new framwork

client/src/service/utils/chat/index.ts

@@ -0,0 +1,167 @@
+import { ChatItemSimpleType } from '@/types/chat';
+import { modelToolMap } from '@/utils/plugin';
+import type { ChatModelType } from '@/constants/model';
+import { ChatRoleEnum } from '@/constants/chat';
+import { OpenAiChatEnum, ClaudeEnum } from '@/constants/model';
+import { chatResponse, openAiStreamResponse } from './openai';
+import { claudChat, claudStreamResponse } from './claude';
+import type { NextApiResponse } from 'next';
+
+export type ChatCompletionType = {
+  apiKey: string;
+  temperature: number;
+  messages: ChatItemSimpleType[];
+  chatId?: string;
+  [key: string]: any;
+};
+export type ChatCompletionResponseType = {
+  streamResponse: any;
+  responseMessages: ChatItemSimpleType[];
+  responseText: string;
+  totalTokens: number;
+};
+export type StreamResponseType = {
+  chatResponse: any;
+  prompts: ChatItemSimpleType[];
+  res: NextApiResponse;
+  [key: string]: any;
+};
+export type StreamResponseReturnType = {
+  responseContent: string;
+  totalTokens: number;
+  finishMessages: ChatItemSimpleType[];
+};
+
+export const modelServiceToolMap: Record<
+  ChatModelType,
+  {
+    chatCompletion: (data: ChatCompletionType) => Promise<ChatCompletionResponseType>;
+    streamResponse: (data: StreamResponseType) => Promise<StreamResponseReturnType>;
+  }
+> = {
+  [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
+      })
+  },
+  [ClaudeEnum.Claude]: {
+    chatCompletion: claudChat,
+    streamResponse: claudStreamResponse
+  }
+};
+
+/* 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;
+}) => {
+  const systemPrompts: ChatItemSimpleType[] = [];
+  const chatPrompts: ChatItemSimpleType[] = [];
+
+  let rawTextLen = 0;
+  prompts.forEach((item) => {
+    const val = simplifyStr(item.value);
+    rawTextLen += val.length;
+
+    const data = {
+      obj: item.obj,
+      value: val
+    };
+
+    if (item.obj === ChatRoleEnum.System) {
+      systemPrompts.push(data);
+    } else {
+      chatPrompts.push(data);
+    }
+  });
+
+  // 长度太小时，不需要进行 token 截断
+  if (rawTextLen < maxTokens * 0.5) {
+    return [...systemPrompts, ...chatPrompts];
+  }
+
+  // 去掉 system 的 token
+  maxTokens -= modelToolMap[model].countTokens({
+    messages: systemPrompts
+  });
+
+  // 根据 tokens 截断内容
+  const chats: ChatItemSimpleType[] = [];
+
+  // 从后往前截取对话内容
+  for (let i = chatPrompts.length - 1; i >= 0; i--) {
+    chats.unshift(chatPrompts[i]);
+
+    const tokens = modelToolMap[model].countTokens({
+      messages: chats
+    });
+
+    /* 整体 tokens 超出范围, system必须保留 */
+    if (tokens >= maxTokens) {
+      chats.shift();
+      break;
+    }
+  }
+
+  return [...systemPrompts, ...chats];
+};
+
+/* stream response */
+export const resStreamResponse = async ({
+  model,
+  res,
+  chatResponse,
+  prompts
+}: StreamResponseType & {
+  model: ChatModelType;
+}) => {
+  // 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');
+
+  const { responseContent, totalTokens, finishMessages } = await modelServiceToolMap[
+    model
+  ].streamResponse({
+    chatResponse,
+    prompts,
+    res
+  });
+
+  return { responseContent, totalTokens, finishMessages };
+};