JARVIS/docs/superpowers/specs/2026-03-20-knowledge-brain-blueprint-notes.md

Notes: Jarvis Knowledge Brain Blueprint

Current-State Findings

• Existing source domains already exist separately: conversations, documents, todos, tasks, forum posts.
• Current long-term memory only comes from conversation extraction via UserMemory.
• Current graph build path only uses indexed document chunks.
• Scheduler infrastructure already exists and can host daily brain-learning jobs.
• Frontend already exposes a 知识大脑 navigation entry, but it currently points to the graph page.

Synthesized Findings

What can be reused

• memory_service as a seed for conversation extraction and recall.
• scheduler_service as the base for daily learning workflows.
• tag_service as an early foundation for brain tags.
• Existing business tables as authoritative raw source records.

What is missing

• Unified event layer across all source systems.
• Candidate memory layer between raw events and durable brain memory.
• Timeline-aware memory model with reinforcement / archival states.
• Retrieval path that combines long-term memory with recent relevant events.
• Brain-specific APIs and a dedicated frontend dashboard module.

Phase 1 objective

• Build the minimum architecture needed for a real event-driven brain:
  • BrainEvent
  • BrainCandidate
  • BrainMemory
  • BrainTag and link tables
  • ingestion services
  • daily learning job
  • retrieval integration
  • brain dashboard APIs

Additional Findings: Knowledge Parsing Normalization

• Current document ingestion parses each format separately and builds chunks directly from ParsedNode items.
• Current chunks already carry structural metadata, but there is no explicit parent-child chunk graph.
• The agreed direction is to use MinerU for PDF only, keep existing parsers for DOCX/XLSX/CSV/MD/TXT, and converge all outputs into structured markdown.
• normalized_content should be persisted on documents so preview, rebuild, and future chunking can reuse the same canonical text.
• Lightweight hierarchy should be represented in chunk metadata first, not in a new relational tree schema.
• Current DOCX upload failure in the running environment is caused by a missing python-docx installation in the active backend environment.