triz-ai 0.8.0

AI-Powered TRIZ Innovation Engine — combines TRIZ methodology with AI and real patent data

triz-ai

AI-powered TRIZ innovation engine — analyze technical problems, classify patents, and discover new inventive principles.

What is this?

triz-ai combines TRIZ (Theory of Inventive Problem Solving) with AI and real patent data. It goes beyond static TRIZ tools by using AI to discover candidate new principles from modern patents, continuing Altshuller's original work.

• Patent-grounded — every suggestion is backed by real patent evidence, with assignee and filing date
• Hybrid search — finds relevant patents using both vector similarity and TRIZ principle/contradiction matching
• Solution directions — generates concrete, actionable solution approaches, not just abstract principles
• 50 engineering parameters — extends Altshuller's 39 with modern domains (security, sustainability, scalability, etc.)
• Evolving principles & parameters — discovers candidate new principles and parameters from modern patents
• Provider-agnostic — works with OpenRouter, Ollama, Anthropic, OpenAI, and 100+ providers via litellm
• Zero infrastructure — local SQLite database with built-in vector search

Installation

pip install triz-ai

Or for development:

# Requires Python 3.12+ and uv
uv sync

Set up your LLM provider API key:

export OPENROUTER_API_KEY="your-key"

Or use a .env file: echo 'OPENROUTER_API_KEY=your-key' > .env

Quick Start

# Analyze a problem — auto-classifies and routes to the best TRIZ tool
triz-ai analyze "How to increase SiC MOSFET switching speed without increasing EMI"
# → Routes to: technical contradiction analysis

triz-ai analyze "The solder joint must be rigid for reliability but flexible for thermal cycling"
# → Routes to: physical contradiction analysis

triz-ai analyze "How to detect delamination without adding sensors"
# → Routes to: Su-Field analysis

triz-ai analyze "The adhesive layer damages the silicon die during thermal cycling"
# → Routes to: function analysis

triz-ai analyze "Reduce the BOM cost of this gate driver circuit"
# → Routes to: trimming analysis

triz-ai analyze "What is the next generation of SiC packaging technology?"
# → Routes to: trends analysis

# Force a specific method
triz-ai analyze "How to detect delamination" --method su-field

# For patent-backed examples, ingest patent data
triz-ai ingest data/patents/battery_patents.json

# Discover underused principles in a domain
triz-ai discover --domain "battery technology"

# Run evolution pipeline to find candidate new principles
triz-ai evolve
triz-ai evolve --review  # interactive accept/reject

# View matrix statistics
triz-ai matrix stats

Commands

Command	Description
analyze	Auto-routes to the best TRIZ tool (6 methods); --method to force one
discover	Find underused principles in a domain and generate patent-grounded ideas
evolve	Discover candidate new TRIZ principles (--parameters for parameters)
ingest	Ingest and auto-classify patents from .txt, .pdf, or .json files
init	Reset the patent database (only needed with --force)
matrix seed	LLM-seed missing matrix cells for params 40-50 (power-user)
matrix stats	Show matrix fill rate and patent observation statistics

Analysis Methods

Method	When to use
technical-contradiction	Improving X worsens Y
physical-contradiction	A part must be both A and B
su-field	Detection/measurement problems
function-analysis	A component causes harm
trimming	Simplify / reduce cost
trends	Where is this technology going?

All commands support --format text|json|markdown and --model to override the LLM model.

Project Structure

src/triz_ai/
  cli.py              # Typer CLI entry point
  config.py            # Config loading (~/.triz-ai/config.yaml)
  data/                # TRIZ JSON data (principles, parameters, matrix, separation, solutions, trends)
  knowledge/           # Loaders for all TRIZ knowledge data
  engine/              # analyzer, router, 5 new pipelines, classifier, generator, evaluator
  patents/             # SQLite + sqlite-vec store, ingestion pipeline
  evolution/           # Candidate principle & parameter discovery + review
  llm/                 # litellm wrapper with pydantic validation
tests/                 # 124 unit tests

Configuration

Config lives at ~/.triz-ai/config.yaml:

llm:
  default_model: openrouter/nvidia/nemotron-3-super-120b-a12b:free
  classify_model: openrouter/nvidia/nemotron-3-nano-30b-a3b:free
  # router_model: null  # model for problem classification (defaults to classify_model)

embeddings:
  model: openrouter/nvidia/llama-nemotron-embed-vl-1b-v2:free
  dimensions: 768

database:
  path: ~/.triz-ai/patents.db

evolution:
  review_threshold: 0.7

Any litellm-supported model string works — just change the model and set the corresponding API key.

Using a custom LLM gateway (e.g., company litellm proxy)

llm:
  default_model: gpt-4o
  classify_model: gpt-4o-mini  # smaller/cheaper model for patent classification during ingest
  api_base: https://llm-proxy.internal/v1
  api_key: your-proxy-token

embeddings:
  model: text-embedding-3-small
  dimensions: 768
  api_base: https://llm-proxy.internal/v1
  api_key: your-proxy-token

You can also override the classify model per-command: triz-ai ingest data/ --classify-model gpt-4o-mini

Development

uv sync                              # Install dependencies
uv run pytest                        # Run tests
uv run pre-commit install            # Install git hooks
uv run pre-commit install --hook-type pre-push

License

MIT