bookdatamaker 0.3.0.5

CLI tool for extracting text with DeepSeek OCR and generating datasets

Book Data Maker

A powerful CLI tool for extracting text from documents using DeepSeek OCR and generating high-quality datasets with LLM assistance.

Table of Contents

🚀 Getting Started

• Features
• Quick Start
• Installation

📖 User Guide

• Extract Text (Stage 1)
• Generate Dataset (Stage 2)
• Export Dataset

🔧 Advanced

• Position Distribution
• Performance Tuning
• Interactive Chat

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Features

• 📄 Multi-Format Support: PDF, EPUB, and images
• 🏠 Self-Hosted OCR: Local transformers for DeepSeek-OCR (no API costs)
• 🤖 Parallel Generation: Multiple LLM threads explore documents simultaneously
• 🎯 Smart Distribution: Control thread starting positions
• 💾 SQLite Storage: Real-time dataset storage with flexible export
• 📊 Multiple Formats: JSONL, Parquet, CSV, JSON
• 🌐 Flexible Modes: API or self-hosted for both stages
• 📈 Progress Tracking: Real-time progress bars

Installation

From PyPI (Recommended)

pip install bookdatamaker

From Source

git clone https://github.com/yourusername/bookdatamaker.git
cd bookdatamaker
pip install -r requirements.txt
pip install -e .

Optional: Local Inference Support

# For self-hosted OCR and LLM generation
pip install bookdatamaker[local]  # From PyPI
# OR
pip install -e ".[local]"  # From source - installs transformers==4.46.3, torch, flash-attn, etc.

Note: The project requires transformers==4.46.3 for optimal compatibility with DeepSeek-OCR. A warning will be displayed if a different version is detected.

System Requirements

For API Mode:

• Python 3.10+
• API keys (OpenAI, DeepSeek, etc.)

For Local Mode:

• Python 3.10-3.12 (3.13 not supported due to vLLM compatibility)
• NVIDIA GPU with CUDA support (or CPU, though slower)
• 16GB+ VRAM recommended for GPU
• transformers==4.46.3
• Linux or WSL2 (recommended)

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Quick Start

Prerequisites

# Set API keys (choose one based on your mode)
export OPENAI_API_KEY=your_openai_key        # For API mode
export DEEPSEEK_API_KEY=your_deepseek_key    # For API OCR mode

Option 1: API Mode (Fastest Setup)

# 1. Install
pip install bookdatamaker

# 2. Extract → Generate → Export
bookdatamaker extract book.pdf -o ./extracted
bookdatamaker generate ./extracted -d dataset.db --distribution "10,10,20,30,20,10"
bookdatamaker export-dataset dataset.db -o output.parquet

Option 2: Self-Hosted Mode (Free, Private)

# 1. Install with local dependencies
pip install bookdatamaker[local]

# 2. Extract with local OCR
bookdatamaker extract book.pdf --mode local --batch-size 8 -o ./extracted

# 3. Generate with vLLM
bookdatamaker generate ./extracted \
  --mode vllm \
  --vllm-model-path meta-llama/Llama-3-8B-Instruct \
  --distribution "25,25,25,25" \
  -d dataset.db

# 4. Export
bookdatamaker export-dataset dataset.db -o output.parquet

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System Requirements

For API Mode:

• Python 3.10+
• API keys (OpenAI, DeepSeek, etc.)

For Local Mode:

• Python 3.10-3.12 (3.13 not supported due to vLLM compatibility)
• NVIDIA GPU with CUDA support (or CPU, though slower)
• 16GB+ VRAM recommended for GPU
• transformers==4.46.3
• Linux or WSL2 (recommended)

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Extract Text (Stage 1)

Extract text from documents using DeepSeek OCR.

Supported Formats

• PDF: Text extraction or OCR from rendered pages
• EPUB: E-book text extraction
• Images: JPG, PNG, BMP, TIFF, WebP

API Mode

Note: DeepSeek does not provide an official OCR API. You need to self-host DeepSeek-OCR using vLLM.

