perf: completion dispatch

client/src/service/moduleDispatch/agent/classifyQuestion.ts

@@ -0,0 +1,103 @@
+import { adaptChatItem_openAI } from '@/utils/plugin/openai';
+import { ChatContextFilter } from '@/service/utils/chat/index';
+import type { ChatHistoryItemResType, ChatItemType } from '@/types/chat';
+import { ChatRoleEnum, TaskResponseKeyEnum } from '@/constants/chat';
+import { getOpenAIApi, axiosConfig } from '@/service/ai/openai';
+import type { ClassifyQuestionAgentItemType } from '@/types/app';
+import { countModelPrice } from '@/service/events/pushBill';
+
+export type CQProps = {
+  systemPrompt?: string;
+  history?: ChatItemType[];
+  userChatInput: string;
+  agents: ClassifyQuestionAgentItemType[];
+};
+export type CQResponse = {
+  [TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
+  [key: string]: any;
+};
+
+const moduleName = 'Classify Question';
+const agentModel = 'gpt-3.5-turbo';
+const agentFunName = 'agent_user_question';
+const maxTokens = 2000;
+
+/* request openai chat */
+export const dispatchClassifyQuestion = async (props: Record<string, any>): Promise<CQResponse> => {
+  const { agents, systemPrompt, history = [], userChatInput } = props as CQProps;
+
+  const messages: ChatItemType[] = [
+    ...(systemPrompt
+      ? [
+          {
+            obj: ChatRoleEnum.System,
+            value: systemPrompt
+          }
+        ]
+      : []),
+    ...history,
+    {
+      obj: ChatRoleEnum.Human,
+      value: userChatInput
+    }
+  ];
+  const filterMessages = ChatContextFilter({
+    model: agentModel,
+    prompts: messages,
+    maxTokens
+  });
+  const adaptMessages = adaptChatItem_openAI({ messages: filterMessages, reserveId: false });
+
+  //   function body
+  const agentFunction = {
+    name: agentFunName,
+    description: '判断用户问题的类型，并返回指定值',
+    parameters: {
+      type: 'object',
+      properties: {
+        type: {
+          type: 'string',
+          description: agents.map((item) => `${item.value}，返回：'${item.key}'`).join('；'),
+          enum: agents.map((item) => item.key)
+        }
+      },
+      required: ['type']
+    }
+  };
+  const chatAPI = getOpenAIApi();
+
+  const response = await chatAPI.createChatCompletion(
+    {
+      model: agentModel,
+      temperature: 0,
+      messages: [...adaptMessages],
+      function_call: { name: agentFunName },
+      functions: [agentFunction]
+    },
+    {
+      ...axiosConfig()
+    }
+  );
+
+  const arg = JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '');
+
+  if (!arg.type) {
+    throw new Error('');
+  }
+
+  const tokens = response.data.usage?.total_tokens || 0;
+
+  const result = agents.find((item) => item.key === arg.type) || agents[0];
+
+  return {
+    [result.key]: 1,
+    [TaskResponseKeyEnum.responseData]: {
+      moduleName,
+      price: countModelPrice({ model: agentModel, tokens }),
+      model: agentModel,
+      tokens,
+      cqList: agents,
+      cqResult: result.value
+    }
+  };
+};