limen-ai 0.3.3

Łukasiewicz Interpretable Markov Engine for Neuralized AI

LIMEN-AI (Łukasiewicz Interpretable Markov Engine for Neuralized AI)

LIMEN-AI is a neurosymbolic library for building transparent, auditable AI applications. It combines Large Language Models (LLMs) with Łukasiewicz fuzzy logic and Energy-Based Models (EBM) to create systems that:

• Extract structured knowledge from unstructured text
• Perform probabilistic logical reasoning with fuzzy truth degrees
• Learn new rules inductively from examples
• Generate natural language explanations traceable to logical inference
• Support EU AI Act compliance requirements (Articles 13, 14, 15)
• Compatible with Ollama, OpenAI, Gemini, Claude, and DeepSeek

Core Use Case: Transform legal documents, policies, or domain texts into queryable knowledge bases with built-in explainability.

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📦 Installation

pip install limen-ai
# Recommended for LLM features:
pip install requests python-dotenv

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🚀 Quick Start (5 Lines)

from limen import LimenClient

# Initialize with local Ollama
client = LimenClient(model_name="ollama/gpt-oss:20b")

# Extract and query
proposal = client.extract("TechCorp is provider. Client is GlobalRetail.")
client.add_knowledge(proposal)
answer = client.ask("Who is provider?")
print(answer.natural_language)  # "TechCorp is provider."

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🛠️ Step-by-Step Guide: Build Your First Application

1. Set Up Environment Variables

Create a .env file in your project directory:

# Choose ONE provider (uncomment relevant section)

# Option A: Local Ollama (Free, good for development)
OLLAMA_BASE_URL=http://localhost:11434/v1

# Option B: OpenAI (Production - best quality)
OPENAI_API_KEY=sk-your-openai-key-here

# Option C: Anthropic Claude (Production - best reasoning)
ANTHROPIC_API_KEY=your-anthropic-key-here

# Option D: Google Gemini (Production - good for long docs)
GEMINI_API_KEY=your-gemini-key-here

# Option E: DeepSeek (Budget-friendly)
DEEPSEEK_API_KEY=your-deepseek-key-here

2. Install and Configure Ollama (Local LLM)

For development without API costs, use Ollama:

# 1. Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 2. Pull recommended model (fits in 16GB VRAM)
ollama pull gpt-oss:20b

# 3. Start Ollama server
ollama run gpt-oss:20b

Recommended Ollama Models:

• gpt-oss:20b - Best reasoning, JSON output (14-16GB VRAM)
• qwen2.5:32b - Best for reasoning (16GB VRAM)
• qwen2.5:14b - Good balance (8GB VRAM)

3. Initialize LIMEN-AI Client

import os
from pathlib import Path
from dotenv import load_dotenv
from limen import LimenClient

load_dotenv()

# Initialize client (choose your provider)
client = LimenClient(
    model_name="ollama/gpt-oss:20b",  # Local Ollama
    # model_name="openai/gpt-4o-mini",  # OpenAI
    # model_name="claude/claude-3-5-sonnet-20241022",  # Anthropic
    # model_name="gemini/gemini-1.5-pro",  # Google
    # model_name="deepseek/deepseek-chat",  # DeepSeek
    base_url=os.getenv("OLLAMA_BASE_URL"),  # For Ollama only
    enable_auto_induction=True,  # Enable rule learning
    use_neurosymbolic_ilp=True    # Use LLM-guided ILP
)

4. Extract Knowledge from Document

# Load your document
contract_path = Path("your_contract.txt")
contract_text = contract_path.read_text(encoding="utf-8")

# Extract structured knowledge
proposal = client.extract(contract_text)

# Review what was extracted
print(proposal.to_markdown())

Expected Output:

## 1. Schema Extensions (New Predicates)
- **legalParty**(entity, role): Links entity to its legal role
- **effectiveDate**(date): Specifies effective date of agreement
- **paymentAmount**(amount, currency): Payment details

## 2. Fact Grounding (Extracted from Context)
| Predicate | Arguments | Confidence | Source |
| :--- | :--- | :--- | :--- |
| legalParty | TechCorp, Provider | 0.99 | Sentence 1 |
| legalParty | GlobalRetail, Client | 0.99 | Sentence 2 |

5. Commit Knowledge to Knowledge Base

# After human verification of proposal, commit to KB
result = client.add_knowledge(proposal)

print(f"Added {len(result.facts_added)} facts to KB")

