wehub-resource-sync
338 lines
9.2 KiB
Go
338 lines
9.2 KiB
Go
package agent
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import (
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"context"
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"crypto/sha256"
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"encoding/hex"
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"strings"
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"sync"
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"time"
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"reasonix/internal/provider"
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)
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// TaskClassifier 判断用户输入是否为任务（需要执行动作）或聊天（对话性质）
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type TaskClassifier interface {
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// IsTask 返回 true 表示输入为任务，false 表示聊天
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IsTask(ctx context.Context, input string) (bool, error)
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}
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// llmClassifier 使用 LLM (Haiku) 进行分类
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type llmClassifier struct {
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provider provider.Provider
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cache *classificationCache
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fallback TaskClassifier
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}
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// heuristicClassifier 使用启发式规则进行分类（作为 fallback）
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type heuristicClassifier struct{}
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// classificationCache 会话级别的分类结果缓存
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type classificationCache struct {
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mu sync.RWMutex
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entries map[string]cacheEntry
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maxSize int
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ttl time.Duration
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}
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type cacheEntry struct {
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isTask bool
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timestamp time.Time
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}
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const (
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defaultCacheMaxSize = 100
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defaultCacheTTL = 5 * time.Minute
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)
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// 分类系统 prompt
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const classificationSystemPrompt = `You are a classifier that determines whether user input is a "task" (requires action/execution) or "chat" (conversational/greeting).
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Task: Any input that asks the assistant to write code, fix bugs, run commands, analyze code, create files, or perform any action.
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Chat: Greetings, acknowledgments, confirmations, questions about the assistant itself, or purely conversational inputs.
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Respond with ONLY one word: "task" or "chat"
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Examples:
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- "fix the auth bug" → task
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- "create a component" → task
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- "run tests" → task
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- "the login isn't working" → task
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- "can you help with this error?" → task
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- "继续处理" → task
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- "修复这个问题" → task
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- "hello" → chat
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- "thanks" → chat
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- "ok" → chat
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- "I see" → chat
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- "你好" → chat
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- "谢谢" → chat
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- "thanks for fixing that" → chat`
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// newLLMClassifier 创建新的 LLM 分类器
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func newLLMClassifier(prov provider.Provider, fallback TaskClassifier) *llmClassifier {
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return &llmClassifier{
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provider: prov,
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cache: newClassificationCache(),
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fallback: fallback,
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}
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}
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// IsTask 使用 LLM 判断输入是否为任务
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func (c *llmClassifier) IsTask(ctx context.Context, input string) (bool, error) {
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// 1. 检查缓存
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if cached, ok := c.cache.Get(input); ok {
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return cached, nil
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}
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// 2. 调用 LLM 进行分类（使用 2s 超时）
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ctx, cancel := context.WithTimeout(ctx, 2*time.Second)
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defer cancel()
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req := provider.Request{
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Messages: []provider.Message{
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{Role: provider.RoleSystem, Content: classificationSystemPrompt},
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{Role: provider.RoleUser, Content: "Classify: " + input},
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},
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MaxTokens: 10,
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Temperature: provider.TemperaturePtr(0),
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}
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ch, err := c.provider.Stream(ctx, req)
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if err != nil {
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// fallback to heuristic
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return c.fallback.IsTask(ctx, input)
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}
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var response strings.Builder
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for chunk := range ch {
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if chunk.Err != nil {
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// fallback to heuristic
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return c.fallback.IsTask(ctx, input)
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}
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response.WriteString(chunk.Text)
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}
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// 3. 解析响应
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result := strings.ToLower(strings.TrimSpace(response.String()))
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isTask := strings.Contains(result, "task")
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// 4. 缓存结果
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c.cache.Set(input, isTask)
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return isTask, nil
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}
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// newHeuristicClassifier 创建新的启发式分类器
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func newHeuristicClassifier() *heuristicClassifier {
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return &heuristicClassifier{}
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}
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// IsTask 使用启发式规则判断输入是否为任务
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func (h *heuristicClassifier) IsTask(ctx context.Context, input string) (bool, error) {
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trimmed := strings.TrimSpace(input)
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if trimmed == "" {
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return false, nil
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}
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normalized := strings.ToLower(strings.Trim(trimmed, " \t\r\n.!?。！？,，;；:："))
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// 1. 非常短的问候语白名单（1-3 个词）
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shortGreetings := []string{
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"hello", "hi", "hey", "你好", "您好", "nihao",
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"thanks", "thank you", "谢谢", "谢了",
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"ok", "okay", "好的", "嗯", "行",
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"got it", "i see", "明白", "了解", "收到", "我知道了", "先不用",
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}
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words := strings.Fields(normalized)
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if len(words) <= 3 {
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for _, greeting := range shortGreetings {
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if normalized == greeting {
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return false, nil
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}
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}
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}
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// 2. Polite acknowledgements can contain action words from the completed
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// task ("thanks for fixing") but should stay conversational.
