React DMS GUI Specification

An ideal React GUI for a sophisticated DMS looks like a task-focused “workspace” rather than just a file browser: opinionated navigation, powerful search + filters, rich preview, and inline workflows (versioning, approvals, annotations).

Below is a concrete breakdown you can turn into components and routes.

Aim for a 3‑pane, app-like layout using something like MUI, Tailwind, or a dashboard kit as a base.¹²³

Left sidebar (global navigation):
- Sections: “My Work”, “Documents”, “Projects/Spaces”, “Tasks”, “Admin”.
- Saved searches / smart folders (e.g., “Needs my review”, “Recently viewed”, “Expiring soon”).
- Tenant / workspace switcher for multi-tenant SaaS.
Top bar:
- Global search bar with quick actions (search across title, content, tags, people).
- User menu: profile, notifications, theme toggle, organization context.
- Quick-create button (“New document”, “New folder”, “New request”).
Main content:
- Swappable views (table, grid, split view) for document lists.
- Right-side contextual panel for metadata, activity, and comments.

For implementation speed, starting with a React admin/dashboard kit (e.g., Material UI dashboard templates, TailAdmin, etc.) gives a solid layout and theming foundation.²³¹

Workflow Checklist

Workflow Steps

Initialize - Set up the environment
Configure - Apply settings
Execute - Run the process
Validate - Check results
Complete - Finalize workflow

Workflow Phases

Phase 1: Initialization

Set up prerequisites and validate inputs.

Phase 2: Processing

Execute the main workflow steps.

Phase 3: Verification

Validate outputs and confirm completion.

Phase 4: Finalization

Clean up and generate reports.

Document browsing and search

The “Documents” area should feel like a smarter file manager, not just a tree. Open-source React file manager components and explorers can act as references.⁴⁵⁶⁷

Key elements:

Primary list view:
- Virtualized table with columns: Name, Type, Owner, Modified, Status, Tags, Version, Retention.
- Multi-select with bulk operations (move, tag, change state, share).
- Toggleable views: table, card grid, hierarchy/tree.
Faceted search sidebar:
- Filters by: type, owner, date, lifecycle state, classification, tags, workspace, retention policy.
- Saved filter sets as user-defined “smart folders”.
Search UX:
- One global search bar (with typeahead and quick filters) + “advanced search” modal.
- Support for query building (e.g., owner:me AND status:pending AND tag:contract).
- Recent searches and pinned searches.

You can borrow patterns from existing React file managers (e.g., @cubone/react-file-manager, react-file-manager repos) for interactions like drag-and-drop, breadcrumb navigation, and split panes.⁶⁷⁴

Document detail, preview, and lifecycle

The document detail view is a core screen: think of it as a “control panel” for one document.

Layout:
- Center: preview pane (PDF/Doc viewer, images, text, code, etc.).
- Left or top: document title, key status badges (state, classification, retention).
- Right sidebar: metadata, activity, and workflow.
Preview & interaction:
- In-place viewing for common formats; open in new tab when needed.
- Zoom, page navigation, thumbnails, search within document (if OCR/text available).
- Section-based comments or anchored annotations for documents that support it.
Metadata panel:
- Core fields: owner, created/modified, version, state, retention schedule.
- Custom fields by document type (contract dates, customer, project, etc.).
- Inline editing with validation and audit logging hints for compliance workflows.⁸⁹
Versioning and history:
- Version list with diff metadata (who, when, what changed), plus restore and compare.
- Activity log: views, edits, approvals, permission changes, external shares.
Lifecycle controls:
- State machine controls like “Submit for review”, “Approve”, “Reject”, “Publish”, “Archive”, “Legal hold”.
- Surface policies: “Retention: 7 years after close”, “On legal hold”, etc., in a prominent status area.⁹⁸

Collaboration, permissions, and workflows

A sophisticated DMS GUI should make collaboration and security discoverable without being noisy.¹⁰¹¹⁸⁹

Sharing & permissions drawer:
- People/groups with roles (view, comment, edit, manage, restricted).
- Link sharing options (organization-only, specific groups, external with expiry).
- Quick presets that align with internal policies (e.g., “Internal read-only”, “Legal-only”).
Comments and tasks:
- Threaded comments, mentions, and notifications.
- Inline tasks tied to documents (“Review by X date”, “Sign-off by Y”).
- “My Tasks” view aggregating approvals and review actions across documents.
Workflow dashboards:
- Kanban or table view of documents by lifecycle stage (Draft, In review, Approved, Published, Archived).
- Filter by workflow type (contract approval, policy review, etc.).
Admin & governance:
- Audit logs UI for access, changes, and permission modifications.⁸⁹¹⁰
- Policy views (retention schedules, classification schemes) and how many documents fall under each.

Concrete component/route structure and next-step prompt

A pragmatic React app structure for this GUI:

Routes:
- /login, /onboarding
- /dashboard – personalized overview (recent docs, tasks, approvals).
- /documents – global library with filters and views.
- /spaces/:spaceId – project/department spaces with scoped docs.
- /documents/:documentId – document detail with preview, metadata, activity.
- /tasks – approvals, review requests.
- /admin/* – policies, roles, audit, tenants.
Key components:
- AppShell (sidebar, topbar, routing outlet)
- GlobalSearchBar, SavedSearchList, FacetFilterPanel
- DocumentTable, DocumentGrid, Breadcrumb, FileTree
- DocumentPreview, MetadataPanel, VersionTimeline, ActivityFeed
- CommentsPanel, TaskList, SharePermissionsDrawer
- WorkflowBoard, AdminAuditLog, PolicyList

A focused follow-up prompt you can reuse:

“Design a React component architecture (with TypeScript interfaces) for a multi-tenant document management GUI that includes: global search, faceted document browsing, document detail with preview/versioning, collaboration (comments/tasks), and lifecycle workflows (review/approval/retention). Assume a separate backend API; focus on React components, state management, and routing patterns.”

If you describe your backend shape (APIs, events, AI capabilities), a next iteration can map this UI to specific data contracts and interaction flows. ¹²¹³¹⁴¹⁵¹⁶¹⁷¹⁸¹⁹²⁰

⁂

THE DOCUMENT MANAGEMENT SYSTEM IS DESIGNED FOR A REGULATED INDUSTRY LIKE HEALTH CARE AND FINANCE AND IS TO BE USED TO SEARCH FIND MANAGE AND INTERACT WITH THE ENTERPRISE KNOWLEDGE BASE which is in the form of thousands of MARKDOWN FILES

An ideal React GUI here is an enterprise knowledge console for regulated content: everything is Markdown under the hood, but users experience compliant search, review, and governance workflows tailored to healthcare/finance.

Top-level mental model

For HIPAA/SEC-type environments the UI must surface: access control, classification, retention, and auditability as first-class citizens, not hidden settings.²¹²²²³²⁴²⁵

Primary objects:
- Knowledge items (Markdown docs) with type, classification, lifecycle state, owner, and retention.
- Collections/spaces (e.g., “Clinical Protocols”, “Policies”, “Product Knowledge”) mapping to business domains.
- Workflows (review, approval, periodic re-certification, legal hold).
Primary views:
- My Work (tasks, reviews, assigned items).
- Knowledge Explorer (search + browse).
- Compliance & Governance (retention, holds, audits).
- Admin (policies, roles, mappings to regulations).

Knowledge explorer (Markdown-centric)

Treat thousands of Markdown files like a GitBook/Docmost/Document360-style structured knowledge base, not a raw file system.²⁶²⁷²⁸²⁹

Left:
- Hierarchical navigation (spaces → sections → pages) derived from folder paths / frontmatter.
- Pinned collections (e.g., “Clinical Policies”, “Risk Procedures”, “KYC Playbooks”).
Center:
- Markdown renderer with:
  - Clean typography, heading TOC, intra-doc link highlighting.
  - Support for code blocks, diagrams (Mermaid/UML), callouts.
- “Book mode” / multi-page reading akin to HackMD or GitBook.³⁰²⁶
Right:
- Metadata: classification (e.g., PHI, confidential), document type, jurisdiction, applicable regulations (HIPAA, GDPR, SEC record type, etc.).²³²⁴²¹
- Version timeline and change summary.
- Activity & audit snippet (who viewed/edited, when).

Search in this view should be hybrid: full-text over Markdown, plus filters over metadata and regulatory properties.²⁷²⁸²⁶

Search and discovery UX

Regulated KB search must let a compliance officer answer “who can see what, and why?” and a practitioner quickly find the right guidance.²²²⁴²⁵³¹²¹²³

Global search bar:
- Query across title, headings, body, tags, and “regulation tags” (e.g., hipaa:breach-notification, sec:17a-4).
- Typeahead sections: “Documents”, “Spaces”, “People/Owners”.
Advanced search panel:
- Facets: document type (policy, SOP, clinical guideline, risk procedure), classification level, retention category, jurisdiction, business unit.
- Status: draft / in review / approved / deprecated / on hold.
- Time-based filters keyed to retention (creation, last review, next re-cert date).
Saved searches:
- “HIPAA policies in review”, “KYC procedures expiring this quarter”, “High-risk procedures without current attestation”.

Integrate RAG-style semantic search in the results panel but always anchored back to specific Markdown docs and sections for auditability.²⁶²⁷

Compliance-first document view

Each Markdown document’s detail view should foreground compliance context.²⁴²⁵³¹²¹²²²³

Header strip (always visible):
- Title, version badge, lifecycle state (Draft/In Review/Effective/Obsolete).
- Classification chip (PHI / Confidential / Internal) and icons if it includes patient/financial identifiers.
- Retention label (e.g., “Retain 7 years after deactivation; SEC 17a‑4 class X”).³²²³²⁴
Metadata tab:
- Regulatory mapping (HIPAA section, FDA/EMA GxP rule, SEC or local banking regs).³³²¹²³²⁴
- Effective/expiry dates and next required review date.
- Linked entities: product, facility, line of business.
Workflow tab:
- Review chain (author → SME → Compliance → Approver), current assignee, due dates.
- Attestation history (who signed off when).
Audit tab:
- Read-access log snippets (for PHI access reporting).²⁵²¹
- Historic permission changes.

