303 lines
9.5 KiB
TypeScript
303 lines
9.5 KiB
TypeScript
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import { GoogleGenAI, Modality, Type } from "@google/genai";
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import { ChatMode, Message, ChatScenario, AppLanguage } from "../types";
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import { TRANSLATIONS, DEFAULT_LANGUAGE } from "../constants";
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// 获取 Gemini 客户端实例
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const getClient = () => {
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// 优先从 LocalStorage 获取用户配置的 API Key
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const storedSettings = localStorage.getItem("sociopal_settings");
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const settings = storedSettings ? JSON.parse(storedSettings) : {};
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const apiKey = settings.apiKey || process.env.API_KEY;
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if (!apiKey) {
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throw new Error("API Key 未配置。请在设置中输入您的 API Key。");
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}
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// Create a new GoogleGenAI instance right before making an API call
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return new GoogleGenAI({ apiKey });
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};
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// --- 模型定义 ---
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export const MODEL_CHAT_STANDARD = "gemini-3-flash-preview";
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export const MODEL_CHAT_DEEP = "gemini-3-pro-preview";
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export const MODEL_CHAT_FAST = "gemini-flash-lite-latest";
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export const MODEL_IMAGE_GEN = "gemini-3-pro-image-preview";
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export const MODEL_VIDEO_GEN = "veo-3.1-fast-generate-preview";
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export const MODEL_TTS = "gemini-2.5-flash-preview-tts";
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// 获取当前模式对应的模型名称
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export const getModelNameForMode = (mode: ChatMode): string => {
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switch (mode) {
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case ChatMode.DEEP: return MODEL_CHAT_DEEP;
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case ChatMode.FAST: return MODEL_CHAT_FAST;
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case ChatMode.STANDARD:
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default:
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return MODEL_CHAT_STANDARD;
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}
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};
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// 格式化模型名称用于展示
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export const formatModelName = (modelId: string): string => {
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if (modelId === MODEL_CHAT_STANDARD) return "Gemini 3 Flash";
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if (modelId === MODEL_CHAT_DEEP) return "Gemini 3 Pro";
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if (modelId === MODEL_CHAT_FAST) return "Gemini Flash Lite";
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if (modelId === MODEL_IMAGE_GEN) return "Gemini 3 Pro Image";
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if (modelId === MODEL_VIDEO_GEN) return "Veo 3.1";
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return modelId;
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};
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// --- 聊天功能 ---
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export const streamChatResponse = async (
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history: Message[],
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currentMessage: string,
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mode: ChatMode,
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targetLanguage: string, // 传入目标语言代码（如 'zh-CN', 'en'）或 'auto'
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scenario: ChatScenario = ChatScenario.GENERAL,
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attachments: { mimeType: string; data: string }[] = [],
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onChunk: (text: string, grounding?: any) => void
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) => {
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const ai = getClient();
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let model = getModelNameForMode(mode);
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// 根据场景构造系统指令
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let baseInstruction = "";
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switch (scenario) {
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case ChatScenario.READING:
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baseInstruction = `You are a distinguished Sociology Professor specializing in Classical Sociological Theory.
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Focus on the works of Marx, Weber, Durkheim, Simmel, and other foundational figures.
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When answering:
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1. Contextualize the text historically.
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2. Explain key arguments precisely.
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3. Discuss the critical reception and legacy.
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4. Use academic yet accessible language.`;
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break;
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case ChatScenario.CONCEPT:
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baseInstruction = `You are an expert Sociological Concept Analyst.
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Your goal is to provide deep, multi-dimensional definitions of sociological terms.
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When defining a concept:
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1. Provide a clear, concise definition.
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2. Explain its etymology or theoretical origin.
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3. Contrast it with related or opposing concepts.
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4. Provide concrete examples of the concept in action.`;
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break;
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case ChatScenario.RESEARCH:
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baseInstruction = `You are a Senior Research Methodology Consultant.
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You help students and researchers design their studies.
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Focus on:
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1. Refining research questions.
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2. Suggesting appropriate methods (Qualitative, Quantitative, Mixed).
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3. Discussing sampling, operationalization, and ethics.
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4. Suggesting theoretical frameworks suitable for the topic.`;
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break;
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case ChatScenario.GENERAL:
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default:
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baseInstruction = `You are a helpful and knowledgeable Sociology Learning Assistant.
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Answer questions clearly using sociological perspectives.
