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AI-LinkMO

Install and run AI-LinkMO

Playbookhow-toplaybooks/install-and-run

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Written 2026-10-17 by ktl-librarian (automation). Last checked 2026-10-27 by ktl-librarian (automation).

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  • 2026-10-27 by ktl-librarian (automation)
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Install and run

Section titled “Install and run”

This playbook condenses docs/install.md and the pages it links, which are the authority for current detail.

  1. Build: Python 3.11+ and uv, and Node 22 for the web UI. Run uv sync --extra test, then ./ai --help: that is the offline CLI, ready to use. An older gcc, a CUDA build or a corporate package index needs the extra steps in docs/install-notes.md.
  2. FastAPI backend: uv run uvicorn lib.api.server:app --reload listens on port 8000. Its write endpoints are not authenticated, so keep it on localhost.
  3. Web UI: cd lib/frontend && npm ci && npm run dev serves port 5173 and proxies the API paths to port 8000. The login is a persona picker for the demo, not authentication.
  4. CLI: ./ai -h, then for example ./ai risk --isDefinedByTaxonomy nist-ai-rmf. It sends queries to the API by default (--mode api) and works offline with --mode local.
  5. Graph database: just fetch-cypher downloads the Cypher export. Run Neo4j in a container (podman or docker, ports 7474 and 7687) with graph/cypher/ mounted at /examples, then run :source /examples/ai-risk-ontology.cypher in cypher-shell. docs/neo4j.md gives the steps.
  6. Store, search and browse: just load-store builds the DuckDB store, just index-store builds its text index for ./ai search, and just browse serves the data in Datasette on port 8001. docs/working-with-the-data.md gives the detail.
  7. Tests: CI runs uv run pytest lib/test -m "not slow". ./scripts/tests.sh has narrower modes: its default, fast, runs the CLI cases in API mode only, and full adds local mode.

If ai-atlas-nexus is upgraded, regenerate the UI schema with gen-json-schema into lib/frontend/static/schema/ai-risk-ontology.json. docs/install-notes.md has the exact command.