vtk-python-docs 0.2.0

VTK Python documentation extraction, stub enhancement, and API generation

VTK Python Documentation Enhancement

A Python package for extracting VTK documentation, generating enhanced Python stubs, and creating markdown API documentation.

🚀 Features

• VTK Documentation Extraction: Extract documentation from VTK using Python introspection
• VTK Type Introspection: Automatic role classification (input, filter, output, etc.) and datatype detection using VTK's type system
• Enhanced Python Stubs: Generate VTK stub files with rich docstrings for IDE IntelliSense
• Markdown Documentation: Generate markdown API documentation organized by modules
• JSONL Database: Consolidated database of all VTK classes for querying
• LLM Classification: AI-powered class metadata using LiteLLM (synopsis, action_phrase, visibility_score)

📋 Requirements

• Python 3.10+
• VTK Python package
• uv package manager

🛠️ Installation

git clone <repository-url>
cd vtk-python-docs
./setup.sh

Or manually with uv:

uv sync --extra dev

This creates a virtual environment and installs the package with all dependencies.

⚙️ LLM Configuration (Optional)

For AI-powered classification (synopsis, action_phrase, visibility_score), copy .env.example to .env and configure your LLM provider:

cp .env.example .env
# Edit .env with your API key

Supported providers via LiteLLM:

• OpenAI: gpt-4o-mini, gpt-4o
• Anthropic: claude-3-haiku-20240307, claude-3-5-sonnet-20241022
• Ollama (local, free): ollama/llama3.2, ollama/mistral
• Google: gemini/gemini-1.5-flash
• And 100+ more providers

If no LLM is configured, classification metadata will be skipped.

📖 Usage

Full Build

uv run vtk-docs build

This generates all outputs (~35 seconds without LLM, longer with LLM due to rate limiting):

• docs/vtk-python-docs.jsonl - JSONL database
• docs/python-stubs-enhanced/ - Enhanced Python stubs
• docs/python-api/ - Markdown documentation

CLI Commands

uv run vtk-docs --help          # Show all commands
uv run vtk-docs build           # Run complete build pipeline
uv run vtk-docs extract         # Extract VTK documentation to JSONL
uv run vtk-docs stubs           # Generate and enhance Python stubs
uv run vtk-docs markdown        # Generate markdown documentation
uv run vtk-docs clean           # Clean generated files
uv run vtk-docs stats           # Show database statistics
uv run vtk-docs search <query>  # Search the documentation

Search Examples

uv run vtk-docs search vtkActor           # Search by class name
uv run vtk-docs search Render -f synopsis # Search in synopsis field
uv run vtk-docs search Core -f module_name -n 20  # Search modules, show 20 results

Programmatic Usage

from vtk_python_docs.build import build_all

# Run full build
build_all()

# Or use individual components
from vtk_python_docs.extract import extract_all
from vtk_python_docs.stubs import generate_all as generate_stubs
from vtk_python_docs.markdown import generate_all as generate_markdown
from vtk_python_docs.config import get_config

config = get_config()
extract_all(config)
generate_stubs(config)
generate_markdown(config)

Querying the JSONL Database

import json
from pathlib import Path

# Stream through JSONL database
for line in open('docs/vtk-python-docs.jsonl'):
    record = json.loads(line)
    if 'Actor' in record['class_name']:
        print(f"{record['class_name']}: {record.get('synopsis', '')}")

📁 Output Structure

docs/
├── vtk-python-docs.jsonl    # All VTK classes (JSONL format)
├── llm-cache.jsonl          # Cached LLM classifications (avoids re-calling LLM)
├── python-stubs-enhanced/   # Enhanced .pyi stub files
│   ├── vtkCommonCore.pyi
│   └── ... (150+ modules)
└── python-api/              # Markdown documentation
    ├── index.md
    └── vtkCommonCore/
        ├── index.md
        └── vtkObject.md

JSONL Record Fields

Each record in vtk-python-docs.jsonl contains:

Field	Source	Description
class_name	VTK	Class name (e.g., vtkActor)
module_name	VTK	Module name (e.g., vtkRenderingCore)
class_doc	VTK	Raw documentation from help()
role	Introspection	Pipeline role: input, filter, output, properties, renderer, etc.
input_datatype	Introspection	Input data type (e.g., vtkPolyData)
output_datatype	Introspection	Output data type
semantic_methods	Introspection	Non-boilerplate methods
synopsis	LLM	One-sentence summary
action_phrase	LLM	Noun-phrase (e.g., "mesh smoothing")
visibility_score	LLM	0.0-1.0 likelihood users mention this class

🔧 Project Structure

vtk-python-docs/
├── pyproject.toml
├── setup.sh
├── tests/               # pytest test suite
└── vtk_python_docs/
    ├── __init__.py
    ├── cli.py           # Typer CLI
    ├── config.py        # Centralized configuration
    ├── build.py         # Build pipeline orchestrator
    ├── extract/         # VTK documentation extraction
    ├── stubs/           # Stub generation & enhancement
    └── markdown/        # Markdown generation

🧪 Development

# Install with dev dependencies
uv sync --extra dev

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=vtk_python_docs

# Lint code
uv run ruff check .

# Type check
uv run pyright vtk_python_docs/

🔑 Environment Variables

Variable	Description	Default
LLM_MODEL	LiteLLM model identifier	(none)
OPENAI_API_KEY	OpenAI API key	(none)
ANTHROPIC_API_KEY	Anthropic API key	(none)
GEMINI_API_KEY	Google Gemini API key	(none)
LLM_RATE_LIMIT	Requests per minute	60
LLM_MAX_CONCURRENT	Max concurrent requests	10

📄 License

This project enhances the official VTK Python bindings. Please refer to VTK's licensing terms for the underlying VTK library.