mirror of
https://gitcode.com/JianFeeeee/HomeAgent.git
synced 2026-10-04 00:03:59 +00:00
feat: complete HomeAgent architecture v2
- IO abstraction layer with OutputChannel routing and capability validation - Three-layer memory (Context-Document-Graph) with TF-IDF relevance pruning - OneBot V11 QQ protocol plugin with Reverse WebSocket client - Plugin system with hot-reload (SKILL.md + native factories) - Knowledge system with TF-IDF vector indexing - Personality system (personal.md) - Text memory (JSONL with rotation) - Change tracker (overlayfs) with rollback - Lua adapter VM - Design document (DESIGN.md) Module: gitcode.com/JianFeeeee/HomeAgent
This commit is contained in:
root
329
internal/memory/indexer.go
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329
internal/memory/indexer.go
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package memory
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import (
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"fmt"
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"log"
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"strings"
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"sync"
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"gitcode.com/JianFeeeee/HomeAgent/internal/memory/vector"
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)
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type Indexer struct {
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db *GraphDB
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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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trained bool
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}
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func NewIndexer(db *GraphDB) *Indexer {
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return &Indexer{
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db: db,
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vec: vector.NewStore(),
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veczer: vector.NewTFIDFVectorizer(2),
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}
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}
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// Sync 从图数据库中同步实体名到向量索引
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func (idx *Indexer) Sync() error {
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idx.mu.Lock()
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defer idx.mu.Unlock()
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if idx.db == nil {
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return nil
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}
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result, err := idx.db.Recall(nil, nil, 1, "")
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if err != nil || result == nil {
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return err
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}
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// 收集实体名
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var names []string
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for _, e := range result.Entities {
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names = append(names, e.Name)
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}
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if len(names) == 0 {
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return nil
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}
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// 训练向量化器
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idx.veczer.Train(names)
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// 重建向量索引
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idx.vec = vector.NewStore()
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for _, e := range result.Entities {
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vec := idx.veczer.Vectorize(e.Name)
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idx.vec.Insert(fmt.Sprintf("entity_%d", e.ID), e.Name, vec, map[string]string{
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"type": "entity",
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"name": e.Name,
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})
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}
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idx.trained = true
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log.Printf("[indexer] synced %d entities to vector index", len(names))
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return nil
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}
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type InjectedContext struct {
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Entities []Entity `json:"entities"`
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Relations []Relation `json:"relations"`
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Summary string `json:"summary"`
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TokenEstimate int `json:"token_estimate"`
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}
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func (idx *Indexer) BuildContext(userInput string) *InjectedContext {
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if idx.db == nil {
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return &InjectedContext{Summary: ""}
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}
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// 1. 向量搜索：从实体名向量索引中找到相关实体
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vectorEntities := idx.vectorSearchEntities(userInput)
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// 2. 关键词搜索：已有逻辑
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keywords := extractKeywords(userInput)
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if len(keywords) == 0 && len(vectorEntities) == 0 {
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keywords = []string{userInput}
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}
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// 合并关键词和向量找到的实体名
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seedNames := make([]string, 0, len(vectorEntities))
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for _, e := range vectorEntities {
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seedNames = append(seedNames, e.Name)
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}
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allKeywords := append(keywords, seedNames...)
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result, err := idx.db.Recall(allKeywords, nil, 2, "")
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if err != nil || result == nil {
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return &InjectedContext{Summary: ""}
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}
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ctx := &InjectedContext{
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Entities: result.Entities,
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Relations: nil,
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}
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if len(result.Entities) > 0 {
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summary := buildIndexSummary(result.Entities)
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ctx.Summary = summary
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ctx.TokenEstimate = estimateTokens(summary) + len(result.Entities)*8
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} else {
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ctx.Summary = ""
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}
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return ctx
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}
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// vectorSearchEntities 在实体名向量索引中搜索
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func (idx *Indexer) vectorSearchEntities(query string) []Entity {
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idx.mu.RLock()
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defer idx.mu.RUnlock()
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if !idx.trained || idx.vec.Size() == 0 {
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return nil
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}
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queryVec := idx.veczer.Vectorize(query)
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results := idx.vec.Search(queryVec, 5)
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var entities []Entity
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for _, r := range results {
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if r.Meta != nil && r.Meta["type"] == "entity" {
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entities = append(entities, Entity{Name: r.Meta["name"]})
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}
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}
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return entities
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}
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func (idx *Indexer) BuildToolPrompt() string {
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return `## 图记忆工具
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你有以下工具可以操作长期图记忆系统：
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### memory_recall
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检索与关键词相关的实体和关系。
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参数:
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- query_intent: 查询关键词，逗号分隔
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- depth: 遍历深度（默认2）
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### memory_commit
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将三元组写入图记忆。
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参数:
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- triples: [{"subject": "实体名", "relation": "关系类型", "object": "目标实体"}]
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### memory_introspect
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查看记忆统计信息。
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### memory_purge
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删除或修正记忆。
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参数:
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- criteria: {"subject_contains": "...", "relation_type": "..."}
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- mode: "soft" | "supersede"
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使用方法：在推理过程中调用对应的 tool，系统会自动执行并返回结果。`
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}
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func (idx *Indexer) FormatContext(ctx *InjectedContext) string {
