perf: chat framwork

src/constants/model.ts

@@ -1,6 +1,7 @@
 import type { ModelSchema } from '@/types/mongoSchema';
 
 export const embeddingModel = 'text-embedding-ada-002';
+export type EmbeddingModelType = 'text-embedding-ada-002';
 
 export enum OpenAiChatEnum {
   'GPT35' = 'gpt-3.5-turbo',
@@ -25,7 +26,7 @@ export const ChatModelMap = {
   },
   [OpenAiChatEnum.GPT432k]: {
     name: 'Gpt4-32k',
-    contextMaxToken: 8000,
+    contextMaxToken: 32000,
     maxTemperature: 1.5,
     price: 30
   }

src/pages/api/chat/chat.ts

@@ -1,14 +1,15 @@
 import type { NextApiRequest, NextApiResponse } from 'next';
 import { connectToDatabase } from '@/service/mongo';
-import { getOpenAIApi, authChat } from '@/service/utils/auth';
+import { authChat } from '@/service/utils/auth';
-import { axiosConfig, openaiChatFilter } from '@/service/utils/tools';
+import { modelServiceToolMap } from '@/service/utils/chat';
 import { ChatItemSimpleType } from '@/types/chat';
 import { jsonRes } from '@/service/response';
 import { PassThrough } from 'stream';
 import { ChatModelMap, ModelVectorSearchModeMap } from '@/constants/model';
 import { pushChatBill } from '@/service/events/pushBill';
-import { gpt35StreamResponse } from '@/service/utils/openai';
+import { resStreamResponse } from '@/service/utils/chat';
-import { searchKb_openai } from '@/service/tools/searchKb';
+import { searchKb } from '@/service/plugins/searchKb';
+import { ChatRoleEnum } from '@/constants/chat';
 
 /* 发送提示词 */
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {
@@ -41,7 +42,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     await connectToDatabase();
     let startTime = Date.now();
 
-    const { model, showModelDetail, content, userApiKey, systemKey, userId } = await authChat({
+    const { model, showModelDetail, content, userApiKey, systemApiKey, userId } = await authChat({
       modelId,
       chatId,
       authorization
@@ -54,9 +55,9 @@ 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_openai({
+      const { code, searchPrompt } = await searchKb({
-        apiKey: userApiKey || systemKey,
+        userApiKey,
-        isPay: !userApiKey,
+        systemApiKey,
         text: prompt.value,
         similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity,
         model,
@@ -73,53 +74,37 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       // 没有用知识库搜索，仅用系统提示词
       model.chat.systemPrompt &&
         prompts.unshift({
-          obj: 'SYSTEM',
+          obj: ChatRoleEnum.System,
           value: model.chat.systemPrompt
         });
     }
 
-    // 控制总 tokens 数量，防止超出
-    const filterPrompts = openaiChatFilter({
-      model: model.chat.chatModel,
-      prompts,
-      maxTokens: modelConstantsData.contextMaxToken - 300
-    });
-
     // 计算温度
     const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
       2
     );
     // console.log(filterPrompts);
-    // 获取 chatAPI
+
-    const chatAPI = getOpenAIApi(userApiKey || systemKey);
     // 发出请求
-    const chatResponse = await chatAPI.createChatCompletion(
+    const { streamResponse } = await modelServiceToolMap[model.chat.chatModel].chatCompletion({
-      {
+      apiKey: userApiKey || systemApiKey,
-        model: model.chat.chatModel,
+      temperature: +temperature,
-        temperature: Number(temperature) || 0,
+      messages: prompts,
-        messages: filterPrompts,
+      stream: true
-        frequency_penalty: 0.5, // 越大，重复内容越少
+    });
-        presence_penalty: -0.5, // 越大，越容易出现新内容
-        stream: true,
-        stop: ['.!?。']
-      },
-      {
-        timeout: 40000,
-        responseType: 'stream',
-        ...axiosConfig()
-      }
-    );
 
     console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
 
     step = 1;
 
-    const { responseContent } = await gpt35StreamResponse({
+    const { totalTokens, finishMessages } = await resStreamResponse({
+      model: model.chat.chatModel,
       res,
       stream,
-      chatResponse,
+      chatResponse: streamResponse,
+      prompts,
       systemPrompt:
-        showModelDetail && filterPrompts[0].role === 'system' ? filterPrompts[0].content : ''
+        showModelDetail && prompts[0].obj === ChatRoleEnum.System ? prompts[0].value : ''
     });
 
     // 只有使用平台的 key 才计费
@@ -128,7 +113,8 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       chatModel: model.chat.chatModel,
       userId,
       chatId,
-      messages: filterPrompts.concat({ role: 'assistant', content: responseContent })
+      textLen: finishMessages.map((item) => item.value).join('').length,
+      tokens: totalTokens
     });
   } catch (err: any) {
     if (step === 1) {

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

@@ -1,14 +1,14 @@
 import type { NextApiRequest, NextApiResponse } from 'next';
 import { connectToDatabase } from '@/service/mongo';
-import { getOpenAIApi, authOpenApiKey, authModel } from '@/service/utils/auth';
+import { authOpenApiKey, authModel } from '@/service/utils/auth';
-import { axiosConfig, openaiChatFilter } from '@/service/utils/tools';
+import { modelServiceToolMap, resStreamResponse } from '@/service/utils/chat';
 import { ChatItemSimpleType } from '@/types/chat';
 import { jsonRes } from '@/service/response';
 import { PassThrough } from 'stream';
 import { ChatModelMap, ModelVectorSearchModeMap } from '@/constants/model';
 import { pushChatBill } from '@/service/events/pushBill';
-import { gpt35StreamResponse } from '@/service/utils/openai';
+import { searchKb } from '@/service/plugins/searchKb';
-import { searchKb_openai } from '@/service/tools/searchKb';
+import { ChatRoleEnum } from '@/constants/chat';
 
 /* 发送提示词 */
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {
@@ -64,9 +64,8 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     if (model.chat.useKb) {
       const similarity = ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22;
 
-      const { code, searchPrompt } = await searchKb_openai({
+      const { code, searchPrompt } = await searchKb({
-        apiKey,
+        systemApiKey: apiKey,
-        isPay: true,
         text: prompts[prompts.length - 1].value,
         similarity,
         model,
@@ -83,69 +82,55 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       // 没有用知识库搜索，仅用系统提示词
       if (model.chat.systemPrompt) {
         prompts.unshift({
-          obj: 'SYSTEM',
+          obj: ChatRoleEnum.System,
           value: model.chat.systemPrompt
         });
       }
     }
 
-    // 控制总 tokens 数量，防止超出
-    const filterPrompts = openaiChatFilter({
-      model: model.chat.chatModel,
-      prompts,
-      maxTokens: modelConstantsData.contextMaxToken - 300
-    });
-
     // 计算温度
     const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
       2
     );
-    // console.log(filterPrompts);
+
-    // 获取 chatAPI
-    const chatAPI = getOpenAIApi(apiKey);
     // 发出请求
-    const chatResponse = await chatAPI.createChatCompletion(
+    const { streamResponse, responseMessages, responseText, totalTokens } =
-      {
+      await modelServiceToolMap[model.chat.chatModel].chatCompletion({
-        model: model.chat.chatModel,
+        apiKey,
-        temperature: Number(temperature) || 0,
+        temperature: +temperature,
-        messages: filterPrompts,
+        messages: prompts,
-        frequency_penalty: 0.5, // 越大，重复内容越少
+        stream: isStream
-        presence_penalty: -0.5, // 越大，越容易出现新内容
+      });
-        stream: isStream,
-        stop: ['.!?。']
-      },
-      {
-        timeout: 180000,
-        responseType: isStream ? 'stream' : 'json',
-        ...axiosConfig()
-      }
-    );
 
     console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
 
-    let responseContent = '';
+    let textLen = 0;
+    let tokens = totalTokens;
 
     if (isStream) {
       step = 1;
-      const streamResponse = await gpt35StreamResponse({
+      const { finishMessages, totalTokens } = await resStreamResponse({
+        model: model.chat.chatModel,
         res,
         stream,
-        chatResponse
+        chatResponse: streamResponse,
+        prompts
       });
-      responseContent = streamResponse.responseContent;
+      textLen = finishMessages.map((item) => item.value).join('').length;
+      tokens = totalTokens;
     } else {
-      responseContent = chatResponse.data.choices?.[0]?.message?.content || '';
+      textLen = responseMessages.map((item) => item.value).join('').length;
       jsonRes(res, {
-        data: responseContent
+        data: responseText
       });
     }
 
