LiuVaayne
29af30b8d2
* feat(Memory): add memory management functionality with settings modal and integration into sidebar
* Add user memory search to chat completion flow
Enable assistants to use memory search results for more personalized
responses when enableMemory is true. Add enableMemory flag to Assistant
type.
* Add MemoryService and memory types interfaces
* Add memory settings tab to AssistantSettings
* feat(memory): implement Phase 1 core backend infrastructure
- Add Memory IPC channels for full CRUD operations
- Create main process MemoryService with LibSQL storage
- Implement memory database schema with history tracking
- Add IPC handlers for all memory operations
- Update preload API to expose memory functionality
- Update renderer MemoryService to use IPC instead of mock data
- Add comprehensive error handling and logging
- Support memory deduplication using SHA256 hashes
- Implement audit trail for all memory changes
Core features:
- Memory add/search/list/delete/update operations
- Full history tracking for changes
- User/agent/run filtering support
- Prepared for vector embeddings (Phase 2)
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* ✨ feat: Implement memory vector search and hybrid search with LibSQL native vector support
- Implemented native vector support using `F32_BLOB` for storing embeddings directly in LibSQL.
- Created vector index using `libsql_vector_idx()` for efficient similarity search.
- Implemented `vectorSearch()` using `vector_top_k()` for fast nearest neighbor search based on cosine similarity.
- Implemented `hybridSearch()` combining text search and vector similarity search.
- Added `addBatchMemories()` to efficiently add multiple memories with embeddings.
- Added embedding model configuration via `setEmbeddingModel()`.
- Integrated embedding generation using `EmbeddingService`.
- Updated database schema with vector column and index.
- Added methods for embedding conversion and similarity calculations.
- Updated documentation with vector search details and schema changes.
- Improved memory management with embedding caching and clearing.
- Added `findSimilarMemories` to avoid duplicates
- Added embedding stats and cache methods
* feat(memory): add EmbeddingService and VectorSearch for memory embedding and search functionality
* fix(memory): update vector operations to match libsql documentation
- Fix vector32() function usage by removing quotes from embedding parameters
- Replace vector_top_k with standard queries to avoid index dependency
- Make embedding dimensions flexible by removing fixed 1536 dimension
- Add NULL checks for embeddings in all vector operations
- Update memory update method to regenerate embeddings
- Use parameterized queries to prevent SQL injection
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* update implement plan
* feat(memory): implement Phase 3 - UI and integration components
- Create Memory Page UI with full CRUD operations
- Add, delete, search, and filter memories
- User and date range filtering
- Bulk operations support
- Implement Memory Processing Pipeline
- Automatic fact extraction from conversations
- Intelligent memory updates (add/update/delete)
- Background processing for non-blocking UI
- Integrate memory search into ApiService
- Inject relevant memories into conversation context
- Post-conversation memory processing
- Assistant-specific memory isolation
- Add Assistant Memory Settings
- Toggle memory per assistant
- View memory statistics
- Configure from assistant settings panel
- Complete memory feature integration
- Redux store already implemented
- Memory service backend ready
- Full end-to-end functionality
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* docs: update memory feature implementation plan to reflect Phase 3 completion
- Mark Phase 3 (UI and Integration) as completed
- Update implementation progress to 90%
- Document all completed components and features
- Add usage instructions for the memory feature
- List key implementation files for reference
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Revert "feat(memory): implement Phase 3 - UI and integration components"
This reverts commit 422463d0f7.
* ✨ feat: Implement memory management for assistants
- Introduced `MemoryProcessor` to handle fact extraction, memory updates, and searches.
- Added `AssistantMemorySettings` component to allow enabling/disabling memory and viewing memory statistics.
- Integrated memory functionality with assistant settings.
- Implemented memory-related prompts and schema validation.
- Added memory service calls for adding, updating, deleting, and searching memories.
* fix: Update memory settings form handling in AssistantMemorySettings component
* feat(memory): implement Phase 3 - UI and integration components
- Create Memory Page UI with full CRUD operations
- Add, delete, search, and filter memories
- User and date range filtering
- Bulk operations support
- Implement Memory Processing Pipeline
- Automatic fact extraction from conversations
- Intelligent memory updates (add/update/delete)
- Background processing for non-blocking UI
- Integrate memory search into ApiService
- Inject relevant memories into conversation context
- Post-conversation memory processing
- Assistant-specific memory isolation
- Add Assistant Memory Settings
- Toggle memory per assistant
- View memory statistics
- Configure from assistant settings panel
- Complete memory feature integration
- Redux store already implemented
- Memory service backend ready
- Full end-to-end functionality
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: update memory search method and integrate form handling in AssistantMemorySettings
* feat(memory): enhance memory service with new IPC channels and configuration updates
* refactor: remove VectorSearch service and update memory configuration methods
- Deleted the VectorSearch service implementation from the memory services.
- Updated the memory configuration method in the preload index from `updateConfig` to `setConfig`.
- Adjusted the corresponding call in the MemoryService to reflect the new method name.
