Lavoisier AI Modules Documentation

Overview

The Lavoisier framework integrates multiple sophisticated AI modules that work together to provide robust, intelligent analysis of mass spectrometry data. This documentation covers all AI modules including:

Core AI Modules:

Diadochi: Multi-domain LLM orchestration and expert routing
Mzekezeke: Bayesian Evidence Network with Fuzzy Logic
Hatata: Markov Decision Process verification layer
Zengeza: Intelligent noise reduction and signal processing
Nicotine: Context verification with cryptographic puzzles
Diggiden: Adversarial testing and vulnerability assessment

Advanced AI Systems:

Models Module: Comprehensive AI model management and deployment
LLM Module: Large Language Model integration and services
Integration Module: AI system orchestration and coordination

1. Diadochi Framework (`lavoisier.diadochi`)

Purpose: Multi-domain LLM orchestration system for intelligent query routing and expert collaboration.

Core Components

1. Core Framework (`lavoisier.diadochi.core`)

The core framework provides the foundational classes and interfaces for multi-domain LLM systems.

Key Classes

`DomainExpert`

Abstract base class for domain-specific expert models.

from lavoisier.diadochi import DomainExpert, DomainSpecification, DomainType

class MyExpert(DomainExpert):
    def __init__(self, expert_id: str, domain_spec: DomainSpecification, model_config: dict):
        super().__init__(expert_id, domain_spec, model_config)
    
    async def generate_response(self, query: str, context=None):
        # Implement your model's response generation
        pass
    
    def estimate_confidence(self, query: str) -> float:
        # Implement confidence estimation
        pass

`DiadochiFramework`

Main orchestration class for managing multiple expert systems.

from lavoisier.diadochi import DiadochiFramework, DomainType

framework = DiadochiFramework()

# Register experts
framework.register_expert(my_expert)

# Create different integration patterns
router_system = framework.create_router_ensemble("router_system")
chain_system = framework.create_sequential_chain("chain_system", expert_sequence=["expert1", "expert2"])

# Process queries automatically
response = await framework.process_query_auto("What is the molecular weight of glucose?")

Domain Types

The framework supports various domain types:

MASS_SPECTROMETRY: Mass spectrometry analysis
PROTEOMICS: Protein analysis
METABOLOMICS: Metabolite analysis
BIOINFORMATICS: Computational biology
STATISTICAL_ANALYSIS: Statistical methods
DATA_VISUALIZATION: Data plotting and visualization
MACHINE_LEARNING: ML/AI methods
CHEMISTRY: Chemical analysis
BIOLOGY: Biological processes
GENERAL: General-purpose analysis

Integration Patterns

Router Ensemble: Direct queries to appropriate domain experts
Sequential Chain: Pass queries through multiple experts in sequence
Mixture of Experts: Process queries through multiple experts in parallel
System Prompts: Use single model with multi-domain expertise
Knowledge Distillation: Train unified models from multiple domain experts
Multi-Domain RAG: Retrieval-augmented generation with domain-specific knowledge

2. Routers (`lavoisier.diadochi.routers`)

Routers intelligently direct queries to the most appropriate domain experts.

Available Routers

`KeywordRouter`

Routes based on keyword matching with domain-specific vocabularies.

from lavoisier.diadochi import KeywordRouter

router = KeywordRouter(confidence_threshold=0.5)
decision = router.route(query, available_experts)

`EmbeddingRouter`

Routes based on semantic similarity using embeddings.

from lavoisier.diadochi import EmbeddingRouter

router = EmbeddingRouter(confidence_threshold=0.6)
decision = router.route(query, available_experts)

`HybridRouter`

Combines multiple routing strategies with weighted scoring.

from lavoisier.diadochi import HybridRouter, KeywordRouter, EmbeddingRouter

hybrid_router = HybridRouter(
    routers=[KeywordRouter(), EmbeddingRouter()],
    router_weights={"keyword_router": 0.3, "embedding_router": 0.7}
)

Router Usage Example

from lavoisier.diadochi import KeywordRouter, DomainExpert

# Create router
router = KeywordRouter()

# Route single query
decision = router.route("What is the m/z ratio for glucose?", available_experts)
print(f"Selected expert: {decision.selected_expert}")
print(f"Confidence: {decision.confidence}")

# Route to multiple experts
decisions = router.route_multiple(query, available_experts, k=3)

3. Chains (`lavoisier.diadochi.chains`)

Chains process queries through multiple domain experts sequentially.