-    // 只有使用平台的 key 才计费
     pushChatBill({
       isPay: true,
       chatModel: model.chat.chatModel,
       userId,
-      messages: filterPrompts.concat({ role: 'assistant', content: responseContent })
+      textLen,
+      tokens
     });
   } catch (err: any) {
     if (step === 1) {

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

@@ -1,144 +0,0 @@
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { connectToDatabase, Model } from '@/service/mongo';
-import { getOpenAIApi, authOpenApiKey } from '@/service/utils/auth';
-import { axiosConfig, openaiChatFilter } from '@/service/utils/tools';
-import { ChatItemSimpleType } from '@/types/chat';
-import { jsonRes } from '@/service/response';
-import { PassThrough } from 'stream';
-import { ChatModelMap } from '@/constants/model';
-import { pushChatBill } from '@/service/events/pushBill';
-import { gpt35StreamResponse } from '@/service/utils/openai';
-
-/* 发送提示词 */
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  let step = 0; // step=1时，表示开始了流响应
-  const stream = new PassThrough();
-  stream.on('error', () => {
-    console.log('error: ', 'stream error');
-    stream.destroy();
-  });
-  res.on('close', () => {
-    stream.destroy();
-  });
-  res.on('error', () => {
-    console.log('error: ', 'request error');
-    stream.destroy();
-  });
-
-  try {
-    const {
-      prompts,
-      modelId,
-      isStream = true
-    } = req.body as {
-      prompts: ChatItemSimpleType[];
-      modelId: string;
-      isStream: boolean;
-    };
-
-    if (!prompts || !modelId) {
-      throw new Error('缺少参数');
-    }
-    if (!Array.isArray(prompts)) {
-      throw new Error('prompts is not array');
-    }
-    if (prompts.length > 30 || prompts.length === 0) {
-      throw new Error('prompts length range 1-30');
-    }
-
-    await connectToDatabase();
-    let startTime = Date.now();
-
-    const { apiKey, userId } = await authOpenApiKey(req);
-
-    const model = await Model.findOne({
-      _id: modelId,
-      userId
-    });
-
-    if (!model) {
-      throw new Error('无权使用该模型');
-    }
-
-    const modelConstantsData = ChatModelMap[model.chat.chatModel];
-
-    // 如果有系统提示词，自动插入
-    if (model.chat.systemPrompt) {
-      prompts.unshift({
-        obj: 'SYSTEM',
-        value: model.chat.systemPrompt
-      });
-    }
-
-    // 控制在 tokens 数量，防止超出
-    const filterPrompts = openaiChatFilter({
-      model: model.chat.chatModel,
-      prompts,
-      maxTokens: modelConstantsData.contextMaxToken - 300
-    });
-
-    // console.log(filterPrompts);
-    // 计算温度
-    const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
-      2
-    );
-    // 获取 chatAPI
-    const chatAPI = getOpenAIApi(apiKey);
-    // 发出请求
-    const chatResponse = await chatAPI.createChatCompletion(
-      {
-        model: model.chat.chatModel,
-        temperature: Number(temperature) || 0,
-        messages: filterPrompts,
-        frequency_penalty: 0.5, // 越大，重复内容越少
-        presence_penalty: -0.5, // 越大，越容易出现新内容
-        stream: isStream,
-        stop: ['.!?。']
-      },
-      {
-        timeout: 40000,
-        responseType: isStream ? 'stream' : 'json',
-        ...axiosConfig()
-      }
-    );
-
-    console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
-
-    let responseContent = '';
-
-    if (isStream) {
-      step = 1;
-      const streamResponse = await gpt35StreamResponse({
-        res,
-        stream,
-        chatResponse
-      });
-      responseContent = streamResponse.responseContent;
-    } else {
-      responseContent = chatResponse.data.choices?.[0]?.message?.content || '';
-      jsonRes(res, {
-        data: responseContent
-      });
-    }
-
-    // 只有使用平台的 key 才计费
-    pushChatBill({
-      isPay: true,
-      chatModel: model.chat.chatModel,
-      userId,
-      messages: filterPrompts.concat({ role: 'assistant', content: responseContent })
-    });
-  } catch (err: any) {
-    if (step === 1) {
-      // 直接结束流
-      console.log('error，结束');
-      stream.destroy();
-    } else {
-      res.status(500);
-      jsonRes(res, {
-        code: 500,
-        error: err
-      });
-    }
-  }
-}

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

@@ -1,14 +1,14 @@
 import type { NextApiRequest, NextApiResponse } from 'next';
 import { connectToDatabase, Model } from '@/service/mongo';
-import { getOpenAIApi, authOpenApiKey } from '@/service/utils/auth';
+import { authOpenApiKey } from '@/service/utils/auth';
-import { axiosConfig, openaiChatFilter } from '@/service/utils/tools';
+import { resStreamResponse, modelServiceToolMap } from '@/service/utils/chat';
 import { ChatItemSimpleType } from '@/types/chat';
 import { jsonRes } from '@/service/response';
 import { PassThrough } from 'stream';
-import { ChatModelMap, ModelVectorSearchModeMap, OpenAiChatEnum } from '@/constants/model';
+import { ChatModelMap, ModelVectorSearchModeMap } from '@/constants/model';
 import { pushChatBill } from '@/service/events/pushBill';
-import { gpt35StreamResponse } from '@/service/utils/openai';
+import { searchKb } from '@/service/plugins/searchKb';
-import { searchKb_openai } from '@/service/tools/searchKb';
+import { ChatRoleEnum } from '@/constants/chat';
 
 /* 发送提示词 */
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {
@@ -57,20 +57,16 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
 
     console.log('laf gpt start');
 
-    // 获取 chatAPI
-    const chatAPI = getOpenAIApi(apiKey);
-
     // 请求一次 chatgpt 拆解需求
-    const promptResponse = await chatAPI.createChatCompletion(
+    const { responseText: resolveText, totalTokens: resolveTokens } = await modelServiceToolMap[
-      {
+      model.chat.chatModel
-        model: OpenAiChatEnum.GPT35,
+    ].chatCompletion({
-        temperature: 0,
+      apiKey,
-        frequency_penalty: 0.5, // 越大，重复内容越少
+      temperature: 0,
-        presence_penalty: -0.5, // 越大，越容易出现新内容
+      messages: [
-        messages: [
+        {
-          {
+          obj: ChatRoleEnum.System,
-            role: 'system',
+          value: `服务端逻辑生成器.根据用户输入的需求,拆解成 laf 云函数实现的步骤,只返回步骤,按格式返回步骤: 1.\n2.\n3.\n ......
-            content: `服务端逻辑生成器.根据用户输入的需求,拆解成 laf 云函数实现的步骤,只返回步骤,按格式返回步骤: 1.\n2.\n3.\n ......
 下面是一些例子:
 一个 hello world 例子
 1. 返回字符串: "hello world"
@@ -103,35 +99,25 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
 5. 获取当前时间,记录为 updateTime.
 6. 更新数据库数据,表为"blogs",更新符合 blogId 的记录的内容为{blogText, tags, updateTime}.
 7. 返回结果 "更新博客记录成功"`
-          },
+        },
-          {
+        {
-            role: 'user',
+          obj: ChatRoleEnum.Human,
-            content: prompt.value
+          value: prompt.value
-          }
+        }
-        ]
+      ],
-      },
+      stream: false
-      {
+    });
-        timeout: 180000,
-        ...axiosConfig()
-      }
-    );
 
-    const promptResolve = promptResponse.data.choices?.[0]?.message?.content || '';
+    prompt.value += ` ${resolveText}`;
-    if (!promptResolve) {
-      throw new Error('gpt 异常');
-    }
-
-    prompt.value += ` ${promptResolve}`;
     console.log('prompt resolve success, time:', `${(Date.now() - startTime) / 1000}s`);
 
     // 读取对话内容
     const prompts = [prompt];
 
     // 获取向量匹配到的提示词
-    const { searchPrompt } = await searchKb_openai({
+    const { searchPrompt } = await searchKb({
-      isPay: true,
+      systemApiKey: apiKey,
-      apiKey,
+      similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity,
-      similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22,
       text: prompt.value,
       model,
       userId
@@ -139,49 +125,41 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
 
     searchPrompt && prompts.unshift(searchPrompt);
 
-    // 控制上下文 tokens 数量，防止超出
-    const filterPrompts = openaiChatFilter({
-      model: model.chat.chatModel,
-      prompts,
-      maxTokens: modelConstantsData.contextMaxToken - 300
-    });
-
-    // console.log(filterPrompts);
     // 计算温度
     const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
       2
     );
-    // 发出请求
-    const chatResponse = await chatAPI.createChatCompletion(
-      {
-        model: model.chat.chatModel,
-        temperature: Number(temperature) || 0,
-        messages: filterPrompts,
-        frequency_penalty: 0.5, // 越大，重复内容越少
-        presence_penalty: -0.5, // 越大，越容易出现新内容
-        stream: isStream
-      },
-      {
-        timeout: 180000,
-        responseType: isStream ? 'stream' : 'json',
-        ...axiosConfig()
-      }
-    );
 
