llmdirtree 0.1.5

A simple directory tree generator for llm

llmdirtree

A directory tree generator and codebase context provider designed specifically for enhancing LLM interactions with your code.

Purpose

llmdirtree helps you work more effectively with Large Language Models (LLMs) by:

• Generating visual directory trees for structural understanding
• Creating contextual summaries of your codebase that respect privacy settings
• Optimizing code information for LLM follow-up questions

Installation

pip install llmdirtree

Key Features

Directory Tree Visualization

• Clean, standardized visualization of project structure
• Unicode box-drawing characters for optimal parsing
• Intelligent filtering of non-essential directories
• Full .gitignore pattern support for accurate representation of your project

LLM Context Generation

• AI-powered analysis of your codebase using OpenAI API
• Security-focused with automatic .gitignore pattern recognition
• File-by-file summaries optimized for follow-up questions
• Intelligent handling of large files with automatic chunking and summarization
• Project overview that captures your codebase's essence
• Customizable OpenAI model selection

Security and Privacy

• Respects .gitignore patterns to avoid exposing sensitive information
• Zero dependencies approach (uses system curl instead of libraries)
• Efficient token usage for minimal data exposure

Usage Examples

Basic Directory Tree

# Generate a simple directory tree
llmdirtree --root /path/to/project --output project_structure.txt

With LLM Context Generation

# Generate both directory tree AND code context
llmdirtree --root /path/to/project --llm-context --openai-key YOUR_API_KEY

This creates two files:

• directory_tree.txt - Visual structure
• llmcontext.txt - AI-generated project overview and file summaries

Additional Options

# Exclude specific directories
llmdirtree --exclude node_modules .git venv dist

# Customize output locations
llmdirtree --output custom_tree.txt --context-output custom_context.txt

# Control file selection for context generation
llmdirtree --max-files 150 --llm-context

# Override gitignore protection (not recommended)
llmdirtree --ignore-gitignore --llm-context

# Specify OpenAI model to use (avoids prompting)
llmdirtree --llm-context --model gpt-4

Example Output

Directory Tree

Directory Tree for: /project
Excluding: .git, node_modules, __pycache__, venv
--------------------------------------------------
project/
├── src/
│   ├── main.py
│   └── utils/
│       └── helpers.py
├── tests/
│   └── test_main.py
└── README.md

LLM Context File

# project-name

> A React web application for tracking personal fitness goals with a Node.js backend and MongoDB database.

## src/components/

- **Dashboard.jsx**: Main dashboard component that displays user fitness stats, recent activities, and goal progress.
- **WorkoutForm.jsx**: Form for creating and editing workout entries with validation and submission handling.

## src/utils/

- **api.js**: Contains functions for making API calls to the backend, handling authentication and data fetching.
- **formatters.js**: Utility functions for formatting dates, weights, and other fitness metrics consistently.

Benefits for LLM Workflows

• Comprehensive context without uploading your entire codebase
• More accurate responses with both structural and semantic understanding
• Security first approach that protects sensitive information
• Time savings from clearer communication with AI assistants
• Handles large codebases by intelligently processing and summarizing large files

Configuration

OpenAI Model Selection

By default, llmdirtree will ask which OpenAI model you want to use once per run:

• Default for batch processing: gpt-3.5-turbo-16k
• Default for project overview: gpt-3.5-turbo

You can skip this prompt by specifying a model directly:

llmdirtree --llm-context --model gpt-4

Large File Handling

llmdirtree automatically handles large files by:

1. Identifying files exceeding the token threshold (default: 8000 tokens)
2. Splitting them into meaningful chunks
3. Summarizing each chunk independently
4. Creating a cohesive final summary

You can adjust this threshold by modifying the max_tokens_per_file variable in the source code.

Technical Details

• No external dependencies required for core functionality
• Progress bar available with optional tqdm installation
• Automatically respects .gitignore patterns for security
• Uses system curl instead of Python libraries for API calls

License

MIT