feat: lafClaude

package.json

@@ -26,7 +26,6 @@
     "axios": "^1.3.3",
     "crypto": "^1.0.1",
     "dayjs": "^1.11.7",
-    "delay": "^5.0.0",
     "eventsource-parser": "^0.1.0",
     "formidable": "^2.1.1",
     "framer-motion": "^9.0.6",

pnpm-lock.yaml

@@ -28,7 +28,6 @@ specifiers:
   axios: ^1.3.3
   crypto: ^1.0.1
   dayjs: ^1.11.7
-  delay: ^5.0.0
   eslint: 8.34.0
   eslint-config-next: 13.1.6
   eventsource-parser: ^0.1.0
@@ -83,7 +82,6 @@ dependencies:
   axios: registry.npmmirror.com/axios/1.3.3
   crypto: registry.npmmirror.com/crypto/1.0.1
   dayjs: registry.npmmirror.com/dayjs/1.11.7
-  delay: 5.0.0
   eventsource-parser: registry.npmmirror.com/eventsource-parser/0.1.0
   formidable: registry.npmmirror.com/formidable/2.1.1
   framer-motion: registry.npmmirror.com/framer-motion/9.0.6_biqbaboplfbrettd7655fr4n2y
@@ -288,11 +286,6 @@ packages:
     dev: false
     optional: true
 
-  /delay/5.0.0:
-    resolution: {integrity: sha512-ReEBKkIfe4ya47wlPYf/gu5ib6yUG0/Aez0JQZQz94kiWtRQvZIQbTiehsnwHvLSWJnQdhVeqYue7Id1dKr0qw==}
-    engines: {node: '>=10'}
-    dev: false
-
   /fsevents/2.3.2:
     resolution: {integrity: sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==}
     engines: {node: ^8.16.0 || ^10.6.0 || >=11.0.0}

public/docs/intro.md

@@ -3,12 +3,13 @@
 [Git 仓库](https://github.com/c121914yu/FastGPT)
 
 ### 交流群/问题反馈
+
 扫码满了，加个小号，定时拉
-wx号: fastgpt123
+wx 号: fastgpt123
 ![](/imgs/wx300.jpg)
 
-
 ### 快速开始
+
 1. 使用手机号注册账号。
 2. 进入账号页面，添加关联账号，目前只有 openai 的账号可以添加，直接去 openai 官网，把 API Key 粘贴过来。
 3. 如果填写了自己的 openai 账号，使用时会直接用你的账号。如果没有填写，需要付费使用平台的账号。
@@ -16,15 +17,16 @@ wx号: fastgpt123
 5. 在模型列表点击【对话】，即可使用 API 进行聊天。
 
 ### 价格表
+
 如果使用了自己的 Api Key，不会计费。可以在账号页，看到详细账单。单纯使用 chatGPT 模型进行对话，只有一个计费项目。使用知识库时，包含**对话**和**索引**生成两个计费项。
 | 计费项 | 价格: 元/ 1K tokens（包含上下文）|
 | --- | --- |
+| claude - 对话 | 免费 |
 | chatgpt - 对话 | 0.03 |
 | 知识库 - 对话 | 0.03 |
 | 知识库 - 索引 | 0.004 |
 | 文件拆分 | 0.03 |
 
-
 ### 定制 prompt
 
 1. 进入模型编辑页

src/api/chat.ts

@@ -24,6 +24,7 @@ export const delChatHistoryById = (id: string) => GET(`/chat/removeHistory?id=${
  */
 export const postSaveChat = (data: {
   modelId: string;
+  newChatId: '' | string;
   chatId: '' | string;
   prompts: ChatItemType[];
 }) => POST<string>('/chat/saveChat', data);

src/api/fetch.ts

@@ -1,5 +1,5 @@
 import { getToken } from '../utils/user';
-import { SYSTEM_PROMPT_PREFIX } from '@/constants/chat';
+import { SYSTEM_PROMPT_HEADER, NEW_CHATID_HEADER } from '@/constants/chat';
 
 interface StreamFetchProps {
   url: string;
@@ -8,60 +8,56 @@ interface StreamFetchProps {
   abortSignal: AbortController;
 }
 export const streamFetch = ({ url, data, onMessage, abortSignal }: StreamFetchProps) =>
-  new Promise<{ responseText: string; systemPrompt: string }>(async (resolve, reject) => {
+  new Promise<{ responseText: string; systemPrompt: string; newChatId: string }>(
-    try {
+    async (resolve, reject) => {
-      const res = await fetch(url, {
+      try {
-        method: 'POST',
+        const res = await fetch(url, {
-        headers: {
+          method: 'POST',
-          'Content-Type': 'application/json',
+          headers: {
-          Authorization: getToken() || ''
+            'Content-Type': 'application/json',
-        },
+            Authorization: getToken() || ''
-        body: JSON.stringify(data),
+          },
-        signal: abortSignal.signal
+          body: JSON.stringify(data),
-      });
+          signal: abortSignal.signal
-      const reader = res.body?.getReader();
+        });
-      if (!reader) return;
+        const reader = res.body?.getReader();
+        if (!reader) return;
 