Setup vLLM OCR Server

Follow the vLLM DeepSeek-OCR recipe to set up your server

Use the API

Once your vLLM server is running:

# Basic usage (default: http://localhost:8000/v1)
bookdatamaker extract book.pdf -o ./extracted

# Custom vLLM endpoint
bookdatamaker extract book.pdf \
  --deepseek-api-url http://your-server:8000/v1 \
  -o ./extracted

# Adjust concurrency for faster processing
bookdatamaker extract book.pdf \
  --api-concurrency 8 \
  -o ./extracted

Performance Options:

• --api-concurrency N: Number of concurrent API requests (default: 4)
  • Higher values = faster processing (if your server can handle it)
  • Adjust based on your vLLM server capacity and network bandwidth
  • Example: 8-16 for powerful servers, 2-4 for smaller setups

Local Mode (Transformers)

Use local transformers model for OCR (DeepSeek-OCR, no API calls):

# Basic usage - uses transformers AutoModel with flash_attention_2
bookdatamaker extract book.pdf --mode local -o ./extracted

# With custom batch size (adjust based on GPU memory)
bookdatamaker extract book.pdf --mode local --batch-size 12 -o ./extracted

# Use CPU instead of GPU
bookdatamaker extract book.pdf --mode local --device cpu -o ./extracted

# Use specific GPU
bookdatamaker extract book.pdf --mode local --device cuda:1 -o ./extracted

# Process directory of images
bookdatamaker extract ./images/ --mode local -o ./extracted

Performance Options:

• --batch-size N: Number of images to process in parallel (default: 8)
  • Higher values = faster processing but more GPU memory
  • Adjust based on available VRAM
  • Example: 4 for 8GB VRAM, 8-16 for 24GB+ VRAM

Device Options:

• cuda (default): Use default CUDA GPU
• cuda:0, cuda:1, etc.: Use specific GPU
• cpu: Use CPU (slower, no GPU required)
• xpu: Use Intel XPU

Plain Text Mode (No OCR)

For PDF with embedded text, skip OCR and extract text directly (much faster):

# Extract plain text from PDF without OCR
bookdatamaker extract book.pdf --plain-text -o ./extracted

Note: EPUB files are automatically extracted as plain text (no OCR needed, no --plain-text flag required):

# EPUB always uses plain text extraction
bookdatamaker extract book.epub -o ./extracted

When to use --plain-text (for PDF):

• ✅ PDF with embedded text (e.g., born-digital documents)
• ✅ Fast extraction without GPU/API requirements
• ✅ Text-only documents

When NOT to use --plain-text:

• ❌ Scanned PDFs (images of text)
• ❌ PDFs with complex layouts requiring OCR
• ❌ Documents where text extraction quality is poor

Output Structure

./extracted/
├── page_001/
│   ├── page_001.png      # Page image
│   └── result.mmd        # Extracted text in markdown
├── page_002/
│   ├── page_002.png
│   └── result.mmd
└── ...

Note: Each page is stored in its own subdirectory with the extracted text in result.mmd format.

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Generate Dataset (Stage 2)

Generate Q&A datasets using parallel LLM threads with page-based navigation.

Navigation Model

The system uses page navigation:

• LLM threads navigate through document pages
• Tools available: get_current_page, next_page, previous_page, jump_to_page, get_page_context
• Each thread starts at a specific page based on distribution
• Threads can move forward/backward through pages to explore content

Checkpoint & Resume

The generation process automatically saves checkpoints to the database:

• Thread state is saved after each successful Q&A submission
• If interrupted (Ctrl+C, crash, etc.), simply rerun the same command
• You'll be prompted to resume from checkpoint or start fresh

# First run (interrupted at 50%)
bookdatamaker generate ./extracted -d dataset.db --distribution "25,25,25,25"
# ^C (interrupted)

# Resume from checkpoint
bookdatamaker generate ./extracted -d dataset.db --distribution "25,25,25,25"
# ⚠️  Found 4 incomplete thread(s) in database:
#   Thread 0: 8/20 pairs, last updated 2024-01-15 10:30:45
#   Thread 1: 10/20 pairs, last updated 2024-01-15 10:30:48
#   Thread 2: 12/20 pairs, last updated 2024-01-15 10:30:50
#   Thread 3: 7/20 pairs, last updated 2024-01-15 10:30:43
# 
# Do you want to resume from checkpoint? [Y/n]: y
# ✓ Resuming from checkpoint...

Features:

• 💾 Automatic checkpoint after each Q&A pair submission
• 🔄 Resume from last position in document
• 💬 Preserves conversation history
• 🎯 Tracks progress per thread

Basic Usage

# 6 threads (from distribution), 20 Q&A pairs per thread
bookdatamaker generate ./extracted \
  -d dataset.db \
  --distribution "10,10,20,30,20,10" \
  --datasets-per-thread 20

Key Concept: Thread count is determined by the number of comma-separated values in --distribution.