* refactor(memory): remove embedding cache and related methods from MemoryService
* feat(memory): update MemoryService initialization to handle configuration updates and errors
* feat(memory): centralize SQL queries for MemoryService to improve maintainability
* feat: Enhance memory management with user context and CRUD operations
- Updated memory feature analysis with current implementation status and new functionalities.
- Added user context management, allowing automatic filtering based on selected user.
- Implemented full CRUD operations for memories, including add, edit, and delete functionalities.
- Introduced user management system with validation rules for user IDs.
- Enhanced internationalization support for memory features across multiple languages.
- Improved UI components for memory management, including user switching and memory editing modals.
- Updated MemoryService and MemoryProcessor to handle user-specific memory operations.
- Added comprehensive testing and documentation for new features.
* feat(memory): enhance UI components and user interactions in MemoriesPage
* feat(memory): integrate user context into memory operations and enhance user management
* feat(memory): add user deletion and reset functionality for specific users
* feat(memory): enhance localization for user management and memory operations in multiple languages
* feat(memory): remove reset functionality and add user listing feature in memory service
* feat(memory): enhance memory processing by adding fallback models and improving configuration handling
* feat(memory): refactor fact extraction and memory update logic to use fetchGenerate for LLM calls
* feat(memory): iinject memory to chat
* feat(memory): enhance memory handling by ensuring fallback to empty array and improving logging in memory search
* feat(memory): implement pagination for memory list operations
- Add offset parameter to MemoryListOptions interface for pagination support
- Update MemoryService.list() method to properly handle offset parameter
- Implement client-side pagination with 50 items per page default
- Add pagination controls with page size options (20, 50, 100, 200)
- Include pagination state management (currentPage, pageSize)
- Load all memories at once and paginate on client side for better search performance
- Reset pagination when switching users or performing searches
- Update memory CRUD operations to maintain current pagination state
- Add styled pagination component with consistent design
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(memory): correct state variable name in user switch handler
* feat(memory): add comprehensive memory feature guide in Chinese
* feat: Enhance memory management and citation handling
- Updated BaseApiClient to retrieve memory references from cache and format them for use.
- Modified CitationBlock to include memory references in rendering logic.
- Enhanced CitationsList to display memory citations with appropriate formatting.
- Improved MemoriesPage layout and functionality, including debounced search and user management.
- Refactored MemoryProcessor to handle memory updates and search queries more effectively.
- Updated API service to include memory in external tool results.
- Adjusted memory-related types and schemas for better integration.
- Enhanced createCitationBlock utility to accommodate memory data.
* feat(memory): update memory localization keys and descriptions for improved clarity
