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| import webConfig from "@/constants/common-env"; | |
| export type ChatRole = "user" | "assistant"; | |
| export type ChatAttachment = { | |
| id: string; | |
| name: string; | |
| type: string; | |
| size: number; | |
| dataUrl: string; | |
| }; | |
| const CHAT_RASTER_IMAGE_MIME_TYPES = new Set([ | |
| "image/png", | |
| "image/jpeg", | |
| "image/webp", | |
| "image/gif", | |
| ]); | |
| export function isRasterImageMimeType(value: string) { | |
| return CHAT_RASTER_IMAGE_MIME_TYPES.has(String(value || "").split(";", 1)[0].trim().toLowerCase()); | |
| } | |
| export function isImageAttachment(attachment: ChatAttachment) { | |
| const dataUrlMime = attachment.dataUrl.match(/^data:([^;,]+)/i)?.[1] || ""; | |
| return isRasterImageMimeType(attachment.type) || isRasterImageMimeType(dataUrlMime); | |
| } | |
| export function resolveChatArtifactUrl(value: string) { | |
| const url = String(value || "").trim(); | |
| if (!url.startsWith("/files/")) return url; | |
| const apiBase = webConfig.apiUrl.replace(/\/$/, ""); | |
| return apiBase ? `${apiBase}${url}` : url; | |
| } | |
| export type ChatCitation = { | |
| title: string; | |
| url: string; | |
| }; | |
| export type ChatToolPart = { | |
| type: "tool"; | |
| toolId: string; | |
| invocationId: string; | |
| status: "pending" | "running" | "success" | "error"; | |
| title: string; | |
| input?: unknown; | |
| output?: unknown; | |
| error?: string; | |
| }; | |
| export type ChatGeneratedImage = { | |
| id: string; | |
| src: string; | |
| prompt: string; | |
| revisedPrompt?: string; | |
| size?: string; | |
| model?: string; | |
| origin?: "generated" | "search"; | |
| sourceUrl?: string; | |
| }; | |
| export type ChatPlace = { | |
| id: string; | |
| title: string; | |
| address: string; | |
| }; | |
| export type ChatPlacePart = { | |
| type: "place"; | |
| places: ChatPlace[]; | |
| }; | |
| export type ChatDilCardRow = { | |
| title: string; | |
| badge?: string; | |
| badgeTone?: "success" | "warning" | "info"; | |
| subtitle?: string; | |
| description?: string; | |
| }; | |
| export type ChatInlineSegment = | |
| | { type: "text"; content: string } | |
| | { type: "place"; places: ChatPlace[] } | |
| | { type: "image_carousel"; queries: string[]; aspectRatio: string } | |
| | { type: "question_card"; questions: { question: string; options: string[] }[] } | |
| | { type: "card_list"; rows: ChatDilCardRow[] }; | |
| export type ChatRichTextPart = { | |
| type: "rich_text"; | |
| segments: ChatInlineSegment[]; | |
| }; | |
| function jsonValueAt(text: string, start: number): { value: unknown; end: number } | null { | |
| const opening = text[start]; | |
| const closing = opening === "[" ? "]" : opening === "{" ? "}" : ""; | |
| if (!closing) return null; | |
| let depth = 0; | |
| let quote = ""; | |
| let escaped = false; | |
| for (let index = start; index < text.length; index += 1) { | |
| const character = text[index]; | |
| if (quote) { | |
| if (escaped) escaped = false; | |
| else if (character === "\\") escaped = true; | |
| else if (character === quote) quote = ""; | |
| continue; | |
| } | |
| if (character === '"' || character === "'") { | |
| quote = character; | |
| continue; | |
| } | |
| if (character === opening) depth += 1; | |
| if (character === closing) { | |
| depth -= 1; | |
| if (depth === 0) { | |
| try { | |
| return { value: JSON.parse(text.slice(start, index + 1)), end: index + 1 }; | |
| } catch { | |
| return null; | |
| } | |
| } | |
| } | |
| } | |
| return null; | |
| } | |
| /** | |
| * ChatGPT 网页端新上的 DIL 声明式卡片标记(<box>/<row>/<AsyncImage>/<Entity>/<badge>/ | |
| * <radio-group> + {@body} 状态 + GenUI.issueNewTurn)。这套协议只有官方前端认识,直接渲染 | |
| * 会漏出原始标签。这里做一层降级转换: | |
| * - {@body} 状态声明、MapWidgetV2/Cite/AsyncImage 等无法本地复刻的组件 → 剥掉; | |
| * - badge/Entity/text/divider → 等价 markdown; | |
| * - radio-group 问卷(含前面的 `<text weight="medium">N. 问题</text>` 标题)→ 抽成 | |
| * question_card,复用站内可点选项卡片。 | |
| */ | |
| function convertDilMarkup(raw: string): { | |
| text: string; | |
| questions: { question: string; options: string[] }[]; | |
| cards: ChatDilCardRow[]; | |
| } { | |
| const text = String(raw || ""); | |
| if (!/<{@body|<radio-group|<MapWidgetV2|<AsyncImage|<Cite|<Entity|<box[\s>]/.test(text)) { | |
| return { text, questions: [], cards: [] }; | |
| } | |
