mirror of
https://github.com/Ed1s0nZ/CyberStrikeAI.git
synced 2026-03-31 16:20:28 +02:00
480 lines
14 KiB
Go
480 lines
14 KiB
Go
package agent
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import (
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"context"
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"errors"
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"fmt"
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"net/http"
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"strings"
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"sync"
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"time"
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"cyberstrike-ai/internal/config"
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"cyberstrike-ai/internal/openai"
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"github.com/pkoukk/tiktoken-go"
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"go.uber.org/zap"
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)
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const (
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// DefaultMinRecentMessage 压缩历史消息时保留的最近消息数量，确保最近的对话上下文不被压缩
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DefaultMinRecentMessage = 5
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// defaultChunkSize 压缩历史消息时每次处理的消息块大小，将旧消息分成多个块进行摘要
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defaultChunkSize = 10
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// defaultMaxImages 压缩时最多保留的图片数量，超过此数量的图片会被移除以节省上下文空间
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defaultMaxImages = 3
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// defaultSummaryTimeout 生成消息摘要时的超时时间
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defaultSummaryTimeout = 10 * time.Minute
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summaryPromptTemplate = `你是一名负责为安全代理执行上下文压缩的助手，任务是在保持所有关键渗透信息完整的前提下压缩扫描数据。
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必须保留的关键信息：
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- 已发现的漏洞与潜在攻击路径
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- 扫描结果与工具输出（可压缩，但需保留核心发现）
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- 获取到的访问凭证、令牌或认证细节
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- 系统架构洞察与潜在薄弱点
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- 当前评估进展
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- 失败尝试与死路（避免重复劳动）
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- 关于测试策略的所有决策记录
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压缩指南：
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- 保留精确技术细节（URL、路径、参数、Payload 等）
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- 将冗长的工具输出压缩成概述，但保留关键发现
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- 记录版本号与识别出的技术/组件信息
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- 保留可能暗示漏洞的原始报错
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- 将重复或相似发现整合成一条带有共性说明的结论
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请牢记：另一位安全代理会依赖这份摘要继续测试，他必须在不损失任何作战上下文的情况下无缝接手。
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需要压缩的对话片段：
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%s
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请给出技术精准且简明扼要的摘要，覆盖全部与安全评估相关的上下文。`
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)
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// MemoryCompressor 负责在调用LLM前压缩历史上下文，以避免Token爆炸。
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type MemoryCompressor struct {
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maxTotalTokens int
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minRecentMessage int
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maxImages int
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chunkSize int
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summaryModel string
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timeout time.Duration
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tokenCounter TokenCounter
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completionClient CompletionClient
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logger *zap.Logger
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}
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// MemoryCompressorConfig 用于初始化 MemoryCompressor。
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type MemoryCompressorConfig struct {
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MaxTotalTokens int
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MinRecentMessage int
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MaxImages int
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ChunkSize int
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SummaryModel string
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Timeout time.Duration
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TokenCounter TokenCounter
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CompletionClient CompletionClient
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Logger *zap.Logger
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// 当 CompletionClient 为空时，可以通过 OpenAIConfig + HTTPClient 构造默认的客户端。
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OpenAIConfig *config.OpenAIConfig
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HTTPClient *http.Client
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}
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// NewMemoryCompressor 创建新的 MemoryCompressor。
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func NewMemoryCompressor(cfg MemoryCompressorConfig) (*MemoryCompressor, error) {
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if cfg.Logger == nil {
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cfg.Logger = zap.NewNop()
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}
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// 如果没有显式配置 MaxTotalTokens，则后续逻辑会根据模型的最大上下文长度进行控制；
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// 优先推荐在 config.yaml 的 openai.max_total_tokens 中统一配置。
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if cfg.MinRecentMessage <= 0 {
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cfg.MinRecentMessage = DefaultMinRecentMessage
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}
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if cfg.MaxImages <= 0 {
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cfg.MaxImages = defaultMaxImages
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}
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if cfg.ChunkSize <= 0 {
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cfg.ChunkSize = defaultChunkSize
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}
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if cfg.Timeout <= 0 {
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cfg.Timeout = defaultSummaryTimeout
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}
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if cfg.SummaryModel == "" && cfg.OpenAIConfig != nil && cfg.OpenAIConfig.Model != "" {
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cfg.SummaryModel = cfg.OpenAIConfig.Model
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}
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if cfg.SummaryModel == "" {
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return nil, errors.New("summary model is required (either SummaryModel or OpenAIConfig.Model must be set)")
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}
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if cfg.TokenCounter == nil {
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cfg.TokenCounter = NewTikTokenCounter()
