# dir2text
A Python library and command-line tool for expressing directory structures and file contents in formats suitable for Large Language Models (LLMs). It combines directory tree visualization with file contents in a memory-efficient, streaming format.
## Features
- Tree-style directory structure visualization
- Complete file contents with proper escaping
- Memory-efficient streaming processing
- Multiple output formats (XML, JSON)
- Easy extensibility for new formats
- Support for exclusion patterns (e.g., .gitignore rules)
- Proper symbolic link handling and loop detection
- Optional token counting for LLM context management
- Summary reporting with configurable output destination
- Safe handling of large files and directories
## Installation
This project uses [Poetry](https://python-poetry.org/) for dependency management. We recommend using Poetry for the best development experience, but we also provide traditional pip installation.
### Using Poetry (Recommended)
1. First, [install Poetry](https://python-poetry.org/docs/#installation) if you haven't already.
2. Install dir2text:
```bash
poetry add dir2text
```
### Using pip
```bash
pip install dir2text
```
### Optional Features
Install with token counting support (for LLM context management):
```bash
# With Poetry
poetry add "dir2text[token_counting]"
# With pip
pip install "dir2text[token_counting]"
```
**Note:** The `token_counting` feature requires the `tiktoken` package, which needs a Rust compiler (e.g., `rustc`) and Cargo to be available during installation.
## Usage
### Command Line Interface
Basic usage:
```bash
dir2text /path/to/project
# Show version information
dir2text --version
# Exclude files matching patterns from one or more exclusion files
dir2text -e .gitignore /path/to/project
dir2text -e .gitignore -e .npmignore -e custom-ignore /path/to/project
# Exclude files with direct patterns
dir2text -i "*.pyc" -i "node_modules/" /path/to/project
# Enable token counting for LLM context management
dir2text -t gpt-4 /path/to/project
# Generate JSON output and save to file
dir2text -f json -o output.json /path/to/project
# Follow symbolic links
dir2text -L /path/to/project
# Skip tree or content sections
dir2text -T /path/to/project # Skip tree visualization
dir2text -C /path/to/project # Skip file contents
# Handle binary files
dir2text -B ignore /path/to/project # Skip binary files silently (default)
dir2text -B warn /path/to/project # Skip binary files with warnings
dir2text -B encode /path/to/project # Include binary files as base64
dir2text -B fail /path/to/project # Stop on binary files
```
### Symbolic Link Handling
By default, symbolic links are represented as symlinks without following them:
```bash
dir2text /path/to/project
```
This shows symlinks clearly marked with their targets in the tree output, and as separate elements in content output.
To follow symbolic links during traversal (similar to Unix `find -L`):
```bash
dir2text -L /path/to/project
```
This includes the content that symlinks point to, while still protecting against symlink loops.
### Summary Reporting
Dir2text can generate a summary describing the processed directory including file counts, line counts, and optionally token counts. You can control where this information is displayed:
```bash
# Print summary to stderr
dir2text -s stderr /path/to/project
# Print summary to stdout
dir2text -s stdout /path/to/project
# Include summary in the output file
dir2text -s file -o output.txt /path/to/project
# Include token counts in summary by specifying a tokenizer model
dir2text -s stderr -t gpt-4 /path/to/project
```
Summary includes counts of directories, files, symlinks, lines, and characters. Token counts are only included when a tokenizer model is specified with the `-t` option.