| const questions: { question: string; options: string[] }[] = []; | |
| const cards: ChatDilCardRow[] = []; | |
| const cardMarker = (index: number) => `\u0000DILCARD${index}\u0000`; | |
| // 0) 抽卡片列表:`<row>`(推荐条目,行间以 divider 分隔)。行内含标题(bold+Entity)、 | |
| // badge、灰色副标题(text color=secondary)、描述正文。转成结构化 rows,原位置留标记。 | |
| const withRows = text.replace( | |
| /<row[^>]*>([\s\S]*?)<\/row>/g, | |
| (rowMatch: string, rowBody: string) => { | |
| const titleMatch = rowBody.match(/\*\*([\s\S]*?)\*\*/); | |
| const title = titleMatch | |
| ? titleMatch[1] | |
| .replace(/<Entity[^>]*?value="([^"]*)"[^>]*\/>/g, "$1") | |
| .replace(/<[^>]+>/g, "") | |
| .trim() | |
| : ""; | |
| if (!title) return rowMatch; | |
| const badgeMatch = rowBody.match(/<badge[^>]*color="([^"]*)"[^>]*>([\s\S]*?)<\/badge>/); | |
| const subtitleMatch = rowBody.match(/<text[^>]*color="secondary"[^>]*>([\s\S]*?)<\/text>/); | |
| const description = rowBody | |
| .replace(/\*\*[\s\S]*?\*\*/g, "") | |
| .replace(/<AsyncImage[^>]*\/>/g, "") | |
| .replace(/<Cite[^>]*\/>/g, "") | |
| .replace(/<badge[\s\S]*?<\/badge>/g, "") | |
| .replace(/<Entity[^>]*?\/>/g, "") | |
| .replace(/<text[^>]*color="secondary"[^>]*>[\s\S]*?<\/text>/g, "") | |
| .replace(/<[^>]+>/g, " ") | |
| .replace(/[ \t]+/g, " ") | |
| .replace(/\s*\n\s*/g, " ") | |
| .trim(); | |
| const tone = String(badgeMatch?.[1] || ""); | |
| cards.push({ | |
| title, | |
| badge: badgeMatch ? badgeMatch[2].trim() : undefined, | |
| badgeTone: tone === "success" ? "success" : tone === "warning" ? "warning" : tone ? "info" : undefined, | |
| subtitle: subtitleMatch ? subtitleMatch[1].trim() : undefined, | |
| description: description || undefined, | |
| }); | |
| return cardMarker(cards.length - 1); | |
| }, | |
| ); | |
| // 1) 抽问卷:`<text weight="medium">N. 问题</text>` 紧跟 `<radio-group>…<radio>label</radio>…</radio-group>`。 | |
| const withQuestions = withRows.replace( | |
| /<text[^>]*weight="medium"[^>]*>\s*([^<]+?)\s*<\/text>\s*<radio-group[^>]*>([\s\S]*?)<\/radio-group>/g, | |
| (_match, questionText: string, groupBody: string) => { | |
| const options: string[] = []; | |
| const radioPattern = /<radio[^>]*>([\s\S]*?)<\/radio>/g; | |
| let radioMatch: RegExpExecArray | null; | |
| while ((radioMatch = radioPattern.exec(groupBody))) { | |
| const label = radioMatch[1].trim(); | |
| if (label) options.push(label); | |
| } | |
| const question = String(questionText || "").trim(); | |
| if (question && options.length) questions.push({ question, options }); | |
| return ""; | |
| }, | |
| ); | |
| let working = withQuestions | |
| // 行间 divider(marker 夹着 divider)不转成 `---`,卡片组件自带分隔线;须在通用 divider 规则之前。 | |
| .replace(/(\u0000DILCARD\d+\u0000)\s*<divider[^>]*\/>/g, "$1") | |
| .replace(/<divider[^>]*\/>\s*(?=\u0000DILCARD)/g, "") | |
| .replace(/\{@body[^}]*\}\n?/g, "") // 状态声明 | |
| .replace(/<MapWidgetV2[\s\S]*?\/>/g, "") // 官方地图组件(无本地等价物) | |
| .replace(/<\/?MapWidgetV2[^>]*>/g, "") | |
| .replace(/<Cite[^>]*\/>/g, "") // 官方引用角标(ref 只在官方会话里有意义) | |
| .replace(/<AsyncImage[^>]*\/>/g, "") // 官方图片搜索占位(本站无按 query 拉图管道) | |
| .replace(/<button[^>]*>[\s\S]*?<\/button>/g, "") // GenUI.issueNewTurn 按钮(交互已由 question_card 承担) | |
| .replace(/<badge[^>]*>([\s\S]*?)<\/badge>/g, "「$1」") | |
| .replace(/<Entity[^>]*?value="([^"]*)"[^>]*\/>/g, "$1") | |
| .replace(/<text[^>]*>([\s\S]*?)<\/text>/g, "$1") | |
| .replace(/<divider[^>]*\/>/g, "\n---\n") | |
| .replace(/<\/?(?:box|row|button|radio-group|radio|text|badge|entity|MapWidgetV2)[^>]*>/g, "") | |
| .replace(/\n{3,}/g, "\n\n") | |
| .trim(); | |
| if (!working && (questions.length || cards.length)) working = ""; | |
| return { text: working, questions, cards }; | |
| } | |
| /** Convert ChatGPT web-client place/card directives into portable UI data. */ | |
| export function parseInlineRichContent(value: string): { content: string; places: ChatPlace[]; segments: ChatInlineSegment[]; hasWidgets: boolean } { | |
| const dil = convertDilMarkup(value); | |
| const text = dil.text; | |
| const places: ChatPlace[] = []; | |
| const seen = new Set<string>(); | |
| const segments: ChatInlineSegment[] = []; | |
| const cardMarkerPattern = /\u0000DILCARD(\d+)\u0000/g; | |
| const addText = (content: string) => { | |
| if (!content) return; | |
| // DIL 卡片列表在文本里的占位标记 → 拆开成 text/card_list 段(保留在原文中的位置)。 | |
| cardMarkerPattern.lastIndex = 0; | |
