mirror of
https://github.com/memohai/Memoh.git
synced 2026-04-27 07:16:19 +09:00
6aebbe9279
Major changes: 1. Core Architecture: Decoupled Bots from Users. Bots now have independent lifecycles, member management (bot_members), and dedicated configurations. 2. Channel Gateway: - Implemented a unified Channel Manager supporting Feishu, Telegram, and Local (Web/CLI) adapters. - Added message processing pipeline to normalize interactions across different platforms. - Introduced a Contact system for identity binding and guest access policies. 3. Database & Tooling: - Consolidated all migrations into 0001_init with updated schema for bots, channels, and contacts. - Optimized sqlc.yaml to automatically track the migrations directory. 4. Agent Enhancements: - Introduced ToolContext to provide Agents with platform-aware execution capabilities (e.g., messaging, contact lookups). - Added tool logging and fallback mechanisms for toolChoice execution. 5. UI & Docs: Updated frontend stores, UI components, and Swagger documentation to align with the new Bot-centric model.
151 lines
4.2 KiB
Go
151 lines
4.2 KiB
Go
package memory
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import (
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"reflect"
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"testing"
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)
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func TestBM25Indexer_TermFrequencies(t *testing.T) {
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indexer := NewBM25Indexer(nil)
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tests := []struct {
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name string
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lang string
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text string
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want map[string]int
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docLen int
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wantErr bool
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}{
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{
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name: "English text",
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lang: "en",
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text: "The quick brown fox jumps over the lazy dog",
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// Note: Bleve English analyzer stems words (jumps -> jump, lazy -> lazi) and removes stop words (the, over)
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want: map[string]int{"quick": 1, "brown": 1, "fox": 1, "jump": 1, "lazi": 1, "dog": 1},
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docLen: 6,
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},
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{
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name: "CJK text",
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lang: "cjk",
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text: "你好世界",
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// Note: Bleve CJK analyzer uses bigrams
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want: map[string]int{"你好": 1, "好世": 1, "世界": 1},
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docLen: 3,
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},
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{
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name: "Mixed text with standard analyzer",
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lang: "",
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text: "Go 语言 123",
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// Note: Standard analyzer splits CJK characters individually
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want: map[string]int{"go": 1, "语": 1, "言": 1, "123": 1},
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docLen: 4,
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},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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got, gotLen, err := indexer.TermFrequencies(tt.lang, tt.text)
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if (err != nil) != tt.wantErr {
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t.Errorf("TermFrequencies() error = %v, wantErr %v", err, tt.wantErr)
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return
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}
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if !reflect.DeepEqual(got, tt.want) {
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t.Errorf("TermFrequencies() got = %v, want %v", got, tt.want)
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}
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if gotLen != tt.docLen {
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t.Errorf("TermFrequencies() gotLen = %v, want %v", gotLen, tt.docLen)
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}
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})
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}
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}
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func TestBM25Indexer_BM25Logic(t *testing.T) {
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indexer := NewBM25Indexer(nil)
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// 1. 添加一个包含 "golang" 的文档
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lang := "en"
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tf1 := map[string]int{"golang": 1, "programming": 1}
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len1 := 2
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indices1, values1 := indexer.AddDocument(lang, tf1, len1)
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// 2. 添加另一个包含 "golang" 但更长的文档
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tf2 := map[string]int{"golang": 1, "tutorial": 1, "advanced": 1, "topics": 1}
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len2 := 4
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indices2, values2 := indexer.AddDocument(lang, tf2, len2)
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// 验证:在 BM25 中,相同词项在短文档中的权重应该比在长文档中高(惩罚长文档)
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var weight1, weight2 float32
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for i, idx := range indices1 {
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if idx == termHash("golang") {
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weight1 = values1[i]
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}
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}
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for i, idx := range indices2 {
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if idx == termHash("golang") {
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weight2 = values2[i]
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}
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}
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if weight1 <= weight2 {
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t.Errorf("Expected weight in shorter doc (%f) to be higher than in longer doc (%f)", weight1, weight2)
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}
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// 3. 添加一个不包含 "golang" 的文档,增加文档总数,验证 IDF 变化
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// IDF 应该随着包含该词的文档比例减少而增加
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oldWeight1 := weight1
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indexer.AddDocument(lang, map[string]int{"rust": 1}, 1)
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indices3, values3 := indexer.AddDocument(lang, tf1, len1) // 再次生成相同文档的向量
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for i, idx := range indices3 {
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if idx == termHash("golang") {
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weight1 = values3[i]
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}
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}
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if weight1 <= oldWeight1 {
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t.Errorf("Expected weight to increase as IDF increases (more docs without the term), got %f -> %f", oldWeight1, weight1)
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}
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}
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func TestBM25Indexer_RemoveDocument(t *testing.T) {
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indexer := NewBM25Indexer(nil)
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lang := "en"
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term := "test"
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// 添加文档
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tf, docLen, _ := indexer.TermFrequencies(lang, term)
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indexer.AddDocument(lang, tf, docLen)
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indexer.mu.RLock()
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stats := indexer.stats["en"]
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if stats.DocCount != 1 || stats.DocFreq[term] != 1 {
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t.Errorf("Expected stats to be updated after add, got count=%d, freq=%d", stats.DocCount, stats.DocFreq[term])
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}
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indexer.mu.RUnlock()
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// 删除文档
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indexer.RemoveDocument(lang, tf, docLen)
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indexer.mu.RLock()
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if stats.DocCount != 0 || stats.DocFreq[term] != 0 {
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t.Errorf("Expected stats to be cleared after remove, got count=%d, freq=%d", stats.DocCount, stats.DocFreq[term])
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}
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indexer.mu.RUnlock()
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}
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func TestTermHash_CollisionResistance(t *testing.T) {
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// 验证不同词项生成的哈希索引在 20bit 空间内是否分布合理(简单检查不冲突)
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h1 := termHash("apple")
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h2 := termHash("orange")
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h3 := termHash("banana")
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if h1 == h2 || h2 == h3 || h1 == h3 {
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t.Errorf("Detected unexpected hash collision in small sample: %d, %d, %d", h1, h2, h3)
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}
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// 验证掩码是否生效
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if h1 > sparseDimMask {
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t.Errorf("Hash %d exceeds mask %d", h1, sparseDimMask)
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}
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}
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