6. Query Your Knowledge Base

# Natural language query
answer = client.ask("Who is the service provider?")

# Get results
print(f"Answer: {answer.natural_language}")

# Access structured inference results
for result in answer.answers:
    predicate = result['predicate']
    args = result['args']
    truth_degree = result['value']  # Fuzzy truth [0, 1]
    print(f"{predicate}({args}) = {truth_degree:.4f}")

# View logical trace (reasoning steps)
for step in answer.logical_trace:
    print(step)

7. Save and Restore Knowledge Base

# Save complete state (schema, facts, formulas, rules)
client.save_state("my_knowledge_base.json")

# Later, restore without re-extracting
client2 = LimenClient(model_name="ollama/gpt-oss:20b")
client2.load_state("my_knowledge_base.json")

# Now query
answer = client2.ask("What is the monthly fee?")

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🔧 Advanced Usage

Direct Predicate Queries (For Local Models)

If using Ollama where natural language queries sometimes fail, query the KB directly:

# After extraction and add_knowledge()

# Method 1: Iterate over all high-confidence facts
for atom, confidence in client.pipeline.assignment.values.items():
    if confidence > 0.8 and atom.predicate.name == "legalParty":
        entity = atom.arguments[0].name
        role = atom.arguments[1].name
        print(f"{entity} is {role} (confidence: {confidence:.2f})")

# Method 2: Query specific predicate
from limen.core import Atom

pred = client.pipeline.kb.get_predicate("legalParty")
provider = client.pipeline.kb.get_constant("TechCorp")
client_role = client.pipeline.kb.get_constant("Provider")
atom = Atom(pred, (provider, client_role))

truth_degree = client.pipeline.assignment.get(atom)
print(f"TechCorp is Provider: {truth_degree:.2f}")

Benefits:

• ✅ Deterministic results (no LLM query translation)
• ✅ Lower latency (no additional LLM calls)
• ✅ Reliable (works with any model quality)

Manual Fact Addition (Fill LLM Gaps)

# If LLM missed critical facts, add them manually
client.set_fact("terminationNotice", ["30 days"], 1.0)
client.set_fact("governingLaw", ["California"], 1.0)
client.set_fact("monthlyFee", [15000, "USD"], 1.0)

Inductive Rule Learning

# Provide labeled examples to teach the system rules
labels = {
    "legalParty": {
        "pos": [("TechCorp", "Provider"), ("GlobalRetail", "Client")],
        "neg": [("TechCorp", "Client"), ("GlobalRetail", "Provider")]
    }
}

# Trigger inductive learning
client.feedback(labels)

# Check learned rules
if client.pipeline.kb.induced_clauses:
    print("Learned rules:")
    for clause in client.pipeline.kb.induced_clauses:
        print(f"  IF {', '.join(clause.body)} THEN {clause.head} (weight: {clause.weight:.2f})")

Human Oversight (Veto Power)

# Override incorrect LLM extraction (Article 14(4) compliance)
client.veto("incorrectPredicate", ["arg1", "arg2"])

# This sets truth degree to 0.0 and adds a high-weight NOT formula
# Future inference will respect this override

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🤖 LLM Provider Configuration

Recommended Models by Use Case

Provider	Model	Extraction	Query Translation	Best For	Cost
Ollama	gpt-oss:20b	⭐⭐⭐⭐	⭐⭐	Development	Free
Ollama	qwen2.5:32b	⭐⭐⭐⭐⭐	⭐⭐⭐	Best local	Free
OpenAI	gpt-4o-mini	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Production	$$$
OpenAI	gpt-4o	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐⭐	Production	$$$$
Anthropic	claude-3.5-sonnet	⭐⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐⭐	Complex	$$$
Google	gemini-1.5-pro	⭐⭐⭐⭐	⭐⭐⭐	Long docs	$$
DeepSeek	deepseek-chat	⭐⭐⭐	⭐⭐⭐	Budget	$

Key Observations:

• Ollama: Good for development, but query translation varies between runs
• GPT-4/Claude: Recommended for production - high quality extraction and querying
• Workaround for Ollama: Use direct predicate queries instead of natural language

Ollama Configuration

client = LimenClient(
    model_name="ollama/gpt-oss:20b",
    base_url=os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1")
)

Installation:

ollama pull gpt-oss:20b  # 14-16GB VRAM
ollama run gpt-oss:20b

OpenAI Configuration

client = LimenClient(
    model_name="openai/gpt-4o-mini",
    api_key=os.getenv("OPENAI_API_KEY")
)

Anthropic Claude Configuration

client = LimenClient(
    model_name="claude/claude-3-5-sonnet-20241022",
    api_key=os.getenv("ANTHROPIC_API_KEY")
)

Google Gemini Configuration

client = LimenClient(
    model_name="gemini/gemini-1.5-pro",
    api_key=os.getenv("GEMINI_API_KEY")
)

DeepSeek Configuration

client = LimenClient(
    model_name="deepseek/deepseek-chat",
    api_key=os.getenv("DEEPSEEK_API_KEY")
)

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📊 Understanding Results

LIMEN-AI returns a StructuredAnswer with multiple components:

answer = client.ask("Who is the provider?")