Tasks, workflows, and lifecycle

Regulated industries require structured, repeatable document lifecycle handling. UI should make this explicit.³¹²¹²²²³²⁴²⁵

My Work dashboard:
- Tiles: “Reviews to complete”, “Attestations due”, “Content to update”, “Legal hold notices”.
- List of assigned items with priority and SLA indicators.
Workflow board:
- Columns by lifecycle state (Draft, SME Review, Compliance, Approved, Effective, Obsolete).
- Filters by regulation, space, owner, risk rating.
Lifecycle actions (buttons on doc view):
- “Submit for review”, “Approve with comment”, “Reject to author”, “Start periodic review”, “Place/Release legal hold”.
- Confirmation modals displaying policy snippets and expected retention / audit impact.²³²⁵³¹

Governance & admin views

Provide specialized views for compliance / records management teams.²¹²²²⁴²⁵³²³¹²³

Retention & classification console:
- Table of retention categories with duration and regulatory basis (e.g., “Patient records – 6 years – HIPAA 45 CFR 164.316(b)(2)(i)”, “Trading records – 7 years – SEC 17a‑4”).²⁴³²²³
- Counts of Markdown docs per category and upcoming destruction/archival events.
Records monitor:
- Items approaching review/expiry.
- Items on legal hold.
- Exceptions (content missing classification, retention, or regulatory mapping).
Audit & reporting:
- Searchable audit log with filters (user, doc, action, regulation context).
- Pre-baked reports: “Access to PHI last 30 days”, “Changes to SEC-relevant procedures”, etc.²⁵³¹²¹
Markdown source management:
- If backed by Git or similar: “Repository sync status”, “Branch mappings” (e.g., draft vs approved), “Import/export” controls.
- Migration helpers (e.g., import from Confluence/Markdown-based tools like Docmost).²⁸²⁹²⁷²⁶

Suggested focused follow-up prompt

“Design a React + TypeScript component architecture for a regulated-industry knowledge console that manages thousands of Markdown documents, including: (1) semantic and faceted search over Markdown + metadata, (2) a compliance-focused document view (classification, retention, audit), (3) workflow views for review/approval/attestation, and (4) governance consoles for retention schedules and audit logs. Assume APIs already expose Markdown content, metadata, and audit trails; focus on component boundaries, state management, and routing.” ³⁴³⁵³⁶³⁷³⁸³⁹⁴⁰

⁂

How should I structure markdown folders for regulatory compliance

What metadata fields are essential for healthcare and finance documents How to design search indexing for thousands of markdown files Which access control model fits regulated document systems best How to implement audit trails and retention policies automatically

For a regulated, Markdown-based knowledge base, treat folders, metadata, indexing, access control, and audit/retention as one coherent “compliance fabric.”

1. Folder structure for regulatory compliance

Use folders for human orientation and broad access boundaries, but push most compliance logic into metadata and policy engines.⁴¹⁴²⁴³⁴⁴

High-level pattern (top 2–3 levels):
- /domain/ – e.g., clinical, operations, risk, trading, kyc.
- /jurisdiction/ – e.g., us, eu, uk, br, or multi-jurisdiction.
- /document-type/ – e.g., policy, procedure, sop, form, runbook, guideline.
Examples:
- clinical/us/policy/hipaa-privacy-officer.md
- risk/eu/procedure/aml-kyc-review.md
- trading/us/policy/sec-17a4-electronic-records.md
Separate “working” vs “controlled” content:
- /draft/… (authoring), /controlled/… (approved/official), /archive/… (obsolete).⁴⁵⁴¹
Enforce:
- Naming convention with IDs and version: DOC-CLN-001-hipaa-privacy-v3.2.md.⁴³
- Automation (RPA/agents) to normalize folder placement and filenames on ingest.⁴⁴

2. Essential metadata fields (healthcare + finance)

Most compliance capabilities should be driven by metadata, not paths.⁴⁶⁴⁷⁴⁸⁴⁹⁴⁵

Minimum metadata (frontmatter + index DB):

Identity:
- doc_id (stable), title, description, language.
Classification & sensitivity:
- security_classification (e.g., public, internal, confidential, restricted, PHI, PCI).⁴⁷⁵⁰⁴⁵
- contains_phi / contains_pii / contains_financial_account_data (booleans).⁵⁰⁴⁵⁴⁷
Regulatory mapping:
- regulations: list of codes, e.g., ["HIPAA-164.316", "GDPR-32", "SEC-17a-4", "FINRA-4511"].⁴⁸⁵¹⁴⁵
- jurisdiction: ["US","EU","BR"].⁴⁸
Lifecycle & retention:
- document_type (policy, SOP, work instruction, clinical protocol, product disclosure, etc.).⁵²⁵³⁴¹
- status (draft, in_review, approved, effective, obsolete).
- effective_date, review_due_date, expiry_date.
- retention_category (mapped to policy table) and derived destroy_after / retain_until.⁵⁴⁵⁵⁴⁵
Ownership & context:
- owner, responsible_role (e.g., “Data Protection Officer”, “Chief Compliance Officer”).⁵⁶⁴⁹
- business_unit, product, process, system (for traceability).⁴⁹⁴⁶
Versioning:
- version, supersedes, superseded_by, change_reason.
Access policy hints:
- allowed_roles, allowed_groups, need_to_know_tags (e.g., “oncology-team”, “equities-desk”).⁵⁷⁵⁸⁵⁶⁵⁰

This can map cleanly to YAML frontmatter in each Markdown, plus a normalized relational or graph store for querying.⁴⁷⁴⁹⁴⁸

3. Search indexing for thousands of Markdown files

Design indexing so that full-text and compliance metadata are equally important.⁵⁹⁴³⁴⁴⁴⁹

Parsing & enrichment pipeline:
- Parse frontmatter → structured metadata.
- Render Markdown to text; extract headings, section anchors, code blocks, tables.
- Generate additional fields:
  - h1_h2_text, sections[], keywords, embeddings (for semantic search).
  - Normalized regulation codes, jurisdiction, retention buckets.⁴⁹⁴⁸
Index schema (e.g., OpenSearch/Solr/Typesense/pg_trgm):
- Text: title, body, headings, tags.
- Facets: document_type, jurisdiction, regulations, status, security_classification, retention_category, business_unit, owner.⁴¹⁴³
- Dates: effective_date, review_due_date, destroy_after.
Search behavior:
- Default search = full-text + boosted matches on title/headings and regulation codes.⁴³⁵⁹
- Filters: classification, regulation, jurisdiction, lifecycle, BU, PHI/PII flags.⁴⁵⁴⁷⁴⁸⁴⁹
- Semantic layer: RAG over Markdown, but answers always cite doc/section IDs for auditability.⁶⁰⁶¹⁴⁹
Maintenance:
- Incremental index updates on Git commits or file changes.
- Periodic integrity checks: documents with missing mandatory metadata or un-indexable content reported to compliance.⁴⁶⁴⁷⁴⁹

4. Access control model for regulated documents

Use RBAC as the backbone, with classification- and attribute-based constraints; many regulated shops approach MAC-like behavior for high-sensitivity content.⁵⁸⁶²⁵⁷⁵⁶⁵⁰⁴⁵

Base model:
- RBAC: roles (e.g., clinician, billing, trader, compliance_officer, records_manager) mapped to permission sets over document types and classifications.⁵⁷⁵⁸⁵⁶
- ABAC: attributes from metadata (jurisdiction, BU, classification) and user profile (location, org unit) to refine decisions.⁶²⁵⁰
Classification-aware rules:
- Example: “PHI documents” readable only by roles with phi_access = true and within same facility/jurisdiction.⁵⁰⁴⁵⁴⁷
- “Trading procedures” limited to specific desks plus compliance and audit.⁶³⁴⁵
Least privilege and SoD:
- Enforce least privilege at role definition; regularly review role–permission mappings.⁵⁸⁵⁶⁵⁰
- Segregation of duties (e.g., author cannot finally approve own policy).⁵⁵⁵²
Implementation detail:
- Central policy engine (OPA, Cedar, or custom PDP) evaluating allow(user, action, doc) using role + attributes + classification rules.⁴⁹
- Access decisions and denials logged for audit.⁵⁶⁴⁵⁴⁷

For highly sensitive subsets (e.g., some financial records, special PHI), you can approximate MAC with system-enforced clearances and non-bypassable rules layered on top of RBAC.⁶²⁵⁷⁵⁰

5. Audit trails and automatic retention

Automate logging and lifecycle transitions so compliance is enforced “by default,” not by convention.⁵¹⁵⁴⁵⁵⁴⁵⁴⁷⁴⁹

Audit trails:
- Log all security- and compliance-relevant events:
  - view, download, create, update, delete, status_change, permission_change, retention_change, hold_applied/removed.⁴⁵⁴⁷⁵⁶
- Required fields per event:
  - event_id, timestamp, user_id, session_id, action, doc_id, old_value/new_value (where relevant), source IP/device, decision (allow/deny).⁴⁷⁴⁵
- Storage requirements:
  - Append-only, tamper-evident store (WORM/log-structured; optionally blockchain-style per some HIPAA/GDPR frameworks).⁵¹⁴⁵
  - Retention for logs themselves aligned to regulatory expectations.⁵⁴⁴⁵
Retention policies:
- Central retention catalog:
  - retention_category_id, description, duration, trigger_event (e.g., “effective_date”, “account_closure”), legal_basis (e.g., HIPAA, SEC 17a-4, local banking rules).⁵⁵⁶³⁵⁴⁴⁵
- Each document links to a retention category; system computes retain_until.⁵⁴⁵⁵⁴⁵
- Background jobs:
  - Periodically scan for documents past retain_until and not on legal hold.⁵⁵⁴⁵⁵⁴
  - Move to archive, anonymize, or destroy according to policy, with approvals where required.
- Legal holds:
  - legal_hold flag + hold_reason + hold_owner prevents deletion regardless of retention expiry.⁴⁵⁵⁵
  - UI for compliance to manage holds and see affected documents.⁵⁵⁴⁵
Automation patterns:
- Event-driven: on status_change -> approved, set effective_date, compute review_due_date, and recalc retain_until.⁵⁶⁴⁹
- Integration with tasking: generate review tasks ahead of review_due_date.⁵²⁴¹

If you want, the next step can be a concrete schema + pipeline sketch (frontmatter YAML spec, Postgres schema for metadata and events, and an indexing + policy-eval flow) tailored to your current stack. ⁶⁴⁶⁵

⁂

Create a compliant folder hierarchy template for healthcare documents

Recommended metadata schema for HIPAA and FINRA documents Indexing strategy for fast search across 100k markdown files How to store and query metadata separately from markdown files Best open source search engines for markdown repositories

For a HIPAA/FINRA-style Markdown corpus, use folders only for high‑signal orientation and boundaries, with metadata + index as the real control plane.

