perf: variabel replace;Feat: prompt optimizer code (#5453)

* feat: add prompt optimizer (#5444) * feat: add prompt optimizer * fix * perf: variabel replace * perf: prompt optimizer code * feat: init charts shell * perf: user error remove --------- Co-authored-by: heheer <heheer@sealos.io>
2025-10-14 15:11:13 +00:00 · 2025-08-14 15:48:22 +08:00
parent 6a02d2a2e5
commit 9fbfabac61
35 changed files with 1968 additions and 202 deletions
--- a/projects/app/src/components/common/PromptEditor/OptimizerPopover/index.tsx
+++ b/projects/app/src/components/common/PromptEditor/OptimizerPopover/index.tsx
@@ -0,0 +1,326 @@
+import { useMemo, useRef, useState } from 'react';
+import type { FlexProps } from '@chakra-ui/react';
+import { Box, Button, Flex, Textarea, useDisclosure } from '@chakra-ui/react';
+import { HUGGING_FACE_ICON } from '@fastgpt/global/common/system/constants';
+import Avatar from '@fastgpt/web/components/common/Avatar';
+import MyPopover from '@fastgpt/web/components/common/MyPopover';
+import MyIcon from '@fastgpt/web/components/common/Icon';
+import MyModal from '@fastgpt/web/components/common/MyModal';
+import { useTranslation } from 'next-i18next';
+import { useRequest2 } from '@fastgpt/web/hooks/useRequest';
+import { useLocalStorageState } from 'ahooks';
+import AIModelSelector from '../../../Select/AIModelSelector';
+import { useSystemStore } from '@/web/common/system/useSystemStore';
+import { onOptimizePrompt } from '@/web/common/api/fetch';
+
+export type OptimizerPromptProps = {
+  onChangeText: (text: string) => void;
+  defaultPrompt?: string;
+};
+
+export type OnOptimizePromptProps = {
+  originalPrompt?: string;
+  input: string;
+  model: string;
+  onResult: (result: string) => void;
+  abortController?: AbortController;
+};
+
+const OptimizerPopover = ({
+  onChangeText,
+  iconButtonStyle,
+  defaultPrompt
+}: OptimizerPromptProps & {
+  iconButtonStyle?: FlexProps;
+}) => {
+  const { t } = useTranslation();
+  const { llmModelList, defaultModels } = useSystemStore();
+
+  const [optimizerInput, setOptimizerInput] = useState('');
+  const [optimizedResult, setOptimizedResult] = useState('');
+  const [selectedModel = '', setSelectedModel] = useLocalStorageState<string>(
+    'prompt-editor-selected-model',
+    {
+      defaultValue: defaultModels.llm?.model || ''
+    }
+  );
+
+  const [abortController, setAbortController] = useState<AbortController | null>(null);
+  const { isOpen: isConfirmOpen, onOpen: onOpenConfirm, onClose: onCloseConfirm } = useDisclosure();
+
+  const closePopoverRef = useRef<() => void>();
+
+  const modelOptions = useMemo(() => {
+    return llmModelList.map((model) => {
+      // const provider = getModelProvider(model.model)
+      return {
+        label: (
+          <Flex alignItems={'center'}>
+            <Avatar
+              src={model.avatar || HUGGING_FACE_ICON}
+              fallbackSrc={HUGGING_FACE_ICON}
+              mr={1.5}
+              w={5}
+            />
+            <Box fontWeight={'normal'} fontSize={'14px'} color={'myGray.900'}>
+              {model.name}
+            </Box>
+          </Flex>
+        ),
+        value: model.model
+      };
+    });
+  }, [llmModelList]);
+
+  const isEmptyOptimizerInput = useMemo(() => {
+    return !optimizerInput.trim();
+  }, [optimizerInput]);
+
+  const { runAsync: handleSendOptimization, loading } = useRequest2(async (isAuto?: boolean) => {
+    if (isEmptyOptimizerInput && !isAuto) return;
+
+    setOptimizedResult('');
+    setOptimizerInput('');
+    const controller = new AbortController();
+    setAbortController(controller);
+
+    await onOptimizePrompt({
+      originalPrompt: defaultPrompt,
+      input: optimizerInput,
+      model: selectedModel,
+      onResult: (result: string) => {
+        if (!controller.signal.aborted) {
+          setOptimizedResult((prev) => prev + result);
+        }
+      },
+      abortController: controller
+    });
+
+    setAbortController(null);
