Task List for GraphMem Project
Deadline: April 17, 2025 (Thursday) Midnight
0. Requirement Gathering (2 hours)
- [x] understand the problem statement
- [x] plan the next 2 days
- [x] Talk to AI about it.
1. Research Phase (6 hours) -
- Literature Review
- [x] Review GraphRAG research paper (1 hour)
- [x] Study Mem0 and Graffiti memory systems (1 hour)
- [x] Research knowledge graph implementations for similar use cases (1 hour)
- [x] Identify best practices for screen content parsing (1 hour)
- Technology Selection
- [x] Evaluate graph database options (Neo4j vs others) (30 mins)
- [x] Compare vector database solutions (30 mins)
- [x] Select embedding models appropriate for the task (30 mins)
- [x] Determine API framework and architecture (30 mins)
2. Proposal Development (10 hours)
- System Architecture Design
- [x] Draft high-level architecture diagram (1 hour)
- [x] Define data flow and component interactions (1 hour)
- [x] Design knowledge graph schema (1 hour)
- [x] Outline query processing pipeline (1 hour)
- API Design
- [x] Define /ingest endpoint schema (30 mins)
- [x] Define /chat_completion endpoint schema (30 mins)
- [x] Document data models and structures (1 hour)
- [x] Design application-specific parsers (1 hour)
- Write Proposal Document
- [x] Draft executive summary and problem statement (30 mins)
- [x] Detail technical approach and architecture (1 hour)
- [x] Outline implementation plan (30 mins)
- [x] Document challenges and mitigations (30 mins)
- [x] Finalize proposal with diagrams (30 mins)
3. POC Development (14 hours)
- Setup Development Environment
- [ ] Initialize Jupyter notebook or Python project (30 mins)
- [ ] Set up database environment (Neo4j + vector DB) (1 hour)
- [ ] Configure embedding model access (30 mins)
- [ ] Create project structure (30 mins)
- [ ] Create raw data
- Core Data Processing
- [ ] Implement basic screen content parser (1 hour)
- [ ] Build entity extraction module (1 hour)
- [ ] Develop relationship identification logic (1 hour)
- [ ] Create knowledge graph construction functions (1 hour)
- Query Processing
- [ ] Implement GraphRAG retrieval algorithm (2 hours)
- [ ] Build query classification system (1 hour)
- [ ] Develop answer generation pipeline (1 hour)
- [ ] Create visualization for knowledge graph (1 hour)
- Test with Example Queries
- [ ] Test "Which people are in my team?" (30 mins)
- [ ] Test "What messages haven't I replied to on WhatsApp?" (30 mins)
- [ ] Test "What PRs need my reviews?" (30 mins)
- [ ] Test 3 additional complex queries (1 hour)
4. System Design Documentation (10.5 hours)