4.8-alpha fix (#1424)

packages/global/common/string/textSplitter.ts

@@ -1,24 +1,61 @@
 import { getErrText } from '../error/utils';
 import { replaceRegChars } from './tools';
 
-/**
+export const CUSTOM_SPLIT_SIGN = '-----CUSTOM_SPLIT_SIGN-----';
- * text split into chunks
+
- * chunkLen - one chunk len. max: 3500
+type SplitProps = {
- * overlapLen - The size of the before and after Text
- * chunkLen > overlapLen
- * markdown
- */
-export const splitText2Chunks = (props: {
   text: string;
   chunkLen: number;
   overlapRatio?: number;
   customReg?: string[];
-}): {
+};
+
+type SplitResponse = {
   chunks: string[];
   chars: number;
-  overlapRatio?: number;
+};
-} => {
+
+// 判断字符串是否为markdown的表格形式
+const strIsMdTable = (str: string) => {
+  const regex = /^(\|.*\|[\r]*)$/m;
+
+  return regex.test(str);
+};
+const markdownTableSplit = (props: SplitProps): SplitResponse => {
+  let { text = '', chunkLen } = props;
+  const splitText2Lines = text.split('\n');
+  const header = splitText2Lines[0];
+
+  const headerSize = header.split('|').length - 2;
+  const mdSplitString = `| ${new Array(headerSize)
+    .fill(0)
+    .map(() => '---')
+    .join(' | ')} |`;
+
+  const chunks: string[] = [];
+  let chunk = `${header}
+${mdSplitString}
+`;
+
+  for (let i = 2; i < splitText2Lines.length; i++) {
+    if (chunk.length + splitText2Lines[i].length > chunkLen * 1.2) {
+      chunks.push(chunk);
+      chunk = `${header}
+${mdSplitString}
+`;
+    }
+    chunk += `${splitText2Lines[i]}\n`;
+  }
+
+  return {
+    chunks,
+    chars: chunks.reduce((sum, chunk) => sum + chunk.length, 0)
+  };
+};
+
+const commonSplit = (props: SplitProps): SplitResponse => {
   let { text = '', chunkLen, overlapRatio = 0.2, customReg = [] } = props;
+
   const splitMarker = 'SPLIT_HERE_SPLIT_HERE';
   const codeBlockMarker = 'CODE_BLOCK_LINE_MARKER';
   const overlapLen = Math.round(chunkLen * overlapRatio);
@@ -253,3 +290,29 @@ export const splitText2Chunks = (props: {
     throw new Error(getErrText(err));
   }
 };
+
+/**
+ * text split into chunks
+ * chunkLen - one chunk len. max: 3500
+ * overlapLen - The size of the before and after Text
+ * chunkLen > overlapLen
+ * markdown
+ */
+export const splitText2Chunks = (props: SplitProps): SplitResponse => {
+  let { text = '' } = props;
+
+  const splitWithCustomSign = text.split(CUSTOM_SPLIT_SIGN);
+
+  const splitResult = splitWithCustomSign.map((item) => {
+    if (strIsMdTable(text)) {
+      return markdownTableSplit(props);
+    }
+
+    return commonSplit(props);
+  });
+
+  return {
+    chunks: splitResult.map((item) => item.chunks).flat(),
+    chars: splitResult.reduce((sum, item) => sum + item.chars, 0)
+  };
+};

packages/service/common/string/tiktoken/index.ts

@@ -57,7 +57,7 @@ export const countGptMessagesTokens = (
 
       // 检测是否有内存泄漏
       // addLog.info(`Count token time: ${Date.now() - start}, token: ${data}`);
-      // console.log(Object.keys(global.tiktokenWorker.callbackMap));
+      // console.log(process.memoryUsage());
     };
 
     worker.postMessage({

packages/service/common/vectorStore/controller.d.ts

@@ -15,6 +15,6 @@ export type InsertVectorProps = {
 export type EmbeddingRecallProps = {
   teamId: string;
   datasetIds: string[];
-  similarity?: number;
+  // similarity?: number;
-  efSearch?: number;
+  // efSearch?: number;
 };

packages/service/common/vectorStore/pg/controller.ts

@@ -129,17 +129,15 @@ export const embeddingRecall = async (
 ): Promise<{
   results: EmbeddingRecallItemType[];
 }> => {
-  const { teamId, datasetIds, vectors, limit, similarity = 0, retry = 2, efSearch = 100 } = props;
+  const { datasetIds, vectors, limit, retry = 2 } = props;
 
