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feat: 封装向量生成和账单

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This commit is contained in:
archer
2023-04-03 10:59:32 +08:00
parent 6c4026ccef
commit cf37992b5c
9 changed files with 82 additions and 85 deletions
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3
src/constants/model.ts
View File

@@ -4,7 +4,8 @@ import type { RedisModelDataItemType } from '@/types/redis';
export enum ChatModelNameEnum {
GPT35 = 'gpt-3.5-turbo',
VECTOR_GPT = 'VECTOR_GPT',
GPT3 = 'text-davinci-003'
GPT3 = 'text-davinci-003',
VECTOR = 'text-embedding-ada-002'
}
export const ChatModelNameMap = {

2
src/constants/user.ts
View File

@@ -3,6 +3,7 @@ export enum BillTypeEnum {
splitData = 'splitData',
QA = 'QA',
abstract = 'abstract',
vector = 'vector',
return = 'return'
}
export enum PageTypeEnum {
@@ -16,5 +17,6 @@ export const BillTypeMap: Record<`${BillTypeEnum}`, string> = {
[BillTypeEnum.splitData]: 'QA拆分',
[BillTypeEnum.QA]: 'QA拆分',
[BillTypeEnum.abstract]: '摘要总结',
[BillTypeEnum.vector]: '索引生成',
[BillTypeEnum.return]: '退款'
};

24
src/pages/api/chat/vectorGpt.ts
View File

@@ -13,6 +13,7 @@ import { pushChatBill } from '@/service/events/pushBill';
import { connectRedis } from '@/service/redis';
import { VecModelDataPrefix } from '@/constants/redis';
import { vectorToBuffer } from '@/utils/tools';
import { openaiCreateEmbedding } from '@/service/utils/openai';
/* 发送提示词 */
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
@@ -57,21 +58,12 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
const prompts = [...chat.content, prompt];
// 获取 chatAPI
const chatAPI = getOpenAIApi(userApiKey || systemKey);
// 把输入的内容转成向量
const promptVector = await chatAPI
.createEmbedding(
{
model: 'text-embedding-ada-002',
input: prompt.value
},
{
timeout: 120000,
httpsAgent
}
)
.then((res) => res?.data?.data?.[0]?.embedding || []);
const { vector: promptVector, chatAPI } = await openaiCreateEmbedding({
isPay: !userApiKey,
apiKey: userApiKey || systemKey,
userId,
text: prompt.value
});
// 搜索系统提示词, 按相似度从 redis 中搜出相关的 q 和 text
const redisData: any[] = await redis.sendCommand([
@@ -79,7 +71,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
`idx:${VecModelDataPrefix}:hash`,
`@modelId:{${String(
chat.modelId._id
)}} @vector:[VECTOR_RANGE 0.15 $blob]=>{$YIELD_DISTANCE_AS: score}`,
)}} @vector:[VECTOR_RANGE 0.2 $blob]=>{$YIELD_DISTANCE_AS: score}`,
// `@modelId:{${String(chat.modelId._id)}}=>[KNN 10 @vector $blob AS score]`,
'RETURN',
'1',

30
src/service/events/generateAbstract.ts
View File

@@ -84,36 +84,6 @@ export async function generateAbstract(next = false): Promise<any> {
const rawContent: string = abstractResponse?.data.choices[0].message?.content || '';
// 从 content 中提取摘要内容
const splitContents = splitText(rawContent);
// console.log(rawContent);
// 生成词向量
// const vectorResponse = await Promise.allSettled(
// splitContents.map((item) =>
// chatAPI.createEmbedding(
// {
// model: 'text-embedding-ada-002',
// input: item.abstract
// },
// {
// timeout: 120000,
// httpsAgent
// }
// )
// )
// );
// 筛选成功的向量请求
// const vectorSuccessResponse = vectorResponse
// .map((item: any, i) => {
// if (item.status !== 'fulfilled') {
// // 没有词向量的【摘要】不要
// console.log('获取词向量错误: ', item);
// return '';
// }
// return {
// abstract: splitContents[i].abstract,
// abstractVector: item?.value?.data?.data?.[0]?.embedding
// };
// })
// .filter((item) => item);
// 插入数据库，并修改状态
await DataItem.findByIdAndUpdate(dataItem._id, {

2
src/service/events/generateQA.ts
View File

@@ -83,7 +83,7 @@ export async function generateQA(next = false): Promise<any> {
]
},
{
timeout: 120000,
timeout: 180000,
httpsAgent
}
)

31
src/service/events/generateVector.ts
View File

@@ -4,6 +4,7 @@ import { connectRedis } from '../redis';
import { VecModelDataIdx } from '@/constants/redis';
import { vectorToBuffer } from '@/utils/tools';
import { ModelDataStatusEnum } from '@/constants/redis';
import { openaiCreateEmbedding, getOpenApiKey } from '../utils/openai';
export async function generateVector(next = false): Promise<any> {
if (global.generatingVector && !next) return;
@@ -17,7 +18,7 @@ export async function generateVector(next = false): Promise<any> {
VecModelDataIdx,
`@status:{${ModelDataStatusEnum.waiting}}`,
{
RETURN: ['q'],
RETURN: ['q', 'userId'],
LIMIT: {
from: 0,
size: 1
@@ -31,30 +32,22 @@ export async function generateVector(next = false): Promise<any> {
return;
}
const dataItem: { id: string; q: string } = {
const dataItem: { id: string; q: string; userId: string } = {
id: searchRes.documents[0].id,
q: String(searchRes.documents[0]?.value?.q || '')
q: String(searchRes.documents[0]?.value?.q || ''),
userId: String(searchRes.documents[0]?.value?.userId || '')
};
// 获取 openapi Key
const openAiKey = process.env.OPENAIKEY as string;
// 获取 openai 请求实例
const chatAPI = getOpenAIApi(openAiKey);
const { userApiKey, systemKey } = await getOpenApiKey(dataItem.userId);
// 生成词向量
const vector = await chatAPI
.createEmbedding(
{
model: 'text-embedding-ada-002',
input: dataItem.q
},
{
timeout: 120000,
httpsAgent
}
)
.then((res) => res?.data?.data?.[0]?.embedding || []);
const { vector } = await openaiCreateEmbedding({
text: dataItem.q,
userId: dataItem.userId,
isPay: !userApiKey,
apiKey: userApiKey || systemKey
});
// 更新 redis 向量和状态数据
await redis.sendCommand([