* feat(memory): add descriptions to memory localization files and improve error handling in MemoryService
* ✨ style: Remove blank lines and fix typo in settings
- Removed blank lines in README files.
- Fixed a typo in the assistant settings label for the memory tab.
revert: revert format changes
revert: revert format changes
* feat(memory): enhance memory update logic and improve prompt clarity
* feat(memory): implement global memory toggle and enhance memory settings UI
* feat(memory): refine hybrid search to focus on vector similarity and update localization files
* feat(i18n): add memory configuration prompts in multiple languages
---------
Co-authored-by: Claude <noreply@anthropic.com>
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Cherry Studio 记忆功能指南
功能介绍
Cherry Studio 的记忆功能是一个强大的工具，能够帮助 AI 助手记住对话中的重要信息、用户偏好和上下文。通过记忆功能，您的 AI 助手可以：
- 📝 记住重要信息：自动从对话中提取并存储关键事实和信息
- 🧠 个性化响应：基于存储的记忆提供更加个性化和相关的回答
- 🔍 智能检索：在需要时自动搜索相关记忆，增强对话的连贯性
- 👥 多用户支持：为不同用户维护独立的记忆上下文
记忆功能特别适用于需要长期保持上下文的场景，例如个人助手、客户服务、教育辅导等。
如何启用记忆功能
1. 全局配置（首次设置）
在使用记忆功能之前，您需要先进行全局配置：
- 点击侧边栏的 记忆 图标（记忆棒图标）进入记忆管理页面
- 点击右上角的 更多 按钮（三个点），选择 设置
- 在设置弹窗中配置以下必要项：
- LLM 模型：选择用于处理记忆的语言模型（推荐使用 GPT-4 或 Claude 等高级模型）
- 嵌入模型：选择用于生成向量嵌入的模型（如 text-embedding-3-small）
- 嵌入维度：输入嵌入模型的维度（通常为 1536）
- 点击 确定 保存配置
⚠️ 注意：嵌入模型和维度一旦设置后无法更改，请谨慎选择。
2. 为助手启用记忆
完成全局配置后，您可以为特定助手启用记忆功能：
- 进入 助手 页面
- 选择要启用记忆的助手，点击 编辑
- 在助手设置中找到 记忆 部分
- 打开记忆功能开关
- 保存助手设置
启用后，该助手将在对话过程中自动提取和使用记忆。
使用方法
查看记忆
- 点击侧边栏的 记忆 图标进入记忆管理页面
- 您可以看到所有存储的记忆卡片，包括：
- 记忆内容
- 创建时间
- 所属用户
添加记忆
手动添加记忆有两种方式：
方式一：在记忆管理页面添加
- 点击右上角的 添加记忆 按钮
- 在弹窗中输入记忆内容
- 点击 添加 保存
方式二：在对话中自动提取
- 当助手启用记忆功能后，系统会自动从对话中提取重要信息并存储为记忆
编辑记忆
- 在记忆卡片上点击 更多 按钮（三个点）
- 选择 编辑
- 修改记忆内容
- 点击 保存
删除记忆
- 在记忆卡片上点击 更多 按钮
- 选择 删除
- 确认删除操作
记忆搜索
记忆管理页面提供了强大的搜索功能：
- 在页面顶部的搜索框中输入关键词
- 系统会实时过滤显示匹配的记忆
- 搜索支持模糊匹配，可以搜索记忆内容的任何部分
用户管理
记忆功能支持多用户，您可以为不同的用户维护独立的记忆库：
切换用户
- 在记忆管理页面，点击右上角的用户选择器
- 选择要切换到的用户
- 页面会自动加载该用户的记忆
添加新用户
- 点击用户选择器
- 选择 添加新用户
- 输入用户 ID（支持字母、数字、下划线和连字符）
- 点击 添加
删除用户
- 切换到要删除的用户
- 点击右上角的 更多 按钮
- 选择 删除用户
- 确认删除（注意：这将删除该用户的所有记忆）
💡 提示：默认用户（default-user）无法删除。
设置说明
LLM 模型
- 用于处理记忆提取和更新的语言模型
- 建议选择能力较强的模型以获得更好的记忆提取效果
- 可随时更改
嵌入模型
- 用于将文本转换为向量，支持语义搜索
- 一旦设置后无法更改（为了保证现有记忆的兼容性）
- 推荐使用 OpenAI 的 text-embedding 系列模型
嵌入维度
- 嵌入向量的维度，需要与选择的嵌入模型匹配
- 常见维度：
- text-embedding-3-small: 1536
- text-embedding-3-large: 3072
- text-embedding-ada-002: 1536
自定义提示词（可选）
- 事实提取提示词：自定义如何从对话中提取信息
- 记忆更新提示词：自定义如何更新现有记忆
最佳实践
1. 合理组织记忆
- 保持记忆简洁明了，每条记忆专注于一个具体信息
- 使用清晰的语言描述事实，避免模糊表达
- 定期审查和清理过时或不准确的记忆
2. 多用户场景
- 为不同的使用场景创建独立用户（如工作、个人、学习等）
- 使用有意义的用户 ID，便于识别和管理
- 定期备份重要用户的记忆数据
3. 模型选择建议
- LLM 模型：GPT-4、Claude 3 等高级模型能更准确地提取和理解信息
- 嵌入模型：选择与您的主要使用语言匹配的模型
4. 性能优化
- 避免存储过多冗余记忆，这可能影响搜索性能
- 定期整理和合并相似的记忆
- 对于大量记忆的场景，考虑按主题或时间进行分类管理
常见问题
Q: 为什么我无法启用记忆功能？
A: 请确保您已经完成全局配置，包括选择 LLM 模型和嵌入模型。
Q: 记忆会自动同步到所有助手吗？
A: 不会。每个助手的记忆功能需要单独启用，且记忆是按用户隔离的。
Q: 如何导出我的记忆数据？
A: 目前系统暂不支持直接导出功能，但所有记忆都存储在本地数据库中。
Q: 删除的记忆可以恢复吗？
A: 删除操作是永久的，无法恢复。建议在删除前仔细确认。
Q: 记忆功能会影响对话速度吗？
A: 记忆功能在后台异步处理，不会明显影响对话响应速度。但过多的记忆可能会略微增加搜索时间。
Q: 如何清空所有记忆？
A: 您可以删除当前用户并重新创建，或者手动删除所有记忆条目。
注意事项
隐私保护
- 所有记忆数据都存储在您的本地设备上，不会上传到云端
- 请勿在记忆中存储敏感信息（如密码、私钥等）
- 定期审查记忆内容，确保没有意外存储的隐私信息
数据安全
- 记忆数据存储在本地数据库中
- 建议定期备份重要数据
- 更换设备时请注意迁移记忆数据
使用限制
- 单条记忆的长度建议不超过 500 字
- 每个用户的记忆数量建议控制在 1000 条以内
- 过多的记忆可能影响系统性能
技术细节
记忆功能使用了先进的 RAG（检索增强生成）技术：
- 信息提取：使用 LLM 从对话中智能提取关键信息
- 向量化存储：通过嵌入模型将文本转换为向量，支持语义搜索
- 智能检索：在对话时自动搜索相关记忆，提供给 AI 作为上下文
- 持续学习：随着对话进行，不断更新和完善记忆库
💡 提示：记忆功能是 Cherry Studio 的高级特性，合理使用可以大大提升 AI 助手的智能程度和用户体验。如有更多问题，欢迎查阅文档或联系支持团队。