-    let responseContent = '';
+    // 发出请求
+    const { streamResponse, responseMessages, responseText, totalTokens } =
+      await modelServiceToolMap[model.chat.chatModel].chatCompletion({
+        apiKey,
+        temperature: +temperature,
+        messages: prompts,
+        stream: isStream
+      });
+
+    console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
+
+    let textLen = resolveText.length;
+    let tokens = resolveTokens;
 
     if (isStream) {
       step = 1;
-      const streamResponse = await gpt35StreamResponse({
+      const { finishMessages, totalTokens } = await resStreamResponse({
+        model: model.chat.chatModel,
         res,
         stream,
-        chatResponse
+        chatResponse: streamResponse,
+        prompts
       });
-      responseContent = streamResponse.responseContent;
+      textLen += finishMessages.map((item) => item.value).join('').length;
+      tokens += totalTokens;
     } else {
-      responseContent = chatResponse.data.choices?.[0]?.message?.content || '';
+      textLen += responseMessages.map((item) => item.value).join('').length;
+      tokens += totalTokens;
       jsonRes(res, {
-        data: responseContent
+        data: responseText
       });
     }
 
@@ -191,7 +169,8 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       isPay: true,
       chatModel: model.chat.chatModel,
       userId,
-      messages: filterPrompts.concat({ role: 'assistant', content: responseContent })
+      textLen,
+      tokens
     });
   } catch (err: any) {
     if (step === 1) {

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

@@ -1,159 +0,0 @@
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { connectToDatabase, Model } from '@/service/mongo';
-import { axiosConfig, openaiChatFilter } from '@/service/utils/tools';
-import { getOpenAIApi, authOpenApiKey } from '@/service/utils/auth';
-import { ChatItemSimpleType } from '@/types/chat';
-import { jsonRes } from '@/service/response';
-import { PassThrough } from 'stream';
-import {
-  ChatModelMap,
-  ModelVectorSearchModeMap,
-  ModelVectorSearchModeEnum
-} from '@/constants/model';
-import { pushChatBill } from '@/service/events/pushBill';
-import { gpt35StreamResponse } from '@/service/utils/openai';
-import { searchKb_openai } from '@/service/tools/searchKb';
-
-/* 发送提示词 */
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  let step = 0; // step=1时，表示开始了流响应
-  const stream = new PassThrough();
-  stream.on('error', () => {
-    console.log('error: ', 'stream error');
-    stream.destroy();
-  });
-  res.on('close', () => {
-    stream.destroy();
-  });
-  res.on('error', () => {
-    console.log('error: ', 'request error');
-    stream.destroy();
-  });
-
-  try {
-    const {
-      prompts,
-      modelId,
-      isStream = true
-    } = req.body as {
-      prompts: ChatItemSimpleType[];
-      modelId: string;
-      isStream: boolean;
-    };
-
-    if (!prompts || !modelId) {
-      throw new Error('缺少参数');
-    }
-    if (!Array.isArray(prompts)) {
-      throw new Error('prompts is not array');
-    }
-    if (prompts.length > 30 || prompts.length === 0) {
-      throw new Error('prompts length range 1-30');
-    }
-
-    await connectToDatabase();
-    let startTime = Date.now();
-
-    /* 凭证校验 */
-    const { apiKey, userId } = await authOpenApiKey(req);
-
-    const model = await Model.findOne({
-      _id: modelId,
-      userId
-    });
-
-    if (!model) {
-      throw new Error('无权使用该模型');
-    }
-
-    const modelConstantsData = ChatModelMap[model.chat.chatModel];
-
-    // 获取向量匹配到的提示词
-    const { code, searchPrompt } = await searchKb_openai({
-      isPay: true,
-      apiKey,
-      similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22,
-      text: prompts[prompts.length - 1].value,
-      model,
-      userId
-    });
-
-    // search result is empty
-    if (code === 201) {
-      return res.send(searchPrompt?.value);
-    }
-
-    searchPrompt && prompts.unshift(searchPrompt);
-
-    // 控制在 tokens 数量，防止超出
-    const filterPrompts = openaiChatFilter({
-      model: model.chat.chatModel,
-      prompts,
-      maxTokens: modelConstantsData.contextMaxToken - 300
-    });
-
-    // console.log(filterPrompts);
-    // 计算温度
-    const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
-      2
-    );
-    const chatAPI = getOpenAIApi(apiKey);
-
-    // 发出请求
-    const chatResponse = await chatAPI.createChatCompletion(
-      {
-        model: model.chat.chatModel,
-        temperature: Number(temperature) || 0,
-        messages: filterPrompts,
-        frequency_penalty: 0.5, // 越大，重复内容越少
-        presence_penalty: -0.5, // 越大，越容易出现新内容
-        stream: isStream,
-        stop: ['.!?。']
-      },
-      {
-        timeout: 180000,
-        responseType: isStream ? 'stream' : 'json',
-        ...axiosConfig()
-      }
-    );
-
-    console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
-
-    let responseContent = '';
-
-    if (isStream) {
-      step = 1;
-      const streamResponse = await gpt35StreamResponse({
-        res,
-        stream,
-        chatResponse
-      });
-      responseContent = streamResponse.responseContent;
-    } else {
-      responseContent = chatResponse.data.choices?.[0]?.message?.content || '';
-      jsonRes(res, {
-        data: responseContent
-      });
-    }
-
-    pushChatBill({
-      isPay: true,
-      chatModel: model.chat.chatModel,
-      userId,
-      messages: filterPrompts.concat({ role: 'assistant', content: responseContent })
-    });
-    // jsonRes(res);
-  } catch (err: any) {
-    if (step === 1) {
-      // 直接结束流
-      console.log('error，结束');
-      stream.destroy();
-    } else {
-      res.status(500);
-      jsonRes(res, {
-        code: 500,
-        error: err
-      });
-    }
-  }
-}

src/service/constants/training.ts

@@ -1,5 +0,0 @@
-export enum OpenAiTuneStatusEnum {
-  cancelled = 'cancelled',
-  succeeded = 'succeeded',
-  pending = 'pending'
-}

src/service/events/generateQA.ts

@@ -1,14 +1,13 @@
 import { SplitData } from '@/service/mongo';
-import { getOpenAIApi } from '@/service/utils/auth';
+import { getApiKey } from '../utils/auth';
-import { axiosConfig } from '@/service/utils/tools';
-import { getOpenApiKey } from '../utils/openai';
-import type { ChatCompletionRequestMessage } from 'openai';
 import { OpenAiChatEnum } from '@/constants/model';
 import { pushSplitDataBill } from '@/service/events/pushBill';
 import { generateVector } from './generateVector';
 import { openaiError2 } from '../errorCode';
 import { PgClient } from '@/service/pg';
 import { ModelSplitDataSchema } from '@/types/mongoSchema';
+import { modelServiceToolMap } from '../utils/chat';
+import { ChatRoleEnum } from '@/constants/chat';
 
 export async function generateQA(next = false): Promise<any> {
   if (process.env.queueTask !== '1') {
@@ -47,11 +46,11 @@ export async function generateQA(next = false): Promise<any> {
 
     // 获取 openapi Key
     let userApiKey = '',
-      systemKey = '';
+      systemApiKey = '';
     try {
-      const key = await getOpenApiKey(dataItem.userId);
+      const key = await getApiKey({ model: OpenAiChatEnum.GPT35, userId: dataItem.userId });
       userApiKey = key.userApiKey;
-      systemKey = key.systemKey;
+      systemApiKey = key.systemApiKey;
     } catch (error: any) {
       if (error?.code === 501) {
         // 余额不够了, 清空该记录
@@ -69,55 +68,44 @@ export async function generateQA(next = false): Promise<any> {
 
     const startTime = Date.now();
 