API Mode Examples

# OpenAI/Azure
bookdatamaker generate ./extracted \
  -d dataset.db \
  --openai-api-url https://api.openai.com/v1 \
  --model gpt-4 \
  --distribution "10,10,20,30,20,10"

# Custom API endpoint
bookdatamaker generate ./extracted \
  --openai-api-url http://localhost:8000/v1 \
  --model your-model-name \
  --distribution "25,25,25,25"

vLLM Direct Mode (Self-Hosted)

Use vLLM directly without API server:

# Single GPU
bookdatamaker generate ./extracted \
  --mode vllm \
  --vllm-model-path meta-llama/Llama-3-8B-Instruct \
  --distribution "25,25,25,25" \
  -d dataset.db

# Multi-GPU (4 GPUs, 6 threads)
bookdatamaker generate ./extracted \
  --mode vllm \
  --vllm-model-path meta-llama/Llama-3-70B-Instruct \
  --tensor-parallel-size 4 \
  --distribution "10,10,20,30,20,10" \
  -d dataset.db

Custom Prompts

Add specific instructions to guide LLM behavior:

# Language specification
bookdatamaker generate ./extracted \
  --custom-prompt "Generate all Q&A in Chinese with simplified characters"

# Format specification
bookdatamaker generate ./extracted \
  --custom-prompt "Questions should be multiple-choice with 4 options"

# Multiple requirements
bookdatamaker generate ./extracted \
  --custom-prompt "Requirements:
1. Generate questions in English
2. Focus on practical applications
3. Include code examples
4. Answer length: 50-150 words
5. Difficulty: intermediate"

Message History Management

Control conversation history to prevent token overflow:

# Limit conversation to 50 messages (keeps system prompt + last 10 when exceeded)
bookdatamaker generate ./extracted \
  --max-messages 50 \
  -d dataset.db

# For models with limited context windows
bookdatamaker generate ./extracted \
  --max-messages 30 \
  --model gpt-3.5-turbo

How it works:

• When message count exceeds --max-messages, history is pruned automatically
• System prompt is always preserved
• Last 10 messages are kept for continuity
• Prevents token overflow errors during long generation sessions
• Useful for models with limited context windows (e.g., 4K, 8K tokens)

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Export Dataset

Export from SQLite database to your preferred format:

# Parquet (recommended for data analysis, default: zstd compression)
bookdatamaker export-dataset dataset.db -o output.parquet

# Parquet with different compression methods
bookdatamaker export-dataset dataset.db -o output.parquet -c snappy  # Faster, larger files
bookdatamaker export-dataset dataset.db -o output.parquet -c gzip    # Smaller, slower
bookdatamaker export-dataset dataset.db -o output.parquet -c brotli  # Best compression
bookdatamaker export-dataset dataset.db -o output.parquet -c none    # No compression

# JSON Lines (easy to stream)
bookdatamaker export-dataset dataset.db -o output.jsonl -f jsonl

# CSV (Excel-friendly)
bookdatamaker export-dataset dataset.db -o output.csv -f csv

# JSON with metadata
bookdatamaker export-dataset dataset.db -o output.json -f json --include-metadata

Compression Comparison

For Parquet files:

Method	Speed	Size	Use Case
zstd (default)	Fast	Small	Best balance, recommended
snappy	Fastest	Larger	Real-time processing
gzip	Medium	Smaller	Network transfer
brotli	Slowest	Smallest	Archival storage
none	Instant	Largest	Debug/testing only

Position Distribution

Control where threads start in the document using distribution percentages.

How It Works

Document: 100 pages
Distribution: "10,10,20,30,20,10" (6 threads)

Thread 0: Start at 0%   → Page 1
Thread 1: Start at 10%  → Page 10
Thread 2: Start at 20%  → Page 20
Thread 3: Start at 50%  → Page 50
Thread 4: Start at 70%  → Page 70
Thread 5: Start at 80%  → Page 80

Distribution Strategies

# Even distribution (4 threads)
--distribution "25,25,25,25"
# Start at: 0%, 25%, 50%, 75%

# Front-heavy (4 threads) - focus on beginning
--distribution "40,30,20,10"
# Start at: 0%, 40%, 70%, 90%

# Middle-heavy (5 threads) - focus on middle
--distribution "10,20,40,20,10"
# Start at: 0%, 10%, 30%, 70%, 90%

# Dense sampling (10 threads) - fine-grained coverage
--distribution "10,10,10,10,10,10,10,10,10,10"

Thread Count Guidelines

• Small documents (<50 pages): 2-4 threads
• Medium documents (50-200 pages): 4-8 threads
• Large documents (>200 pages): 8-16 threads

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Performance Tuning

Optimize extraction and generation speeds based on your hardware and requirements.

