mirror of
https://gitcode.com/JianFeeeee/HomeAgent.git
synced 2026-09-24 10:58:13 +00:00
refactor(memory): 移除 media_refs/引用计数，媒体成为一等记忆块
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。
This commit is contained in:
JianFeeeee
@ -4,6 +4,7 @@ import (
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"encoding/json"
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"fmt"
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"log"
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"math"
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"os"
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"path/filepath"
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"sort"
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@ -13,6 +14,7 @@ import (
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"gitcode.com/JianFeeeee/HomeAgent/internal/memory"
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"gitcode.com/JianFeeeee/HomeAgent/internal/memory/vector"
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"gitcode.com/JianFeeeee/HomeAgent/internal/tfidf"
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)
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// ChannelCleaner 按事件来源查找输入通道的 Cleaner 函数。
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@ -21,51 +23,97 @@ type ChannelCleaner func(source string) func(string) string
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// Doc — 记忆文档：由上下文提炼而来
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type Doc struct {
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ID string `json:"id"`
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Summary string `json:"summary"`
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Content string `json:"content"`
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Tags []string `json:"tags"`
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Entities []string `json:"entities"`
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CreatedAt time.Time `json:"created_at"`
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UpdatedAt time.Time `json:"updated_at"`
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Source string `json:"source"` // context / graph / manual
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Meta map[string]string `json:"meta,omitempty"`
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AccessCount int `json:"access_count"` // 访问次数
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LastAccess time.Time `json:"last_access"` // 最后访问时间
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// Media 是这篇 L2 文档原生持有的多模态块 digest。坐标保存在 media.Store，
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// 文档只持引用；被 Consume 召回到 L0 或归档到 L3 时必须随文本一起迁移。
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Media []string `json:"media,omitempty"`
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Vector vector.Vector `json:"vector,omitempty"` // 预计算文本向量（TF-IDF 稀疏，与 context 同空间）
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DenseVec []float64 `json:"dense_vec,omitempty"` // 多模态稠密向量（与媒体共享空间）
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ID string `json:"id"`
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Summary string `json:"summary"`
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Content string `json:"content"`
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Tags []string `json:"tags"`
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Entities []string `json:"entities"`
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CreatedAt time.Time `json:"created_at"`
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UpdatedAt time.Time `json:"updated_at"`
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Source string `json:"source"`
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Meta map[string]string `json:"meta,omitempty"`
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AccessCount int `json:"access_count"`
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LastAccess time.Time `json:"last_access"`
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Blocks []memory.MemoryBlock `json:"blocks,omitempty"` // 一等记忆块（text/image/video/audio）
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Vector tfidf.Vector `json:"vector,omitempty"` // TF-IDF 稀疏向量（fallback 时持久化）
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DenseVec []float64 `json:"dense_vec,omitempty"` // 多模态稠密向量（主路径）
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}
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// Store — 文档记忆存储，包含向量索引
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// Store — 文档记忆存储。
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// 主路径：denseSpace（稠密多模态向量，与媒体共享空间）。
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// Fallback：tfidf（TF-IDF 倒排索引，仅稠密空间不可用时加载）。
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type Store struct {
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dir string
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vec *vector.Store
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veczer *vector.TFIDFVectorizer
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mu sync.RWMutex
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docs map[string]*Doc
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summaries []string // 用于训练向量化器，最大 10000 条
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vectorizer vector.Vectorizer // 可选：与 context 同空间的向量化器
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denseSpace vector.MultimodalEmbedder // 可选：稠密多模态向量空间
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dir string
