monorepo packages (#344)

projects/app/src/pages/api/openapi/plugin/vector.ts

@@ -0,0 +1,102 @@
+import type { NextApiRequest, NextApiResponse } from 'next';
+import { jsonRes } from '@/service/response';
+import { authBalanceByUid, authUser } from '@/service/utils/auth';
+import { withNextCors } from '@/service/utils/tools';
+import { getAIChatApi, axiosConfig } from '@/service/lib/openai';
+import { pushGenerateVectorBill } from '@/service/common/bill/push';
+
+type Props = {
+  model: string;
+  input: string[];
+  billId?: string;
+};
+type Response = {
+  tokenLen: number;
+  vectors: number[][];
+};
+
+export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
+  try {
+    const { userId } = await authUser({ req });
+    let { input, model } = req.query as Props;
+
+    if (!Array.isArray(input)) {
+      throw new Error('缺少参数');
+    }
+
+    jsonRes<Response>(res, {
+      data: await getVector({ userId, input, model })
+    });
+  } catch (err) {
+    console.log(err);
+    jsonRes(res, {
+      code: 500,
+      error: err
+    });
+  }
+});
+
+export async function getVector({
+  model = 'text-embedding-ada-002',
+  userId,
+  input,
+  billId
+}: { userId?: string } & Props) {
+  userId && (await authBalanceByUid(userId));
+
+  for (let i = 0; i < input.length; i++) {
+    if (!input[i]) {
+      return Promise.reject({
+        code: 500,
+        message: '向量生成模块输入内容为空'
+      });
+    }
+  }
+
+  // 获取 chatAPI
+  const chatAPI = getAIChatApi();
+
+  // 把输入的内容转成向量
+  const result = await chatAPI
+    .createEmbedding(
+      {
+        model,
+        input
+      },
+      {
+        timeout: 60000,
+        ...axiosConfig()
+      }
+    )
+    .then(async (res) => {
+      if (!res.data?.data?.[0]?.embedding) {
+        console.log(res.data);
+        // @ts-ignore
+        return Promise.reject(res.data?.err?.message || 'Embedding API Error');
+      }
+      return {
+        tokenLen: res.data.usage.total_tokens || 0,
+        vectors: await Promise.all(res.data.data.map((item) => unityDimensional(item.embedding)))
+      };
+    });
+
+  userId &&
+    pushGenerateVectorBill({
+      userId,
+      tokenLen: result.tokenLen,
+      model,
+      billId
+    });
+
+  return result;
+}
+
+function unityDimensional(vector: number[]) {
+  if (vector.length > 1536) return Promise.reject('向量维度不能超过 1536');
+  let resultVector = vector;
+  const vectorLen = vector.length;
+
+  const zeroVector = new Array(1536 - vectorLen).fill(0);
+
+  return resultVector.concat(zeroVector);
+}