-      const decoder = new TextDecoder();
+        const decoder = new TextDecoder();
-      let responseText = '';
-      let systemPrompt = '';
 
-      const read = async () => {
+        const systemPrompt = decodeURIComponent(res.headers.get(SYSTEM_PROMPT_HEADER) || '');
-        try {
+        const newChatId = decodeURIComponent(res.headers.get(NEW_CHATID_HEADER) || '');
-          const { done, value } = await reader?.read();
+
-          if (done) {
+        let responseText = '';
-            if (res.status === 200) {
+
-              resolve({ responseText, systemPrompt });
+        const read = async () => {
-            } else {
+          try {
-              const parseError = JSON.parse(responseText);
+            const { done, value } = await reader?.read();
-              reject(parseError?.message || '请求异常');
+            if (done) {
+              if (res.status === 200) {
+                resolve({ responseText, systemPrompt, newChatId });
+              } else {
+                const parseError = JSON.parse(responseText);
+                reject(parseError?.message || '请求异常');
+              }
+
+              return;
             }
-
+            const text = decoder.decode(value).replace(/<br\/>/g, '\n');
-            return;
+            responseText += text;
+            onMessage(text);
+            read();
+          } catch (err: any) {
+            if (err?.message === 'The user aborted a request.') {
+              return resolve({ responseText, systemPrompt, newChatId });
+            }
+            reject(typeof err === 'string' ? err : err?.message || '请求异常');
           }
-          let text = decoder.decode(value).replace(/<br\/>/g, '\n');
+        };
-          // check system prompt
+        read();
-          if (text.includes(SYSTEM_PROMPT_PREFIX)) {
+      } catch (err: any) {
-            const arr = text.split(SYSTEM_PROMPT_PREFIX);
+        console.log(err, '====');
-            systemPrompt = arr.pop() || '';
+        reject(typeof err === 'string' ? err : err?.message || '请求异常');
-
+      }
-            text = arr.join('');
-          }
-          responseText += text;
-          onMessage(text);
-
-          read();
-        } catch (err: any) {
-          if (err?.message === 'The user aborted a request.') {
-            return resolve({ responseText, systemPrompt });
-          }
-          reject(typeof err === 'string' ? err : err?.message || '请求异常');
-        }
-      };
-
-      read();
-    } catch (err: any) {
-      console.log(err, '====');
-      reject(typeof err === 'string' ? err : err?.message || '请求异常');
     }
-  });
+  );

src/constants/chat.ts

@@ -1,4 +1,5 @@
-export const SYSTEM_PROMPT_PREFIX = 'SYSTEM_PROMPT:';
+export const SYSTEM_PROMPT_HEADER = 'System-Prompt-Header';
+export const NEW_CHATID_HEADER = 'Chat-Id-Header';
 
 export enum ChatRoleEnum {
   System = 'System',

src/constants/model.ts

@@ -8,13 +8,17 @@ export enum OpenAiChatEnum {
   'GPT4' = 'gpt-4',
   'GPT432k' = 'gpt-4-32k'
 }
+export enum ClaudeEnum {
+  'Claude' = 'Claude'
+}
 
-export type ChatModelType = `${OpenAiChatEnum}`;
+export type ChatModelType = `${OpenAiChatEnum}` | `${ClaudeEnum}`;
 
 export type ChatModelItemType = {
   chatModel: ChatModelType;
   name: string;
   contextMaxToken: number;
+  systemMaxToken: number;
   maxTemperature: number;
   price: number;
 };
@@ -24,6 +28,7 @@ export const ChatModelMap = {
     chatModel: OpenAiChatEnum.GPT35,
     name: 'ChatGpt',
     contextMaxToken: 4096,
+    systemMaxToken: 3000,
     maxTemperature: 1.5,
     price: 3
   },
@@ -31,6 +36,7 @@ export const ChatModelMap = {
     chatModel: OpenAiChatEnum.GPT4,
     name: 'Gpt4',
     contextMaxToken: 8000,
+    systemMaxToken: 4000,
     maxTemperature: 1.5,
     price: 30
   },
@@ -38,12 +44,24 @@ export const ChatModelMap = {
     chatModel: OpenAiChatEnum.GPT432k,
     name: 'Gpt4-32k',
     contextMaxToken: 32000,
+    systemMaxToken: 4000,
     maxTemperature: 1.5,
     price: 30
+  },
+  [ClaudeEnum.Claude]: {
+    chatModel: ClaudeEnum.Claude,
+    name: 'Claude(免费体验)',
+    contextMaxToken: 9000,
+    systemMaxToken: 2500,
+    maxTemperature: 1,
+    price: 0
   }
 };
 
