doctok 1.0.0

Production-grade token counter for PDF, TXT, DOCX, MD, and PPTX files using real GPT tokenization via tiktoken

Token Calculator

Production-grade token counter for PDF, TXT, DOCX, MD, and PPTX files using real GPT tokenization via tiktoken

Features • Installation • Quick Start • API • CLI

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Features

✨ Real GPT Tokenization - Uses OpenAI's tiktoken library for accurate token counting (not approximations)

⚡ Multi-Format Support - Handles PDF, TXT, MD, DOCX, PPTX files seamlessly

🔄 Streaming Architecture - Constant-memory processing for large files (handles 500MB+ efficiently)

🚀 Adaptive Concurrency - Automatic CPU detection and parallel batch processing

🔍 OCR/Scanned PDF Detection - Automatically identifies whether PDFs are text-based or scanned images

⏱️ Timeout Enforcement - Prevents hanging on malformed or problematic files

💾 Memory Protected - Soft (512MB) and hard (2048MB) memory limits with enforcement

🎯 Enterprise Ready - Designed for RAG, LLM preprocessing, token budgeting, and bulk analytics

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Installation

From PyPI

pip install doctok

From source

git clone https://github.com/Pranesh-2005/Token-Calculator.git
cd Token-Calculator
pip install -e .

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Quick Start

Command Line

# Count tokens in a single file
doctok document.pdf

# Process an entire directory
doctok ./documents/

# Output results as JSON
doctok document.pdf --output results.json --format json

# Compute SHA256 hashes
doctok document.pdf --hash

# Specify timeout (seconds)
doctok large-file.pdf --timeout 600

# Use multiple workers (default: auto-detected)
doctok ./documents/ --workers 8

Python API

from token_calculator import count_file, count_files_batch

# Single file
result = count_file("document.pdf")
print(f"Tokens: {result.gpt_tokens}")
print(f"Words: {result.word_count}")
print(f"Characters: {result.char_count}")
print(f"Pages: {result.pages}")
print(f"Status: {result.status}")  # 'ok', 'scanned', 'timeout', 'error'

# Batch processing
results = count_files_batch(
    ["doc1.pdf", "doc2.docx", "doc3.txt"],
    max_workers=4,
    compute_hash=True
)
print(f"Total tokens: {results.total_tokens}")
print(f"Total pages: {results.total_pages}")
print(f"Elapsed time: {results.elapsed_sec:.2f}s")

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API Reference

Core Functions

count_file(path: str, timeout_sec: float = 300, compute_hash: bool = False) -> FileResult

Process a single file and return detailed metrics.

Parameters:

• path (str): Path to the file to process
• timeout_sec (float): Timeout in seconds (default: 300)
• compute_hash (bool): Compute SHA256 hash (default: False)

Returns:

• FileResult object with:
  • gpt_tokens (int): Token count using GPT tokenizer
  • char_count (int): Character count
  • word_count (int): Word count
  • pages (int): Page count
  • extractable_pages (int): Pages with extractable text
  • skipped_pages (int): Pages without text (scanned PDFs)
  • status (str): 'ok', 'scanned', 'timeout', 'error'
  • error_msg (str): Error details if applicable
  • elapsed_sec (float): Processing time

Example:

from token_calculator import count_file

result = count_file("paper.pdf")
if result.status == "ok":
    print(f"{result.gpt_tokens} tokens")
elif result.status == "scanned":
    print("PDF is scanned (no OCR)")
else:
    print(f"Error: {result.error_msg}")

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count_files_batch(paths: list[str], max_workers: Optional[int] = None, compute_hash: bool = False) -> BatchResult

Process multiple files in parallel.

Parameters:

• paths (list[str]): List of file paths
• max_workers (int): Max parallel workers (default: auto-detected, max 8)
• compute_hash (bool): Compute SHA256 hashes (default: False)

Returns:

• BatchResult object with:
  • files (list[FileResult]): Results for each file
  • total_tokens (int): Sum of all tokens
  • total_words (int): Sum of all words
  • total_chars (int): Sum of all characters
  • total_pages (int): Sum of all pages
  • file_count (int): Number of files processed
  • error_count (int): Number of failed files
  • elapsed_sec (float): Total elapsed time

Example:

from token_calculator import count_files_batch
import json

results = count_files_batch([
    "doc1.pdf",
    "doc2.docx",
    "doc3.txt"
])

# Export as JSON
output = {
    "total_tokens": results.total_tokens,
    "files": [
        {
            "name": f.filename,
            "tokens": f.gpt_tokens,
            "status": f.status
        }
        for f in results.files
    ]
}
print(json.dumps(output, indent=2))