-    // 获取 openai 请求实例
-    const chatAPI = getOpenAIApi(userApiKey || systemKey);
-    const systemPrompt: ChatCompletionRequestMessage = {
-      role: 'system',
-      content: `你是出题人
-${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:
-A1:
-Q2:
-A2:
-...`
-    };
-
     // 请求 chatgpt 获取回答
     const response = await Promise.allSettled(
       textList.map((text) =>
-        chatAPI
+        modelServiceToolMap[OpenAiChatEnum.GPT35]
-          .createChatCompletion(
+          .chatCompletion({
-            {
+            apiKey: userApiKey || systemApiKey,
-              model: OpenAiChatEnum.GPT35,
+            temperature: 0.8,
-              temperature: 0.8,
+            messages: [
-              n: 1,
+              {
-              messages: [
+                obj: ChatRoleEnum.System,
-                systemPrompt,
+                value: `你是出题人
-                {
+          ${dataItem.prompt || '下面是"一段长文本"'}
-                  role: 'user',
+          从中选出5至20个题目和答案.答案详细.按格式返回: Q1:
-                  content: text
+          A1:
-                }
+          Q2:
-              ]
+          A2:
-            },
+          ...`
-            {
+              },
-              timeout: 180000,
+              {
-              ...axiosConfig()
+                obj: 'Human',
-            }
+                value: text
-          )
+              }
-          .then((res) => {
+            ],
-            const rawContent = res?.data.choices[0].message?.content || ''; // chatgpt 原本的回复
+            stream: false
-            const result = formatSplitText(res?.data.choices[0].message?.content || ''); // 格式化后的QA对
+          })
+          .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: !userApiKey && result.length > 0,
               userId: dataItem.userId,
               type: 'QA',
-              text: systemPrompt.content + text + rawContent,
+              textLen: responseMessages.map((item) => item.value).join('').length,
-              tokenLen: res.data.usage?.total_tokens || 0
+              totalTokens
             });
             return {
-              rawContent,
+              rawContent: responseText,
               result
             };
           })

src/service/events/generateVector.ts

@@ -1,6 +1,8 @@
-import { openaiCreateEmbedding, getOpenApiKey } from '../utils/openai';
+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') {
@@ -40,11 +42,11 @@ export async function generateVector(next = false): Promise<any> {
     dataId = dataItem.id;
 
     // 获取 openapi Key
-    let userApiKey, systemKey;
+    let userApiKey, systemApiKey;
     try {
-      const res = await getOpenApiKey(dataItem.userId);
+      const res = await getApiKey({ model: embeddingModel, userId: dataItem.userId });
       userApiKey = res.userApiKey;
-      systemKey = res.systemKey;
+      systemApiKey = res.systemApiKey;
     } catch (error: any) {
       if (error?.code === 501) {
         await PgClient.delete('modelData', {
@@ -61,8 +63,8 @@ export async function generateVector(next = false): Promise<any> {
     const { vector } = await openaiCreateEmbedding({
       text: dataItem.q,
       userId: dataItem.userId,
-      isPay: !userApiKey,
+      userApiKey,
-      apiKey: userApiKey || systemKey
+      systemApiKey
     });
 
     // 更新 pg 向量和状态数据

src/service/events/pushBill.ts

@@ -1,60 +1,54 @@
 import { connectToDatabase, Bill, User } from '../mongo';
 import { ChatModelMap, OpenAiChatEnum, ChatModelType, embeddingModel } from '@/constants/model';
 import { BillTypeEnum } from '@/constants/user';
-import { countChatTokens } from '@/utils/tools';
 
 export const pushChatBill = async ({
   isPay,
   chatModel,
   userId,
   chatId,
-  messages
+  textLen,
+  tokens
 }: {
   isPay: boolean;
   chatModel: ChatModelType;
   userId: string;
   chatId?: '' | string;
-  messages: { role: 'system' | 'user' | 'assistant'; content: string }[];
+  textLen: number;
+  tokens: number;
 }) => {
+  console.log(`chat generate success. text len: ${textLen}. token len: ${tokens}. pay:${isPay}`);
+  if (!isPay) return;
+
   let billId = '';
 
   try {
-    // 计算 token 数量
+    await connectToDatabase();
-    const tokens = countChatTokens({ model: chatModel, messages });
-    const text = messages.map((item) => item.content).join('');
 
-    console.log(
+    // 计算价格
-      `chat generate success. text len: ${text.length}. token len: ${tokens}. pay:${isPay}`
+    const unitPrice = ChatModelMap[chatModel]?.price || 5;
-    );
+    const price = unitPrice * tokens;
 
-    if (isPay) {
+    try {
-      await connectToDatabase();
+      // 插入 Bill 记录
+      const res = await Bill.create({
+        userId,
+        type: 'chat',
+        modelName: chatModel,
+        chatId: chatId ? chatId : undefined,
+        textLen,
+        tokenLen: tokens,
+        price
+      });
+      billId = res._id;
 
-      // 计算价格
+      // 账号扣费
-      const unitPrice = ChatModelMap[chatModel]?.price || 5;
+      await User.findByIdAndUpdate(userId, {
-      const price = unitPrice * tokens;
+        $inc: { balance: -price }
-
+      });
-      try {
+    } catch (error) {
-        // 插入 Bill 记录
+      console.log('创建账单失败:', error);
-        const res = await Bill.create({
+      billId && Bill.findByIdAndDelete(billId);
-          userId,
-          type: 'chat',
-          modelName: chatModel,
-          chatId: chatId ? chatId : undefined,
-          textLen: text.length,
-          tokenLen: tokens,
-          price
-        });
-        billId = res._id;
-
-        // 账号扣费
-        await User.findByIdAndUpdate(userId, {
-          $inc: { balance: -price }
-        });
-      } catch (error) {
-        console.log('创建账单失败:', error);
-        billId && Bill.findByIdAndDelete(billId);
-      }
     }
   } catch (error) {
     console.log(error);
@@ -64,54 +58,49 @@ export const pushChatBill = async ({
 export const pushSplitDataBill = async ({
   isPay,
   userId,
-  tokenLen,
+  totalTokens,
-  text,
+  textLen,
   type
 }: {
   isPay: boolean;
   userId: string;
-  tokenLen: number;
+  totalTokens: number;
-  text: string;
+  textLen: number;
   type: `${BillTypeEnum}`;
 }) => {
-  await connectToDatabase();
+  console.log(
+    `splitData generate success. text len: ${textLen}. token len: ${totalTokens}. pay:${isPay}`
+  );
+  if (!isPay) return;
 
   let billId;
 
   try {
-    console.log(
+    await connectToDatabase();
-      `splitData generate success. text len: ${text.length}. token len: ${tokenLen}. pay:${isPay}`
-    );
 
-    if (isPay) {
+    // 获取模型单价格, 都是用 gpt35 拆分
-      try {
+    const unitPrice = ChatModelMap[OpenAiChatEnum.GPT35].price || 3;
-        // 获取模型单价格, 都是用 gpt35 拆分
+    // 计算价格
-        const unitPrice = ChatModelMap[OpenAiChatEnum.GPT35]?.price || 3;
+    const price = unitPrice * totalTokens;
-        // 计算价格
-        const price = unitPrice * tokenLen;
 
-        // 插入 Bill 记录
+    // 插入 Bill 记录
-        const res = await Bill.create({
+    const res = await Bill.create({
-          userId,
+      userId,
-          type,
+      type,
-          modelName: OpenAiChatEnum.GPT35,
+      modelName: OpenAiChatEnum.GPT35,
-          textLen: text.length,
+      textLen,
-          tokenLen,
+      tokenLen: totalTokens,
-          price
+      price
-        });
+    });
-        billId = res._id;
+    billId = res._id;
 
-        // 账号扣费
+    // 账号扣费
-        await User.findByIdAndUpdate(userId, {
+    await User.findByIdAndUpdate(userId, {
-          $inc: { balance: -price }
+      $inc: { balance: -price }
-        });
+    });
-      } catch (error) {
-        console.log('创建账单失败:', error);
-        billId && Bill.findByIdAndDelete(billId);
-      }
-    }
   } catch (error) {
-    console.log(error);
+    console.log('创建账单失败:', error);
+    billId && Bill.findByIdAndDelete(billId);
   }
 };
 
@@ -126,41 +115,40 @@ export const pushGenerateVectorBill = async ({
   text: string;
   tokenLen: number;
 }) => {
-  await connectToDatabase();
+  console.log(
+    `vector generate success. text len: ${text.length}. token len: ${tokenLen}. pay:${isPay}`
+  );
+  if (!isPay) return;
 
   let billId;
 
   try {
-    console.log(
+    await connectToDatabase();
-      `vector generate success. text len: ${text.length}. token len: ${tokenLen}. pay:${isPay}`
-    );
 
-    if (isPay) {
+    try {
-      try {
+      const unitPrice = 0.4;
-        const unitPrice = 0.4;
+      // 计算价格. 至少为1
-        // 计算价格. 至少为1
+      let price = unitPrice * tokenLen;
-        let price = unitPrice * tokenLen;
+      price = price > 1 ? price : 1;
-        price = price > 1 ? price : 1;
 
