f81f137791
后端： 1. 流式思考链路从 raw reasoning_content 切到 `thinking_summary` 摘要协议，补齐摘要 prompt、digestor 与 Lite 压缩链路，plan / execute / fallback 统一改为“只出摘要、不透原始推理”，正文开始后自动关停摘要流。 2. thinking_summary 打通 timeline / SSE / outbox 持久化闭环，只落 detail_summary 与必要 metadata，并补强 seq 自检、冲突幂等识别与补 seq 回填，提升重放恢复稳定性。 3. 会话历史口径继续收紧，assistant 正文与时间线不再回写 raw reasoning_content，仅保留正文与思考耗时，避免刷新恢复时再次暴露内部推理文本。 前端： 4. 助手页开始接入 thinking_summary 实时流与历史恢复，补齐短摘要状态、长摘要折叠区、正文开流后自动收口，并增加调试入口用于协议联调与验收。 5. 当前前端助手页仍是残次过渡态，本版先以 thinking_summary 协议接通和基础渲染为主，样式、交互与细节体验暂未收平，下一版集中修复。 仓库： 6. 补充 thinking_summary 对接说明，明确 SSE 协议、timeline 恢复口径与 short/detail summary 的使用边界。
113 lines
3.3 KiB
Go
113 lines
3.3 KiB
Go
package agentsvc
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import (
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"context"
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"errors"
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"log"
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"strings"
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infrallm "github.com/LoveLosita/smartflow/backend/infra/llm"
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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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)
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const reasoningSummaryMaxTokens = 700
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type reasoningSummaryLLMResponse struct {
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ShortSummary string `json:"short_summary"`
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DetailSummary string `json:"detail_summary"`
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}
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// makeReasoningSummaryFunc 把便宜模型封装成 stream 层可注入的摘要函数。
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//
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// 职责边界：
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// 1. service 层负责选择模型与 prompt，stream 层只负责调度和闸门；
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// 2. 这里不持久化摘要，持久化统一走 ChunkEmitter 的 extra hook；
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// 3. 摘要失败时返回 error，由 ReasoningDigestor 吞掉并等待下一次水位线/Flush 兜底。
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func (s *AgentService) makeReasoningSummaryFunc(client *infrallm.Client) newagentstream.ReasoningSummaryFunc {
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if client == nil {
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return nil
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}
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return func(ctx context.Context, input newagentstream.ReasoningSummaryInput) (newagentstream.StreamThinkingSummaryExtra, error) {
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previousSummary := ""
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if input.PreviousSummary != nil {
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previousSummary = input.PreviousSummary.DetailSummary
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if strings.TrimSpace(previousSummary) == "" {
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previousSummary = input.PreviousSummary.ShortSummary
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}
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}
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messages := newagentprompt.BuildReasoningSummaryMessages(newagentprompt.ReasoningSummaryPromptInput{
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FullReasoning: input.FullReasoning,
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DeltaReasoning: input.DeltaReasoning,
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PreviousSummary: previousSummary,
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CandidateSeq: input.CandidateSeq,
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Final: input.Final,
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DurationSeconds: input.DurationSeconds,
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})
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resp, rawResult, err := infrallm.GenerateJSON[reasoningSummaryLLMResponse](
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ctx,
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client,
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messages,
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infrallm.GenerateOptions{
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Temperature: 0.1,
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MaxTokens: reasoningSummaryMaxTokens,
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Thinking: infrallm.ThinkingModeDisabled,
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Metadata: map[string]any{
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"stage": "reasoning_summary",
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"candidate_seq": input.CandidateSeq,
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"final": input.Final,
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},
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},
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)
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if err != nil {
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log.Printf("[WARN] reasoning 摘要模型调用失败 seq=%d final=%v err=%v raw=%s",
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input.CandidateSeq,
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input.Final,
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err,
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truncateReasoningSummaryRaw(rawResult),
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)
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return newagentstream.StreamThinkingSummaryExtra{}, err
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}
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summary := newagentstream.StreamThinkingSummaryExtra{
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ShortSummary: strings.TrimSpace(resp.ShortSummary),
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DetailSummary: limitReasoningDetailSummary(
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resp.DetailSummary,
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newagentprompt.ReasoningSummaryDetailRuneLimit(input.FullReasoning, input.DeltaReasoning),
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),
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}
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if summary.ShortSummary == "" && summary.DetailSummary == "" {
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return newagentstream.StreamThinkingSummaryExtra{}, errors.New("reasoning 摘要模型返回空摘要")
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}
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return summary, nil
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}
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}
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func limitReasoningDetailSummary(text string, maxRunes int) string {
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text = strings.TrimSpace(text)
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if text == "" || maxRunes <= 0 {
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return text
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}
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runes := []rune(text)
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if len(runes) <= maxRunes {
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return text
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}
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return string(runes[:maxRunes])
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}
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func truncateReasoningSummaryRaw(raw *infrallm.TextResult) string {
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if raw == nil {
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return ""
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}
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text := strings.TrimSpace(raw.Text)
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runes := []rune(text)
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if len(runes) <= 200 {
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return text
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}
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return string(runes[:200]) + "..."
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}
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