| let last = 0; | |
| let markerMatch: RegExpExecArray | null; | |
| while ((markerMatch = cardMarkerPattern.exec(content))) { | |
| if (markerMatch.index > last) segments.push({ type: "text", content: content.slice(last, markerMatch.index) }); | |
| const rows = dil.cards[Number(markerMatch[1])]; | |
| if (rows) segments.push({ type: "card_list", rows: [rows] }); | |
| last = markerMatch.index + markerMatch[0].length; | |
| } | |
| if (last < content.length) segments.push({ type: "text", content: content.slice(last) }); | |
| }; | |
| let cursor = 0; | |
| while (cursor < text.length) { | |
| const match = /[\[{]/.exec(text.slice(cursor)); | |
| if (!match) { | |
| addText(text.slice(cursor)); | |
| break; | |
| } | |
| const start = cursor + match.index; | |
| addText(text.slice(cursor, start)); | |
| const parsed = jsonValueAt(text, start); | |
| if (!parsed) { | |
| addText(text[start]); | |
| cursor = start + 1; | |
| continue; | |
| } | |
| const { value: item, end } = parsed; | |
| const isPlace = Array.isArray(item) && String(item[0] || "").toLowerCase() === "point_of_interest"; | |
| const isCarousel = !Array.isArray(item) | |
| && Boolean(item) | |
| && typeof item === "object" | |
| && Array.isArray((item as { query?: unknown }).query); | |
| if (isPlace) { | |
| const title = String(item[1] || "").trim(); | |
| const address = String(item[2] || "").trim(); | |
| const key = `${title}\u0000${address}`; | |
| if (title && !seen.has(key)) { | |
| seen.add(key); | |
| const place = { id: `inline-place-${places.length + 1}`, title, address }; | |
| places.push(place); | |
| segments.push({ type: "place", places: [place] }); | |
| } | |
| cursor = end; | |
| continue; | |
| } | |
| if (isCarousel) { | |
| const metadata = item as { query?: unknown; aspect_ratio?: unknown }; | |
| const queries = (metadata.query as unknown[]).map((query) => String(query || "").trim()).filter(Boolean); | |
| if (queries.length) { | |
| segments.push({ type: "image_carousel", queries, aspectRatio: String(metadata.aspect_ratio || "16:9").trim() || "16:9" }); | |
| } | |
| cursor = end; | |
| continue; | |
| } | |
| // 上游模型偶尔会按 ChatGPT 官方客户端的「小程序块」习惯输出 {"app_block":{title,variant,icon,content}} | |
| // (content 是一段可交互 HTML,通常带 <canvas>/<script>)。本站没有这个协议,直接渲染会漏出 | |
| // 一坨转义 JSON;这里把它转成 ```html 围栏,交给 AssistantMarkdown 的 CodeBlock —— 那边已有 | |
| // sandbox="allow-scripts" 的交互式 iframe 预览(CSP 允许内联脚本),游戏/小工具可以直接玩。 | |
| const isAppBlock = !Array.isArray(item) && Boolean(item) && typeof item === "object" | |
| && "app_block" in (item as Record<string, unknown>); | |
| if (isAppBlock) { | |
| const block = ((item as { app_block?: unknown }).app_block || {}) as { title?: unknown; content?: unknown }; | |
| const html = String(block.content || "").trim(); | |
| if (html) { | |
| const title = String(block.title || "").trim(); | |
| addText(`\n\n${title ? `**${title}**\n\n` : ""}\`\`\`html\n${html}\n\`\`\`\n\n`); | |
| cursor = end; | |
| continue; | |
| } | |
| } | |
| // 本站自有的「澄清选择题」协议(仿官网 canvas 流程的问卷卡片):模型对含糊的应用/游戏请求 | |
| // 先输出 {"question_card":{"questions":[{"question":"...","options":["..."]}]}}, | |
| // 前端渲染成可点击的选项卡片,点选项即以该文本作为用户消息发送。 | |
| const isQuestionCard = !Array.isArray(item) && Boolean(item) && typeof item === "object" | |
| && "question_card" in (item as Record<string, unknown>); | |
| if (isQuestionCard) { | |
| const card = ((item as { question_card?: unknown }).question_card || {}) as { | |
| questions?: unknown; question?: unknown; options?: unknown; | |
| }; | |
| const rawQuestions: unknown[] = Array.isArray(card.questions) && card.questions.length | |
| ? card.questions | |
| : (card.question ? [card] : []); | |
| const questions = rawQuestions | |
| .map((entry) => { | |
| const q = (entry || {}) as { question?: unknown; options?: unknown }; | |
| return { | |
| question: String(q.question || "").trim(), | |
| options: (Array.isArray(q.options) ? q.options : []) | |
| .map((option) => String(option || "").trim()) | |
| .filter(Boolean) | |
| .slice(0, 6), | |
| }; | |
| }) | |
| .filter((q) => q.question && q.options.length) | |
| .slice(0, 3); | |
| if (questions.length) { | |
| segments.push({ type: "question_card", questions }); | |
| cursor = end; | |
| continue; | |
| } | |
| } | |
| addText(text.slice(start, end)); | |
| cursor = end; | |
| } | |
