mirror of
https://github.com/FloatTech/ZeroBot-Plugin.git
synced 2026-02-12 18:20:27 +00:00
feat(aichat): agent add memory
This commit is contained in:
源文雨
246
plugin/llm/main.go
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246
plugin/llm/main.go
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@@ -0,0 +1,246 @@
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// Package llm 大模型聊天和群聊总结
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package llm
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import (
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"strconv"
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"strings"
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"time"
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"github.com/fumiama/deepinfra"
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"github.com/fumiama/deepinfra/model"
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"github.com/tidwall/gjson"
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zero "github.com/wdvxdr1123/ZeroBot"
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"github.com/wdvxdr1123/ZeroBot/extension/single"
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"github.com/wdvxdr1123/ZeroBot/message"
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ctrl "github.com/FloatTech/zbpctrl"
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"github.com/FloatTech/zbputils/chat"
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"github.com/FloatTech/zbputils/control"
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"github.com/FloatTech/zbputils/ctxext"
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)
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var (
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// en data [8 temp] [8 rate] LSB
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en = control.AutoRegister(&ctrl.Options[*zero.Ctx]{
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DisableOnDefault: false,
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Brief: "大模型聊天和群聊总结",
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Help: "- 群聊总结 [消息数目]|群聊总结 1000\n" +
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"- /gpt [内容] （使用大模型聊天）\n",
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}).ApplySingle(single.New(
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single.WithKeyFn(func(ctx *zero.Ctx) int64 {
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if ctx.Event.GroupID == 0 {
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return -ctx.Event.UserID
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}
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return ctx.Event.GroupID
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}),
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// no post option, silently quit
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))
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)
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var (
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limit = ctxext.NewLimiterManager(time.Second*30, 1)
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)
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func init() {
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// 添加群聊总结功能
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en.OnRegex(`^群聊总结\s?(\d*)$`, chat.EnsureConfig, zero.OnlyGroup, zero.AdminPermission).SetBlock(true).Limit(limit.LimitByGroup).Handle(func(ctx *zero.Ctx) {
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ctx.SendChain(message.Text("少女思考中..."))
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gid := ctx.Event.GroupID
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if gid == 0 {
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gid = -ctx.Event.UserID
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}
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p, _ := strconv.ParseInt(ctx.State["regex_matched"].([]string)[1], 10, 64)
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if p > 1000 {
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p = 1000
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}
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if p == 0 {
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p = 200
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}
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group := ctx.GetGroupInfo(gid, false)
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if group.MemberCount == 0 {
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ctx.SendChain(message.Text(zero.BotConfig.NickName[0], "未加入", group.Name, "(", gid, "),无法获取总结"))
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return
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}
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var messages []string
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h := ctx.GetGroupMessageHistory(gid, 0, p, false)
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h.Get("messages").ForEach(func(_, msgObj gjson.Result) bool {
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nickname := msgObj.Get("sender.nickname").Str
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text := strings.TrimSpace(message.ParseMessageFromString(msgObj.Get("raw_message").Str).ExtractPlainText())
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if text != "" {
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messages = append(messages, nickname+": "+text)
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}
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return true
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})
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if len(messages) == 0 {
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ctx.SendChain(message.Text("ERROR: 历史消息为空或者无法获得历史消息"))
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return
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}
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// 构造总结请求提示 (使用通用版省流提示词)
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// 使用反引号定义多行字符串，更清晰
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promptTemplate := `请对以下群聊对话进行【极简总结】。
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要求：
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1. 剔除客套与废话，直击主题。
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2. 使用 Markdown 列表格式。
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3. 按以下结构输出：
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- 🎯 核心议题：(一句话概括)
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- 💡 关键观点/结论：(提取3-5个重点)
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- ✅ 下一步/待办：(如果有，明确谁做什么)
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群聊对话内容如下：
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`
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summaryPrompt := promptTemplate + strings.Join(messages, "\n")
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stor, err := chat.NewStorage(ctx, gid)
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if err != nil {
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ctx.SendChain(message.Text("ERROR: ", err))
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return
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}
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// 调用大模型API进行总结
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summary, err := llmchat(summaryPrompt, stor.Temp())
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if err != nil {
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ctx.SendChain(message.Text("ERROR: ", err))
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return
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}
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var b strings.Builder
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b.WriteString("群 ")
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b.WriteString(group.Name)
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b.WriteByte('(')
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b.WriteString(strconv.FormatInt(gid, 10))
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b.WriteString(") 的 ")
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b.WriteString(strconv.FormatInt(p, 10))
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b.WriteString(" 条消息总结:\n\n")
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b.WriteString(summary)
