feat: 重构向量存储架构和优化思维导图生成

[jimmyken]

主要改进：

向量存储架构优化

• 实现每个笔记本独立的向量表（vec_{notebookId}），替代全局 vec_embeddings
• 添加 vec_metadata 表追踪每个笔记本的向量维度
• 支持动态向量维度（768, 1024, 1536等），自动检测嵌入模型输出维度
• 修复维度不匹配错误（Expected 1024 dimensions but received 768）

AI 提供商兼容性

• 修复 Qwen 提供商与 AI SDK v5 的兼容性问题
• 将 Qwen 从 qwen-ai-provider 迁移到 @ai-sdk/openai-compatible
• 解决 UnsupportedModelVersionError 错误

思维导图生成改进

• 优化提示词结构，添加明确的格式要求和示例
• 修复 schema 验证错误：chunkIds 和 keywords 字段支持 null 值
• 增强内容聚合：MAX_CHUNKS_PER_DOC 从 10 提升到 30
• 添加详细的调试日志（输入提示词、模型输出、错误信息）

技术细节

• 修改文件：9个核心文件
• 新增功能：动态向量表管理、维度自动检测
• 性能优化：提升内容聚合效率
• 调试改进：完整的输入输出日志追踪

此次重构解决了多个关键问题，提升了系统的灵活性和稳定性。

[MrSibe]

Code review

Found 1 issue:

1. Missing data migration - PR changes vector storage architecture from single vec_embeddings table to per-notebook tables (vec_{notebookId}), but provides no migration strategy. Existing users will lose all vector indexes on upgrade.

KnowNote/src/main/db/index.ts

Lines 225 to 246 in d37edfa

	const tableName = `vec_${notebookId.replace(/[^a-zA-Z0-9]/g, '_')}`
	try {
	// 检查元数据表中是否有记录
	const metadata = sqlite
	.prepare(`SELECT table_name, dimensions FROM vec_metadata WHERE notebook_id = ?`)
	.get(notebookId) as { table_name: string; dimensions: number } | undefined
	if (metadata) {
	// 表已存在，检查维度是否匹配
	if (metadata.dimensions !== dimensions) {
	console.warn(
	`[Database] Vector table ${metadata.table_name} exists with dimensions ${metadata.dimensions}, ` +
	`but requested ${dimensions}. Dropping and recreating table.`
	)
	// 删除旧表
	try {
	sqlite.exec(`DROP TABLE IF EXISTS ${metadata.table_name}`)
	} catch (err) {
	console.error(`[Database] Failed to drop old table ${metadata.table_name}:`, err)

The PR introduces a new createNotebookVectorTable() function that creates separate vector tables for each notebook, completely changing from the original architecture that used a single vec_embeddings table with notebook_id column (established in commit a785075). Without migration code, existing users' vector data will be inaccessible after upgrading.

🤖 Generated with Claude Code

_{- If this code review was useful, please react with 👍. Otherwise, react with 👎.}

[MrSibe]

Code review

Found 1 issue:

1. Missing data migration - PR changes vector storage architecture from single vec_embeddings table to per-notebook tables (vec_{notebookId}), but provides no migration strategy. Existing users will lose all vector indexes on upgrade.

KnowNote/src/main/db/index.ts

Lines 225 to 246 in d37edfa

	const tableName = `vec_${notebookId.replace(/[^a-zA-Z0-9]/g, '_')}`
	try {
	// 检查元数据表中是否有记录
	const metadata = sqlite
	.prepare(`SELECT table_name, dimensions FROM vec_metadata WHERE notebook_id = ?`)
	.get(notebookId) as { table_name: string; dimensions: number } | undefined
	if (metadata) {
	// 表已存在，检查维度是否匹配
	if (metadata.dimensions !== dimensions) {
	console.warn(
	`[Database] Vector table ${metadata.table_name} exists with dimensions ${metadata.dimensions}, ` +
	`but requested ${dimensions}. Dropping and recreating table.`
	)
	// 删除旧表
	try {
	sqlite.exec(`DROP TABLE IF EXISTS ${metadata.table_name}`)
	} catch (err) {
	console.error(`[Database] Failed to drop old table ${metadata.table_name}:`, err)

The PR introduces a new createNotebookVectorTable() function that creates separate vector tables for each notebook, completely changing from the original architecture that used a single vec_embeddings table with notebook_id column (established in commit a785075). Without migration code, existing users' vector data will be inaccessible after upgrading.

🤖 Generated with Claude Code

• If this code review was useful, please react with 👍. Otherwise, react with 👎.

@jimmyken 感谢贡献！可以运行一下数据库迁移的指令吗？自动生成一下SQLite的迁移SQL