rootandClaude Opus 4.6 bf033b2d9d Metacognitive knowledge extraction: 496 topics, 300 decisions, 100 insights ...

- extract_knowledge.py: LLM processes sampled pages from all conversations
- Extracts structured JSON: topics, decisions, insights, open questions
- Stores as queryable Parquet tables (knowledge_topics, knowledge_decisions,
  knowledge_insights, knowledge_questions)
- Each finding traces to source conversation + page number
- Fixed Unicode chunker crash (byte boundary on multi-byte chars)
- Tested on 8 real ChatGPT PDFs, 477 pages:
  496 topics, 300 decisions, 100 insights, 200 open questions extracted

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-03-27 23:06:36 -05:00

..

extract_knowledge.py

Metacognitive knowledge extraction: 496 topics, 300 decisions, 100 insights

2026-03-27 23:06:36 -05:00

generate_demo.py

Phase 6: Ingest pipeline — CSV, JSON, PDF, text file support

2026-03-27 08:07:31 -05:00

scale_test.py

Scale test: 2.47M rows + 10K vector index benchmarked

2026-03-27 08:31:37 -05:00

serve_ui.py

Systemd services: gateway, sidecar, UI survive reboots

2026-03-27 22:06:28 -05:00

staffing_demo.py

PRD v2: production roadmap with ingest, vector search, hot cache phases

2026-03-27 07:54:24 -05:00

stress_test.py

Stress test suite: 9/9 passed — architecture validated

2026-03-27 22:13:27 -05:00