perf: chat framwork

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);
+  }
+};