后端:
1.阶段 6 CP4/CP5 目录收口与共享边界纯化
- 将 backend 根目录收口为 services、client、gateway、cmd、shared 五个一级目录
- 收拢 bootstrap、inits、infra/kafka、infra/outbox、conv、respond、pkg、middleware,移除根目录旧实现与空目录
- 将 utils 下沉到 services/userauth/internal/auth,将 logic 下沉到 services/schedule/core/planning
- 将迁移期 runtime 桥接实现统一收拢到 services/runtime/{conv,dao,eventsvc,model},删除 shared/legacy 与未再被 import 的旧 service 实现
- 将 gateway/shared/respond 收口为 HTTP/Gin 错误写回适配,shared/respond 仅保留共享错误语义与状态映射
- 将 HTTP IdempotencyMiddleware 与 RateLimitMiddleware 收口到 gateway/middleware
- 将 GormCachePlugin 下沉到 shared/infra/gormcache,将共享 RateLimiter 下沉到 shared/infra/ratelimit,将 agent token budget 下沉到 services/agent/shared
- 删除 InitEino 兼容壳,收缩 cmd/internal/coreinit 仅保留旧组合壳残留域初始化语义
- 更新微服务迁移计划与桌面 checklist,补齐 CP4/CP5 当前切流点、目录终态与验证结果
- 完成 go test ./...、git diff --check 与最终真实 smoke;health、register/login、task/create+get、schedule/today、task-class/list、memory/items、agent chat/meta/timeline/context-stats 全部 200,SSE 合并结果为 CP5_OK 且 [DONE] 只有 1 个
302 lines
10 KiB
Go
302 lines
10 KiB
Go
package agentnode
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import (
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"context"
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"encoding/json"
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"fmt"
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"log"
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agentmodel "github.com/LoveLosita/smartflow/backend/services/agent/model"
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agentprompt "github.com/LoveLosita/smartflow/backend/services/agent/prompt"
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agentshared "github.com/LoveLosita/smartflow/backend/services/agent/shared"
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agentstream "github.com/LoveLosita/smartflow/backend/services/agent/stream"
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llmservice "github.com/LoveLosita/smartflow/backend/services/llm"
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"github.com/cloudwego/eino/schema"
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)
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// UnifiedCompactInput 是统一压缩入口的参数。
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//
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// 设计说明:
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// 1. 从 ExecuteNodeInput 中提取压缩所需的公共字段,消除对 Execute 的直接依赖;
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// 2. 各节点(Plan/Chat/Deliver)构造此参数时从自己的 NodeInput 中提取对应字段;
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// 3. StageName 和 StatusBlockID 用于区分日志来源和 SSE 状态推送。
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type UnifiedCompactInput struct {
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// Client 用于调用 LLM 压缩 msg1/msg2。
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Client *llmservice.Client
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// CompactionStore 用于持久化压缩摘要和 token 统计,为 nil 时跳过持久化。
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CompactionStore agentmodel.CompactionStore
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// FlowState 提供 userID / chatID / roundUsed 等定位信息。
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FlowState *agentmodel.CommonState
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// Emitter 用于推送压缩进度 SSE 事件。
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Emitter *agentstream.ChunkEmitter
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// StageName 标识当前阶段(如 "execute"/"plan"/"chat"/"deliver"),用于日志和缓存 key。
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StageName string
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// StatusBlockID 是 SSE 状态推送的 block ID,各节点使用自己的 block ID。
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StatusBlockID string
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}
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// compactUnifiedMessagesIfNeeded 检查统一消息结构的 token 预算,
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// 超限时对 msg1(历史对话)和 msg2(阶段工作区)执行 LLM 压缩。
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//
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// 消息布局约定(由 buildUnifiedStageMessages 返回):
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//
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// [0] system — msg0: 系统规则 + 工具简表
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// [1] assistant — msg1: 历史对话上下文
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// [2] assistant — msg2: 阶段工作区(Execute=ReAct Loop,其余="暂无")
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// [3] system — msg3: 阶段状态 + 记忆 + 指令
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//
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// 压缩策略:
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// 1. msg1 超过可用预算一半时触发 LLM 压缩(合并已有摘要 + 新内容);
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// 2. msg1 压缩后仍超限,则对 msg2 也做 LLM 压缩;
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// 3. 压缩结果持久化到 CompactionStore,下一轮可复用摘要避免重复计算。
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func compactUnifiedMessagesIfNeeded(
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ctx context.Context,
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messages []*schema.Message,
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input UnifiedCompactInput,
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) []*schema.Message {
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if input.FlowState == nil {
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log.Printf("[COMPACT:%s] FlowState is nil, skip token stats refresh", input.StageName)
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return messages
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}
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// 1. 非严格 4 段式时,退化成按角色汇总的统计,确保 context_token_stats 仍然刷新。
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if len(messages) != 4 {
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breakdown := estimateFallbackStageTokenBreakdown(messages)
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log.Printf(
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"[COMPACT:%s] fallback token stats refresh: total=%d budget=%d count=%d (msg0=%d msg1=%d msg2=%d msg3=%d)",
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input.StageName, breakdown.Total, breakdown.Budget, len(messages),
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breakdown.Msg0, breakdown.Msg1, breakdown.Msg2, breakdown.Msg3,
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)
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saveUnifiedTokenStats(ctx, input, breakdown)
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return messages
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}
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// 2. 提取四条消息的文本内容。
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msg0 := messages[0].Content
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msg1 := messages[1].Content
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msg2 := messages[2].Content
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msg3 := messages[3].Content
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// 3. Token 预算检查。
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breakdown, overBudget, needCompactMsg1, needCompactMsg2 := agentshared.CheckStageTokenBudget(msg0, msg1, msg2, msg3)
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log.Printf(
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"[COMPACT:%s] token budget check: total=%d budget=%d over=%v compactMsg1=%v compactMsg2=%v (msg0=%d msg1=%d msg2=%d msg3=%d)",
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input.StageName, breakdown.Total, breakdown.Budget, overBudget, needCompactMsg1, needCompactMsg2,
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breakdown.Msg0, breakdown.Msg1, breakdown.Msg2, breakdown.Msg3,
