界(设计 §3.1):产出限跨会话可复用经验,
|
||||
/// 未完成事项/待办是会话任务态不是知识,归 L4 目标快照与停靠说明承接,不进知识表。
|
||||
@@ -1356,7 +1374,7 @@ fn conv_title_or_placeholder(title: Option<String>) -> String {
|
||||
/// LLM 产出单条 → candidate 知识记录(常规/终态提炼共用的落库统一口径)。
|
||||
/// kind 缺失或标题/正文为空返回 None(调用方跳过该条);status 恒 candidate(LLM 写入
|
||||
/// 不自动发布,与工具沉淀同信任级);归属与溯源 source_ref 由调用方按统一口径给定。
|
||||
fn candidate_record_from_item(
|
||||
pub(crate) fn candidate_record_from_item(
|
||||
item: &serde_json::Value,
|
||||
source_project: Option<&str>,
|
||||
source_ref: Option<String>,
|
||||
@@ -1417,8 +1435,19 @@ async fn run_extraction_core(
|
||||
Some(mid) => format!("conv_msg:{}", mid),
|
||||
None => format!("conv:{}", conv_id),
|
||||
});
|
||||
// P0 写入防重(2026-10-23):提炼前按窗口关键词查已有知识 top5 随 prompt 附给 LLM,
|
||||
// 重复经验走 update 融合而非同义新增——治理审查实测零查重是 10 月 695 条膨胀的主因。
|
||||
// 全局检索(project=None):知识重复不分项目。检索失败非阻断(降级为无候选,行为回旧)。
|
||||
let existing = match extract_existing_candidates(db, conv_text).await {
|
||||
Ok(v) => v,
|
||||
Err(e) => {
|
||||
tracing::warn!("[knowledge] 查重候选检索失败,降级无查重提炼(非阻断): {e}");
|
||||
Vec::new()
|
||||
}
|
||||
};
|
||||
let system_full = append_existing_block(system_prompt, &existing);
|
||||
let extract_messages = vec![
|
||||
ChatMessage::system(system_prompt),
|
||||
ChatMessage::system(&system_full),
|
||||
ChatMessage::user(&format!("请从以下对话中提炼可复用知识:\n\n{}", conv_text)),
|
||||
];
|
||||
// F-01 阶段5: 知识提炼路由 — TaskRequirements(Standard,无工具)。
|
||||
@@ -1498,7 +1527,45 @@ async fn run_extraction_core(
|
||||
tracing::info!(conv_id, project_id = %pid, "[KNOWLEDGE-EXTRACT] 本轮产出知识归属项目");
|
||||
}
|
||||
let mut inserted = 0;
|
||||
let mut updated = 0;
|
||||
// update 分派的合法性锚:target_id 必须命中本轮附给 LLM 的候选集(防幻觉 id 误改他条)
|
||||
let existing_ids: std::collections::HashSet<&str> =
|
||||
existing.iter().map(|r| r.id.as_str()).collect();
|
||||
for item in &items {
|
||||
// P0 查重融合路径:action=update 且 target_id 命中候选集 → 覆盖已有条目而非新建。
|
||||
// content 缺失/空白与 target 未命中都降级走新建(内容不丢,与旧行为一致)。
|
||||
let update_target = (item.get("action").and_then(|v| v.as_str()) == Some("update"))
|
||||
.then(|| item.get("target_id").and_then(|v| v.as_str()))
|
||||
.flatten()
|
||||
.filter(|tid| existing_ids.contains(*tid));
|
||||
if let Some(tid) = update_target {
|
||||
let Some(new_content) = item
|
||||
.get("content")
|
||||
.and_then(|v| v.as_str())
|
||||
.map(str::trim)
|
||||
.filter(|s| !s.is_empty())
|
||||
else {
|
||||
continue;
|
||||
};
|
||||
let reasoning_line = format!(
|
||||
"update@{}: {}",
|
||||
chrono::Utc::now().format("%Y-%m-%d"),
|