   try {
     const results: any = await PgClient.query(
       `BEGIN;
-        SET LOCAL hnsw.ef_search = ${efSearch};
+        SET LOCAL hnsw.ef_search = ${global.systemEnv?.pgHNSWEfSearch || 100};
         select id, collection_id, vector <#> '[${vectors[0]}]' AS score 
           from ${PgDatasetTableName} 
-          where team_id='${teamId}' 
+          where dataset_id IN (${datasetIds.map((id) => `'${String(id)}'`).join(',')})
-              AND dataset_id IN (${datasetIds.map((id) => `'${String(id)}'`).join(',')})
-              AND vector <#> '[${vectors[0]}]' < -${similarity}
           order by score limit ${limit};
         COMMIT;`
     );

packages/service/core/dataset/search/controller.ts

@@ -85,8 +85,7 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
       teamId,
       datasetIds,
       vectors,
-      limit,
+      limit
-      efSearch: global.systemEnv?.pgHNSWEfSearch
     });
 
     // get q and a

packages/service/core/workflow/dispatch/agent/classifyQuestion.ts

@@ -127,8 +127,8 @@ const completions = async ({
   });
   const answer = data.choices?.[0].message?.content || '';
 
-  console.log(JSON.stringify(chats2GPTMessages({ messages, reserveId: false }), null, 2));
+  // console.log(JSON.stringify(chats2GPTMessages({ messages, reserveId: false }), null, 2));
-  console.log(answer, '----');
+  // console.log(answer, '----');
 
   const id =
     agents.find((item) => answer.includes(item.key))?.key ||

packages/service/worker/file/extension/csv.ts

@@ -10,9 +10,13 @@ export const readCsvRawText = async (params: ReadRawTextByBuffer): Promise<ReadF
 
   const header = csvArr[0];
 
-  const formatText = header
+  // format to md table
-    ? csvArr.map((item) => item.map((item, i) => `${header[i]}:${item}`).join('\n')).join('\n')
+  const formatText = `| ${header.join(' | ')} |
-    : '';
+| ${header.map(() => '---').join(' | ')} |
+${csvArr
+  .slice(1)
+  .map((row) => `| ${row.map((item) => item.replace(/\n/g, '\\n')).join(' | ')} |`)
+  .join('\n')}`;
 
   return {
     rawText,

packages/service/worker/file/extension/xlsx.ts

@@ -1,3 +1,4 @@
+import { CUSTOM_SPLIT_SIGN } from '@fastgpt/global/common/string/textSplitter';
 import { ReadRawTextByBuffer, ReadFileResponse } from '../type';
 import xlsx from 'node-xlsx';
 import Papa from 'papaparse';
@@ -18,25 +19,25 @@ export const readXlsxRawText = async ({
   });
 
   const rawText = format2Csv.map((item) => item.csvText).join('\n');
+
   const formatText = format2Csv
     .map((item) => {
       const csvArr = Papa.parse(item.csvText).data as string[][];
       const header = csvArr[0];
 
-      const formatText = header
+      if (!header) return;
-        ? csvArr
-            .map((item) =>
-              item
-                .map((item, i) => (item ? `${header[i]}:${item}` : ''))
-                .filter(Boolean)
-                .join('\n')
-            )
-            .join('\n')
-        : '';
 
-      return `${item.title}\n${formatText}`;
+      const formatText = `| ${header.join(' | ')} |
+      | ${header.map(() => '---').join(' | ')} |
+      ${csvArr
+        .slice(1)
+        .map((row) => `| ${row.map((item) => item.replace(/\n/g, '\\n')).join(' | ')} |`)
+        .join('\n')}`;
+
+      return formatText;
     })
-    .join('\n');
+    .filter(Boolean)
+    .join(CUSTOM_SPLIT_SIGN);
 
   return {
     rawText: rawText,

packages/service/worker/file/read.ts

@@ -67,5 +67,5 @@ parentPort?.on('message', async (props: ReadRawTextProps<Uint8Array>) => {
     });
   }
 
