dirac-vector-kb-ops

name	dirac-vector-kb-ops
description	Use when the user asks to build, refresh, validate, or inspect the Dirac DFT/TDDFT vector knowledge base, including ingestion status and retrieval quality.

Dirac Vector KB Ops Skill

Use When

• User asks to "supplement vector KB", "refresh KB", or "check KB sync".
• Need deterministic proof that KB ingestion happened and sync state was updated.
• Need a quick retrieval probe after ingestion.

Primary Tool

• scripts/run_vector_kb_ops.py

Typical Commands

# Full workflow: ingest + query probe + sync snapshot
python scripts/run_vector_kb_ops.py --mode full --base-url http://127.0.0.1:8001

# Force reingest and include web references from manifest
python scripts/run_vector_kb_ops.py --mode ingest --force-reingest --include-web

# Query-only probe
python scripts/run_vector_kb_ops.py --mode query --query "Octopus TDDFT convergence" --top-k 5

# Status-only snapshot from sync file
python scripts/run_vector_kb_ops.py --mode status

Required Outputs

• Ingestion command exit code.
• Ingestion report path from kb_sync.report_json / kb_sync.report_md.
• Query endpoint used and hits_count.
• Sync snapshot fields: total_sources, ingested_sources, total_chunks_added.

Rules

1. Always return structured evidence from tool output before narrative summary.
2. If ingestion fails, report stderr and stop claiming KB is updated.
3. For incremental no-change runs, explicitly report unchanged_sources.
4. If retrieval hits are empty, suggest concrete source/topic gaps.