mirror of
https://gitcode.com/JianFeeeee/HomeAgent.git
synced 2026-09-21 17:38:10 +00:00
147d0baaf9
- agent: 工具轮请求尾部补 user 占位(zen 网关强制),tool 消息正确配对 - agent: 工具提醒/中断以 system 角色注入并带 [中断消息] 前缀,不进用户履历;系统提示词说明中断消息格式 - agentcli: 基于 ConPTY 的交互式终端(ptywin fork),terminal_create/read/write/resize/close/watch - webui: server 输出通道适配器(保留 reasoning_content/disable_thinking) - GUI: 沉浸式标题栏、icon 圆角重制、mascot 等打磨
598 lines
14 KiB
Go
598 lines
14 KiB
Go
package document
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import (
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"encoding/json"
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"fmt"
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"log"
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"os"
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"path/filepath"
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"sort"
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"strings"
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"sync"
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"time"
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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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)
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// ChannelCleaner 按事件来源查找输入通道的 Cleaner 函数。
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// 返回 nil 表示不使用额外清洗。
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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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Vector vector.Vector `json:"vector,omitempty"` // 预计算向量（与 context 同空间），nil 则用 TF-IDF 兜底
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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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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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dirty bool
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}
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func (s *Store) SetVectorizer(v vector.Vectorizer) {
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s.vectorizer = v
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}
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// ReindexWithVectorizer 用给定的向量化器重建所有文档的向量索引
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func (s *Store) ReindexWithVectorizer(v vector.Vectorizer) {
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s.mu.Lock()
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defer s.mu.Unlock()
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log.Printf("[document memory] reindex with vectorizer (%d docs)", len(s.docs))
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s.vec = vector.NewStore()
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for _, doc := range s.docs {
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doc.Vector = v.Vectorize(doc.Summary + " " + doc.Content)
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s.vec.Insert(doc.ID, doc.Summary, doc.Vector, doc.Meta)
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}
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log.Printf("[document memory] reindex with vectorizer complete (%d vectors)", s.vec.Size())
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}
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const maxSummaries = 10000
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func NewStore(dir string) *Store {
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return &Store{
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dir: dir,
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vec: vector.NewStore(),
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veczer: vector.NewTFIDFVectorizer(memory.TokenizeWords),
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docs: make(map[string]*Doc),
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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, %d vectors", len(s.docs), s.vec.Size())
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return nil
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}
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func (s *Store) Stop() {
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s.flush()
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}
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// Insert 创建/更新文档
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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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// 增量训练向量化器并加入向量索引
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s.addSummary(doc.Summary)
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vec := doc.Vector
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if vec == nil {
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vec = s.veczer.Vectorize(doc.Summary + " " + doc.Content)
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}
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s.vec.Insert(doc.ID, doc.Summary, vec, doc.Meta)
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// 立即写盘
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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 — 将一段上下文对话历史提炼为文档（带内容去重）
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// cleanFn 可选，在计算层前统一过滤文本，不影响原文存储。
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// toolCleanFn 可选，func(name, output string) string，按工具名对输出进行过滤/清洗：
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// - 返回 "" → 跳过该工具输出（NoMemory）
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// - 返回清洗后文本 → 用于计算层（Cleaner），原文不受影响
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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) {
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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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// 去重
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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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s.dirty = true
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s.mu.Unlock()
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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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var docVec vector.Vector
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if vec != nil {
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docVec = vec.Vectorize(summary + " " + content)
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} else {
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docVec = s.veczer.Vectorize(summary + " " + content)
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}
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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,
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Summary: summary,
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Content: content,
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Tags: tags,
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Entities: entities,
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CreatedAt: time.Now(),
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UpdatedAt: time.Now(),
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LastAccess: time.Now(),
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AccessCount: 1,
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Source: source,
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Meta: meta,
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Vector: docVec,
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}
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s.docs[id] = doc
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// 加入向量索引
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s.addSummary(summary)
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s.vec.Insert(id, summary, doc.Vector, nil)
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s.dirty = true
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s.mu.Unlock()
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// 立即写盘
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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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return doc, nil
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}
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// 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）
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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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vec := s.vectorizeQuery(text)
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results := s.vec.Search(vec, 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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// vectorizeQuery 用语义向量化器（首选）或 TF-IDF（兜底）处理查询文本
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func (s *Store) vectorizeQuery(text string) vector.Vector {
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if s.vectorizer != nil {
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return s.vectorizer.Vectorize(text)
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}
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return s.veczer.Vectorize(text)
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}
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// Query — 向量相似度查询文档
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func (s *Store) Query(text string, topK int) []*Doc {
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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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}
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vec := s.vectorizeQuery(text)
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results := s.vec.Search(vec, 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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d.AccessCount++
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d.LastAccess = time.Now()
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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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// Reindex — 重新训练并重建向量索引
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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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log.Printf("[document memory] reindexing %d docs", len(s.docs))
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s.veczer.Train(s.summaries)
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s.vec = vector.NewStore()
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for _, doc := range s.docs {
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vec := doc.Vector
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if vec == nil {
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vec = s.veczer.Vectorize(doc.Summary + " " + doc.Content)
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}
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s.vec.Insert(doc.ID, doc.Summary, vec, doc.Meta)
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}
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log.Printf("[document memory] reindex complete (%d vectors)", s.vec.Size())
