SheetCompressor 0.1.1

SheetCompressor

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.

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

Install

dotnet add package SheetCompressor

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. Per-encoding reader prompt templates are available via PromptsLoader.Instance and ship with the package.

For reading .xlsx files, add the optional SheetCompressor.Xlsx package.

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.

License

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