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mu sync.RWMutex
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docs map[string]*Doc
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dirty bool
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// fallback 路径（仅稠密空间不可用时加载）
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tfidfEmb *tfidf.Embedder
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tfidfIdx *tfidf.SearchableIndex
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trainTexts []string // 缓存训练文本，延迟训练
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tfidfOnce sync.Once
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// 主路径
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denseSpace vector.MultimodalEmbedder
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}
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// SetDenseSpace 设置稠密多模态向量空间。配置后文档检索使用稠密余弦（brute-force），
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// 与媒体检索共享同一向量空间，实现真正的统一跨模态检索。
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const maxSummaries = 10000
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// NewStore 创建文档存储。tokenizer 由外层注入（如 jieba），核心不直接依赖分词库。
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func NewStore(dir string, tokenizer tfidf.Tokenizer) *Store {
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return &Store{
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dir: dir,
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docs: make(map[string]*Doc),
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// tfidf 延迟初始化：只在需要 fallback 时创建
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tfidfEmb: tfidf.NewEmbedder(tokenizer, 4096),
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}
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}
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// ensureTFIDF 延迟初始化 TF-IDF 索引（仅 fallback 路径）。
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// 调用方已持有 s.mu。
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func (s *Store) ensureTFIDF() {
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s.tfidfOnce.Do(func() {
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s.tfidfIdx = tfidf.NewSearchableIndex(s.tfidfEmb)
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// 延迟训练：用缓存的文本建立索引
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texts := make(map[string]string, len(s.trainTexts)/2)
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for i := 0; i+1 < len(s.trainTexts); i += 2 {
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texts[s.trainTexts[i]] = s.trainTexts[i+1]
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}
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s.tfidfIdx.Train(texts)
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s.trainTexts = nil // 释放缓存
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s.tfidfEmb.Train(func() []string {
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out := make([]string, 0, len(texts))
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for _, t := range texts {
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out = append(out, t)
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}
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return out
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}())
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log.Printf("[document memory] tfidf fallback loaded: %d docs", len(texts))
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})
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}
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func (s *Store) Start() error {
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if err := os.MkdirAll(s.dir, 0755); err != nil {
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return fmt.Errorf("document store dir: %w", err)
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}
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if err := s.loadAll(); err != nil {
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log.Printf("[document memory] load error: %v", err)
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}
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log.Printf("[document memory] started with %d docs", len(s.docs))
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return nil
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}
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func (s *Store) Stop() { s.flush() }
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// SetDenseSpace 设置稠密多模态向量空间（主路径）。
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func (s *Store) SetDenseSpace(ds vector.MultimodalEmbedder) {
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s.mu.Lock()
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defer s.mu.Unlock()
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s.denseSpace = ds
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}
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// buildDenseIndex 为所有文档计算稠密向量并建立 brute-force 索引。
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// 在启动时或配置变更后调用一次。492 篇文档 brute-force ~300ms，可接受。
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// BuildDenseIndex 为所有文档计算稠密向量并建立 brute-force 索引。
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// 在启动时或配置变更后调用一次。492 篇文档 brute-force ~300ms，可接受。
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// BuildDenseIndex 为所有文档计算稠密向量。
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func (s *Store) BuildDenseIndex(ds vector.MultimodalEmbedder) {