| if (dil.questions.length && !segments.some((segment) => segment.type === "question_card")) { | |
| segments.push({ type: "question_card", questions: dil.questions }); | |
| } | |
| // 相邻的 DIL 卡片行合并成一个 card_list(官方是一个 box 里 divider 分行);中间的纯空白文本段丢弃。 | |
| const mergedSegments: ChatInlineSegment[] = []; | |
| for (const segment of segments) { | |
| const last = mergedSegments[mergedSegments.length - 1]; | |
| if (segment.type === "text" && !segment.content.trim() && last?.type === "card_list") continue; | |
| if (segment.type === "card_list" && last?.type === "card_list") { | |
| last.rows.push(...segment.rows); | |
| continue; | |
| } | |
| mergedSegments.push(segment); | |
| } | |
| const content = mergedSegments | |
| .filter((segment): segment is Extract<ChatInlineSegment, { type: "text" }> => segment.type === "text") | |
| .map((segment) => segment.content) | |
| .join("") | |
| .replace(/\n{3,}/g, "\n\n") | |
| .trim(); | |
| return { content, places, segments: mergedSegments, hasWidgets: mergedSegments.some((segment) => segment.type !== "text") }; | |
| } | |
| export function extractInlinePlaceCards(value: string): { content: string; places: ChatPlace[] } { | |
| const parsed = parseInlineRichContent(value); | |
| return { content: parsed.content, places: parsed.places }; | |
| } | |
| export type ChatImagePart = { | |
| type: "image"; | |
| images: ChatGeneratedImage[]; | |
| }; | |
| export type ChatTextPart = { | |
| type: "text"; | |
| text: string; | |
| }; | |
| export type ChatReasoningPart = { | |
| type: "reasoning"; | |
| title: string; | |
| summary: string; | |
| status: "running" | "complete" | "error"; | |
| }; | |
| export type ChatSkillRunStage = "preparing" | "awaiting_input" | "reading" | "executing" | "postprocessing" | "complete" | "error"; | |
| export type ChatSkillRunPart = { | |
| type: "skill_run"; | |
| runId: string; | |
| skillId: string; | |
| skillName: string; | |
| stage: ChatSkillRunStage; | |
| status: "running" | "complete" | "error"; | |
| summary: string; | |
| completed?: number; | |
| total?: number; | |
| failed?: number; | |
| startedAt: string; | |
| updatedAt: string; | |
| tools?: ChatToolPart[]; | |
| error?: string; | |
| }; | |
| export type ChatMessagePart = ChatTextPart | ChatToolPart | ChatImagePart | ChatPlacePart | ChatRichTextPart | ChatReasoningPart | ChatSkillRunPart; | |
| export type ChatMessage = { | |
| id: string; | |
| role: ChatRole; | |
| content: string; | |
| attachments: ChatAttachment[]; | |
| parts: ChatMessagePart[]; | |
| citations: ChatCitation[]; | |
| createdAt: string; | |
| status?: "sending" | "complete" | "error"; | |
| error?: string; | |
| requestMode?: ChatCapabilityMode; | |
| }; | |
| export type ChatReasoningEffort = "" | "low" | "medium" | "high" | "xhigh"; | |
| export type ChatCapabilityMode = "auto" | "chat" | "web_search" | "image_generation"; | |
| export type ChatConversation = { | |
| id: string; | |
| title: string; | |
| model: string; | |
| reasoningEffort: ChatReasoningEffort; | |
| createdAt: string; | |
| updatedAt: string; | |
| messages: ChatMessage[]; | |
| }; | |
| export type ChatCompletionResponse = { | |
| choices?: Array<{ | |
| message?: { | |
| role?: string; | |
| content?: unknown; | |
| attachments?: Array<{ | |
| id?: string; | |
| name?: string; | |
| type?: string; | |
| size?: number; | |
| url?: string; | |
| preview_url?: string; | |
| download_url?: string; | |
| data_url?: string; | |
| }>; | |
| images?: Array<{ | |
| id?: string; | |
| title?: string; | |
| content_url?: string; | |
| thumbnail_url?: string; | |
| source_url?: string; | |
| query?: string; | |
| width?: number; | |
| height?: number; | |
| }>; | |
| places?: Array<{ | |
| id?: string; | |
| title?: string; | |
| address?: string; | |
| }>; | |
| annotations?: Array<{ | |
| type?: string; | |
| url_citation?: { | |
| title?: string; | |
| url?: string; | |
| }; | |
| }>; | |
| }; | |
| }>; | |
| }; | |
| export type ChatCapability = { | |
| id: string; | |
| kind: "builtin" | "skill"; | |
| title: string; | |
| description: string; | |
| enabled: boolean; | |
| command?: string; | |
| }; | |
| /** | |
| * The chat surface consumes capabilities through this stable contract. Search, | |
| * installed skills and other tools can be registered later without coupling | |
| * their transport or result UI to the page component. | |
| */ | |
