# Supported File Formats — Omna Help

> Omna can slice any of these file types before sending to AI. Each format is handled differently based on its structure.

Canonical: https://omna.dev/help/supported-files

Omna can slice any of these file types before sending to AI. Each format is handled differently based on its structure.

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## Tabular files (rows and columns)

These are sliced row-by-row. Omna finds the rows most relevant to your question and sends only those.

| Format | Extensions | Notes |
|---|---|---|
| **CSV** | `.csv` | Most common. All columns preserved. |
| **Excel** | `.xlsx`, `.xls`, `.xlsb` | All sheets are concatenated into one table. |
| **OpenDocument Spreadsheet** | `.ods` | Same as Excel handling. |
| **Parquet** | `.parquet` | Column-store format common in data engineering. Fully supported — no size limit beyond the 10 GB file cap. |

**Output format:** A clean CSV with a header row and only the relevant rows. Column names are preserved exactly as they appear in the original.

**What counts as a "relevant row":** Omna uses a combination of keyword matching (BM25) and semantic similarity (sentence embeddings) to score every row against your question. Rows with scores below a minimum threshold are dropped. For numeric questions ("over $500", "between 10 and 20"), an exact arithmetic filter runs first — all rows that arithmetically satisfy the condition are kept, regardless of semantic score.

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## Documents (text with structure)

These are sliced section-by-section. Omna finds the sections most relevant to your question.

| Format | Extensions | Notes |
|---|---|---|
| **Word** | `.docx`, `.doc`, `.odt`, `.rtf` | Sliced by paragraph or heading section. |
| **PDF** | `.pdf` | Sliced by page. Each page becomes one searchable unit. |

**Output format:** A `.txt` file with structural markers. Word docs include `[§ Heading Name]` markers before each section. PDFs include `[p. N]` markers before each page's content.

**Why PDFs are sliced by page:** Unlike tabular data where each row is an independent fact, PDFs contain flowing text where context within a page matters. Splitting mid-paragraph would break meaning. One page = one chunk is a safe, predictable unit.

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## Plain text files

These are sliced line-by-line.

| Format | Extensions | Notes |
|---|---|---|
| **Plain text** | `.txt`, `.md`, `.log` | Each non-empty line is one searchable unit. |
| **JSON** | `.json` | Each top-level array element or top-level object key is one unit. |

**Markdown files:** Treated as plain text. Omna doesn't parse markdown structure — each line is indexed independently. For large `.md` files with clear headings, results will typically cluster around the relevant sections naturally.

**Log files:** Log line format varies widely. Omna indexes each line as a unit and uses keyword + semantic search to surface the relevant entries. Works well for error logs, access logs, and structured log lines.

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## File size limits

| Limit | Value | Why |
|---|---|---|
| Maximum file size | **10 GB** | Hard cap per file |
| Maximum rows for full indexing | **5,000,000 rows** | Above this, only BM25 keyword index is built (no embeddings) |
| Minimum row length for embeddings | **50 tokens** | Very short rows (like single IDs) are BM25-only |

Files above 5M rows can still be sliced — the keyword search runs on all rows, but the semantic reranking step only applies to the BM25 survivors.

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## What is NOT supported

### Screenshots and images (OCR-sliced)

`.png`, `.jpg`, `.jpeg`, `.webp`, `.heic` files are supported via OCR.

When you drop a screenshot onto the capsule or attach it in the browser extension, Omna uses macOS Vision to extract the text, then slices and masks it exactly like a `.txt` file. The AI receives extracted text (~200–400 tokens) instead of the raw image (~1,600 Vision API tokens).

AI Vision APIs charge ~1,600 tokens per image regardless of content. OCR + slicing can cut that by 70–90% when the image contains relevant text.

**Unsupported image formats:** `.gif`, `.bmp`, `.svg` — these are not screenshot formats and are released to the AI unchanged.

### Other unsupported formats

| Format | Status |
|---|---|
| Images (`.gif`, `.bmp`, `.svg`) | Not supported — not screenshot formats |
| Audio (`.mp3`, `.m4a`, `.wav`) | Not supported |
| Video (`.mp4`, `.mov`) | Not supported |
| Encrypted / password-protected files | Not supported — Omna cannot read encrypted content |
| ZIP / RAR archives | Not supported — extract first, then drop the individual file |
| Binary files (`.exe`, `.bin`, `.dmg`) | Not supported |

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## Extension vs. desktop app — same formats?

Yes. The browser extension uses the same slicing engine as the desktop capsule (the Mac app). When you attach a file on claude.ai or chatgpt.com, the extension sends it to the Mac app for slicing and swaps the result in — same formats, same quality, same output.

**One difference:** The extension does its own PDF and Word text extraction in Chrome (using pdfjs-dist and mammoth) before sending to the Mac app. The Mac app handles all formats natively. End result for the user is identical.

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## How Omna picks the output format

| Input | Output |
|---|---|
| Tabular (CSV, Excel, Parquet) | `.csv` — header + relevant rows |
| PDF | `.txt` — page markers + relevant page content |
| Word / RTF / ODF | `.txt` — section markers + relevant sections |
| Plain text / log / markdown | `.txt` — relevant lines |
| JSON | `.json` — relevant top-level elements |
| Image (PNG/JPG/JPEG/WEBP/HEIC) | `.txt` — OCR'd text, relevant lines sliced |

The output file is attached to the AI chat as if you'd attached it yourself. The AI sees the sliced file — it never sees the original.