pdf2md-tui

README

pdf2md-tui

High-speed PDF → Markdown ingestion engine for multimodal RAG pipelines. Extracts structured text + isolated images so downstream chunkers, LlamaIndex, and VLM agents get context that actually works.

---

Why?

PDFs break AI pipelines. Binary encoding, embedded fonts, and layout metadata inflate token counts — but the real damage is structural. Legacy parsers flatten documents into raw text, destroying tables, orphaning image references, and producing vector embeddings that are functionally useless for retrieval.

pdf2md-tui solves this by extracting structured Markdown with preserved table layouts alongside an isolated ./images/ directory — the exact format that modern RAG frameworks (LlamaIndex, LangChain) and Vision-Language Models expect.

Document	Naive VLM / Image Processing (est. API cost)	Clean Markdown (est. token cost)	Context Quality
50-page technical spec	~42,500 tokens (Image API)	~15,000 tokens (Clean text)	Noise-free, structured
200-page legal contract	~170,000 tokens (Image API)	~60,000 tokens (Clean text)	Noise-free, structured
Research paper (12 pages)	~10,200 tokens (Image API)	~3,500 tokens (Clean text)	Noise-free, structured

Estimates reflect common API token costs for Vision vs. Text ingestion. Actual savings depend on document density and VLM provider.

Built for the "Look Twice" methodology — extract text + isolate images at ingestion time (Phase 1), then let your downstream VLM pipeline handle deep visual reasoning at retrieval time (Phase 2).

Use cases:

• Preprocessing document archives for RAG pipelines (LlamaIndex SimpleDirectoryReader compatible)
• Building multimodal knowledge bases with text + image vector stores
• Feeding autonomous AI agents via MCP with structured, parseable document context
• Reducing token costs when processing large document sets via API

---

Features

• Chunking-safe tables — positional text analysis detects column alignment and emits GFM pipe tables as indivisible atomic units, preventing downstream chunkers from destroying table integrity
• Two-path extraction — positional extraction first (preserves structure); falls back to plain-text for edge cases
• Worker pool — concurrent conversion using runtime.NumCPU() workers by default; configurable via --workers
• Live TUI — Bubble Tea dashboard for batch conversion with live stats, recent activity, and a completion menu
• Interactive Menu — run pdf2md-tui without arguments in a terminal to launch a guided configuration wizard
• Graceful OCR detection — scanned/image-only PDFs are detected and skipped cleanly, reported in the summary (no empty output files)
• --strip-noise — aggressively removes page numbers, repeated headers/footers, and excess whitespace for maximum token density
• Smart Overwrites — prompts before overwrite in a terminal; non-interactive and --quiet runs fail unless --force is set
• Automation-friendly modes — --quiet emits a JSON summary; non-TTY non-quiet runs fall back to a plain-text summary without alt-screen UI
• Date-stamped outputs — report_2026-05-06.md so you always know which version was processed
• Single static binary — no runtime dependencies; CGO_ENABLED=0, pure Go

---

Installation

Homebrew (macOS and Linux)

brew tap nawodyaishan/tap
brew install pdf2md-tui

Go install

go install github.com/nawodyaishan/pdf2md-tui/cmd/pdf2md-tui@latest

Binary download

Download a pre-built binary for your platform from the latest release.

Platforms: Linux, macOS, Windows × amd64 / arm64. Packages: .tar.gz, .zip, .deb, .rpm.

---

Usage

Interactive Mode

Run the tool without arguments in a terminal to launch the guided interactive wizard. It prompts for the target directory and common flags, and currently preselects Extract images in the multiselect:

pdf2md-tui

The zero-argument menu is only used when both stdin and stdout are interactive terminals. Otherwise the command defaults to converting the current directory.

CLI Mode

# Convert all PDFs in ./docs, write Markdown to ./docs/md/
pdf2md-tui convert ./docs

# Recurse into subdirectories and strip layout noise for LLM ingestion
pdf2md-tui convert ./archive --recursive --strip-noise

# Use 8 workers, custom output directory, no date suffix, and force overwrite existing files
pdf2md-tui convert ./papers --workers 8 --output out --date-format none --force

# Extract embedded images and inject markdown links into the output
pdf2md-tui convert ./docs --extract-images

# CI-friendly mode: emit only JSON to stdout
pdf2md-tui convert ./docs --quiet

# Print version and build info
pdf2md-tui version

Go Library Usage

Since the refactoring to Clean Architecture, you can embed the conversion engine directly into your own Go applications.

[!NOTE] The core logic currently resides in internal/ packages. To use this in an external module, you should move internal/domain, internal/service, and internal/repository to a public directory like pkg/ or the root.

import (
	"github.com/nawodyaishan/pdf2md-tui/internal/domain"
	"github.com/nawodyaishan/pdf2md-tui/internal/repository/pdf"
	"github.com/nawodyaishan/pdf2md-tui/internal/repository/storage"
	"github.com/nawodyaishan/pdf2md-tui/internal/service"
)

func main() {
	// 1. Initialize configuration
	cfg := domain.NewConfig()
	cfg.ExtractImages = true
	cfg.StripNoise = true

	// 2. Initialize dependencies (Clean Architecture)
	store := storage.NewStorage()
	parser := pdf.NewParser()

	// 3. Initialize the service
	conv := service.NewConverterService(cfg, store, parser)