-        // 插入 Bill 记录
+      // 插入 Bill 记录
-        const res = await Bill.create({
+      const res = await Bill.create({
-          userId,
+        userId,
-          type: BillTypeEnum.vector,
+        type: BillTypeEnum.vector,
-          modelName: embeddingModel,
+        modelName: embeddingModel,
-          textLen: text.length,
+        textLen: text.length,
-          tokenLen,
+        tokenLen,
-          price
+        price
-        });
+      });
-        billId = res._id;
+      billId = res._id;
 
-        // 账号扣费
+      // 账号扣费
-        await User.findByIdAndUpdate(userId, {
+      await User.findByIdAndUpdate(userId, {
-          $inc: { balance: -price }
+        $inc: { balance: -price }
-        });
+      });
-      } catch (error) {
+    } catch (error) {
-        console.log('创建账单失败:', error);
+      console.log('创建账单失败:', error);
-        billId && Bill.findByIdAndDelete(billId);
+      billId && Bill.findByIdAndDelete(billId);
-      }
     }
   } catch (error) {
     console.log(error);

src/service/models/chat.ts

@@ -1,5 +1,6 @@
 import { Schema, model, models, Model } from 'mongoose';
 import { ChatSchema as ChatType } from '@/types/mongoSchema';
+import { ChatRoleMap } from '@/constants/chat';
 
 const ChatSchema = new Schema({
   userId: {
@@ -36,7 +37,7 @@ const ChatSchema = new Schema({
         obj: {
           type: String,
           required: true,
-          enum: ['Human', 'AI', 'SYSTEM']
+          enum: Object.keys(ChatRoleMap)
         },
         value: {
           type: String,

src/service/plugins/searchKb.ts

@@ -1,22 +1,23 @@
-import { openaiCreateEmbedding } from '../utils/openai';
 import { PgClient } from '@/service/pg';
 import { ModelDataStatusEnum, ModelVectorSearchModeEnum, ChatModelMap } from '@/constants/model';
 import { ModelSchema } from '@/types/mongoSchema';
-import { systemPromptFilter } from '../utils/tools';
+import { openaiCreateEmbedding } from '../utils/chat/openai';
+import { ChatRoleEnum } from '@/constants/chat';
+import { sliceTextByToken } from '@/utils/chat';
 
 /**
  *  use openai embedding search kb
  */
-export const searchKb_openai = async ({
+export const searchKb = async ({
-  apiKey,
+  userApiKey,
-  isPay = true,
+  systemApiKey,
   text,
   similarity = 0.2,
   model,
   userId
 }: {
-  apiKey: string;
+  userApiKey?: string;
-  isPay: boolean;
+  systemApiKey: string;
   text: string;
   model: ModelSchema;
   userId: string;
@@ -24,7 +25,7 @@ export const searchKb_openai = async ({
 }): Promise<{
   code: 200 | 201;
   searchPrompt?: {
-    obj: 'Human' | 'AI' | 'SYSTEM';
+    obj: `${ChatRoleEnum}`;
     value: string;
   };
 }> => {
@@ -32,8 +33,8 @@ export const searchKb_openai = async ({
 
   // 获取提示词的向量
   const { vector: promptVector } = await openaiCreateEmbedding({
-    isPay,
+    userApiKey,
-    apiKey,
+    systemApiKey,
     userId,
     text
   });
@@ -61,7 +62,7 @@ export const searchKb_openai = async ({
     return {
       code: 201,
       searchPrompt: {
-        obj: 'AI',
+        obj: ChatRoleEnum.AI,
         value: '对不起，你的问题不在知识库中。'
       }
     };
@@ -72,7 +73,7 @@ export const searchKb_openai = async ({
       code: 200,
       searchPrompt: model.chat.systemPrompt
         ? {
-            obj: 'SYSTEM',
+            obj: ChatRoleEnum.System,
             value: model.chat.systemPrompt
           }
         : undefined
@@ -81,16 +82,16 @@ export const searchKb_openai = async ({
 
   // 有匹配情况下，system 添加知识库内容。
   // 系统提示词过滤，最多 65% tokens
-  const filterSystemPrompt = systemPromptFilter({
+  const filterSystemPrompt = sliceTextByToken({
     model: model.chat.chatModel,
-    prompts: systemPrompts,
+    text: systemPrompts.join('\n'),
-    maxTokens: Math.floor(modelConstantsData.contextMaxToken * 0.65)
+    length: Math.floor(modelConstantsData.contextMaxToken * 0.65)
   });
 
   return {
     code: 200,
     searchPrompt: {
-      obj: 'SYSTEM',
+      obj: ChatRoleEnum.System,
       value: `
 ${model.chat.systemPrompt}
 ${

src/service/utils/auth.ts

@@ -1,14 +1,18 @@
-import { Configuration, OpenAIApi } from 'openai';
 import type { NextApiRequest } from 'next';
 import jwt from 'jsonwebtoken';
 import { Chat, Model, OpenApi, User } from '../mongo';
 import type { ModelSchema } from '@/types/mongoSchema';
-import { getOpenApiKey } from './openai';
 import type { ChatItemSimpleType } from '@/types/chat';
 import mongoose from 'mongoose';
 import { defaultModel } from '@/constants/model';
 import { formatPrice } from '@/utils/user';
 import { ERROR_ENUM } from '../errorCode';
+import {
+  ChatModelType,
+  OpenAiChatEnum,
+  embeddingModel,
+  EmbeddingModelType
+} from '@/constants/model';
 
 /* 校验 token */
 export const authToken = (token?: string): Promise<string> => {
@@ -29,13 +33,63 @@ export const authToken = (token?: string): Promise<string> => {
   });
 };
 
-export const getOpenAIApi = (apiKey: string) => {
+/* 获取 api 请求的 key */
-  const configuration = new Configuration({
+export const getApiKey = async ({
-    apiKey,
+  model,
-    basePath: process.env.OPENAI_BASE_URL
+  userId
-  });
+}: {
+  model: ChatModelType | EmbeddingModelType;
+  userId: string;
+}) => {
+  const user = await User.findById(userId);
+  if (!user) {
+    return Promise.reject({
+      code: 501,
+      message: '找不到用户'
+    });
+  }
 
-  return new OpenAIApi(configuration);
+  const keyMap = {
+    [OpenAiChatEnum.GPT35]: {
+      userApiKey: user.openaiKey || '',
+      systemApiKey: process.env.OPENAIKEY as string
+    },
+    [OpenAiChatEnum.GPT4]: {
+      userApiKey: user.openaiKey || '',
+      systemApiKey: process.env.OPENAIKEY as string
+    },
+    [OpenAiChatEnum.GPT432k]: {
+      userApiKey: user.openaiKey || '',
+      systemApiKey: process.env.OPENAIKEY as string
+    },
+    [embeddingModel]: {
+      userApiKey: user.openaiKey || '',
+      systemApiKey: process.env.OPENAIKEY as string
+    }
+  };
+
+  // 有自己的key
+  if (keyMap[model].userApiKey) {
+    return {
+      user,
+      userApiKey: keyMap[model].userApiKey,
+      systemApiKey: ''
+    };
+  }
+
+  // 平台账号余额校验
+  if (formatPrice(user.balance) <= 0) {
+    return Promise.reject({
+      code: 501,
+      message: '账号余额不足'
+    });
+  }
+
+  return {
+    user,
+    userApiKey: '',
+    systemApiKey: keyMap[model].systemApiKey
+  };
 };
 
