Files
FastGPT/client/src/pages/api/openapi/v1/chat/completions.ts
2023-07-04 21:24:32 +08:00

337 lines
9.4 KiB
TypeScript

import type { NextApiRequest, NextApiResponse } from 'next';
import { connectToDatabase } from '@/service/mongo';
import { authUser, authModel, getApiKey, authShareChat } from '@/service/utils/auth';
import { modelServiceToolMap, V2_StreamResponse } from '@/service/utils/chat';
import { jsonRes } from '@/service/response';
import { ChatModelMap } from '@/constants/model';
import { pushChatBill, updateShareChatBill } from '@/service/events/pushBill';
import { ChatRoleEnum, sseResponseEventEnum } from '@/constants/chat';
import { withNextCors } from '@/service/utils/tools';
import { BillTypeEnum } from '@/constants/user';
import { appKbSearch } from '../../../openapi/kb/appKbSearch';
import type { CreateChatCompletionRequest } from 'openai';
import { gptMessage2ChatType, textAdaptGptResponse } from '@/utils/adapt';
import { getChatHistory } from './getHistory';
import { saveChat } from '@/pages/api/chat/saveChat';
import { sseResponse } from '@/service/utils/tools';
import { type ChatCompletionRequestMessage } from 'openai';
import { Types } from 'mongoose';
import { sensitiveCheck } from '../../text/sensitiveCheck';
export type MessageItemType = ChatCompletionRequestMessage & { _id?: string };
type FastGptWebChatProps = {
chatId?: string; // undefined: nonuse history, '': new chat, 'xxxxx': use history
appId?: string;
};
type FastGptShareChatProps = {
password?: string;
shareId?: string;
};
export type Props = CreateChatCompletionRequest &
FastGptWebChatProps &
FastGptShareChatProps & {
messages: MessageItemType[];
};
export type ChatResponseType = {
newChatId: string;
quoteLen?: number;
};
/* 发送提示词 */
export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse) {
res.on('close', () => {
res.end();
});
res.on('error', () => {
console.log('error: ', 'request error');
res.end();
});
let { chatId, appId, shareId, password = '', stream = false, messages = [] } = req.body as Props;
let step = 0;
try {
if (!messages) {
throw new Error('Prams Error');
}
if (!Array.isArray(messages)) {
throw new Error('messages is not array');
}
await connectToDatabase();
let startTime = Date.now();
/* user auth */
const {
userId,
appId: authAppid,
authType
} = await (shareId
? authShareChat({
shareId,
password
})
: authUser({ req }));
appId = appId ? appId : authAppid;
if (!appId) {
throw new Error('appId is empty');
}
// auth app permission
const { model, showModelDetail } = await authModel({
userId,
modelId: appId,
authOwner: false,
reserveDetail: true
});
const showAppDetail = !shareId && showModelDetail;
/* get api key */
const { systemAuthKey: apiKey, userOpenAiKey } = await getApiKey({
model: model.chat.chatModel,
userId,
mustPay: authType !== 'token'
});
// get history
const { history } = await getChatHistory({ chatId, userId });
const prompts = history.concat(gptMessage2ChatType(messages));
// adapt fastgpt web
if (prompts[prompts.length - 1].obj === 'AI') {
prompts.pop();
}
// user question
const prompt = prompts[prompts.length - 1];
const {
rawSearch = [],
userSystemPrompt = [],
userLimitPrompt = [],
quotePrompt = []
} = await (async () => {
// 使用了知识库搜索
if (model.chat.relatedKbs?.length > 0) {
const { rawSearch, quotePrompt, userSystemPrompt, userLimitPrompt } = await appKbSearch({
model,
userId,
fixedQuote: history[history.length - 1]?.quote,
prompt,
similarity: model.chat.searchSimilarity,
limit: model.chat.searchLimit
});
return {
rawSearch,
userSystemPrompt,
userLimitPrompt,
quotePrompt: [quotePrompt]
};
}
return {
userSystemPrompt: model.chat.systemPrompt
? [
{
obj: ChatRoleEnum.System,
value: model.chat.systemPrompt
}
]
: [],
userLimitPrompt: model.chat.limitPrompt
? [
{
obj: ChatRoleEnum.Human,
value: model.chat.limitPrompt
