diff --git a/app/i18n/locales/en/translation.json b/app/i18n/locales/en/translation.json index e045e63..6c54671 100644 --- a/app/i18n/locales/en/translation.json +++ b/app/i18n/locales/en/translation.json @@ -9,6 +9,7 @@ "生成轮数": "Generation Rounds", "时间范围": "Range of literature release dates, from this time to this year", "更新文中的上标，使得数字顺序排列": "Update the superscript in the text to make the numbers in order", + "停止生成": "Stop Generation", "+ Add Paper": "+ Add Paper", "Buy VIP TO UNLOCK Cloud Sync and Edit Mutiple Papers Simultaneously": "Buy VIP TO UNLOCK Cloud Sync and Edit Mutiple Papers Simultaneously", "Paper Management": "Paper Management", diff --git a/app/i18n/locales/zh-CN/translation.json b/app/i18n/locales/zh-CN/translation.json index f27d055..5db06b8 100644 --- a/app/i18n/locales/zh-CN/translation.json +++ b/app/i18n/locales/zh-CN/translation.json @@ -9,6 +9,7 @@ "生成轮数": "生成轮数", "时间范围": "文献发布日期范围，从这个时间到今年", "更新文中的上标，使得数字顺序排列": "更新文中的上标，使得数字顺序排列", + "停止生成": "停止生成", "+ Add Paper": "+ 添加新论文（会直接替换编辑器里的内容）", "Buy VIP TO UNLOCK Cloud Sync and Edit Mutiple Papers Simultaneously": "购买VIP解锁云同步和同时编辑多篇论文", "Paper Management": "论文管理", diff --git a/components/QuillEditor.tsx b/components/QuillEditor.tsx index 023d5a5..92b550c 100644 --- a/components/QuillEditor.tsx +++ b/components/QuillEditor.tsx @@ -116,11 +116,12 @@ const QEditor = ({ lng }) => { //选择时间范围 const [timeRange, setTimeRange] = useLocalStorage("时间范围", "2019"); const [generateNumber, setGenerateNumber] = useState(0); //当前任务的进行数 - const [openProgressBar, setOpenProgressBar] = useState(false); + const [openProgressBar, setOpenProgressBar] = useState(false); //设置进度条是否打开 const [showAnnouncement, setShowAnnouncement] = useLocalStorage( "显示公告", true ); // 是否显示公告 + const [controller, setController] = useState(null); // 创建 AbortController 的状态 //redux const dispatch = useAppDispatch(); @@ -287,240 +288,280 @@ const QEditor = ({ lng }) => { }; // 处理AI写作 const handleAIWrite = async () => { - quill!.setSelection(cursorPosition!, 0); // 将光标移动到原来的位置 + try { + setOpenProgressBar(true); //开启进度条 + // 创建一个新的 AbortController 实例 + const newController = new AbortController(); + setController(newController); + quill!.setSelection(cursorPosition!, 0); // 将光标移动到原来的位置 - const prompt = "请帮助用户完成论文写作，使用用户所说的语言完成"; - await sendMessageToOpenAI( - userInput, - quill!, - selectedModel!, - apiKey, - upsreamUrl, - prompt, - cursorPosition! - ); - // 清空input内容 - setUserInput(""); - // 重新获取更新后的内容并更新 Redux store - const updatedContent = quill!.root.innerHTML; - dispatch(setEditorContent(updatedContent)); - toast.success(`AI写作完成`, { - position: "top-center", - autoClose: 2000, - pauseOnHover: true, - }); + const prompt = "请帮助用户完成论文写作，使用用户所说的语言完成"; + await sendMessageToOpenAI( + userInput, + quill!, + selectedModel!, + apiKey, + upsreamUrl, + prompt, + cursorPosition!, + true, + newController.signal // 传递 AbortSignal + ); + // 清空input内容 + setUserInput(""); + // 重新获取更新后的内容并更新 Redux store + const updatedContent = quill!.root.innerHTML; + dispatch(setEditorContent(updatedContent)); + toast.success(`AI写作完成`, { + position: "top-center", + autoClose: 2000, + pauseOnHover: true, + }); + } catch (error) { + toast.error(`AI写作出现错误: ${error}`, { + position: "top-center", + autoClose: 3000, + pauseOnHover: true, + }); + } finally { + setOpenProgressBar(false); //关闭进度条 + } }; // 处理paper2AI async function paper2AI(topic: string) { - quill!.setSelection(cursorPosition!, 0); // 将光标移动到原来的位置 - let offset = -1; - if (generatedPaperNumber != 1) offset = 0; //如果生成的数量不为1，则从0开始 - setOpenProgressBar(true); //开启进度条 - //如果说要评估主题是否匹配的话,就要多获取一些文献 - let limit = 2; - if (isEvaluateTopicMatch) { - limit = 4; - } + try { + // 创建一个新的 AbortController 实例 + const newController = new AbortController(); + setController(newController); + quill!.setSelection(cursorPosition!, 0); // 将光标移动到原来的位置 + let offset = -1; + if (generatedPaperNumber != 1) offset = 0; //如果生成的数量不为1，则从0开始 + setOpenProgressBar(true); //开启进度条 + //如果说要评估主题是否匹配的话,就要多获取一些文献 + let limit = 2; + if (isEvaluateTopicMatch) { + limit = 4; + } - for (let i = 0; i < generatedPaperNumber!; i++) { - try { - if (!topic) { - //使用ai提取当前要请求的论文主题 - const prompt = - "As a topic extraction assistant, you can help me extract the current discussion of the paper topic, I will enter the content of the paper, you extract the paper topic , no more than two, Hyphenated query terms yield no matches (replace it with space to find matches) return format is: topic1 topic2"; - const userMessage = getTextBeforeCursor(quill!, 2000); - topic = await sendMessageToOpenAI( - userMessage, - null, + for (let i = 0; i < generatedPaperNumber!; i++) { + try { + if (!topic) { + //使用ai提取当前要请求的论文主题 + const prompt = + "As a topic extraction assistant, you can help me extract the current discussion of the paper topic, I will enter the content of the paper, you extract the paper topic , no more than two, Hyphenated query terms yield no matches (replace it with space to find matches) return format is: topic1 topic2"; + const userMessage = getTextBeforeCursor(quill!, 2000); + topic = await sendMessageToOpenAI( + userMessage, + null, + selectedModel!, + apiKey, + upsreamUrl, + prompt, + null, + false, + newController.signal // 传递 AbortSignal + ); + console.log("topic in AI before removeSpecialCharacters", topic); + topic = removeSpecialCharacters(topic); + topic = topic.split(" ").slice(0, 2).join(" "); + //如果超过十个字符就截断 + if (topic.length > 10) { + topic = topic.slice(0, 10); + } + } + console.log("topic in AI", topic); + console.log("offset in paper2AI", offset); + console.log("limit in paper2AI", limit); + let rawData, dataString, newReferences; + if (selectedSource === "arxiv") { + rawData = await getArxivPapers(topic, limit, offset); + //判断返回的文献是否跟用户输入的主题相关 + if (isEvaluateTopicMatch) { + const { relevantPapers, nonRelevantPapers } = + await evaluateTopicMatch( + rawData, + apiKey, + upsreamUrl, + selectedModel!, + topic, + newController.signal + ); + rawData = relevantPapers; + } + console.log("arxiv rawdata:", rawData); + // 将 rawData 转换为引用数组 + newReferences = rawData.map((entry: any) => ({ + url: entry.id, + title: entry.title, + year: entry.published, + author: entry.authors?.slice(0, 3).join(", "), + })); + dataString = rawData + .map((entry: any) => { + return `ID: ${entry.id}\nTime: ${entry.published}\nTitle: ${entry.title}\nSummary: ${entry.abstract}\n\n`; + }) + .join(""); + } else if (selectedSource === "semanticScholar") { + rawData = await getSemanticPapers( + topic, + `${timeRange}-2024`, + offset, + limit + ); + //判断返回的文献是否跟用户输入的主题相关 + if (isEvaluateTopicMatch) { + const { relevantPapers, nonRelevantPapers } = + await evaluateTopicMatch( + rawData, + apiKey, + upsreamUrl, + selectedModel!, + topic, + newController.signal + ); + rawData = relevantPapers; + } + // 将 rawData 转换为引用数组 + newReferences = rawData.map((entry: any) => ({ + url: entry.url, + title: entry.title, + year: entry.year, + author: entry.authors?.slice(0, 3).join(", "), + venue: entry.venue, + journal: formatJournalReference(entry), + doi: entry.externalIds.DOI, + })); + dataString = rawData + .map((entry: any) => { + return `Time: ${entry.year}\nTitle: ${entry.title}\nSummary: ${entry.abstract}\n\n`; + }) + .join(""); + } else if (selectedSource === "pubmed") { + rawData = await fetchPubMedData( + topic, + Number(timeRange)!, + offset, + limit + ); + if (!rawData) { + throw new Error("未搜索到文献 from PubMed."); + } + //判断返回的文献是否跟用户输入的主题相关 + if (isEvaluateTopicMatch) { + const { relevantPapers, nonRelevantPapers } = + await evaluateTopicMatch( + rawData, + apiKey, + upsreamUrl, + selectedModel!, + topic, + newController.signal + ); + rawData = relevantPapers; + } + newReferences = rawData.map((entry: any) => ({ + id: entry.id, // 文章的 PubMed ID + title: entry.title, // 文章的标题 + abstract: entry.abstract, // 文章的摘要 + author: entry.authors?.slice(0, 3).join(", "), // 文章的作者列表，假设为字符串数组 + year: entry.year, // 文章的发表日期 + journal: entry.journal, // 文章的发表杂志 + url: entry.url, // 文章的 URL + source: "PubMed", // 指示这些引用来自 PubMed + doi: entry.doi, // 文章的 DOI + })); + + // 打印 newReferences + console.log(newReferences); + dataString = rawData + .map((entry: any) => { + return `Time: ${entry.year}\nTitle: ${entry.title}\nSummary: ${entry.abstract}\n\n`; + }) + .join(""); + } + + // 确保搜索到的论文不超过 3000 个字符 + const trimmedMessage = + dataString.length > 3000 ? dataString.slice(0, 3000) : dataString; + //slate的方法 + // const content = `需要完成的论文主题：${topic}, 搜索到的论文内容:${trimmedMessage},之前已经完成的内容上下文：${extractText( + // editorValue + // )}`; + const content = `之前用户已经完成的内容上下文：${getTextBeforeCursor( + quill!, + 800 + )},搜索到的论文内容:${trimmedMessage},需要完成的论文主题：${topic},请根据搜索到的论文内容完成用户的论文`; + showExpandableToast(`搜索论文完成，搜索到的论文:${trimmedMessage}`); + await sendMessageToOpenAI( + content, + quill!, selectedModel!, apiKey, upsreamUrl, - prompt, - null, - false + systemPrompt, + cursorPosition!, + true, + newController.signal // 传递 AbortSignal ); - console.log("topic in AI before removeSpecialCharacters", topic); - topic = removeSpecialCharacters(topic); - topic = topic.split(" ").slice(0, 2).join(" "); - //如果超过十个字符就截断 - if (topic.length > 10) { - topic = topic.slice(0, 10); - } - } - console.log("topic in AI", topic); - console.log("offset in paper2AI", offset); - console.log("limit in paper2AI", limit); - let rawData, dataString, newReferences; - if (selectedSource === "arxiv") { - rawData = await getArxivPapers(topic, limit, offset); - //判断返回的文献是否跟用户输入的主题相关 - if (isEvaluateTopicMatch) { - const { relevantPapers, nonRelevantPapers } = - await evaluateTopicMatch( - rawData, - apiKey, - upsreamUrl, - selectedModel!, - topic - ); - rawData = relevantPapers; - } - console.log("arxiv rawdata:", rawData); - // 将 rawData 转换为引用数组 - newReferences = rawData.map((entry: any) => ({ - url: entry.id, - title: entry.title, - year: entry.published, - author: entry.authors?.slice(0, 3).join(", "), - })); - dataString = rawData - .map((entry: any) => { - return `ID: ${entry.id}\nTime: ${entry.published}\nTitle: ${entry.title}\nSummary: ${entry.abstract}\n\n`; + setUserInput(""); + // 重新获取更新后的内容并更新 Redux store + const updatedContent = quill!.root.innerHTML; + dispatch(setEditorContent(updatedContent)); + //在对应的位置添加文献 + const nearestNumber = getNumberBeforeCursor(quill!); + dispatch( + addReferencesRedux({ + references: newReferences, + position: nearestNumber, }) - .join(""); - } else if (selectedSource === "semanticScholar") { - rawData = await getSemanticPapers( - topic, - `${timeRange}-2024`, - offset, - limit ); - //判断返回的文献是否跟用户输入的主题相关 - if (isEvaluateTopicMatch) { - const { relevantPapers, nonRelevantPapers } = - await evaluateTopicMatch( - rawData, - apiKey, - upsreamUrl, - selectedModel!, - topic - ); - rawData = relevantPapers; + if (isVip) { + //在云端同步supabase + const data = await submitPaper( + supabase, + updatedContent, + references, + paperNumberRedux + ); } - // 将 rawData 转换为引用数组 - newReferences = rawData.map((entry: any) => ({ - url: entry.url, - title: entry.title, - year: entry.year, - author: entry.authors?.slice(0, 3).join(", "), - venue: entry.venue, - journal: formatJournalReference(entry), - doi: entry.externalIds.DOI, - })); - dataString = rawData - .map((entry: any) => { - return `Time: ${entry.year}\nTitle: ${entry.title}\nSummary: ${entry.abstract}\n\n`; - }) - .join(""); - } else if (selectedSource === "pubmed") { - rawData = await fetchPubMedData( - topic, - Number(timeRange)!, - offset, - limit - ); - if (!rawData) { - throw new Error("未搜索到文献 from PubMed."); - } - //判断返回的文献是否跟用户输入的主题相关 - if (isEvaluateTopicMatch) { - const { relevantPapers, nonRelevantPapers } = - await evaluateTopicMatch( - rawData, - apiKey, - upsreamUrl, - selectedModel!, - topic - ); - rawData = relevantPapers; - } - newReferences = rawData.map((entry: any) => ({ - id: entry.id, // 文章的 PubMed ID - title: entry.title, // 文章的标题 - abstract: entry.abstract, // 文章的摘要 - author: entry.authors?.slice(0, 3).join(", "), // 文章的作者列表，假设为字符串数组 - year: entry.year, // 文章的发表日期 - journal: entry.journal, // 文章的发表杂志 - url: entry.url, // 文章的 URL - source: "PubMed", // 指示这些引用来自 PubMed - doi: entry.doi, // 文章的 DOI - })); - - // 打印 newReferences - console.log(newReferences); - dataString = rawData - .map((entry: any) => { - return `Time: ${entry.year}\nTitle: ${entry.title}\nSummary: ${entry.abstract}\n\n`; - }) - .join(""); + //修改offset使得按照接下来的顺序进行获取文献 + offset += 2; + setGenerateNumber(i + 1); + toast.success(`AI写作完成`, { + position: "top-center", + autoClose: 2000, + pauseOnHover: true, + }); + } catch (error) { + console.error("Paper2AI出现错误", error); + // 在处理错误后，再次抛出这个错误 + // throw new Error(`Paper2AI出现错误: ${error}`); + toast.error(`Paper2AI出现错误: ${error}`, { + position: "top-center", + autoClose: 3000, + pauseOnHover: true, + }); } - - // 确保搜索到的论文不超过 3000 个字符 - const trimmedMessage = - dataString.length > 3000 ? dataString.slice(0, 3000) : dataString; - //slate的方法 - // const content = `需要完成的论文主题：${topic}, 搜索到的论文内容:${trimmedMessage},之前已经完成的内容上下文：${extractText( - // editorValue - // )}`; - const content = `之前用户已经完成的内容上下文：${getTextBeforeCursor( - quill!, - 800 - )},搜索到的论文内容:${trimmedMessage},需要完成的论文主题：${topic},请根据搜索到的论文内容完成用户的论文`; - showExpandableToast(`搜索论文完成，搜索到的论文:${trimmedMessage}`); - await