||||
item.get("reasoning").and_then(|v| v.as_str()).unwrap_or("融合重复经验")
|
||||
);
|
||||
match knowledge_repo
|
||||
.update_content_merged(tid, new_content, &reasoning_line)
|
||||
.await
|
||||
{
|
||||
Ok(true) => {
|
||||
updated += 1;
|
||||
tracing::info!("AI 提炼知识融合更新: {tid}(重复经验并入已有条目)");
|
||||
timeline.record_updated(tid, &["content"], source_ref.as_deref().unwrap_or("extract")).await;
|
||||
}
|
||||
Ok(false) => tracing::warn!("知识融合更新未命中(id 已消失?),降级新建: {tid}"),
|
||||
Err(e) => tracing::warn!("知识融合更新失败(非阻断,降级新建): {e}"),
|
||||
}
|
||||
continue;
|
||||
}
|
||||
let Some(record) = candidate_record_from_item(item, source_project.as_deref(), source_ref.clone()) else {
|
||||
continue;
|
||||
};
|
||||
@@ -1520,18 +1587,78 @@ async fn run_extraction_core(
|
||||
last_assistant_msg_id,
|
||||
)
|
||||
.await;
|
||||
// 阶段1 连边:related_ids ∩ 候选集 → similar 边(方向=新条目→相关已有条目)。
|
||||
// 幂等(insert_edge 双向去重),单边失败非阻断只 warn。
|
||||
let related = item
|
||||
.get("related_ids")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.iter().filter_map(|x| x.as_str()).collect::<Vec<_>>())
|
||||
.unwrap_or_default();
|
||||
for rid in related.into_iter().take(3) {
|
||||
if !existing_ids.contains(rid) || rid == record.id {
|
||||
continue;
|
||||
}
|
||||
if let Err(e) = knowledge_repo.insert_edge(&record.id, rid, "similar", "llm-tag").await {
|
||||
tracing::warn!("知识关联边建立失败(非阻断): {}→{}: {e}", record.id, rid);
|
||||
}
|
||||
}
|
||||
}
|
||||
Err(e) => tracing::warn!("知识候选插入失败(非阻断): {}", e),
|
||||
}
|
||||
}
|
||||
if inserted > 0 {
|
||||
tracing::info!("知识提炼完成: 对话 {} 产出 {} 条 candidate", conv_id, inserted);
|
||||
// 水位语义「有产出才推进」:update 与 new 同为产出(0 new + N update 不再误判无产出)
|
||||
if inserted + updated > 0 {
|
||||
tracing::info!(
|
||||
"知识提炼完成: 对话 {conv_id} 新建 {inserted} 条 / 融合更新 {updated} 条"
|
||||
);
|
||||
}
|
||||
Ok(inserted)
|
||||
Ok(inserted + updated)
|
||||
}
|
||||
|
||||
/// 提炼查重候选检索:按窗口关键词查已有知识 top5(P0 写入防重,2026-10-23)
|
||||
///
|
||||
/// 走与注入同源的 [`extract_query_keywords`] 分词 + [`KnowledgeRepo::search_keywords`]
|
||||
/// (include_candidates=true 与检索面同口径)。降级链:分词空词/0 命中 → 空表(LLM 收不到
|
||||
/// 已有块,全部走 new,行为回旧)。
|
||||
async fn extract_existing_candidates(
|
||||
db: &Arc<Database>,
|
||||
conv_text: &str,
|
||||
) -> anyhow::Result<Vec<KnowledgeRecord>> {
|
||||
let keywords = extract_query_keywords(conv_text);
|
||||
if keywords.is_empty() {
|
||||
return Ok(Vec::new());
|
||||
}
|
||||
let repo = KnowledgeRepo::new(db);
|
||||
Ok(repo.search_keywords(&keywords, true, KNOWLEDGE_DEDUP_CANDIDATES).await?)