+  });
+
+  const handleStopRequest = () => {
+    if (abortController) {
+      abortController.abort();
+      setAbortController(null);
+    }
+  };
+
+  const handleKeyDown = (e: React.KeyboardEvent) => {
+    if (e.key === 'Enter' && !e.shiftKey && !e.nativeEvent.isComposing) {
+      e.preventDefault();
+      if (!loading) {
+        handleSendOptimization();
+      }
+    }
+  };
+
+  return (
+    <>
+      <MyPopover
+        Trigger={
+          <Flex {...iconButtonStyle}>
+            <MyIcon name={'optimizer'} w={'18px'} />
+          </Flex>
+        }
+        trigger="click"
+        placement={'auto'}
+        w="482px"
+        onBackdropClick={() => {
+          if (optimizedResult) {
+            onOpenConfirm();
+          } else {
+            closePopoverRef.current?.();
+          }
+        }}
+      >
+        {({ onClose }) => {
+          closePopoverRef.current = onClose;
+          return (
+            <Box p={optimizedResult ? 8 : 4}>
+              {/* Result */}
+              {optimizedResult && (
+                <Box
+                  px={'10px'}
+                  maxHeight={'300px'}
+                  overflowY={'auto'}
+                  fontSize={'14px'}
+                  color={'gray.700'}
+                  whiteSpace={'pre-wrap'}
+                  wordBreak={'break-word'}
+                  mb={4}
+                >
+                  {optimizedResult}
+                </Box>
+              )}
+              {/* Button */}
+              <Flex mb={3} alignItems={'center'} gap={3}>
+                {!loading && (
+                  <>
+                    {!optimizedResult && !!defaultPrompt && (
+                      <Button
+                        variant={'whiteBase'}
+                        size={'sm'}
+                        color={'myGray.600'}
+                        onClick={() => handleSendOptimization(true)}
+                      >
+                        {t('app:AutoOptimize')}
+                      </Button>
+                    )}
+                    {optimizedResult && (
+                      <>
+                        <Button
+                          variant={'primaryGhost'}
+                          size={'sm'}
+                          px={2}
+                          border={'0.5px solid'}
+                          color={'primary.600'}
+                          onClick={() => {
+                            onChangeText?.(optimizedResult);
+                            setOptimizedResult('');
+                            setOptimizerInput('');
+                            onClose();
+                          }}
+                        >
+                          {t('app:Optimizer_Replace')}
+                        </Button>
+                        <Button
+                          variant={'whiteBase'}
+                          size={'sm'}
+                          fontSize={'12px'}
+                          onClick={() => {
+                            setOptimizedResult('');
+                            handleSendOptimization();
+                          }}
+                        >
+                          {t('app:Optimizer_Reoptimize')}
+                        </Button>
+                      </>
+                    )}
+                  </>
+                )}
+
+                <Box flex={1} />
+                {modelOptions && modelOptions.length > 0 && (
+                  <AIModelSelector
+                    borderColor={'transparent'}
+                    _hover={{
+                      border: '1px solid',
+                      borderColor: 'primary.400'
+                    }}
+                    size={'sm'}
+                    value={selectedModel}
+                    list={modelOptions}
+                    onChange={setSelectedModel}
+                  />
+                )}
+              </Flex>
+
+              {/* Input */}
+              <Flex
+                alignItems={'center'}
+                gap={2}
