feat: 第四轮功能增强 - LLM 思维记忆优化、DevTools 记忆UI、9个新工具、5分钟自我思考

- 优化 LLM 思维方式和记忆方法（类别/重要性/关键词/相似度合并/衰减） - DevTools 记忆查询 UI 重新设计（类别筛选/排序/星标/搜索） - 新增 9 个 LLM 工具：calculator, datetime, file_ops, http_request, json_ops, text, random, crypto, markdown - 管理员主对话 5 分钟自我思考增强（工具调用/记忆提取/记忆维护）
2026-05-18 12:13:49 +08:00
parent 07781eda0e
commit b6ec36886c
20 changed files with 4654 additions and 320 deletions
@@ -0,0 +1,345 @@
+package tools
+
+import (
+	"context"
+	"fmt"
+	"regexp"
+	"strings"
+	"unicode"
+)
+
+// TextTool provides text processing operations for the LLM.
+// Supports counting, summarizing, translation, and pattern extraction.
+type TextTool struct{}
+
+// NewTextTool creates a text processing tool.
+func NewTextTool() *TextTool {
+	return &TextTool{}
+}
+
+// Definition returns the tool definition for LLM function calling.
+func (t *TextTool) Definition() ToolDefinition {
+	return ToolDefinition{
+		Name:        "text",
+		Description: "文本处理工具。统计文本、生成摘要、翻译文本、正则提取信息。用于处理用户提供的文本内容。",
+		Parameters: map[string]interface{}{
+			"type": "object",
+			"properties": map[string]interface{}{
+				"action": map[string]interface{}{
+					"type":        "string",
+					"enum":        []string{"count", "summarize", "translate", "extract"},
+					"description": "操作类型。count: 统计字符/单词/行/段落数；summarize: 提取首段+关键句生成简单摘要；translate: 翻译文本（需指定target_lang）；extract: 正则提取邮箱/电话/URL等",
+				},
+				"text": map[string]interface{}{
+					"type":        "string",
+					"description": "输入文本，需要处理的文本内容",
+				},
+				"target_lang": map[string]interface{}{
+					"type":        "string",
+					"enum":        []string{"en", "zh", "ja", "ko", "fr", "de"},
+					"description": "翻译目标语言代码。en: 英语, zh: 中文, ja: 日语, ko: 韩语, fr: 法语, de: 德语",
+				},
+				"pattern": map[string]interface{}{
+					"type":        "string",
+					"description": "正则表达式模式，用于 extract 操作。常用预设: email(邮箱), phone(电话), url(网址)",
+				},
+			},
+			"required": []string{"action", "text"},
+		},
+	}
+}
+
+// Execute performs text processing operations.
+func (t *TextTool) Execute(ctx context.Context, arguments map[string]interface{}) (*ToolResult, error) {
+	action, ok := arguments["action"].(string)
+	if !ok || action == "" {
+		return &ToolResult{
+			ToolName: "text",
+			Success:  false,
+			Error:    "缺少 action 参数",
+		}, nil
+	}
+
+	text, ok := arguments["text"].(string)
+	if !ok || strings.TrimSpace(text) == "" {
+		return &ToolResult{
+			ToolName: "text",
+			Success:  false,
+			Error:    "缺少 text 参数或文本为空",
+		}, nil
+	}
+
+	switch action {
+	case "count":
+		return t.handleCount(text)
+	case "summarize":
+		return t.handleSummarize(text)
+	case "translate":
+		return t.handleTranslate(arguments)
+	case "extract":
+		return t.handleExtract(arguments)
+	default:
+		return &ToolResult{
+			ToolName: "text",
+			Success:  false,
+			Error:    fmt.Sprintf("未知操作: %s，支持: count, summarize, translate, extract", action),
+		}, nil
+	}
+}
+
+// handleCount counts characters, words, lines, and paragraphs in the text.