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if ds == nil || !ds.Loaded() {
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return
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@ -76,7 +124,7 @@ func (s *Store) BuildDenseIndex(ds vector.MultimodalEmbedder) {
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count := 0
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for _, doc := range s.docs {
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if doc.DenseVec != nil && len(doc.DenseVec) == ds.Dim() {
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continue // 已有向量，跳过
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continue
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}
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text := doc.Summary + " " + doc.Content
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vec, err := ds.VectorizeDense(text)
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@ -90,8 +138,225 @@ func (s *Store) BuildDenseIndex(ds vector.MultimodalEmbedder) {
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log.Printf("[document memory] dense index built: %d new vectors", count)
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}
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// denseSearchScored 对所有文档做 brute-force 余弦检索，返回 topK 个最相似的候选。
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// 仅在 denseSpace 配置后使用；未配置时退化到 TF-IDF 倒排检索。
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// Reindex 重建 TF-IDF 索引（fallback 路径变更时调用）。
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func (s *Store) Reindex() {
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s.mu.Lock()
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defer s.mu.Unlock()
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s.tfidfOnce = sync.Once{} // 重置延迟初始化
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texts := make(map[string]string, len(s.docs))
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for _, doc := range s.docs {
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texts[doc.ID] = doc.Summary + " " + doc.Content
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}
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// 缓存文本供 ensureTFIDF 延迟训练
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s.trainTexts = make([]string, 0, len(texts)*2)
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for id, t := range texts {
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s.trainTexts = append(s.trainTexts, id, t)
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}
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s.ensureTFIDF()
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}
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func (s *Store) Insert(doc *Doc) error {
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s.mu.Lock()
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defer s.mu.Unlock()
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if doc.ID == "" {
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doc.ID = fmt.Sprintf("doc_%d", time.Now().UnixNano())
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doc.CreatedAt = time.Now()
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}
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doc.UpdatedAt = time.Now()
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doc.LastAccess = time.Now()
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if doc.AccessCount == 0 {
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doc.AccessCount = 1
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}
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s.docs[doc.ID] = doc
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text := doc.Summary + " " + doc.Content
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// 主路径：稠密向量
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if s.denseSpace != nil && s.denseSpace.Loaded() && len(doc.DenseVec) == 0 {
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if dv, err := s.denseSpace.VectorizeDense(text); err == nil {
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doc.DenseVec = dv
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}
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}
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// Fallback 路径：缓存文本，延迟训练
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if s.tfidfIdx != nil {
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s.tfidfIdx.Add(doc.ID, text)
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} else {
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s.trainTexts = append(s.trainTexts, doc.ID, text)
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}
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path := filepath.Join(s.dir, doc.ID+".json")
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data, _ := json.MarshalIndent(doc, "", " ")
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os.WriteFile(path, data, 0644)
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s.dirty = true
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return nil
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}
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// ContextToDoc 将上下文对话历史提炼为文档。
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func (s *Store) ContextToDoc(source string, entries []ContextEntry, _ interface{}, cleanFn func(string) string, toolCleanFn func(name, output string) string, channelCleaner ChannelCleaner) (*Doc, error) {
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if len(entries) == 0 {
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return nil, nil