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)
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if !overBudget {
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// 4. 未超限,记录 token 分布后直接返回。
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saveUnifiedTokenStats(ctx, input, breakdown)
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return messages
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}
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// 5. msg1 压缩(历史对话 → LLM 摘要)。
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if needCompactMsg1 {
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msg1 = compactUnifiedMsg1(ctx, input, msg1)
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messages[1].Content = msg1
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// 压缩 msg1 后重算预算。
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breakdown = agentshared.EstimateStageMessagesTokens(msg0, msg1, msg2, msg3)
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}
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// 6. msg2 压缩(阶段工作区 → LLM 摘要)。
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if needCompactMsg2 || breakdown.Total > agentshared.StageTokenBudget {
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msg2 = compactUnifiedMsg2(ctx, input, msg2)
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messages[2].Content = msg2
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breakdown = agentshared.EstimateStageMessagesTokens(msg0, msg1, msg2, msg3)
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}
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// 7. 记录最终 token 分布。
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saveUnifiedTokenStats(ctx, input, breakdown)
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log.Printf(
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"[COMPACT:%s] after compaction: total=%d budget=%d (msg0=%d msg1=%d msg2=%d msg3=%d)",
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input.StageName, breakdown.Total, breakdown.Budget,
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breakdown.Msg0, breakdown.Msg1, breakdown.Msg2, breakdown.Msg3,
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)
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return messages
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}
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// estimateFallbackStageTokenBreakdown 在非统一 4 段式场景下按消息角色做近似统计。
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//
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// 步骤说明:
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// 1. 先按消息类型汇总 token,保证总量准确;
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// 2. 再把最后一个 user 消息尽量视作 msg3,保留阶段指令语义;
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// 3. 其他历史内容归入 msg1 / msg2,确保上下文统计不会因为结构不标准而断更。
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func estimateFallbackStageTokenBreakdown(messages []*schema.Message) agentshared.StageTokenBreakdown {
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breakdown := agentshared.StageTokenBreakdown{Budget: agentshared.StageTokenBudget}
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if len(messages) == 0 {
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return breakdown
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}
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lastUserIndex := -1
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for i := len(messages) - 1; i >= 0; i-- {
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msg := messages[i]
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if msg == nil {
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continue
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}
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if msg.Role == schema.User {
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lastUserIndex = i
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break
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}
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}
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for i, msg := range messages {
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if msg == nil {
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continue
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}
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tokens := agentshared.EstimateMessageTokens(msg)
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breakdown.Total += tokens
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switch msg.Role {
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case schema.System:
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breakdown.Msg0 += tokens
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case schema.User:
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if i == lastUserIndex {
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breakdown.Msg3 += tokens
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} else {
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breakdown.Msg1 += tokens
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}
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case schema.Tool:
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breakdown.Msg2 += tokens
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case schema.Assistant:
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if len(msg.ToolCalls) > 0 {
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breakdown.Msg2 += tokens
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} else {
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breakdown.Msg1 += tokens
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}
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default:
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breakdown.Msg1 += tokens
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}
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}
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return breakdown
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}
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// compactUnifiedMsg1 对 msg1(历史对话)执行 LLM 压缩。
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//
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// 步骤化说明:
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// 1. CompactionStore 为 nil 时跳过(测试环境 / 骨架期);
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// 2. 先加载该阶段已有的压缩摘要,与当前 msg1 合并后调 LLM 压缩;
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// 3. 压缩失败时降级为原始文本,不中断主流程;
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// 4. 压缩成功后持久化新摘要,供下一轮复用。
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func compactUnifiedMsg1(
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ctx context.Context,
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input UnifiedCompactInput,
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msg1 string,
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) string {
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// 1. CompactionStore 为 nil 时无法加载/保存摘要,跳过压缩。
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if input.CompactionStore == nil {
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log.Printf("[COMPACT:%s] CompactionStore is nil, skip msg1 compaction", input.StageName)
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return msg1
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}
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// 2. 加载该阶段已有的压缩摘要(可能为空)。
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existingSummary, _, err := input.CompactionStore.LoadStageCompaction(ctx, input.FlowState.UserID, input.FlowState.ConversationID, input.StageName)
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if err != nil {