+                border={'1px solid'}
+                borderColor={'gray.200'}
+                borderRadius={'md'}
+                p={2}
+                mb={3}
+                _focusWithin={{ borderColor: 'primary.600' }}
+              >
+                <MyIcon name={'optimizer'} alignSelf={'flex-start'} mt={0.5} w={5} />
+                <Textarea
+                  placeholder={t('app:Optimizer_Placeholder')}
+                  resize={'none'}
+                  rows={1}
+                  minHeight={'24px'}
+                  lineHeight={'24px'}
+                  maxHeight={'96px'}
+                  overflowY={'hidden'}
+                  border={'none'}
+                  _focus={{
+                    boxShadow: 'none'
+                  }}
+                  fontSize={'sm'}
+                  p={0}
+                  borderRadius={'none'}
+                  value={optimizerInput}
+                  autoFocus
+                  onKeyDown={handleKeyDown}
+                  isDisabled={loading}
+                  onChange={(e) => {
+                    const textarea = e.target;
+                    setOptimizerInput(e.target.value);
+
+                    textarea.style.height = '24px';
+                    const maxHeight = 96;
+                    const newHeight = Math.min(textarea.scrollHeight, maxHeight);
+                    textarea.style.height = `${newHeight}px`;
+
+                    if (textarea.scrollHeight > maxHeight) {
+                      textarea.style.overflowY = 'auto';
+                    } else {
+                      textarea.style.overflowY = 'hidden';
+                    }
+                  }}
+                  flex={1}
+                />
+                <MyIcon
+                  name={loading ? 'stop' : 'core/chat/sendLight'}
+                  w={'1rem'}
+                  alignSelf={'flex-end'}
+                  mb={1}
+                  color={loading || !isEmptyOptimizerInput ? 'primary.600' : 'gray.400'}
+                  cursor={loading || !isEmptyOptimizerInput ? 'pointer' : 'not-allowed'}
+                  onClick={() => {
+                    if (loading) {
+                      handleStopRequest();
+                    } else {
+                      void handleSendOptimization();
+                    }
+                  }}
+                />
+              </Flex>
+            </Box>
+          );
+        }}
+      </MyPopover>
+
+      <MyModal
+        isOpen={isConfirmOpen}
+        onClose={onCloseConfirm}
+        title={t('app:Optimizer_CloseConfirm')}
+        iconSrc={'common/confirm/deleteTip'}
+        size="md"
+        zIndex={2000}
+      >
+        <Box p={4}>
+          <Box fontSize={'sm'} color={'myGray.700'} mb={4}>
+            {t('app:Optimizer_CloseConfirmText')}
+          </Box>
+          <Flex justifyContent={'flex-end'} gap={3}>
+            <Button variant={'whiteBase'} onClick={onCloseConfirm}>
+              {t('common:Cancel')}
+            </Button>
+            <Button
+              variant={'dangerFill'}
+              onClick={() => {
+                setOptimizedResult('');
+                setOptimizerInput('');
+                if (abortController) {
+                  abortController.abort();
+                  setAbortController(null);
+                }
+                onCloseConfirm();
+                closePopoverRef.current?.();
+              }}
+            >
+              {t('app:Optimizer_CloseConfirm')}
+            </Button>
+          </Flex>
+        </Box>
+      </MyModal>
+    </>
+  );
+};
+
+export default OptimizerPopover;
--- a/projects/app/src/components/core/app/formRender/index.tsx
+++ b/projects/app/src/components/core/app/formRender/index.tsx
@@ -73,6 +73,7 @@ const InputRender = (props: InputRenderProps) => {
          maxLength={props.maxLength}
          minH={100}
          maxH={300}
+          ExtensionPopover={props.ExtensionPopover}
        />
      );
    }