-export const chatModelList: ChatModelItemType[] = [ChatModelMap[OpenAiChatEnum.GPT35]];
+export const chatModelList: ChatModelItemType[] = [
+  ChatModelMap[OpenAiChatEnum.GPT35],
+  ChatModelMap[ClaudeEnum.Claude]
+];
 
 export enum ModelStatusEnum {
   running = 'running',

src/pages/api/chat/chat.ts

@@ -42,11 +42,12 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     await connectToDatabase();
     let startTime = Date.now();
 
-    const { model, showModelDetail, content, userApiKey, systemApiKey, userId } = await authChat({
+    const { model, showModelDetail, content, userOpenAiKey, systemAuthKey, userId } =
-      modelId,
+      await authChat({
-      chatId,
+        modelId,
-      authorization
+        chatId,
-    });
+        authorization
+      });
 
     const modelConstantsData = ChatModelMap[model.chat.chatModel];
 
@@ -56,8 +57,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     // 使用了知识库搜索
     if (model.chat.useKb) {
       const { code, searchPrompt } = await searchKb({
-        userApiKey,
+        userOpenAiKey,
-        systemApiKey,
         prompts,
         similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity,
         model,
@@ -86,10 +86,12 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
 
     // 发出请求
     const { streamResponse } = await modelServiceToolMap[model.chat.chatModel].chatCompletion({
-      apiKey: userApiKey || systemApiKey,
+      apiKey: userOpenAiKey || systemAuthKey,
       temperature: +temperature,
       messages: prompts,
-      stream: true
+      stream: true,
+      res,
+      chatId
     });
 
     console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
@@ -108,7 +110,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
 
     // 只有使用平台的 key 才计费
     pushChatBill({
-      isPay: !userApiKey,
+      isPay: !userOpenAiKey,
       chatModel: model.chat.chatModel,
       userId,
       chatId,

src/pages/api/chat/saveChat.ts

@@ -9,7 +9,8 @@ import mongoose from 'mongoose';
 /* 聊天内容存存储 */
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {
   try {
-    const { chatId, modelId, prompts } = req.body as {
+    const { chatId, modelId, prompts, newChatId } = req.body as {
+      newChatId: '' | string;
       chatId: '' | string;
       modelId: string;
       prompts: ChatItemType[];
@@ -35,6 +36,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     // 没有 chatId, 创建一个对话
     if (!chatId) {
       const { _id } = await Chat.create({
+        _id: newChatId ? new mongoose.Types.ObjectId(newChatId) : undefined,
         userId,
         modelId,
         content,

src/pages/api/openapi/chat/chat.ts

@@ -65,7 +65,6 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       const similarity = ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22;
 
       const { code, searchPrompt } = await searchKb({
-        systemApiKey: apiKey,
         prompts,
         similarity,
         model,

src/pages/api/openapi/chat/lafGpt.ts

@@ -116,7 +116,6 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
 
     // 获取向量匹配到的提示词
     const { searchPrompt } = await searchKb({
-      systemApiKey: apiKey,
       similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity,
       prompts,
       model,

src/pages/chat/index.tsx

@@ -206,7 +206,7 @@ const Chat = ({ modelId, chatId }: { modelId: string; chatId: string }) => {
       };
 
       // 流请求，获取数据
-      const { responseText, systemPrompt } = await streamFetch({
+      let { responseText, systemPrompt, newChatId } = await streamFetch({
         url: '/api/chat/chat',
         data: {
           prompt,
@@ -234,10 +234,10 @@ const Chat = ({ modelId, chatId }: { modelId: string; chatId: string }) => {
         return;
       }
 
-      let newChatId = '';
       // save chat record
       try {
         newChatId = await postSaveChat({
+          newChatId, // 如果有newChatId，会自动以这个Id创建对话框
           modelId,
           chatId,
           prompts: [

src/service/events/generateQA.ts

@@ -45,12 +45,12 @@ export async function generateQA(next = false): Promise<any> {
     const textList: string[] = dataItem.textList.slice(-5);
 