1. Folder hierarchy template (healthcare-focused, extendable to finance)

Use a consistent 3–4 level hierarchy; drive compliance from metadata, but keep folders human-readable and audit-friendly.⁶⁶⁶⁷⁶⁸⁶⁹

Top-level:

clinical/ – care delivery policies, protocols, order sets.
administrative/ – HR, operations, facilities.
security-privacy/ – HIPAA, GDPR, security policies.
billing-revenue/ – coding, billing, collections.
research/ – IRB, study procedures.
finance/ – trading, products, risk, disclosures (for FINRA/SEC overlap).

Within each domain:

us/, eu/, uk/, br/, global/ (jurisdiction).⁷⁰
policy/, procedure/, sop/, work-instruction/, form/, guideline/.⁷¹⁶⁶
controlled/, draft/, archive/ to distinguish official vs working vs obsolete content.⁷²⁶⁶

Example paths:

clinical/us/policy/controlled/CLN-001-hipaa-privacy-officer-v3.2.md
security-privacy/us/procedure/draft/SEC-17a4-electronic-records-v0.9.md
finance/us/policy/controlled/FINRA-4511-recordkeeping-v2.1.md

Use IDs + short slugs in filenames to help eDiscovery and cross-systems referencing.⁷³⁶⁸

2. Recommended metadata schema (HIPAA + FINRA)

Metadata should cover descriptive, structural, administrative, technical, and provenance aspects, with explicit regulatory and retention signals.⁷⁴⁷⁵⁷⁶⁷³

Core fields (YAML frontmatter + DB):

Identity:
- doc_id: stable identifier.
- title, summary, language.
Domain & type:
- domain: clinical, security-privacy, billing-revenue, finance, etc.⁶⁶⁷¹
- document_type: policy, procedure, sop, guideline, form, runbook.⁷⁷⁶⁶
Regulatory mapping:
- regulations: e.g., ["HIPAA-164.316", "HIPAA-164.312", "FINRA-4511", "SEC-17a-4"].⁷⁸⁷⁹⁷⁵⁷⁶⁷³
- jurisdiction: e.g., ["US"], ["US","EU"].⁸⁰⁷⁰
Sensitivity & classification:
- security_classification: public, internal, confidential, restricted.
- contains_phi: bool; contains_pii: bool; contains_financial_data: bool.⁸¹⁷⁶⁷⁸⁷⁴
Lifecycle & retention:
- status: draft, in_review, approved, effective, obsolete.⁷²⁶⁶
- effective_date, review_due_date, expiry_date.
- retention_category: e.g., PHI-6Y, FINRA-6Y, SEC-7Y.⁷⁶⁸²⁷³
- retain_until: computed date; legal_hold: bool; legal_hold_reason.⁸²⁸³⁷³
Ownership & access:
- owner_user_id, owner_role (e.g., “Privacy Officer”, “Head of Trading Compliance”).⁷⁵⁷⁴
- business_unit, facility, desk (for finance).⁸⁴⁷⁶
- allowed_roles, allowed_groups, need_to_know_tags.⁸⁵⁸⁶⁸⁷⁸⁸
Provenance & versioning:
- version, created_at, created_by, last_modified_at, last_modified_by.
- supersedes, superseded_by, change_summary.⁸⁹⁷⁴⁷³

For HIPAA/FINRA, treat metadata (timestamps, authorship, classification, retention, lineage) as part of the “record” and preserve it immutably with content for WORM-style compliance.⁷⁴⁷³⁸⁹⁷⁶

3. Indexing strategy for 100k Markdown files

100k Markdown docs are well within range for a serious full-text engine; focus on a content pipeline and rich fields.⁹⁰⁹¹⁹²⁹³⁹⁴

Ingestion pipeline:

Step 1 – Parse:
- Read frontmatter → structured metadata.
- Render Markdown to plain text; extract:
  - headings (H1–H3), sections with anchors.
  - code_blocks, tables if relevant.
Step 2 – Enrich:
- Normalize regulation codes, jurisdictions, and retention categories.⁷³⁷⁶⁷⁰
- Derive tokens and maybe embeddings for semantic search.⁹¹⁹³
Step 3 – Index document (per file) with fields:
- Text:
  - title, headings, body, tags, regulation_text (e.g., codes + human labels).
- Facets/filterable fields:
  - domain, document_type, jurisdiction, regulations, security_classification, status, retention_category, business_unit, owner.⁷⁶⁷⁴⁷³
- Sortable/date:
  - effective_date, review_due_date, retain_until, last_modified_at.⁹³⁸²
Indexing performance considerations:
- Batch insert/update in chunks of thousands (depending on engine) to speed up indexing and reduce overhead.⁹⁵⁹³
- Prefer bigger payloads over many small ones; 100k documents is generally safe to index in a few batches.⁹³⁹⁵
- Use incremental indexing triggered by VCS hooks or filesystem events for continuous updates.⁹⁰⁹¹

Query model:

Default query = full-text over title + headings + body with boosts on title, headings, regulations.⁹¹⁹⁰⁹³
Filters:
- Combine text search with facets for regulations, classification, jurisdiction, status, retention_category.⁷⁴⁷³⁷⁶
For compliance and explainability, always return:
- doc_id, path, matched fields, and highlight snippets referencing stored Markdown anchors.⁹⁰⁹¹

4. Storing and querying metadata separately from Markdown

Keep Markdown as the source of truth for content; use a database for metadata, joins, and analytics.⁹⁴⁸⁹⁷³⁷⁴

Recommended split:

Markdown:
- Stored in Git, object storage, or a content repo; path + hash referenced by doc_id.⁹⁶⁹⁷⁹¹
Metadata store:
- Relational DB (PostgreSQL is ideal) with:
  - documents table (doc_id, path, hash, timestamps).
  - document_metadata (doc_id FK, all normalized fields like domain, type, regulation codes, classifications, retention, BU).⁹⁴⁷⁴
  - document_regulations (doc_id, regulation_code) for many-to-many if needed.⁷⁰⁷⁶
  - document_tags, document_facilities, etc., as junction tables.
Query patterns:
- UI / APIs:
  - Query DB first (filter on metadata), get doc_ids, then query search engine with those IDs as a constraint; or vice versa.⁹¹⁹⁴⁷⁴
- Analytics / compliance dashboards:
  - Use SQL directly over metadata (counts by regulation, classification, jurisdiction, retention bucket).⁸²⁷³⁷⁶
Synchronization:
- On Markdown change:
  - Parse frontmatter → upsert metadata row(s).
  - Trigger reindex in search engine with new doc/fields.⁹⁰⁹¹
- Ensure immutable snapshots of metadata are kept for WORM/WORM-like compliance (e.g., append-only metadata versions for FINRA/SEC).⁸⁹⁷³⁷⁶

5. Open source search engines for Markdown repositories

Several engines work well with Markdown once you provide a parsing pipeline.⁹²⁹⁷⁹⁶⁹³⁹¹⁹⁰

Good fits:

Elasticsearch / OpenSearch:
- Mature full-text, faceting, highlighting; widely used for document search at 100k+ scale.⁹²
- Works well for mixed text + structured metadata; supports analyzers for multiple languages.
Meilisearch:
- Lightweight, fast, simple HTTP API; great relevance for document-style search; good for 100k docs.⁹³
- Supports searchable attributes, filterable attributes, synonyms, and typo tolerance.
Typesense:
- Similar to Meilisearch; strong for instant search and faceted UIs.
Solr:
- Traditional, battle-tested; strong document indexing, used in many enterprise DMS setups.⁹²
PostgreSQL full-text (tsvector/tsquery):
- If you want to avoid extra infra, can work well with 100k documents using FTS + GIN indexes.⁹⁴
Markdown-specific examples and inspiration:
- GitHub project “Search engine for markdown files with tagging” (Whoosh-based), which boosts filenames and Markdown structure (headlines, emphasis).⁹⁰
- Guides on building Markdown search APIs (e.g., ZeroEntropy’s approach) show indexing flows specific to .md repositories.⁹¹
- Static-site ecosystems (MkDocs, Docusaurus) bundle client-side search (Lunr/Algolia-like) that can be reused or adapted for server-side search pipelines.⁹⁷⁹⁶

For a regulated KB with 100k Markdown docs, a typical architecture is:

PostgreSQL (metadata + audit).
OpenSearch/Elasticsearch or Meilisearch (text + facets).
Markdown in Git/object store.
Event-driven sync to maintain metadata DB and search index integrity.⁷³⁸⁹⁹³⁹⁴

If useful, next step can be: concrete Postgres schema + Meilisearch index JSON definition + a small ingestion pipeline sketch tailored to your RBAC/ABAC model. ⁹⁸⁹⁹¹⁰⁰¹⁰¹

⁂

Show a HIPAA focused folder template with retention rules List mandatory metadata fields for FINRA audits only Design a scalable index schema for 100k markdown files Compare storing metadata in sidecar files versus a DB How to implement immutable WORM storage for records and metadata. next step can be: concrete Postgres schema + Meilisearch index JSON definition + a small ingestion pipeline sketch tailored to your RBAC/ABAC model.

A compliant HIPAA/FINRA Markdown repo needs: (1) a retention-aware folder pattern, (2) audit-ready metadata, (3) an index schema that separates content from metadata, and (4) an immutable storage layer for records + metadata.