|
||||
}
|
||||
|
||||
/// 把已有知识候选块追加到提炼 system prompt(action=update 协议的上下文载体)
|
||||
///
|
||||
/// 每条带 id/title/content 截断(500 字,LLM 判断重复足够,不撑提炼输入预算);
|
||||
/// 候选为空返回原 prompt(零开销)。
|
||||
fn append_existing_block(system_prompt: &str, existing: &[KnowledgeRecord]) -> String {
|
||||
if existing.is_empty() {
|
||||
return system_prompt.to_owned();
|
||||
}
|
||||
let mut block = String::from("\n\n已有知识库条目(判断查重用):\n");
|
||||
for r in existing {
|
||||
let truncated: String;
|
||||
let content = if r.content.chars().count() > 500 {
|
||||
truncated = r.content.chars().take(500).collect();
|
||||
truncated.as_str()
|
||||
} else {
|
||||
r.content.as_str()
|
||||
};
|
||||
block.push_str(&format!(
|
||||
"- id={} | {} | {}\n",
|
||||
r.id, r.title, content
|
||||
));
|
||||
}
|
||||
format!("{system_prompt}{block}")
|
||||
}
|
||||
|
||||
/// 剥离 LLM 输出可能的 ```json ... ``` 代码块包裹
|
||||
fn strip_code_fence(s: &str) -> &str {
|
||||
pub(crate) fn strip_code_fence(s: &str) -> &str {
|
||||
let s = s.trim();
|
||||
if let Some(rest) = s.strip_prefix("```json") {
|
||||
return rest.trim().trim_end_matches("```").trim();
|
||||
|
||||
@@ -92,6 +92,7 @@ pub fn register(registry: &mut AiToolRegistry, db: &Arc<Database>) {
|
||||
.iter()
|
||||
.map(|k| {
|
||||
serde_json::json!({
|
||||
"id": k.id,
|
||||
"title": k.title,
|
||||
"kind": k.kind,
|
||||
"content": truncate_chars_ellipsis(&k.content, SEARCH_CONTENT_SNIPPET_LEN),
|
||||
@@ -100,22 +101,46 @@ pub fn register(registry: &mut AiToolRegistry, db: &Arc<Database>) {
|
||||
})
|
||||
})
|
||||
.collect();
|
||||
Ok(serde_json::json!({ "results": results }))
|
||||
// 图召回·激活扩散(阶段2,2026-10-26):命中集沿边扩一跳,把「连着的知识」带出
|
||||
// 检索面——排除已命中与 archived,按边权重降序限 5 条;related 不占 limit 配额,
|
||||
// 空数组=无关联(语义清晰,LLM 可忽略)。
|
||||
let seed_ids: Vec<String> = hits.iter().map(|k| k.id.clone()).collect();
|
||||
let related: Vec<serde_json::Value> = repo
|
||||
.list_neighbors_for_seeds(&seed_ids, 5)
|
||||
.await
|
||||
.unwrap_or_default()
|
||||
.iter()
|
||||
.map(|n| {
|
||||
serde_json::json!({
|
||||
"id": n.knowledge.id,
|
||||
"title": n.knowledge.title,
|
||||
"kind": n.knowledge.kind,
|
||||
"edge_kind": n.edge.kind,
|
||||
"direction": n.direction,
|
||||
})
|
||||
})
|
||||
.collect();
|
||||
Ok(serde_json::json!({ "results": results, "related": related }))
|
||||
}
|
||||
);
|
||||
|
||||
// 沉淀工具:用户说「记住/记一下/以后注意」时把要点落为候选知识
|
||||
// 沉淀工具:用户说「记住/记一下/以后注意」时把要点落为候选知识。
|
||||
// P0 写入防重(2026-10-23):工具无「再问一次 LLM」的查重机会,防重靠描述引导
|
||||
// (先 search 后写) + update_target_id 融合口径——agent 检索发现重复时传已有条目
|
||||
// id 走融合更新,禁同义新增。
|
||||
declare_tool!(
|
||||
registry,
|
||||
db: Arc<Database>,
|
||||
"knowledge_insert",
|
||||
"把一条经验/约定/踩坑要点沉淀进本地知识库(写入为候选知识,经复用或人工审核后转正,不会立即进入检索)。使用时机:用户说「记住/记一下/以后注意/这是我踩过的坑」时调用,先提炼要点再写入。参数:title=一句话概括(必填),content=要点详情(必填),kind=知识类型(可选,常用:pitfall 踩坑/architecture_pattern 架构模式/diagnosis 诊断/review_rule 评审规则/prompt_template 提示词模板/deployment_note 部署要点/workflow_optimization 流程优化,默认 pitfall),tags=标签数组(可选)。回答前查已有知识请用 knowledge_search",
