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perf: template
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@@ -51,7 +51,7 @@ export const dispatchClassifyQuestion = async (props: Record<string, any>): Prom
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// function body
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const agentFunction = {
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name: agentFunName,
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description: '判断用户问题的类型,并返回指定值',
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description: '判断用户问题的类型属于哪方面,返回对应的枚举字段',
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parameters: {
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type: 'object',
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properties: {
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@@ -81,13 +81,9 @@ export const dispatchClassifyQuestion = async (props: Record<string, any>): Prom
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const arg = JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '');
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if (!arg.type) {
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throw new Error('');
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}
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const tokens = response.data.usage?.total_tokens || 0;
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const result = agents.find((item) => item.key === arg.type) || agents[0];
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const result = agents.find((item) => item.key === arg?.type) || agents[0];
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return {
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[result.key]: 1,
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@@ -31,7 +31,7 @@ const simplifyStr = (str = '') =>
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.replace(/[^\S\r\n]+/g, ' ') // 连续空白内容
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.trim();
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/* 聊天上下文 tokens 截断 */
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/* slice chat context by tokens */
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export const ChatContextFilter = ({
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model,
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prompts,
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@@ -41,33 +41,19 @@ export const ChatContextFilter = ({
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prompts: ChatItemType[];
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maxTokens: number;
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}) => {
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const systemPrompts: ChatItemType[] = [];
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const chatPrompts: ChatItemType[] = [];
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const rawTextLen = prompts.reduce((sum, item) => sum + item.value.length, 0);
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let rawTextLen = 0;
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prompts.forEach((item) => {
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const val = simplifyStr(item.value);
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rawTextLen += val.length;
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const data = {
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_id: item._id,
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obj: item.obj,
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value: val
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};
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if (item.obj === ChatRoleEnum.System) {
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systemPrompts.push(data);
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} else {
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chatPrompts.push(data);
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}
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});
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// 长度太小时,不需要进行 token 截断
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// If the text length is less than half of the maximum token, no calculation is required
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if (rawTextLen < maxTokens * 0.5) {
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return [...systemPrompts, ...chatPrompts];
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return prompts;
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}
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// 去掉 system 的 token
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// filter startWith system prompt
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const chatStartIndex = prompts.findIndex((item) => item.obj !== ChatRoleEnum.System);
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const systemPrompts: ChatItemType[] = prompts.slice(0, chatStartIndex);
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const chatPrompts: ChatItemType[] = prompts.slice(chatStartIndex);
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// reduce token of systemPrompt
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maxTokens -= modelToolMap.countTokens({
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model,
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messages: systemPrompts
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