limen-ai 0.2.3

Łukasiewicz Interpretable Markov Engine for Neuralized AI

LIMEN-AI

LIMEN-AI (Łukasiewicz Interpretable Markov Engine for Neuralized AI) is a Python reference implementation of the LIMEN-AI SRM engine. It provides:

• Łukasiewicz fuzzy semantics and differentiable rule evaluation
• Energy-based inference (importance sampling, explanations, deduction)
• KB-driven inductive rule discovery using configurable templates

Installation

cd code
pip install -e .

Documentation

• docs/api_overview.md describes the main APIs and workflow.
• notebooks/limen_ai_walkthrough.ipynb demonstrates KB creation, inference, and induction.
• notebooks/limen_llm_orchestration.ipynb shows how to pair an LLM with the new ingestion/query/response helpers.

LLM-Oriented Pipelines

The limen.pipeline package now provides building blocks for production-grade integrations:

• SchemaRegistry / PredicateSchema – declare the KB schema so prompts stay aligned with predicate arities.
• DocumentIngestionPipeline – chunk/normalize text, call an LLM with the extraction prompt, parse JSON facts, validate arguments, and upsert them into the KB.
• QueryTranslator – translate arbitrary user questions into structured predicate calls with schema validation/backoff.
• ResponseGenerator – turn LIMEN-AI's structured answers/explanations back into natural language (optionally via an LLM).

These helpers are LLM-agnostic: plug any open-source model by providing a callable completion function or a custom LLMClient.