### Python API
Basic usage:
```python
from dir2text import StreamingDir2Text
from dir2text.exclusion_rules.git_rules import GitIgnoreExclusionRules
# Create exclusion rules (optional)
rules = GitIgnoreExclusionRules()
rules.add_rule("*.pyc") # Add rules directly
# OR load from files
rules.load_rules(".gitignore")
# Initialize the analyzer
analyzer = StreamingDir2Text("path/to/project", exclusion_rules=rules)
# Stream the directory tree
for line in analyzer.stream_tree():
print(line, end='')
# Stream file contents
for chunk in analyzer.stream_contents():
print(chunk, end='')
# Get metrics
print(f"Processed {analyzer.file_count} files in {analyzer.directory_count} directories")
print(f"Found {analyzer.symlink_count} symbolic links")
```
Memory-efficient processing with token counting:
```python
from dir2text import StreamingDir2Text
from dir2text.exclusion_rules.git_rules import GitIgnoreExclusionRules
# Create exclusion rules from multiple files
rules = GitIgnoreExclusionRules()
rules.load_rules(".gitignore")
rules.load_rules(".npmignore")
rules.add_rule("custom.ignore")
# Initialize with options
analyzer = StreamingDir2Text(
directory="path/to/project",
exclusion_rules=rules,
output_format="json",
tokenizer_model="gpt-4",
follow_symlinks=False, # Default behavior, don't follow symlinks
binary_action="ignore" # How to handle binary files: "ignore", "warn", "encode", or "fail"
)
# Process content incrementally
with open("output.json", "w") as f:
for line in analyzer.stream_tree():
f.write(line)
for chunk in analyzer.stream_contents():
f.write(chunk)
# Print statistics
print(f"Files: {analyzer.file_count}")
print(f"Directories: {analyzer.directory_count}")
print(f"Symlinks: {analyzer.symlink_count}")
print(f"Lines: {analyzer.line_count}")
print(f"Tokens: {analyzer.token_count}")
print(f"Characters: {analyzer.character_count}")
```
Immediate processing (for smaller directories):
```python
from dir2text import Dir2Text
from dir2text.exclusion_rules.git_rules import GitIgnoreExclusionRules
# Create exclusion rules
rules = GitIgnoreExclusionRules()
rules.load_rules(".gitignore")
# Process everything immediately
analyzer = Dir2Text(
"path/to/project",
exclusion_rules=rules,
follow_symlinks=True, # Optionally follow symlinks
binary_action="encode" # Include binary files as base64
)
# Access complete content
print(analyzer.tree_string)
print(analyzer.content_string)
```
## Output Formats
### XML Format
```xml
<file path="relative/path/to/file.py" content_type="text" tokens="150">
def example():
print("Hello, world!")
</file>
<symlink path="docs/api.md" target="../README.md" />
```
### JSON Format
```json
{
"type": "file",
"path": "relative/path/to/file.py",
"content_type": "text",
"content": "def example():\n print(\"Hello, world!\")",
"tokens": 150
}
{
"type": "symlink",
"path": "docs/api.md",
"target": "../README.md"
}
```
## Signal Handling
When using dir2text as a command-line tool, it handles system signals gracefully to ensure proper resource management and clean exits:
- **SIGPIPE**: When piping output to programs like `head`, `less`, or `grep` that may terminate before reading all input, dir2text detects the closed pipe and exits cleanly without error messages.
- **SIGINT** (Ctrl+C): Properly handles user interruption, ensuring all resources are cleaned up.
This means you can safely pipe dir2text output to other commands without worrying about error messages when those commands exit:
```bash
# The first 10 lines of output
dir2text /path/to/project | head -n 10
# Only files containing "function"
dir2text /path/to/project | grep "function"
```
## Development
### Setup Development Environment
1. Clone the repository:
```bash
git clone https://github.com/rlichtenwalter/dir2text.git
cd dir2text
```
2. Install development dependencies:
```bash
poetry install --with dev
```
3. Install pre-commit hooks:
```bash
poetry run pre-commit install
```
### Running Tests
```bash
# Run specific quality control categories
poetry run tox -e format # Run formatters
poetry run tox -e lint # Run linters
poetry run tox -e test # Run tests
poetry run tox -e coverage # Run test coverage analysis
```
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the repository
2. Create a new branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run the test suite
5. Commit your changes (`git commit -m 'Add some amazing feature'`)
6. Push to the branch (`git push origin feature/amazing-feature`)
7. Open a Pull Request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- This project uses [anytree](https://github.com/c0fec0de/anytree) for tree data structures
- .gitignore pattern matching uses [pathspec](https://github.com/cpburnz/python-pathspec)
- Token counting functionality is provided by OpenAI's [tiktoken](https://github.com/openai/tiktoken)
## Requirements
- Python 3.9+
- Poetry (recommended) or pip
- Optional: Rust compiler and Cargo (for token counting feature)
## Project Status
This project is actively maintained. Issues and pull requests are welcome.
## FAQ
**Q: Why use streaming processing?**
A: Streaming allows processing of large directories and files with constant memory usage, making it suitable for processing repositories of any size.
**Q: How does dir2text handle symbolic links?**
A: By default, dir2text represents symlinks as symbolic links in both tree and content output without following them. With the `-L` option, it follows symlinks similar to Unix tools like `find -L`. In both modes, symlink loop detection prevents infinite recursion.
**Q: Can I use this with binary files?**
A: Yes! dir2text provides flexible binary file handling with the `-B/--binary-action` option:
- `ignore` (default): Skip binary files silently
- `warn`: Skip binary files with warnings to stderr
- `encode`: Include binary files as base64-encoded content
- `fail`: Stop processing when a binary file is encountered
You can also exclude binary files entirely using the exclusion rules feature for better performance.
