ede891f2ce
Combined all three under verification-monopoly.org with title: 'The Evaluation Harness — Collective Regression Suite as Certification Monopoly' Structure: (1) vision from monopoly, (2) service from harness, (3) spec from collective-regression. All three IDs preserved in PROPERTIES. Deleted evaluation-harness.org and collective-regression-suite.org.
84 lines
3.4 KiB
Org Mode
84 lines
3.4 KiB
Org Mode
:PROPERTIES:
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:ID: 7f4e6b9a-2c1d-5e8f-9a3b-6d7c4e5f2a1b
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:CREATED: [2026-05-23 Sat]
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:END:
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#+title: Passepartout Native Org-Mode Knowledge Base
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#+filetags: :passepartout:roadmap:knowledge:org:gbrain:
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** What
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[[id:28c46769-c14b-42aa-ac7a-69d310157f8f][Passepartout]] should be able to use Org-mode files directly as its
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knowledge base — no pandoc conversion, no markdown intermediary.
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Currently gbrain provides vector search + entity linking over markdown,
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but we bridge via a conversion layer (org → pandoc → markdown → gbrain).
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This loses Org-mode semantics: properties drawers become flat YAML, tag
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inheritance is lost, file: links become relative markdown links, TODO
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states vanish, and the tree structure (headings with content subtrees)
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collapses into flat markdown headings.
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** Why
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Org-mode's data model is strictly richer than markdown's. A Passepartout
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that can ingest, index, and query org files natively has:
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- Property-based entity extraction (no separate links: frontmatter needed)
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- Tag-inheritance for automatic categorization
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- TODO/priority/timestamps for knowledge freshness signals
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- ID-based stable cross-references (org-id) that survive file moves
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- Heading-level chunking (one heading = one knowledge unit)
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- The same file format for everything — no split between "authoring format"
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and "knowledge base format"
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** What it replaces
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The current pipeline: org file → pandoc → markdown file → gbrain import →
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gbrain embed → gbrain query. This is four serial steps with a conversion
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at each boundary that degrades the data model.
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The target: org file → (Passepartout-native indexer) → query. Zero
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conversion, zero data loss.
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** Architecture sketch
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A Passepartout-native knowledge module that directly ingests
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ideas/*.org:
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- Parser: extract each heading as a chunk. Preserve:
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- Heading path (H1 → H2 → H3) as a hierarchical path
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- Properties drawer as structured metadata
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- file: links as typed entity references
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- org-id as stable identifier
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- Tags (inherited from parent headings)
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- TODO state, priority, timestamps
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- Embedder: vector-embed each heading chunk with metadata prefix
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- Query: hybrid search over headings + full-text over content.
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Result includes the heading path + sibling headings for context.
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- Cross-reference graph: build a typed entity graph from:
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- file: links → typed reference
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- org-id links → stable cross-doc reference
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- Tag co-occurrence → implicit relationship
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- Same-property values → attribute-based grouping
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- Dream cycle: auto-discover entities from org properties and file:
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links. Enrich thin sections. Flag sections with stale timestamps.
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** Priority
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Below the gate stack and ACL2 planner (core dependencies) but above
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the Lisp Machine hardware. Target: after TUI stabilization and eval harness, once Screamer
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planner is stable enough to route queries through the knowledge base.
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The short-term bridge (current) is gbrain with nightly org→md sync.
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This is adequate while the gate stack and planner are the priority.
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The native org module replaces gbrain entirely once built.
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The nightly pipeline uses gbrain to provide hybrid search across the existing
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org files. The [[id:36e5b948-e07b-477f-9036-4dfe88254347][compliance framework mapping]] is the largest single
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dataset this would serve, and the broader
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[[id:28c46769-c14b-42aa-ac7a-69d310157f8f][Passepartout economics]] knowledge base demonstrates the value of
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native org querying at scale.
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