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}
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func (s *Store) Stats() map[string]interface{} {
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s.mu.RLock()
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defer s.mu.RUnlock()
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return map[string]interface{}{
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"doc_count": len(s.docs),
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"vector_count": s.vec.Size(),
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"summary_count": len(s.summaries),
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"dir": s.dir,
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}
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}
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// FindColdDocs — 查找冷文档：超过 maxAge 未访问且访问次数 <= minAccess
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func (s *Store) FindColdDocs(maxAge time.Duration, minAccess int) []*Doc {
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s.mu.RLock()
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defer s.mu.RUnlock()
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cutoff := time.Now().Add(-maxAge)
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var cold []*Doc
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for _, d := range s.docs {
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if d.AccessCount <= minAccess && d.LastAccess.Before(cutoff) {
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cold = append(cold, d)
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}
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}
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return cold
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}
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func (s *Store) RecentDocs(n int) []*Doc {
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s.mu.RLock()
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defer s.mu.RUnlock()
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var list []*Doc
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for _, d := range s.docs {
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list = append(list, d)
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}
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sort.Slice(list, func(i, j int) bool {
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return list[i].CreatedAt.After(list[j].CreatedAt)
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})
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if len(list) > n {
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list = list[:n]
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}
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return list
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}
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// Remove 从文档存储中删除指定 ID 的文档
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func (s *Store) Remove(id string) {
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s.mu.Lock()
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defer s.mu.Unlock()
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if _, ok := s.docs[id]; ok {
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s.removeDoc(id)
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s.dirty = true
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}
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}
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// ——— internal ———
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// addSummary 添加一条摘要到训练集，超限时截断并触发重索引。
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// 调用方必须已持有 s.mu 写锁。
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func (s *Store) addSummary(summary string) {
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s.summaries = append(s.summaries, summary)
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if len(s.summaries) > maxSummaries {
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n := maxSummaries / 2
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copy(s.summaries, s.summaries[len(s.summaries)-n:])
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s.summaries = s.summaries[:n]
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s.veczer.Train(s.summaries)
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s.vec = vector.NewStore()
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for _, doc := range s.docs {
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vec := s.veczer.Vectorize(doc.Summary + " " + doc.Content)
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s.vec.Insert(doc.ID, doc.Summary, vec, nil)
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}
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}
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}
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// removeDoc 从内存索引和磁盘删除文档。
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// 调用方必须已持有 s.mu 写锁。
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func (s *Store) removeDoc(id string) {
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delete(s.docs, id)
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s.vec.Remove(id)
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path := filepath.Join(s.dir, id+".json")
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os.Remove(path)
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}
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func (s *Store) loadAll() error {
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entries, err := os.ReadDir(s.dir)
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if err != nil {
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return err
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}
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for _, e := range entries {
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if !strings.HasSuffix(e.Name(), ".json") || !strings.HasPrefix(e.Name(), "doc_") {
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continue
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}
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path := filepath.Join(s.dir, e.Name())
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data, err := os.ReadFile(path)
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if err != nil {
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continue
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}
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var doc Doc
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if err := json.Unmarshal(data, &doc); err != nil {
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continue
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}
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s.docs[doc.ID] = &doc
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s.summaries = append(s.summaries, doc.Summary)
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}
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// 训练向量化器
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if len(s.summaries) > 0 {
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s.veczer.Train(s.summaries)
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}
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// 重建向量索引
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for _, doc := range s.docs {
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vec := doc.Vector
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if vec == nil {
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vec = s.veczer.Vectorize(doc.Summary + " " + doc.Content)
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}
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s.vec.Insert(doc.ID, doc.Summary, vec, nil)
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}
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return nil
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}
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func (s *Store) flush() {
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s.mu.Lock()
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defer s.mu.Unlock()
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if !s.dirty {
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return
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}
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for _, doc := range s.docs {
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path := filepath.Join(s.dir, doc.ID+".json")
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data, err := json.MarshalIndent(doc, "", " ")
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if err != nil {
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continue
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}
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os.WriteFile(path, data, 0644)
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}
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s.dirty = false
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}
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type ToolResultItem struct {
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Name string
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Output string
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}
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type ContextEntry struct {
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Timestamp time.Time
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Source string
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Content string
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Response string
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ToolResults []ToolResultItem
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}
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func summarizeEntries(entries []ContextEntry, cleanText func(string) string, toolCleanFn func(name, output string) string, channelCleaner ChannelCleaner) string {
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if len(entries) == 0 {
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return ""
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}
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sources := make(map[string]int)
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var topics []string
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for _, e := range entries {
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sources[e.Source]++
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content := e.Content
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if channelCleaner != nil {
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if c := channelCleaner(e.Source); c != nil {
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content = c(content)
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}
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}
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words := memory.ExtractKeywords(cleanText(content))
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topics = append(topics, words...)