}
]
: []
};
})();
// search result is empty
if (model.chat.relatedKbs?.length > 0 && !quotePrompt[0]?.value && model.chat.searchEmptyText) {
const response = model.chat.searchEmptyText;
if (stream) {
sseResponse({
res,
event: sseResponseEventEnum.answer,
data: textAdaptGptResponse({
text: response,
model: model.chat.chatModel,
finish_reason: 'stop'
})
});
return res.end();
} else {
return res.json({
id: chatId || '',
model: model.chat.chatModel,
usage: { prompt_tokens: 0, completion_tokens: 0, total_tokens: 0 },
choices: [
{ message: { role: 'assistant', content: response }, finish_reason: 'stop', index: 0 }
]
});
}
}
// api messages. [quote,context,systemPrompt,question]
const completePrompts = [
...quotePrompt,
...userSystemPrompt,
...prompts.slice(0, -1),
...userLimitPrompt,
prompt
];
// chat temperature
const modelConstantsData = ChatModelMap[model.chat.chatModel];
// FastGpt temperature range: 1~10
const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
2
);
await sensitiveCheck({
input: `${userSystemPrompt[0]?.value}\n${userLimitPrompt[0]?.value}\n${prompt.value}`
});
// start model api. responseText and totalTokens: valid only if stream = false
const { streamResponse, responseMessages, responseText, totalTokens } =
await modelServiceToolMap.chatCompletion({
model: model.chat.chatModel,
apiKey: userOpenAiKey || apiKey,
temperature: +temperature,
maxToken: model.chat.maxToken,
messages: completePrompts,
stream,
res
});
console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
if (res.closed) return res.end();
// create a chatId
const newChatId = chatId === '' ? new Types.ObjectId() : undefined;
// response answer
const {
textLen = 0,
answer = responseText,
tokens = totalTokens
} = await (async () => {
if (stream) {
// 创建响应流
res.setHeader('Content-Type', 'text/event-stream;charset-utf-8');
res.setHeader('Access-Control-Allow-Origin', '*');
res.setHeader('Transfer-Encoding', 'chunked');
res.setHeader('X-Accel-Buffering', 'no');
res.setHeader('Cache-Control', 'no-cache, no-transform');
step = 1;
try {
// response newChatId and quota
sseResponse({
res,
event: sseResponseEventEnum.chatResponse,
data: JSON.stringify({
newChatId,
quoteLen: rawSearch.length
})
});
// response answer
const { finishMessages, totalTokens, responseContent } = await V2_StreamResponse({
model: model.chat.chatModel,
res,
chatResponse: streamResponse,
prompts: responseMessages
});
return {
answer: responseContent,
textLen: finishMessages.map((item) => item.value).join('').length,
tokens: totalTokens
};
} catch (error) {
return Promise.reject(error);
}
} else {
return {
textLen: responseMessages.map((item) => item.value).join('').length
};
}
})();
// save chat history
if (typeof chatId === 'string') {
await saveChat({
newChatId,
chatId,
modelId: appId,
prompts: [
prompt,
{
_id: messages[messages.length - 1]._id,
obj: ChatRoleEnum.AI,
value: answer,
...(showAppDetail
? {
quote: rawSearch,
systemPrompt: `${userSystemPrompt[0]?.value}\n\n${userLimitPrompt[0]?.value}`
}
: {})
}
],
userId
});
}
// close response
if (stream) {
res.end();
} else {
res.json({
...(showAppDetail
? {
rawSearch
}
: {}),
newChatId,
id: chatId || '',
model: model.chat.chatModel,
usage: { prompt_tokens: 0, completion_tokens: 0, total_tokens: tokens },
choices: [
{ message: { role: 'assistant', content: answer }, finish_reason: 'stop', index: 0 }
]
});
}
pushChatBill({
isPay: !userOpenAiKey,
chatModel: model.chat.chatModel,
userId,
textLen,
tokens,
type: authType === 'apikey' ? BillTypeEnum.openapiChat : BillTypeEnum.chat
});
shareId &&
updateShareChatBill({
shareId,
tokens
});
} catch (err: any) {
res.status(500);
if (step === 1) {
sseResponse({
res,
event: sseResponseEventEnum.error,
data: JSON.stringify(err)
});
res.end();
} else {
jsonRes(res, {
code: 500,
error: err
});
}
}
});