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docs: overhaul retrieval accuracy benchmark

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This commit is contained in:
Johann Schopplich
2025-10-28 20:22:43 +01:00
parent efbe4ded88
commit 67c0df8cb0
22 changed files with 1553 additions and 27288 deletions
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34
benchmarks/src/formatters.ts
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@@ -1,20 +1,3 @@
/**
* Format converters for TOON benchmarks
*
* Converts data to different formats for comparison:
* - JSON
* - TOON
* - CSV
* - XML
* - YAML
*
* ## Semantic Equivalence
*
* All formatters attempt to preserve semantic equivalence with the source data,
* meaning the converted data should represent the same information. However,
* CSV has inherent limitations with nested structures (see `toCSV` docs).
*/
import { stringify as stringifyCSV } from 'csv-stringify/sync'
import { XMLBuilder } from 'fast-xml-parser'
import { stringify as stringifyYAML } from 'yaml'
@@ -23,7 +6,10 @@ import { encode as encodeToon } from '../../src/index'
/**
* Format converters registry
*
* Each formatter takes unknown data and returns a string representation
* @remarks
* All formatters attempt to preserve semantic equivalence with the source data,
* meaning the converted data should represent the same information. However,
* CSV has inherent limitations with nested structures (see `toCSV` docs).
*/
export const formatters: Record<string, (data: unknown) => string> = {
json: data => JSON.stringify(data, undefined, 2),
@@ -37,11 +23,13 @@ export const formatters: Record<string, (data: unknown) => string> = {
* Convert data to CSV format
*
* @remarks
* **Limitations**: CSV is designed for flat tabular data only. This formatter:
* - Only handles top-level objects with arrays of flat objects
* - Cannot properly represent deeply nested structures (nested arrays/objects within rows)
* - Loses nested structure information during conversion
* - May produce misleading results for datasets with complex nesting (e.g., e-commerce orders with nested items)
* Limitations: CSV is designed for flat tabular data only.
*
* This formatter:
* - Only handles top-level objects with arrays of flat objects
* - Cannot properly represent deeply nested structures (nested arrays/objects within rows)
* - Loses nested structure information during conversion
* - May produce misleading results for datasets with complex nesting (e.g., e-commerce orders with nested items)
*
* For datasets with nested structures, CSV comparisons may not be fair or representative
* of how CSV would typically be used in practice.