mirror of
https://gitcode.com/JianFeeeee/HomeAgent.git
synced 2026-09-22 01:48:11 +00:00
feat: multi-language embedding with CleanTemplateText + three-branch vector strategy
- StaticEmbedder: pre-trained ConceptNet Numberbatch/fastText word embeddings, auto-download with TF-IDF fallback, comma-separated multi-model paths - CleanTemplateText: regex stripping of QQ tool call templates and noise - textForVector: per-source vector strategy (agent→Response, user→Input, cold_storage→both) - Indexer.BuildContext and ExtractKeywords now clean input before vectorization - Protect recent 10 events in Prune (regression fix: use local var not const)
This commit is contained in:
309
internal/memory/real_context_test.go
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309
internal/memory/real_context_test.go
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package memory
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import (
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"encoding/json"
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"os"
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"sort"
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"strings"
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"testing"
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"time"
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)
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type realEvent struct {
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Timestamp time.Time `json:"timestamp"`
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Source string `json:"source"`
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Input string `json:"input"`
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Response string `json:"response"`
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}
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func TestCleanTemplateText(t *testing.T) {
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cases := []struct {
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input string
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expected string
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contains string
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}{
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{
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input: `来自A的（扶高升学咨询群）群聊消息，通过id36使用qq_get_message工具获取消息正文。获取内容后使用 output_send(channel="qq") 回复该群聊，content 设为 JSON 字符串：{"content":"你的回复","group_id":979911915}`,
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expected: "来自A的（扶高升学咨询群）群聊消息",
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},
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{
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input: `【重要！老大消息】来自—/的私聊消息，通过id54使用qq_get_message工具获取消息正文。获取内容后使用 output_send(channel="qq") 回复对方，content 设为 JSON 字符串：{"content":"你的回复","user_id":2198972886}`,
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expected: "【重要！老大消息】来自—/的私聊消息",
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},
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{
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input: `[12:05] agent: 已经回复老大啦～继续去搞 vanblog 换端口的事 😊`,
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contains: "已经回复老大啦",
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},
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{
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input: `加载文档记忆: 扶高升学咨询群 河南医药大学 回复 招生 专业 录取`,
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expected: "加载文档记忆: 扶高升学咨询群 河南医药大学 回复 招生 专业 录取",
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},
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{
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input: `通过id10使用qq_get_message工具获取消息正文。获取后必须使用qq_send_private_msg工具回复对方，不得使用其他非回复工具。你只能通过qq_get_message先看消息，然后直接用%!s(MISSING)send_private_msg回复，中间的思考过程禁止调用任何其他工具 → 已经回复老大啦～`,
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contains: "已经回复老大啦",
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},
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{
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input: "",
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expected: "",
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},
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{
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input: `处理错误: all 9 providers failed, last error: lua transform_request: adapter tesy not loaded`,
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contains: "处理错误",
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},
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}
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for i, c := range cases {
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got := CleanTemplateText(c.input)
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if c.expected != "" && got != c.expected {
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t.Errorf("case %d:\n input: %q\n expected: %q\n got: %q", i, trimLen(c.input, 60), c.expected, got)
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}
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if c.contains != "" && !strings.Contains(got, c.contains) {
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t.Errorf("case %d: expected to contain %q, got %q", i, c.contains, got)
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}
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t.Logf("case %d: %q → %q", i, trimLen(c.input, 60), got)
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}
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}
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func TestRealContextPerSourceVector(t *testing.T) {
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modelPath := "/tmp/cc.zh.sample.vec"
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if _, err := os.Stat(modelPath); os.IsNotExist(err) {
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t.Skip("real embedding file not found")
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}
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e := NewStaticEmbedder(modelPath)
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data, err := os.ReadFile("/tmp/context.json")
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if err != nil {
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t.Fatal(err)
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}
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var raw []realEvent
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if err := json.Unmarshal(data, &raw); err != nil {
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t.Fatal(err)
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}
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t.Logf("loaded %d real events", len(raw))
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type scored struct {
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idx int
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source string
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text string
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score float64
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}
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clean := func(ev realEvent) string {
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switch {
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case ev.Source == "agent" && ev.Response != "":
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return CleanTemplateText(ev.Response)
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case ev.Source == "cold_storage":
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return CleanTemplateText(ev.Input + " " + ev.Response)
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default:
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return CleanTemplateText(ev.Input)
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}
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}
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t.Run("医药大学_不同源向量", func(t *testing.T) {
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q := "河南医药大学招生分数录取排名"
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qVec := e.VectorizeClean(q)
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all := make([]scored, len(raw))
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for i, ev := range raw {
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text := clean(ev)
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all[i] = scored{idx: i, source: ev.Source, text: text[:min(len(text), 200)], score: cosineSim(qVec, e.Vectorize(text))}
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}
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sort.Slice(all, func(i, j int) bool { return all[i].score > all[j].score })
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t.Log("top 5:")
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for _, s := range all[:5] {
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t.Logf(" [%.4f] [%-13s] %s", s.score, s.source, trimLen(s.text, 80))
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}
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var univHigh bool
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for _, s := range all[:8] {
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if strings.Contains(s.text, "医药大学") || strings.Contains(s.text, "升学") {
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univHigh = true
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break
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}
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}
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if !univHigh {
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t.Error("expected university-related events in top 8")
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}
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})
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t.Run("老大私聊_agent主用Response", func(t *testing.T) {
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q := "老大私聊说了什么"
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qVec := e.VectorizeClean(q)
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all := make([]scored, len(raw))
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for i, ev := range raw {
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text := clean(ev)
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all[i] = scored{idx: i, source: ev.Source, text: text[:min(len(text), 200)], score: cosineSim(qVec, e.Vectorize(text))}
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}
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sort.Slice(all, func(i, j int) bool { return all[i].score > all[j].score })
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t.Log("top 5:")
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for _, s := range all[:5] {
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t.Logf(" [%.4f] [%-13s] %s", s.score, s.source, trimLen(s.text, 80))
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}
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var bossFound bool