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// 分割总结内容为多段（按1000字符长度切割）
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summaryText := b.String()
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msg := make(message.Message, 0)
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for len(summaryText) > 0 {
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if len(summaryText) <= 1000 {
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msg = append(msg, ctxext.FakeSenderForwardNode(ctx, message.Text(summaryText)))
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break
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}
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// 查找1000字符内的最后一个换行符，尽量在换行处分割
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chunk := summaryText[:1000]
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lastNewline := strings.LastIndex(chunk, "\n")
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if lastNewline > 0 {
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chunk = summaryText[:lastNewline+1]
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}
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msg = append(msg, ctxext.FakeSenderForwardNode(ctx, message.Text(chunk)))
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summaryText = summaryText[len(chunk):]
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}
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if len(msg) > 0 {
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ctx.Send(msg)
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}
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})
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// 添加 /gpt 命令处理（同时支持回复消息和直接使用）
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en.OnKeyword("/gpt", chat.EnsureConfig).SetBlock(true).Handle(func(ctx *zero.Ctx) {
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gid := ctx.Event.GroupID
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if gid == 0 {
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gid = -ctx.Event.UserID
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}
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text := ctx.MessageString()
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var query string
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var replyContent string
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// 检查是否是回复消息 (使用MessageElement检查而不是CQ码)
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for _, elem := range ctx.Event.Message {
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if elem.Type == "reply" {
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// 提取被回复的消息ID
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replyIDStr := elem.Data["id"]
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replyID, err := strconv.ParseInt(replyIDStr, 10, 64)
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if err == nil {
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// 获取被回复的消息内容
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replyMsg := ctx.GetMessage(replyID)
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if replyMsg.Elements != nil {
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replyContent = replyMsg.Elements.ExtractPlainText()
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}
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}
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break // 找到回复元素后退出循环
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}
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}
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// 提取 /gpt 后面的内容
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parts := strings.SplitN(text, "/gpt", 2)
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var gContent string
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if len(parts) > 1 {
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gContent = strings.TrimSpace(parts[1])
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}
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// 组合内容：优先使用回复内容，如果同时有/gpt内容则拼接
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switch {
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case replyContent != "" && gContent != "":
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query = replyContent + "\n" + gContent
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case replyContent != "":
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query = replyContent
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case gContent != "":
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query = gContent
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default:
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return
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}
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stor, err := chat.NewStorage(ctx, gid)
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if err != nil {
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ctx.SendChain(message.Text("ERROR: ", err))
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return
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}
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// 调用大模型API进行聊天
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reply, err := llmchat(query, stor.Temp())
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if err != nil {
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ctx.SendChain(message.Text("ERROR: ", err))
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return
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}
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// 分割总结内容为多段（按1000字符长度切割）
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msg := make(message.Message, 0)
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for len(reply) > 0 {
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if len(reply) <= 1000 {
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msg = append(msg, ctxext.FakeSenderForwardNode(ctx, message.Text(reply)))
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break
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}
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// 查找1000字符内的最后一个换行符，尽量在换行处分割
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chunk := reply[:1000]
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lastNewline := strings.LastIndex(chunk, "\n")
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if lastNewline > 0 {
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chunk = reply[:lastNewline+1]
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}
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msg = append(msg, ctxext.FakeSenderForwardNode(ctx, message.Text(chunk)))
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reply = reply[len(chunk):]
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}
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if len(msg) > 0 {
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ctx.Send(msg)
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}
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})
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}
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// llmchat 调用大模型API包装
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func llmchat(prompt string, temp float32) (string, error) {
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topp, maxn := chat.AC.MParams()
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x := deepinfra.NewAPI(chat.AC.API, string(chat.AC.Key))
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mod, err := chat.AC.Type.Protocol(chat.AC.ModelName, temp, topp, maxn)
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if err != nil {
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return "", nil
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}
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data, err := x.Request(mod.User(model.NewContentText(prompt)))
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if err != nil {
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return "", err
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}
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return strings.TrimSpace(data), nil
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}
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