| export const CHAT_CAPABILITIES: readonly ChatCapability[] = [ | |
| { | |
| id: "web_search", | |
| kind: "builtin", | |
| title: "联网搜索", | |
| description: "搜索互联网并在回复中附带来源", | |
| enabled: true, | |
| command: "$搜索", | |
| }, | |
| { | |
| id: "image_generation", | |
| kind: "builtin", | |
| title: "图片生成 / 编辑", | |
| description: "生成图片;上传参考图后自动改用图片编辑", | |
| enabled: true, | |
| command: "$生图", | |
| }, | |
| ]; | |
| function textFromUnknownContent(value: unknown): string { | |
| if (typeof value === "string") return value; | |
| if (!Array.isArray(value)) return ""; | |
| return value | |
| .map((part) => { | |
| if (typeof part === "string") return part; | |
| if (!part || typeof part !== "object") return ""; | |
| const item = part as { type?: unknown; text?: unknown; content?: unknown }; | |
| if (["text", "output_text"].includes(String(item.type || ""))) { | |
| return String(item.text || item.content || ""); | |
| } | |
| return ""; | |
| }) | |
| .join(""); | |
| } | |
| export function normalizeAssistantContent(value: unknown): string { | |
| const text = textFromUnknownContent(value).replace(/^\uFEFF/, "").trim(); | |
| if (!text) return ""; | |
| // Some compatible providers expose hidden reasoning in their text payload. | |
| // Keep the user-facing answer while avoiding a raw <think> block in Markdown. | |
| return text.replace(/<think>[\s\S]*?<\/think>/gi, "").trim(); | |
| } | |
| function utf8DataUrl(type: string, source: string) { | |
| return `data:${type};charset=utf-8,${encodeURIComponent(source)}`; | |
| } | |
| function safeArtifactStem(value: string, fallback: string) { | |
| const stem = value | |
| .replace(/[<>:"/\\|?*\u0000-\u001f]/g, " ") | |
| .replace(/\s+/g, " ") | |
| .trim() | |
| .slice(0, 64); | |
| return stem || fallback; | |
| } | |
| /** | |
| * Compatible providers do not always expose generated files through the | |
| * message.attachments protocol. Preserve the real source in that case by | |
| * materialising complete inline SVG/HTML documents as browser attachments. | |
| */ | |
| function materializeInlineArtifacts(content: string): { content: string; attachments: ChatAttachment[] } { | |
| let display = content; | |
| const attachments: ChatAttachment[] = []; | |
| const candidates: Array<{ kind: "svg" | "html"; source: string; fullMatch: string }> = []; | |
| const fencedPattern = /```(?:svg|xml|html)\s*\n([\s\S]*?)```/gi; | |
| for (const match of content.matchAll(fencedPattern)) { | |
| const source = String(match[1] || "").trim(); | |
| const kind = /^\s*(?:<!doctype\s+html|<html[\s>])/i.test(source) ? "html" : "svg"; | |
| if ((kind === "svg" && /<svg[\s>][\s\S]*<\/svg>/i.test(source)) | |
| || (kind === "html" && /<html[\s>][\s\S]*<\/html>/i.test(source))) { | |
| candidates.push({ kind, source, fullMatch: match[0] }); | |
| } | |
| } | |
| if (!candidates.length) { | |
| const svg = content.match(/<svg[\s>][\s\S]*?<\/svg>/i); | |
| if (svg?.[0] && svg[0].length >= 120) candidates.push({ kind: "svg", source: svg[0], fullMatch: svg[0] }); | |
| const html = content.match(/(?:<!doctype\s+html[^>]*>\s*)?<html[\s>][\s\S]*?<\/html>/i); | |
| if (html?.[0] && html[0].length >= 160) candidates.push({ kind: "html", source: html[0], fullMatch: html[0] }); | |
| } | |
| for (const [index, candidate] of candidates.entries()) { | |
| const label = candidate.kind === "svg" | |
| ? candidate.source.match(/aria-label=["']([^"']+)["']/i)?.[1] | |
| : candidate.source.match(/<title[^>]*>([^<]+)<\/title>/i)?.[1]; | |
| const extension = candidate.kind === "svg" ? "svg" : "html"; | |
| const type = candidate.kind === "svg" ? "image/svg+xml" : "text/html"; | |
| attachments.push({ | |
| id: createChatId(`inline-${extension}`), | |
| name: `${safeArtifactStem(label || "", `generated-${extension}-${index + 1}`)}.${extension}`, | |
| type, | |
| size: new TextEncoder().encode(candidate.source).byteLength, | |
| dataUrl: utf8DataUrl(type, candidate.source), | |
| }); | |
| display = display.replace(candidate.fullMatch, `\n\n[已提取完整 ${extension.toUpperCase()} 成品,可在下方预览或下载。]\n\n`); | |
| } | |
| return { content: display.replace(/\n{3,}/g, "\n\n").trim(), attachments }; | |
| } | |
| export function parseChatCompletionResponse(response: ChatCompletionResponse) { | |
| const message = response.choices?.[0]?.message; | |
| let content = normalizeAssistantContent(message?.content); | |