Available Chain Types

`SequentialChain`

Basic sequential processing through multiple experts.

from lavoisier.diadochi import SequentialChain

chain = SequentialChain(
    system_id="basic_chain",
    experts=[ms_expert, stats_expert, viz_expert]
)

response = await chain.process_query("Analyze this mass spectrometry data")

`SummarizingChain`

Sequential chain with automatic summarization to manage context length.

from lavoisier.diadochi import SummarizingChain

chain = SummarizingChain(
    system_id="summarizing_chain",
    experts=experts,
    max_context_length=4000
)

`HierarchicalChain`

Processes queries through hierarchical groups of experts.

from lavoisier.diadochi import HierarchicalChain

expert_groups = {
    "analysis": [ms_expert, stats_expert],
    "visualization": [viz_expert, plotting_expert],
    "interpretation": [bio_expert, chem_expert]
}

chain = HierarchicalChain(
    system_id="hierarchical_chain",
    expert_groups=expert_groups
)

`AdaptiveChain`

Dynamically adapts the expert sequence based on intermediate results.

from lavoisier.diadochi import AdaptiveChain

chain = AdaptiveChain(
    system_id="adaptive_chain",
    experts=experts,
    adaptation_strategy="confidence_based"
)

Quick Start Guide

1. Basic Setup

from lavoisier.diadochi import DiadochiFramework, DomainType, DomainSpecification

# Create framework
framework = DiadochiFramework()

# Define domain specifications
ms_domain = DomainSpecification(
    domain_type=DomainType.MASS_SPECTROMETRY,
    name="Mass Spectrometry Expert",
    description="Expert in mass spectrometry analysis",
    keywords=["mass spec", "ms", "ion", "fragmentation"],
    expertise_areas=["peak detection", "fragmentation", "ionization"]
)

# Create and register experts
# (Implementation depends on your specific LLM setup)

2. Using Router Ensemble

# Create router-based system
router_system = framework.create_router_ensemble(
    system_id="ms_router",
    router_type="hybrid"
)

# Process query
response = await router_system.process_query(
    "What is the molecular ion peak for caffeine in positive ESI mode?"
)

3. Using Sequential Chain

# Create sequential chain
chain_system = framework.create_sequential_chain(
    system_id="analysis_chain",
    expert_sequence=["ms_expert", "stats_expert", "viz_expert"],
    chain_type="summarizing"
)

# Process query
response = await chain_system.process_query(
    "Analyze the mass spectrometry data for metabolite identification"
)

4. Automatic Pattern Selection

# Let framework choose optimal pattern
response = await framework.process_query_auto(
    "Compare the fragmentation patterns of two peptides"
)

Advanced Usage

Custom Expert Implementation

from lavoisier.diadochi import DomainExpert, ExpertResponse
import asyncio

class CustomMSExpert(DomainExpert):
    def __init__(self, expert_id: str, domain_spec: DomainSpecification, model_config: dict):
        super().__init__(expert_id, domain_spec, model_config)
        # Initialize your model here
    
    async def generate_response(self, query: str, context=None):
        # Implement your model's response generation
        response_text = await self._call_model(query)
        confidence = self._calculate_confidence(query, response_text)
        
        return ExpertResponse(
            expert_id=self.expert_id,
            domain_type=self.domain_spec.domain_type,
            response=response_text,
            confidence=confidence,
            processing_time=0.5
        )
    
    def estimate_confidence(self, query: str) -> float:
        # Implement confidence estimation logic
        ms_keywords = ["mass spec", "ms", "ion", "fragmentation"]
        query_lower = query.lower()
        matches = sum(1 for keyword in ms_keywords if keyword in query_lower)
        return min(1.0, matches / len(ms_keywords))

Custom Router Implementation

from lavoisier.diadochi import BaseRouter, RoutingDecision

class CustomRouter(BaseRouter):
    def route(self, query: str, available_experts, context=None):
        # Implement custom routing logic
        scores = {}
        for expert in available_experts:
            scores[expert.expert_id] = self._calculate_score(query, expert)
        
        best_expert = max(scores.keys(), key=lambda k: scores[k])
        
        return RoutingDecision(
            selected_expert=best_expert,
            confidence=scores[best_expert],
            reasoning="Custom routing logic",
            alternatives=list(scores.items()),
            processing_time=0.1,
            metadata={}
        )