 // 模型使用权校验
@@ -122,11 +176,11 @@ export const authChat = async ({
     ]);
   }
   // 获取 user 的 apiKey
-  const { userApiKey, systemKey } = await getOpenApiKey(userId);
+  const { userApiKey, systemApiKey } = await getApiKey({ model: model.chat.chatModel, userId });
 
   return {
     userApiKey,
-    systemKey,
+    systemApiKey,
     content,
     userId,
     model,

src/service/utils/chat/index.ts

@@ -0,0 +1,155 @@
+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 { OpenAiChatEnum } from '@/constants/model';
+import { chatResponse, openAiStreamResponse } from './openai';
+import type { NextApiResponse } from 'next';
+import type { PassThrough } from 'stream';
+
+export type ChatCompletionType = {
+  apiKey: string;
+  temperature: number;
+  messages: ChatItemSimpleType[];
+  stream: boolean;
+};
+export type StreamResponseType = {
+  stream: PassThrough;
+  chatResponse: any;
+  prompts: ChatItemSimpleType[];
+};
+
+export const modelServiceToolMap = {
+  [OpenAiChatEnum.GPT35]: {
+    chatCompletion: (data: ChatCompletionType) =>
+      chatResponse({ model: OpenAiChatEnum.GPT35, ...data }),
+    streamResponse: (data: StreamResponseType) =>
+      openAiStreamResponse({
+        model: OpenAiChatEnum.GPT35,
+        ...data
+      })
+  },
+  [OpenAiChatEnum.GPT4]: {
+    chatCompletion: (data: ChatCompletionType) =>
+      chatResponse({ model: OpenAiChatEnum.GPT4, ...data }),
+    streamResponse: (data: StreamResponseType) =>
+      openAiStreamResponse({
+        model: OpenAiChatEnum.GPT4,
+        ...data
+      })
+  },
+  [OpenAiChatEnum.GPT432k]: {
+    chatCompletion: (data: ChatCompletionType) =>
+      chatResponse({ model: OpenAiChatEnum.GPT432k, ...data }),
+    streamResponse: (data: StreamResponseType) =>
+      openAiStreamResponse({
+        model: OpenAiChatEnum.GPT432k,
+        ...data
+      })
+  }
+};
+
+/* delete invalid symbol */
+const simplifyStr = (str: string) =>
+  str
+    .replace(/\n+/g, '\n') // 连续空行
+    .replace(/[^\S\r\n]+/g, ' ') // 连续空白内容
+    .trim();
+
+/* 聊天上下文 tokens 截断 */
+export const ChatContextFilter = ({
+  model,
+  prompts,
+  maxTokens
+}: {
+  model: ChatModelType;
+  prompts: ChatItemSimpleType[];
+  maxTokens: number;
+}) => {
+  let rawTextLen = 0;
+  const formatPrompts = prompts.map<ChatItemSimpleType>((item) => {
+    const val = simplifyStr(item.value);
+    rawTextLen += val.length;
+    return {
+      obj: item.obj,
+      value: val
+    };
+  });
+
+  // 长度太小时，不需要进行 token 截断
+  if (formatPrompts.length <= 2 || rawTextLen < maxTokens * 0.5) {
+    return formatPrompts;
+  }
+
+  // 根据 tokens 截断内容
+  const chats: ChatItemSimpleType[] = [];
+  let systemPrompt: ChatItemSimpleType | null = null;
+
+  //  System 词保留
+  if (formatPrompts[0].obj === ChatRoleEnum.System) {
+    const prompt = formatPrompts.shift();
+    if (prompt) {
+      systemPrompt = prompt;
+    }
+  }
+
+  let messages: ChatItemSimpleType[] = [];
+
+  // 从后往前截取对话内容
+  for (let i = formatPrompts.length - 1; i >= 0; i--) {
+    chats.unshift(formatPrompts[i]);
+
+    messages = systemPrompt ? [systemPrompt, ...chats] : chats;
+
+    const tokens = modelToolMap[model].countTokens({
+      messages
+    });
+
+    /* 整体 tokens 超出范围 */
+    if (tokens >= maxTokens) {
+      return systemPrompt ? [systemPrompt, ...chats.slice(1)] : chats.slice(1);
+    }
+  }
+
+  return messages;
+};
+
+/* stream response */
+export const resStreamResponse = async ({
+  model,
+  res,
+  stream,
+  chatResponse,
+  systemPrompt,
+  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');
+  stream.pipe(res);
+
+  const { responseContent, totalTokens, finishMessages } = await modelServiceToolMap[
+    model
+  ].streamResponse({
+    chatResponse,
+    stream,
+    prompts
+  });
+
+  // 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();
+
+  return { responseContent, totalTokens, finishMessages };
+};

src/service/utils/chat/openai.ts

@@ -0,0 +1,174 @@
+import { Configuration, OpenAIApi } from 'openai';
+import { createParser, ParsedEvent, ReconnectInterval } from 'eventsource-parser';
+import { axiosConfig } from '../tools';
+import { ChatModelMap, embeddingModel, OpenAiChatEnum } from '@/constants/model';
+import { pushGenerateVectorBill } from '../../events/pushBill';
+import { adaptChatItem_openAI } from '@/utils/chat/openai';
+import { modelToolMap } from '@/utils/chat';
+import { ChatCompletionType, ChatContextFilter, StreamResponseType } from './index';
+import { ChatRoleEnum } from '@/constants/chat';
+
+export const getOpenAIApi = (apiKey: string) => {
+  const configuration = new Configuration({
+    apiKey,
+    basePath: process.env.OPENAI_BASE_URL
+  });
+
+  return new OpenAIApi(configuration);
+};
+
+/* 获取向量 */
+export const openaiCreateEmbedding = async ({
+  userApiKey,
+  systemApiKey,
+  userId,
+  text
+}: {
+  userApiKey?: string;
+  systemApiKey: string;
+  userId: string;
+  text: string;
+}) => {
+  // 获取 chatAPI
+  const chatAPI = getOpenAIApi(userApiKey || systemApiKey);
+
+  // 把输入的内容转成向量
+  const res = await chatAPI
+    .createEmbedding(
+      {
+        model: embeddingModel,
+        input: text
+      },
+      {
+        timeout: 60000,
+        ...axiosConfig()
+      }
+    )
+    .then((res) => ({
+      tokenLen: res.data.usage.total_tokens || 0,
+      vector: res.data.data?.[0]?.embedding || []
+    }));
+
+  pushGenerateVectorBill({
+    isPay: !userApiKey,
+    userId,
+    text,
+    tokenLen: res.tokenLen
+  });
+
+  return {
+    vector: res.vector,
+    chatAPI
+  };
+};
+
+/* 模型对话 */
+export const chatResponse = async ({
+  model,
+  apiKey,
+  temperature,
+  messages,
+  stream
+}: ChatCompletionType & { model: `${OpenAiChatEnum}` }) => {
+  const filterMessages = ChatContextFilter({
+    model,
+    prompts: messages,
+    maxTokens: Math.ceil(ChatModelMap[model].contextMaxToken * 0.9)
+  });
+
+  const adaptMessages = adaptChatItem_openAI({ messages: filterMessages });
+  const chatAPI = getOpenAIApi(apiKey);
+
+  const response = await chatAPI.createChatCompletion(
+    {
+      model,
+      temperature: Number(temperature) || 0,
+      messages: adaptMessages,
+      frequency_penalty: 0.5, // 越大，重复内容越少
+      presence_penalty: -0.5, // 越大，越容易出现新内容
+      stream,
+      stop: ['.!?。']
+    },
+    {
+      timeout: stream ? 40000 : 240000,
+      responseType: stream ? 'stream' : 'json',
+      ...axiosConfig()
+    }
+  );
+
+  let responseText = '';
+  let totalTokens = 0;
+
+  // adapt data
+  if (!stream) {
+    responseText = response.data.choices[0].message?.content || '';
+    totalTokens = response.data.usage?.total_tokens || 0;
+  }
+
+  return {
+    streamResponse: response,
+    responseMessages: filterMessages.concat({ obj: 'AI', value: responseText }),
+    responseText,
+    totalTokens
+  };
+};
+
+/* openai stream response */
+export const openAiStreamResponse = async ({
+  model,
+  stream,
+  chatResponse,
+  prompts
+}: StreamResponseType & {
+  model: `${OpenAiChatEnum}`;
+}) => {
+  try {
+    let responseContent = '';
+
+    const onParse = async (event: ParsedEvent | ReconnectInterval) => {
+      if (event.type !== 'event') return;
+      const data = event.data;
+      if (data === '[DONE]') return;
+      try {
+        const json = JSON.parse(data);
+        const content: string = json?.choices?.[0].delta.content || '';
+        responseContent += content;
+
+        !stream.destroyed && content && stream.push(content.replace(/\n/g, '<br/>'));
+      } catch (error) {
+        error;
+      }
+    };
+
+    try {
+      const decoder = new TextDecoder();
+      const parser = createParser(onParse);
+      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;
+        }
+        parser.feed(decoder.decode(chunk, { stream: true }));
+      }
+    } catch (error) {
+      console.log('pipe error', error);
+    }
+
+    // count tokens
+    const finishMessages = prompts.concat({
+      obj: ChatRoleEnum.AI,
+      value: responseContent
+    });
+    const totalTokens = modelToolMap[model].countTokens({
+      messages: finishMessages
+    });
+
+    return {
+      responseContent,
+      totalTokens,
+      finishMessages
+    };
+  } catch (error) {
+    return Promise.reject(error);
+  }
+};

