update:1.优化章节管理（1->N）模式下支持修改章节标题 2.AI创作章节内容支持加载模型列表，使用不同模型创作
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xiamuceer
@@ -2,7 +2,7 @@
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<div align="center">
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@@ -351,7 +351,7 @@ MuMuAINovel/
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- 提交 [Issue](https://github.com/xiamuceer-j/MuMuAINovel/issues)
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- Linux DO [讨论](https://linux.do/t/topic/1106333)
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- 加入QQ群 [QQ群](frontend/public/qq.jpg)
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- 加入WX群 [WX群](frontend/public/WX.jpg)
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- 加入WX群 [WX群](frontend/public/WX.png)
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---
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@@ -953,6 +953,7 @@ async def generate_chapter_content_stream(
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style_id = generate_request.style_id
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target_word_count = generate_request.target_word_count or 3000
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enable_mcp = generate_request.enable_mcp if hasattr(generate_request, 'enable_mcp') else True
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custom_model = generate_request.model if hasattr(generate_request, 'model') else None
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# 预先验证章节存在性（使用临时会话）
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async for temp_db in get_db(request):
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try:
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@@ -1310,12 +1311,20 @@ async def generate_chapter_content_stream(
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logger.info(f"开始AI流式创作章节 {chapter_id}")
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# 准备生成参数
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generate_kwargs = {"prompt": prompt}
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if custom_model:
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logger.info(f" 使用自定义模型: {custom_model}")
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generate_kwargs["model"] = custom_model
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# 注意：这里使用用户配置的AI服务，模型参数会覆盖默认模型
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# 如果需要切换provider，需要在前端传递provider参数
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# 流式生成内容
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full_content = ""
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chunk_count = 0
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last_progress = 0
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async for chunk in user_ai_service.generate_text_stream(prompt=prompt):
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async for chunk in user_ai_service.generate_text_stream(**generate_kwargs):
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full_content += chunk
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chunk_count += 1
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@@ -2494,7 +2503,10 @@ async def generate_single_chapter_for_batch(
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# 非流式生成内容
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full_content = ""
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async for chunk in ai_service.generate_text_stream(prompt=prompt):
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async for chunk in ai_service.generate_text_stream(
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prompt=prompt,
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model=None # 批量生成时使用用户默认模型，后续可扩展
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):
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full_content += chunk
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# 更新章节内容到数据库（使用锁保护）
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@@ -78,6 +78,7 @@ class ChapterGenerateRequest(BaseModel):
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le=10000 # 最大10000字
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)
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enable_mcp: bool = Field(True, description="是否启用MCP工具增强（搜索参考资料）")
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model: Optional[str] = Field(None, description="指定使用的AI模型，不提供则使用用户默认模型")
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class BatchGenerateRequest(BaseModel):
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@@ -94,6 +95,7 @@ class BatchGenerateRequest(BaseModel):
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enable_analysis: bool = Field(False, description="是否启用同步分析")
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enable_mcp: bool = Field(True, description="是否启用MCP工具增强（搜索参考资料）")
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max_retries: int = Field(3, description="每个章节的最大重试次数", ge=0, le=5)
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model: Optional[str] = Field(None, description="指定使用的AI模型，不提供则使用用户默认模型")
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class BatchGenerateResponse(BaseModel):
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@@ -1,7 +1,7 @@
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{
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"name": "frontend",
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"private": true,
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"version": "1.0.8",
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"version": "1.0.9",
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"type": "module",
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"scripts": {
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"dev": "vite",
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@@ -27,6 +27,8 @@ export default function Chapters() {
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const [writingStyles, setWritingStyles] = useState<WritingStyle[]>([]);
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const [selectedStyleId, setSelectedStyleId] = useState<number | undefined>();
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const [targetWordCount, setTargetWordCount] = useState<number>(3000);
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const [availableModels, setAvailableModels] = useState<Array<{value: string, label: string}>>([]);
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const [selectedModel, setSelectedModel] = useState<string | undefined>();
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const [analysisVisible, setAnalysisVisible] = useState(false);
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const [analysisChapterId, setAnalysisChapterId] = useState<string | null>(null);
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// 分析任务状态管理
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@@ -187,6 +189,37 @@ export default function Chapters() {
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}
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};
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const loadAvailableModels = async () => {
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try {
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// 从设置API获取用户配置的模型列表
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const settingsResponse = await fetch('/api/settings');
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if (settingsResponse.ok) {
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const settings = await settingsResponse.json();
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const { api_key, api_base_url, api_provider } = settings;
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if (api_key && api_base_url) {
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try {
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const modelsResponse = await fetch(
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`/api/settings/models?api_key=${encodeURIComponent(api_key)}&api_base_url=${encodeURIComponent(api_base_url)}&provider=${api_provider}`
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);
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if (modelsResponse.ok) {
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const data = await modelsResponse.json();
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if (data.models && data.models.length > 0) {
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setAvailableModels(data.models);