Performance Monitoring

Framework Statistics

# Get overall framework statistics
stats = framework.get_framework_statistics()
print(f"Total queries processed: {stats['total_queries']}")
print(f"Average processing time: {stats['avg_processing_time']}")
print(f"Pattern usage distribution: {stats['pattern_usage']}")

Expert Performance

# Get performance metrics for specific expert
expert_metrics = expert.get_performance_metrics()
print(f"Average confidence: {expert_metrics['avg_confidence']}")
print(f"Success rate: {expert_metrics['success_rate']}")

Router Performance

# Get routing statistics
routing_stats = router.get_routing_statistics()
print(f"Average routing confidence: {routing_stats['avg_confidence']}")
print(f"Expert selection distribution: {routing_stats['expert_selection_distribution']}")

Configuration Management

Export Configuration

# Export framework configuration
framework.export_configuration("diadochi_config.json")

Load Configuration

# Load configuration from file
framework.load_configuration("diadochi_config.json")

Integration with Lavoisier

Mass Spectrometry Analysis

from lavoisier.diadochi import DiadochiFramework
from lavoisier.numerical import MSAnalysisPipeline

# Create framework with MS-specific experts
framework = DiadochiFramework()

# Integrate with numerical pipeline
async def analyze_ms_data(mzml_files):
    # Use AI framework for intelligent analysis
    analysis_plan = await framework.process_query_auto(
        f"Create analysis plan for {len(mzml_files)} mzML files"
    )
    
    # Execute using numerical pipeline
    pipeline = MSAnalysisPipeline()
    results = pipeline.process_files(mzml_files)
    
    # Use AI for interpretation
    interpretation = await framework.process_query_auto(
        f"Interpret these MS analysis results: {results}"
    )
    
    return interpretation

Best Practices

Domain Specification: Define clear domain specifications with relevant keywords and expertise areas.
Confidence Thresholds: Set appropriate confidence thresholds for routers and experts.
Context Management: Use QueryContext to provide additional information for better routing and processing.
Performance Monitoring: Regularly monitor expert and router performance to optimize the system.
Prompt Engineering: Customize prompt templates for different chain positions and expert types.
Error Handling: Implement robust error handling for model failures and timeouts.

Troubleshooting

Common Issues

Low Routing Confidence: Adjust confidence thresholds or improve domain specifications.
Slow Processing: Use parallel processing where possible and monitor expert performance.
Poor Response Quality: Review prompt templates and expert selection logic.
Memory Issues: Use SummarizingChain for long conversations to manage context length.

Debugging

# Enable debug logging
import logging
logging.getLogger('lavoisier.diadochi').setLevel(logging.DEBUG)

# Check framework state
print(f"Registered experts: {list(framework.experts.keys())}")
print(f"Available systems: {list(framework.systems.keys())}")

API Reference

Core Classes

DomainExpert: Abstract base class for domain experts
MultiDomainSystem: Abstract base class for multi-domain systems
DiadochiFramework: Main orchestration framework
DomainSpecification: Domain configuration
QueryContext: Query context and configuration
ExpertResponse: Response from individual expert
IntegratedResponse: Final integrated response

Router Classes

BaseRouter: Abstract base router class
KeywordRouter: Keyword-based routing
EmbeddingRouter: Embedding-based routing
HybridRouter: Combined routing strategies

Chain Classes

SequentialChain: Basic sequential processing
SummarizingChain: Sequential with summarization
HierarchicalChain: Hierarchical group processing
AdaptiveChain: Adaptive sequence modification

Enums

IntegrationPattern: Available integration patterns
DomainType: Supported domain types

For detailed API documentation, see the individual module docstrings and method signatures.

2. Mzekezeke: Bayesian Evidence Network (`lavoisier.ai_modules.mzekezeke`)

Purpose: Sophisticated annotation system combining Bayesian evidence networks with fuzzy logic for probabilistic MS annotations.

Key Features

Bayesian Evidence Networks: Probabilistic reasoning for compound identification
Fuzzy Logic Integration: Handles uncertainty in m/z ratio matching
Network-Based Identification: Graph-based evidence correlation
Dynamic Evidence Updates: Continuous learning from new data

Core Classes

`MzekezekeBayesianNetwork`

Main class implementing the Bayesian evidence network with fuzzy logic.

from lavoisier.ai_modules.mzekezeke import MzekezekeBayesianNetwork, EvidenceType

# Initialize network
network = MzekezekeBayesianNetwork(
    mass_tolerance_ppm=5.0,
    fuzzy_width_multiplier=2.0,
    min_evidence_nodes=2
)

