rootandClaude Opus 4.6 2189a6dbee Multi-pass deep analysis workflow: dynamic knowledge discovery ...

5-pass analysis over extracted knowledge:
1. Corpus overview: LLM characterizes what the data is about
2. Cross-conversation patterns: recurring themes found via SQL + semantic search
3. Decision evolution: contradictions, progressive refinement, consistent principles
4. Gap analysis: recovered knowledge (questions answered elsewhere),
   real gaps (unresolved), blind spots (never discussed)
5. Synthesis: actionable recommendations based on patterns

Demonstrates: SQL (DataFusion) + Vector Search (HNSW) + Local LLM (qwen2.5)
working together for structured analytical workflow.
All local, no data leaves the machine.

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

2026-03-27 23:12:13 -05:00

..

deep_analysis.py

Multi-pass deep analysis workflow: dynamic knowledge discovery

2026-03-27 23:12:13 -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