| const inlineRichContent = parseInlineRichContent(content); | |
| content = inlineRichContent.content; | |
| const citationMap = new Map<string, ChatCitation>(); | |
| for (const annotation of message?.annotations || []) { | |
| if (annotation?.type !== "url_citation") continue; | |
| const url = String(annotation.url_citation?.url || "").trim(); | |
| if (!/^https?:\/\//i.test(url) || citationMap.has(url)) continue; | |
| citationMap.set(url, { | |
| url, | |
| title: String(annotation.url_citation?.title || url).trim() || url, | |
| }); | |
| } | |
| const attachments = (message?.attachments || []).flatMap((item, index) => { | |
| const dataUrl = resolveChatArtifactUrl(String(item.data_url || item.preview_url || item.download_url || item.url || "").trim()); | |
| if (!dataUrl || !(/^(?:data:|https?:\/\/|\/)/i.test(dataUrl))) return []; | |
| const name = String(item.name || `artifact-${index + 1}`).trim() || `artifact-${index + 1}`; | |
| return [{ | |
| id: String(item.id || createChatId("artifact")), | |
| name, | |
| type: String(item.type || "application/octet-stream"), | |
| size: Number(item.size) || 0, | |
| dataUrl, | |
| } satisfies ChatAttachment]; | |
| }); | |
| if (!attachments.some((attachment) => /\.(?:svg|html?)$/i.test(attachment.name))) { | |
| const inline = materializeInlineArtifacts(content); | |
| content = inline.content; | |
| attachments.push(...inline.attachments); | |
| } | |
| const images = (message?.images || []).flatMap((item, index) => { | |
| const src = String(item.content_url || item.thumbnail_url || "").trim(); | |
| if (!/^https?:\/\//i.test(src)) return []; | |
| return [{ | |
| id: String(item.id || createChatId("search-image")), | |
| src, | |
| prompt: String(item.query || item.title || `搜索图片 ${index + 1}`).trim(), | |
| revisedPrompt: String(item.title || item.query || "搜索图片").trim(), | |
| origin: "search" as const, | |
| sourceUrl: String(item.source_url || "").trim() || undefined, | |
| } satisfies ChatGeneratedImage]; | |
| }); | |
| const places = (message?.places || []).flatMap((item, index) => { | |
| const title = String(item.title || "").trim(); | |
| if (!title) return []; | |
| return [{ | |
| id: String(item.id || `place-${index + 1}`), | |
| title, | |
| address: String(item.address || "").trim(), | |
| } satisfies ChatPlace]; | |
| }); | |
| for (const place of inlineRichContent.places) { | |
| if (!places.some((item) => item.title === place.title && item.address === place.address)) places.push(place); | |
| } | |
| if (!images.length) { | |
| const fallbackUrls = new Set<string>(); | |
| const addImageUrl = (rawUrl: string) => { | |
| const url = rawUrl.trim().replace(/[`'"),.;]+$/g, ""); | |
| if (/^https?:\/\/[^\s]+\.(?:png|jpe?g|webp|gif)(?:\?[^\s]*)?$/i.test(url)) fallbackUrls.add(url); | |
| }; | |
| for (const citation of citationMap.values()) addImageUrl(citation.url); | |
| for (const match of content.matchAll(/https?:\/\/[^\s<>"']+/gi)) addImageUrl(match[0]); | |
| let index = 0; | |
| for (const url of fallbackUrls) { | |
| index += 1; | |
| const matchingCitation = Array.from(citationMap.values()).find((citation) => citation.url.replace(/[`'"),.;]+$/g, "") === url); | |
| images.push({ | |
| id: createChatId("search-image"), | |
| src: url, | |
| prompt: matchingCitation?.title || `搜索图片 ${index}`, | |
| revisedPrompt: matchingCitation?.title || `搜索图片 ${index}`, | |
| origin: "search", | |
| sourceUrl: matchingCitation?.url || url, | |
| }); | |
| } | |
| } | |
| return { content, citations: Array.from(citationMap.values()), attachments, images, places, inlineSegments: inlineRichContent.segments, hasInlineWidgets: inlineRichContent.hasWidgets }; | |
| } | |
| const CHAT_SURFACE_SYSTEM_PROMPT = `This is a full chat workspace with managed artifact previews and downloads. | |
| When the user asks you to create or modify a file, create the real file in /mnt/data and include its sandbox:/mnt/data/... link in the final answer. Never claim a file exists without returning that link. | |
| When the user requests complete source or content that is too long for one reply, put the complete content in the generated file; do not refuse because of message length. Briefly explain that the full content is available through the artifact's preview/source view. | |