     // 获取 openapi Key
-    let userApiKey = '',
+    let userOpenAiKey = '',
-      systemApiKey = '';
+      systemAuthKey = '';
     try {
       const key = await getApiKey({ model: OpenAiChatEnum.GPT35, userId: dataItem.userId });
-      userApiKey = key.userApiKey;
+      userOpenAiKey = key.userOpenAiKey;
-      systemApiKey = key.systemApiKey;
+      systemAuthKey = key.systemAuthKey;
     } catch (error: any) {
       if (error?.code === 501) {
         // 余额不够了, 清空该记录
@@ -73,18 +73,18 @@ export async function generateQA(next = false): Promise<any> {
       textList.map((text) =>
         modelServiceToolMap[OpenAiChatEnum.GPT35]
           .chatCompletion({
-            apiKey: userApiKey || systemApiKey,
+            apiKey: userOpenAiKey || systemAuthKey,
             temperature: 0.8,
             messages: [
               {
                 obj: ChatRoleEnum.System,
                 value: `你是出题人
-          ${dataItem.prompt || '下面是"一段长文本"'}
+${dataItem.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:
+从中选出5至20个题目和答案.答案详细.按格式返回: Q1:
-          A1:
+A1:
-          Q2:
+Q2:
-          A2:
+A2:
-          ...`
+...`
               },
               {
                 obj: 'Human',
@@ -98,7 +98,7 @@ export async function generateQA(next = false): Promise<any> {
             console.log(`split result length: `, result.length);
             // 计费
             pushSplitDataBill({
-              isPay: !userApiKey && result.length > 0,
+              isPay: !userOpenAiKey && result.length > 0,
               userId: dataItem.userId,
               type: 'QA',
               textLen: responseMessages.map((item) => item.value).join('').length,

src/service/events/generateVector.ts

@@ -2,7 +2,6 @@ import { openaiCreateEmbedding } from '../utils/chat/openai';
 import { getApiKey } from '../utils/auth';
 import { openaiError2 } from '../errorCode';
 import { PgClient } from '@/service/pg';
-import { embeddingModel } from '@/constants/model';
 
 export async function generateVector(next = false): Promise<any> {
   if (process.env.queueTask !== '1') {
@@ -42,11 +41,10 @@ export async function generateVector(next = false): Promise<any> {
     dataId = dataItem.id;
 
     // 获取 openapi Key
-    let userApiKey, systemApiKey;
+    let userOpenAiKey;
     try {
-      const res = await getApiKey({ model: embeddingModel, userId: dataItem.userId });
+      const res = await getApiKey({ model: 'gpt-3.5-turbo', userId: dataItem.userId });
-      userApiKey = res.userApiKey;
+      userOpenAiKey = res.userOpenAiKey;
-      systemApiKey = res.systemApiKey;
     } catch (error: any) {
       if (error?.code === 501) {
         await PgClient.delete('modelData', {
@@ -63,8 +61,7 @@ export async function generateVector(next = false): Promise<any> {
     const { vectors } = await openaiCreateEmbedding({
       textArr: [dataItem.q],
       userId: dataItem.userId,
-      userApiKey,
+      userOpenAiKey
-      systemApiKey
     });
 
     // 更新 pg 向量和状态数据

src/service/plugins/searchKb.ts

@@ -3,22 +3,20 @@ import { ModelDataStatusEnum, ModelVectorSearchModeEnum, ChatModelMap } from '@/
 import { ModelSchema } from '@/types/mongoSchema';
 import { openaiCreateEmbedding } from '../utils/chat/openai';
 import { ChatRoleEnum } from '@/constants/chat';
-import { sliceTextByToken } from '@/utils/chat';
+import { modelToolMap } from '@/utils/chat';
 import { ChatItemSimpleType } from '@/types/chat';
 
 /**
  *  use openai embedding search kb
  */
 export const searchKb = async ({
-  userApiKey,
+  userOpenAiKey,
-  systemApiKey,
   prompts,
   similarity = 0.2,
   model,
   userId
 }: {
-  userApiKey?: string;
+  userOpenAiKey?: string;
-  systemApiKey: string;
   prompts: ChatItemSimpleType[];
   model: ModelSchema;
   userId: string;
@@ -33,8 +31,7 @@ export const searchKb = async ({
   async function search(textArr: string[] = []) {
     // 获取提示词的向量
     const { vectors: promptVectors } = await openaiCreateEmbedding({
-      userApiKey,
+      userOpenAiKey,
-      systemApiKey,
       userId,
       textArr
     });
@@ -81,11 +78,24 @@ export const searchKb = async ({
   ].filter((item) => item);
   const systemPrompts = await search(searchArr);
 