1. HIPAA-focused folder template with retention hints

HIPAA generally requires retaining HIPAA-related documentation (policies, procedures, notices, complaints) at least six years from creation or last effective date.¹⁰²¹⁰³¹⁰⁴¹⁰⁵

Example top-level layout:

clinical/
administrative/
security-privacy/
billing-revenue/
research/

Within each:

us/, state-<xx>/ (where state law drives longer retention), global/.¹⁰⁶¹⁰⁷¹⁰⁸¹⁰⁹
policy/, procedure/, sop/, form/, notice/, log/.¹¹⁰¹⁰³¹⁰²
controlled/, draft/, archive/.¹⁰⁴¹¹⁰

Concrete template with retention category encoded in folder name (for ops clarity, while actual enforcement is via metadata & jobs):

security-privacy/us/policy/ret-6y/
- For HIPAA-required policies, procedures, and notices (min 6 years).¹⁰³¹⁰⁵¹⁰²
clinical/us/record/ret-6y-plus-state/
- Where HIPAA is 6 years but state law (e.g., pediatrics) may require longer.¹⁰⁹¹⁰⁶¹¹⁰
billing-revenue/us/record/ret-7y/
- If internal policy aligns with common 7‑year practices for financial records.¹¹⁰

Filename pattern:

HSP-POL-001-privacy-notice-v3.0.md
CLN-SOP-010-medication-reconciliation-v1.4.md

The folder name ret-6y is advisory; the authoritative retention is in metadata and a central retention table.¹⁰⁷¹⁰³¹¹⁰

2. Mandatory metadata fields for FINRA audits (documents only)

FINRA Rule 4511 points to SEC Rule 17a‑4 for how records must be made and preserved: accurate, complete, immutable (WORM or equivalent), and retained for specified periods.¹¹¹¹¹²¹¹³¹¹⁴¹¹⁵

For Markdown-based “books and records,” minimum per-record metadata should include:

Identity:
- record_id (stable, unique).
- record_type (e.g., “customer communication”, “order ticket”, “supervisory procedure”, “trade blotter”).¹¹²¹¹³¹¹⁵
Business context:
- account_id or customer_id where applicable.
- business_unit / desk / product.¹¹⁴¹¹²
Regulatory mapping:
- rules: e.g., ["FINRA-4511", "SEC-17a-4", "SEC-17a-3"].¹¹³¹¹⁵¹¹¹¹¹²¹¹⁴
Authorship and timestamps:
- created_at, created_by.
- received_at (for inbound comms), sent_at (for outbound).
- last_modified_at (content side; for WORM, later “modifications” are new records or versions, not in-place edits).¹¹¹¹¹²¹¹⁴
Retention:
- retention_category (e.g., communications-6y, trade-record-3y).
- retention_period_years (e.g., 3, 6, 7) with legal basis.
- retain_until (computed).
- legal_hold (bool), legal_hold_reason.¹¹⁴¹¹¹
Integrity and storage:
- content_hash (e.g., SHA‑256 of the Markdown payload).
- worm_storage_location / archive_bucket_id.¹¹⁶¹¹⁷¹¹⁸¹¹¹
Status & lineage:
- status (active, superseded, archived).
- supersedes_record_id, superseded_by_record_id.¹¹¹¹¹⁴

Auditors will focus heavily on: accurate timestamps, clear mapping to books/records rules, retention duration, and demonstrable immutability of the record and its metadata.¹¹⁵¹¹²¹¹³¹¹⁴¹¹¹

3. Scalable index schema for 100k Markdown files

100k Markdown docs are moderate scale; a good design is “one indexed document per Markdown, plus structured fields for compliance filters.”¹¹⁹¹²⁰¹²¹¹²²

Logical index document (for Meilisearch/OpenSearch/etc.):

id: doc_id.
Content:
- title
- headings: array of strings (H1–H3).
- body: plain text of Markdown.
- sections: array of { anchor, heading, text_snippet } for section-level highlighting.
Compliance & metadata fields (filterable/faceted):
- domain (clinical, security-privacy, finance, etc.).
- document_type (policy, sop, record, communication).
- jurisdiction (us, state-ca, eu, etc.).
- regulations (array of codes).
- security_classification
- contains_phi, contains_financial_data.
- status
- retention_category
- business_unit, desk, facility.
- owner_role, owner_user_id.¹²³¹²⁴¹¹¹
Dates (sortable & filterable):
- created_at
- effective_date
- review_due_date
- retain_until
- last_modified_at.¹²²¹²⁵
Integrity & storage (for linking to WORM store):
- content_hash
- worm_location_id (if helpful at query time).¹¹⁷¹¹⁶¹¹¹

Indexing choices:

Mark all metadata fields (domain, regulations, status, retention_category, etc.) as filterable/faceted.¹²²
For Meilisearch:
- searchableAttributes: ["title", "headings", "body"].
- filterableAttributes: everything compliance-related and date fields.¹²²
- sortableAttributes: ["effective_date", "last_modified_at", "retain_until"].¹²²
Use batched indexing and incremental updates based on Git commits or FS events; 100k docs is well within Meilisearch/OpenSearch abilities with correct batching.¹²¹¹²⁶¹²²

4. Sidecar metadata files vs DB

Both patterns are used; regulators care that metadata is preserved immutably and consistently, not where it lives.¹²⁷¹²⁴¹²³¹¹¹

Aspect	Sidecar/YAML-only (per .md)	Central DB (e.g., Postgres)
Source of truth	Metadata travels with content in Git or object store. ¹²³¹²⁰¹²⁸	Clear, queryable system of record for metadata. ¹²³¹²⁹
Human review	Easy to view/edit in editors; good for dev workflows. ¹²⁰¹²⁸	Requires UI/tooling to see/edit; less diff-friendly.
Query & analytics	Needs indexing step; no ad‑hoc SQL. ¹¹⁹¹²⁰	Natural for dashboards, compliance queries, joins. ¹²⁵¹²⁴¹²⁹
Immutability (WORM)	Commit history can act as immutable log; but Git alone may not satisfy WORM. ¹¹¹¹³⁰	DB must be backed by WORM/append-only logs or replicated into immutable archive. ¹¹¹¹²⁷¹¹⁶
Complexity	Simpler infra; more complex analytics. ¹¹⁹¹²⁰	More infra; simpler analytics and API queries. ¹²⁹
Regulatory expectations	Must still prove metadata wasn’t altered independently; strong story if entire repo is archived in WORM snapshots. ¹¹¹¹¹⁶	Stronger central controls for mandatory fields, validations, consistency. ¹²³¹²⁴¹²⁹

For HIPAA/FINRA‑grade systems, a hybrid is typically best:

Authoring: metadata in frontmatter/sidecar tightly coupled with Markdown.¹²⁰¹²⁸¹³¹
Operational: parsed metadata normalized into Postgres (or similar) and archived along with content to WORM storage so both can be shown as preserved together.¹²⁴¹²⁷¹¹⁶¹¹¹

5. Implementing immutable WORM storage for records + metadata

SEC 17a‑4 and FINRA 4511 require “non‑rewriteable, non‑erasable” records storage (classic WORM or equivalent) for specified periods; HIPAA requires retrievable, safeguarded records and auditable activity logs (often 6+ years).¹⁰⁵¹⁰³¹⁰⁴¹¹⁵¹¹⁴¹¹¹

Core principles:

Immutability:
- Once a record + its metadata are written, they cannot be modified or deleted until retain_until.¹¹⁸¹¹⁶¹¹⁷¹¹¹
- New versions are new immutable records linked by lineage, not in-place edits.¹¹⁴¹¹¹
Integrity & verification:
- Hash each stored object; optionally chain hashes or anchor periodic “top hashes” to an external timestamp service or blockchain to prove non-tampering.¹³⁰¹²⁷¹¹⁸¹¹¹
Separation of duties:
- Admins cannot bypass WORM or silently alter retention rules; changes are logged and may themselves be WORM‑archived.¹¹⁶¹¹⁸¹¹¹¹¹⁴

Practical pattern:

Use cloud or on-prem WORM-capable storage:
- Object storage with immutable buckets / object lock (S3 Object Lock, similar features on other providers) configured in compliance mode for required retention periods.¹¹⁷¹¹⁶
- Store both Markdown content and a serialized metadata blob (e.g., JSON with all fields) as a single logical object or tightly coupled objects.¹¹⁸¹¹⁶¹¹⁷¹¹¹
Write path:
- Application validates metadata, calculates retain_until from retention category.¹²⁵¹⁰⁷¹¹⁰
- Serializes {markdown, metadata} with hash and writes to WORM store with a retention setting at least as long as policy requires.¹¹⁶¹¹⁷¹¹¹
- Records a pointer (WORM object ID, hash) in Postgres for fast lookup and search indexing.¹²⁴¹¹¹¹¹⁶
Update / versioning:
- On “edit”, write a new WORM object with incremented version and new metadata; link to previous via supersedes and superseded_by.¹¹¹¹¹⁴
- Old versions remain immutable until their retention expires; policies may require keeping superseded records for full retention.¹¹⁵¹¹⁴¹¹¹
Deletion:
- A scheduled process identifies records whose retain_until has passed and are not on legal hold, then:
  - Issues deletion/expiry requests to WORM system if allowed by the mode (or lets configured retention lapse).¹¹⁷¹¹⁶¹¹¹
  - Logs destruction events (who/what/when) in a separate, also WORM-backed audit log.¹²⁷¹¹⁸¹¹⁶¹¹¹
Audit logs:
- Store access, configuration, and retention-change logs in an append-only, tamper-evident log that is itself archived in WORM.¹³⁰¹²⁷¹¹⁸¹¹⁶

6. Next step: Postgres + Meilisearch + ingestion pipeline (sketch)

Below is a compact, implementation-ready sketch you can expand.

