Implement robust and scalable AI chat personalization #199
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Co-authored-by: rei.mizushima <rei.mizushima@go.sfcollege.edu>
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Summary of Changes
Hello @lqvp, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request delivers a foundational overhaul of the AI chat's personalization capabilities. It establishes a robust, modular framework for understanding user context, maintaining memory across interactions, and dynamically tailoring AI responses. The changes aim to significantly enhance the user experience by providing more relevant and consistent AI conversations, while also offering users direct control over their personal data.
Highlights
- New Personalization System: Introduced a comprehensive AI chat personalization system, replacing an unstable legacy implementation. This new architecture addresses issues like fragmented memory and lack of context awareness.
- Core Components: Implemented several key modules: a
PersonalizationEngineas the central orchestrator, aUserProfileManagerfor dynamic user profiles, aHybridMemorySystemfor short-term and long-term memory management, aContextEnginefor intelligent prompt construction, and aUserMemoryInterfacefor user control over their data. - AI Chat Integration: The existing
aichatmodule has been updated to integrate with the new personalization engine. This includes initializing the engine, passing user and session IDs to the Gemini text generation, and processing both user messages and AI responses through the personalization system to build context and store information. - User Data Management: Users can now interact with the personalization system via commands like
/profile,/memories,/forget,/update_info,/export_data, and/delete_all_data, providing transparency and control over their personalized data. - Automated Memory Management: The system includes background tasks for memory consolidation (converting short-term to long-term memories) and a 'forgetting mechanism' that applies decay to memories and profile data over time, ensuring relevance and managing data retention.
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Code Review
This pull request introduces a comprehensive and well-architected personalization system, which is a major feature enhancement. The code is well-structured into distinct components like PersonalizationEngine, HybridMemorySystem, and UserProfileManager, with clear responsibilities. The inclusion of a user-facing command interface for managing personal data is an excellent privacy-conscious feature. The test suite is also thorough and covers the key functionalities.
My review focuses on a few critical areas:
- Security and Privacy: There's a critical issue with hardcoded privacy settings that needs to be addressed.
- Correctness: I've identified a couple of potential bugs related to incorrect
userIdusage and a division-by-zero risk. - Maintainability: There are opportunities to improve maintainability by centralizing duplicated logic, removing unused code, and enhancing error logging.
Overall, this is a very strong contribution. Addressing the identified issues will make the new personalization system more robust, secure, and maintainable.
| privacySettings: { | ||
| userId, | ||
| allowInference: true, | ||
| allowLongTermStorage: true, | ||
| dataRetentionDays: 365, | ||
| sensitiveTopics: [], | ||
| autoDeletePatterns: [] | ||
| } |
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Hardcoding privacy settings, especially allowInference: true and allowLongTermStorage: true, is a significant privacy and security risk. These settings should be retrieved from the user's profile or a dedicated privacy configuration store, respecting the user's choices. Forcing these values to true could violate user consent and lead to data being processed and stored against their wishes.
| // Consolidate based on various factors | ||
| // This is a simplified version | ||
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| const session = await this.memorySystem.getOrCreateSession('', sessionId); |
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Calling getOrCreateSession with an empty string for userId is incorrect and will likely lead to bugs. It will either fail to find the session or create a new session with an empty userId.
To fix this, you should:
- Update the
shouldConsolidateMemorymethod signature to acceptuserId:
private async shouldConsolidateMemory(sessionId: string, userId: string): Promise<boolean> - Update the call to this method in
processResponse(line 161) to pass theuserId:
const shouldConsolidate = await this.shouldConsolidateMemory(sessionId, userId); - Use the passed
userIdhere.
| if (traits.openness > 0.7) { | ||
| prompt += "The user appreciates creative and novel ideas. "; | ||
| } | ||
| if (traits.conscientiousness > 0.7) { | ||
| prompt += "The user values detailed, well-organized responses. "; | ||
| } |
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The value 0.7 is a magic number. It should be defined as a named constant at the top of the class or in a shared constants file to improve readability and maintainability. For example: private readonly PERSONALITY_TRAIT_THRESHOLD = 0.7;.
| sessionId?: string | ||
| ): Promise<ShortTermMemory> { | ||
| // Clean up old sessions first | ||
| await this.cleanupOldSessions(); |
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Calling cleanupOldSessions on every call to getOrCreateSession could introduce performance issues, especially if the number of sessions grows large. This cleanup operation iterates over sessions and could become slow. It would be more efficient to run cleanupOldSessions as a periodic background task (e.g., using setInterval) rather than in the hot path of session creation/retrieval.
| const shouldConsolidate = | ||
| session.workingMemory.length >= 10 || | ||
| Date.now() - session.lastUpdate > 20 * 60 * 1000; // 20 minutes idle |
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This logic for determining if a session should be consolidated seems to be duplicated and slightly different from the logic in ContextEngine.shouldConsolidateMemory. For example, this uses a working memory length of 10, while ContextEngine uses 8. To improve maintainability and ensure consistency, this logic should be centralized in a single place, likely within ContextEngine.shouldConsolidateMemory, and this background task should call that method.
Co-authored-by: rei.mizushima <rei.mizushima@go.sfcollege.edu>
Co-authored-by: rei.mizushima <rei.mizushima@go.sfcollege.edu>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: rei.mizushima <rei.mizushima@go.sfcollege.edu>
Co-authored-by: rei.mizushima <rei.mizushima@go.sfcollege.edu>
Implement a robust and scalable personalization system to provide deeply optimized, context-aware AI chat experiences.
This PR introduces a completely new architecture to replace the unstable
cursor/aichat-b339implementation. It fundamentally solves issues like fragmented and inconsistent memory, lack of context awareness, and scalability limitations by integrating dynamic user profiles, a hybrid memory system, and a context engine for intelligent prompt construction and response processing.Open in Web • Open in Cursor • Open Docs