csharp

sheet-compressor (C#)

Independent C# implementation of the SheetCompressor encoding from the SpreadsheetLLM paper (Dong et al., Microsoft, 2024). Turns a spreadsheet grid into compact, LLM-friendly text — three interchangeable encodings plus token estimates — and makes no LLM calls itself. Conforms to the shared golden corpus in fixtures/corpus/; see spec/SPEC.md for the language-neutral contract.

Independent, community implementation. Not affiliated with or endorsed by Microsoft. Part of the multi-language sheet-compressor project.

Two NuGet packages:

• SheetCompressor — the pure core (src/SheetCompressor).
• SheetCompressor.Xlsx — optional .xlsx reader built on ClosedXML (src/SheetCompressor.Xlsx).

Install

dotnet add package SheetCompressor          # core
dotnet add package SheetCompressor.Xlsx     # optional .xlsx reader (ClosedXML)

Usage

using SheetCompressor;

var grid = new Grid
{
    Origin = new Origin(1, 1),
    Rows = new IReadOnlyList<string>[]
    {
        new[] { "Name", "Qty", "Price" },
        new[] { "Apple", "3", "1.50" },
    },
};

var result = Compressor.Compress(grid);
Console.WriteLine(result.Encodings.Anchor.String);          // the LLM-ready text
Console.WriteLine($"{result.Encodings.Anchor.TokenEstimate} vs {result.RawBaseline.TokenEstimate}");

Each result carries three encodings — Anchor, InvertedIndex, FormatAggregation — every one with a .String, a .Json, and a .TokenEstimate, plus RawBaseline and any Charts.

Read an .xlsx instead of hand-building the grid (separate SheetCompressor.Xlsx package):

using SheetCompressor;
using SheetCompressor.Xlsx;
var result = Compressor.Compress(XlsxReader.ReadSheetFile("workbook.xlsx"));

The three encodings

The same sparse two-table sheet, in each encoding (.String shown; each group also has .Json and .TokenEstimate). Raw baseline 100 tokens → 80 / 77 / 23:

# Encodings.Anchor.String  — addresses + values, empty rows dropped
A1,Product|B1,Q1|C1,Q2|D1,Q3|E1,Q4
A2,Apples|B2,100|C2,150|D2,200|E2,120
A15,Region|B15,Cost|C15,Margin|D15,Profit|E15,Status
A16,North|B16,500|C16,0.15|D16,75|E16,good

# Encodings.InvertedIndex.String  — value → cell(s); repeats collapse (B4|D18,60)
A1,Product
B4|D18,60
E16|E18,good

# Encodings.FormatAggregation.String  — values → type over ranges
IntNum: B2:E4,B16:B18,D16:D18
FloatNum: C16:C18
Text: A1:E1,A2:A4,A15:E15,A16:A18,E16:E18

See the project README for the complete strings.

Prompts — read the output with an LLM

The shared prompt templates ship with the package and load via PromptsLoader.Instance: reader explainers (PromptsLoader.Instance.Readers.Anchor / .InvertedIndex / .FormatAggregation) that teach a model to decode each encoding, task templates (PromptsLoader.Instance.Tasks.SheetQA / .CellValueLookup / .TableRegionDetection) with {ENCODING} / {ADDRESS} / {QUESTION} placeholders, and .Snippets.ChartDescriptor. The library makes no LLM calls — assemble the messages and send them to any chat model:

var result = Compressor.Compress(grid);

// reader explainer → system prompt; task template with placeholders filled → user message
var system = PromptsLoader.Instance.Readers.Anchor;
var user = PromptsLoader.Instance.Tasks.SheetQA
    .Replace("{ENCODING}", result.Encodings.Anchor.String)
    .Replace("{QUESTION}", "Which region had the highest profit?");

// Send { system, user } to your chat model (e.g. the Anthropic .NET SDK) and read the reply.

Match the reader to the encoding you send (PromptsLoader.Instance.Readers.InvertedIndex for the inverted index, and so on).

Conformance

dotnet test SheetCompressor.sln

The suite walks every fixture under fixtures/corpus/ and asserts byte-equal output against the goldens — the same contract as the other language ports.

License

MIT. The SheetCompressor algorithm originates from the SpreadsheetLLM paper, credited above.