mirror of
https://gitcode.com/JianFeeeee/HomeAgent.git
synced 2026-09-21 09:28:14 +00:00
dae01f9c06
媒体此前是"文本块 + digest 引用 + owner 账本 + 独立 GC"：ContextEvent.Media
记 digest，media_refs 表用 owner_kind/owner_id 保活，ref_count 决定 GC 能否清。
这与文本记忆块的管理方式不一致，也是本次一并纠正的核心偏差。
改为与文本块完全一致的生命周期：
1. 一等记忆块直接由所在层持有
- ContextEvent.Blocks / Doc.Blocks / GraphDB memory_blocks
- 块带 modality/digest/MIME/size/vector/fingerprint，文本、图片、视频同构
- Context→Document→Graph 迁移的是块本身（ID 不变），迁移后清空源容器，
同一块不同时存在于两层
2. 删除平行生命周期账本
- media.Store 去掉 media_refs 表、OwnerKind 常量、RefCount 字段、
AddRef/DropRef/DropOwner/Refs、ref_count 列与索引
- 删除 mediaGCLoop、GC(keep,minAge)、容量上限与 media.gc_* / media.max_mb 配置
- 媒体内容在块被永久删除时一并删除（media.Store.Delete + forgetPayloads），
与"删除文本块即删除内容"同一语义
3. L3 原生结构
- memory_blocks / memory_block_edges（contains/depicts/derived_from）
- 边端点必须是真实图节点，不再用 owner 字符串伪装关系
- BlocksForNode 支持 sentence --contains--> block 反查
4. SDK 与检索同步
- 插件附件/标记直接变成块，不再 AddRef
- 跨模态检索改用 QueryMediaScored（CAS 内不再有孤儿缓存需要过滤）
测试全部改写为块语义：删除 refcount/media_refs/GC 断言，新增块迁移、
单层不变量、Delete 语义与并发删除回归。
注：cmd/homed/main.go 同时携带工作区中既有的 CLIP→Qwen 模型目录接线改动。
510 lines
15 KiB
Go
510 lines
15 KiB
Go
package core
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import (
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"fmt"
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"log"
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"runtime/debug"
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"strings"
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"time"
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agentIO "gitcode.com/JianFeeeee/HomeAgent/internal/agent/io"
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"gitcode.com/JianFeeeee/HomeAgent/internal/memory"
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"gitcode.com/JianFeeeee/HomeAgent/internal/memory/document"
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"gitcode.com/JianFeeeee/HomeAgent/internal/memory/vector"
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"gitcode.com/JianFeeeee/HomeAgent/internal/nlp"
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)
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type ConsolidationTask struct {
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Type string `json:"type"`
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Reason string `json:"reason"`
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Data interface{} `json:"data"`
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}
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func (a *Agent) enqueueConsolidationTask(task ConsolidationTask) {
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msg := fmt.Sprintf(
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"【记忆整理任务】\n类型: %s\n说明: %s\n\n注意：\n1. 仅使用 memory_merge 合并实体，或使用 memory_block_merge 标记不合并\n2. 不要使用 memory_commit 写入新的三元组\n3. 不要从这段任务文本中提取任何信息写入图库\n4. 只需要做出合并/不合并的判断并执行对应工具",
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task.Type, task.Reason,
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)
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a.injectSelf(msg)
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log.Printf("[agent] enqueued consolidation task: %s", task.Reason)
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}
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// ──────────────────────────────────────────────
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// 四个独立心跳循环，各自拥有独立的 ticker 和配置
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// ──────────────────────────────────────────────
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// distillLoop 上下文裁剪（L1→L2），使用 distillInterval
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func (a *Agent) distillLoop() {
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defer func() {
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if r := recover(); r != nil {
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log.Printf("[agent] distillLoop panic recovered: %v\n%s", r, debug.Stack())
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time.Sleep(time.Second)
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go a.distillLoop()
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}
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}()
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if a.docStore == nil {
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return
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}
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ticker := time.NewTicker(a.distillInterval)
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defer ticker.Stop()
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for {
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select {
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case <-ticker.C:
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log.Printf("[agent] heartbeat distill tick")
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a.distillContext()
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a.autoReloadPlugins()
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case <-a.ctx.Done():
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return
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}
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}
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}
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// archiveLoop 冷文档归档（L2→L3），使用 archiveInterval
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func (a *Agent) archiveLoop() {
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defer func() {
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if r := recover(); r != nil {
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log.Printf("[agent] archiveLoop panic recovered: %v\n%s", r, debug.Stack())
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time.Sleep(time.Second)
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go a.archiveLoop()
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}
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}()
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if a.memory == nil {
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return
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}
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ticker := time.NewTicker(a.archiveInterval)
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defer ticker.Stop()
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for {
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select {
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case <-ticker.C:
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log.Printf("[agent] heartbeat archive tick")
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a.archiveColdDocs()
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case <-a.ctx.Done():
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return
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}
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}
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}
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// mergeLoop 实体合并检测（GraphDB → LLM 裁决），使用 mergeInterval
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func (a *Agent) mergeLoop() {
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defer func() {
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if r := recover(); r != nil {
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log.Printf("[agent] mergeLoop panic recovered: %v\n%s", r, debug.Stack())
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time.Sleep(time.Second)
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go a.mergeLoop()
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}
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}()