# Add evidence nodes
node_id = network.add_evidence_node(
    mz_value=180.0634,
    intensity=1000.0,
    evidence_type=EvidenceType.MASS_MATCH
)

# Connect related evidence
network.auto_connect_related_evidence(correlation_threshold=0.5)

# Update network probabilities
network.update_bayesian_network()

# Generate annotations
compound_database = [
    {'name': 'Glucose', 'exact_mass': 180.0634, 'formula': 'C6H12O6'}
]
annotations = network.generate_annotations(compound_database)

Evidence Types

MASS_MATCH: Exact mass matching evidence
ISOTOPE_PATTERN: Isotope pattern matching
FRAGMENTATION: MS/MS fragmentation evidence
RETENTION_TIME: Chromatographic retention time
ADDUCT_FORMATION: Adduct formation patterns
NEUTRAL_LOSS: Neutral loss patterns
DATABASE_MATCH: Database search results
SPECTRAL_SIMILARITY: Spectral library matching

Fuzzy Logic Integration

from lavoisier.ai_modules.mzekezeke import FuzzyMembership

# Create fuzzy membership function
fuzzy_func = FuzzyMembership(
    center=180.0634,
    width=0.005,  # 5 mDa uncertainty
    shape='gaussian',
    confidence=0.8
)

# Calculate membership degree
membership = fuzzy_func.membership_degree(180.0640)  # Returns ~0.97

Advanced Features

Network Analysis

# Get comprehensive network summary
summary = network.get_network_summary()
print(f"Network density: {summary['network_connectivity']['density']}")
print(f"Mean confidence: {summary['confidence_statistics']['mean_confidence']}")

# Export network for visualization
network.export_network('evidence_network.json')

Evidence Correlation

# Automatic evidence correlation based on chemical relationships
network.auto_connect_related_evidence(correlation_threshold=0.6)

# Manual evidence connection
network.connect_evidence_nodes(node1_id, node2_id, connection_strength=0.8)

3. Hatata: MDP Verification Layer (`lavoisier.ai_modules.hatata`)

Purpose: Markov Decision Process verification system providing stochastic validation of evidence networks through goal-oriented state transitions.

Key Features

Stochastic Validation: MDP-based verification of analysis workflows
Goal-Oriented State Transitions: Optimize analysis pathways
Utility Function Optimization: Multi-objective decision making
Quality Assurance: Probabilistic validation of results

Core Classes

`HatataMDPVerifier`

Main MDP verification system with multiple utility functions.

from lavoisier.ai_modules.hatata import HatataMDPVerifier, MDPState, MDPAction

# Initialize MDP verifier
verifier = HatataMDPVerifier(
    discount_factor=0.95,
    convergence_threshold=1e-6,
    max_iterations=1000
)

# Solve MDP for optimal policy
policy = verifier.solve_mdp()

# Select actions based on context
context = {
    'num_evidence_nodes': 15,
    'avg_posterior_probability': 0.8,
    'network_density': 0.3
}

action = verifier.select_action(context)
next_state, reward = verifier.execute_action(action, context)

MDP States

EVIDENCE_COLLECTION: Gathering evidence from MS data
BAYESIAN_INFERENCE: Updating probability distributions
FUZZY_EVALUATION: Applying fuzzy logic rules
NETWORK_ANALYSIS: Analyzing network topology
ANNOTATION_GENERATION: Creating compound annotations
CONTEXT_VERIFICATION: Verifying analysis context
VALIDATION_COMPLETE: Analysis successfully completed
ERROR_DETECTED: Issues detected in analysis
RECOVERY_MODE: Attempting error recovery

Utility Functions

The system optimizes multiple utility functions:

Evidence Quality: Measures reliability of collected evidence
Network Coherence: Evaluates logical consistency
Computational Efficiency: Optimizes resource usage
Annotation Confidence: Maximizes annotation reliability
Context Preservation: Ensures context integrity

# Calculate total utility for current context
total_utility = verifier.calculate_total_utility(context)
print(f"Total utility: {total_utility:.4f}")

# Get detailed validation report
report = verifier.get_validation_report()

Advanced Usage

Custom Utility Functions

from lavoisier.ai_modules.hatata import UtilityFunction

def custom_utility(context):
    return context.get('custom_metric', 0.0) * 0.8

custom_func = UtilityFunction(
    name="custom_quality",
    weight=0.2,
    function=custom_utility,
    description="Custom quality metric"
)

verifier.utility_functions.append(custom_func)

MDP Analysis

# Export complete MDP model
verifier.export_mdp_model('mdp_analysis.json')

# Get validation statistics
validation_stats = verifier.get_validation_report()

4. Zengeza: Intelligent Noise Reduction (`lavoisier.ai_modules.zengeza`)

Purpose: Sophisticated noise detection and removal using statistical analysis, spectral entropy, and machine learning techniques.