src/service/utils/openai.ts

@@ -1,179 +0,0 @@
-import type { NextApiResponse } from 'next';
-import type { PassThrough } from 'stream';
-import { createParser, ParsedEvent, ReconnectInterval } from 'eventsource-parser';
-import { getOpenAIApi } from '@/service/utils/auth';
-import { axiosConfig } from './tools';
-import { User } from '../models/user';
-import { formatPrice } from '@/utils/user';
-import { embeddingModel } from '@/constants/model';
-import { pushGenerateVectorBill } from '../events/pushBill';
-import { SYSTEM_PROMPT_PREFIX } from '@/constants/chat';
-
-/* 获取用户 api 的 openai 信息 */
-export const getUserApiOpenai = async (userId: string) => {
-  const user = await User.findById(userId);
-
-  const userApiKey = user?.openaiKey;
-
-  if (!userApiKey) {
-    return Promise.reject('缺少ApiKey, 无法请求');
-  }
-
-  return {
-    user,
-    openai: getOpenAIApi(userApiKey),
-    apiKey: userApiKey
-  };
-};
-
-/* 获取 open api key，如果用户没有自己的key，就用平台的，用平台记得加账单 */
-export const getOpenApiKey = async (userId: string) => {
-  const user = await User.findById(userId);
-  if (!user) {
-    return Promise.reject({
-      code: 501,
-      message: '找不到用户'
-    });
-  }
-
-  const userApiKey = user?.openaiKey;
-
-  // 有自己的key
-  if (userApiKey) {
-    return {
-      user,
-      userApiKey,
-      systemKey: ''
-    };
-  }
-
-  // 平台账号余额校验
-  if (formatPrice(user.balance) <= 0) {
-    return Promise.reject({
-      code: 501,
-      message: '账号余额不足'
-    });
-  }
-
-  return {
-    user,
-    userApiKey: '',
-    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 res = await chatAPI
-    .createEmbedding(
-      {
-        model: embeddingModel,
-        input: text
-      },
-      {
-        timeout: 60000,
-        ...axiosConfig()
-      }
-    )
-    .then((res) => ({
-      tokenLen: res.data.usage.total_tokens || 0,
-      vector: res.data.data?.[0]?.embedding || []
-    }));
-
-  pushGenerateVectorBill({
-    isPay,
-    userId,
-    text,
-    tokenLen: res.tokenLen
-  });
-
-  return {
-    vector: res.vector,
-    chatAPI
-  };
-};
-
-/* gpt35 响应 */
-export const gpt35StreamResponse = ({
-  res,
-  stream,
-  chatResponse,
-  systemPrompt = ''
-}: {
-  res: NextApiResponse;
-  stream: PassThrough;
-  chatResponse: any;
-  systemPrompt?: string;
-}) =>
-  new Promise<{ responseContent: string }>(async (resolve, reject) => {
-    try {
-      // 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');
-      stream.pipe(res);
-
-      let responseContent = '';
-
-      const onParse = async (event: ParsedEvent | ReconnectInterval) => {
-        if (event.type !== 'event') return;
-        const data = event.data;
-        if (data === '[DONE]') return;
-        try {
-          const json = JSON.parse(data);
-          const content: string = json?.choices?.[0].delta.content || '';
-          responseContent += content;
-
-          if (!stream.destroyed && content) {
-            stream.push(content.replace(/\n/g, '<br/>'));
-          }
-        } catch (error) {
-          error;
-        }
-      };
-
-      try {
-        const decoder = new TextDecoder();
-        const parser = createParser(onParse);
-        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;
-          }
-          parser.feed(decoder.decode(chunk, { stream: true }));
-        }
-      } catch (error) {
-        console.log('pipe error', error);
-      }
-
-      // 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();
-
-      resolve({
-        responseContent
-      });
-    } catch (error) {
-      reject(error);
-    }
-  });

src/service/utils/tools.ts

@@ -1,9 +1,5 @@
 import crypto from 'crypto';
 import jwt from 'jsonwebtoken';
-import { ChatItemSimpleType } from '@/types/chat';
-import { countChatTokens, sliceTextByToken } from '@/utils/tools';
-import { ChatCompletionRequestMessageRoleEnum, ChatCompletionRequestMessage } from 'openai';
-import type { ChatModelType } from '@/constants/model';
 
 /* 密码加密 */
 export const hashPassword = (psw: string) => {
@@ -30,92 +26,3 @@ export const axiosConfig = () => ({
     auth: process.env.OPENAI_BASE_URL_AUTH || ''
   }
 });
-
-/* delete invalid symbol */
-const simplifyStr = (str: string) =>
-  str
-    .replace(/\n+/g, '\n') // 连续空行
-    .replace(/[^\S\r\n]+/g, ' ') // 连续空白内容
-    .trim();
-
-/* 聊天内容 tokens 截断 */
-export const openaiChatFilter = ({
-  model,
-  prompts,
-  maxTokens
-}: {
-  model: ChatModelType;
-  prompts: ChatItemSimpleType[];
-  maxTokens: number;
-}) => {
-  // role map
-  const map = {
-    Human: ChatCompletionRequestMessageRoleEnum.User,
-    AI: ChatCompletionRequestMessageRoleEnum.Assistant,
-    SYSTEM: ChatCompletionRequestMessageRoleEnum.System
-  };
-
-  let rawTextLen = 0;
-  const formatPrompts = prompts.map((item) => {
-    const val = simplifyStr(item.value);
-    rawTextLen += val.length;
-    return {
-      role: map[item.obj],
-      content: val
-    };
-  });
-
-  // 长度太小时，不需要进行 token 截断
-  if (rawTextLen < maxTokens * 0.5) {
-    return formatPrompts;
-  }
-
-  // 根据 tokens 截断内容
-  const chats: ChatCompletionRequestMessage[] = [];
-  let systemPrompt: ChatCompletionRequestMessage | null = null;
-
-  //  System 词保留
-  if (formatPrompts[0]?.role === 'system') {
-    systemPrompt = formatPrompts.shift() as ChatCompletionRequestMessage;
-  }
-
-  let messages: { role: ChatCompletionRequestMessageRoleEnum; content: string }[] = [];
-
-  // 从后往前截取对话内容
-  for (let i = formatPrompts.length - 1; i >= 0; i--) {
-    chats.unshift(formatPrompts[i]);
-
-    messages = systemPrompt ? [systemPrompt, ...chats] : chats;
-
-    const tokens = countChatTokens({
-      model,
-      messages
-    });
-
-    /* 整体 tokens 超出范围 */
-    if (tokens >= maxTokens) {
-      return systemPrompt ? [systemPrompt, ...chats.slice(1)] : chats.slice(1);
-    }
-  }
-
-  return messages;
-};
-
-/* system 内容截断. 相似度从高到低 */
-export const systemPromptFilter = ({
-  model,
-  prompts,
-  maxTokens
-}: {
-  model: 'gpt-4' | 'gpt-4-32k' | 'gpt-3.5-turbo';
-  prompts: string[];
-  maxTokens: number;
-}) => {
-  const systemPrompt = prompts.join('\n');
-
-  return sliceTextByToken({
-    model,
-    text: systemPrompt,
-    length: maxTokens
-  });
-};

src/types/chat.d.ts

@@ -1,5 +1,7 @@
+import { ChatRoleEnum } from '@/constants/chat';
+
 export type ChatItemSimpleType = {
-  obj: 'Human' | 'AI' | 'SYSTEM';
+  obj: `${ChatRoleEnum}`;
   value: string;
   systemPrompt?: string;
 };

src/utils/chat/index.ts

@@ -0,0 +1,39 @@
+import { OpenAiChatEnum } from '@/constants/model';
+import type { ChatModelType } from '@/constants/model';
+import type { ChatItemSimpleType } from '@/types/chat';
+import { countOpenAIToken, getOpenAiEncMap, adaptChatItem_openAI } from './openai';
+
+export type CountTokenType = { messages: ChatItemSimpleType[] };
+
+export const modelToolMap = {
+  [OpenAiChatEnum.GPT35]: {
+    countTokens: ({ messages }: CountTokenType) =>
+      countOpenAIToken({ model: OpenAiChatEnum.GPT35, messages }),
+    adaptChatMessages: adaptChatItem_openAI
+  },
+  [OpenAiChatEnum.GPT4]: {
+    countTokens: ({ messages }: CountTokenType) =>
+      countOpenAIToken({ model: OpenAiChatEnum.GPT4, messages }),
+    adaptChatMessages: adaptChatItem_openAI
+  },
+  [OpenAiChatEnum.GPT432k]: {
+    countTokens: ({ messages }: CountTokenType) =>
+      countOpenAIToken({ model: OpenAiChatEnum.GPT432k, messages }),
+    adaptChatMessages: adaptChatItem_openAI
+  }
+};
+
+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