-  // filter system prompt
+  // filter system prompts.
-  if (
+  const filterRateMap: Record<number, number[]> = {
-    systemPrompts.length === 0 &&
+    1: [1],
-    model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity
+    2: [0.7, 0.3]
-  ) {
+  };
+  const filterRate = filterRateMap[systemPrompts.length] || filterRateMap[0];
+
+  const filterSystemPrompt = filterRate
+    .map((rate, i) =>
+      modelToolMap[model.chat.chatModel].sliceText({
+        text: systemPrompts[i],
+        length: Math.floor(modelConstantsData.systemMaxToken * rate)
+      })
+    )
+    .join('\n');
+
+  /* 高相似度+不回复 */
+  if (!filterSystemPrompt && model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity) {
     return {
       code: 201,
       searchPrompt: {
@@ -95,7 +105,7 @@ export const searchKb = async ({
     };
   }
   /* 高相似度+无上下文，不添加额外知识,仅用系统提示词 */
-  if (systemPrompts.length === 0 && model.chat.searchMode === ModelVectorSearchModeEnum.noContext) {
+  if (!filterSystemPrompt && model.chat.searchMode === ModelVectorSearchModeEnum.noContext) {
     return {
       code: 200,
       searchPrompt: model.chat.systemPrompt
@@ -107,25 +117,7 @@ export const searchKb = async ({
     };
   }
 
-  /* 有匹配情况下，system 添加知识库内容。 */
+  /* 有匹配 */
-
-  // filter system prompts. max 70% tokens
-  const filterRateMap: Record<number, number[]> = {
-    1: [0.7],
-    2: [0.5, 0.2]
-  };
-  const filterRate = filterRateMap[systemPrompts.length] || filterRateMap[0];
-
-  const filterSystemPrompt = filterRate
-    .map((rate, i) =>
-      sliceTextByToken({
-        model: model.chat.chatModel,
-        text: systemPrompts[i],
-        length: Math.floor(modelConstantsData.contextMaxToken * rate)
-      })
-    )
-    .join('\n');
-
   return {
     code: 200,
     searchPrompt: {
@@ -133,9 +125,9 @@ export const searchKb = async ({
       value: `
 ${model.chat.systemPrompt}
 ${
-  model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity ? `不回答知识库外的内容.` : ''
+  model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity ? '不回答知识库外的内容.' : ''
 }
-知识库内容为: ${filterSystemPrompt}'
+知识库内容为: '${filterSystemPrompt}'
 `
     }
   };

src/service/utils/auth.ts

@@ -4,15 +4,10 @@ import { Chat, Model, OpenApi, User } from '../mongo';
 import type { ModelSchema } from '@/types/mongoSchema';
 import type { ChatItemSimpleType } from '@/types/chat';
 import mongoose from 'mongoose';
-import { defaultModel } from '@/constants/model';
+import { ClaudeEnum, defaultModel } from '@/constants/model';
 import { formatPrice } from '@/utils/user';
 import { ERROR_ENUM } from '../errorCode';
-import {
+import { ChatModelType, OpenAiChatEnum } from '@/constants/model';
-  ChatModelType,
-  OpenAiChatEnum,
-  embeddingModel,
-  EmbeddingModelType
-} from '@/constants/model';
 
 /* 校验 token */
 export const authToken = (token?: string): Promise<string> => {
@@ -34,13 +29,7 @@ export const authToken = (token?: string): Promise<string> => {
 };
 
 /* 获取 api 请求的 key */
-export const getApiKey = async ({
+export const getApiKey = async ({ model, userId }: { model: ChatModelType; userId: string }) => {
-  model,
-  userId
-}: {
-  model: ChatModelType | EmbeddingModelType;
-  userId: string;
-}) => {
   const user = await User.findById(userId);
   if (!user) {
     return Promise.reject({
@@ -51,29 +40,29 @@ export const getApiKey = async ({
 
   const keyMap = {
     [OpenAiChatEnum.GPT35]: {
-      userApiKey: user.openaiKey || '',
+      userOpenAiKey: user.openaiKey || '',
-      systemApiKey: process.env.OPENAIKEY as string
+      systemAuthKey: process.env.OPENAIKEY as string
     },
     [OpenAiChatEnum.GPT4]: {
-      userApiKey: user.openaiKey || '',
+      userOpenAiKey: user.openaiKey || '',
-      systemApiKey: process.env.OPENAIKEY as string
+      systemAuthKey: process.env.OPENAIKEY as string
     },
     [OpenAiChatEnum.GPT432k]: {
-      userApiKey: user.openaiKey || '',
+      userOpenAiKey: user.openaiKey || '',
-      systemApiKey: process.env.OPENAIKEY as string
+      systemAuthKey: process.env.OPENAIKEY as string
     },
-    [embeddingModel]: {
+    [ClaudeEnum.Claude]: {
-      userApiKey: user.openaiKey || '',
+      userOpenAiKey: '',
-      systemApiKey: process.env.OPENAIKEY as string
+      systemAuthKey: process.env.LAFKEY as string
     }
   };
 