| Attachments identified by <conversation_artifact_context> were generated by you earlier in this same conversation. Inspect their actual contents and treat short follow-ups such as "continue", "optimize it", or "modify this" as referring to the most recent matching artifact. | |
| Structured data inside <search_result_context> comes from an earlier web search in this same local conversation. Preserve its displayed order and use its exact titles, source URLs, and direct image URLs when the user refers to prior search results or asks for an artifact built from them. | |
| Satisfy every requested modification before returning the artifact. | |
| When you want to include an interactive app, game, or widget in the reply, return it as one complete \`\`\`html code block (the UI renders it as a sandboxed interactive preview). Do not wrap it in {"app_block": ...} or any other custom JSON envelope. | |
| For an app, game, or widget request that leaves key choices open (such as game type, visual style, controls, or where it should run), do NOT guess and build immediately. First reply with one short lead-in sentence followed by exactly one JSON object on its own line: {"question_card":{"questions":[{"question":"...","options":["...","..."]}]}} — at most 2 questions, 3-4 short options each (the UI renders them as clickable choice cards). Do not wrap it in any other envelope and do not emit extra JSON objects. The user may answer with one numbered line per question. Skip the questions entirely when the request is already specific. Once the user answers, build the complete \`\`\`html code block in one go.`; | |
| function toolExecutionContext(message: ChatMessage) { | |
| const tools = message.parts | |
| .filter((part): part is ChatToolPart => part.type === "tool") | |
| .map((part) => ({ | |
| tool_id: part.toolId, | |
| status: part.status, | |
| input: part.input, | |
| output: part.output, | |
| error: part.error, | |
| })); | |
| if (!tools.length) return ""; | |
| const serialized = JSON.stringify(tools); | |
| return `<tool_execution_context>\n${serialized.slice(0, 16000)}\n</tool_execution_context>`; | |
| } | |
| const MAX_SEARCH_CONTEXT_CHARS = 16_000; | |
| const MAX_TOTAL_SEARCH_CONTEXT_CHARS = 24_000; | |
| const MAX_SEARCH_CONTEXT_CITATIONS = 12; | |
| const MAX_SEARCH_CONTEXT_IMAGES = 8; | |
| function boundedSearchText(value: unknown, maxLength: number) { | |
| return String(value || "").trim().slice(0, maxLength); | |
| } | |
| function searchResultContext(message: ChatMessage) { | |
| const citations = message.citations | |
| .slice(0, MAX_SEARCH_CONTEXT_CITATIONS) | |
| .map((citation, index) => ({ | |
| position: index + 1, | |
| title: boundedSearchText(citation.title, 300), | |
| url: boundedSearchText(citation.url, 1_200), | |
| })); | |
| const images = message.parts | |
| .filter((part): part is ChatImagePart => part.type === "image") | |
| .flatMap((part) => part.images) | |
| .filter((image) => image.origin === "search") | |
| .slice(0, MAX_SEARCH_CONTEXT_IMAGES) | |
| .map((image, index) => ({ | |
| position: index + 1, | |
| title: boundedSearchText(image.revisedPrompt || image.prompt, 300), | |
| query: boundedSearchText(image.prompt, 500), | |
| image_url: boundedSearchText(image.src, 1_500), | |
| source_url: boundedSearchText(image.sourceUrl, 1_200), | |
| })); | |
| if (!citations.length && !images.length) return ""; | |
| const payload = { | |
| description: "Structured results from an earlier web search in this local conversation. Use these exact titles and URLs for follow-up questions and requested artifacts. Positions are one-based and preserve the displayed order.", | |
| citations, | |
| images, | |
| }; | |
| let serialized = JSON.stringify(payload); | |
| while (serialized.length > MAX_SEARCH_CONTEXT_CHARS && (citations.length > 1 || images.length > 1)) { | |
| if (citations.length > images.length && citations.length > 1) citations.pop(); | |
| else if (images.length > 1) images.pop(); | |
| else citations.pop(); | |
| serialized = JSON.stringify(payload); | |
| } | |
| return `<search_result_context>\n${serialized.slice(0, MAX_SEARCH_CONTEXT_CHARS)}\n</search_result_context>`; | |
| } | |
| function recentSearchResultContexts(messages: ChatMessage[]) { | |
| const contexts = new Map<string, string>(); | |
| let remainingChars = MAX_TOTAL_SEARCH_CONTEXT_CHARS; | |
| for (const message of [...messages].reverse()) { | |
