ragctl 0.1.1

Intelligent RAG system with advanced OCR, semantic chunking, and production-ready batch processing CLI

🚀 Atlas-RAG

Production-ready document processing CLI for RAG applications

Process documents, extract text with advanced OCR, chunk intelligently, and prepare data for RAG systems - all from the command line.

---

🎯 What is Atlas-RAG?

Atlas-RAG is a command-line tool for processing documents into chunks ready for Retrieval-Augmented Generation (RAG) systems. It handles the dirty work of document ingestion, OCR, and intelligent chunking so you can focus on building your RAG application.

Key capabilities:

• 📄 Universal document loading (PDF, DOCX, images, HTML, Markdown, etc.)
• 🔍 Advanced OCR with automatic fallback (EasyOCR → PaddleOCR → pytesseract)
• ✂️ Intelligent semantic chunking using LangChain
• 📦 Production-ready batch processing with auto-retry
• 💾 Multiple export formats (JSON, JSONL, CSV)
• 🗄️ Direct ingestion into Qdrant vector store

---

✨ Features

📄 Universal Document Processing

• Supported formats: PDF, DOCX, ODT, TXT, HTML, Markdown, Images (JPEG, PNG)
• Smart OCR cascade:
  1. EasyOCR (best quality, multi-language)
  2. PaddleOCR (fast, good for complex layouts)
  3. pytesseract (fallback, most tolerant)
• Quality detection: Automatically rejects unreadable documents
• Multi-language: French, English, German, Spanish, Italian, Portuguese, and more

✂️ Intelligent Chunking

• Semantic chunking: Context-aware text splitting using LangChain RecursiveCharacterTextSplitter
• Multiple strategies:
  • semantic - Smart splitting by meaning (default)
  • sentence - Split by sentences
  • token - Fixed token-based splitting
• Configurable: Token limits (50-2000), overlap (0-500), model selection
• Rich metadata: Source file, chunk index, token count, strategy, timestamps

🔄 Production-Ready Batch Processing

• Automatic retry: Up to 3 attempts with exponential backoff (1s, 2s, 4s...)
• Interactive error handling:
  • interactive - Prompt user on each error (default)
  • auto-continue - Continue on errors (CI/CD mode)
  • auto-stop - Stop on first error (validation mode)
  • auto-skip - Skip failed files automatically
• Complete history: Every run saved to ~/.atlasrag/history/
• Retry capability: atlas-rag retry to rerun failed files only
• Per-file output: One chunk file per document for better traceability

💾 Flexible Export & Storage

• Export formats: JSON, JSONL (streaming), CSV (Excel-compatible)
• Vector store integration: Direct ingestion into Qdrant
• No database required: Pure file-based export for easy sharing

⚙️ Configuration System

• Hierarchical config: CLI flags > Environment variables > YAML file > Defaults
• Example config: config.example.yml with detailed documentation
• Easy customization: Override any setting via command line

---

🚀 Quick Start

Installation

# Clone repository
git clone [e-mail]:horiz-data/atlas-rag.git
cd atlas-rag

# Install with pip
pip install -e .

# Verify installation
atlas-rag --version

Basic Usage

# Process a single document
atlas-rag chunk document.pdf --show

# Process with advanced OCR for scanned documents
atlas-rag chunk scanned.pdf --advanced-ocr -o chunks.json

# Batch process a folder
atlas-rag batch ./documents --output ./chunks/

# Batch with auto-retry for CI/CD
atlas-rag batch ./documents --output ./chunks/ --auto-continue

---

💡 Usage Examples

Single Document Processing

# Simple text file
atlas-rag chunk document.txt --show

# PDF with semantic chunking (default)
atlas-rag chunk report.pdf -o report_chunks.json

# Scanned image with OCR
atlas-rag chunk contract.jpeg --advanced-ocr --show

# Custom chunking parameters
atlas-rag chunk document.pdf \
  --strategy semantic \
  --max-tokens 500 \
  --overlap 100 \
  -o output.jsonl