   // 有自己的key
-  if (keyMap[model].userApiKey) {
+  if (keyMap[model].userOpenAiKey) {
     return {
       user,
-      userApiKey: keyMap[model].userApiKey,
+      userOpenAiKey: keyMap[model].userOpenAiKey,
-      systemApiKey: ''
+      systemAuthKey: ''
     };
   }
 
@@ -87,8 +76,8 @@ export const getApiKey = async ({
 
   return {
     user,
-    userApiKey: '',
+    userOpenAiKey: '',
-    systemApiKey: keyMap[model].systemApiKey
+    systemAuthKey: keyMap[model].systemAuthKey
   };
 };
 
@@ -176,11 +165,11 @@ export const authChat = async ({
     ]);
   }
   // 获取 user 的 apiKey
-  const { userApiKey, systemApiKey } = await getApiKey({ model: model.chat.chatModel, userId });
+  const { userOpenAiKey, systemAuthKey } = await getApiKey({ model: model.chat.chatModel, userId });
 
   return {
-    userApiKey,
+    userOpenAiKey,
-    systemApiKey,
+    systemAuthKey,
     content,
     userId,
     model,

src/service/utils/chat/claude.ts

@@ -0,0 +1,103 @@
+import { modelToolMap } from '@/utils/chat';
+import { ChatCompletionType, StreamResponseType } from './index';
+import { ChatRoleEnum } from '@/constants/chat';
+import axios from 'axios';
+import mongoose from 'mongoose';
+import { NEW_CHATID_HEADER } from '@/constants/chat';
+import { ClaudeEnum } from '@/constants/model';
+
+/* 模型对话 */
+export const lafClaudChat = async ({
+  apiKey,
+  messages,
+  stream,
+  chatId,
+  res
+}: ChatCompletionType) => {
+  const conversationId = chatId || String(new mongoose.Types.ObjectId());
+  // create a new chat
+  !chatId &&
+    messages.filter((item) => item.obj === 'Human').length === 1 &&
+    res?.setHeader(NEW_CHATID_HEADER, conversationId);
+
+  // get system prompt
+  const systemPrompt = messages
+    .filter((item) => item.obj === 'System')
+    .map((item) => item.value)
+    .join('\n');
+  const systemPromptText = systemPrompt ? `这是我的知识:'${systemPrompt}'\n` : '';
+
+  const prompt = systemPromptText + messages[messages.length - 1].value;
+
+  const lafResponse = await axios.post(
+    'https://hnvacz.laf.run/claude-gpt',
+    {
+      prompt,
+      stream,
+      conversationId
+    },
+    {
+      headers: {
+        Authorization: apiKey
+      },
+      timeout: stream ? 40000 : 240000,
+      responseType: stream ? 'stream' : 'json'
+    }
+  );
+
+  let responseText = '';
+  let totalTokens = 0;
+
+  if (!stream) {
+    responseText = lafResponse.data?.text || '';
+  }
+
+  return {
+    streamResponse: lafResponse,
+    responseMessages: messages.concat({ obj: ChatRoleEnum.AI, value: responseText }),
+    responseText,
+    totalTokens
+  };
+};
+
+/* openai stream response */
+export const lafClaudStreamResponse = async ({
+  stream,
+  chatResponse,
+  prompts
+}: StreamResponseType) => {
+  try {
+    let responseContent = '';
+
+    try {
+      const decoder = new TextDecoder();
+      for await (const chunk of chatResponse.data as any) {
+        if (stream.destroyed) {
+          // 流被中断了，直接忽略后面的内容
+          break;
+        }
+        const content = decoder.decode(chunk);
+        responseContent += content;
+        content && stream.push(content.replace(/\n/g, '<br/>'));
+      }
+    } catch (error) {
+      console.log('pipe error', error);
+    }
+    // count tokens
+    const finishMessages = prompts.concat({
+      obj: ChatRoleEnum.AI,
+      value: responseContent
+    });
+    const totalTokens = modelToolMap[ClaudeEnum.Claude].countTokens({
+      messages: finishMessages
+    });
+
+    return {
+      responseContent,
+      totalTokens,
+      finishMessages
+    };
+  } catch (error) {
+    return Promise.reject(error);
+  }
+};