6.1 Postgres schema (core tables)

-- documents: logical docs; markdown lives in content store
CREATE TABLE documents (
  doc_id              UUID PRIMARY KEY,
  path                TEXT NOT NULL,                -- repo path
  content_hash        TEXT NOT NULL,                -- SHA-256
  current_version     INT  NOT NULL,
  worm_object_id      TEXT NOT NULL,                -- pointer into WORM
  created_at          TIMESTAMPTZ NOT NULL,
  created_by          TEXT NOT NULL,
  last_modified_at    TIMESTAMPTZ NOT NULL,
  last_modified_by    TEXT NOT NULL
);

CREATE TABLE document_versions (
  doc_id              UUID NOT NULL REFERENCES documents(doc_id),
  version             INT  NOT NULL,
  worm_object_id      TEXT NOT NULL,
  content_hash        TEXT NOT NULL,
  created_at          TIMESTAMPTZ NOT NULL,
  created_by          TEXT NOT NULL,
  supersedes_version  INT,
  PRIMARY KEY (doc_id, version)
);

-- core metadata (flattened for easy indexing)
CREATE TABLE document_metadata (
  doc_id               UUID PRIMARY KEY REFERENCES documents(doc_id),
  title                TEXT NOT NULL,
  summary              TEXT,
  domain               TEXT NOT NULL,
  document_type        TEXT NOT NULL,
  jurisdiction         TEXT[] NOT NULL,
  regulations          TEXT[] NOT NULL,
  security_class       TEXT NOT NULL,
  contains_phi         BOOLEAN NOT NULL DEFAULT FALSE,
  contains_financial   BOOLEAN NOT NULL DEFAULT FALSE,
  status               TEXT NOT NULL,
  effective_date       DATE,
  review_due_date      DATE,
  expiry_date          DATE,
  retention_category   TEXT NOT NULL,
  retain_until         DATE NOT NULL,
  legal_hold           BOOLEAN NOT NULL DEFAULT FALSE,
  legal_hold_reason    TEXT,
  business_unit        TEXT,
  desk                 TEXT,
  facility             TEXT,
  owner_user_id        TEXT NOT NULL,
  owner_role           TEXT NOT NULL,
  created_at           TIMESTAMPTZ NOT NULL,
  last_modified_at     TIMESTAMPTZ NOT NULL
);

CREATE TABLE document_tags (
  doc_id  UUID NOT NULL REFERENCES documents(doc_id),
  tag     TEXT NOT NULL,
  PRIMARY KEY (doc_id, tag)
);

This schema keeps metadata queryable and ties each document to immutable WORM objects via worm_object_id.¹²⁹¹²⁴¹¹⁶¹¹¹

6.2 Meilisearch index settings (JSON)

{
  "uid": "documents",
  "primaryKey": "doc_id",
  "searchableAttributes": [
    "title",
    "headings",
    "body"
  ],
  "filterableAttributes": [
    "domain",
    "document_type",
    "jurisdiction",
    "regulations",
    "security_class",
    "contains_phi",
    "contains_financial",
    "status",
    "retention_category",
    "business_unit",
    "desk",
    "facility",
    "owner_role",
    "owner_user_id",
    "effective_date",
    "review_due_date",
    "retain_until",
    "created_at",
    "last_modified_at"
  ],
  "sortableAttributes": [
    "effective_date",
    "review_due_date",
    "retain_until",
    "created_at",
    "last_modified_at"
  ]
}

Each indexed document you send would look like:

{
  "doc_id": "uuid",
  "title": "HIPAA Privacy Officer Policy",
  "headings": ["Purpose", "Scope", "Responsibilities"],
  "body": "Plain text…",
  "domain": "security-privacy",
  "document_type": "policy",
  "jurisdiction": ["US"],
  "regulations": ["HIPAA-164.316"],
  "security_class": "confidential",
  "contains_phi": true,
  "contains_financial": false,
  "status": "effective",
  "retention_category": "HIPAA-6Y",
  "business_unit": "Compliance",
  "desk": null,
  "facility": "Hospital-A",
  "owner_role": "Privacy Officer",
  "owner_user_id": "u123",
  "effective_date": "2025-01-01",
  "review_due_date": "2027-01-01",
  "retain_until": "2031-01-01",
  "created_at": "2025-01-01T10:00:00Z",
  "last_modified_at": "2025-01-10T11:00:00Z"
}

6.3 Ingestion pipeline (RBAC/ABAC-aware)

High-level steps:

Discovery & parsing:
- Walk the Markdown repo (Git clone or object listing).
- For each .md:
  - Extract frontmatter → metadata object.
  - Render Markdown → plain text; extract headings/sections.¹²⁸¹³¹¹²⁰¹¹⁹
  - Compute content_hash.
Retention and policy enrichment:
- Lookup retention_category in a retention_policies table to derive retain_until, using effective/trigger dates and duration.¹⁰⁷¹²⁵¹¹⁰
- Normalize regulation codes and jurisdiction values.¹³²¹²⁴¹¹¹
RBAC/ABAC projection:
- From metadata, derive ABAC attributes:
  - domain, document_type, jurisdiction, security_class, contains_phi, contains_financial, business_unit, desk, facility.¹³³¹³¹¹²⁷
- Store these attributes in document_metadata to be used by your PDP (OPA/Cedar/custom) when evaluating allow(user, action, doc).¹³⁴¹³³
WORM write:
- Serialize {markdown, metadata} into an archive format (e.g., JSON with Markdown embedded, or two objects with cross-links).
- Write to WORM storage with object-lock retention set to computed retain_until (or stricter).¹¹⁸¹¹⁶¹¹⁷¹¹¹
- Capture worm_object_id and content_hash.
Database upsert:
- In Postgres:
  - Upsert documents (doc_id, path, hashes, worm_object_id, created/modified).
  - Upsert document_metadata and document_tags.
  - Insert a new row in document_versions if content_hash changed.¹²⁹¹²⁴¹¹¹
Search indexing:
- Build index document from DB row + parsed text.
- Upsert into Meilisearch with the settings above.¹²⁰¹²²
Audit logging:
- For each ingest/update:
  - Log event_type (create, update, re-index), actor (system or user), doc_id, version, worm_object_id, timestamp into an append-only audit_events table and/or tamper-evident log that is also periodically archived to WORM.¹²⁷¹³⁰¹¹⁶¹¹⁸¹¹¹

If you want, next iteration can be: concrete RBAC/ABAC policy examples (e.g., OPA/Cedar snippets) wired to the Postgres schema and Meilisearch filters for HIPAA + FINRA scenarios. ¹³⁵

⁂

next iteration can be: concrete RBAC/ABAC policy examples (e.g., OPA/Cedar snippets) wired to the Postgres schema and Meilisearch filters for HIPAA + FINRA scenarios.

Related Provide a concrete Postgres schema for records and metadata Show Meilisearch index JSON definition for HIPAA and FINRA fields Sketch a small ingestion pipeline for markdown files to Postgres and Meilisearch How to map RBAC and ABAC attributes into the Postgres schema Retention automation rules and SQL procedures for record expiry and destruction

Below is a concrete, implementation-ready next step that ties together Postgres, Meilisearch, RBAC/ABAC, and retention automation for a HIPAA + FINRA Markdown repo.

1. Postgres schema for records, metadata, RBAC/ABAC

This focuses on: documents, versions, metadata, RBAC roles/permissions, ABAC attributes, and retention.

-- Core documents (logical record)
CREATE TABLE documents (
  doc_id            UUID PRIMARY KEY,
  path              TEXT NOT NULL,              -- repo path to .md
  current_version   INT  NOT NULL,
  content_hash      TEXT NOT NULL,              -- hash of current content
  worm_object_id    TEXT NOT NULL,              -- immutable storage pointer
  created_at        TIMESTAMPTZ NOT NULL,
  created_by        TEXT NOT NULL,
  last_modified_at  TIMESTAMPTZ NOT NULL,
  last_modified_by  TEXT NOT NULL
);

-- Immutable versions (link to WORM objects)
CREATE TABLE document_versions (
  doc_id            UUID NOT NULL REFERENCES documents(doc_id),
  version           INT  NOT NULL,
  worm_object_id    TEXT NOT NULL,
  content_hash      TEXT NOT NULL,
  created_at        TIMESTAMPTZ NOT NULL,
  created_by        TEXT NOT NULL,
  supersedes_version INT,
  PRIMARY KEY (doc_id, version)
);

-- Flattened metadata (HIPAA + FINRA)
CREATE TABLE document_metadata (
  doc_id               UUID PRIMARY KEY REFERENCES documents(doc_id),
  title                TEXT NOT NULL,
  summary              TEXT,
  domain               TEXT NOT NULL,               -- clinical, security-privacy, finance, etc.
  document_type        TEXT NOT NULL,               -- policy, sop, record, communication, etc.
  jurisdiction         TEXT[] NOT NULL,             -- ["US"], ["US","EU"], etc.
  regulations          TEXT[] NOT NULL,             -- ["HIPAA-164.316","FINRA-4511",...]
  security_class       TEXT NOT NULL,               -- public/internal/confidential/restricted
  contains_phi         BOOLEAN NOT NULL DEFAULT FALSE,
  contains_pii         BOOLEAN NOT NULL DEFAULT FALSE,
  contains_financial   BOOLEAN NOT NULL DEFAULT FALSE,
  status               TEXT NOT NULL,               -- draft/in_review/effective/obsolete
  effective_date       DATE,
  review_due_date      DATE,
  expiry_date          DATE,
  retention_category   TEXT NOT NULL,               -- HIPAA-6Y, FINRA-6Y, etc.
  retention_period_y   INT  NOT NULL,               -- in years
  retain_until         DATE NOT NULL,
  legal_hold           BOOLEAN NOT NULL DEFAULT FALSE,
  legal_hold_reason    TEXT,
  business_unit        TEXT,
  desk                 TEXT,                        -- trading desk, etc.
  facility             TEXT,                        -- hospital/facility
  owner_user_id        TEXT NOT NULL,
  owner_role           TEXT NOT NULL,
  created_at           TIMESTAMPTZ NOT NULL,
  last_modified_at     TIMESTAMPTZ NOT NULL
);