| if (message.role !== "assistant") continue; | |
| const context = searchResultContext(message); | |
| if (!context) continue; | |
| if (context.length > remainingChars) break; | |
| contexts.set(message.id, context); | |
| remainingChars -= context.length; | |
| } | |
| return contexts; | |
| } | |
| export function buildChatCompletionMessages( | |
| messages: ChatMessage[], | |
| options: { includePriorImages?: boolean } = {}, | |
| ) { | |
| const lastUserIndex = messages.findLastIndex((message) => message.role === "user" && message.status !== "error"); | |
| const latestGeneratedImages = options.includePriorImages ? messages | |
| .slice(0, Math.max(0, lastUserIndex)) | |
| .reverse() | |
| .flatMap((message) => [...message.parts].reverse()) | |
| .find((part): part is ChatImagePart => part.type === "image" && part.images.some((image) => image.origin !== "search")) | |
| ?.images.filter((image) => image.origin !== "search") || [] : []; | |
| const latestUploadedImages = options.includePriorImages | |
| ? [...messages.slice(0, Math.max(0, lastUserIndex))] | |
| .reverse() | |
| .find((message) => message.role === "user" && message.attachments.some(isImageAttachment)) | |
| ?.attachments.filter(isImageAttachment) || [] | |
| : []; | |
| const searchContexts = recentSearchResultContexts(messages); | |
| const conversationMessages = messages | |
| .filter((message) => message.status !== "error") | |
| .map((message) => { | |
| const messageIndex = messages.indexOf(message); | |
| const isLatestUser = message.role === "user" && messageIndex === lastUserIndex; | |
| const contextImages = isLatestUser ? latestGeneratedImages.slice(0, 4) : []; | |
| const contextUploads = isLatestUser ? latestUploadedImages.slice(0, Math.max(0, 4 - contextImages.length)) : []; | |
| if (isLatestUser && (message.attachments.length > 0 || contextImages.length > 0 || contextUploads.length > 0)) { | |
| const imageAttachments = message.attachments.filter(isImageAttachment); | |
| const fileAttachments = message.attachments.filter((attachment) => !isImageAttachment(attachment)); | |
| return { | |
| role: message.role, | |
| content: [ | |
| ...(message.content.trim() | |
| ? [{ type: "text" as const, text: message.content }] | |
| : []), | |
| ...(contextImages.length | |
| ? [{ type: "text" as const, text: "以下是本对话中助手最近生成的图片,请在回答和后续修改时继续参考这些图片。" }] | |
| : []), | |
| ...imageAttachments.map((attachment) => ({ | |
| type: "image_url" as const, | |
| image_url: { url: attachment.dataUrl }, | |
| })), | |
| ...fileAttachments.map((attachment) => ({ | |
| type: "file" as const, | |
| file: { | |
| filename: attachment.name, | |
| file_data: attachment.dataUrl, | |
| }, | |
| })), | |
| ...contextImages.map((image) => ({ | |
| type: "image_url" as const, | |
| image_url: { url: image.src }, | |
| })), | |
| ...contextUploads.map((attachment) => ({ | |
| type: "image_url" as const, | |
| image_url: { url: attachment.dataUrl }, | |
| })), | |
| ], | |
| }; | |
| } | |
| if (message.role === "user" && message.attachments.length > 0) { | |
| const attachmentNames = message.attachments.map((attachment) => attachment.name).filter(Boolean); | |
| return { | |
| role: message.role, | |
| content: [ | |
| message.content, | |
| attachmentNames.length ? `[Earlier attachments: ${attachmentNames.join(", ")}]` : "", | |
| ].filter(Boolean).join("\n\n"), | |
| }; | |
| } | |
| const assistantContexts = message.role === "assistant" | |
| ? [toolExecutionContext(message), searchContexts.get(message.id)].filter(Boolean) | |
| : []; | |
| return { | |
| role: message.role, | |
| content: [message.content, ...assistantContexts].filter(Boolean).join("\n\n"), | |
| }; | |
| }); | |
| return [ | |
| { role: "system" as const, content: CHAT_SURFACE_SYSTEM_PROMPT }, | |
| ...conversationMessages, | |
| ]; | |
| } | |
| export function createChatId(prefix: string) { | |
| const id = typeof crypto !== "undefined" && typeof crypto.randomUUID === "function" | |
| ? crypto.randomUUID() | |
| : `${Date.now()}-${Math.random().toString(36).slice(2)}`; | |
| return `${prefix}-${id}`; | |
| } | |
| export function createConversationTitle(text: string, hasImages = false) { | |
| const normalized = text.replace(/\s+/g, " ").trim(); | |
| if (!normalized) return hasImages ? "图片对话" : "新对话"; | |
| return normalized.length > 26 ? `${normalized.slice(0, 26)}…` : normalized; | |
| } | |