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for _, tr := range e.ToolResults {
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out := tr.Output
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if toolCleanFn != nil {
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if c := toolCleanFn(tr.Name, tr.Output); c == "" {
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continue
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} else {
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out = c
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}
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}
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toolWords := memory.ExtractKeywords(out)
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topics = append(topics, toolWords...)
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}
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}
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summary := fmt.Sprintf("来自 %d 个来源的 %d 条对话", len(sources), len(entries))
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var srcList []string
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for s := range sources {
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srcList = append(srcList, s)
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}
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summary += " (" + strings.Join(srcList, ", ") + ")"
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if len(topics) > 0 {
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seen := make(map[string]bool)
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var uniq []string
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for _, t := range topics {
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if !seen[t] {
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seen[t] = true
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uniq = append(uniq, t)
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}
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}
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if len(uniq) > 5 {
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uniq = uniq[:5]
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}
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summary += " 涉及: " + strings.Join(uniq, ", ")
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}
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return summary
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}
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|
||
func extractTags(entries []ContextEntry, cleanText func(string) string, toolCleanFn func(name, output string) string, channelCleaner ChannelCleaner) []string {
|
||
tagSet := make(map[string]bool)
|
||
for _, e := range entries {
|
||
content := e.Content
|
||
if channelCleaner != nil {
|
||
if c := channelCleaner(e.Source); c != nil {
|
||
content = c(content)
|
||
}
|
||
}
|
||
for _, kw := range memory.ExtractKeywords(cleanText(content)) {
|
||
tagSet[kw] = true
|
||
}
|
||
for _, tr := range e.ToolResults {
|
||
out := tr.Output
|
||
if toolCleanFn != nil {
|
||
if c := toolCleanFn(tr.Name, tr.Output); c == "" {
|
||
continue
|
||
} else {
|
||
out = c
|
||
}
|
||
}
|
||
for _, kw := range memory.ExtractKeywords(out) {
|
||
tagSet[kw] = true
|
||
}
|
||
}
|
||
}
|
||
var tags []string
|
||
for t := range tagSet {
|
||
if len(tags) >= 10 {
|
||
break
|
||
}
|
||
tags = append(tags, t)
|
||
}
|
||
return tags
|
||
}
|
||
|
||
func extractEntities(entries []ContextEntry, cleanText func(string) string, toolCleanFn func(name, output string) string, channelCleaner ChannelCleaner) []string {
|
||
var entities []string
|
||
seen := make(map[string]bool)
|
||
for _, e := range entries {
|
||
content := e.Content
|
||
if channelCleaner != nil {
|
||
if c := channelCleaner(e.Source); c != nil {
|
||
content = c(content)
|
||
}
|
||
}
|
||
for _, kw := range memory.ExtractKeywords(cleanText(content)) {
|
||
if len(kw) >= 2 && !seen[kw] {
|
||
seen[kw] = true
|
||
entities = append(entities, kw)
|
||
}
|
||
}
|
||
for _, tr := range e.ToolResults {
|
||
out := tr.Output
|
||
if toolCleanFn != nil {
|
||
if c := toolCleanFn(tr.Name, tr.Output); c == "" {
|
||
continue
|
||
} else {
|
||
out = c
|
||
}
|
||
}
|
||
for _, kw := range memory.ExtractKeywords(out) {
|
||
if len(kw) >= 2 && !seen[kw] {
|
||
seen[kw] = true
|
||
entities = append(entities, kw)
|
||
}
|
||
}
|
||
}
|
||
}
|
||
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]) + "..."
|
||
}
|
||
return s
|
||
}
|
||
|
||
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)
|
||
}
|
||
return fmt.Sprintf("h%08x", h)
|
||
}
|