**Q: What models are supported for token counting?**
A: The token counting feature uses OpenAI's tiktoken library with the following primary models and encodings:
- cl100k_base encoding:
- GPT-4 models (gpt-4, gpt-4-32k)
- GPT-3.5-Turbo models (gpt-3.5-turbo)
- p50k_base encoding:
- Text Davinci models (text-davinci-003)
For other language models, using a similar model's tokenizer (like gpt-4) can provide useful approximations of token counts. While the counts may not exactly match your target model's tokenization, they can give a good general estimate. The default model is "gpt-4", which uses cl100k_base encoding and provides a good general-purpose tokenization.
**Q: What happens if I specify a model that doesn't have a dedicated tokenizer?**
A: The library will suggest using a well-supported model like 'gpt-4' or 'text-davinci-003' for token counting. While token counts may not exactly match your target model, they can provide useful approximations for most modern language models.
**Q: How can I control where summary information is displayed?**
A: Use the `-s/--summary` option to control where summary information is displayed:
- `-s stderr`: Print summary to stderr
- `-s stdout`: Print summary to stdout
- `-s file`: Include summary in the output file (requires `-o`)
**Q: Is token counting required for summary reporting?**
A: No. Basic statistics (e.g., file count, directory count, etc.,) are available without token counting. Including token counts in summary requires the `-t/--tokenizer` option to be specified along with `-s/--summary`.
## Contact
Ryan N. Lichtenwalter - rlichtenwalter@gmail.com
Project Link: [https://github.com/rlichtenwalter/dir2text](https://github.com/rlichtenwalter/dir2text)
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"description": "# dir2text\n\nA Python library and command-line tool for expressing directory structures and file contents in formats suitable for Large Language Models (LLMs). It combines directory tree visualization with file contents in a memory-efficient, streaming format.\n\n## Features\n\n- Tree-style directory structure visualization\n- Complete file contents with proper escaping\n- Memory-efficient streaming processing\n- Multiple output formats (XML, JSON)\n- Easy extensibility for new formats\n- Support for exclusion patterns (e.g., .gitignore rules)\n- Proper symbolic link handling and loop detection\n- Optional token counting for LLM context management\n- Summary reporting with configurable output destination\n- Safe handling of large files and directories\n\n## Installation\n\nThis project uses [Poetry](https://python-poetry.org/) for dependency management. We recommend using Poetry for the best development experience, but we also provide traditional pip installation.\n\n### Using Poetry (Recommended)\n\n1. First, [install Poetry](https://python-poetry.org/docs/#installation) if you haven't already.\n2. Install dir2text:\n ```bash\n poetry add dir2text\n ```\n\n### Using pip\n\n```bash\npip install dir2text\n```\n\n### Optional Features\n\nInstall with token counting support (for LLM context management):\n```bash\n# With Poetry\npoetry add \"dir2text[token_counting]\"\n\n# With pip\npip install \"dir2text[token_counting]\"\n```\n\n**Note:** The `token_counting` feature requires the `tiktoken` package, which needs a Rust compiler (e.g., `rustc`) and Cargo to be available during installation.\n\n## Usage\n\n### Command Line Interface\n\nBasic usage:\n```bash\ndir2text /path/to/project\n\n# Show version information\ndir2text --version\n\n# Exclude files matching patterns from one or more exclusion files\ndir2text -e .gitignore /path/to/project\ndir2text -e .gitignore -e .npmignore -e custom-ignore /path/to/project\n\n# Exclude files with direct patterns\ndir2text -i \"*.pyc\" -i \"node_modules/\" /path/to/project\n\n# Enable token counting for LLM context management\ndir2text -t gpt-4 /path/to/project\n\n# Generate JSON output and save to file\ndir2text -f json -o output.json /path/to/project\n\n# Follow symbolic links\ndir2text -L /path/to/project\n\n# Skip tree or content sections\ndir2text -T /path/to/project # Skip tree visualization\ndir2text -C /path/to/project # Skip file contents\n\n# Handle binary files\ndir2text -B ignore /path/to/project # Skip binary files silently (default)\ndir2text -B warn /path/to/project # Skip binary files with warnings\ndir2text -B encode /path/to/project # Include binary files as base64\ndir2text -B fail /path/to/project # Stop on binary files\n```\n\n### Symbolic Link Handling\n\nBy default, symbolic links are represented as symlinks without following them:\n\n```bash\ndir2text /path/to/project\n```\n\nThis shows symlinks clearly marked with their targets in the tree output, and as separate elements in content output.