--- a/projects/app/src/components/core/app/formRender/type.d.ts
+++ b/projects/app/src/components/core/app/formRender/type.d.ts
@@ -5,6 +5,7 @@ import type {
 import type { InputTypeEnum } from './constant';
 import type { UseFormReturn } from 'react-hook-form';
 import type { BoxProps } from '@chakra-ui/react';
+import type { EditorProps } from '@fastgpt/web/components/common/Textarea/PromptEditor/Editor';

 type CommonRenderProps = {
  placeholder?: string;
@@ -18,14 +19,16 @@ type CommonRenderProps = {
 } & Omit<BoxProps, 'onChange' | 'list' | 'value'>;

 type SpecificProps =
-  | {
+  | ({
      // input & textarea
      inputType: InputTypeEnum.input | InputTypeEnum.textarea;
      variables?: EditorVariablePickerType[];
      variableLabels?: EditorVariableLabelPickerType[];
      title?: string;
      maxLength?: number;
-    }
+    } & {
+      ExtensionPopover?: EditorProps['ExtensionPopover'];
+    })
  | {
      // numberInput
      inputType: InputTypeEnum.numberInput;
--- a/projects/app/src/pageComponents/app/detail/SimpleApp/EditForm.tsx
+++ b/projects/app/src/pageComponents/app/detail/SimpleApp/EditForm.tsx
@@ -1,4 +1,4 @@
-import React, { useEffect, useMemo, useTransition } from 'react';
+import React, { useCallback, useEffect, useMemo, useTransition } from 'react';
 import {
  Box,
  Flex,
@@ -31,6 +31,8 @@ import FormLabel from '@fastgpt/web/components/common/MyBox/FormLabel';
 import VariableTip from '@/components/common/Textarea/MyTextarea/VariableTip';
 import { getWebLLMModel } from '@/web/common/system/utils';
 import ToolSelect from './components/ToolSelect';
+import { useSystemStore } from '@/web/common/system/useSystemStore';
+import OptimizerPopover from '@/components/common/PromptEditor/OptimizerPopover';

 const DatasetSelectModal = dynamic(() => import('@/components/core/app/DatasetSelectModal'));
 const DatasetParamsModal = dynamic(() => import('@/components/core/app/DatasetParamsModal'));
@@ -69,6 +71,7 @@ const EditForm = ({
  const { appDetail } = useContextSelector(AppContext, (v) => v);
  const selectDatasets = useMemo(() => appForm?.dataset?.datasets, [appForm]);
  const [, startTst] = useTransition();
+  const { llmModelList, defaultModels } = useSystemStore();

  const {
    isOpen: isOpenDatasetSelect,
@@ -126,6 +129,27 @@ const EditForm = ({
    }
  }, [selectedModel, setAppForm]);

+  const OptimizerPopverComponent = useCallback(
+    ({ iconButtonStyle }: { iconButtonStyle: Record<string, any> }) => {
+      return (
+        <OptimizerPopover
+          iconButtonStyle={iconButtonStyle}
+          defaultPrompt={appForm.aiSettings.systemPrompt}
+          onChangeText={(e) => {
+            setAppForm((state) => ({
+              ...state,
+              aiSettings: {
+                ...state.aiSettings,
+                systemPrompt: e
+              }
+            }));
+          }}
+        />
+      );
+    },
+    [appForm.aiSettings.systemPrompt, setAppForm]
+  );
+
  return (
    <>
      <Box>
@@ -196,6 +220,7 @@ const EditForm = ({
                variables={formatVariables}
                placeholder={t('common:core.app.tip.systemPromptTip')}
                title={t('common:core.ai.Prompt')}
+                ExtensionPopover={[OptimizerPopverComponent]}
              />
            </Box>
          </Box>
--- a/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/render/RenderInput/templates/CommonInputForm.tsx
+++ b/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/render/RenderInput/templates/CommonInputForm.tsx
@@ -13,6 +13,8 @@ import { getEditorVariables } from '@/pageComponents/app/detail/WorkflowComponen
 import { InputTypeEnum } from '@/components/core/app/formRender/constant';
 import { llmModelTypeFilterMap } from '@fastgpt/global/core/ai/constants';
 import { getWebDefaultLLMModel } from '@/web/common/system/utils';
+import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
+import OptimizerPopover from '@/components/common/PromptEditor/OptimizerPopover';