-  global?.close?.();
+  process.exit();
 });

packages/service/worker/htmlStr2Md/index.ts

@@ -15,6 +15,5 @@ parentPort?.on('message', (params: { html: string }) => {
       data: error
     });
   }
-
+  process.exit();
-  global?.close?.();
 });

packages/service/worker/tiktoken/countGptMessagesTokens.ts

@@ -26,91 +26,96 @@ parentPort?.on(
     tools?: ChatCompletionTool[];
     functionCall?: ChatCompletionCreateParams.Function[];
   }) => {
-    const start = Date.now();
+    try {
-    /* count one prompt tokens */
+      /* count one prompt tokens */
-    const countPromptTokens = (
+      const countPromptTokens = (
-      prompt: string | ChatCompletionContentPart[] | null | undefined = '',
+        prompt: string | ChatCompletionContentPart[] | null | undefined = '',
-      role: '' | `${ChatCompletionRequestMessageRoleEnum}` = ''
+        role: '' | `${ChatCompletionRequestMessageRoleEnum}` = ''
-    ) => {
+      ) => {
-      const promptText = (() => {
+        const promptText = (() => {
-        if (!prompt) return '';
+          if (!prompt) return '';
-        if (typeof prompt === 'string') return prompt;
+          if (typeof prompt === 'string') return prompt;
-        let promptText = '';
+          let promptText = '';
-        prompt.forEach((item) => {
+          prompt.forEach((item) => {
-          if (item.type === 'text') {
+            if (item.type === 'text') {
-            promptText += item.text;
+              promptText += item.text;
-          } else if (item.type === 'image_url') {
+            } else if (item.type === 'image_url') {
-            promptText += item.image_url.url;
+              promptText += item.image_url.url;
-          }
+            }
-        });
+          });
-        return promptText;
+          return promptText;
-      })();
-
-      const text = `${role}\n${promptText}`.trim();
-
-      try {
-        const encodeText = enc.encode(text);
-        const supplementaryToken = role ? 4 : 0;
-        return encodeText.length + supplementaryToken;
-      } catch (error) {
-        return text.length;
-      }
-    };
-    const countToolsTokens = (
-      tools?: ChatCompletionTool[] | ChatCompletionCreateParams.Function[]
-    ) => {
-      if (!tools || tools.length === 0) return 0;
-
-      const toolText = tools
-        ? JSON.stringify(tools)
-            .replace('"', '')
-            .replace('\n', '')
-            .replace(/( ){2,}/g, ' ')
-        : '';
-
-      return enc.encode(toolText).length;
-    };
-
-    const total =
-      messages.reduce((sum, item) => {
-        // Evaluates the text of toolcall and functioncall
-        const functionCallPrompt = (() => {
-          let prompt = '';
-          if (item.role === ChatCompletionRequestMessageRoleEnum.Assistant) {
-            const toolCalls = item.tool_calls;
-            prompt +=
-              toolCalls
-                ?.map((item) => `${item?.function?.name} ${item?.function?.arguments}`.trim())
-                ?.join('') || '';
-
-            const functionCall = item.function_call;
-            prompt += `${functionCall?.name} ${functionCall?.arguments}`.trim();
-          }
-          return prompt;
         })();
 
-        const contentPrompt = (() => {
+        const text = `${role}\n${promptText}`.trim();
-          if (!item.content) return '';
-          if (typeof item.content === 'string') return item.content;
-          return item.content
-            .map((item) => {
-              if (item.type === 'text') return item.text;
-              return '';
-            })
-            .join('');
-        })();
 
-        return sum + countPromptTokens(`${contentPrompt}${functionCallPrompt}`, item.role);
+        try {
-      }, 0) +
+          const encodeText = enc.encode(text);
-      countToolsTokens(tools) +
+          const supplementaryToken = role ? 4 : 0;
-      countToolsTokens(functionCall);
+          return encodeText.length + supplementaryToken;
+        } catch (error) {
+          return text.length;
+        }
+      };
+      const countToolsTokens = (
+        tools?: ChatCompletionTool[] | ChatCompletionCreateParams.Function[]
+      ) => {
+        if (!tools || tools.length === 0) return 0;
 
-    parentPort?.postMessage({
+        const toolText = tools
-      id,
+          ? JSON.stringify(tools)
-      type: 'success',
+              .replace('"', '')
-      data: total
+              .replace('\n', '')
-    });
+              .replace(/( ){2,}/g, ' ')
+          : '';
 
