AI Documents

Let users upload files and ask questions about them in chat, with citations, downloads, and built-in support for text, images, and tabular data.

Quick Start

builder.Services.AddCrestAppsCore(crestApps => crestApps
    .AddAISuite(ai => ai
        .AddMarkdown()
        .AddChatInteractions()
        .AddDocumentProcessing(documentProcessing => documentProcessing
            .AddEntityCoreStores()
            .AddOpenXml()
            .AddPdf()
            .AddReferenceDownloads()
        )
        .AddOpenAI()
    )
    .AddEntityCoreSqliteDataStore("Data Source=app.db")
);

app.AddChatApiEndpoints()
    .AddDownloadAIDocumentEndpoint();

What It Gives You

With AI Documents enabled, your users can:

• Upload knowledge files for chat, profiles, or templates
• Ask questions about uploaded content and get cited answers
• Search document content semantically instead of by exact keyword only
• Work with spreadsheets and CSV files through a tabular workflow
• Download generated files such as exports and AI-authored documents
• Include supported images in chat flows

Supported Experiences

Text and knowledge files

For text-heavy files such as Markdown, text, Word, PDF, HTML, JSON, or XML, CrestApps.Core extracts the useful content and makes it available during conversation. This is the main path for summaries, Q&A, reviews, rewrites, extraction, and similar knowledge tasks.

Tabular files

CSV and Excel uploads are handled as structured data instead of plain text. That means the AI can filter, update, reshape, and export rows without asking the model to copy large tables into the prompt.

Multi-sheet Excel workbooks are supported: each worksheet is loaded as its own queryable table, and column value types — numbers (including currency and thousands-separated amounts) and dates — are detected from the row data rather than the spreadsheet cell format, so the AI can aggregate, sort, and filter accurately.

This is the recommended path for tasks such as:

• filling blank cells
• filtering rows
• adding calculated columns
• exporting an updated spreadsheet for download

Under the hood, tabular workflows are handled by the built-in Tabular Data Agent. It is a code-defined, always-available system agent that stays hidden from the AI Profile and Chat Interaction agent pickers, yet still participates in orchestration and is exposed through the A2A host for remote clients.

Spreadsheet formatting

Exported .xlsx files are written with real cell types: a numeric column becomes numbers and a date column becomes date serials, so the recipient can sum, sort, filter, and chart the result rather than receiving a sheet of text. A column whose leading zeros matter — a postal code or an account number — is detected and kept as text.

On top of that, the agent's hidden format_tabular_data tool records how the exported workbook should look, and export_tabular_data applies whatever is currently recorded:

Capability	What the user can ask for
Number formats	currency, accounting, number, percent, date, date/time, time, duration, scientific, text, or an explicit format code, with decimal places, a currency symbol, and negatives in red
Cell styling	bold, italic, underline, font name and size, text color, fill color, alignment, text wrapping, borders, column widths
Sheet layout	worksheet name, styled header row, frozen header, filter dropdowns, banded rows
Conditional formatting	gradient color scales, data bars, icon sets, duplicate highlighting, and comparisons (greater than, less than, equal to, between, contains text)
Formulas	calculated columns written as live formulas that reference other columns by name (={Actual}-{Planned}), and a total row built from SUBTOTAL so it follows the reader's filtering
Charts	column, bar, line, pie, and area charts embedded in the worksheet and bound to its cell ranges, so they redraw when the data changes

The recorded formatting is stored alongside the workspace data, so formatting requested in one turn still applies when the file is exported in a later one, and a follow-up request refines it instead of replacing it.

Requires AddOpenXml(); other formats (such as CSV) ignore the formatting and export the data alone.

Charts in the conversation

A chart embedded in the workbook is for the downloaded file. To render a chart in the chat itself, the agent calls the generate_chart system tool with the actual values (labels and series), which builds the chart configuration directly from those numbers.

Images

When your deployment supports vision, users can upload supported image files alongside standard documents. This enables image-aware chat scenarios such as describing screenshots, extracting visible text, or answering questions about diagrams and photos.

Download Links and Citations

Uploaded documents and generated deliverables can both appear as downloadable references in chat.

Use these registrations together when you want document references to render as clickable downloads:

builder.Services.AddCrestAppsCore(crestApps => crestApps
    .AddAISuite(ai => ai
        .AddDocumentProcessing(documentProcessing => documentProcessing
            .AddEntityCoreStores()
            .AddOpenXml()
            .AddPdf()
            .AddReferenceDownloads()
        )
    )
);

app.AddChatApiEndpoints()
    .AddDownloadAIDocumentEndpoint();

Generated downloads are kept separate from user-uploaded source documents, which helps hosts clean up conversation artifacts without touching knowledge uploads.

File Types

Out of the box, the document features support common text, document, image, and tabular formats. Add the packages you need:

• AddOpenXml() for Office formats such as Word, PowerPoint, and Excel
• AddPdf() for PDF reading
• AddMarkdown() for Markdown-aware normalization and chunking

Use the document upload options to control which extensions your app accepts.

Common Setup Choices

Entity Framework Core stores

.AddDocumentProcessing(documentProcessing => documentProcessing
    .AddEntityCoreStores()
    .AddOpenXml()
    .AddPdf()
    .AddReferenceDownloads()
)

YesSql stores

.AddDocumentProcessing(documentProcessing => documentProcessing
    .AddYesSqlStores()
    .AddOpenXml()
    .AddPdf()
    .AddReferenceDownloads()
)

Pick the store stack that matches the rest of your app.

Upload Configuration

Use ChatDocumentsOptions to decide which file types users can attach.

services.Configure<ChatDocumentsOptions>(options =>
{
    options.Add(".rtf", embeddable: true);
    options.Add(".tsv", embeddable: false);
});

Use the configured option values in both your UI and server-side validation so the visible upload guidance matches what the app actually supports.

Storage

Uploaded files are stored through IDocumentFileStore. The default setup uses local storage, but you can replace it when you want a different backend such as cloud blob storage.

builder.Services.AddSingleton<IDocumentFileStore, AzureBlobDocumentFileStore>();

Extending the Experience

If you need another file format, register a custom reader for that extension and keep the rest of the document pipeline the same.

builder.Services.AddCoreAIIngestionDocumentReader<MyCustomReader>(".custom", ".myformat");

When to Use AI Documents

Choose AI Documents when your app needs any of the following:

• chat over uploaded knowledge files
• searchable document context with citations
• spreadsheet and CSV workflows in chat
• downloadable generated files tied to a conversation
• multimodal chat that includes image uploads