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for _, s := range all[:8] {
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if strings.Contains(s.text, "老大") {
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bossFound = true
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break
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}
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}
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if !bossFound {
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t.Error("expected events mentioning 老大 in top 8")
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}
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var agentFound bool
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for _, s := range all[:5] {
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if s.source == "agent" {
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agentFound = true
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break
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}
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}
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t.Logf("agent in top5: %v (source strategy: agent events use Response for vector)", agentFound)
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})
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t.Run("图片转SVG_去模版后效果", func(t *testing.T) {
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q := "图片转换SVG工具"
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qVec := e.VectorizeClean(q)
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all := make([]scored, len(raw))
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for i, ev := range raw {
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text := clean(ev)
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all[i] = scored{idx: i, source: ev.Source, text: text[:min(len(text), 200)], score: cosineSim(qVec, e.Vectorize(text))}
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}
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sort.Slice(all, func(i, j int) bool { return all[i].score > all[j].score })
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t.Log("top 5:")
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for _, s := range all[:5] {
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t.Logf(" [%.4f] [%-13s] %s", s.score, s.source, trimLen(s.text, 80))
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}
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var img bool
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for _, s := range all[:5] {
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if strings.Contains(s.text, "图片") || strings.Contains(s.text, "SVG") {
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img = true
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break
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}
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}
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if !img {
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t.Error("expected image-related events in top 5")
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}
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})
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t.Run("南航航空航天", func(t *testing.T) {
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q := "南航航空航天专业转电气"
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qVec := e.VectorizeClean(q)
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all := make([]scored, len(raw))
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for i, ev := range raw {
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text := clean(ev)
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all[i] = scored{idx: i, source: ev.Source, text: text[:min(len(text), 200)], score: cosineSim(qVec, e.Vectorize(text))}
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}
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sort.Slice(all, func(i, j int) bool { return all[i].score > all[j].score })
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t.Log("top 5:")
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for _, s := range all[:5] {
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t.Logf(" [%.4f] [%-13s] %s", s.score, s.source, trimLen(s.text, 80))
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}
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var nau bool
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for _, s := range all[:5] {
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if strings.Contains(s.text, "南航") {
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nau = true
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break
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}
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}
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if !nau {
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t.Error("expected 南航 in top 5")
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}
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})
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t.Run("跨域区分度", func(t *testing.T) {
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pairs := []struct {
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a, b string
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}{
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{"老大私聊说了什么", "河南医药大学招生分数"},
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{"老大私聊说了什么", "图片转换SVG工具"},
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{"南航航空航天电气", "河南医药大学录取"},
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{"图片转换SVG工具", "老大私聊"},
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}
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for _, p := range pairs {
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va := e.VectorizeClean(p.a)
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vb := e.VectorizeClean(p.b)
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s := cosineSim(va, vb)
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t.Logf(" sim(%q, %q) = %.4f", p.a, p.b, s)
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}
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univVec := e.VectorizeClean("河南医药大学招生")
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bossVec := e.VectorizeClean("老大私聊说了什么")
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t.Logf("cross-domain sim(医药大学, 老大私聊) = %.4f", cosineSim(univVec, bossVec))
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})
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t.Run("去模版节省量", func(t *testing.T) {
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var savedTotal int
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for i, ev := range raw {
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orig := len(ev.Input + " " + ev.Response)
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after := len(clean(ev))
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saved := orig - after
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savedTotal += saved
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if saved > 100 {
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t.Logf(" [%2d] [%-13s] 节省 %d 字符 (raw=%d clean=%d)", i, ev.Source, saved, orig, after)
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}
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}
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t.Logf("总计节省 %d 字符", savedTotal)
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})
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}
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func TestRealContextEmbedderStats(t *testing.T) {
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e := NewStaticEmbedder("/tmp/cc.zh.sample.vec")
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if !e.Loaded() {
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t.Skip("embedder not loaded")
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}
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data, err := os.ReadFile("/tmp/context.json")
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if err != nil {
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t.Fatal(err)
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}
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var raw []realEvent
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if err := json.Unmarshal(data, &raw); err != nil {
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t.Fatal(err)
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}
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for _, ev := range raw[:5] {
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var text string
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switch {
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case ev.Source == "agent" && ev.Response != "":
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text = CleanTemplateText(ev.Response)
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case ev.Source == "cold_storage":
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text = CleanTemplateText(ev.Input + " " + ev.Response)
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default:
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text = CleanTemplateText(ev.Input)
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}
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vec := e.Vectorize(text)
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origLen := len(ev.Input + ev.Response)
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t.Logf("[%-13s] raw=%d cleaned=%d dims=%d", ev.Source, origLen, len(text), len(vec))
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for k := range vec {
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if !isNumericKey(k) {
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t.Errorf("non-numeric key %q — should be dense space", k)
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}
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}
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}
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}
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func containsAny(s string, subs []string) bool {
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for _, sub := range subs {
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if sub != "" && strings.Contains(s, sub) {
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return true
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}
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}
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return false
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}
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func trimLen(s string, n int) string {
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if len(s) > n {
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return s[:n]
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}
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return s
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}
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