 const CommonInputForm = ({ item, nodeId }: RenderInputProps) => {
  const { t } = useTranslation();
@@ -80,6 +82,22 @@ const CommonInputForm = ({ item, nodeId }: RenderInputProps) => {
    return item.value;
  }, [inputType, item.value, defaultModel, handleChange]);

+  const canOptimizePrompt = item.key === NodeInputKeyEnum.aiSystemPrompt;
+  const OptimizerPopverComponent = useCallback(
+    ({ iconButtonStyle }: { iconButtonStyle: Record<string, any> }) => {
+      return (
+        <OptimizerPopover
+          iconButtonStyle={iconButtonStyle}
+          defaultPrompt={item.value}
+          onChangeText={(e) => {
+            handleChange(e);
+          }}
+        />
+      );
+    },
+    [item.value, handleChange]
+  );
+
  return (
    <InputRender
      inputType={inputType}
@@ -93,6 +111,7 @@ const CommonInputForm = ({ item, nodeId }: RenderInputProps) => {
      max={item.max}
      list={item.list}
      modelList={modelList}
+      ExtensionPopover={canOptimizePrompt ? [OptimizerPopverComponent] : undefined}
    />
  );
 };
--- a/projects/app/src/pages/api/admin/initv4121.ts
+++ b/projects/app/src/pages/api/admin/initv4121.ts
@@ -0,0 +1,167 @@
+import type { ApiRequestProps, ApiResponseType } from '@fastgpt/service/type/next';
+import { NextAPI } from '@/service/middleware/entry';
+import { authCert } from '@fastgpt/service/support/permission/auth/common';
+import { MongoChat } from '@fastgpt/service/core/chat/chatSchema';
+import { MongoChatItem } from '@fastgpt/service/core/chat/chatItemSchema';
+import { MongoAppChatLog } from '@fastgpt/service/core/app/logs/chatLogsSchema';
+import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
+import { addLog } from '@fastgpt/service/common/system/log';
+import type { ChatSchemaType } from '@fastgpt/global/core/chat/type';
+
+export type SyncAppChatLogQuery = {};
+
+export type SyncAppChatLogBody = {
+  batchSize?: number;
+};
+
+export type SyncAppChatLogResponse = {};
+
+/* 
+    将 chats 表全部扫一遍，来获取统计数据
+*/
+async function handler(
+  req: ApiRequestProps<SyncAppChatLogBody, SyncAppChatLogQuery>,
+  res: ApiResponseType<SyncAppChatLogResponse>
+) {
+  await authCert({ req, authRoot: true });
+
+  const { batchSize = 10 } = req.body;
+
+  console.log('开始同步AppChatLog数据...');
+  console.log(`批处理大小: ${batchSize}`);
+
+  let success = 0;
+  const total = await MongoChat.countDocuments({});
+  console.log(`总共需要处理的chat记录数: ${total}`);
+
+  res.json({
+    data: '同步任务已开始，可在日志中看到进度'
+  });
+
+  while (true) {
+    console.log(`对话同步处理进度: ${success}/${total}`);
+
+    try {
+      const chats = await MongoChat.find({
+        initStatistics: { $exists: false }
+      })
+        .sort({ _id: -1 })
+        .limit(batchSize)
+        .lean();
+
+      if (chats.length === 0) break;
+
+      const result = await Promise.allSettled(chats.map((chat) => processChatRecord(chat)));
+      success += result.filter((r) => r.status === 'fulfilled').length;
+    } catch (error) {
+      addLog.error('处理chat记录失败', error);
+    }
+  }
+
+  console.log('同步对话完成');
+}
+
+async function processChatRecord(chat: ChatSchemaType) {
+  async function calculateChatItemStats(chatId: string) {
+    const chatItems = await MongoChatItem.find({ chatId }).lean();
+
+    let chatItemCount = chatItems.length;
+    let errorCount = 0;
+    let totalPoints = 0;
+    let goodFeedbackCount = 0;
+    let badFeedbackCount = 0;
+    let totalResponseTime = 0;
+
+    for (const item of chatItems) {
+      const itemData = item as any;
+
+      if (itemData.userGoodFeedback && itemData.userGoodFeedback.trim() !== '') {
+        goodFeedbackCount++;
+      }
+      if (itemData.userBadFeedback && itemData.userBadFeedback.trim() !== '') {
+        badFeedbackCount++;
+      }
+
+      if (itemData.durationSeconds) {
+        totalResponseTime += itemData.durationSeconds;
+      } else if (
+        itemData[DispatchNodeResponseKeyEnum.nodeResponse] &&
+        Array.isArray(itemData[DispatchNodeResponseKeyEnum.nodeResponse])
