new framwork

client/src/service/events/generateQA.ts

@@ -0,0 +1,229 @@
+import { TrainingData } from '@/service/mongo';
+import { getApiKey } from '../utils/auth';
+import { OpenAiChatEnum } from '@/constants/model';
+import { pushSplitDataBill } from '@/service/events/pushBill';
+import { openaiAccountError } from '../errorCode';
+import { modelServiceToolMap } from '../utils/chat';
+import { ChatRoleEnum } from '@/constants/chat';
+import { BillTypeEnum } from '@/constants/user';
+import { pushDataToKb } from '@/pages/api/openapi/kb/pushData';
+import { TrainingModeEnum } from '@/constants/plugin';
+import { ERROR_ENUM } from '../errorCode';
+import { sendInform } from '@/pages/api/user/inform/send';
+
+const reduceQueue = () => {
+  global.qaQueueLen = global.qaQueueLen > 0 ? global.qaQueueLen - 1 : 0;
+};
+
+export async function generateQA(): Promise<any> {
+  const maxProcess = Number(process.env.QA_MAX_PROCESS || 10);
+
+  if (global.qaQueueLen >= maxProcess) return;
+  global.qaQueueLen++;
+
+  let trainingId = '';
+  let userId = '';
+
+  try {
+    const match = {
+      mode: TrainingModeEnum.qa,
+      lockTime: { $lte: new Date(Date.now() - 4 * 60 * 1000) }
+    };
+    // random get task
+    const agree = await TrainingData.aggregate([
+      {
+        $match: match
+      },
+      { $sample: { size: 1 } },
+      {
+        $project: {
+          _id: 1
+        }
+      }
+    ]);
+
+    // no task
+    if (agree.length === 0) {
+      reduceQueue();
+      global.qaQueueLen <= 0 && console.log(`没有需要【QA】的数据, ${global.qaQueueLen}`);
+      return;
+    }
+
+    const data = await TrainingData.findOneAndUpdate(
+      {
+        _id: agree[0]._id,
+        ...match
+      },
+      {
+        lockTime: new Date()
+      }
+    ).select({
+      _id: 1,
+      userId: 1,
+      kbId: 1,
+      prompt: 1,
+      q: 1,
+      source: 1
+    });
+
+    // task preemption
+    if (!data) {
+      reduceQueue();
+      return generateQA();
+    }
+
+    trainingId = data._id;
+    userId = String(data.userId);
+    const kbId = String(data.kbId);
+
+    // 余额校验并获取 openapi Key
+    const { systemAuthKey } = await getApiKey({
+      model: OpenAiChatEnum.GPT35,
+      userId,
+      type: 'training',
+      mustPay: true
+    });
+
+    const startTime = Date.now();
+
+    // 请求 chatgpt 获取回答
+    const response = await Promise.all(
+      [data.q].map((text) =>
+        modelServiceToolMap[OpenAiChatEnum.GPT35]
+          .chatCompletion({
+            apiKey: systemAuthKey,
+            temperature: 0.8,
+            messages: [
+              {
+                obj: ChatRoleEnum.System,
+                value: `你是出题人
+${data.prompt || '下面是"一段长文本"'}
+从中选出5至20个题目和答案.答案详细.按格式返回: Q1:
+A1:
+Q2:
+A2:
+...`
+              },
+              {
+                obj: 'Human',
+                value: text
+              }
+            ],
+            stream: false
+          })
+          .then(({ totalTokens, responseText, responseMessages }) => {
+            const result = formatSplitText(responseText); // 格式化后的QA对
+            console.log(`split result length: `, result.length);
+            // 计费
+            pushSplitDataBill({
+              isPay: result.length > 0,
+              userId: data.userId,
+              type: BillTypeEnum.QA,
+              textLen: responseMessages.map((item) => item.value).join('').length,
+              totalTokens
+            });
+            return {
+              rawContent: responseText,
+              result
+            };
+          })
+          .catch((err) => {
+            console.log('QA拆分错误');
+            console.log(err.response?.status, err.response?.statusText, err.response?.data);
+            return Promise.reject(err);
+          })
+      )
+    );
+
+    const responseList = response.map((item) => item.result).flat();
+
+    // 创建 向量生成 队列
+    await pushDataToKb({
+      kbId,
+      data: responseList.map((item) => ({
+        ...item,
+        source: data.source
+      })),
+      userId,
+      mode: TrainingModeEnum.index
+    });
+
+    // delete data from training
+    await TrainingData.findByIdAndDelete(data._id);
+
+    console.log('生成QA成功，time:', `${(Date.now() - startTime) / 1000}s`);
+
+    reduceQueue();
+    generateQA();
+  } catch (err: any) {
+    reduceQueue();
+    // log
+    if (err?.response) {
+      console.log('openai error: 生成QA错误');
+      console.log(err.response?.status, err.response?.statusText, err.response?.data);
+    } else {
+      console.log('生成QA错误:', err);
+    }
+
+    // message error or openai account error
+    if (
+      err?.message === 'invalid message format' ||
+      err.response?.statusText === 'Unauthorized' ||
+      openaiAccountError[err?.response?.data?.error?.code || err?.response?.data?.error?.type]
+    ) {
+      await TrainingData.findByIdAndRemove(trainingId);
+    }
+
+    // 账号余额不足，删除任务
+    if (userId && err === ERROR_ENUM.insufficientQuota) {
+      sendInform({
+        type: 'system',
+        title: 'QA 任务中止',
+        content: '由于账号余额不足，QA 任务中止，重新充值后将会继续。',
+        userId
+      });
+      console.log('余额不足，暂停向量生成任务');
+      await TrainingData.updateMany(
+        {
+          userId
+        },
+        {
+          lockTime: new Date('2999/5/5')
+        }
+      );
+      return generateQA();
+    }
+
+    // unlock
+    await TrainingData.findByIdAndUpdate(trainingId, {
+      lockTime: new Date('2000/1/1')
+    });
+
+    setTimeout(() => {
+      generateQA();
+    }, 1000);
+  }
+}
+
+/**
+ * 检查文本是否按格式返回
+ */
+function formatSplitText(text: string) {
+  const regex = /Q\d+:(\s*)(.*)(\s*)A\d+:(\s*)([\s\S]*?)(?=Q|$)/g; // 匹配Q和A的正则表达式
+  const matches = text.matchAll(regex); // 获取所有匹配到的结果
+
+  const result = []; // 存储最终的结果
+  for (const match of matches) {
+    const q = match[2];
+    const a = match[5];
+    if (q && a) {
+      // 如果Q和A都存在，就将其添加到结果中
+      result.push({
+        q,
+        a: a.trim().replace(/\n\s*/g, '\n')
+      });
+    }
+  }
+
+  return result;
+}