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log.Printf("[COMPACT:%s] load existing compaction failed: %v, proceed without cache", input.StageName, err)
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}
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// 3. SSE: 压缩开始。
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tokenBefore := agentshared.EstimateTextTokens(msg1)
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_ = input.Emitter.EmitStatus(
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input.StatusBlockID, input.StageName, "context_compact_start",
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fmt.Sprintf("正在压缩对话历史(%d tokens)...", tokenBefore),
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false,
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)
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// 4. 调用 LLM 压缩:将 msg1 全文 + 已有摘要合并为一份紧凑摘要。
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newSummary, err := agentprompt.CompactMsg1(ctx, input.Client, msg1, existingSummary)
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if err != nil {
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log.Printf("[COMPACT:%s] compact msg1 failed: %v", input.StageName, err)
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_ = input.Emitter.EmitStatus(
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input.StatusBlockID, input.StageName, "context_compact_done",
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"对话历史压缩失败,使用原始文本",
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false,
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)
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return msg1
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}
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// 5. SSE: 压缩完成。
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tokenAfter := agentshared.EstimateTextTokens(newSummary)
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_ = input.Emitter.EmitStatus(
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input.StatusBlockID, input.StageName, "context_compact_done",
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fmt.Sprintf("对话历史已压缩:%d → %d tokens", tokenBefore, tokenAfter),
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false,
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)
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// 6. 持久化压缩结果,下一轮可直接复用摘要。
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if err := input.CompactionStore.SaveStageCompaction(ctx, input.FlowState.UserID, input.FlowState.ConversationID, input.StageName, newSummary, input.FlowState.RoundUsed); err != nil {
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log.Printf("[COMPACT:%s] save compaction failed: %v", input.StageName, err)
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}
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return newSummary
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}
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// compactUnifiedMsg2 对 msg2(阶段工作区)执行 LLM 压缩。
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//
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// 步骤化说明:
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// 1. 非 Execute 阶段的 msg2 通常是"暂无",压缩无意义但不会出错;
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// 2. Execute 阶段的 msg2 包含 ReAct loop 记录,压缩可显著节省 token;
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// 3. 压缩失败时降级为原始文本,不中断主流程。
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func compactUnifiedMsg2(
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ctx context.Context,
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input UnifiedCompactInput,
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msg2 string,
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) string {
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// 1. SSE: 压缩开始。
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tokenBefore := agentshared.EstimateTextTokens(msg2)
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_ = input.Emitter.EmitStatus(
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input.StatusBlockID, input.StageName, "context_compact_start",
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fmt.Sprintf("正在压缩执行记录(%d tokens)...", tokenBefore),
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false,
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)
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// 2. 调用 LLM 压缩。
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compressed, err := agentprompt.CompactMsg2(ctx, input.Client, msg2)
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if err != nil {
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log.Printf("[COMPACT:%s] compact msg2 failed: %v", input.StageName, err)
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_ = input.Emitter.EmitStatus(
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input.StatusBlockID, input.StageName, "context_compact_done",
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"执行记录压缩失败,使用原始文本",
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false,
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)
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return msg2
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}
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// 3. SSE: 压缩完成。
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tokenAfter := agentshared.EstimateTextTokens(compressed)
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_ = input.Emitter.EmitStatus(
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input.StatusBlockID, input.StageName, "context_compact_done",
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fmt.Sprintf("执行记录已压缩:%d → %d tokens", tokenBefore, tokenAfter),
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false,
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)
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return compressed
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}
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// saveUnifiedTokenStats 持久化当前 token 分布到 DB。
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//
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// 步骤化说明:
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// 1. CompactionStore 为 nil 时跳过(测试环境 / 骨架期);
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// 2. 序列化失败只记日志,不中断主流程;
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// 3. 写入失败只记日志,不中断主流程。
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func saveUnifiedTokenStats(
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ctx context.Context,
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input UnifiedCompactInput,
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breakdown agentshared.StageTokenBreakdown,
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) {
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if input.CompactionStore == nil || input.FlowState == nil {
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return
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}
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statsJSON, err := json.Marshal(breakdown)
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if err != nil {
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log.Printf("[COMPACT:%s] marshal token stats failed: %v", input.StageName, err)
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return
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}
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if err := input.CompactionStore.SaveContextTokenStats(ctx, input.FlowState.UserID, input.FlowState.ConversationID, string(statsJSON)); err != nil {
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log.Printf("[COMPACT:%s] save token stats failed: %v", input.StageName, err)
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}
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}
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