Key Features

Statistical Noise Modeling: Advanced noise characterization
Spectral Entropy Analysis: Entropy-based noise detection
Machine Learning Denoising: Isolation Forest for outlier detection
Adaptive Filtering: Context-aware noise removal

Core Classes

`ZengezaNoiseReducer`

Advanced noise reduction system for mass spectrometry data.

from lavoisier.ai_modules.zengeza import ZengezaNoiseReducer

# Initialize noise reducer
reducer = ZengezaNoiseReducer(
    entropy_window=50,
    isolation_contamination=0.1,
    wavelet='db8',
    adaptive_threshold=0.95
)

# Analyze noise characteristics
mz_array = np.array([100.0, 101.0, 102.0, ...])
intensity_array = np.array([1000, 800, 1200, ...])

noise_profile = reducer.analyze_noise_characteristics(
    mz_array, 
    intensity_array, 
    spectrum_id="spectrum_001"
)

# Remove noise from spectrum
clean_mz, clean_intensity = reducer.remove_noise(
    mz_array, 
    intensity_array,
    spectrum_id="spectrum_001"
)

Noise Profile Analysis

# Get comprehensive noise analysis report
noise_report = reducer.get_noise_report("spectrum_001")

print(f"Baseline noise level: {noise_report['baseline_noise_level']:.2f}")
print(f"Signal-to-noise ratio: {noise_report['signal_to_noise_ratio']:.2f}")
print(f"Noise pattern type: {noise_report['noise_pattern_type']}")
print(f"Analysis confidence: {noise_report['analysis_confidence']:.2f}")

Noise Reduction Techniques

Baseline Noise Removal: Statistical baseline correction
Systematic Noise Removal: FFT-based periodic noise removal
Wavelet Denoising: Wavelet transform-based denoising
Entropy-Based Filtering: Spectral entropy thresholding
Isolation Forest Filtering: ML-based outlier detection

Advanced Features

# Analyze multiple noise patterns
noise_patterns = ['gaussian', 'poisson', 'uniform', 'mixed']

for pattern in noise_patterns:
    if noise_profile.noise_pattern == pattern:
        print(f"Detected {pattern} noise pattern")
        # Apply pattern-specific denoising

5. Nicotine: Context Verification System (`lavoisier.ai_modules.nicotine`)

Purpose: Context verification system using sophisticated cryptographic puzzles and pattern recognition to ensure AI maintains proper analysis context.

Key Features

Cryptographic Puzzles: Non-human readable verification challenges
Pattern Recognition: Complex pattern-based context validation
Temporal Consistency: Time-based context integrity checks
AI Integrity Assurance: Prevents context drift in AI systems

Core Classes

`NicotineContextVerifier`

Advanced context verification system with cryptographic puzzle challenges.

from lavoisier.ai_modules.nicotine import NicotineContextVerifier, PuzzleType

# Initialize context verifier
verifier = NicotineContextVerifier(
    puzzle_complexity=5,
    verification_frequency=100,
    max_context_age=3600,
    puzzle_timeout=30
)

# Create context snapshot
snapshot_id = verifier.create_context_snapshot(
    evidence_nodes=evidence_data,
    network_topology=network_graph,
    bayesian_states=probability_states,
    fuzzy_memberships=fuzzy_data,
    annotation_candidates=annotations
)

# Verify puzzle solutions
success, result = verifier.verify_context(puzzle_id, proposed_solution)

Puzzle Types

CRYPTOGRAPHIC_HASH: Multi-stage cryptographic challenges
PATTERN_RECONSTRUCTION: Complex pattern reconstruction tasks
TEMPORAL_SEQUENCE: Time-series pattern analysis
SPECTRAL_FINGERPRINT: Mass spectrum fingerprint challenges
MOLECULAR_TOPOLOGY: Molecular structure topology puzzles
EVIDENCE_CORRELATION: Evidence correlation matrix puzzles
BAYESIAN_CONSISTENCY: Bayesian network consistency checks
FUZZY_LOGIC_PROOF: Fuzzy logic verification challenges

