64b946816f
后端： 1.Plan节点实现两阶段LLM调用：Phase1无thinking快速评估复杂度，复杂任务自动开启Phase2深度规划 2.Execute节点新增GoalCheck自省机制：LLM输出next_plan/done时必须附带对照done_when的完成验证，为空则追加修正重试 前端：无 仓库：无
259 lines
8.8 KiB
Go
259 lines
8.8 KiB
Go
package newagentnode
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import (
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"context"
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"fmt"
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"strings"
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"time"
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"github.com/google/uuid"
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newagentllm "github.com/LoveLosita/smartflow/backend/newAgent/llm"
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newagentmodel "github.com/LoveLosita/smartflow/backend/newAgent/model"
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newagentprompt "github.com/LoveLosita/smartflow/backend/newAgent/prompt"
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newagentstream "github.com/LoveLosita/smartflow/backend/newAgent/stream"
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"github.com/cloudwego/eino/schema"
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)
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const (
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planStageName = "plan"
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planStatusBlockID = "plan.status"
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planSpeakBlockID = "plan.speak"
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planPinnedKey = "current_plan"
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planCurrentStepKey = "current_step"
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planCurrentStepTitle = "当前步骤"
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planFullPlanTitle = "当前完整计划"
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)
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// PlanNodeInput 描述单轮规划节点执行所需的最小依赖。
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type PlanNodeInput struct {
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RuntimeState *newagentmodel.AgentRuntimeState
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ConversationContext *newagentmodel.ConversationContext
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UserInput string
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Client *newagentllm.Client
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ChunkEmitter *newagentstream.ChunkEmitter
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ResumeNode string
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}
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// RunPlanNode 执行一轮规划节点逻辑。
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//
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// 步骤说明：
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// 1. 先校验最小依赖，并推送一条”正在规划”的状态，避免用户空等；
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// 2. Phase 1（快速评估）：不开 thinking，让 LLM 同时产出复杂度评估和规划结果；
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// 3. Phase 2（深度规划）：若 LLM 自评需要深度思考且规划已完成，开 thinking 重跑；
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// 4. 若模型先对用户说了话，则先把 speak 伪流式推给前端，并写回 history；
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// 5. 最后按 action 推进流程：
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// 5.1 continue：继续停留在 planning；
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// 5.2 ask_user：打开 pending interaction，后续交给 interrupt 收口；
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// 5.3 plan_done：固化完整计划，刷新 pinned context，并进入 waiting_confirm。
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func RunPlanNode(ctx context.Context, input PlanNodeInput) error {
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runtimeState, conversationContext, emitter, err := preparePlanNodeInput(input)
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if err != nil {
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return err
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}
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flowState := runtimeState.EnsureCommonState()
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// 1. 先发一条阶段状态，让前端知道当前已经进入规划环节。
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if err := emitter.EmitStatus(
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planStatusBlockID,
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planStageName,
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"planning",
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"正在梳理目标并补全执行计划。",
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false,
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); err != nil {
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return fmt.Errorf("规划阶段状态推送失败: %w", err)
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}
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// 2. 构造本轮规划输入。
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messages := newagentprompt.BuildPlanMessages(flowState, conversationContext, input.UserInput)
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// 3. Phase 1：快速评估（不开 thinking），让 LLM 同时产出复杂度评估和规划结果。
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decision, rawResult, err := newagentllm.GenerateJSON[newagentmodel.PlanDecision](
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ctx,
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input.Client,
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messages,
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newagentllm.GenerateOptions{
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Temperature: 0.2,
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MaxTokens: 1600,
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Thinking: newagentllm.ThinkingModeDisabled,
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Metadata: map[string]any{
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"stage": planStageName,
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"phase": "assessment",
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},
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},
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)
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if err != nil {
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if rawResult != nil && strings.TrimSpace(rawResult.Text) != "" {
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return fmt.Errorf("规划评估解析失败，原始输出=%s，错误=%w", strings.TrimSpace(rawResult.Text), err)
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}
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return fmt.Errorf("规划评估阶段模型调用失败: %w", err)
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}
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if err := decision.Validate(); err != nil {
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return fmt.Errorf("规划评估决策不合法: %w", err)
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}
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// 4. Phase 2：若 LLM 自评需要深度思考且本轮规划已完成，则开启 thinking 重跑。
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// 条件：NeedThinking=true + Action=plan_done → 说明 LLM 认为当前无 thinking 的计划质量不够。
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// 其他 action（continue / ask_user）不需要 thinking，直接用 Phase 1 结果。
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if decision.NeedThinking && decision.Action == newagentmodel.PlanActionDone {
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if err := emitter.EmitStatus(
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planStatusBlockID,
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planStageName,
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"deep_planning",
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"正在深入思考，生成更完善的计划。",
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false,
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); err != nil {
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return fmt.Errorf("深度规划状态推送失败: %w", err)
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}
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deepDecision, _, deepErr := newagentllm.GenerateJSON[newagentmodel.PlanDecision](
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ctx,
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input.Client,
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messages,
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newagentllm.GenerateOptions{
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Temperature: 0.2,
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MaxTokens: 3200,
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Thinking: newagentllm.ThinkingModeEnabled,
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Metadata: map[string]any{
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"stage": planStageName,
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"phase": "deep_planning",
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},
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},
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)
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if deepErr == nil && deepDecision != nil {
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if validateErr := deepDecision.Validate(); validateErr == nil {
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decision = deepDecision
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}
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}
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// 深度规划失败时静默降级到 Phase 1 结果，不中断流程。
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}
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// 5. 若模型先对用户说了话，则先以伪流式推送，再写回 history，保证上下文连续。