CREATE TABLE document_tags (
  doc_id  UUID NOT NULL REFERENCES documents(doc_id),
  tag     TEXT NOT NULL,
  PRIMARY KEY (doc_id, tag)
);

-- Retention catalog (policy table)
CREATE TABLE retention_policies (
  retention_category   TEXT PRIMARY KEY,
  description          TEXT NOT NULL,
  period_years_default INT  NOT NULL,
  legal_basis          TEXT NOT NULL,     -- "HIPAA-164.316", "FINRA-4511/SEC-17a-4", etc.
  min_years            INT  NOT NULL      -- safeguard against too-short periods
);

-- RBAC: users, roles, permissions
CREATE TABLE users (
  user_id       TEXT PRIMARY KEY,
  display_name  TEXT NOT NULL,
  email         TEXT NOT NULL,
  active        BOOLEAN NOT NULL DEFAULT TRUE
);

CREATE TABLE roles (
  role_id       TEXT PRIMARY KEY,         -- "clinician", "privacy_officer", "trader", "finra_compliance"
  description   TEXT NOT NULL
);

CREATE TABLE user_roles (
  user_id       TEXT NOT NULL REFERENCES users(user_id),
  role_id       TEXT NOT NULL REFERENCES roles(role_id),
  PRIMARY KEY (user_id, role_id)
);

CREATE TABLE permissions (
  permission_id TEXT PRIMARY KEY,         -- "doc.read", "doc.write", "doc.approve", "doc.view_phi", ...
  description   TEXT NOT NULL
);

CREATE TABLE role_permissions (
  role_id       TEXT NOT NULL REFERENCES roles(role_id),
  permission_id TEXT NOT NULL REFERENCES permissions(permission_id),
  PRIMARY KEY (role_id, permission_id)
);

-- ABAC-like policy hints stored per doc (consumed by PDP)
CREATE TABLE document_access_attributes (
  doc_id               UUID PRIMARY KEY REFERENCES documents(doc_id),
  required_roles       TEXT[] DEFAULT '{}',    -- e.g., ["privacy_officer"]
  allowed_business_units TEXT[] DEFAULT '{}',  -- e.g., ["Compliance","Oncology"]
  allowed_desks        TEXT[] DEFAULT '{}',    -- for finance
  allowed_facilities   TEXT[] DEFAULT '{}',    -- site-level scoping
  max_class_for_role   TEXT                    -- optional (e.g., define a ceiling on classification for a role)
);

This schema mirrors the RBAC/ABAC patterns described for OPA/Cedar-style evaluation, where user roles and resource attributes are fed into a policy engine.¹³⁶¹³⁷¹³⁸¹³⁹¹⁴⁰¹⁴¹¹⁴²¹⁴³

2. Meilisearch index JSON (HIPAA + FINRA filter fields)

Index settings for a documents index:

{
  "uid": "documents",
  "primaryKey": "doc_id",
  "searchableAttributes": [
    "title",
    "headings",
    "body"
  ],
  "filterableAttributes": [
    "domain",
    "document_type",
    "jurisdiction",
    "regulations",
    "security_class",
    "contains_phi",
    "contains_pii",
    "contains_financial",
    "status",
    "retention_category",
    "business_unit",
    "desk",
    "facility",
    "owner_role",
    "owner_user_id",
    "effective_date",
    "review_due_date",
    "retain_until",
    "created_at",
    "last_modified_at"
  ],
  "sortableAttributes": [
    "effective_date",
    "review_due_date",
    "retain_until",
    "created_at",
    "last_modified_at"
  ]
}

searchableAttributes limit text search to the Markdown content fields.¹⁴⁴¹⁴⁵¹⁴⁶¹⁴⁷
filterableAttributes drive compliance filters and fine-grained access filtering (e.g., contains_phi = true AND facility = "Hospital-A").¹⁴⁵¹⁴⁸¹⁴⁹¹⁴⁴
You can also configure attribute patterns if you want families of fields filterable without enumerating all names.¹⁴⁴¹⁴⁵

Each indexed doc should map 1:1 from documents + document_metadata rows plus parsed content, with doc_id as PK.

3. Ingestion pipeline sketch (Markdown → Postgres + Meilisearch)

High-level, idempotent pipeline:

Discover changed files
- From Git (commits) or filesystem events, collect changed .md paths and their content hashes.
Parse Markdown
- Extract YAML frontmatter → metadata object (title, domain, document_type, regulations, retention_category, etc.).
- Render Markdown → plain text for body.
- Extract headings and section anchors for headings/sections.
Enrich metadata
- Lookup retention_category in retention_policies to get period_years_default and legal_basis.
- Compute retain_until = effective_date + max(period_years_default, min_years).¹⁵⁰¹⁵¹¹⁵²¹⁵³
Write to WORM storage
- Serialize {markdown_content, frontmatter_metadata, doc_id, version} as a JSON or archive blob.
- Write to WORM-capable store with retention >= retain_until (e.g., S3 Object Lock).¹⁵⁴¹⁵⁵¹⁵⁶¹⁵⁷¹⁵⁸
- Capture worm_object_id and content_hash.
Upsert Postgres
- If doc_id new:
  - INSERT INTO documents (doc_id, path, current_version=1, content_hash, worm_object_id, timestamps).
  - INSERT INTO document_versions with version 1.
- Else:
  - Increment current_version, add row to document_versions.
  - Update documents.content_hash, worm_object_id, last_modified_at, last_modified_by.
- Upsert into document_metadata with enriched values.
- Upsert tags / document_access_attributes if present.
Index in Meilisearch
- Build index doc:

{
  "doc_id": "...",
  "title": "...",
  "headings": ["..."],
  "body": "plain text…",
  "domain": "clinical",
  "document_type": "policy",
  "jurisdiction": ["US"],
  "regulations": ["HIPAA-164.316"],
  "security_class": "confidential",
  "contains_phi": true,
  "contains_pii": false,
  "contains_financial": false,
  "status": "effective",
  "retention_category": "HIPAA-6Y",
  "business_unit": "Compliance",
  "desk": null,
  "facility": "Hospital-A",
  "owner_role": "Privacy Officer",
  "owner_user_id": "u123",
  "effective_date": "2025-01-01",
  "review_due_date": "2027-01-01",
  "retain_until": "2031-01-01",
  "created_at": "2025-01-01T10:00:00Z",
  "last_modified_at": "2025-01-10T11:00:00Z"
}

- `POST /indexes/documents/documents` in batches.[^6_13][^6_14][^6_12][^6_24]

7. Audit the pipeline - For each create/update, insert into an audit_events table (append-only) and periodically archive that table into WORM as well.¹⁵⁵¹⁵⁶¹⁵⁸¹⁵⁹

4. Mapping RBAC + ABAC into the schema

RBAC mapping

Users ↔ users table.
Roles ↔ roles table (e.g., clinician, privacy_officer, trader, finra_compliance).
Permissions ↔ permissions (doc.read, doc.write, doc.approve, doc.view_phi, doc.view_financial).
Role-permission assignments ↔ role_permissions.¹³⁷¹³⁸¹³⁹¹⁴⁰¹⁴²¹³⁶

At decision time, your PDP (OPA/Cedar) receives:

{
  "subject": {
    "user_id": "u123",
    "roles": ["privacy_officer", "clinician"],
    "business_unit": "Compliance",
    "facility": "Hospital-A"
  },
  "action": "doc.read",
  "resource": {
    "doc_id": "d0001",
    "domain": "security-privacy",
    "document_type": "policy",
    "jurisdiction": ["US"],
    "regulations": ["HIPAA-164.316"],
    "security_class": "confidential",
    "contains_phi": true,
    "contains_financial": false,
    "business_unit": "Compliance",
    "facility": "Hospital-A"
  }
}

User and resource attributes are loaded from Postgres into OPA or into Cedar policies.¹³⁸¹³⁹¹⁴⁰¹⁴¹¹³⁶¹³⁷

ABAC mapping

Typical ABAC conditions:

contains_phi = true ⇒ user must have doc.view_phi permission and facility match.
contains_financial = true AND regulations contains FINRA-4511 ⇒ user must have finra_compliance or approved front-office role.
domain = "security-privacy" ⇒ only privacy_officer or security_officer roles can update.¹⁴⁰¹⁴¹¹⁴²¹³⁶¹³⁷¹³⁸

These attributes are directly read from document_metadata and user profile tables, then turned into input.resource and input.subject attributes in Rego/Cedar policy.

At query time, you can also pre-filter Meilisearch results by ABAC attributes (e.g., filter: 'facility = "Hospital-A" AND security_class != "restricted"') in combination with PDP decisions.¹⁴⁹¹⁴⁶¹⁴⁵¹⁴⁸¹⁴⁴

5. Retention automation rules and SQL procedures

Retention enforcement belongs in a scheduled job or background worker that uses retain_until and legal_hold.¹⁵¹¹⁵²¹⁵³¹⁵⁰

Retention update helper

Whenever metadata changes effective_date or retention_category, recompute retain_until:

CREATE OR REPLACE FUNCTION recompute_retain_until()
RETURNS TRIGGER AS $$
DECLARE
  pol retention_policies;
  years INT;
BEGIN
  SELECT * INTO pol
  FROM retention_policies
  WHERE retention_category = NEW.retention_category;

  IF NOT FOUND THEN
    RAISE EXCEPTION 'Unknown retention_category: %', NEW.retention_category;
  END IF;

  years := GREATEST(pol.period_years_default, pol.min_years);

  IF NEW.effective_date IS NULL THEN
    RAISE EXCEPTION 'effective_date required for retention computation';
  END IF;

  NEW.retention_period_y := years;
  NEW.retain_until := (NEW.effective_date + (years || ' years')::INTERVAL)::DATE;

  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER trg_recompute_retain_until
BEFORE INSERT OR UPDATE OF effective_date, retention_category
ON document_metadata
FOR EACH ROW
EXECUTE FUNCTION recompute_retain_until();

This mirrors guidance on automating retention windows at the DB level.¹⁵²¹⁵³¹⁵⁰¹⁵¹

Scheduled expiry job (logical destruction queue)