	// 4. Run conversion
	// Convert(pdfPath, outDir) returns a domain.Result
	res := conv.Convert("input.pdf", "output_dir")

	if res.Err != nil {
		fmt.Printf("Conversion failed: %v\n", res.Err)
		return
	}

	fmt.Printf("Successfully converted %s to %s (Saved %d bytes)\n", 
		res.InputPath, res.OutputPath, res.InputBytes - res.OutputBytes)
}

Flags

Flag	Short	Default	Description
--output	-o	md/	Output subdirectory (relative to the target directory)
--recursive	-r	false	Scan subdirectories
--workers	-w	NumCPU	Number of concurrent conversion workers
--date-format		2006-01-02	Date suffix format (Go reference time); none disables the suffix
--force	-f	false	Overwrite existing output files without prompting
--strip-noise		false	Aggressively remove page numbers, headers/footers, and excess whitespace
--extract-images		false	Extract embedded images and inject markdown references
--quiet	-q	false	Suppress the TUI and emit a JSON summary to stdout
--verbose	-v	false	Print per-file errors to stderr
--log-file		pdf2md.log	Path for detailed conversion logs

Runtime Modes

• Interactive terminal: shows the pterm banner/discovery phase, then launches the Bubble Tea dashboard during conversion. When the batch completes, the dashboard offers Open Output Directory, View Detailed Log when failures occurred, and Exit.
• Non-interactive without --quiet: skips the Bubble Tea alt-screen and prints a concise text summary after conversion finishes.
• --quiet: prints only the JSON summary to stdout. If any conversions fail, the command still exits non-zero.

Overwrite and Log Behavior

• Existing output files require confirmation only in an interactive terminal.
• Non-interactive runs and --quiet refuse to overwrite existing outputs unless --force is set.
• When failures occur, inspect the configured --log-file path for details. The completion menu exposes View Detailed Log in the dashboard, and non-interactive summaries print the log path directly.

Output structure

./docs/
├── report.pdf
├── spec.pdf
└── md/
    ├── images/
    │   ├── report/
    │   │   ├── report_1_5.png
    │   │   └── report_2_11.jpg
    │   └── spec/
    │       └── spec_1_8.png
    ├── report_2026-05-06.md
    └── spec_2026-05-06.md

---

How it works

1. Discovery — scans the target directory (optionally recursive) for .pdf files.
2. Worker pool — distributes files across N goroutines via a buffered channel.
3. Extraction — for each page, attempts positional character extraction to preserve table structure; falls back to plain-text if the page is image-only or yields no content.
4. Table detection — identifies column-aligned rows (≥3 columns, ≥50 pt gaps, appearing in ≥40% of rows) and renders them as GFM pipe tables.
5. Output — writes one .md file per PDF to the output directory.

See docs/ARCHITECTURE.md for a detailed breakdown of the extraction pipeline and concurrency model.

---

Roadmap Status

• 🛡️ Graceful OCR Detection — Detect and skip scanned PDFs without failing.
• 🖼️ Image Extraction Pipeline — Extract raw images for "Look Twice" VLM workflows.
• ⚡ Zero-Arg Usability — Run pdf2md-tui in any folder with no arguments.
• 🏗️ Clean Architecture — Decoupled domain/service/repository structure for scaling.
• 🧱 Chunking-Safe Tables — Atomic GFM pipe tables for hybrid vector chunkers.
• 🔇 CI-Friendly Quiet Mode — Non-interactive JSON output for automation.
• 🔌 MCP Server Wrapper — Native tool support for Model Context Protocol agents.
• ☁️ VLM Cloud Integration — High-accuracy Markdown generation via GPT-4o/Claude.

See ROADMAP.md for the full strategic vision.

---

Building from source

git clone https://github.com/nawodyaishan/pdf2md-tui.git
cd pdf2md-tui
make build          # → bin/pdf2md-tui

make test           # run tests with race detector
make lint           # golangci-lint
make check          # fmt + vet + lint + test
make help           # list all targets

Git hooks

This repository uses Lefthook as a Go-friendly alternative to Husky.

The hook split follows the usual Go workflow:

• pre-commit: format staged Go files with gofmt -w and run go vet ./...
• pre-push: run golangci-lint run ./..., go test -race ./..., and go build ./cmd/pdf2md-tui

Install Lefthook and wire the hooks into .git/hooks:

brew install lefthook
make hooks-install

Official Go-based install is also supported:

go install github.com/evilmartians/lefthook/v2@v2.1.6
make hooks-install

You can also run the hook suites manually:

make hooks-run-pre-commit
make hooks-run-pre-push

---

Roadmap

The roadmap is organized around maximizing context quality for downstream AI pipelines:

• Near-term (v0.x) — Image extraction pipeline (pdfcpu), graceful OCR detection, chunking-safe table output, --quiet JSON mode for CI/MCP
• Mid-term (v1.x) — "Look Twice" VLM pipeline (cloud vision providers), MCP server prototype, .docx/.txt ingestion
• Long-term (v2.x) — Pluggable post-processors, full MCP server + REST API

See ROADMAP.md for the full vision, strategic goals, and how to contribute.

---

Contributing

Bug reports and feature requests: open an issue using the provided templates.

For code contributions:

1. Check ROADMAP.md and open issues for help wanted items.
2. Fork, branch, implement, add tests, and open a PR.
3. PRs must pass make check (fmt + vet + lint + test with race detector).

---

License

MIT — see LICENSE.