src/service/utils/chat/index.ts

@@ -1,19 +1,19 @@
 import { ChatItemSimpleType } from '@/types/chat';
 import { modelToolMap } from '@/utils/chat';
 import type { ChatModelType } from '@/constants/model';
-import { ChatRoleEnum, SYSTEM_PROMPT_PREFIX } from '@/constants/chat';
+import { ChatRoleEnum, SYSTEM_PROMPT_HEADER } from '@/constants/chat';
-import { OpenAiChatEnum } from '@/constants/model';
+import { OpenAiChatEnum, ClaudeEnum } from '@/constants/model';
 import { chatResponse, openAiStreamResponse } from './openai';
+import { lafClaudChat, lafClaudStreamResponse } from './claude';
 import type { NextApiResponse } from 'next';
 import type { PassThrough } from 'stream';
-import delay from 'delay';
 
 export type ChatCompletionType = {
   apiKey: string;
   temperature: number;
   messages: ChatItemSimpleType[];
   stream: boolean;
-  params?: any;
+  [key: string]: any;
 };
 export type ChatCompletionResponseType = {
   streamResponse: any;
@@ -25,6 +25,9 @@ export type StreamResponseType = {
   stream: PassThrough;
   chatResponse: any;
   prompts: ChatItemSimpleType[];
+  res: NextApiResponse;
+  systemPrompt?: string;
+  [key: string]: any;
 };
 export type StreamResponseReturnType = {
   responseContent: string;
@@ -65,6 +68,10 @@ export const modelServiceToolMap: Record<
         model: OpenAiChatEnum.GPT432k,
         ...data
       })
+  },
+  [ClaudeEnum.Claude]: {
+    chatCompletion: lafClaudChat,
+    streamResponse: lafClaudStreamResponse
   }
 };
 
@@ -143,14 +150,13 @@ export const resStreamResponse = async ({
   prompts
 }: StreamResponseType & {
   model: ChatModelType;
-  res: NextApiResponse;
-  systemPrompt?: string;
 }) => {
   // 创建响应流
   res.setHeader('Content-Type', 'text/event-stream;charset-utf-8');
   res.setHeader('Access-Control-Allow-Origin', '*');
   res.setHeader('X-Accel-Buffering', 'no');
   res.setHeader('Cache-Control', 'no-cache, no-transform');
+  systemPrompt && res.setHeader(SYSTEM_PROMPT_HEADER, encodeURIComponent(systemPrompt));
   stream.pipe(res);
 
   const { responseContent, totalTokens, finishMessages } = await modelServiceToolMap[
@@ -158,16 +164,11 @@ export const resStreamResponse = async ({
   ].streamResponse({
     chatResponse,
     stream,
-    prompts
+    prompts,
+    res,
+    systemPrompt
   });
 
-  await delay(100);
-
-  // push system prompt
-  !stream.destroyed &&
-    systemPrompt &&
-    stream.push(`${SYSTEM_PROMPT_PREFIX}${systemPrompt.replace(/\n/g, '<br/>')}`);
-
   // close stream
   !stream.destroyed && stream.push(null);
   stream.destroy();

src/service/utils/chat/openai.ts

@@ -19,18 +19,18 @@ export const getOpenAIApi = (apiKey: string) => {
 
 /* 获取向量 */
 export const openaiCreateEmbedding = async ({
-  userApiKey,
+  userOpenAiKey,
-  systemApiKey,
   userId,
   textArr
 }: {
-  userApiKey?: string;
+  userOpenAiKey?: string;
-  systemApiKey: string;
   userId: string;
   textArr: string[];
 }) => {
+  const systemAuthKey = process.env.OPENAIKEY as string;
+
   // 获取 chatAPI
-  const chatAPI = getOpenAIApi(userApiKey || systemApiKey);
+  const chatAPI = getOpenAIApi(userOpenAiKey || systemAuthKey);
 
   // 把输入的内容转成向量
   const res = await chatAPI
@@ -50,7 +50,7 @@ export const openaiCreateEmbedding = async ({
     }));
 
   pushGenerateVectorBill({
-    isPay: !userApiKey,
+    isPay: !userOpenAiKey,
     userId,
     text: textArr.join(''),
     tokenLen: res.tokenLen

src/utils/chat/claude.ts

@@ -0,0 +1,3 @@
+export const ClaudeSliceTextByToken = ({ text, length }: { text: string; length: number }) => {
+  return text.slice(0, length);
+};