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}
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if cleanFn == nil {
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cleanFn = func(text string) string { return text }
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}
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var parts []string
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for _, e := range entries {
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line := fmt.Sprintf("[%s] %s: %s", e.Timestamp.Format("15:04"), e.Source, e.Content)
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if e.Response != "" {
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line += fmt.Sprintf(" → %s", truncate(e.Response, 100))
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}
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for _, tr := range e.ToolResults {
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line += fmt.Sprintf("\n [工具] %s: %s", tr.Name, truncate(tr.Output, 200))
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}
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parts = append(parts, line)
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}
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content := strings.Join(parts, "\n")
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contentHash := simpleHash(content)
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summary := summarizeEntries(entries, cleanFn, toolCleanFn, channelCleaner)
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tags := extractTags(entries, cleanFn, toolCleanFn, channelCleaner)
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entities := extractEntities(entries, cleanFn, toolCleanFn, channelCleaner)
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s.mu.Lock()
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defer s.mu.Unlock()
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for _, d := range s.docs {
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if d.Meta != nil && d.Meta["content_hash"] == contentHash {
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d.UpdatedAt = time.Now()
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d.LastAccess = time.Now()
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d.Content = content
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d.Source = source
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d.Summary = summary
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d.Tags = tags
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d.Entities = entities
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d.Blocks = blocksFromEntries(entries)
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s.dirty = true
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return d, nil
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}
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}
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id := fmt.Sprintf("doc_%d", time.Now().UnixNano())
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meta := map[string]string{"content_hash": contentHash}
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if source == "context_archived" {
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meta["is_archived_context"] = "true"
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}
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doc := &Doc{
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ID: id, Summary: summary, Content: content, Tags: tags,
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Entities: entities, CreatedAt: time.Now(), UpdatedAt: time.Now(),
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LastAccess: time.Now(), AccessCount: 1, Source: source, Meta: meta,
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Blocks: blocksFromEntries(entries),
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}
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s.docs[id] = doc
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text := summary + " " + content
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if s.tfidfIdx != nil {
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s.tfidfIdx.Add(id, text)
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} else {
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s.trainTexts = append(s.trainTexts, id, text)
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}
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path := filepath.Join(s.dir, id+".json")
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data, _ := json.MarshalIndent(doc, "", " ")
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os.WriteFile(path, data, 0644)
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s.dirty = true
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return doc, nil
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}
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// Consume 向量相似度查询并移除文档
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func (s *Store) Consume(text string, topK int) []*Doc {
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s.mu.Lock()
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defer s.mu.Unlock()
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if topK <= 0 {
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topK = 5
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}
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// 主路径：稠密检索
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if s.denseSpace != nil && s.denseSpace.Loaded() {
