Recipe 22: CMDB asset matching #22

Recipe 22 of 60 · Level 2 (Easy) · Data quality & knowledge graphs · Choice

Use case

Link differently worded software asset descriptions to a canonical configuration-management record from a bounded candidate list or a no-match outcome.

What to build

The folder recipes/22-cmdb-asset-matching/ with notebook.ipynb, README.md, and fixtures/, meeting the recipe contract. Start from python tools/new_recipe.py 22 and the template in recipes/_template/.

Build notes

• Python looks up a bounded candidate list of canonical records. Options are record identifiers plus no_match.
• Fixtures vary vendor aliases, version formats, and edition names.
• Include two records that differ only by major version.

Decision design

• Choice. Options are a fixed set supplied by Python, including the fallback outcome this use case names. Show the choice, the per-option probabilities, and the confidence for individual examples before aggregating.

Level 2 scope

Context-sensitive matching, scoring, or verification with candidate sets and explicit uncertain or no-match outcomes.

Acceptance criteria

• recipes/22-cmdb-asset-matching/notebook.ipynb runs top to bottom offline, with no key and no network, and its outputs are committed
• Live mode is opt-in (JEV_COOKBOOK_LIVE=1 with TYPESAFE_API_KEY) and uses the same question definitions as the offline run
• Fixtures are synthetic, include the hard cases named in the build notes, carry gold labels, and pass the fixture validator
• Every response fixture records its provenance, and the notebook states which mode it ran in
• Evaluation: Link accuracy and the false-link rate.
• Candidate sets are assembled by Python and shown to the reader
• The uncertain or no-match outcome is exercised by several fixtures and reported separately in the evaluation
• No statement about Jev's quality, latency, or cost appears without recorded live inference; in a synthetic run, metrics are labelled as pipeline checks
• recipes/22-cmdb-asset-matching/README.md says what the recipe teaches, how to run it, and what was and was not measured live
• The pull request changes only recipes/22-cmdb-asset-matching/ and CI is green

Dependencies

• Blocked by Foundation: CI executes every notebook offline and validates fixtures #69 (CI executes every notebook offline and validates fixtures)
• Tracked in Epic: Level 2 · Easy recipes #76

Sources

Read these before writing the questions.

• S02 · TypeSafe AI: Primitives (Questions): Choice, Noul, Score, independent questions, and dependent follow-up calls.
• S03 · TypeSafe AI: Confidence: Distribution-derived confidence and task-specific thresholds informed by observed outcomes.
• S06 · TypeSafe AI: Cookbooks: Worked patterns for extraction, citation checking, skill selection, hierarchical classification, and feature discovery.

Out of scope

• Changes to shared code, the catalog, or the root README. If the shared package is missing something, open a foundation issue and link it here.
• Real side effects of any kind. Actions are simulated.