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)