Catalog Record Linker · AI Engineering

Help a librarian or organizer reconcile inconsistent item records from two CSV exports. Generate candidate pairs using normalized fields and token similarity, collect explicit match decisions, and export a source-preserving crosswalk and reviewed merged catalog.

The implementation uses Python 3.10+ and standard libraries. Samples are original teaching data. Read API.md for exact input, output and error contracts.

Build your version

bash

python3 scripts/project_test.py catalog-record-linker --init learning-artifacts/catalog-record-linker
python3 scripts/project_test.py catalog-record-linker --stage 1 --path learning-artifacts/catalog-record-linker --strict

A fresh starter fails clearly until you implement the first contract. Later stages retain your source file. The supplied CLI and presentation helpers are scaffolding; all domain decisions call your implementation.

bash

python3 scripts/project_test.py catalog-record-linker --all --path learning-artifacts/catalog-record-linker --strict
cd learning-artifacts/catalog-record-linker
python3 cli.py samples/input.json --output my-output

Run the reference and your own input

bash

python3 scripts/project_test.py catalog-record-linker --all --solution --strict
cd projects/catalog-record-linker/solution
python3 cli.py samples/input.json --output demo-output
python3 cli.py /absolute/path/to/your-input.json --output your-output

Input has left_csv, right_csv and optional threshold. Outputs are crosswalk.csv, merged-catalog.json, review.html. Download decisions.json, add {left,right,match,values} entries in pairs, then pass --decisions decisions.json. A fingerprint rejects reviews for different source inputs.

Worked example and limits

left:map-1 and right:r-7 may describe the same atlas, but neither source record is discarded.

No similarity automatically merges records. ready reports resolved accepted merges; it does not claim every candidate was inspected.

Stages

Primary references

Technical reference