+func (t *TextTool) handleCount(text string) (*ToolResult, error) {
+	charCount := len([]rune(text))
+	byteCount := len(text)
+
+	words := strings.Fields(text)
+	wordCount := len(words)
+
+	lines := strings.Split(text, "\n")
+	lineCount := len(lines)
+
+	// Count paragraphs (separated by double newlines)
+	paragraphs := regexp.MustCompile(`\n\s*\n`).Split(text, -1)
+	paraCount := 0
+	for _, p := range paragraphs {
+		if strings.TrimSpace(p) != "" {
+			paraCount++
+		}
+	}
+
+	// Count Chinese characters
+	chineseCount := 0
+	for _, r := range text {
+		if unicode.Is(unicode.Han, r) {
+			chineseCount++
+		}
+	}
+
+	return &ToolResult{
+		ToolName: "text",
+		Success:  true,
+		Data: fmt.Sprintf("文本统计结果:\n- 字符数 (含空格): %d\n- 字符数 (不含空格): %d\n- 字节数: %d\n- 单词数: %d\n- 行数: %d\n- 段落数: %d\n- 中文字符数: %d",
+			charCount, len([]rune(strings.ReplaceAll(text, " ", ""))),
+			byteCount, wordCount, lineCount, paraCount, chineseCount),
+	}, nil
+}
+
+// handleSummarize generates a simple summary by extracting the first paragraph and key sentences.
+func (t *TextTool) handleSummarize(text string) (*ToolResult, error) {
+	var result strings.Builder
+	result.WriteString("文本摘要:\n\n")
+
+	// Extract first paragraph
+	paragraphs := regexp.MustCompile(`\n\s*\n`).Split(text, -1)
+	var firstPara string
+	for _, p := range paragraphs {
+		if trimmed := strings.TrimSpace(p); trimmed != "" {
+			firstPara = trimmed
+			break
+		}
+	}
+
+	if firstPara != "" {
+		result.WriteString("【首段】\n")
+		// Truncate if very long
+		runes := []rune(firstPara)
+		if len(runes) > 300 {
+			firstPara = string(runes[:300]) + "..."
+		}
+		result.WriteString(firstPara)
+		result.WriteString("\n\n")
+	}
+
+	// Extract key sentences (longer sentences with important keywords)
+	sentences := t.splitSentences(text)
+	keySentences := t.extractKeySentences(sentences, 5)
+
+	if len(keySentences) > 0 {
+		result.WriteString("【关键句】\n")
+		for i, s := range keySentences {
+			result.WriteString(fmt.Sprintf("%d. %s\n", i+1, s))
+		}
+	}
+
+	// Overall stats
+	lines := strings.Split(text, "\n")
+	words := strings.Fields(text)
+	result.WriteString(fmt.Sprintf("\n【概况】共 %d 段、%d 句、%d 词、%d 行",
+		len(paragraphs), len(sentences), len(words), len(lines)))
+
+	return &ToolResult{
+		ToolName: "text",
+		Success:  true,
+		Data:     result.String(),
+	}, nil
+}
+
+// splitSentences splits text into sentences based on punctuation.
+func (t *TextTool) splitSentences(text string) []string {
+	re := regexp.MustCompile(`[^。！？.!?\n]+[。！？.!?\n]?`)
+	return re.FindAllString(text, -1)
+}
+
+// extractKeySentences selects the most informative sentences (longer ones with keyword hints).
+func (t *TextTool) extractKeySentences(sentences []string, maxCount int) []string {
+	type scored struct {
+		text  string
+		score int
+	}
+
+	var scoredList []scored
+	keywords := []string{"重要", "关键", "核心", "主要", "首先", "最后", "因此", "所以", "总结",
+		"important", "key", "critical", "significant", "therefore", "conclusion", "summary"}
+
+	for _, s := range sentences {
+		trimmed := strings.TrimSpace(s)
+		if len([]rune(trimmed)) < 10 {
+			continue
+		}
+
+		score := len([]rune(trimmed)) // longer sentences are more likely informative
+		lower := strings.ToLower(trimmed)
+		for _, kw := range keywords {
+			if strings.Contains(lower, kw) {
+				score += 50
+			}
+		}
+		scoredList = append(scoredList, scored{text: trimmed, score: score})
+	}
+
+	// Sort by score descending (simple bubble sort for small lists)
+	for i := 0; i < len(scoredList); i++ {
+		for j := i + 1; j < len(scoredList); j++ {
+			if scoredList[j].score > scoredList[i].score {
+				scoredList[i], scoredList[j] = scoredList[j], scoredList[i]
+			}
+		}
+	}
+
+	result := make([]string, 0, maxCount)
+	for i := 0; i < len(scoredList) && i < maxCount; i++ {
+		result = append(result, scoredList[i].text)
+	}
+	return result
+}
+
+// handleTranslate provides a translation placeholder (actual translation requires LLM).