|
||||
"把一条经验/约定/踩坑要点沉淀进本地知识库(写入为候选知识,经复用或人工审核后转正,不会立即进入检索)。使用时机:用户说「记住/记一下/以后注意/这是我踩过的坑」时调用,先提炼要点再写入。写入前必须先 knowledge_search 查重:与已有条目重复或强相关时不要新增,改传 update_target_id=该条 ID 做融合更新(此时 content 给融合后的完整内容,title 忽略),禁止同义新增造成知识重复。参数:title=一句话概括(新增时必填),content=要点详情(必填),kind=知识类型(可选,常用:pitfall 踩坑/architecture_pattern 架构模式/diagnosis 诊断/review_rule 评审规则/prompt_template 提示词模板/deployment_note 部署要点/workflow_optimization 流程优化,默认 pitfall),tags=标签数组(可选),update_target_id=已有知识ID(可选,融合更新而非新增)",
|
||||
RiskLevel::Low,
|
||||
schema: object_schema(vec![
|
||||
("title", "string", true),
|
||||
("content", "string", true),
|
||||
("kind", "string", false),
|
||||
("tags", "array", false),
|
||||
("update_target_id", "string", false),
|
||||
("related_ids", "array", false),
|
||||
]),
|
||||
args => {
|
||||
let title = args["title"].as_str().ok_or_else(|| anyhow::anyhow!("缺少 title"))?;
|
||||
@@ -123,6 +148,28 @@ pub fn register(registry: &mut AiToolRegistry, db: &Arc<Database>) {
|
||||
if title.trim().is_empty() || content.trim().is_empty() {
|
||||
anyhow::bail!("title 和 content 不能为空");
|
||||
}
|
||||
let repo = df_storage::crud::KnowledgeRepo::new(&db);
|
||||
// P0 融合更新路径:update_target_id 命中已有条目 → 覆盖 content 并留轨迹;
|
||||
// 未命中(false=条目已消失)向 agent 明确报错,由其降级重新调用(不带该参数)
|
||||
if let Some(tid) = args["update_target_id"].as_str().map(str::trim).filter(|s| !s.is_empty()) {
|
||||
let reasoning_line = format!(
|
||||
"update@{}: agent 对话沉淀融合(来源 {})",
|
||||
chrono::Utc::now().format("%Y-%m-%d"),
|
||||
caller_conv().map(|c| format!("conv:{c}")).unwrap_or_else(|| "ai-chat".into())
|
||||
);
|
||||
let merged = repo
|
||||
.update_content_merged(tid, content.trim(), &reasoning_line)
|
||||
.await?;
|
||||
if !merged {
|
||||
anyhow::bail!("update_target_id={tid} 的知识不存在(可能已删除),请不带该参数重新调用以新增");
|
||||
}
|
||||
return Ok(serde_json::json!({
|
||||
"id": tid,
|
||||
"content": content,
|
||||
"action": "update",
|
||||
"note": "已融合更新已有知识,未新增条目"
|
||||
}));
|
||||
}
|
||||
// 非空即透传不做硬白名单(见模块注释:snippet 先例,过严校验制造假失败)
|
||||
let kind = match args["kind"].as_str() {
|
||||
Some(k) if !k.trim().is_empty() => k.trim().to_string(),
|
||||
@@ -169,11 +216,29 @@ pub fn register(registry: &mut AiToolRegistry, db: &Arc<Database>) {
|
||||
let kind_out = record.kind.clone();
|
||||
let repo = df_storage::crud::KnowledgeRepo::new(&db);
|
||||
repo.insert(record).await?;
|
||||
// 阶段1 连边:related_ids(知识检索结果里的已有条目)逐个校验存在后建 similar 边,
|
||||
// 幂等且非阻断(单边失败只 warn,不回滚主写入)
|
||||
let mut linked = 0usize;
|
||||
if let Some(arr) = args["related_ids"].as_array() {
|
||||
for rid in arr.iter().filter_map(|v| v.as_str()).take(3) {
|
||||
if rid == id {
|
||||
continue;
|
||||
}
|
||||
if repo.get_by_id(rid).await.unwrap_or(None).is_none() {
|
||||
tracing::warn!("related_id 不存在,跳过建边: {rid}");
|
||||
continue;
|
||||
}
|
||||
if repo.insert_edge(&id, rid, "similar", "llm-tag").await.is_ok() {