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}
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if cfg.CompletionClient == nil {
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if cfg.OpenAIConfig == nil {
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return nil, errors.New("memory compressor requires either CompletionClient or OpenAIConfig")
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}
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if cfg.HTTPClient == nil {
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cfg.HTTPClient = &http.Client{
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Timeout: 5 * time.Minute,
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}
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}
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cfg.CompletionClient = NewOpenAICompletionClient(cfg.OpenAIConfig, cfg.HTTPClient, cfg.Logger)
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}
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return &MemoryCompressor{
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maxTotalTokens: cfg.MaxTotalTokens,
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minRecentMessage: cfg.MinRecentMessage,
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maxImages: cfg.MaxImages,
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chunkSize: cfg.ChunkSize,
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summaryModel: cfg.SummaryModel,
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timeout: cfg.Timeout,
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tokenCounter: cfg.TokenCounter,
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completionClient: cfg.CompletionClient,
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logger: cfg.Logger,
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}, nil
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}
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// UpdateConfig 更新OpenAI配置（用于动态更新模型配置）
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func (mc *MemoryCompressor) UpdateConfig(cfg *config.OpenAIConfig) {
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if cfg == nil {
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return
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}
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// 更新summaryModel字段
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if cfg.Model != "" {
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mc.summaryModel = cfg.Model
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}
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// 更新completionClient中的配置（如果是OpenAICompletionClient）
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if openAIClient, ok := mc.completionClient.(*OpenAICompletionClient); ok {
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openAIClient.UpdateConfig(cfg)
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mc.logger.Info("MemoryCompressor配置已更新",
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zap.String("model", cfg.Model),
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)
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}
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}
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// CompressHistory 根据Token限制压缩历史消息。
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func (mc *MemoryCompressor) CompressHistory(ctx context.Context, messages []ChatMessage) ([]ChatMessage, bool, error) {
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if len(messages) == 0 {
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return messages, false, nil
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}
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mc.handleImages(messages)
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systemMsgs, regularMsgs := mc.splitMessages(messages)
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if len(regularMsgs) <= mc.minRecentMessage {
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return messages, false, nil
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}
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totalTokens := mc.countTotalTokens(systemMsgs, regularMsgs)
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if totalTokens <= int(float64(mc.maxTotalTokens)*0.9) {
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return messages, false, nil
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}
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recentStart := len(regularMsgs) - mc.minRecentMessage
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recentStart = mc.adjustRecentStartForToolCalls(regularMsgs, recentStart)
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oldMsgs := regularMsgs[:recentStart]
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recentMsgs := regularMsgs[recentStart:]
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mc.logger.Info("memory compression triggered",
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zap.Int("total_tokens", totalTokens),
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zap.Int("max_total_tokens", mc.maxTotalTokens),
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zap.Int("system_messages", len(systemMsgs)),
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zap.Int("regular_messages", len(regularMsgs)),
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zap.Int("old_messages", len(oldMsgs)),
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zap.Int("recent_messages", len(recentMsgs)))
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var compressed []ChatMessage
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for i := 0; i < len(oldMsgs); i += mc.chunkSize {
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end := i + mc.chunkSize
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if end > len(oldMsgs) {
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end = len(oldMsgs)
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}
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chunk := oldMsgs[i:end]
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if len(chunk) == 0 {
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continue
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}
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summary, err := mc.summarizeChunk(ctx, chunk)
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if err != nil {
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mc.logger.Warn("chunk summary failed, fallback to raw chunk",
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zap.Error(err),
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zap.Int("start", i),
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zap.Int("end", end))
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compressed = append(compressed, chunk...)
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continue
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}
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compressed = append(compressed, summary)
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}
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finalMessages := make([]ChatMessage, 0, len(systemMsgs)+len(compressed)+len(recentMsgs))
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finalMessages = append(finalMessages, systemMsgs...)
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finalMessages = append(finalMessages, compressed...)
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finalMessages = append(finalMessages, recentMsgs...)