\n\nTo follow symbolic links during traversal (similar to Unix `find -L`):\n\n```bash\ndir2text -L /path/to/project\n```\n\nThis includes the content that symlinks point to, while still protecting against symlink loops.\n\n### Summary Reporting\n\nDir2text can generate a summary describing the processed directory including file counts, line counts, and optionally token counts. You can control where this information is displayed:\n\n```bash\n# Print summary to stderr\ndir2text -s stderr /path/to/project\n\n# Print summary to stdout\ndir2text -s stdout /path/to/project\n\n# Include summary in the output file\ndir2text -s file -o output.txt /path/to/project\n\n# Include token counts in summary by specifying a tokenizer model\ndir2text -s stderr -t gpt-4 /path/to/project\n```\n\nSummary includes counts of directories, files, symlinks, lines, and characters. Token counts are only included when a tokenizer model is specified with the `-t` option.\n\n### Python API\n\nBasic usage:\n```python\nfrom dir2text import StreamingDir2Text\nfrom dir2text.exclusion_rules.git_rules import GitIgnoreExclusionRules\n\n# Create exclusion rules (optional)\nrules = GitIgnoreExclusionRules()\nrules.add_rule(\"*.pyc\") # Add rules directly\n# OR load from files\nrules.load_rules(\".gitignore\")\n\n# Initialize the analyzer\nanalyzer = StreamingDir2Text(\"path/to/project\", exclusion_rules=rules)\n\n# Stream the directory tree\nfor line in analyzer.stream_tree():\n print(line, end='')\n\n# Stream file contents\nfor chunk in analyzer.stream_contents():\n print(chunk, end='')\n\n# Get metrics\nprint(f\"Processed {analyzer.file_count} files in {analyzer.directory_count} directories\")\nprint(f\"Found {analyzer.symlink_count} symbolic links\")\n```\n\nMemory-efficient processing with token counting:\n```python\nfrom dir2text import StreamingDir2Text\nfrom dir2text.exclusion_rules.git_rules import GitIgnoreExclusionRules\n\n# Create exclusion rules from multiple files\nrules = GitIgnoreExclusionRules()\nrules.load_rules(\".gitignore\")\nrules.load_rules(\".npmignore\")\nrules.add_rule(\"custom.ignore\")\n\n# Initialize with options\nanalyzer = StreamingDir2Text(\n directory=\"path/to/project\",\n exclusion_rules=rules,\n output_format=\"json\",\n tokenizer_model=\"gpt-4\",\n follow_symlinks=False, # Default behavior, don't follow symlinks\n binary_action=\"ignore\" # How to handle binary files: \"ignore\", \"warn\", \"encode\", or \"fail\"\n)\n\n# Process content incrementally\nwith open(\"output.json\", \"w\") as f:\n for line in analyzer.stream_tree():\n f.write(line)\n for chunk in analyzer.stream_contents():\n f.write(chunk)\n\n# Print statistics\nprint(f\"Files: {analyzer.file_count}\")\nprint(f\"Directories: {analyzer.directory_count}\")\nprint(f\"Symlinks: {analyzer.symlink_count}\")\nprint(f\"Lines: {analyzer.line_count}\")\nprint(f\"Tokens: {analyzer.token_count}\")\nprint(f\"Characters: {analyzer.character_count}\")\n```\n\nImmediate processing (for smaller directories):\n```python\nfrom dir2text import Dir2Text\nfrom dir2text.exclusion_rules.git_rules import GitIgnoreExclusionRules\n\n# Create exclusion rules\nrules = GitIgnoreExclusionRules()\nrules.load_rules(\".gitignore\")\n\n# Process everything immediately\nanalyzer = Dir2Text(\n \"path/to/project\", \n exclusion_rules=rules,\n follow_symlinks=True, # Optionally follow symlinks\n binary_action=\"encode\" # Include binary files as base64\n)\n\n# Access complete content\nprint(analyzer.tree_string)\nprint(analyzer.content_string)\n```\n\n## Output Formats\n\n### XML Format\n```xml\n<file path=\"relative/path/to/file.py\" content_type=\"text\" tokens=\"150\">\ndef example():\n print(\"Hello, world!\")\n</file>\n<symlink path=\"docs/api.md\" target=\"../README.md\" />\n```\n\n### JSON Format\n```json\n{\n \"type\": \"file\",\n \"path\": \"relative/path/to/file.py\",\n \"content_type\": \"text\",\n \"content\": \"def example():\\n print(\\\"Hello, world!\\\")\",\n \"tokens\": 150\n}\n{\n \"type\": \"symlink\",\n \"path\": \"docs/api.md\",\n \"target\": \"../README.md\"\n}\n```\n\n## Signal Handling\n\nWhen using dir2text as a command-line tool, it handles system signals gracefully to ensure proper resource management and clean exits:\n\n- **SIGPIPE**: When piping output to programs like `head`, `less`, or `grep` that may terminate before reading all input, dir2text detects the closed pipe and exits cleanly without error messages.\n- **SIGINT** (Ctrl+C): Properly handles user interruption, ensuring all resources are cleaned up.