This proposal outlines a system architecture for a context-aware AI assistant that can understand and respond to complex queries about a user's digital environment. The system continuously ingests screen content, builds a rich knowledge graph representation of entities and relationships, and enables sophisticated question answering that goes beyond traditional RAG approaches.
Traditional RAG systems fall short when dealing with questions that require:
Understanding relationships between entities (e.g., "which people are in my team?")
Temporal reasoning (e.g., "what messages haven't I replied to?")
Cross-application context (e.g., "what PRs need my reviews?")
Inferential reasoning based on multiple pieces of information
Handling complex queries requiring domain-specific knowledge or expertise (e.g., "which code changes are most likely to cause regressions?")
Difficulty in identifying dependencies between tasks across multiple applications (e.g., "which tasks in JIRA are blocked by unresolved issues in GitHub?")
Lack of a unified view of user activity, making it hard to track progress or identify pending actions (e.g., "what documents have I accessed but not finalized?")
Limited ability to synthesize and summarize information across numerous sources (e.g., "summarize recent conversations across Slack and email related to project X")
Challenges in understanding and resolving conflicts in schedules or priorities (e.g., "which meetings are overlapping or conflicting with my deadlines?")
Inability to provide proactive, context-aware recommendations based on historical patterns and relationships (e.g., "suggest potential reviewers for this code change based on similar past contributions")
I propose GraphMem, a hybrid memory system that combines the strengths of knowledge graphs, vector databases, and LLM reasoning to create a comprehensive contextual understanding system.