-    global?.close?.();
+        return enc.encode(toolText).length;
+      };
+
+      const total =
+        messages.reduce((sum, item) => {
+          // Evaluates the text of toolcall and functioncall
+          const functionCallPrompt = (() => {
+            let prompt = '';
+            if (item.role === ChatCompletionRequestMessageRoleEnum.Assistant) {
+              const toolCalls = item.tool_calls;
+              prompt +=
+                toolCalls
+                  ?.map((item) => `${item?.function?.name} ${item?.function?.arguments}`.trim())
+                  ?.join('') || '';
+
+              const functionCall = item.function_call;
+              prompt += `${functionCall?.name} ${functionCall?.arguments}`.trim();
+            }
+            return prompt;
+          })();
+
+          const contentPrompt = (() => {
+            if (!item.content) return '';
+            if (typeof item.content === 'string') return item.content;
+            return item.content
+              .map((item) => {
+                if (item.type === 'text') return item.text;
+                return '';
+              })
+              .join('');
+          })();
+
+          return sum + countPromptTokens(`${contentPrompt}${functionCallPrompt}`, item.role);
+        }, 0) +
+        countToolsTokens(tools) +
+        countToolsTokens(functionCall);
+
+      parentPort?.postMessage({
+        id,
+        type: 'success',
+        data: total
+      });
+    } catch (error) {
+      parentPort?.postMessage({
+        id,
+        type: 'success',
+        data: 0
+      });
+    }
   }
 );

packages/service/worker/utils.ts

@@ -25,9 +25,12 @@ export const runWorker = <T = any>(name: WorkerNameEnum, params?: Record<string,
     });
 
     worker.on('error', (err) => {
-      worker.terminate();
-
       reject(err);
+      worker.terminate();
+    });
+    worker.on('messageerror', (err) => {
+      reject(err);
+      worker.terminate();
     });
   });
 };

projects/app/public/docs/versionIntro.md

@@ -1,10 +1,32 @@
-### FastGPT V4.7.1
+### FastGPT V4.8
 
-1. 新增 - 语音输入完整配置。支持选择是否打开语音输入（包括分享页面），支持语音输入后自动发送，支持语音输入后自动语音播放（流式）。
+本次更新的重点是对工作流 (高级编排) 进行了重构，使其更加简洁和强大。但由于新旧工作流机制有较大变化，尽管我们进行了一定的自动转换，仍有部分工作流需要您手动重建。请尽快更新到新版本，并对工作流进行必要的调试和重新发布。
-2. 新增 - Pptx 和 xlsx 文件读取。但所有文件读取都放服务端，会消耗更多的服务器资源，以及无法在上传时预览更多内容。
+
-3. 新增 - 集成 Laf 云函数，可以读取 Laf 账号中的云函数作为 HTTP 模块。
+❗ 重要提示：
-4. 修改 - csv导入模板，取消 header 校验，自动获取前两列。
+1️⃣ 旧工作流更新后暂不失效，打开旧工作流会弹出自动转换提示，重新编排后点 “发布” 按钮发布新工作流
-5. 修复 - 问题补全历史记录BUG
+2️⃣ 发布新工作流前，工作流自动保存功能暂不生效
-6. [点击查看高级编排介绍文档](https://doc.fastgpt.in/docs/workflow/intro)
+3️⃣ 应用和插件新增 version 字段，标识适用新/旧版工作流，以实现兼容
-7. [使用文档](https://doc.fastgpt.in/docs/intro/)
+
-8. [点击查看商业版](https://doc.fastgpt.in/docs/commercial/)
+✨ 新增功能亮点：
+1️⃣ 判断器：支持 if/elseIf/else 判断逻辑，工作流控制更灵活
+2️⃣ 变量更新节点：运行中可动态修改工作流输出变量或全局变量值
+3️⃣ 工作流自动保存和版本管理：自动保存修改，支持查看和回滚历史版本
+4️⃣ 工作流调试模式：更直观高效，可调试单节点或逐步执行，实时查看输入输出数据
+5️⃣ 定时执行应用：支持简单配置实现各种定时任务
+
+🛠️ 其他优化与修复：
+- 优化工作流节点连线方式，支持四向连接，易构建循环工作流
+- 显著提升工作流上下文数据传递性能
+- 简易模式下修改配置自动刷新调试框，免手动保存
+- 改进 worker 进程管理，支持 Token 计算任务分配，提高效率
+- 工具调用支持 string、boolean、number 数据类型
+- 完善 completions 接口对 size 参数限制
+- 重构 Node.js API 中间件和服务端代码
+- 对话记录长度调整为偶数，最大长度增至 50 轮，避免奇数导致部分模型不兼容
+- HTTP 节点出错将终止进程，避免异常影响
+- 修复工具调用名称不能以数字开头问题
+- 修复分享链接 query 参数缓存 bug
+- 修复工具调用和 HTTP 模块兼容性问题
+- [点击查看高级编排介绍文档](https://doc.fastgpt.in/docs/workflow/intro)
+- [使用文档](https://doc.fastgpt.in/docs/intro/)
+- [点击查看商业版](https://doc.fastgpt.in/docs/commercial/)

projects/app/src/components/ChatBox/MessageInput.tsx

@@ -55,6 +55,8 @@ const MessageInput = ({
   const { t } = useTranslation();
 
   const havInput = !!inputValue || fileList.length > 0;
+  const hasFileUploading = fileList.some((item) => !item.url);
+  const canSendMessage = havInput && !hasFileUploading;
 