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chatPhrases := []string{
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"thanks for", "thank you for", "i'll check later", "i will check later",
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"i'll test it later", "i will test it later", "that test was helpful", "the test was helpful",
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"谢谢你", "辛苦了",
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}
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for _, phrase := range chatPhrases {
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if strings.Contains(normalized, phrase) {
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return false, nil
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}
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}
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// 3. 文件引用检测（任务的强信号）
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if strings.Contains(trimmed, "@") || strings.Contains(trimmed, ".go") ||
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strings.Contains(trimmed, ".js") || strings.Contains(trimmed, ".py") ||
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strings.Contains(trimmed, ".ts") {
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return true, nil
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}
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// 4. Failure/help descriptions are actionable even when phrased without an
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// imperative verb, e.g. "the auth isn't working".
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taskPhrases := []string{
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"not working", "isn't working", "doesn't work", "dont work", "don't work",
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"can you help", "help with", "broken", "error", "bug", "issue", "failed", "failing", "crash", "cannot", "can't",
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"问题", "不工作", "无法", "不能", "报错", "错误", "失败", "崩溃", "异常",
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"卡住", "卡住了", "没反应", "不生效", "异常退出",
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}
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for _, phrase := range taskPhrases {
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if strings.Contains(normalized, phrase) {
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return true, nil
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}
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}
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// 5. 动作关键词检测
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actionNeedles := []string{
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"fix", "debug", "repair", "resolve", "reproduce",
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"create", "add", "write", "edit", "update", "change", "delete", "remove", "rename",
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"review", "inspect", "analyze", "check", "test", "run", "build", "implement", "refactor",
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"continue work", "continue the", "continue this",
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"修复", "调试", "解决", "复现", "创建", "新建", "添加", "编写", "编辑", "修改", "更新",
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"删除", "移除", "重命名", "评审", "检查", "分析", "测试", "运行", "构建", "实现", "重构", "继续处理",
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"看看", "看下", "帮我看", "帮我看下", "处理下", "处理一下", "排查", "定位",
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}
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for _, needle := range actionNeedles {
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if containsTaskNeedle(normalized, needle) {
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return true, nil
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}
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}
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// 默认：不确定时，根据长度判断
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// 理由：false negative（任务→聊天）比 false positive（聊天→任务）更严重
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// 短的模糊输入 → 聊天；长的输入 → 任务
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return len(words) > 5, nil
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}
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func containsTaskNeedle(input, needle string) bool {
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if needle == "" {
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return false
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}
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if containsNonASCII(needle) || strings.Contains(needle, " ") {
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return strings.Contains(input, needle)
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}
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for _, word := range strings.FieldsFunc(input, func(r rune) bool {
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return !(r >= 'a' && r <= 'z') && !(r >= '0' && r <= '9') && r != '_'
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}) {
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if word == needle {
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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 containsNonASCII(s string) bool {
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for _, r := range s {
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if r > 127 {
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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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// newClassificationCache 创建新的分类缓存
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func newClassificationCache() *classificationCache {
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return &classificationCache{
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entries: make(map[string]cacheEntry),
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maxSize: defaultCacheMaxSize,
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ttl: defaultCacheTTL,
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}
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}
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// Get 从缓存中获取分类结果
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func (c *classificationCache) Get(input string) (bool, bool) {
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c.mu.RLock()
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defer c.mu.RUnlock()
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key := normalizeInputForCache(input)
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entry, ok := c.entries[key]
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if !ok {
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return false, false
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}
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// 检查 TTL
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if time.Since(entry.timestamp) > c.ttl {
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return false, false
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}
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return entry.isTask, true
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}
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// Set 将分类结果存入缓存
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func (c *classificationCache) Set(input string, isTask bool) {
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c.mu.Lock()
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defer c.mu.Unlock()
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key := normalizeInputForCache(input)
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// LRU 淘汰：如果缓存满了，清除最老的 20% 条目
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if len(c.entries) >= c.maxSize {
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c.evictOldest()
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}
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c.entries[key] = cacheEntry{
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isTask: isTask,
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timestamp: time.Now(),
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}
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}
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// Clear 清除所有缓存
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func (c *classificationCache) Clear() {
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c.mu.Lock()
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defer c.mu.Unlock()
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c.entries = make(map[string]cacheEntry)
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}
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// evictOldest 淘汰最老的 20% 条目（LRU）
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func (c *classificationCache) evictOldest() {
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// 找出所有条目按时间排序
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type entry struct {
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key string
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timestamp time.Time
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}
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var entries []entry
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for k, v := range c.entries {
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entries = append(entries, entry{key: k, timestamp: v.timestamp})
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}
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// 如果条目少，不淘汰
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if len(entries) == 0 {
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return
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}
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// 按时间排序
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for i := 0; i < len(entries)-1; i++ {
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for j := i + 1; j < len(entries); j++ {
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if entries[i].timestamp.After(entries[j].timestamp) {
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entries[i], entries[j] = entries[j], entries[i]
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}
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}
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}
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// 删除最老的 20%
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toDelete := len(entries) / 5
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if toDelete == 0 {
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toDelete = 1
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}
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for i := 0; i < toDelete && i < len(entries); i++ {
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delete(c.entries, entries[i].key)
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}
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}
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// normalizeInputForCache 规范化输入用于缓存键
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func normalizeInputForCache(input string) string {
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normalized := strings.ToLower(strings.TrimSpace(input))
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// 折叠内部空白，保留结构
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normalized = strings.Join(strings.Fields(normalized), " ")
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// 使用 SHA256 hash 作为键，避免超长键
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hash := sha256.Sum256([]byte(normalized))
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return hex.EncodeToString(hash[:])
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}
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