+      ) {
+        for (const response of itemData[DispatchNodeResponseKeyEnum.nodeResponse]) {
+          if (response.runningTime) {
+            totalResponseTime += response.runningTime / 1000;
+          }
+        }
+      }
+
+      if (
+        itemData[DispatchNodeResponseKeyEnum.nodeResponse] &&
+        Array.isArray(itemData[DispatchNodeResponseKeyEnum.nodeResponse])
+      ) {
+        for (const response of itemData[DispatchNodeResponseKeyEnum.nodeResponse]) {
+          if (response.errorText) {
+            errorCount++;
+            break;
+          }
+
+          if (response.totalPoints) {
+            totalPoints += response.totalPoints;
+          }
+        }
+      }
+    }
+
+    return {
+      chatItemCount,
+      errorCount,
+      totalPoints,
+      goodFeedbackCount,
+      badFeedbackCount,
+      totalResponseTime
+    };
+  }
+
+  async function checkIsFirstChat(chat: any): Promise<boolean> {
+    const earliestChat = await MongoChat.findOne(
+      {
+        userId: chat.userId,
+        appId: chat.appId
+      },
+      {},
+      { sort: { createTime: 1 } }
+    ).lean();
+
+    return earliestChat?._id.toString() === chat._id.toString();
+  }
+
+  const chatItemStats = await calculateChatItemStats(chat.chatId);
+  const isFirstChat = await checkIsFirstChat(chat);
+
+  const chatLogData = {
+    appId: chat.appId,
+    teamId: chat.teamId,
+    chatId: chat.chatId,
+    userId: String(chat.outLinkUid || chat.tmbId),
+    source: chat.source,
+    sourceName: chat.sourceName,
+    createTime: chat.createTime,
+    updateTime: chat.updateTime,
+    chatItemCount: chatItemStats.chatItemCount,
+    errorCount: chatItemStats.errorCount,
+    totalPoints: chatItemStats.totalPoints,
+    goodFeedbackCount: chatItemStats.goodFeedbackCount,
+    badFeedbackCount: chatItemStats.badFeedbackCount,
+    totalResponseTime: chatItemStats.totalResponseTime,
+    isFirstChat
+  };
+
+  await MongoAppChatLog.updateOne(
+    { appId: chat.appId, chatId: chat.chatId },
+    { $set: chatLogData },
+    { upsert: true }
+  );
+  await MongoChat.updateOne({ _id: chat._id }, { $set: { initStatistics: true } });
+}
+
+export default NextAPI(handler);
--- a/projects/app/src/pages/api/core/ai/optimizePrompt.ts
+++ b/projects/app/src/pages/api/core/ai/optimizePrompt.ts
@@ -0,0 +1,223 @@
+import type { ApiRequestProps, ApiResponseType } from '@fastgpt/service/type/next';
+import { NextAPI } from '@/service/middleware/entry';
+import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
+import { responseWrite } from '@fastgpt/service/common/response';
+import { sseErrRes } from '@fastgpt/service/common/response';
+import { createChatCompletion } from '@fastgpt/service/core/ai/config';
+import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
+import { authCert } from '@fastgpt/service/support/permission/auth/common';
+import { loadRequestMessages } from '@fastgpt/service/core/chat/utils';
+import { llmCompletionsBodyFormat, parseLLMStreamResponse } from '@fastgpt/service/core/ai/utils';
+import { countGptMessagesTokens } from '@fastgpt/service/common/string/tiktoken/index';
+import { formatModelChars2Points } from '@fastgpt/service/support/wallet/usage/utils';
+import { createUsage } from '@fastgpt/service/support/wallet/usage/controller';
+import { UsageSourceEnum } from '@fastgpt/global/support/wallet/usage/constants';
+import { ModelTypeEnum } from '@fastgpt/global/core/ai/model';
+import { i18nT } from '@fastgpt/web/i18n/utils';
+import { addLog } from '@fastgpt/service/common/system/log';
+
+type OptimizePromptBody = {
+  originalPrompt: string;
+  optimizerInput: string;
+  model: string;
+};
+
+const getPromptOptimizerSystemPrompt = () => {
+  return `# Role
+Prompt工程师
+
+## Skills
+- 了解LLM的技术原理和局限性，包括它的训练数据、构建方式等，以便更好地设计Prompt
+- 具有丰富的自然语言处理经验，能够设计出符合语法、语义的高质量Prompt
+- 迭代优化能力强，能通过不断调整和测试Prompt的表现，持续改进Prompt质量
+- 能结合具体业务需求设计Prompt，使LLM生成的内容符合业务要求
+- 擅长分析用户需求，设计结构清晰、逻辑严谨的Prompt框架