@@ -0,0 +1,106 @@
+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 Graphemer from 'graphemer';
+
+const textDecoder = new TextDecoder();
+const graphemer = new Graphemer();
+
+export const adaptChatItem_openAI = ({
+  messages
+}: {
+  messages: ChatItemSimpleType[];
+}): ChatCompletionRequestMessage[] => {
+  const map = {
+    [ChatRoleEnum.AI]: ChatCompletionRequestMessageRoleEnum.Assistant,
+    [ChatRoleEnum.Human]: ChatCompletionRequestMessageRoleEnum.User,
+    [ChatRoleEnum.System]: ChatCompletionRequestMessageRoleEnum.System
+  };
+  return messages.map((item) => ({
+    role: map[item.obj] || ChatCompletionRequestMessageRoleEnum.System,
+    content: item.value || ''
+  }));
+};
+
+/* count openai chat token*/
+let OpenAiEncMap: Record<string, Tiktoken>;
+export const getOpenAiEncMap = () => {
+  if (OpenAiEncMap) return OpenAiEncMap;
+  OpenAiEncMap = {
+    'gpt-3.5-turbo': encoding_for_model('gpt-3.5-turbo', {
+      '<|im_start|>': 100264,
+      '<|im_end|>': 100265,
+      '<|im_sep|>': 100266
+    }),
+    'gpt-4': encoding_for_model('gpt-4', {
+      '<|im_start|>': 100264,
+      '<|im_end|>': 100265,
+      '<|im_sep|>': 100266
+    }),
+    'gpt-4-32k': encoding_for_model('gpt-4-32k', {
+      '<|im_start|>': 100264,
+      '<|im_end|>': 100265,
+      '<|im_sep|>': 100266
+    })
+  };
+  return OpenAiEncMap;
+};
+export function countOpenAIToken({
+  messages,
+  model
+}: {
+  messages: ChatItemSimpleType[];
+  model: 'gpt-3.5-turbo' | 'gpt-4' | 'gpt-4-32k';
+}) {
+  function getChatGPTEncodingText(
+    messages: { role: 'system' | 'user' | 'assistant'; content: string; name?: string }[],
+    model: 'gpt-3.5-turbo' | 'gpt-4' | 'gpt-4-32k'
+  ) {
+    const isGpt3 = model === 'gpt-3.5-turbo';
+
+    const msgSep = isGpt3 ? '\n' : '';
+    const roleSep = isGpt3 ? '\n' : '<|im_sep|>';
+
+    return [
+      messages
+        .map(({ name = '', role, content }) => {
+          return `<|im_start|>${name || role}${roleSep}${content}<|im_end|>`;
+        })
+        .join(msgSep),
+      `<|im_start|>assistant${roleSep}`
+    ].join(msgSep);
+  }
+  function text2TokensLen(encoder: Tiktoken, inputText: string) {
+    const encoding = encoder.encode(inputText, 'all');
+    const segments: { text: string; tokens: { id: number; idx: number }[] }[] = [];
+
+    let byteAcc: number[] = [];
+    let tokenAcc: { id: number; idx: number }[] = [];
+    let inputGraphemes = graphemer.splitGraphemes(inputText);
+
+    for (let idx = 0; idx < encoding.length; idx++) {
+      const token = encoding[idx]!;
+      byteAcc.push(...encoder.decode_single_token_bytes(token));
+      tokenAcc.push({ id: token, idx });
+
+      const segmentText = textDecoder.decode(new Uint8Array(byteAcc));
+      const graphemes = graphemer.splitGraphemes(segmentText);
+
+      if (graphemes.every((item, idx) => inputGraphemes[idx] === item)) {
+        segments.push({ text: segmentText, tokens: tokenAcc });
+
+        byteAcc = [];
+        tokenAcc = [];
+        inputGraphemes = inputGraphemes.slice(graphemes.length);
+      }
+    }
+
+    return segments.reduce((memo, i) => memo + i.tokens.length, 0) ?? 0;
+  }
+
+  const adaptMessages = adaptChatItem_openAI({ messages });
+
+  return text2TokensLen(getOpenAiEncMap()[model], getChatGPTEncodingText(adaptMessages, model));
+}

src/utils/file.ts

@@ -1,6 +1,6 @@
 import mammoth from 'mammoth';
 import Papa from 'papaparse';
-import { getEncMap } from './tools';
+import { getOpenAiEncMap } from './chat/openai';
 
 /**
  * 读取 txt 文件内容
@@ -154,7 +154,7 @@ export const splitText_token = ({
   maxLen: number;
   slideLen: number;
 }) => {
-  const enc = getEncMap()['gpt-3.5-turbo'];
+  const enc = getOpenAiEncMap()['gpt-3.5-turbo'];
   // filter empty text. encode sentence
   const encodeText = enc.encode(text);
 

src/utils/tools.ts

@@ -1,33 +1,5 @@
 import crypto from 'crypto';
 import { useToast } from '@/hooks/useToast';
-import { encoding_for_model, type Tiktoken } from '@dqbd/tiktoken';
-import Graphemer from 'graphemer';
-import type { ChatModelType } from '@/constants/model';
-
-const textDecoder = new TextDecoder();
-const graphemer = new Graphemer();
-let encMap: Record<string, Tiktoken>;
-export const getEncMap = () => {
-  if (encMap) return encMap;
-  encMap = {
-    'gpt-3.5-turbo': encoding_for_model('gpt-3.5-turbo', {
-      '<|im_start|>': 100264,
-      '<|im_end|>': 100265,
-      '<|im_sep|>': 100266
-    }),
-    'gpt-4': encoding_for_model('gpt-4', {
-      '<|im_start|>': 100264,
-      '<|im_end|>': 100265,
-      '<|im_sep|>': 100266
-    }),
-    'gpt-4-32k': encoding_for_model('gpt-4-32k', {
-      '<|im_start|>': 100264,
-      '<|im_end|>': 100265,
-      '<|im_sep|>': 100266
-    })
-  };
-  return encMap;
-};
 
 /**
  * copy text data
@@ -79,75 +51,3 @@ export const Obj2Query = (obj: Record<string, string | number>) => {
   }
   return queryParams.toString();
 };
-
-/* 格式化 chat 聊天内容 */
-function getChatGPTEncodingText(
-  messages: { role: 'system' | 'user' | 'assistant'; content: string; name?: string }[],
-  model: 'gpt-3.5-turbo' | 'gpt-4' | 'gpt-4-32k'
-) {
-  const isGpt3 = model === 'gpt-3.5-turbo';
-
-  const msgSep = isGpt3 ? '\n' : '';
-  const roleSep = isGpt3 ? '\n' : '<|im_sep|>';
-
-  return [
-    messages
-      .map(({ name = '', role, content }) => {
-        return `<|im_start|>${name || role}${roleSep}${content}<|im_end|>`;
-      })
-      .join(msgSep),
-    `<|im_start|>assistant${roleSep}`
-  ].join(msgSep);
-}
-function text2TokensLen(encoder: Tiktoken, inputText: string) {
-  const encoding = encoder.encode(inputText, 'all');
-  const segments: { text: string; tokens: { id: number; idx: number }[] }[] = [];
-
-  let byteAcc: number[] = [];
-  let tokenAcc: { id: number; idx: number }[] = [];
-  let inputGraphemes = graphemer.splitGraphemes(inputText);
-
-  for (let idx = 0; idx < encoding.length; idx++) {
-    const token = encoding[idx]!;
-    byteAcc.push(...encoder.decode_single_token_bytes(token));
-    tokenAcc.push({ id: token, idx });
-
-    const segmentText = textDecoder.decode(new Uint8Array(byteAcc));
-    const graphemes = graphemer.splitGraphemes(segmentText);
-
-    if (graphemes.every((item, idx) => inputGraphemes[idx] === item)) {
-      segments.push({ text: segmentText, tokens: tokenAcc });
-
-      byteAcc = [];
-      tokenAcc = [];
-      inputGraphemes = inputGraphemes.slice(graphemes.length);
-    }
-  }
-
-  return segments.reduce((memo, i) => memo + i.tokens.length, 0) ?? 0;
-}
-export const countChatTokens = ({
-  model = 'gpt-3.5-turbo',
-  messages
-}: {
-  model?: ChatModelType;
-  messages: { role: 'system' | 'user' | 'assistant'; content: string }[];
-}) => {
-  const text = getChatGPTEncodingText(messages, model);
-  return text2TokensLen(getEncMap()[model], text);
-};
-
-export const sliceTextByToken = ({
-  model = 'gpt-3.5-turbo',
-  text,
-  length
-}: {
-  model?: ChatModelType;
-  text: string;
-  length: number;
-}) => {
-  const enc = getEncMap()[model];
-  const encodeText = enc.encode(text);
-  const decoder = new TextDecoder();
-  return decoder.decode(enc.decode(encodeText.slice(0, length)));
-};