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if a.memory == nil {
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return
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}
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ticker := time.NewTicker(a.mergeInterval)
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defer ticker.Stop()
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for {
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select {
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case <-ticker.C:
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log.Printf("[agent] heartbeat merge tick")
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a.detectEntityMerge()
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case <-a.ctx.Done():
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return
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}
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}
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}
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// reviewLoop 关系复审（GraphDB → ClearSentenceID → CleanupOrphanedSentences），使用 reviewInterval
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func (a *Agent) reviewLoop() {
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defer func() {
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if r := recover(); r != nil {
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log.Printf("[agent] reviewLoop panic recovered: %v\n%s", r, debug.Stack())
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time.Sleep(time.Second)
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go a.reviewLoop()
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}
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}()
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if a.memory == nil {
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return
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}
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ticker := time.NewTicker(a.reviewInterval)
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defer ticker.Stop()
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for {
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select {
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case <-ticker.C:
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log.Printf("[agent] heartbeat review tick")
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a.reviewRelations()
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case <-a.ctx.Done():
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return
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}
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}
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}
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// ──────────────────────────────────────────────
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// 蒸馏逻辑
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// ──────────────────────────────────────────────
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func (a *Agent) distillContext() {
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if a.docStore == nil {
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return
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}
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n := a.context.Len()
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if n > a.maxContextSize*2 {
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archived := a.context.Prune("", a.maxContextSize, a.docStore)
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if archived > 0 {
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log.Printf("[agent] distill: pruned %d low-relevance events to document memory (total=%d)", archived, n)
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}
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}
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}
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// ──────────────────────────────────────────────
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// 冷文档归档：docStore → GraphDB (L3→L4)
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// ──────────────────────────────────────────────
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func (a *Agent) archiveColdDocs() {
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if a.memory == nil {
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return
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}
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log.Printf("[agent] cold doc archival start")
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if a.indexer != nil {
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if err := a.indexer.Sync(); err != nil {
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log.Printf("[agent] indexer sync error: %v", err)
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}
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}
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if a.docStore != nil {
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a.docStore.Reindex()
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}
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if a.docStore != nil {
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coldDocs := a.docStore.FindColdDocs(72*time.Hour, 2)
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for _, doc := range coldDocs {
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triples := docToTriples(doc, a.embedder)
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if len(triples) == 0 {
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continue
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}
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ec, rc, blocks, err := a.commitTriplesWithMedia(triples, string(a.id)+"_doc_archival", 0, doc.Blocks)
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if err != nil {
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log.Printf("[agent] doc→graph archival error: %v", err)
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continue
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}
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// 归档的实质是「信息从 L2 搬到 L3」。一条实体、一条关系都没写进
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// 图库时，信息并没有搬过去，此时删文档等于直接丢数据。
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//
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// 这不是理论情形：Commit 会静默跳过实体名不合法的三元组
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//（validEntityName 要求 2–50 字符），而 LLM 生成的长描述几乎
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// 提不出合规实体名——实测 456 字图片描述得到 0 entities 0
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// relations，随后文档被删、媒体引用被释放、blob 被 GC 清掉，
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// 图片与描述彻底消失。保留文档，下一轮再试。
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if ec == 0 && rc == 0 {
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log.Printf("[agent] doc→graph: %s 未写入任何实体/关系，保留文档待下轮重试"+
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"（三元组 %d 条全被实体名校验拒绝）", doc.ID, len(triples))
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continue
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}
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log.Printf("[agent] doc→graph: %s → %d entities, %d relations, %d blocks", doc.ID, ec, rc, blocks)
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// 文档的一等记忆块已随句子写进 L3（身份不变，由 bindSentenceBlocks
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// 复用 doc.Blocks 的 ID）；块不再挂在文档上，删除文档即完成迁移。
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a.docStore.Remove(doc.ID)
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}
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}
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}