\n\nThis means you can safely pipe dir2text output to other commands without worrying about error messages when those commands exit:\n\n```bash\n# The first 10 lines of output\ndir2text /path/to/project | head -n 10\n\n# Only files containing \"function\"\ndir2text /path/to/project | grep \"function\"\n```\n\n## Development\n\n### Setup Development Environment\n\n1. Clone the repository:\n ```bash\n git clone https://github.com/rlichtenwalter/dir2text.git\n cd dir2text\n ```\n\n2. Install development dependencies:\n ```bash\n poetry install --with dev\n ```\n\n3. Install pre-commit hooks:\n ```bash\n poetry run pre-commit install\n ```\n\n### Running Tests\n\n```bash\n# Run specific quality control categories\npoetry run tox -e format # Run formatters\npoetry run tox -e lint # Run linters\npoetry run tox -e test # Run tests\npoetry run tox -e coverage # Run test coverage analysis\n```\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n1. Fork the repository\n2. Create a new branch (`git checkout -b feature/amazing-feature`)\n3. Make your changes\n4. Run the test suite\n5. Commit your changes (`git commit -m 'Add some amazing feature'`)\n6. Push to the branch (`git push origin feature/amazing-feature`)\n7. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n- This project uses [anytree](https://github.com/c0fec0de/anytree) for tree data structures\n- .gitignore pattern matching uses [pathspec](https://github.com/cpburnz/python-pathspec)\n- Token counting functionality is provided by OpenAI's [tiktoken](https://github.com/openai/tiktoken)\n\n## Requirements\n\n- Python 3.9+\n- Poetry (recommended) or pip\n- Optional: Rust compiler and Cargo (for token counting feature)\n\n## Project Status\n\nThis project is actively maintained. Issues and pull requests are welcome.\n\n## FAQ\n\n**Q: Why use streaming processing?** \nA: Streaming allows processing of large directories and files with constant memory usage, making it suitable for processing repositories of any size.\n\n**Q: How does dir2text handle symbolic links?** \nA: By default, dir2text represents symlinks as symbolic links in both tree and content output without following them. With the `-L` option, it follows symlinks similar to Unix tools like `find -L`. In both modes, symlink loop detection prevents infinite recursion.\n\n**Q: Can I use this with binary files?** \nA: Yes! dir2text provides flexible binary file handling with the `-B/--binary-action` option:\n- `ignore` (default): Skip binary files silently\n- `warn`: Skip binary files with warnings to stderr \n- `encode`: Include binary files as base64-encoded content\n- `fail`: Stop processing when a binary file is encountered\n\nYou can also exclude binary files entirely using the exclusion rules feature for better performance.\n\n**Q: What models are supported for token counting?** \nA: The token counting feature uses OpenAI's tiktoken library with the following primary models and encodings:\n- cl100k_base encoding:\n - GPT-4 models (gpt-4, gpt-4-32k)\n - GPT-3.5-Turbo models (gpt-3.5-turbo)\n- p50k_base encoding:\n - Text Davinci models (text-davinci-003)\n\nFor other language models, using a similar model's tokenizer (like gpt-4) can provide useful approximations of token counts. While the counts may not exactly match your target model's tokenization, they can give a good general estimate. The default model is \"gpt-4\", which uses cl100k_base encoding and provides a good general-purpose tokenization.\n\n**Q: What happens if I specify a model that doesn't have a dedicated tokenizer?** \nA: The library will suggest using a well-supported model like 'gpt-4' or 'text-davinci-003' for token counting. While token counts may not exactly match your target model, they can provide useful approximations for most modern language models.\n\n**Q: How can I control where summary information is displayed?** \nA: Use the `-s/--summary` option to control where summary information is displayed:\n - `-s stderr`: Print summary to stderr\n - `-s stdout`: Print summary to stdout\n - `-s file`: Include summary in the output file (requires `-o`)\n\n**Q: Is token counting required for summary reporting?** \nA: No. Basic statistics (e.g., file count, directory count, etc.,) are available without token counting. Including token counts in summary requires the `-t/--tokenizer` option to be specified along with `-s/--summary`.\n\n## Contact\n\nRyan N. Lichtenwalter - rlichtenwalter@gmail.com\n\nProject Link: [https://github.com/rlichtenwalter/dir2text](https://github.com/rlichtenwalter/dir2text)",
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