A daily job (cron or PG background worker) can mark records for destruction:

-- Table capturing destruction events (logical delete)
CREATE TABLE destruction_queue (
  id              BIGSERIAL PRIMARY KEY,
  doc_id          UUID NOT NULL REFERENCES documents(doc_id),
  queued_at       TIMESTAMPTZ NOT NULL DEFAULT now(),
  processed_at    TIMESTAMPTZ,
  status          TEXT NOT NULL DEFAULT 'queued',   -- queued|processing|completed|failed
  reason          TEXT NOT NULL                     -- "retention_expired"
);

-- Query to enqueue eligible docs (no legal hold, retention passed)
INSERT INTO destruction_queue (doc_id, reason)
SELECT dm.doc_id, 'retention_expired'
FROM document_metadata dm
LEFT JOIN destruction_queue dq ON dq.doc_id = dm.doc_id AND dq.status IN ('queued','processing','completed')
WHERE dq.doc_id IS NULL
  AND dm.legal_hold = FALSE
  AND dm.retain_until <= CURRENT_DATE;

The worker that processes destruction_queue would:

Read queued items.
Issue delete/expiry operations against WORM storage if allowed, or mark them logically deleted.¹⁵⁷¹⁵⁸¹⁵⁴¹⁵⁵
Update destruction_queue.status and processed_at.
Optionally set a status = 'archived' or similar in document_metadata/documents.
Log events into audit_events and (optionally) Meilisearch (e.g., status = "expired").¹⁵⁶¹⁵⁸¹⁵⁹¹⁵⁵

For performance, this can be batched and run on a schedule (e.g., daily, off-peak), consistent with common practices for DB- and storage-level retention jobs.¹⁵³¹⁵⁰¹⁵¹¹⁵²

If you want, next iteration can be specific Rego or Cedar policy snippets that consume these tables (e.g., allow_read for HIPAA PHI vs FINRA records, plus example Meilisearch filter expressions bound to PDP results). ¹⁶⁰¹⁶¹¹⁶²

⁂

CODITECT Document Management API

1.0.0 OAS 3.1 /openapi.json CODITECT Document Management API Enterprise-grade document management with AI-powered semantic search. Features Semantic Search: Vector similarity search with pgvector Hybrid Search: Combined vector + keyword using RRF fusion GraphRAG: Graph-based relationship traversal Document Management: Full CRUD with chunking and embeddings Analytics: Real-time metrics and usage tracking Authentication All endpoints (except health checks) require JWT authentication. Include the token in the Authorization header: Authorization: Bearer

Rate Limiting Standard tier: 100 requests/minute Premium tier: 1000 requests/minute Enterprise tier: 10000 requests/minute Authorize Health GET /health Health Check GET /health/ready Readiness Check GET /health/live Liveness Check GET /health/version Version Check Search POST /api/v1/search Search Documents POST /api/v1/search/hybrid Hybrid Search POST /api/v1/search/graphrag Graphrag Search GET /api/v1/search/suggest Get Suggestions GET /api/v1/search/modes Get Search Modes Documents GET /api/v1/documents List Documents POST /api/v1/documents Create Document POST /api/v1/documents/upload Upload Document Upload a document file. Accepts various file formats: Markdown (.md) Text (.txt) PDF (.pdf) Word (.docx) Parameters CancelReset No parameters Request body multipart/form-data file * string($binary) Execute Responses Code Description Links 201 Successful Response Media type application/json Controls Accept header. Example Value Schema { "created_at": "2025-12-30T08:07:07.245Z", "updated_at": "2025-12-30T08:07:07.245Z", "id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "filename": "string", "filepath": "string", "mime_type": "string", "file_size": 0, "file_hash": "string", "status": "pending", "document_type": "reference", "title": "string", "version": "string", "summary": "string", "keywords": [ "string" ], "tags": [ "string" ], "chunk_count": 0, "processing_error": "string" } No links 422 Validation Error Media type application/json Example Value Schema { "detail": [ { "loc": [ "string", 0 ], "msg": "string", "type": "string" } ] } No links GET /api/v1/documents/{doc_id} Get Document PUT /api/v1/documents/{doc_id} Update Document DELETE /api/v1/documents/{doc_id} Delete Document GET /api/v1/documents/{doc_id}/chunks Get Document Chunks POST /api/v1/documents/{doc_id}/reprocess Reprocess Document GET /api/v1/documents/{doc_id}/status Get Processing Status Analytics GET /api/v1/analytics/dashboard Get Dashboard POST /api/v1/analytics/metrics Query Metrics GET /api/v1/analytics/documents Get Document Metrics GET /api/v1/analytics/search Get Search Analytics GET /api/v1/analytics/usage Get Usage Metrics GET /api/v1/analytics/costs Get Cost Metrics Schemas Body_upload_document_api_v1_documents_upload_post Expand allobject ChunkResponse Expand allobject CostMetrics Expand allobject DashboardResponse Expand allobject DocumentChunksResponse Expand allobject DocumentCreate Expand allobject DocumentListResponse Expand allobject DocumentMetrics Expand allobject DocumentProcessingStatus Expand allobject DocumentResponse Expand allobject DocumentStatus Expand allstring DocumentType Expand allstring DocumentUpdate Expand allobject GraphRAGNode Expand allobject GraphRAGRelationship Expand allobject GraphRAGRequest Expand allobject GraphRAGResponse Expand allobject HTTPValidationError Expand allobject HealthResponse Expand allobject MetricSeries Expand allobject MetricType Expand allstring MetricsRequest Expand allobject MetricsResponse Expand allobject ProcessingMetrics Expand allobject SearchAnalytics Expand allobject SearchMetrics Expand allobject SearchMode Expand allstring SearchRequest Expand allobject SearchResponse Expand allobject SearchResultItem Expand allobject SuggestResponse Expand allobject TimeGranularity Expand allstring TimeSeriesPoint Expand allobject TopSearchQuery Expand allobject UsageMetrics Expand allobject ValidationError Expand allobject

The existing CODITECT DMS API already gives you most of the primitives you need; the missing pieces are (a) richer compliance metadata, (b) retention state, and (c) a policy layer that ties RBAC/ABAC to search and CRUD.

Below is a concise, implementation-oriented next step tailored to what you posted.

1. Concrete Postgres schema aligned to CODITECT API

Map the existing /api/v1/documents schema into relational tables and extend with compliance/ABAC fields.

-- Raw file + high-level doc info (maps to DocumentResponse)
CREATE TABLE documents (
  id               UUID PRIMARY KEY,
  filename         TEXT NOT NULL,
  filepath         TEXT NOT NULL,
  mime_type        TEXT NOT NULL,
  file_size        BIGINT NOT NULL,
  file_hash        TEXT NOT NULL,
  status           TEXT NOT NULL,  -- pending, processed, error, deleted
  document_type    TEXT NOT NULL,  -- reference, policy, record, etc.
  title            TEXT NOT NULL,
  version          TEXT NOT NULL,
  summary          TEXT,
  chunk_count      INT  NOT NULL DEFAULT 0,
  processing_error TEXT,
  created_at       TIMESTAMPTZ NOT NULL,
  updated_at       TIMESTAMPTZ NOT NULL
);

CREATE TABLE document_keywords (
  document_id UUID NOT NULL REFERENCES documents(id),
  keyword     TEXT NOT NULL,
  PRIMARY KEY (document_id, keyword)
);

CREATE TABLE document_tags (
  document_id UUID NOT NULL REFERENCES documents(id),
  tag         TEXT NOT NULL,
  PRIMARY KEY (document_id, tag)
);

-- Compliance-focused metadata for HIPAA + FINRA
CREATE TABLE document_metadata (
  document_id          UUID PRIMARY KEY REFERENCES documents(id),
  domain               TEXT NOT NULL,         -- clinical, security-privacy, finance, etc.
  jurisdiction         TEXT[] NOT NULL,       -- ["US"], ["US","EU"], etc.
  regulations          TEXT[] NOT NULL,       -- ["HIPAA-164.316","FINRA-4511"]
  security_class       TEXT NOT NULL,         -- public/internal/confidential/restricted
  contains_phi         BOOLEAN NOT NULL DEFAULT FALSE,
  contains_pii         BOOLEAN NOT NULL DEFAULT FALSE,
  contains_financial   BOOLEAN NOT NULL DEFAULT FALSE,
  status               TEXT NOT NULL,         -- draft/in_review/effective/obsolete
  effective_date       DATE,
  review_due_date      DATE,
  expiry_date          DATE,
  retention_category   TEXT NOT NULL,         -- HIPAA-6Y, FINRA-6Y, etc.
  retention_period_y   INT  NOT NULL,
  retain_until         DATE NOT NULL,
  legal_hold           BOOLEAN NOT NULL DEFAULT FALSE,
  legal_hold_reason    TEXT,
  business_unit        TEXT,
  desk                 TEXT,
  facility             TEXT,
  owner_user_id        TEXT NOT NULL,
  owner_role           TEXT NOT NULL,
  created_at           TIMESTAMPTZ NOT NULL,
  last_modified_at     TIMESTAMPTZ NOT NULL
);

-- Optional: chunk metadata (aligned with /documents/{id}/chunks)
CREATE TABLE document_chunks (
  id           UUID PRIMARY KEY,
  document_id  UUID NOT NULL REFERENCES documents(id),
  index        INT  NOT NULL,
  content      TEXT NOT NULL,
  embedding    VECTOR,          -- pgvector
  created_at   TIMESTAMPTZ NOT NULL
);

This lets you hydrate both /api/v1/documents and compliance-aware UIs from one schema, while still using pgvector for semantic search chunks.¹⁶³¹⁶⁴¹⁶⁵

2. Meilisearch index definition for HIPAA + FINRA

You already have pgvector for semantic; Meilisearch (or similar) can own the text + metadata faceting for UI search filters.