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Exception Handling

from token_calculator import (
    count_file,
    TokenCounterError,
    FileTooLargeError,
    UnsupportedFileError,
    FileTimeoutError,
)

try:
    result = count_file("document.pdf")
except FileTooLargeError:
    print("File exceeds 500MB limit")
except UnsupportedFileError as e:
    print(f"Format not supported: {e}")
except FileTimeoutError:
    print("Processing timed out")
except TokenCounterError as e:
    print(f"Processing error: {e}")

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Supported Formats

Format	Extension	Features
PDF	.pdf	Text extraction, page count, scanned PDF detection
Plain Text	.txt	Encoding auto-detection, streaming processing
Markdown	.md	Standard markdown text extraction
Word	.docx	Paragraph and table extraction
PowerPoint	.pptx	Slide and shape text extraction

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Configuration

Default Limits

Max file size: 500 MB
Global timeout: 300 seconds
PDF page timeout: 10 seconds
Soft memory limit: 512 MB
Hard memory limit: 2048 MB
Max workers: CPU count (capped at 8)

To use different limits, process files locally and modify core.py constants.

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Performance

• Small files (< 10MB): < 1 second
• Medium files (10-100MB): 2-10 seconds
• Large files (100-500MB): 30-120 seconds
• Batch of 100 files: ~2 minutes (with 8 workers)

Memory usage remains constant regardless of file size due to streaming architecture.

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Use Cases

• 📊 RAG Pipeline Optimization - Calculate token costs before ingestion
• 🧠 LLM Preprocessing - Validate document sizes for model context windows
• 💰 Token Budgeting - Estimate API costs for document processing
• 📈 Enterprise Analytics - Batch analyze large document corpora
• 🔍 Content Indexing - Organize documents by token complexity

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Examples

Example 1: Calculate API Cost

from token_calculator import count_file

result = count_file("paper.pdf")

# Pricing: $0.01 per 1K tokens (example)
cost = (result.gpt_tokens / 1000) * 0.01
print(f"Estimated API cost: ${cost:.2f}")

Example 2: Batch Processing with Error Handling

from token_calculator import count_files_batch
from pathlib import Path

pdf_files = list(Path("./documents").glob("*.pdf"))

results = count_files_batch([str(p) for p in pdf_files], max_workers=8)

print(f"Successfully processed: {results.file_count - results.error_count}")
print(f"Failed: {results.error_count}")
print(f"Total tokens: {results.total_tokens:,}")

for file_result in results.files:
    if file_result.status != "ok":
        print(f"⚠️  {file_result.filename}: {file_result.error_msg}")

Example 3: RAG Ingestion Check

from token_calculator import count_file

# Check if document fits in context window
MAX_CONTEXT = 8000  # tokens

result = count_file("document.pdf")

if result.gpt_tokens > MAX_CONTEXT:
    print(f"⚠️  Document is {result.gpt_tokens} tokens (exceeds {MAX_CONTEXT})")
    print(f"Split into {result.gpt_tokens // MAX_CONTEXT + 1} chunks")
else:
    print(f"✓ Document fits in context ({result.gpt_tokens} tokens)")

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CLI Options

usage: token_calculator [-h] [-w WORKERS] [-t TIMEOUT] [-o OUTPUT] [--hash] [--format {text,json}] path

positional arguments:
  path                  File or directory to process

optional arguments:
  -h, --help           Show help message
  -w, --workers N      Max parallel workers (default: auto)
  -t, --timeout SEC    Per-file timeout in seconds (default: 300)
  -o, --output FILE    Write JSON results to file
  --hash               Compute SHA256 hash for each file
  --format {text,json} Output format (default: text)

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Logging

import logging
from token_calculator import setup_logging, count_file

# Enable debug logging
setup_logging(logging.DEBUG)

result = count_file("document.pdf")

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License

MIT License - see LICENSE file for details

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Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Make your changes
4. Run tests (pytest)
5. Submit a pull request

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Support

• 📖 GitHub Issues
• 💬 GitHub Discussions

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Changelog

v1.0.0 (Initial Release)

• Multi-format document support (PDF, TXT, MD, DOCX, PPTX)
• Real GPT tokenization via tiktoken
• Streaming extraction with constant-memory processing
• Batch processing with adaptive concurrency
• OCR/scanned PDF detection
• CLI and Python API
• Comprehensive error handling
• Memory protection and timeout enforcement

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Roadmap

• Async API support
• Web API endpoint
• Language-specific tokenizer support
• Streaming file uploads
• Token cost calculator for multiple LLM providers

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Made with ❤️ by Pranesh