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if strings.TrimSpace(decision.Speak) != "" {
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if err := emitter.EmitPseudoAssistantText(
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ctx,
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planSpeakBlockID,
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planStageName,
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decision.Speak,
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newagentstream.DefaultPseudoStreamOptions(),
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); err != nil {
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return fmt.Errorf("规划文案推送失败: %w", err)
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}
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conversationContext.AppendHistory(schema.AssistantMessage(decision.Speak, nil))
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}
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// 6. 按规划动作推进流程状态。
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switch decision.Action {
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case newagentmodel.PlanActionContinue:
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flowState.Phase = newagentmodel.PhasePlanning
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return nil
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case newagentmodel.PlanActionAskUser:
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question := resolvePlanAskUserText(decision)
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runtimeState.OpenAskUserInteraction(uuid.NewString(), question, strings.TrimSpace(input.ResumeNode))
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return nil
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case newagentmodel.PlanActionDone:
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// 4.1 直接把结构化 PlanStep 固化到 CommonState，避免 state 层丢失 done_when。
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// 4.2 再把完整自然语言计划写入 pinned context，保证后续 execute 优先看到。
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// 4.3 最后进入 waiting_confirm，等待用户确认整体计划。
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flowState.FinishPlan(decision.PlanSteps)
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writePlanPinnedBlocks(conversationContext, decision.PlanSteps)
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return nil
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default:
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// 1. LLM 输出了不支持的 action，不应直接报错终止，而应给它修正机会。
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// 2. 使用通用修正函数追加错误反馈，让 Graph 继续循环。
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// 3. LLM 下一轮会看到错误反馈并修正自己的输出。
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llmOutput := decision.Speak
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if strings.TrimSpace(llmOutput) == "" {
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llmOutput = decision.Reason
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}
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AppendLLMCorrectionWithHint(
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conversationContext,
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llmOutput,
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fmt.Sprintf("你输出的 action \"%s\" 不是合法的执行动作。", decision.Action),
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"合法的 action 包括：continue（继续当前步骤）、ask_user（追问用户）、next_plan（推进到下一步）、done（任务完成）。",
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)
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return nil
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}
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}
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func preparePlanNodeInput(input PlanNodeInput) (*newagentmodel.AgentRuntimeState, *newagentmodel.ConversationContext, *newagentstream.ChunkEmitter, error) {
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if input.RuntimeState == nil {
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return nil, nil, nil, fmt.Errorf("plan node: runtime state 不能为空")
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}
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if input.Client == nil {
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return nil, nil, nil, fmt.Errorf("plan node: plan client 未注入")
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}
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input.RuntimeState.EnsureCommonState()
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if input.ConversationContext == nil {
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input.ConversationContext = newagentmodel.NewConversationContext("")
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}
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if input.ChunkEmitter == nil {
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input.ChunkEmitter = newagentstream.NewChunkEmitter(newagentstream.NoopPayloadEmitter(), "", "", time.Now().Unix())
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}
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return input.RuntimeState, input.ConversationContext, input.ChunkEmitter, nil
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}
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func resolvePlanAskUserText(decision *newagentmodel.PlanDecision) string {
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if decision == nil {
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return "我还缺一点关键信息，想先向你确认一下。"
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}
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if strings.TrimSpace(decision.Speak) != "" {
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return strings.TrimSpace(decision.Speak)
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}
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if strings.TrimSpace(decision.Reason) != "" {
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return strings.TrimSpace(decision.Reason)
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}
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return "我还缺一点关键信息，想先向你确认一下。"
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}
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func writePlanPinnedBlocks(ctx *newagentmodel.ConversationContext, steps []newagentmodel.PlanStep) {
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if ctx == nil {
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return
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}
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fullPlanText := buildPinnedPlanText(steps)
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if strings.TrimSpace(fullPlanText) != "" {
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ctx.UpsertPinnedBlock(newagentmodel.ContextBlock{
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Key: planPinnedKey,
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Title: planFullPlanTitle,
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Content: fullPlanText,
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})
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}
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if len(steps) == 0 {
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return
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}
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firstStep := strings.TrimSpace(steps[0].Content)
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if strings.TrimSpace(steps[0].DoneWhen) != "" {
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firstStep = fmt.Sprintf("%s\n完成判定：%s", firstStep, strings.TrimSpace(steps[0].DoneWhen))
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}
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ctx.UpsertPinnedBlock(newagentmodel.ContextBlock{
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Key: planCurrentStepKey,
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Title: planCurrentStepTitle,
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Content: firstStep,
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})
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}
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func buildPinnedPlanText(steps []newagentmodel.PlanStep) string {
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if len(steps) == 0 {
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return ""
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}
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lines := make([]string, 0, len(steps))
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for i, step := range steps {
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content := strings.TrimSpace(step.Content)
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if content == "" {
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continue
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}
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line := fmt.Sprintf("%d. %s", i+1, content)
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if strings.TrimSpace(step.DoneWhen) != "" {
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line += fmt.Sprintf("\n完成判定：%s", strings.TrimSpace(step.DoneWhen))
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}
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lines = append(lines, line)
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}
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return strings.TrimSpace(strings.Join(lines, "\n\n"))
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}
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