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if qv, err := s.denseSpace.VectorizeDense(text); err == nil {
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results := s.denseSearchScored(qv, topK)
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var docs []*Doc
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for _, r := range results {
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if d, ok := s.docs[r.Doc.ID]; ok {
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s.removeDoc(r.Doc.ID)
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s.dirty = true
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docs = append(docs, d)
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}
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}
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return docs
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}
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}
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|
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// Fallback：TF-IDF 倒排检索（延迟初始化）
|
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s.ensureTFIDF()
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results := s.tfidfIdx.Search(text, topK)
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var docs []*Doc
|
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for _, r := range results {
|
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if d, ok := s.docs[r.ID]; ok {
|
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s.removeDoc(r.ID)
|
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s.dirty = true
|
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docs = append(docs, d)
|
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}
|
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}
|
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return docs
|
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}
|
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|
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func (s *Store) Query(text string, topK int) []*Doc {
|
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hits := s.QueryScored(text, topK)
|
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out := make([]*Doc, len(hits))
|
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for i, h := range hits {
|
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out[i] = h.Doc
|
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}
|
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return out
|
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}
|
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|
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// DocHit 是一篇文档记忆的相似度候选及原始分数。
|
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type DocHit struct {
|
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Doc *Doc
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Score float64
|
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}
|
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|
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func (s *Store) QueryScored(text string, topK int) []DocHit {
|
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s.mu.RLock()
|
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defer s.mu.RUnlock()
|
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if topK <= 0 {
|
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topK = 5
|
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}
|
||||
|
||||
// 主路径
|
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if s.denseSpace != nil && s.denseSpace.Loaded() {
|
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if qv, err := s.denseSpace.VectorizeDense(text); err == nil {
|
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results := s.denseSearchScored(qv, topK)
|
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for i := range results {
|
||||
if d, ok := s.docs[results[i].Doc.ID]; ok {
|
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d.AccessCount++
|
||||
d.LastAccess = time.Now()
|
||||
results[i].Doc = d
|
||||
}
|
||||
}
|
||||
return results
|
||||
}
|
||||
}
|
||||
|
||||
// Fallback（需要写锁来 ensureTFIDF）
|
||||
s.mu.RUnlock()
|
||||
s.mu.Lock()
|
||||
s.ensureTFIDF()
|
||||
s.mu.Unlock()
|
||||
s.mu.RLock()
|
||||
|
||||
results := s.tfidfIdx.Search(text, topK)
|
||||
var out []DocHit
|
||||
for _, r := range results {
|
||||
if d, ok := s.docs[r.ID]; ok {
|
||||
d.AccessCount++
|
||||
d.LastAccess = time.Now()
|
||||
out = append(out, DocHit{Doc: d, Score: r.Score})
|
||||
}
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
func (s *Store) denseSearchScored(queryVec []float64, topK int) []DocHit {
|
||||
if len(queryVec) == 0 {
|
||||
return nil
|
||||
@ -101,13 +366,11 @@ func (s *Store) denseSearchScored(queryVec []float64, topK int) []DocHit {
|
||||
score float64
|
||||
}
|
||||
var results []scored
|
||||
s.mu.RLock()
|
||||
defer s.mu.RUnlock()
|
||||
for _, doc := range s.docs {
|
||||
if len(doc.DenseVec) != len(queryVec) {
|
||||
continue
|
||||
}
|
||||
score := vector.DenseCosine(queryVec, doc.DenseVec)
|
||||
score := denseCosine(queryVec, doc.DenseVec)
|
||||
if score > 0.01 {
|
||||
results = append(results, scored{doc.ID, score})
|
||||
}
|
||||
@ -126,332 +389,36 @@ func (s *Store) denseSearchScored(queryVec []float64, topK int) []DocHit {
|
||||
return out
|
||||
}
|
||||
|
||||
// denseCosine 计算两个 []float64 向量的余弦相似度（已迁移到 vector.DenseCosine，此处保留兼容）。
|
||||
func denseCosine(a, b []float64) float64 {
|
||||
return vector.DenseCosine(a, b)