# Natural language response (LLM-generated)
print(answer.natural_language)  # "TechCorp is the provider."

# Structured inference results (from sampling)
for result in answer.answers:
    predicate = result['predicate']
    args = result['args']
    truth_degree = result['value']  # Fuzzy truth [0, 1]
    print(f"{predicate}({args}) = {truth_degree:.4f}")

# Logical trace (reasoning steps)
for step in answer.logical_trace:
    print(step)

Key Insight: LIMEN-AI performs probabilistic inference using importance sampling. Truth degrees represent expected value under induced distribution, not simple lookup values.

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📈 Domain-Specific Usage Patterns

Legal Domain (Contracts, Regulations)

# Extract legal entities, obligations, terms
client.extract(contract_text)

# Query for compliance, obligations, parties
answer = client.ask("What are the termination conditions?")
answer = client.ask("Who is liable for breach?")
answer = client.ask("What is the governing law?")

Cybersecurity Domain (Incidents, Controls)

# Extract security concepts, threats, controls
client.extract(security_policy_text)

# Query for incident response, controls, risks
answer = client.ask("What is the incident response time?")
answer = client.ask("What encryption is required?")
answer = client.ask("Which controls mitigate this threat?")

Financial Domain (Risk, Compliance)

# Extract risk factors, controls, metrics
client.extract(risk_framework_text)

# Query for compliance status, risk ratings, controls
answer = client.ask("What is the overall risk rating?")
answer = client.ask("Which controls are in place for AML?")
answer = client.ask("Is PCI-DSS compliance met?")

Key: LIMEN-AI works across any domain. Just provide domain-specific text and ask questions in that domain.

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❓ Troubleshooting & FAQ

Q: I get a ConnectionError when using Ollama. A: Ensure Ollama is running: ollama run <model>. Check base_url is correct (usually http://localhost:11434/v1).

Q: Queries return "The fact is not supported" even though I see data in extraction. A: This is a query translation issue. The LLM failed to map your natural language question to the correct predicates.

• Solution 1: Use a better model (GPT-4, Claude) - recommended for production
• Solution 2: Query predicates directly instead of natural language (see "Direct Predicate Queries" section)

Q: The extraction is missing some facts. A: Small local models have extraction limitations.

• Solution 1: Use GPT-4, Claude, or Gemini for complete extraction
• Solution 2: Manually add missing facts with client.set_fact("predicate", ["arg1", "arg2"], 1.0)

Q: How do I save my Knowledge Base? A: Use client.save_state("my_kb.json") to save complete pipeline state:

• Schema (predicates with descriptions)
• Facts (ground truth assignments)
• Formulas (weighted rules)
• Induced clauses (learned rules)

Later, restore with client.load_state("my_kb.json").

Q: What's the difference between save_state() and save_knowledge_base()? A: save_state() (JSON) saves everything including facts and learned rules. save_knowledge_base() (SQLite) only saves KB structure (predicates/formulas) without facts. Recommendation: Always use save_state() for complete persistence.

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🧪 Scientific Validation

LIMEN-AI is built on rigorous mathematical foundations:

Łukasiewicz Logic

• T-norm: min(a ⊕ b, 1 - a ⊖ b)
• Implication: a → b = 1 - a + b
• Conjunction: a ⊙ b = min(a, b)

Energy-Based Models

• Probability distribution: P(X) = (1/Z) * exp(E(X))
• Inference via sampling (Importance Sampling, Langevin MCMC)
• Learning via gradient-based optimization

Neurosymbolic Integration

• LLM extracts schema and facts from natural language
• Probabilistic logic engine performs reasoning
• Inductive Logic Programming learns rules from examples

No Experimental Data: All examples in documentation use either:

• Hand-calculable pedagogical examples (clearly labeled as such)
• Real document processing (showing extraction and inference on actual texts)

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📄 License

LIMEN-AI is licensed under the AGPL-3.0. See LICENSE file for details.

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🤝 Contributing

We welcome contributions! Please see our GitHub repository.

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