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return finalMessages, true, nil
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}
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func (mc *MemoryCompressor) handleImages(messages []ChatMessage) {
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if mc.maxImages <= 0 {
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return
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}
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count := 0
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for i := len(messages) - 1; i >= 0; i-- {
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content := messages[i].Content
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if !strings.Contains(content, "[IMAGE]") {
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continue
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}
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count++
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if count > mc.maxImages {
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messages[i].Content = "[Previously attached image removed to preserve context]"
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}
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}
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}
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func (mc *MemoryCompressor) splitMessages(messages []ChatMessage) (systemMsgs, regularMsgs []ChatMessage) {
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for _, msg := range messages {
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if strings.EqualFold(msg.Role, "system") {
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systemMsgs = append(systemMsgs, msg)
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} else {
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regularMsgs = append(regularMsgs, msg)
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}
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}
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return
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}
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func (mc *MemoryCompressor) countTotalTokens(systemMsgs, regularMsgs []ChatMessage) int {
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total := 0
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for _, msg := range systemMsgs {
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total += mc.countTokens(msg.Content)
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}
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for _, msg := range regularMsgs {
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total += mc.countTokens(msg.Content)
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}
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return total
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}
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// getModelName 获取当前使用的模型名称（优先从completionClient获取最新配置）
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func (mc *MemoryCompressor) getModelName() string {
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// 如果completionClient是OpenAICompletionClient，从它获取最新的模型名称
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if openAIClient, ok := mc.completionClient.(*OpenAICompletionClient); ok {
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if openAIClient.config != nil && openAIClient.config.Model != "" {
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return openAIClient.config.Model
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}
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}
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// 否则使用保存的summaryModel
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return mc.summaryModel
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}
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func (mc *MemoryCompressor) countTokens(text string) int {
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if mc.tokenCounter == nil {
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return len(text) / 4
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}
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modelName := mc.getModelName()
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count, err := mc.tokenCounter.Count(modelName, text)
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if err != nil {
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return len(text) / 4
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}
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return count
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}
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// totalTokensFor provides token statistics without mutating the message list.
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func (mc *MemoryCompressor) totalTokensFor(messages []ChatMessage) (totalTokens int, systemCount int, regularCount int) {
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if len(messages) == 0 {
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return 0, 0, 0
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}
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systemMsgs, regularMsgs := mc.splitMessages(messages)
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return mc.countTotalTokens(systemMsgs, regularMsgs), len(systemMsgs), len(regularMsgs)
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}
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func (mc *MemoryCompressor) summarizeChunk(ctx context.Context, chunk []ChatMessage) (ChatMessage, error) {
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if len(chunk) == 0 {
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return ChatMessage{}, errors.New("chunk is empty")
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}
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formatted := make([]string, 0, len(chunk))
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for _, msg := range chunk {
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formatted = append(formatted, fmt.Sprintf("%s: %s", msg.Role, mc.extractMessageText(msg)))
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}
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conversation := strings.Join(formatted, "\n")
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prompt := fmt.Sprintf(summaryPromptTemplate, conversation)
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// 使用动态获取的模型名称，而不是保存的summaryModel
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modelName := mc.getModelName()
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summary, err := mc.completionClient.Complete(ctx, modelName, prompt, mc.timeout)
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if err != nil {
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return ChatMessage{}, err
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}
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summary = strings.TrimSpace(summary)
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if summary == "" {
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return chunk[0], nil
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}
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return ChatMessage{
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Role: "assistant",
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Content: fmt.Sprintf("<context_summary message_count='%d'>%s</context_summary>", len(chunk), summary),
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}, nil
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}
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func (mc *MemoryCompressor) extractMessageText(msg ChatMessage) string {
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return msg.Content
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}
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func (mc *MemoryCompressor) adjustRecentStartForToolCalls(msgs []ChatMessage, recentStart int) int {
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if recentStart <= 0 || recentStart >= len(msgs) {
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return recentStart
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}
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adjusted := recentStart
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for adjusted > 0 && strings.EqualFold(msgs[adjusted].Role, "tool") {
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adjusted--
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}
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if adjusted != recentStart {
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mc.logger.Debug("adjusted recent window to keep tool call context",
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zap.Int("original_recent_start", recentStart),
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zap.Int("adjusted_recent_start", adjusted),
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)
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}
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return adjusted
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}
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// TokenCounter 用于计算文本Token数量。