Batch Processing

# Process all files in a directory
atlas-rag batch ./documents --output ./chunks/

# Process only PDFs recursively
atlas-rag batch ./documents \
  --pattern "*.pdf" \
  --recursive \
  --output ./chunks/

# CI/CD mode - continue on errors
atlas-rag batch ./documents \
  --output ./chunks/ \
  --auto-continue \
  --save-history

# Per-file output (default):
# chunks/
# ├── doc1_chunks.jsonl  (25 chunks)
# ├── doc2_chunks.jsonl  (42 chunks)
# └── doc3_chunks.jsonl  (18 chunks)

# Single-file output (all chunks combined):
atlas-rag batch ./documents \
  --output ./all_chunks.jsonl \
  --single-file

Retry Failed Files

# Show last failed run
atlas-rag retry --show

# Retry all failed files from last run
atlas-rag retry

# Retry specific run by ID
atlas-rag retry run_20251028_133403

Vector Store Integration

# Ingest chunks into Qdrant
atlas-rag ingest chunks.jsonl \
  --collection my-docs \
  --url http://localhost:6333

# Get system info
atlas-rag info

Evaluate Chunking Quality

# Evaluate chunking strategy
atlas-rag eval document.pdf \
  --strategies semantic sentence token \
  --metrics coverage overlap coherence

# Compare strategies with visualization
atlas-rag eval document.pdf --compare --output eval_results.json

---

📚 Documentation

Document	Description
Getting Started	Installation and first steps
CLI Guide	Complete command reference
Security	Security features and best practices
Full Documentation	Complete documentation index

---

⚙️ Configuration

Create ~/.atlasrag/config.yml or use CLI flags:

# OCR settings
ocr:
  use_advanced_ocr: false
  enable_fallback: true

# Chunking settings
chunking:
  strategy: semantic
  max_tokens: 400
  overlap: 50

# Output settings
output:
  format: jsonl
  include_metadata: true
  pretty_print: true

Configuration hierarchy: CLI flags > Environment variables > YAML config > Defaults

---

🧪 Testing

# Run all tests
make test

# Run CLI tests
make test-cli

# Quick validation
atlas-rag --version
atlas-rag chunk tests/data/sample.txt --show

Test Coverage: 129 tests, 96% coverage

---

📊 Performance

Processing Speed

• Text documents: ~100-200 docs/minute
• PDFs with OCR: ~5-10 docs/minute (depends on page count)
• Batch processing: Parallel-ready with retry mechanism

Quality Metrics

• OCR accuracy: 95%+ with EasyOCR on clear scans
• Chunk quality: 90% readability threshold enforced
• Semantic coherence: LangChain's RecursiveCharacterTextSplitter optimized for context

---

🛠️ CLI Commands

Command	Description
atlas-rag chunk	Process a single document
atlas-rag batch	Batch process multiple files
atlas-rag retry	Retry failed files from history
atlas-rag ingest	Ingest chunks into Qdrant
atlas-rag eval	Evaluate chunking quality
atlas-rag info	System information

Run atlas-rag COMMAND --help for detailed options.

---

🐛 Troubleshooting

Common Issues

NumPy incompatibility

# For OCR support, use NumPy 1.x
pip install "numpy<2.0"

Missing system dependencies

# Ubuntu/Debian
sudo apt-get install tesseract-ocr poppler-utils

# macOS
brew install tesseract poppler

"Document unreadable" errors

• Try lowering quality threshold: --ocr-threshold 0.2
• Use advanced OCR: --advanced-ocr
• Check document is not corrupted

Import errors

# Reinstall dependencies
pip install -e .

More help: Getting Started Guide

---

🔧 Development

# Install dev dependencies
make install-dev

# Format code
make format

# Run linters
make lint

# Install pre-commit hooks
make pre-commit-install

# Run all CI checks
make ci-all

---

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

---

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

See CONTRIBUTING.md for detailed guidelines (coming soon).

---

📧 Support

• Documentation: docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions

---

🙏 Acknowledgments

Built with:

• LangChain - Text splitting and document loading
• EasyOCR - OCR engine
• PaddleOCR - Alternative OCR engine
• Unstructured - Document parsing
• Typer - CLI framework
• Rich - Terminal formatting

---

Version: 0.1.0 | Status: Beta | License: MIT