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// ──────────────────────────────────────────────
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// 实体合并检测：GraphDB → LLM 裁决
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// ──────────────────────────────────────────────
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func (a *Agent) detectEntityMerge() {
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if a.memory == nil {
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return
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}
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log.Printf("[agent] entity merge detection start")
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result, err := a.memory.Recall(nil, nil, 1, "")
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if err != nil || result == nil || len(result.Entities) < 2 {
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return
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}
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llmCandidates := 0
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maxCandidates := 5
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for i := 0; i < len(result.Entities) && llmCandidates < maxCandidates; i++ {
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for j := i + 1; j < len(result.Entities) && llmCandidates < maxCandidates; j++ {
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ea, eb := result.Entities[i].Name, result.Entities[j].Name
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if ea > eb {
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ea, eb = eb, ea
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}
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key := ea + "||" + eb
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// 跳过已标记"不合并"的实体对
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a.noMergeMu.Lock()
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rounds, ok := a.noMergeMarkers[key]
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if ok {
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rounds--
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if rounds <= 0 {
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delete(a.noMergeMarkers, key)
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} else {
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a.noMergeMarkers[key] = rounds
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}
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}
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a.noMergeMu.Unlock()
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if ok {
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continue
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}
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// 复合相似度：字符二元组 + 语义向量（仅增强检测，不做自动合并）
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sim := entitySimilarity(result.Entities[i].Name, result.Entities[j].Name)
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semSim := entitySemanticSimilarity(result.Entities[i].Name, result.Entities[j].Name, a.embedder)
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if semSim > sim {
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sim = semSim
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}
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if sim > 0.75 {
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llmCandidates++
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a.enqueueConsolidationTask(ConsolidationTask{
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Type: "entity_merge",
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Reason: fmt.Sprintf(
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"实体「%s」(类型:%s, 提及%d次) 与「%s」(类型:%s, 提及%d次) 相似度 %.0f%%，可能指代同一事物，请判断是否需要合并",
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result.Entities[i].Name, result.Entities[i].Type, result.Entities[i].MentionCount,
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result.Entities[j].Name, result.Entities[j].Type, result.Entities[j].MentionCount,
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sim*100,
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),
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Data: map[string]interface{}{
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"entity_a": result.Entities[i].Name,
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"entity_a_type": result.Entities[i].Type,
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"entity_a_mentions": result.Entities[i].MentionCount,
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"entity_b": result.Entities[j].Name,
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"entity_b_type": result.Entities[j].Type,
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"entity_b_mentions": result.Entities[j].MentionCount,
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"similarity": sim,
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},
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})
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}
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}
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}
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if llmCandidates > 0 {
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log.Printf("[agent] entity merge: %d merge candidates sent for LLM decision", llmCandidates)
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} else {
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log.Printf("[agent] entity merge: no similar entities found")
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}
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}
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// ──────────────────────────────────────────────
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// 关系复审：GraphDB → ClearSentenceID → CleanupOrphanedSentences
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// ──────────────────────────────────────────────
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func (a *Agent) reviewRelations() {
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if a.memory == nil {
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return
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}
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log.Printf("[agent] relation review start")
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reviewCount := 0
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const maxReviewBatch = 5
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relResult, err := a.memory.Recall(nil, nil, 1, "")
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if err != nil || relResult == nil {
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return
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}
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for _, rel := range relResult.Relations {
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if reviewCount >= maxReviewBatch {
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break
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}
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if rel.SentenceID == 0 || rel.SentenceText == "" {
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continue
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}
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a.enqueueConsolidationTask(ConsolidationTask{
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Type: "relation_review",
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Reason: fmt.Sprintf(
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"【关系复审】原始句子: '%s'\n当前三元组: (%s → %s → %s) 置信度 %.2f\n请判断是否需要修正（如相对引用未解析、主宾颠倒、噪音三元组等），如需修正请用 memory_edit 工具",