Stage 1: OCR Extraction

API Mode (vLLM):

# Increase concurrent requests (default: 4)
bookdatamaker extract book.pdf --api-concurrency 8

# Guidelines:
# - 2-4:  Small vLLM server (1-2 GPUs)
# - 4-8:  Medium server (2-4 GPUs)
# - 8-16: Large server (4+ GPUs)
# - Monitor server load and adjust accordingly

Local Mode (Transformers):

# Increase batch size (default: 8)
bookdatamaker extract book.pdf --mode local --batch-size 16

# Guidelines based on GPU VRAM:
# - 8GB VRAM:   batch-size 2-4
# - 16GB VRAM:  batch-size 4-8
# - 24GB VRAM:  batch-size 8-12
# - 40GB+ VRAM: batch-size 12-16

Stage 2: Dataset Generation

Thread Count:

# More threads = faster generation (if LLM server can handle it)
bookdatamaker generate ./extracted \
  --distribution "10,10,10,10,10,10,10,10,10,10" \
  --threads 10

# Guidelines:
# - API mode: 4-16 threads (based on rate limits)
# - vLLM mode: 4-8 threads (based on GPU capacity)
# - Local mode: 2-4 threads (memory intensive)

Message History Management:

# Limit conversation history to prevent memory issues
bookdatamaker generate ./extracted \
  --max-messages 20 \
  -d dataset.db

# Default: 20 messages (system message + last 10 exchanges)
# Lower values = less memory, potentially less context
# Higher values = more memory, better context retention

Duplicate Detection:

• Automatically enabled with 95% similarity threshold
• Uses rapidfuzz for efficient fuzzy matching
• Prevents redundant Q&A pairs in the dataset

Performance Tips

1. Start Small: Test with small concurrency/batch sizes first
2. Monitor Resources: Watch GPU memory, CPU usage, and network
3. Balance Quality vs Speed: Higher concurrency may reduce quality
4. Network Bandwidth: API mode performance depends on network speed
5. vLLM Configuration: Use tensor parallelism for multi-GPU setups

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Interactive Chat

Chat with an LLM that can access your document through MCP tools. Perfect for exploring documents interactively or testing Q&A generation.

Start Chat Session

# Basic chat with GPT-4
bookdatamaker chat ./extracted

# With vLLM server
bookdatamaker chat ./extracted \
  --openai-api-url http://localhost:8000/v1 \
  --model Qwen/Qwen3-4B-Thinking-2507

# With custom database
bookdatamaker chat ./extracted --db my_dataset.db

Debug Mode

Set environment variable for verbose logging:

export LOG_LEVEL=DEBUG
bookdatamaker generate ./extracted -d dataset.db

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Development

Project Structure

bookdatamaker/
├── src/bookdatamaker/
│   ├── cli.py                    # CLI interface
│   ├── ocr/
│   │   ├── extractor.py          # OCR extraction
│   │   └── document_parser.py    # Document parsing
│   ├── mcp/
│   │   └── server.py             # MCP server
│   ├── llm/
│   │   └── parallel_generator.py # Parallel generation
│   ├── dataset/
│   │   ├── builder.py            # Dataset building
│   │   └── dataset_manager.py    # SQLite management
│   └── utils/
│       ├── page_manager.py       # Page navigation
│       └── status.py             # Progress indicators
└── tests/                        # Test files

Development Setup

# Clone repository
git clone https://github.com/yourusername/bookdatamaker.git
cd bookdatamaker

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

# Code formatting
black src/
ruff check src/

# Type checking
mypy src/

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Ensure all tests pass
5. Submit a pull request

Testing

# Run all tests
pytest

# Run specific test file
pytest tests/test_ocr.py

# Run with coverage
pytest --cov=bookdatamaker tests/

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License

MIT License - see LICENSE file for details.