30
src/service/events/pushBill.ts
View File

@@ -2,6 +2,7 @@ import { connectToDatabase, Bill, User } from '../mongo';
import { modelList, ChatModelNameEnum } from '@/constants/model';
import { encode } from 'gpt-token-utils';
import { formatPrice } from '@/utils/user';
import { BillTypeEnum } from '@/constants/user';
import type { DataType } from '@/types/data';
export const pushChatBill = async ({
@@ -23,8 +24,7 @@ export const pushChatBill = async ({
// 计算 token 数量
const tokens = encode(text);
console.log('text len: ', text.length);
console.log('token len:', tokens.length);
console.log(`chat generate success. text len: ${text.length}. token len: ${tokens.length}`);
if (isPay) {
await connectToDatabase();
@@ -34,7 +34,7 @@ export const pushChatBill = async ({
// 计算价格
const unitPrice = modelItem?.price || 5;
const price = unitPrice * tokens.length;
console.log(`chat bill, unit price: ${unitPrice}, price: ${formatPrice(price)}元`);
console.log(`unit price: ${unitPrice}, price: ${formatPrice(price)}元`);
try {
// 插入 Bill 记录
@@ -82,8 +82,9 @@ export const pushSplitDataBill = async ({
// 计算 token 数量
const tokens = encode(text);
console.log('text len: ', text.length);
console.log('token len:', tokens.length);
console.log(
`splitData generate success. text len: ${text.length}. token len: ${tokens.length}`
);
if (isPay) {
try {
@@ -93,7 +94,7 @@ export const pushSplitDataBill = async ({
// 计算价格
const price = unitPrice * tokens.length;
console.log(`splitData bill, price: ${formatPrice(price)}元`);
console.log(`price: ${formatPrice(price)}元`);
// 插入 Bill 记录
const res = await Bill.create({
@@ -123,13 +124,11 @@ export const pushSplitDataBill = async ({
export const pushGenerateVectorBill = async ({
isPay,
userId,
text,
type
text
}: {
isPay: boolean;
userId: string;
text: string;
type: DataType;
}) => {
await connectToDatabase();
@@ -139,24 +138,21 @@ export const pushGenerateVectorBill = async ({
// 计算 token 数量
const tokens = encode(text);
console.log('text len: ', text.length);
console.log('token len:', tokens.length);
console.log(`vector generate success. text len: ${text.length}. token len: ${tokens.length}`);
if (isPay) {
try {
// 获取模型单价格, 都是用 gpt35 拆分
const modelItem = modelList.find((item) => item.model === ChatModelNameEnum.GPT35);
const unitPrice = modelItem?.price || 5;
const unitPrice = 1;
// 计算价格
const price = unitPrice * tokens.length;
console.log(`splitData bill, price: ${formatPrice(price)}元`);
console.log(`price: ${formatPrice(price)}元`);
// 插入 Bill 记录
const res = await Bill.create({
userId,
type,
modelName: ChatModelNameEnum.GPT35,
type: BillTypeEnum.vector,
modelName: ChatModelNameEnum.VECTOR,
textLen: text.length,
tokenLen: tokens.length,
price

2
src/service/models/bill.ts
View File

@@ -16,7 +16,7 @@ const BillSchema = new Schema({
},
modelName: {
type: String,
enum: modelList.map((item) => item.model),
enum: [...modelList.map((item) => item.model), 'text-embedding-ada-002'],
required: true
},
chatId: {

43
src/service/utils/openai.ts
View File

@@ -3,6 +3,8 @@ import { getOpenAIApi } from '@/service/utils/chat';
import { httpsAgent } from './tools';
import { User } from '../models/user';
import { formatPrice } from '@/utils/user';
import { ChatModelNameEnum } from '@/constants/model';
import { pushGenerateVectorBill } from '../events/pushBill';
/* 判断 apikey 是否还有余额 */
export const checkKeyGrant = async (apiKey: string) => {
@@ -87,3 +89,44 @@ export const getOpenApiKey = async (userId: string, checkGrant = false) => {
systemKey: process.env.OPENAIKEY as string
};
};
/* 获取向量 */
export const openaiCreateEmbedding = async ({
isPay,
userId,
apiKey,
text
}: {
isPay: boolean;
userId: string;
apiKey: string;
text: string;
}) => {
// 获取 chatAPI
const chatAPI = getOpenAIApi(apiKey);
// 把输入的内容转成向量
const vector = await chatAPI
.createEmbedding(
{
model: ChatModelNameEnum.VECTOR,
input: text
},
{
timeout: 60000,
httpsAgent
}
)
.then((res) => res?.data?.data?.[0]?.embedding || []);
pushGenerateVectorBill({
isPay,
userId,
text
});
return {
vector,
chatAPI
};
};