+func (t *TextTool) handleTranslate(arguments map[string]interface{}) (*ToolResult, error) {
+	text, _ := arguments["text"].(string)
+	targetLang, _ := arguments["target_lang"].(string)
+	if targetLang == "" {
+		targetLang = "zh"
+	}
+
+	langNames := map[string]string{
+		"en": "英语",
+		"zh": "中文",
+		"ja": "日语",
+		"ko": "韩语",
+		"fr": "法语",
+		"de": "德语",
+	}
+
+	langName, ok := langNames[targetLang]
+	if !ok {
+		langName = targetLang
+	}
+
+	return &ToolResult{
+		ToolName: "text",
+		Success:  true,
+		Data: fmt.Sprintf("【翻译请求】\n目标语言: %s (%s)\n原文 (%d 字符):\n---\n%s\n---\n\n提示: 实际翻译由LLM完成，请基于以上原文和目标语言进行翻译。",
+			langName, targetLang, len([]rune(text)), text),
+	}, nil
+}
+
+// handleExtract extracts patterns like emails, phones, URLs from text using regex.
+func (t *TextTool) handleExtract(arguments map[string]interface{}) (*ToolResult, error) {
+	text, _ := arguments["text"].(string)
+	pattern, _ := arguments["pattern"].(string)
+
+	// Predefined patterns
+	presets := map[string]string{
+		"email": `[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,}`,
+		"phone": `(?:\+?86[\-\s]?)?1[3-9]\d{9}`,
+		"url":   `https?://[^\s<>"{}|\\^` + "`" + `\[\]]+`,
+	}
+
+	if preset, ok := presets[strings.ToLower(pattern)]; ok {
+		pattern = preset
+	}
+
+	if pattern == "" {
+		// Extract all common patterns when no specific pattern given
+		var result strings.Builder
+		result.WriteString("文本提取结果:\n\n")
+
+		for name, p := range presets {
+			re, err := regexp.Compile(p)
+			if err != nil {
+				continue
+			}
+			matches := re.FindAllString(text, -1)
+			if len(matches) > 0 {
+				result.WriteString(fmt.Sprintf("【%s】(共 %d 个):\n", name, len(matches)))
+				seen := make(map[string]bool)
+				for _, m := range matches {
+					if !seen[m] {
+						result.WriteString(fmt.Sprintf("  - %s\n", m))
+						seen[m] = true
+					}
+				}
+				result.WriteString("\n")
+			}
+		}
+
+		if result.Len() == len("文本提取结果:\n\n") {
+			return &ToolResult{
+				ToolName: "text",
+				Success:  true,
+				Data:     "未提取到匹配的内容（邮箱、电话、URL）",
+			}, nil
+		}
+
+		return &ToolResult{
+			ToolName: "text",
+			Success:  true,
+			Data:     result.String(),
+		}, nil
+	}
+
+	// Use custom regex pattern
+	re, err := regexp.Compile(pattern)
+	if err != nil {
+		return &ToolResult{
+			ToolName: "text",
+			Success:  false,
+			Error:    fmt.Sprintf("正则表达式无效: %v", err),
+		}, nil
+	}
+
+	matches := re.FindAllString(text, -1)
+	if len(matches) == 0 {
+		return &ToolResult{
+			ToolName: "text",
+			Success:  true,
+			Data:     fmt.Sprintf("未找到匹配模式 '%s' 的内容", pattern),
+		}, nil
+	}
+
+	var result strings.Builder
+	result.WriteString(fmt.Sprintf("正则提取结果 (模式: %s, 共 %d 个匹配):\n", pattern, len(matches)))
+	seen := make(map[string]bool)
+	for _, m := range matches {
+		if !seen[m] {
+			result.WriteString(fmt.Sprintf("  - %s\n", m))
+			seen[m] = true
+		}
+	}
+
+	return &ToolResult{
+		ToolName: "text",
+		Success:  true,
+		Data:     result.String(),
+	}, nil
+}