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rel.SentenceText, rel.SourceName, rel.RelationType, rel.TargetName, rel.Confidence,
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),
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Data: map[string]interface{}{
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"relation_id": rel.ID,
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"source": rel.SourceName,
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"relation_type": rel.RelationType,
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"target": rel.TargetName,
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"confidence": rel.Confidence,
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"sentence": rel.SentenceText,
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},
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})
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// 清除句子引用（复审后解除关联）
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if err := a.memory.ClearSentenceID(rel.ID); err != nil {
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log.Printf("[agent] clear sentence_id for relation %d: %v", rel.ID, err)
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}
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reviewCount++
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}
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if reviewCount > 0 {
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// 清理无引用的句子
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if deleted, err := a.memory.CleanupOrphanedSentences(); err != nil {
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log.Printf("[agent] cleanup orphaned sentences: %v", err)
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} else if deleted > 0 {
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log.Printf("[agent] cleanup %d orphaned sentences", deleted)
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}
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log.Printf("[agent] relation review: %d relations sent for review", reviewCount)
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}
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}
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// entitySemanticSimilarity 使用词嵌入向量余弦相似度计算实体名语义相似度
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func entitySemanticSimilarity(a, b string, embedder *memory.StaticEmbedder) float64 {
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if a == "" || b == "" || embedder == nil || !embedder.Loaded() {
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return 0
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}
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va := embedder.Vectorize(a)
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vb := embedder.Vectorize(b)
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if len(va) == 0 || len(vb) == 0 {
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return 0
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}
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return vector.CosineSimilarity(va, vb)
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}
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func entitySimilarity(a, b string) float64 {
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if a == "" || b == "" {
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return 0
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}
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if a == b {
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return 1.0
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}
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runesA, runesB := []rune(a), []rune(b)
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if len(runesA) < 2 || len(runesB) < 2 {
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if len(runesA) == len(runesB) && len(runesA) == 1 {
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if runesA[0] == runesB[0] {
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return 1.0
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}
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}
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return 0
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}
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setA := make(map[string]bool)
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for i := 0; i < len(runesA)-1; i++ {
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setA[string(runesA[i:i+2])] = true
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}
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setB := make(map[string]bool)
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for i := 0; i < len(runesB)-1; i++ {
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setB[string(runesB[i:i+2])] = true
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}
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intersect := 0
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for bg := range setA {
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if setB[bg] {
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intersect++
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}
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}
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union := len(setA) + len(setB) - intersect
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if union <= 0 {
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return 0
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}
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return float64(intersect) / float64(union)
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}
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func docToTriples(doc *document.Doc, embedder nlp.Vectorizer) []memory.Triple {
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var triples []memory.Triple
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if doc == nil {
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return triples
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}
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if doc.Source == "graph" || doc.Source == "" {
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return nil
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}
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isArchivedContext := doc.Meta != nil && doc.Meta["is_archived_context"] == "true"
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// 文档元数据:仅当 summary 合理(非空、非模板化、长度适中)时才写「主题」
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if !isArchivedContext && doc.Summary != "" && len([]rune(doc.Summary)) < 80 && !isTemplateSummary(doc.Summary) {
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triples = append(triples, memory.Triple{
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Subject: "文档",
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SubjectType: "Concept",
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Relation: "主题",
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Object: doc.Summary,
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ObjectType: "Topic",
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Confidence: 1.0,
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})
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}
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// 媒体三元组：确定性产出，先于 NLP 提取。
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//
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// 媒体入 L3 曾完全依赖提取器碰巧从描述文本里提出合规三元组——实测
|
||
// LLM 的 477 字图片描述只产出「水平 -分割-> 成」这类语法碎片，
|
||
// obj 仅 1 字被 validEntityName 拒掉，整条媒体记忆就进不了图库
|
||
//（阶段性表现是"时好时坏"，取决于提取器运气）。媒体自身的
|
||
// digest / mime / 描述都是确定的，直接建三元组而不经提取器。
|
||
triples = append(triples, mediaTriplesFromText(doc.Content)...)
