Heinrich: The Inventing Machine

An open-source AI system for TRIZ-based inventive problem solving with interpretable, step-by-step reasoning flow

🌍 Languages: English | 中文 | Русский | العربية

Overview

Heinrich is an open-source AI engine that combines classical TRIZ (Theory of Inventive Problem Solving) methodology with modern Large Language Models to provide systematic, interpretable inventive problem-solving capabilities. Named after Genrich Altshuller, the creator of TRIZ, Heinrich embodies the systematic thinking approach of the original methodology while leveraging contemporary AI advances.

Key Features

• Systematic TRIZ Pipeline: 7-module workflow following classical TRIZ methodology
• Interpretable Reasoning: Every step is logged and traceable to TRIZ principles
• Knowledge Base: Complete 39 parameters, 40 principles, contradiction matrix
• Multi-LLM Support: Google Gemini, Ollama (local), and more coming soon
• Multi-modal Support: Text problems, technical specifications, patent analysis
• Evaluation Suite: Curated case base with reference solutions
• Multilingual: Full documentation in English, Chinese, Russian, and Arabic
• Ethical AI: Transparent persona with safety guidelines

Getting Started

Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Install from source

1. Clone the repository:

git clone https://github.com/NickScherbakov/Heinrich-The-Inventing-Machine.git
cd Heinrich-The-Inventing-Machine

2. Install dependencies:

pip install -r requirements.txt

3. Install Heinrich in development mode:

pip install -e .

Dependencies

Heinrich requires the following Python packages:

• PyYAML>=6.0 - For TRIZ knowledge base (YAML files)
• numpy>=1.21.0 - For numerical computations
• dataclasses-json>=0.5.0 - For data serialization
• google-generativeai>=0.3.0 - For Google Gemini LLM integration
• requests>=2.31.0 - For HTTP requests (Ollama)

All dependencies are automatically installed when using pip install.

LLM Providers

Heinrich supports multiple LLM backends:

Provider	Setup	Best For
Google Gemini	API key from AI Studio	Production, cloud deployment
Ollama	Local installation from ollama.ai	Local development, privacy
Vertex AI	GCP project with Vertex AI enabled	Enterprise, GCP integration

Using Google Gemini (Recommended)

from heinrich.llm.adapters import GeminiAdapter

adapter = GeminiAdapter({
    "api_key": "your-gemini-api-key",
    "model": "gemini-1.5-pro",
    "temperature": 0.7,
})

response = adapter.generate("Analyze this TRIZ problem...")

See examples/gemini_usage.py for a complete example.

Usage

Quick Example

Here's a simple example to get started with Heinrich:

from problem_parser import ProblemParser

# Create a problem parser
parser = ProblemParser()

# Analyze a problem
problem = "We need to make a car faster, but increasing engine power makes it consume more fuel."
result = parser.parse(problem)