sendMessageToOpenAI( - content, - quill!, - selectedModel!, - apiKey, - upsreamUrl, - systemPrompt, - cursorPosition! - ); - setUserInput(""); - // 重新获取更新后的内容并更新 Redux store - const updatedContent = quill!.root.innerHTML; - dispatch(setEditorContent(updatedContent)); - //在对应的位置添加文献 - const nearestNumber = getNumberBeforeCursor(quill!); - dispatch( - addReferencesRedux({ - references: newReferences, - position: nearestNumber, - }) - ); - if (isVip) { - //在云端同步supabase - const data = await submitPaper( - supabase, - updatedContent, - references, - paperNumberRedux - ); - } - //修改offset使得按照接下来的顺序进行获取文献 - offset += 2; - setGenerateNumber(i + 1); - toast.success(`AI写作完成`, { - position: "top-center", - autoClose: 2000, - pauseOnHover: true, - }); - } catch (error) { - console.error("Paper2AI出现错误", error); - // 在处理错误后，再次抛出这个错误 - // throw new Error(`Paper2AI出现错误: ${error}`); - toast.error(`Paper2AI出现错误: ${error}`, { - position: "top-center", - autoClose: 3000, - pauseOnHover: true, - }); } + } catch (error) { + toast.error(`Paper2AI出现错误: ${error}`, { + position: "top-center", + autoClose: 3000, + pauseOnHover: true, + }); + } finally { + setOpenProgressBar(false); + setGenerateNumber(0); //总的已经生成的数量 } - setOpenProgressBar(false); - setGenerateNumber(0); //总的已经生成的数量 } + const handleStop = () => { + if (controller) { + controller.abort(); // 取消请求 + setController(null); // 重置 controller 状态 + } + }; return (
@@ -601,6 +642,16 @@ const QEditor = ({ lng }) => {
+ {/* 停止生成的按钮只有在开始对话之后才会出现 */} + {openProgressBar ? ( + + ) : null} +
); diff --git a/components/chatAI.tsx b/components/chatAI.tsx index ac22433..dadf213 100644 --- a/components/chatAI.tsx +++ b/components/chatAI.tsx @@ -8,6 +8,7 @@ import { convertToSuperscript, deleteSameBracketNumber, } from "@/utils/others/quillutils"; +import { faSignal } from "@fortawesome/free-solid-svg-icons"; //redux不能在普通函数使用 interface ChatData { @@ -29,7 +30,8 @@ const sendMessageToOpenAI = async ( upsreamUrl: string, prompt: string, cursorPosition: number | null, - useEditorFlag = true // 新增的标志，用于决定操作 + useEditorFlag = true, // 新增的标志，用于决定操作 + signal: AbortSignal ) => { //识别应该使用的模型 let model = selectedModel; @@ -37,6 +39,7 @@ const sendMessageToOpenAI = async ( // 设置API请求参数 const requestOptions = { method: "POST", + signal: signal, headers: { "Content-Type": "application/json", // "Upstream-Url": upsreamUrl, @@ -106,7 +109,12 @@ const sendMessageToOpenAI = async ( const content = data.choices[0].message.content; return content; // 或根据需要处理并返回数据 } - } catch (error) { + } catch (error: any) { + if (error.name === "AbortError") { + console.log("Fetch operation was aborted"); + //这里不用产生报错因为是手动停止 + return; + } console.error("Error:", error); // 如果有响应，返回响应的原始内容 if (response) { diff --git a/utils/others/aiutils.ts b/utils/others/aiutils.ts index 82c2a3c..1d28504 100644 --- a/utils/others/aiutils.ts +++ b/utils/others/aiutils.ts @@ -74,7 +74,8 @@ export async function evaluateTopicMatch( apiKey: string, upsreamUrl: string, selectedModel: string, - topic: string + topic: string, + signal: AbortSignal ): Promise<{ relevantPapers: string[]; nonRelevantPapers: string[] }> { const prompt = "请判断文献是否跟用户输入的主题相关,只需要返回true或false的数组"; @@ -117,7 +118,8 @@ export async function evaluateTopicMatch( upsreamUrl, prompt, null, - false + false, + signal ); console.log("isrelevantResults in 相关性检查", isRelevantResults); // 处理每篇文献的相关性结果