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Encourage critical thinking and connect daily life examples to sociological theories.`;
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break;
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}
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// 语言指令：根据 targetLanguage 动态生成
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let languageInstruction = "";
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if (targetLanguage === 'auto') {
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languageInstruction = "Reply in the language used by the user in their latest message. If the user explicitly asks to switch languages, follow their request.";
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} else {
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// 强制使用特定语言
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languageInstruction = `You MUST reply in ${targetLanguage}. Do NOT use other languages unless explicitly asked for translation examples.`;
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}
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let config: any = {
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systemInstruction: `${baseInstruction}\n\n${languageInstruction}`,
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};
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// 根据模式配置参数
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if (mode === ChatMode.STANDARD) {
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config.tools = [{ googleSearch: {} }];
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} else if (mode === ChatMode.DEEP) {
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config.thinkingConfig = { thinkingBudget: 32768 }; // Pro 模型最大思考预算
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}
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// Fast 模式仅切换模型，无需额外配置
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const chat = ai.chats.create({
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model,
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config,
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history: history.slice(0, -1).map(m => ({
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role: m.role,
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parts: [
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{ text: m.content },
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...(m.attachments || []).map(a => ({
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inlineData: { mimeType: a.mimeType, data: a.data }
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}))
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]
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}))
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});
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const parts: any[] = [{ text: currentMessage }];
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attachments.forEach(att => {
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parts.push({ inlineData: { mimeType: att.mimeType, data: att.data } });
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});
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try {
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const result = await chat.sendMessageStream({
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message: parts
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});
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for await (const chunk of result) {
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const text = chunk.text;
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const grounding = chunk.candidates?.[0]?.groundingMetadata;
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if (text || grounding) {
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onChunk(text || '', grounding);
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}
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}
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} catch (e) {
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console.error("Chat error", e);
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throw e;
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}
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};
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// --- 图像生成 ---
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export const generateImage = async (
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prompt: string,
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size: "1K" | "2K" | "4K"
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): Promise<string[]> => {
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const ai = getClient();
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const response = await ai.models.generateContent({
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model: MODEL_IMAGE_GEN,
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contents: { parts: [{ text: prompt }] },
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config: {
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imageConfig: {
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imageSize: size,
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}
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}
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});
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const images: string[] = [];
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if (response.candidates?.[0]?.content?.parts) {
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for (const part of response.candidates[0].content.parts) {
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if (part.inlineData && part.inlineData.data) {
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images.push(`data:${part.inlineData.mimeType};base64,${part.inlineData.data}`);
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}
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}
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}
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return images;
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};
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// --- 视频生成 ---
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export const generateVideo = async (
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prompt: string,
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aspectRatio: "16:9" | "9:16"
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): Promise<string> => {
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const ai = getClient();
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let operation = await ai.models.generateVideos({
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model: MODEL_VIDEO_GEN,
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prompt: prompt,
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config: {
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numberOfVideos: 1,
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aspectRatio: aspectRatio,
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resolution: '720p',
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}
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});
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while (!operation.done) {
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await new Promise(resolve => setTimeout(resolve, 5000));
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operation = await ai.operations.getVideosOperation({ operation: operation });
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}
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const uri = operation.response?.generatedVideos?.[0]?.video?.uri;
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if (!uri) throw new Error("No video URI returned");
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// Fetch with explicit key, prioritizing local setting
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const storedSettings = localStorage.getItem("sociopal_settings");
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const settings = storedSettings ? JSON.parse(storedSettings) : {};
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const apiKey = settings.apiKey || process.env.API_KEY;
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const fetchResponse = await fetch(`${uri}&key=${apiKey}`);
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const blob = await fetchResponse.blob();
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return URL.createObjectURL(blob);
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};
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// --- 语音转录 ---
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export const transcribeAudio = async (
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audioBase64: string,
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mimeType: string,
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language: AppLanguage
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): Promise<string> => {
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const ai = getClient();
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const t = TRANSLATIONS[language] || TRANSLATIONS[DEFAULT_LANGUAGE];
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const response = await ai.models.generateContent({
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model: MODEL_CHAT_STANDARD,
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contents: {
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parts: [
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{ inlineData: { mimeType, data: audioBase64 } },
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{ text: t.transcribePrompt }
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]
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}
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});
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return response.text || "";
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};
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// --- 语音合成 ---
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export const generateSpeech = async (
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text: string
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): Promise<AudioBuffer> => {
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const ai = getClient();
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const response = await ai.models.generateContent({
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model: MODEL_TTS,
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contents: { parts: [{ text }] },
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config: {
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responseModalities: [Modality.AUDIO],
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speechConfig: {
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voiceConfig: {
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prebuiltVoiceConfig: { voiceName: 'Kore' },
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},
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},
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},
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});
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const base64Audio = response.candidates?.[0]?.content?.parts?.[0]?.inlineData?.data;
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if (!base64Audio) throw new Error("No audio generated");
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const audioContext = new (window.AudioContext || (window as any).webkitAudioContext)({sampleRate: 24000});
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const audioBuffer = await decodeAudioData(
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decode(base64Audio),
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audioContext,
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24000,
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1
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);
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return audioBuffer;
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};
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// 辅助函数
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function decode(base64: string) {
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const binaryString = atob(base64);
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const len = binaryString.length;
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const bytes = new Uint8Array(len);
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for (let i = 0; i < len; i++) {
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bytes[i] = binaryString.charCodeAt(i);
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}
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return bytes;
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}
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async function decodeAudioData(
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data: Uint8Array,
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ctx: AudioContext,
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sampleRate: number,
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numChannels: number,
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): Promise<AudioBuffer> {
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const dataInt16 = new Int16Array(data.buffer);
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const frameCount = dataInt16.length / numChannels;
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const buffer = ctx.createBuffer(numChannels, frameCount, sampleRate);
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for (let channel = 0; channel < numChannels; channel++) {
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const channelData = buffer.getChannelData(channel);
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for (let i = 0; i < frameCount; i++) {
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channelData[i] = dataInt16[i * numChannels + channel] / 32768.0;
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}
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}
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return buffer;
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}
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