|
||
|
||
// NLP 通用提取
|
||
e := nlp.NewExtractor(nil)
|
||
if embedder != nil {
|
||
e.SetEmbedder(embedder)
|
||
}
|
||
result := e.Extract(doc.Content)
|
||
if result != nil {
|
||
for _, nt := range result.Triples {
|
||
mt := nlp.ToMemoryTriple(nt)
|
||
if mt.Subject != "" && mt.Relation != "" && mt.Object != "" {
|
||
triples = append(triples, mt)
|
||
}
|
||
}
|
||
}
|
||
|
||
// 仅当来源非归档上下文且非空时写「来源」——归档文档写死模板三元组属于垃圾
|
||
if doc.Source != "" && doc.Source != "context_archived" {
|
||
triples = append(triples, memory.Triple{
|
||
Subject: "文档",
|
||
SubjectType: "Concept",
|
||
Relation: "来源",
|
||
Object: doc.Source,
|
||
ObjectType: "Source",
|
||
Confidence: 1.0,
|
||
})
|
||
}
|
||
|
||
return triples
|
||
}
|
||
|
||
// isTemplateSummary 识别 summarizeEntries 生成的模板化摘要
|
||
// （形如「来自 N 个来源的 M 条对话 (src1, src2) 涉及: kw1, kw2」），
|
||
// 这类摘要无独立信息量,不应作为「主题」实体写入图库。
|
||
func isTemplateSummary(s string) bool {
|
||
if s == "" {
|
||
return true
|
||
}
|
||
return strings.HasPrefix(s, "来自 ") && strings.Contains(s, "条对话")
|
||
}
|
||
|
||
func (a *Agent) emitMemoryCandidate(source, input, response string, toolResults []ToolResultItem, toolsUsed []string) {
|
||
a.io.EmitOutput("memory", "memory_candidate", map[string]interface{}{
|
||
"source": source,
|
||
"input": input,
|
||
"response": response,
|
||
"tool_results": toolResults,
|
||
"tools_used": toolsUsed,
|
||
"agent_id": string(a.id),
|
||
"timestamp": time.Now().Unix(),
|
||
})
|
||
}
|
||
|
||
func (a *Agent) processConsolidation(evt *agentIO.InputEvent, input string) {
|
||
start := time.Now()
|
||
a.currentOutputChannel = "_consolidation_"
|
||
|
||
stageCtx := a.stageCtxFromInput(input, evt.Source, "")
|
||
stageCtx.Extra["output_channel"] = evt.OutputChannel
|
||
a.injectSourceContext(stageCtx, evt)
|
||
|
||
_, toolsUsed, _, err := a.process(input, stageCtx)
|
||
if err != nil {
|
||
log.Printf("[agent] consolidation error: %v", err)
|
||
return
|
||
}
|
||
|
||
log.Printf("[agent] consolidation done (%dms, tools=%v)", time.Since(start).Milliseconds(), toolsUsed)
|
||
}
|