|
||||
linked += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(serde_json::json!({
|
||||
"id": id,
|
||||
"title": title,
|
||||
"kind": kind_out,
|
||||
"status": "candidate",
|
||||
"linked": linked,
|
||||
"note": "已记录为候选知识,复用或审核后转正"
|
||||
}))
|
||||
}
|
||||
|
||||
@@ -7,7 +7,7 @@ use serde::Deserialize;
|
||||
use tauri::State;
|
||||
|
||||
use df_types::types::new_id;
|
||||
use df_storage::models::{KnowledgeEventRecord, KnowledgeRecord};
|
||||
use df_storage::models::{KnowledgeEventRecord, KnowledgeRecord, RelatedKnowledge};
|
||||
|
||||
use crate::state::{AppState, KnowledgeConfig};
|
||||
|
||||
@@ -303,6 +303,80 @@ pub async fn knowledge_retry_embedding(
|
||||
Ok(count)
|
||||
}
|
||||
|
||||
/// 淘汰批:零复用 + low 置信 + 超龄条目批量归档(P0 淘汰机制,2026-10-23)
|
||||
///
|
||||
/// 背景(知识库治理审查 2026-10-23):71% 条目零复用长尾稀释检索排序,检索面是
|
||||
/// status 白名单,archived 天然出池。逐条留痕(status_changed,source_ref=archive_stale)
|
||||
/// 可审计可回滚。默认阈值 90 天/批 500——从生产画像看 low 置信存量极少(实测 1 条),
|
||||
/// 阈值偏保守,宁可分批多跑不误伤。
|
||||
///
|
||||
/// 归档不可逆性:archived 条目退出检索面,可通过 knowledge_update_status 手动转回
|
||||
/// published(状态机合法迁移),留痕链完整。
|
||||
#[tauri::command]
|
||||
pub async fn knowledge_archive_stale(
|
||||
state: State<'_, AppState>,
|
||||
older_than_days: Option<i64>,
|
||||
limit: Option<i64>,
|
||||
) -> Result<Vec<String>, String> {
|
||||
let days = older_than_days.unwrap_or(90).clamp(1, 3650);
|
||||
let limit = limit.unwrap_or(500).clamp(1, 5000);
|
||||
let ids = state
|
||||
.knowledge
|
||||
.archive_stale(days, limit)
|
||||
.await
|
||||
.map_err(err_str)?;
|
||||
// 逐条留痕 fire-and-forget(与晋升留痕同风格,吞错只 warn)。
|
||||
// from 记 "stale" 而非逐条旧值(批量 UPDATE 已合并原状态,淘汰批标记便于审计回滚)
|
||||
let timeline = KnowledgeTimeline::new(&state.db);
|
||||
for id in &ids {
|
||||
timeline.record_status_change(id, "stale", "archived").await;
|
||||
}
|
||||
if !ids.is_empty() {
|
||||
tracing::info!("[knowledge] 淘汰批: {} 条零复用低置信超龄条目归档(>{days}天)", ids.len());
|
||||
}
|
||||
Ok(ids)
|
||||
}
|
||||
|
||||
/// 关联知识列表(知识中枢阶段1:详情页「关联知识」区块数据源)
|
||||
///
|
||||
/// 双向读边表 + 对端条目展开,archived 对端不展示(与检索面口径一致)。
|
||||
#[tauri::command]
|
||||
pub async fn knowledge_list_related(
|
||||
state: State<'_, AppState>,
|
||||
id: String,
|
||||
limit: Option<i64>,
|
||||
) -> Result<Vec<RelatedKnowledge>, String> {
|
||||
let limit = limit.unwrap_or(10).clamp(1, 50) as usize;
|
||||
state
|
||||
.knowledge
|
||||
.list_related(&id, limit)
|
||||
.await
|
||||
.map_err(err_str)
|
||||
}
|
||||
|
||||
/// 存量蒸馏合并(治理 P0 深化,2026-10-24):对指定 kind 的已归档分组跑一次 LLM 蒸馏,
|
||||
/// 产出单条合订本(candidate)并建 merges 边连组内条目。返回合订本 id(组内不足 2 条
|
||||
/// 或模型判 skip 返回 None)。
|
||||
#[tauri::command]
|
||||
pub async fn knowledge_distill_group(
|
||||
state: State<'_, AppState>,
|
||||
kind: String,
|
||||
limit: Option<i64>,
|
||||
) -> Result<Option<String>, String> {
|
||||
let provider_config = crate::commands::ai::prompt::get_active_provider(&state)