src/utils/chat/index.ts

@@ -1,46 +1,30 @@
-import { OpenAiChatEnum } from '@/constants/model';
+import { ClaudeEnum, OpenAiChatEnum } from '@/constants/model';
 import type { ChatModelType } from '@/constants/model';
 import type { ChatItemSimpleType } from '@/types/chat';
-import { countOpenAIToken, getOpenAiEncMap, adaptChatItem_openAI } from './openai';
+import { countOpenAIToken, openAiSliceTextByToken } from './openai';
-import { ChatCompletionRequestMessage } from 'openai';
+import { ClaudeSliceTextByToken } from './claude';
-
-export type CountTokenType = { messages: ChatItemSimpleType[] };
 
 export const modelToolMap: Record<
   ChatModelType,
   {
-    countTokens: (data: CountTokenType) => number;
+    countTokens: (data: { messages: ChatItemSimpleType[] }) => number;
-    adaptChatMessages: (data: CountTokenType) => ChatCompletionRequestMessage[];
+    sliceText: (data: { text: string; length: number }) => string;
   }
 > = {
   [OpenAiChatEnum.GPT35]: {
-    countTokens: ({ messages }: CountTokenType) =>
+    countTokens: ({ messages }) => countOpenAIToken({ model: OpenAiChatEnum.GPT35, messages }),
-      countOpenAIToken({ model: OpenAiChatEnum.GPT35, messages }),
+    sliceText: (data) => openAiSliceTextByToken({ model: OpenAiChatEnum.GPT35, ...data })
-    adaptChatMessages: adaptChatItem_openAI
   },
   [OpenAiChatEnum.GPT4]: {
-    countTokens: ({ messages }: CountTokenType) =>
+    countTokens: ({ messages }) => countOpenAIToken({ model: OpenAiChatEnum.GPT4, messages }),
-      countOpenAIToken({ model: OpenAiChatEnum.GPT4, messages }),
+    sliceText: (data) => openAiSliceTextByToken({ model: OpenAiChatEnum.GPT35, ...data })
-    adaptChatMessages: adaptChatItem_openAI
   },
   [OpenAiChatEnum.GPT432k]: {
-    countTokens: ({ messages }: CountTokenType) =>
+    countTokens: ({ messages }) => countOpenAIToken({ model: OpenAiChatEnum.GPT432k, messages }),
-      countOpenAIToken({ model: OpenAiChatEnum.GPT432k, messages }),
+    sliceText: (data) => openAiSliceTextByToken({ model: OpenAiChatEnum.GPT35, ...data })
-    adaptChatMessages: adaptChatItem_openAI
+  },
+  [ClaudeEnum.Claude]: {
+    countTokens: () => 0,
+    sliceText: ClaudeSliceTextByToken
   }
 };
-
-export const sliceTextByToken = ({
-  model = 'gpt-3.5-turbo',
-  text,
-  length
-}: {
-  model: ChatModelType;
-  text: string;
-  length: number;
-}) => {
-  const enc = getOpenAiEncMap()[model];
-  const encodeText = enc.encode(text);
-  const decoder = new TextDecoder();
-  return decoder.decode(enc.decode(encodeText.slice(0, length)));
-};

src/utils/chat/openai.ts

@@ -2,7 +2,7 @@ import { encoding_for_model, type Tiktoken } from '@dqbd/tiktoken';
 import type { ChatItemSimpleType } from '@/types/chat';
 import { ChatRoleEnum } from '@/constants/chat';
 import { ChatCompletionRequestMessage, ChatCompletionRequestMessageRoleEnum } from 'openai';
-
+import { OpenAiChatEnum } from '@/constants/model';
 import Graphemer from 'graphemer';
 
 const textDecoder = new TextDecoder();
@@ -52,7 +52,7 @@ export function countOpenAIToken({
   model
 }: {
   messages: ChatItemSimpleType[];
-  model: 'gpt-3.5-turbo' | 'gpt-4' | 'gpt-4-32k';
+  model: `${OpenAiChatEnum}`;
 }) {
   function getChatGPTEncodingText(
     messages: { role: 'system' | 'user' | 'assistant'; content: string; name?: string }[],
@@ -104,3 +104,18 @@ export function countOpenAIToken({
 
   return text2TokensLen(getOpenAiEncMap()[model], getChatGPTEncodingText(adaptMessages, model));
 }
+
+export const openAiSliceTextByToken = ({
+  model = 'gpt-3.5-turbo',
+  text,
+  length
+}: {
+  model: `${OpenAiChatEnum}`;
+  text: string;
+  length: number;
+}) => {
+  const enc = getOpenAiEncMap()[model];
+  const encodeText = enc.encode(text);
+  const decoder = new TextDecoder();
+  return decoder.decode(enc.decode(encodeText.slice(0, length)));
+};