{
  "uid": "documents",
  "primaryKey": "id",
  "searchableAttributes": [
    "title",
    "summary",
    "body",
    "keywords",
    "tags"
  ],
  "filterableAttributes": [
    "document_type",
    "domain",
    "jurisdiction",
    "regulations",
    "security_class",
    "contains_phi",
    "contains_pii",
    "contains_financial",
    "status",
    "retention_category",
    "business_unit",
    "desk",
    "facility",
    "owner_role",
    "owner_user_id",
    "effective_date",
    "review_due_date",
    "retain_until",
    "created_at",
    "updated_at"
  ],
  "sortableAttributes": [
    "effective_date",
    "review_due_date",
    "retain_until",
    "created_at",
    "updated_at"
  ]
}

Frontend can pass filters like contains_phi = true AND security_class = "confidential" AND jurisdiction = "US".¹⁶⁵¹⁶⁶¹⁶⁷¹⁶⁸

3. Ingestion pipeline (Markdown → Postgres + Meilisearch + pgvector)

Core steps, assuming you already have the chunking/embedding logic for /api/v1/documents/upload:

File upload (via POST /api/v1/documents/upload):
- Store file, compute hash.
- Insert into documents with status = 'pending', basic fields from request.
Processor worker (kicks on pending docs):
- Parse Markdown:
  - YAML frontmatter → compliance metadata (domain, regulations, etc.).
  - Body → plain text.
  - Headings, keywords, tags.
- Enrich metadata:
  - Look up retention_category in a retention_policies table and compute retain_until.¹⁶⁹¹⁷⁰¹⁷¹
- Chunk + embed:
  - Split body; store chunks in document_chunks with pgvector embeddings.
- Upsert metadata:
  - Fill document_metadata for HIPAA/FINRA semantics.
- Index into Meilisearch:
  - Build a doc like:

{
  "id": "uuid",
  "title": "HIPAA Privacy Officer Policy",
  "summary": "...",
  "body": "plain text...",
  "keywords": ["hipaa","privacy","officer"],
  "tags": ["policy","hipaa"],
  "document_type": "policy",
  "domain": "security-privacy",
  "jurisdiction": ["US"],
  "regulations": ["HIPAA-164.316"],
  "security_class": "confidential",
  "contains_phi": true,
  "contains_pii": false,
  "contains_financial": false,
  "status": "effective",
  "retention_category": "HIPAA-6Y",
  "business_unit": "Compliance",
  "facility": "Hospital-A",
  "owner_role": "Privacy Officer",
  "owner_user_id": "u123",
  "effective_date": "2025-01-01",
  "review_due_date": "2027-01-01",
  "retain_until": "2031-01-01",
  "created_at": "2025-01-01T10:00:00Z",
  "updated_at": "2025-01-10T11:00:00Z"
}

- Update `documents.status = 'processed'`, `chunk_count`, `updated_at`.

3. Search orchestration (for /api/v1/search/hybrid): - Use pgvector for k‑NN over document_chunks.embedding to get candidate doc IDs. - Query Meilisearch with filter incorporating RBAC/ABAC constraints (see below) and candidate IDs. - Fuse scores (you already use RRF).

4. Mapping RBAC/ABAC attributes into Postgres and filters

Extend with user + policy tables; your policy engine (OPA/Cedar/permit.io/etc.) reads from there.¹⁷²¹⁷³¹⁷⁴¹⁷⁵¹⁷⁶¹⁷⁷¹⁷⁸

CREATE TABLE users (
  id            TEXT PRIMARY KEY,
  display_name  TEXT NOT NULL,
  email         TEXT NOT NULL,
  business_unit TEXT,
  desk          TEXT,
  facility      TEXT
);

CREATE TABLE roles (
  id           TEXT PRIMARY KEY,
  description  TEXT NOT NULL
);

CREATE TABLE user_roles (
  user_id   TEXT NOT NULL REFERENCES users(id),
  role_id   TEXT NOT NULL REFERENCES roles(id),
  PRIMARY KEY (user_id, role_id)
);

CREATE TABLE permissions (
  id          TEXT PRIMARY KEY,   -- "doc.read", "doc.view_phi", ...
  description TEXT NOT NULL
);

CREATE TABLE role_permissions (
  role_id       TEXT NOT NULL REFERENCES roles(id),
  permission_id TEXT NOT NULL REFERENCES permissions(id),
  PRIMARY KEY (role_id, permission_id)
);

ABAC lives in document_metadata and the user record:

Resource attributes: domain, document_type, jurisdiction, regulations, security_class, contains_phi, contains_financial, business_unit, desk, facility, owner_role.
Subject attributes: user roles + business_unit, desk, facility.

Authorization flow per request:

Decode JWT → user_id, tenant, maybe roles.
Load user + roles + perms from Postgres; build subject claims.
For each candidate doc:
- Load row(s) from document_metadata.
- Call PDP with subject, action, resource.¹⁷³¹⁷⁴¹⁷⁶¹⁷⁷¹⁷⁸¹⁷²
For list/search APIs:
- Precompute a filter expression equivalent to the PDP’s rules (e.g., facility = "Hospital-A" AND security_class != "restricted" for a clinician), and inject into Meilisearch filter to avoid over-fetch + per-doc rejection.¹⁶⁶¹⁶⁷¹⁶⁸¹⁷⁹¹⁶⁵

This gives you a single, consistent attribute model used both in Meilisearch filters and in fine-grained policy decisions.

5. Retention automation: rules + SQL

You already have retention_category, retention_period_y, retain_until, and legal_hold in document_metadata.

Retention policy table

CREATE TABLE retention_policies (
  category            TEXT PRIMARY KEY,      -- "HIPAA-6Y", "FINRA-6Y"
  description         TEXT NOT NULL,
  period_years        INT  NOT NULL,
  legal_basis         TEXT NOT NULL,         -- "HIPAA-164.316", "FINRA-4511/SEC-17a-4"
  min_years           INT  NOT NULL          -- defensive lower bound
);

Trigger to compute `retain_until`

CREATE OR REPLACE FUNCTION set_retention_fields()
RETURNS TRIGGER AS $$
DECLARE
  pol retention_policies;
  years INT;
BEGIN
  SELECT * INTO pol
  FROM retention_policies
  WHERE category = NEW.retention_category;

  IF NOT FOUND THEN
    RAISE EXCEPTION 'Unknown retention_category: %', NEW.retention_category;
  END IF;

  years := GREATEST(pol.period_years, pol.min_years);

  IF NEW.effective_date IS NULL THEN
    RAISE EXCEPTION 'effective_date required for retention computation';
  END IF;

  NEW.retention_period_y := years;
  NEW.retain_until := (NEW.effective_date + (years || ' years')::INTERVAL)::DATE;
  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER trg_set_retention
BEFORE INSERT OR UPDATE OF effective_date, retention_category
ON document_metadata
FOR EACH ROW
EXECUTE FUNCTION set_retention_fields();

Scheduled expiry queue

CREATE TABLE destruction_queue (
  id            BIGSERIAL PRIMARY KEY,
  document_id   UUID NOT NULL REFERENCES documents(id),
  queued_at     TIMESTAMPTZ NOT NULL DEFAULT now(),
  processed_at  TIMESTAMPTZ,
  status        TEXT NOT NULL DEFAULT 'queued',   -- queued|processing|completed|failed
  reason        TEXT NOT NULL
);

-- daily job:
INSERT INTO destruction_queue (document_id, reason)
SELECT dm.document_id, 'retention_expired'
FROM document_metadata dm
LEFT JOIN destruction_queue dq
  ON dq.document_id = dm.document_id
 AND dq.status IN ('queued','processing','completed')
WHERE dq.document_id IS NULL
  AND dm.legal_hold = FALSE
  AND dm.retain_until <= CURRENT_DATE;

A worker then:

Talks to your WORM/object store to delete/expire,
Marks status → completed,
Updates documents.status = 'deleted',
Logs an audit event for regulators.¹⁸⁰¹⁸¹¹⁸²¹⁸³¹⁸⁴¹⁶⁹¹⁷⁰

If you want a follow-up, the next iteration can be: concrete Rego (OPA) policies that consume document_metadata + users + roles and example Meilisearch filter strings derived from those policies for HIPAA PHI vs FINRA record access. ¹⁸⁵

⁂

Core layout and navigation​

Workflow Checklist​

Workflow Steps​

Workflow Phases​

Phase 1: Initialization​

Phase 2: Processing​

Phase 3: Verification​

Phase 4: Finalization​

Document browsing and search​

Document detail, preview, and lifecycle​

Collaboration, permissions, and workflows​

Concrete component/route structure and next-step prompt​

THE DOCUMENT MANAGEMENT SYSTEM IS DESIGNED FOR A REGULATED INDUSTRY LIKE HEALTH CARE AND FINANCE AND IS TO BE USED TO SEARCH FIND MANAGE AND INTERACT WITH THE ENTERPRISE KNOWLEDGE BASE which is in the form of thousands of MARKDOWN FILES

Top-level mental model​

Knowledge explorer (Markdown-centric)​

Search and discovery UX​

Compliance-first document view​

Tasks, workflows, and lifecycle​

Governance & admin views​

Suggested focused follow-up prompt​

How should I structure markdown folders for regulatory compliance

1. Folder structure for regulatory compliance​

2. Essential metadata fields (healthcare + finance)​

3. Search indexing for thousands of Markdown files​

4. Access control model for regulated documents​

5. Audit trails and automatic retention​

Create a compliant folder hierarchy template for healthcare documents

1. Folder hierarchy template (healthcare-focused, extendable to finance)​

2. Recommended metadata schema (HIPAA + FINRA)​

3. Indexing strategy for 100k Markdown files​

4. Storing and querying metadata separately from Markdown​

5. Open source search engines for Markdown repositories​

Related

1. HIPAA-focused folder template with retention hints​

2. Mandatory metadata fields for FINRA audits (documents only)​

3. Scalable index schema for 100k Markdown files​

4. Sidecar metadata files vs DB​

5. Implementing immutable WORM storage for records + metadata​

6. Next step: Postgres + Meilisearch + ingestion pipeline (sketch)​

6.1 Postgres schema (core tables)​

6.2 Meilisearch index settings (JSON)​

6.3 Ingestion pipeline (RBAC/ABAC-aware)​