|
||||
}
|
||||
|
||||
func (s *Store) SetVectorizer(v vector.Vectorizer) {
|
||||
s.vectorizer = v
|
||||
}
|
||||
|
||||
// ReindexWithVectorizer 用给定的向量化器重建所有文档的向量索引
|
||||
func (s *Store) ReindexWithVectorizer(v vector.Vectorizer) {
|
||||
s.mu.Lock()
|
||||
defer s.mu.Unlock()
|
||||
|
||||
log.Printf("[document memory] reindex with vectorizer (%d docs)", len(s.docs))
|
||||
s.vec = vector.NewStore()
|
||||
for _, doc := range s.docs {
|
||||
doc.Vector = v.Vectorize(doc.Summary + " " + doc.Content)
|
||||
s.vec.Insert(doc.ID, doc.Summary, doc.Vector, doc.Meta)
|
||||
var dot, na, nb float64
|
||||
for i := range a {
|
||||
dot += a[i] * b[i]
|
||||
na += a[i] * a[i]
|
||||
nb += b[i] * b[i]
|
||||
}
|
||||
log.Printf("[document memory] reindex with vectorizer complete (%d vectors)", s.vec.Size())
|
||||
}
|
||||
|
||||
const maxSummaries = 10000
|
||||
|
||||
func NewStore(dir string) *Store {
|
||||
return &Store{
|
||||
dir: dir,
|
||||
vec: vector.NewStore(),
|
||||
veczer: vector.NewTFIDFVectorizer(memory.TokenizeWords),
|
||||
docs: make(map[string]*Doc),
|
||||
if na == 0 || nb == 0 {
|
||||
return 0
|
||||
}
|
||||
}
|
||||
|
||||
func (s *Store) Start() error {
|
||||
if err := os.MkdirAll(s.dir, 0755); err != nil {
|
||||
return fmt.Errorf("document store dir: %w", err)
|
||||
}
|
||||
if err := s.loadAll(); err != nil {
|
||||
log.Printf("[document memory] load error: %v", err)
|
||||
}
|
||||
log.Printf("[document memory] started with %d docs, %d vectors", len(s.docs), s.vec.Size())
|
||||
return nil
|
||||
}
|
||||
|
||||
func (s *Store) Stop() {
|
||||
s.flush()
|
||||
}
|
||||
|
||||
// Insert 创建/更新文档
|
||||
func (s *Store) Insert(doc *Doc) error {
|
||||
s.mu.Lock()
|
||||
defer s.mu.Unlock()
|
||||
|
||||
if doc.ID == "" {
|
||||
doc.ID = fmt.Sprintf("doc_%d", time.Now().UnixNano())
|
||||
doc.CreatedAt = time.Now()
|
||||
}
|
||||
doc.UpdatedAt = time.Now()
|
||||
doc.LastAccess = time.Now()
|
||||
if doc.AccessCount == 0 {
|
||||
doc.AccessCount = 1
|
||||
}
|
||||
|
||||
s.docs[doc.ID] = doc
|
||||
|
||||
// 增量训练向量化器并加入向量索引
|
||||
s.addSummary(doc.Summary)
|
||||
vec := doc.Vector
|
||||
if vec == nil {
|
||||
vec = s.veczer.Vectorize(doc.Summary + " " + doc.Content)
|
||||
}
|
||||
s.vec.Insert(doc.ID, doc.Summary, vec, doc.Meta)
|
||||
|
||||
// 若配置了稠密空间，为新文档计算稠密向量
|
||||
if s.denseSpace != nil && s.denseSpace.Loaded() && len(doc.DenseVec) == 0 {
|
||||
if dv, err := s.denseSpace.VectorizeDense(doc.Summary + " " + doc.Content); err == nil {
|
||||
doc.DenseVec = dv
|
||||
}
|
||||
}
|
||||
|
||||
// 立即写盘
|
||||
path := filepath.Join(s.dir, doc.ID+".json")
|
||||
data, _ := json.MarshalIndent(doc, "", " ")
|
||||
os.WriteFile(path, data, 0644)
|
||||
|
||||
s.dirty = true
|
||||
return nil
|
||||
}
|
||||
|
||||
// ContextToDoc — 将一段上下文对话历史提炼为文档（带内容去重）
|
||||
// cleanFn 可选，在计算层前统一过滤文本，不影响原文存储。
|
||||
// toolCleanFn 可选，func(name, output string) string，按工具名对输出进行过滤/清洗：
|
||||
// - 返回 "" → 跳过该工具输出（NoMemory）
|
||||
// - 返回清洗后文本 → 用于计算层（Cleaner），原文不受影响
|
||||
func (s *Store) ContextToDoc(source string, entries []ContextEntry, vec vector.Vectorizer, cleanFn func(string) string, toolCleanFn func(name, output string) string, channelCleaner ChannelCleaner) (*Doc, error) {
|
||||
if len(entries) == 0 {
|
||||
return nil, nil
|
||||
}
|
||||
|
||||
if cleanFn == nil {
|
||||
cleanFn = func(text string) string { return text }
|
||||
}
|
||||
|
||||
var parts []string
|
||||
for _, e := range entries {
|
||||
line := fmt.Sprintf("[%s] %s: %s", e.Timestamp.Format("15:04"), e.Source, e.Content)
|
||||
if e.Response != "" {
|
||||
line += fmt.Sprintf(" → %s", truncate(e.Response, 100))
|
||||
}
|
||||
for _, tr := range e.ToolResults {
|
||||
line += fmt.Sprintf("\n [工具] %s: %s", tr.Name, truncate(tr.Output, 200))
|
||||
}
|
||||
parts = append(parts, line)
|
||||
}
|
||||
content := strings.Join(parts, "\n")
|
||||
contentHash := simpleHash(content)
|
||||
|
||||
summary := summarizeEntries(entries, cleanFn, toolCleanFn, channelCleaner)
|
||||
tags := extractTags(entries, cleanFn, toolCleanFn, channelCleaner)
|
||||
entities := extractEntities(entries, cleanFn, toolCleanFn, channelCleaner)
|
||||
|
||||
s.mu.Lock()
|
||||
|
||||
// 去重
|
||||
for _, d := range s.docs {
|
||||
if d.Meta != nil && d.Meta["content_hash"] == contentHash {
|
||||
d.UpdatedAt = time.Now()
|
||||
d.LastAccess = time.Now()
|
||||
d.Content = content
|
||||
d.Source = source
|
||||
d.Summary = summary
|
||||
d.Tags = tags
|
||||
d.Entities = entities
|
||||
d.Media = mediaDigestsFromEntries(entries)
|
||||
s.dirty = true
|
||||
s.mu.Unlock()
|
||||
return d, nil
|
||||
}
|
||||
}
|
||||
|
||||
id := fmt.Sprintf("doc_%d", time.Now().UnixNano())
|
||||
var docVec vector.Vector
|
||||
if vec != nil {
|
||||
docVec = vec.Vectorize(summary + " " + content)
|
||||
} else {
|
||||
docVec = s.veczer.Vectorize(summary + " " + content)
|
||||
}
|
||||
meta := map[string]string{"content_hash": contentHash}
|
||||
if source == "context_archived" {
|
||||
meta["is_archived_context"] = "true"
|
||||
}
|
||||
doc := &Doc{
|
||||
ID: id,
|
||||
Summary: summary,
|
||||
Content: content,
|
||||
Tags: tags,
|
||||
Entities: entities,
|
||||
CreatedAt: time.Now(),
|
||||
UpdatedAt: time.Now(),
|
||||
LastAccess: time.Now(),
|
||||
AccessCount: 1,
|
||||
Source: source,
|
||||
Meta: meta,
|
||||
Media: mediaDigestsFromEntries(entries),
|
||||
Vector: docVec,
|
||||
}
|
||||
s.docs[id] = doc
|
||||
|
||||
// 加入向量索引
|
||||
s.addSummary(summary)
|
||||
s.vec.Insert(id, summary, doc.Vector, nil)
|
||||
|
||||
s.dirty = true
|
||||
s.mu.Unlock()
|
||||
|
||||