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type TokenCounter interface {
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Count(model, text string) (int, error)
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}
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// TikTokenCounter 基于 tiktoken 的 Token 统计器。
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type TikTokenCounter struct {
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mu sync.RWMutex
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cache map[string]*tiktoken.Tiktoken
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fallbackEncoding *tiktoken.Tiktoken
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}
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// NewTikTokenCounter 创建新的 TikTokenCounter。
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func NewTikTokenCounter() *TikTokenCounter {
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return &TikTokenCounter{
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cache: make(map[string]*tiktoken.Tiktoken),
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}
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}
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// Count 实现 TokenCounter 接口。
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func (tc *TikTokenCounter) Count(model, text string) (int, error) {
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enc, err := tc.encodingForModel(model)
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if err != nil {
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return len(text) / 4, err
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}
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tokens := enc.Encode(text, nil, nil)
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return len(tokens), nil
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}
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func (tc *TikTokenCounter) encodingForModel(model string) (*tiktoken.Tiktoken, error) {
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tc.mu.RLock()
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if enc, ok := tc.cache[model]; ok {
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tc.mu.RUnlock()
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return enc, nil
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}
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tc.mu.RUnlock()
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tc.mu.Lock()
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defer tc.mu.Unlock()
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if enc, ok := tc.cache[model]; ok {
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return enc, nil
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}
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enc, err := tiktoken.EncodingForModel(model)
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if err != nil {
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if tc.fallbackEncoding == nil {
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tc.fallbackEncoding, err = tiktoken.GetEncoding("cl100k_base")
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if err != nil {
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return nil, err
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}
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}
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tc.cache[model] = tc.fallbackEncoding
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return tc.fallbackEncoding, nil
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}
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tc.cache[model] = enc
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return enc, nil
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}
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// CompletionClient 对话压缩时使用的补全接口。
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type CompletionClient interface {
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Complete(ctx context.Context, model string, prompt string, timeout time.Duration) (string, error)
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}
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// OpenAICompletionClient 基于 OpenAI Chat Completion。
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type OpenAICompletionClient struct {
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config *config.OpenAIConfig
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client *openai.Client
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logger *zap.Logger
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}
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// NewOpenAICompletionClient 创建 OpenAICompletionClient。
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func NewOpenAICompletionClient(cfg *config.OpenAIConfig, client *http.Client, logger *zap.Logger) *OpenAICompletionClient {
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if logger == nil {
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logger = zap.NewNop()
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}
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return &OpenAICompletionClient{
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config: cfg,
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client: openai.NewClient(cfg, client, logger),
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logger: logger,
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}
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}
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// UpdateConfig 更新底层配置。
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func (c *OpenAICompletionClient) UpdateConfig(cfg *config.OpenAIConfig) {
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c.config = cfg
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if c.client != nil {
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c.client.UpdateConfig(cfg)
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}
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}
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// Complete 调用OpenAI获取摘要。
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func (c *OpenAICompletionClient) Complete(ctx context.Context, model string, prompt string, timeout time.Duration) (string, error) {
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if c.config == nil {
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return "", errors.New("openai config is required")
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}
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if model == "" {
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return "", errors.New("model name is required")
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}
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reqBody := OpenAIRequest{
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Model: model,
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Messages: []ChatMessage{
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{Role: "user", Content: prompt},
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},
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}
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requestCtx := ctx
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var cancel context.CancelFunc
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if timeout > 0 {
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requestCtx, cancel = context.WithTimeout(ctx, timeout)
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defer cancel()
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}
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var completion OpenAIResponse
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if c.client == nil {
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return "", errors.New("openai completion client not initialized")
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}
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if err := c.client.ChatCompletion(requestCtx, reqBody, &completion); err != nil {
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if apiErr, ok := err.(*openai.APIError); ok {
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return "", fmt.Errorf("openai completion failed, status: %d, body: %s", apiErr.StatusCode, apiErr.Body)
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}
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return "", err
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}
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if completion.Error != nil {
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return "", errors.New(completion.Error.Message)
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}
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if len(completion.Choices) == 0 || completion.Choices[0].Message.Content == "" {
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return "", errors.New("empty completion response")
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}
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return completion.Choices[0].Message.Content, nil
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}
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