A CLI utility to consolidate project context for LLMs.
Project description
DeepBase
DeepBase is a command-line tool that analyzes a project directory, extracts the folder structure and the content of all significant code files, and consolidates them into a single text/markdown file.
This unified "context" is perfect for providing to a Large Language Model (LLM) to enable it to deeply understand the entire codebase.
Features
- Project Structure: Generates a tree view of the folder and file structure.
- Smart Filtering: Automatically ignores common unnecessary directories (e.g.,
.git,venv,node_modules). - Token Optimization (TOON): Capable of generating "Semantic Skeletons" (class definitions, function signatures, docstrings) instead of full code to save up to 90% of tokens.
- Hybrid Focus Mode: Combine lightweight context for the whole project with full content only for specific files or folders.
- Configurable: Customize ignored directories and included extensions via a
.deepbase.tomlfile. - Unified Output: Combines everything into a single file, easy to copy and paste.
- PyPI Ready: Easy to install via
pip.
Installation
You can install DeepBase directly from PyPI:
pip install deepbase
How to Use
Once installed, use the deepbase command followed by the target (directory or file).
1. Basic Project Analysis
Structure Only (Default) Quickly generate a tree view of your project folders and files. No code content is included.
deepbase .
Include All Content
To generate the full context including the code of all significant files, use the --all (or -a) flag.
Warning: use this only for small projects.
deepbase . --all
2. Smart Token Optimization (TOON)
For large projects, sending all code to an LLM is expensive and inefficient. TOON (Token Oriented Object Notation) extracts only the semantic "skeleton" of your code (classes, signatures, docstrings), ignoring implementations.
deepbase . --toon
# or
deepbase . -t
Result: LLMs understand your architecture using minimal tokens.
3. Hybrid Mode (Focus)
This is the power user feature. You can provide the TOON skeleton for the entire project (background context) while focusing on specific files (full content).
Focus via CLI:
Use -f or --focus with glob patterns (e.g., *auth*, src/utils/*).
deepbase . --toon --focus "server/controllers/*" --focus "client/src/login.js"
Focus via File:
Instead of typing patterns every time, create a text file (e.g., context_task.txt) with the list of files/folders you are working on.
content of context_task.txt:
server/routes/auth.js
server/models/User.js
client/src/components/LoginForm.jsx
Run deepbase loading the file:
deepbase . --toon --focus-file context_task.txt
4. Single File Analysis
DeepBase supports analyzing a single specific file.
Structure Only (Default) Extracts only the outline/headers. Useful for large documentation files.
deepbase README.md
Structure + Content Appends the full content after the structure.
deepbase README.md --all
Configuration (.deepbase.toml)
You can customize behavior by creating a .deepbase.toml file in your project root:
ignore_dirs = ["my_assets", "experimental"]
significant_extensions = [".cfg", "Makefile", ".tsx"]
Development Workflow
If you want to contribute or test the tool locally:
# Install in editable mode
pip install -e ".[dev]"
# Run tests
pytest
License
This project is released under the GPL 3 license. See the LICENSE file for details.
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