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https://github.com/labring/FastGPT.git
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perf: search prompt
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@@ -55,7 +55,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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// 使用了知识库搜索
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if (model.chat.useKb) {
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const { code, searchPrompt, aiPrompt } = await searchKb({
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const { code, searchPrompts } = await searchKb({
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userOpenAiKey,
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prompts,
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similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity,
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@@ -65,13 +65,10 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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// search result is empty
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if (code === 201) {
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return res.send(searchPrompt?.value);
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return res.send(searchPrompts[0]?.value);
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}
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if (aiPrompt) {
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prompts.splice(prompts.length - 1, 0, aiPrompt);
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}
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searchPrompt && prompts.unshift(searchPrompt);
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prompts.splice(prompts.length - 1, 0, ...searchPrompts);
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} else {
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// 没有用知识库搜索,仅用系统提示词
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model.chat.systemPrompt &&
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@@ -73,7 +73,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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if (model.chat.useKb) {
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const similarity = ModelVectorSearchModeMap[model.chat.searchMode]?.similarity || 0.22;
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const { code, searchPrompt } = await searchKb({
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const { code, searchPrompts } = await searchKb({
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prompts,
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similarity,
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model,
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@@ -82,10 +82,9 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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// search result is empty
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if (code === 201) {
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return res.send(searchPrompt?.value);
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return res.send(searchPrompts[0]?.value);
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}
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searchPrompt && prompts.unshift(searchPrompt);
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prompts.splice(prompts.length - 1, 0, ...searchPrompts);
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} else {
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// 没有用知识库搜索,仅用系统提示词
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if (model.chat.systemPrompt) {
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@@ -122,14 +122,14 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
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const prompts = [prompt];
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// 获取向量匹配到的提示词
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const { searchPrompt } = await searchKb({
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const { searchPrompts } = await searchKb({
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similarity: ModelVectorSearchModeMap[model.chat.searchMode]?.similarity,
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prompts,
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model,
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userId
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});
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searchPrompt && prompts.unshift(searchPrompt);
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prompts.splice(prompts.length - 1, 0, ...searchPrompts);
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// 计算温度
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const temperature = (modelConstantsData.maxTemperature * (model.chat.temperature / 10)).toFixed(
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@@ -23,14 +23,10 @@ export const searchKb = async ({
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similarity?: number;
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}): Promise<{
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code: 200 | 201;
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searchPrompt?: {
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obj: `${ChatRoleEnum.System}`;
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searchPrompts: {
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obj: ChatRoleEnum;
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value: string;
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};
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aiPrompt?: {
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obj: `${ChatRoleEnum.AI}`;
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value: string;
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};
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}[];
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}> => {
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async function search(textArr: string[] = []) {
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// 获取提示词的向量
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@@ -102,41 +98,58 @@ export const searchKb = async ({
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if (!filterSystemPrompt && model.chat.searchMode === ModelVectorSearchModeEnum.hightSimilarity) {
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return {
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code: 201,
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searchPrompt: {
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searchPrompts: [
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{
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obj: ChatRoleEnum.System,
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value: '对不起,你的问题不在知识库中。'
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}
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]
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};
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}
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/* 高相似度+无上下文,不添加额外知识,仅用系统提示词 */
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if (!filterSystemPrompt && model.chat.searchMode === ModelVectorSearchModeEnum.noContext) {
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return {
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code: 200,
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searchPrompt: model.chat.systemPrompt
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? {
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searchPrompts: model.chat.systemPrompt
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? [
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{
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obj: ChatRoleEnum.System,
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value: model.chat.systemPrompt
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}
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: undefined
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]
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: []
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};
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}
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/* 有匹配 */
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return {
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code: 200,
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searchPrompt: {
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searchPrompts: [
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{
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obj: ChatRoleEnum.System,
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value: `知识库:'${filterSystemPrompt}'`
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},
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aiPrompt: ModelVectorSearchModeEnum.hightSimilarity
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? {
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obj: 'AI',
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value: `我来玩一个问答游戏,规则为:
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1.我完全忘记我已有的知识
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2.我只能回答关于"${model.chat.systemPrompt || model.name}"的问题
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3.我只能从知识库中选择内容进行回答
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4.如果问题不在知识库中,我会回答"我不知道。"`
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...(model.chat.systemPrompt
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? [
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{
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obj: ChatRoleEnum.System,
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value: model.chat.systemPrompt
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}
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: undefined
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]
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: []),
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...(model.chat.searchMode !== ModelVectorSearchModeEnum.noContext
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? [
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{
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obj: ChatRoleEnum.System,
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value: `我们来玩问答游戏,规则为:
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1.你完全忘记你已有的知识
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2.你只能回答关于"${model.name}"的问题
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3.你只能从知识库中选择内容进行回答
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4.如果问题不在知识库中,你会回答"我不知道。"
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务必遵守规则`
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}
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]
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: [])
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]
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};
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};
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@@ -27,7 +27,7 @@ export const lafClaudChat = async ({
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.join('\n');
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const systemPromptText = systemPrompt ? `\n知识库内容:'${systemPrompt}'\n` : '';
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const prompt = `${systemPromptText}我的问题:'${messages[messages.length - 1].value}'`;
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const prompt = `${systemPromptText}\n我的问题是:'${messages[messages.length - 1].value}'`;
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const lafResponse = await axios.post(
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'https://hnvacz.laf.run/claude-gpt',
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