|
||||
.await
|
||||
.map_err(err_str)?;
|
||||
crate::commands::ai::knowledge_distill::distill_archived_group(
|
||||
&state.db,
|
||||
&provider_config,
|
||||
&state.llm_concurrency,
|
||||
&kind,
|
||||
limit.map(|n| n as usize),
|
||||
)
|
||||
.await
|
||||
.map_err(err_str)
|
||||
}
|
||||
|
||||
// ============================================================
|
||||
// 配置 + 手动提炼
|
||||
// ============================================================
|
||||
@@ -367,14 +441,17 @@ pub struct UpdateKnowledgeInput {
|
||||
pub reasoning: Option<String>,
|
||||
}
|
||||
|
||||
/// 知识详情聚合负载(基本信息 + 全部生命线事件)
|
||||
/// 知识详情聚合负载(基本信息 + 全部生命线事件 + 关联知识)
|
||||
#[derive(Debug, Serialize)]
|
||||
pub struct KnowledgeDetailPayload {
|
||||
pub knowledge: KnowledgeRecord,
|
||||
pub events: Vec<KnowledgeEventRecord>,
|
||||
/// 关联知识(阶段1 连边,双向读+对端展开);老前端不消费此字段无影响
|
||||
#[serde(default)]
|
||||
pub related: Vec<RelatedKnowledge>,
|
||||
}
|
||||
|
||||
/// 知识详情(基本信息 + 生命线事件) — 详情页一次拉全
|
||||
/// 知识详情(基本信息 + 生命线事件 + 关联知识) — 详情页一次拉全
|
||||
#[tauri::command]
|
||||
pub async fn knowledge_get_detail(
|
||||
state: State<'_, AppState>,
|
||||
@@ -391,7 +468,9 @@ pub async fn knowledge_get_detail(
|
||||
.list_by_knowledge(&id)
|
||||
.await
|
||||
.map_err(err_str)?;
|
||||
Ok(KnowledgeDetailPayload { knowledge, events })
|
||||
// 关联边读取失败非阻断(边表为空/极老库未到 V54 时降级空列表)
|
||||
let related = state.knowledge.list_related(&id, 10).await.unwrap_or_default();
|
||||
Ok(KnowledgeDetailPayload { knowledge, events, related })
|
||||
}
|
||||
|
||||
/// 编辑知识(部分更新 title/content/tags/confidence/reasoning)
|
||||
|
||||
@@ -149,7 +149,7 @@ export default {
|
||||
confirmStatus: 'Change idea "{title}" status from {from} to {to}?',
|
||||
promoteFailed: 'Promotion failed',
|
||||
// Archive P1: idea archive box
|
||||
filterArchived: '📦 Archive',
|
||||
filterArchived: 'Archive',
|
||||
archiveBoxEmpty: 'No archived ideas',
|
||||
quickArchive: '📦 Archive',
|
||||
quickUnarchive: '📤 Unarchive',
|
||||
|
||||
@@ -46,6 +46,7 @@ export default {
|
||||
originUnknown: '—',
|
||||
reasoningLabel: '🤖 AI Reasoning',
|
||||
refTitle: '🔄 References ({n})',
|
||||
relatedTitle: '🔗 Related ({n})',
|
||||
refEmpty: 'Not referenced yet',
|
||||
loadMore: 'Load more ({n} left)',
|
||||
lifecycleTitle: '📅 Lifecycle',
|
||||
|
||||
@@ -149,7 +149,7 @@ export default {
|
||||
confirmStatus: '将灵感「{title}」状态从 {from} 改为 {to}?',
|
||||
promoteFailed: '立项失败',
|
||||
// 归档功能 P1(灵感归档箱)
|
||||
filterArchived: '📦 归档箱',
|
||||
filterArchived: '归档箱',
|
||||
archiveBoxEmpty: '暂无归档灵感',
|
||||
quickArchive: '📦 归档到归档箱',
|
||||
quickUnarchive: '📤 取消归档',
|
||||
|
||||
@@ -55,6 +55,7 @@ export default {
|
||||
reasoningLabel: '🤖 AI 判断依据',
|
||||
// 引用
|
||||
refTitle: '🔄 引用记录（{n} 次）',
|
||||
relatedTitle: '🔗 关联知识（{n} 条）',
|
||||
refEmpty: '尚未被引用',
|
||||
loadMore: '加载更多（剩 {n} 条）',
|
||||
// 生命周期
|
||||
|
||||
Reference in New Issue
Block a user