+
+## Goals
+- 分析用户的Prompt，理解其核心需求和意图
+- 设计一个结构清晰、符合逻辑的Prompt框架
+- 生成高质量的结构化Prompt
+- 提供针对性的优化建议
+
+## Constrains
+- 确保所有内容符合各个学科的最佳实践
+- 在任何情况下都不要跳出角色
+- 不要胡说八道和编造事实
+- 保持专业性和准确性
+- 输出必须包含优化建议部分
+
+## Suggestions
+- 深入分析用户原始Prompt的核心意图，避免表面理解
+- 采用结构化思维，确保各个部分逻辑清晰且相互呼应
+- 优先考虑实用性，生成的Prompt应该能够直接使用
+- 注重细节完善，每个部分都要有具体且有价值的内容
+- 保持专业水准，确保输出的Prompt符合行业最佳实践
+- **特别注意**：Suggestions部分应该专注于角色内在的工作方法，而不是与用户互动的策略`;
+};
+
+const getPromptOptimizerUserPrompt = (originalPrompt: string, optimizerInput: string) => {
+  return `请严格遵循用户的优化需求: 
+<OptimizerInput>
+${optimizerInput}
+</OptimizerInput>
+
+分析并优化以下Prompt，将其转化为结构化的高质量Prompt：
+<OriginalPrompt>
+${originalPrompt}
+</OriginalPrompt>
+
+## 注意事项：
+- 直接输出优化后的Prompt，不要添加解释性文字，不要用代码块包围
+- 每个部分都要有具体内容，不要使用占位符
+- **数量要求**：Skills、Goals、Constrains、Workflow、Suggestions各部分需要5个要点，OutputFormat需要3个要点
+- **Suggestions是给角色的内在工作方法论**，专注于角色自身的技能提升和工作优化方法，避免涉及与用户互动的建议
+- **必须包含完整结构**：确保包含Role、Background、Attention、Profile、Skills、Goals、Constrains、Workflow、OutputFormat、Suggestions、Initialization等所有部分
+- 保持内容的逻辑性和连贯性，各部分之间要相互呼应`;
+};
+
+async function handler(req: ApiRequestProps<OptimizePromptBody>, res: ApiResponseType) {
+  try {
+    const { originalPrompt, optimizerInput, model } = req.body;
+
+    const { teamId, tmbId } = await authCert({
+      req,
+      authToken: true,
+      authApiKey: true
+    });
+
+    res.setHeader('Content-Type', 'text/event-stream;charset=utf-8');
+    res.setHeader('X-Accel-Buffering', 'no');
+    res.setHeader('Cache-Control', 'no-cache, no-transform');
+
+    const messages: ChatCompletionMessageParam[] = [
+      {
+        role: 'system',
+        content: getPromptOptimizerSystemPrompt()
+      },
+      {
+        role: 'user',
+        content: getPromptOptimizerUserPrompt(originalPrompt, optimizerInput)
+      }
+    ];
+
+    const requestMessages = await loadRequestMessages({
+      messages,
+      useVision: false
+    });
+
+    const { response, isStreamResponse } = await createChatCompletion({
+      body: llmCompletionsBodyFormat(
+        {
+          model,
+          messages: requestMessages,
+          temperature: 0.1,
+          max_tokens: 2000,
+          stream: true
+        },
+        model
+      )
+    });
+
+    const { inputTokens, outputTokens } = await (async () => {
+      if (isStreamResponse) {
+        const { parsePart, getResponseData } = parseLLMStreamResponse();
+
+        let optimizedText = '';
+
+        for await (const part of response) {
+          const { responseContent } = parsePart({
+            part,
+            parseThinkTag: true,
+            retainDatasetCite: false
+          });
+
+          if (responseContent) {
+            optimizedText += responseContent;
+            responseWrite({
+              res,
+              event: SseResponseEventEnum.answer,
+              data: JSON.stringify({
+                choices: [
+                  {
+                    delta: {
+                      content: responseContent
+                    }
+                  }
+                ]
+              })
+            });
+          }
+        }
+
+        const { content: answer, usage } = getResponseData();
+        return {
+          content: answer,
+          inputTokens: usage?.prompt_tokens || (await countGptMessagesTokens(requestMessages)),
+          outputTokens:
+            usage?.completion_tokens ||
+            (await countGptMessagesTokens([{ role: 'assistant', content: optimizedText }]))
+        };
+      } else {
+        const usage = response.usage;
+        const content = response.choices?.[0]?.message?.content || '';
+
+        responseWrite({
+          res,
+          event: SseResponseEventEnum.answer,
+          data: JSON.stringify({
+            choices: [
+              {
+                delta: {
+                  content
+                }
+              }
+            ]
+          })
+        });
+
+        return {
+          content,