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if ctx == nil || len(ctx.Entities) == 0 {
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return ""
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}
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var b strings.Builder
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b.WriteString("【记忆索引】")
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if ctx.Summary != "" {
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b.WriteString(" ")
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b.WriteString(ctx.Summary)
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}
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b.WriteString(fmt.Sprintf(" 索引: "))
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for i, e := range ctx.Entities {
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if i >= 5 {
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b.WriteString("…")
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break
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}
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if i > 0 {
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b.WriteString(", ")
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}
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b.WriteString(e.Name)
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if e.Type != "Concept" {
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b.WriteString("(" + e.Type + ")")
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}
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}
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b.WriteString(" | 需更多细节请用 memory_recall 查询")
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return b.String()
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}
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func (idx *Indexer) GetToolDefinitions() []map[string]interface{} {
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return []map[string]interface{}{
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{
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"type": "function",
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"function": map[string]interface{}{
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"name": "memory_recall",
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"description": "检索图记忆。输入查询意图关键词，返回相关实体和关系。",
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"parameters": map[string]interface{}{
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"type": "object",
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"properties": map[string]interface{}{
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"query_intent": map[string]interface{}{
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"type": "string",
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"description": "查询意图，支持逗号分隔多个关键词",
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},
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"depth": map[string]interface{}{
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"type": "integer",
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"description": "遍历深度，默认2",
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"default": 2,
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},
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},
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"required": []string{"query_intent"},
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},
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},
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},
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{
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"type": "function",
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"function": map[string]interface{}{
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"name": "memory_commit",
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"description": "写入图记忆。将三元组列表写入长期记忆。",
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"parameters": map[string]interface{}{
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"type": "object",
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"properties": map[string]interface{}{
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"triples": map[string]interface{}{
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"type": "array",
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"description": "三元组列表",
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"items": map[string]interface{}{
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"type": "object",
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"properties": map[string]interface{}{
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"subject": map[string]interface{}{"type": "string"},
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"relation": map[string]interface{}{"type": "string"},
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"object": map[string]interface{}{"type": "string"},
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},
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"required": []string{"subject", "relation", "object"},
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},
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},
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},
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"required": []string{"triples"},
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},
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},
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},
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{
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"type": "function",
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"function": map[string]interface{}{
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"name": "memory_introspect",
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"description": "查看图记忆统计信息：实体数量、关系数量、热点实体。",
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"parameters": map[string]interface{}{
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"type": "object",
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"properties": map[string]interface{}{},
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},
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},
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},
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}
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}
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func extractKeywords(input string) []string {
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stopWords := map[string]bool{
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"的": true, "了": true, "是": true, "在": true, "有": true,
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"和": true, "就": true, "不": true, "人": true, "都": true,
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"一": true, "一个": true, "上": true, "也": true, "很": true,
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"到": true, "说": true, "要": true, "去": true, "你": true,
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"会": true, "着": true, "没有": true, "看": true, "好": true,
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"自己": true, "这": true, "他": true, "她": true, "它": true,
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"什么": true, "怎么": true, "为什么": true, "如何": true,
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}
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var keywords []string
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seen := make(map[string]bool)
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runes := []rune(input)
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bigram := []rune{}
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for _, r := range runes {
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bigram = append(bigram, r)
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if len(bigram) >= 2 {
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word := string(bigram)
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if !stopWords[word] && !seen[word] {
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seen[word] = true
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keywords = append(keywords, word)
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}
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bigram = bigram[1:]
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}
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}
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if len(keywords) == 0 && len(runes) > 0 {
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keywords = []string{string(runes)}
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}
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if len(keywords) > 5 {
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keywords = keywords[:5]
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}
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return keywords
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}
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func buildIndexSummary(entities []Entity) string {
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if len(entities) == 0 {
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return ""
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}
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var b strings.Builder
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b.WriteString(fmt.Sprintf("关联 %d 个记忆实体", len(entities)))
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topN := 3
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if len(entities) < topN {
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topN = len(entities)
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}
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b.WriteString("，高频：")
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for i := 0; i < topN; i++ {
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if i > 0 {
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b.WriteString("、")
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}
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b.WriteString(entities[i].Name)
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}
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return b.String()
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}
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func estimateTokens(s string) int {
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return len(s) / 2
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}
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