   /* file selector and upload */
   const { File, onOpen: onOpenSelectFile } = useSelectFile({
@@ -142,7 +144,8 @@ const MessageInput = ({
   );
 
   /* on send */
-  const handleSend = useCallback(async () => {
+  const handleSend = async () => {
+    if (!canSendMessage) return;
     const textareaValue = TextareaDom.current?.value || '';
 
     onSendMessage({
@@ -150,7 +153,7 @@ const MessageInput = ({
       files: fileList
     });
     replaceFile([]);
-  }, [TextareaDom, fileList, onSendMessage, replaceFile]);
+  };
 
   /* whisper init */
   const {
@@ -466,16 +469,20 @@ const MessageInput = ({
                 h={['28px', '32px']}
                 w={['28px', '32px']}
                 borderRadius={'md'}
-                bg={isSpeaking || isChatting ? '' : !havInput ? '#E5E5E5' : 'primary.500'}
+                bg={
+                  isSpeaking || isChatting
+                    ? ''
+                    : !havInput || hasFileUploading
+                      ? '#E5E5E5'
+                      : 'primary.500'
+                }
                 cursor={havInput ? 'pointer' : 'not-allowed'}
                 lineHeight={1}
                 onClick={() => {
                   if (isChatting) {
                     return onStop();
                   }
-                  if (havInput) {
+                  return handleSend();
-                    return handleSend();
-                  }
                 }}
               >
                 {isChatting ? (

projects/app/src/components/Markdown/index.tsx

@@ -53,7 +53,7 @@ const Markdown = ({
   );
 
   const formatSource = source
-    .replace(/\\n/g, '\n ')
+    // .replace(/\\n/g, '\n')
     .replace(/(http[s]?:\/\/[^\s，。]+)([。，])/g, '$1 $2')
     .replace(/\n*(\[QUOTE SIGN\]\(.*\))/g, '$1');
 

projects/app/src/pages/api/plugins/textEditor/v2/index.ts

@@ -40,3 +40,9 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse<
     res.status(500).send(getErrText(err));
   }
 }
+
+export const config = {
+  api: {
+    responseLimit: '16mb'
+  }
+};

projects/app/src/pages/api/v1/chat/completions.ts

@@ -362,12 +362,6 @@ async function handler(req: NextApiRequest, res: NextApiResponse) {
 }
 export default NextAPI(handler);
 
-export const config = {
-  api: {
-    responseLimit: '20mb'
-  }
-};
-
 const authShareChat = async ({
   chatId,
   ...data
@@ -526,3 +520,9 @@ const authHeaderRequest = async ({
     canWrite
   };
 };
+
+export const config = {
+  api: {
+    responseLimit: '20mb'
+  }
+};

projects/app/src/pages/dataset/detail/components/Import/diffSource/TableLocal.tsx

@@ -28,7 +28,7 @@ export default React.memo(FileLocal);
 
 const csvTemplate = `"第一列内容","第二列内容"
 "必填列","可选列。CSV 中请注意内容不能包含双引号，双引号是列分割符号"
-"只会讲第一和第二列内容导入，其余列会被忽略",""
+"只会将第一和第二列内容导入，其余列会被忽略",""
 "结合人工智能的演进历程,AIGC的发展大致可以分为三个阶段，即:早期萌芽阶段(20世纪50年代至90年代中期)、沉淀积累阶段(20世纪90年代中期至21世纪10年代中期),以及快速发展展阶段(21世纪10年代中期至今)。",""
 "AIGC发展分为几个阶段？","早期萌芽阶段(20世纪50年代至90年代中期)、沉淀积累阶段(20世纪90年代中期至21世纪10年代中期)、快速发展展阶段(21世纪10年代中期至今)"`;
 

projects/app/src/web/core/dataset/store/searchTest.ts

@@ -32,7 +32,7 @@ export const useSearchTestStore = create<State>()(
         datasetTestList: [],
         pushDatasetTestItem(data) {
           set((state) => {
-            state.datasetTestList = [data, ...state.datasetTestList].slice(0, 100);
+            state.datasetTestList = [data, ...state.datasetTestList].slice(0, 50);
           });
         },
         delDatasetTestItemById(id) {