// 立即写盘
|
||||
path := filepath.Join(s.dir, id+".json")
|
||||
data, _ := json.MarshalIndent(doc, "", " ")
|
||||
os.WriteFile(path, data, 0644)
|
||||
|
||||
return doc, nil
|
||||
}
|
||||
|
||||
// Consume — 向量相似度查询并移除文档（召回后即从冷存储删除，避免重复记忆）
|
||||
func (s *Store) Consume(text string, topK int) []*Doc {
|
||||
s.mu.Lock()
|
||||
defer s.mu.Unlock()
|
||||
|
||||
if topK <= 0 {
|
||||
topK = 5
|
||||
}
|
||||
|
||||
// 优先稠密检索（与媒体共享空间）；未配置时退化到 TF-IDF 倒排检索。
|
||||
if s.denseSpace != nil && s.denseSpace.Loaded() {
|
||||
queryVec, err := s.denseSpace.VectorizeDense(text)
|
||||
if err == nil {
|
||||
results := s.denseSearchScored(queryVec, topK)
|
||||
var docs []*Doc
|
||||
for _, r := range results {
|
||||
if d, ok := s.docs[r.Doc.ID]; ok {
|
||||
s.removeDoc(r.Doc.ID)
|
||||
s.dirty = true
|
||||
docs = append(docs, d)
|
||||
}
|
||||
}
|
||||
return docs
|
||||
}
|
||||
log.Printf("[document memory] dense query failed, falling back to TF-IDF: %v", err)
|
||||
}
|
||||
|
||||
vec := s.vectorizeQuery(text)
|
||||
results := s.vec.Search(vec, topK)
|
||||
|
||||
var docs []*Doc
|
||||
for _, r := range results {
|
||||
if d, ok := s.docs[r.ID]; ok {
|
||||
s.removeDoc(r.ID)
|
||||
s.dirty = true
|
||||
docs = append(docs, d)
|
||||
}
|
||||
}
|
||||
return docs
|
||||
}
|
||||
|
||||
// vectorizeQuery 用语义向量化器（首选）或 TF-IDF（兜底）处理查询文本
|
||||
func (s *Store) vectorizeQuery(text string) vector.Vector {
|
||||
if s.vectorizer != nil {
|
||||
return s.vectorizer.Vectorize(text)
|
||||
}
|
||||
return s.veczer.Vectorize(text)
|
||||
}
|
||||
|
||||
// Query — 向量相似度查询文档
|
||||
func (s *Store) Query(text string, topK int) []*Doc {
|
||||
hits := s.QueryScored(text, topK)
|
||||
out := make([]*Doc, len(hits))
|
||||
for i, h := range hits {
|
||||
out[i] = h.Doc
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
// DocHit 是一篇文档记忆的相似度候选及原始分数（供跨模态融合归一化）。
|
||||
type DocHit struct {
|
||||
Doc *Doc
|
||||
Score float64
|
||||
}
|
||||
|
||||
// QueryScored 与 Query 同语义，但返回带原始 cosine 分数的候选。
|
||||
func (s *Store) QueryScored(text string, topK int) []DocHit {
|
||||
s.mu.RLock()
|
||||
defer s.mu.RUnlock()
|
||||
|
||||
if topK <= 0 {
|
||||
topK = 5
|
||||
}
|
||||
|
||||
// 优先稠密检索；退化到 TF-IDF。
|
||||
if s.denseSpace != nil && s.denseSpace.Loaded() {
|
||||
queryVec, err := s.denseSpace.VectorizeDense(text)
|
||||
if err == nil {
|
||||
results := s.denseSearchScored(queryVec, topK)
|
||||
for i := range results {
|
||||
if d, ok := s.docs[results[i].Doc.ID]; ok {
|
||||
d.AccessCount++
|
||||
d.LastAccess = time.Now()
|
||||
results[i].Doc = d
|
||||
}
|
||||
}
|
||||
return results
|
||||
}
|
||||
}
|
||||
|
||||
vec := s.vectorizeQuery(text)
|
||||
results := s.vec.SearchScored(vec, topK)
|
||||
|
||||
var out []DocHit
|
||||
for _, r := range results {
|
||||
if d, ok := s.docs[r.Doc.ID]; ok {
|
||||
d.AccessCount++
|
||||
d.LastAccess = time.Now()
|
||||
out = append(out, DocHit{Doc: d, Score: r.Score})
|
||||
}
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
// Reindex — 重新训练并重建向量索引
|
||||
func (s *Store) Reindex() {
|
||||
s.mu.Lock()
|
||||
defer s.mu.Unlock()
|
||||
|
||||
log.Printf("[document memory] reindexing %d docs", len(s.docs))
|
||||
|
||||
s.veczer.Train(s.summaries)
|
||||
|
||||
s.vec = vector.NewStore()
|
||||
for _, doc := range s.docs {
|
||||
vec := doc.Vector
|
||||
if vec == nil {
|
||||
vec = s.veczer.Vectorize(doc.Summary + " " + doc.Content)
|
||||
}
|
||||
s.vec.Insert(doc.ID, doc.Summary, vec, doc.Meta)
|
||||
}
|
||||
|
||||
log.Printf("[document memory] reindex complete (%d vectors)", s.vec.Size())
|
||||
return dot / math.Sqrt(na*nb)
|
||||
}
|
||||
|
||||
func (s *Store) Stats() map[string]interface{} {
|
||||
s.mu.RLock()
|
||||
defer s.mu.RUnlock()
|
||||
|
||||
idxSize := 0
|
||||
if s.tfidfIdx != nil {
|
||||
idxSize = s.tfidfIdx.Size()
|
||||
}
|
||||
return map[string]interface{}{
|
||||
"doc_count": len(s.docs),
|
||||
"vector_count": s.vec.Size(),
|
||||
"summary_count": len(s.summaries),
|
||||
"dir": s.dir,
|
||||
"doc_count": len(s.docs),
|
||||
"index_count": idxSize,
|
||||
"dir": s.dir,
|
||||
}
|
||||
}
|
||||
|
||||
// FindColdDocs — 查找冷文档：超过 maxAge 未访问且访问次数 <= minAccess
|
||||
func (s *Store) FindColdDocs(maxAge time.Duration, minAccess int) []*Doc {
|
||||
s.mu.RLock()
|
||||
defer s.mu.RUnlock()
|
||||
|
||||
cutoff := time.Now().Add(-maxAge)
|
||||
var cold []*Doc
|
||||
for _, d := range s.docs {
|
||||
@ -462,60 +429,53 @@ func (s *Store) FindColdDocs(maxAge time.Duration, minAccess int) []*Doc {
|
||||
return cold
|
||||
}
|
||||
|
||||
// Get 返回指定文档（不存在时为 nil）。
|
||||
func (s *Store) Get(id string) *Doc {
|
||||
s.mu.RLock()
|
||||
defer s.mu.RUnlock()
|
||||
return s.docs[id]
|
||||
}
|
||||
|
||||
// Blocks 返回全部文档持有的一等记忆块（供跨层存活判定）。
|
||||
func (s *Store) Blocks() []memory.MemoryBlock {
|
||||
s.mu.RLock()
|
||||
defer s.mu.RUnlock()
|
||||
var out []memory.MemoryBlock
|
||||
for _, d := range s.docs {
|
||||
out = append(out, d.Blocks...)
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
func (s *Store) RecentDocs(n int) []*Doc {
|
||||
s.mu.RLock()
|
||||
defer s.mu.RUnlock()
|
||||
|
||||
var list []*Doc
|
||||
for _, d := range s.docs {
|
||||
list = append(list, d)
|
||||
}
|
||||
sort.Slice(list, func(i, j int) bool {
|
||||
return list[i].CreatedAt.After(list[j].CreatedAt)
|
||||
})
|
||||
sort.Slice(list, func(i, j int) bool { return list[i].CreatedAt.After(list[j].CreatedAt) })
|
||||
if len(list) > n {
|
||||
list = list[:n]
|
||||
}
|
||||
return list
|
||||
}
|
||||
|
||||
// Remove 从文档存储中删除指定 ID 的文档
|
||||
func (s *Store) Remove(id string) {
|
||||
s.mu.Lock()
