Tests
88 passing
Problem
What the build needed to solve
My contribution
Built the query-variant retrieval, OpenAlex normalization/deduplication/filtering, ranking and clustering, the classic map and v2 explorer (graph, mind map, trend radar, quality report), and the deterministic test and fixture suite.
A single query to a scholarly database returns a noisy, partial slice of a field. A useful starting map needs multi-query coverage, de-duplicated candidates, transparent ranking, and honest limits—without pretending citation counts predict future impact.
Constraints
Design boundaries
Run without an OpenAI key using deterministic signals; embeddings optional.
Deduplicate and normalize OpenAlex records before ranking.
Keep ranking signals as reading-order cues, not impact predictions.
Make evaluation repeatable with frozen OpenAlex fixtures.
Build log
Design evolution
Iterations, issues, and fixes documented as a compact notebook timeline.
Revision
Failure mode
Design change
Result
Multi-query retrieval
One query under-covers a field.
Generate query variants, then normalize, deduplicate, and filter the OpenAlex results.
Broader candidate coverage before ranking.
Transparent ranking
Opaque or impact-implying ranks mislead the reader.
Rank on keyword, metadata, and citation signals as explicit reading-order cues, with optional embeddings.
A source-linked map, graph, roadmap, and quality report.
Repeatable evaluation
Live OpenAlex changes make results non-reproducible.
Froze OpenAlex captures as fixtures behind an 88-test suite across 9 files.
2 of 5 audit-topic fixtures active; keyed precision/recall evaluation still pending.
Testing
Results
Test suites
9
Topic fixtures
2 of 5
Primary source
OpenAlex
Views
Map + explorer
Keyed eval
Pending
The automated suite covers deduplication, relevance diagnostics, scoring, project-idea constraints, roadmap, trend, and clustering across 9 files and 88 tests, using two frozen OpenAlex captures.
Precision, recall, and ranking quality across mechanical-engineering fields are explicitly not yet evaluated; the keyed relevance run is the next measurement.
Scope note: Deterministic components are tested with frozen fixtures. A keyed relevance evaluation—precision at fixed result counts and ranking versus an OpenAlex baseline—has not been run yet, and citation ranks are reading-order signals, not impact predictions.
Reflection
Engineering lessons
- Coverage is won in retrieval (query variants plus deduplication), not only in ranking.
- Frozen fixtures make ranking checks repeatable without live-API drift.
- A shipped app still needs a real relevance evaluation before claiming ranking quality.
Gallery
CAD, fabrication, and test views
Architecture diagram
Retrieval pipeline: query variants, OpenAlex works and authors, normalize/deduplicate/filter, rank and cluster, research map and quality report (architecture schematic, not a product screenshot).
Links