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// 设置默认模型为当前配置的模型
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setSelectedModel(settings.llm_model);
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}
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}
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} catch (error) {
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console.log('获取模型列表失败，将使用默认模型');
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}
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}
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}
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} catch (error) {
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console.error('加载可用模型失败:', error);
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}
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};
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// 检查并恢复批量生成任务
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const checkAndRestoreBatchTask = async () => {
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if (!currentProject?.id) return;
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@@ -270,6 +303,10 @@ export default function Chapters() {
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try {
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await updateChapter(editingId, values);
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// 刷新章节列表以获取完整的章节数据（包括outline_title等联查字段）
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await refreshChapters();
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message.success('章节更新成功');
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setIsModalOpen(false);
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form.resetFields();
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@@ -288,6 +325,8 @@ export default function Chapters() {
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});
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setEditingId(id);
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setIsEditorOpen(true);
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// 打开编辑窗口时加载模型列表
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loadAvailableModels();
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}
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};
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@@ -335,7 +374,8 @@ export default function Chapters() {
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// 进度回调
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setSingleChapterProgress(progressValue);
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setSingleChapterProgressMessage(progressMsg);
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}
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},
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selectedModel // 传递选中的模型
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);
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message.success('AI创作成功，正在分析章节内容...');
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@@ -1517,9 +1557,21 @@ export default function Chapters() {
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<Form.Item
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label="章节标题"
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name="title"
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tooltip="章节标题由大纲管理，建议在大纲页面统一修改"
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tooltip={
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currentProject.outline_mode === 'one-to-one'
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? "章节标题由大纲管理，建议在大纲页面统一修改"
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: "一对多模式下可以修改章节标题"
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}
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rules={
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currentProject.outline_mode === 'one-to-many'
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? [{ required: true, message: '请输入章节标题' }]
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: undefined
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}
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>
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<Input placeholder="输入章节标题" disabled />
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<Input
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placeholder="输入章节标题"
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disabled={currentProject.outline_mode === 'one-to-one'}
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/>
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</Form.Item>
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<Form.Item
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@@ -1666,6 +1718,32 @@ export default function Chapters() {
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</div>
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</Form.Item>
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<Form.Item
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label="AI模型"
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tooltip="选择用于生成章节内容的AI模型，不选择则使用默认模型"
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>
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<Select
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placeholder="使用默认模型"
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value={selectedModel}
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onChange={setSelectedModel}
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size="large"
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allowClear
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disabled={isGenerating}
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style={{ width: '100%' }}
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showSearch
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optionFilterProp="label"
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>
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{availableModels.map(model => (
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<Select.Option key={model.value} value={model.value} label={model.label}>
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{model.label}
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</Select.Option>
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))}
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</Select>
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<div style={{ color: '#666', fontSize: 12, marginTop: 4 }}>
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不同模型有不同的特点和定价，请根据需要选择
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</div>
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</Form.Item>
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<Form.Item label="章节内容" name="content">
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<TextArea
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ref={contentTextAreaRef}
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@@ -286,7 +286,8 @@ export function useChapterSync() {
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onProgress?: (content: string) => void,
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styleId?: number,
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targetWordCount?: number,
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onProgressUpdate?: (message: string, progress: number) => void
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onProgressUpdate?: (message: string, progress: number) => void,
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model?: string
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) => {
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try {
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// 使用fetch处理流式响应
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@@ -297,7 +298,8 @@ export function useChapterSync() {
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},
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body: JSON.stringify({
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style_id: styleId,
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target_word_count: targetWordCount
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target_word_count: targetWordCount,
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model: model
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}),
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});
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