|
||||
defer s.mu.Unlock()
|
||||
|
||||
if _, ok := s.docs[id]; ok {
|
||||
s.removeDoc(id)
|
||||
s.dirty = true
|
||||
}
|
||||
}
|
||||
|
||||
// ——— internal ———
|
||||
|
||||
// addSummary 添加一条摘要到训练集，超限时截断并触发重索引。
|
||||
// 调用方必须已持有 s.mu 写锁。
|
||||
func (s *Store) addSummary(summary string) {
|
||||
s.summaries = append(s.summaries, summary)
|
||||
if len(s.summaries) > maxSummaries {
|
||||
n := maxSummaries / 2
|
||||
copy(s.summaries, s.summaries[len(s.summaries)-n:])
|
||||
s.summaries = s.summaries[:n]
|
||||
s.veczer.Train(s.summaries)
|
||||
s.vec = vector.NewStore()
|
||||
for _, doc := range s.docs {
|
||||
vec := s.veczer.Vectorize(doc.Summary + " " + doc.Content)
|
||||
s.vec.Insert(doc.ID, doc.Summary, vec, nil)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// removeDoc 从内存索引和磁盘删除文档。
|
||||
// 调用方必须已持有 s.mu 写锁。
|
||||
func (s *Store) removeDoc(id string) {
|
||||
delete(s.docs, id)
|
||||
s.vec.Remove(id)
|
||||
path := filepath.Join(s.dir, id+".json")
|
||||
os.Remove(path)
|
||||
if s.tfidfIdx != nil {
|
||||
s.tfidfIdx.Remove(id)
|
||||
}
|
||||
os.Remove(filepath.Join(s.dir, id+".json"))
|
||||
}
|
||||
|
||||
func (s *Store) loadAll() error {
|
||||
@ -523,63 +483,43 @@ func (s *Store) loadAll() error {
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
for _, e := range entries {
|
||||
if !strings.HasSuffix(e.Name(), ".json") || !strings.HasPrefix(e.Name(), "doc_") {
|
||||
continue
|
||||
}
|
||||
path := filepath.Join(s.dir, e.Name())
|
||||
data, err := os.ReadFile(path)
|
||||
data, err := os.ReadFile(filepath.Join(s.dir, e.Name()))
|
||||
if err != nil {
|
||||
continue
|
||||
}
|
||||
var doc Doc
|
||||
if err := json.Unmarshal(data, &doc); err != nil {
|
||||
if json.Unmarshal(data, &doc) != nil || doc.ID == "" {
|
||||
continue
|
||||
}
|
||||
s.docs[doc.ID] = &doc
|
||||
s.summaries = append(s.summaries, doc.Summary)
|
||||
// 缓存文本，延迟训练（确保TFIDF在首次需要时才加载）
|
||||
s.trainTexts = append(s.trainTexts, doc.ID, doc.Summary+" "+doc.Content)
|
||||
}
|
||||
|
||||
// 训练向量化器
|
||||
if len(s.summaries) > 0 {
|
||||
s.veczer.Train(s.summaries)
|
||||
}
|
||||
|
||||
// 重建向量索引
|
||||
for _, doc := range s.docs {
|
||||
vec := doc.Vector
|
||||
if vec == nil {
|
||||
vec = s.veczer.Vectorize(doc.Summary + " " + doc.Content)
|
||||
}
|
||||
s.vec.Insert(doc.ID, doc.Summary, vec, nil)
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func (s *Store) flush() {
|
||||
s.mu.Lock()
|
||||
defer s.mu.Unlock()
|
||||
|
||||
if !s.dirty {
|
||||
return
|
||||
}
|
||||
|
||||
for _, doc := range s.docs {
|
||||
path := filepath.Join(s.dir, doc.ID+".json")
|
||||
data, err := json.MarshalIndent(doc, "", " ")
|
||||
if err != nil {
|
||||
continue
|
||||
}
|
||||
os.WriteFile(path, data, 0644)
|
||||
data, _ := json.MarshalIndent(doc, "", " ")
|
||||
os.WriteFile(filepath.Join(s.dir, doc.ID+".json"), data, 0644)
|
||||
}
|
||||
s.dirty = false
|
||||
}
|
||||
|
||||
// ——— 内部工具函数（从上下文提炼文档所需）———
|
||||
|
||||
type ToolResultItem struct {
|
||||
Name string
|
||||
Output string
|
||||
Name string `json:"name"`
|
||||
Output string `json:"output"`
|
||||
}
|
||||
|
||||
type ContextEntry struct {
|
||||
@ -588,19 +528,20 @@ type ContextEntry struct {
|
||||
Content string
|
||||
Response string
|
||||
ToolResults []ToolResultItem
|
||||
Media []string
|
||||
Blocks []memory.MemoryBlock // 一等记忆块随事件一起迁移到文档
|
||||
}
|
||||
|
||||
func mediaDigestsFromEntries(entries []ContextEntry) []string {
|
||||
func blocksFromEntries(entries []ContextEntry) []memory.MemoryBlock {
|
||||
seen := make(map[string]bool)
|
||||
var out []string
|
||||
var out []memory.MemoryBlock
|
||||
for _, e := range entries {
|
||||
for _, d := range e.Media {
|
||||
if d == "" || seen[d] {
|
||||
for i := range e.Blocks {
|
||||
b := e.Blocks[i]
|
||||
if b.ID == "" || seen[b.ID] {
|
||||
continue
|
||||
}
|
||||
seen[d] = true
|
||||
out = append(out, d)
|
||||
seen[b.ID] = true
|
||||
out = append(out, b)
|
||||
}
|
||||
}
|
||||
return out
|
||||
@ -635,14 +576,12 @@ func summarizeEntries(entries []ContextEntry, cleanText func(string) string, too
|
||||
topics = append(topics, toolWords...)
|
||||
}
|
||||
}
|
||||
|
||||
summary := fmt.Sprintf("来自 %d 个来源的 %d 条对话", len(sources), len(entries))
|
||||
var srcList []string
|
||||
for s := range sources {
|
||||
srcList = append(srcList, s)
|
||||
}
|
||||
summary += " (" + strings.Join(srcList, ", ") + ")"
|
||||
|
||||
if len(topics) > 0 {
|
||||
seen := make(map[string]bool)
|
||||
var uniq []string
|
||||
@ -657,7 +596,6 @@ func summarizeEntries(entries []ContextEntry, cleanText func(string) string, too
|
||||
}
|
||||
summary += " 涉及: " + strings.Join(uniq, ", ")
|
||||
}
|
||||
|
||||
return summary
|
||||
}
|
||||
|
||||
@ -730,25 +668,20 @@ func extractEntities(entries []ContextEntry, cleanText func(string) string, tool
|
||||
}
|
||||
}
|
||||
}
|
||||
if len(entities) > 20 {
|
||||
entities = entities[:20]
|
||||
}
|
||||
return entities
|
||||
}
|
||||
|
||||
func truncate(s string, max int) string {
|
||||
runes := []rune(s)
|
||||
if len(runes) > max {
|
||||
return string(runes[:max]) + "..."
|
||||
if len([]rune(s)) <= max {
|
||||
return s
|
||||
}
|
||||
return s
|
||||
return string([]rune(s)[:max]) + "..."
|
||||
}
|
||||
|
||||
func simpleHash(s string) string {
|
||||
// 简单的基于内容的哈希，用于去重
|
||||
h := 0
|
||||
for _, r := range s {
|
||||
h = h*31 + int(r)
|
||||
h := fmt.Sprintf("%x", len(s))
|
||||
for _, c := range s {
|
||||
h += fmt.Sprintf("%x", c)
|
||||
}
|
||||
return fmt.Sprintf("h%08x", h)
|
||||
return h
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user