+          inputTokens: usage?.prompt_tokens || (await countGptMessagesTokens(requestMessages)),
+          outputTokens:
+            usage?.completion_tokens ||
+            (await countGptMessagesTokens([{ role: 'assistant', content: content }]))
+        };
+      }
+    })();
+    responseWrite({
+      res,
+      event: SseResponseEventEnum.answer,
+      data: '[DONE]'
+    });
+
+    const { totalPoints, modelName } = formatModelChars2Points({
+      model,
+      inputTokens,
+      outputTokens,
+      modelType: ModelTypeEnum.llm
+    });
+
+    createUsage({
+      teamId,
+      tmbId,
+      appName: i18nT('common:support.wallet.usage.Optimize Prompt'),
+      totalPoints,
+      source: UsageSourceEnum.optimize_prompt,
+      list: [
+        {
+          moduleName: i18nT('common:support.wallet.usage.Optimize Prompt'),
+          amount: totalPoints,
+          model: modelName,
+          inputTokens,
+          outputTokens
+        }
+      ]
+    });
+  } catch (error: any) {
+    addLog.error('Optimize prompt error', error);
+    sseErrRes(res, error);
+  }
+  res.end();
+}
+
+export default NextAPI(handler);
--- a/projects/app/src/service/support/permission/auth/chat.ts
+++ b/projects/app/src/service/support/permission/auth/chat.ts
@@ -1,4 +1,4 @@
-import { type ChatHistoryItemResType, type ChatSchema } from '@fastgpt/global/core/chat/type';
+import { type ChatHistoryItemResType, type ChatSchemaType } from '@fastgpt/global/core/chat/type';
 import { MongoChat } from '@fastgpt/service/core/chat/chatSchema';
 import { type AuthModeType } from '@fastgpt/service/support/permission/type';
 import { authOutLink } from './outLink';
@@ -51,7 +51,7 @@ export async function authChatCrud({
  teamId: string;
  tmbId: string;
  uid: string;
-  chat?: ChatSchema;
+  chat?: ChatSchemaType;
  responseDetail: boolean;
  showNodeStatus: boolean;
  showRawSource: boolean;
--- a/projects/app/src/types/app.d.ts
+++ b/projects/app/src/types/app.d.ts
@@ -10,7 +10,6 @@ import {
 } from '@fastgpt/global/core/workflow/type/io.d';
 import type { StoreNodeItemType } from '@fastgpt/global/core/workflow/type/node.d';
 import type { FlowNodeTemplateType } from '@fastgpt/global/core/workflow/type/node';
-import type { ChatSchema } from '@fastgpt/global/core/chat/type';
 import type { AppSchema } from '@fastgpt/global/core/app/type';
 import { ChatModelType } from '@/constants/model';
 import { TeamMemberStatusEnum } from '@fastgpt/global/support/user/team/constant';
--- a/projects/app/src/web/common/api/fetch.ts
+++ b/projects/app/src/web/common/api/fetch.ts
@@ -10,6 +10,7 @@ import { TeamErrEnum } from '@fastgpt/global/common/error/code/team';
 import { useSystemStore } from '../system/useSystemStore';
 import { formatTime2YMDHMW } from '@fastgpt/global/common/string/time';
 import { getWebReqUrl } from '@fastgpt/web/common/system/utils';
+import type { OnOptimizePromptProps } from '@/components/common/PromptEditor/OptimizerPopover';

 type StreamFetchProps = {
  url?: string;
@@ -272,3 +273,27 @@ export const streamFetch = ({
      failedFinish(err);
    }
  });
+
+export const onOptimizePrompt = async ({
+  originalPrompt,
+  model,
+  input,
+  onResult,
+  abortController
+}: OnOptimizePromptProps) => {
+  const controller = abortController || new AbortController();
+  await streamFetch({
+    url: '/api/core/ai/optimizePrompt',
+    data: {
+      originalPrompt,
+      optimizerInput: input,
+      model
+    },
+    onMessage: ({ event, text }) => {
+      if (event === SseResponseEventEnum.answer && text) {
+        onResult(text);
+      }
+    },
+    abortCtrl: controller
+  });
+};
--- a/projects/app/src/web/common/system/useSystemStore.ts
+++ b/projects/app/src/web/common/system/useSystemStore.ts
@@ -103,7 +103,7 @@ export const useSystemStore = create<State>()(
          return null;
        },

-        gitStar: 20000,
+        gitStar: 25000,
        async loadGitStar() {
          if (!get().feConfigs?.show_git) return;
          try {