Advanced Usage

Context Monitoring

# Get verification system status
status = verifier.get_verification_status()
print(f"Active puzzles: {status['active_puzzles']}")
print(f"Average difficulty: {status['average_difficulty']:.1f}")

# Clean up expired puzzles
verifier.cleanup_expired_puzzles()

# Export puzzle analytics
verifier.export_puzzle_analytics('puzzle_analytics.json')

Custom Puzzle Creation

# The system automatically creates context-specific puzzles
# Each puzzle is uniquely derived from the current analysis context
# Solutions require maintaining proper context throughout analysis

6. Diggiden: Adversarial Testing System (`lavoisier.ai_modules.diggiden`)

Purpose: Sophisticated adversarial testing system that actively searches for vulnerabilities in evidence networks through systematic attacks.

Key Features

Vulnerability Assessment: Systematic testing of evidence networks
Multiple Attack Vectors: Comprehensive attack strategy library
Security Analysis: Detailed security evaluation reports
Robustness Validation: Adversarial robustness verification

Core Classes

`DiggidenAdversarialTester`

Advanced adversarial testing system with multiple attack strategies.

from lavoisier.ai_modules.diggiden import DiggidenAdversarialTester, AttackType

# Initialize adversarial tester
tester = DiggidenAdversarialTester(
    attack_intensity=0.7,
    max_attack_iterations=1000,
    vulnerability_threshold=0.6,
    detection_evasion_level=0.8
)

# Set targets for testing
tester.set_targets(
    evidence_network=evidence_system,
    bayesian_system=bayesian_network,
    fuzzy_system=fuzzy_logic_system,
    context_verifier=context_system
)

# Launch comprehensive attack campaign
vulnerabilities = tester.launch_comprehensive_attack()

# Generate security report
security_report = tester.generate_security_report()

Attack Types

NOISE_INJECTION: Statistical noise injection attacks
EVIDENCE_CORRUPTION: Data integrity corruption attacks
NETWORK_FRAGMENTATION: Network topology attacks
BAYESIAN_POISONING: Probabilistic reasoning attacks
FUZZY_MANIPULATION: Fuzzy logic manipulation attacks
CONTEXT_DISRUPTION: Context verification attacks
ANNOTATION_SPOOFING: False annotation injection
TEMPORAL_INCONSISTENCY: Time-based consistency attacks

Security Analysis

Vulnerability Reporting

# Get attack statistics
attack_stats = tester.get_attack_statistics()
print(f"Total attacks: {attack_stats['total_attacks']}")
print(f"Success rate: {attack_stats['attack_success_rate']:.2%}")
print(f"Vulnerabilities found: {attack_stats['vulnerabilities_found']}")

# Export detailed security report
tester.export_security_report('security_assessment.json')

Vulnerability Assessment

# Analyze discovered vulnerabilities
for vuln in vulnerabilities:
    print(f"Vulnerability: {vuln.vulnerability_id}")
    print(f"Severity: {vuln.severity_score:.2f}")
    print(f"Attack type: {vuln.attack_type.value}")
    print(f"Affected components: {vuln.affected_components}")
    print(f"Recommended fixes: {vuln.recommended_fixes}")

7. Models Module (`lavoisier.models`)

The Models module provides a comprehensive framework for managing, deploying, and working with specialized AI models for mass spectrometry analysis.

7.1 Chemical Language Models (`lavoisier.models.chemical_language_models`)

Integration of transformer-based models for molecular property prediction and chemical understanding.

ChemBERTa Model

Purpose: Pre-trained transformer for molecular property prediction and SMILES encoding.

from lavoisier.models import ChemBERTaModel, create_chemberta_model

# Initialize model
model = ChemBERTaModel(
    model_id="DeepChem/ChemBERTa-77M-MLM",
    pooling_strategy="cls",  # 'cls', 'mean', or 'max'
    device="cuda"  # or "cpu"
)

# Encode SMILES strings
smiles = ["CCO", "c1ccccc1", "CC(=O)O"]
embeddings = model.encode_smiles(smiles)
print(f"Embeddings shape: {embeddings.shape}")

# Convenience function
model = create_chemberta_model(pooling_strategy="mean")

Features:

Multiple pooling strategies (CLS token, mean, max)
Batch processing support
GPU acceleration
Integration with DeepChem models

MoLFormer Model

Purpose: Large-scale molecular representation learning for advanced chemical understanding.

from lavoisier.models import MoLFormerModel, create_molformer_model

# Initialize MoLFormer
model = MoLFormerModel(
    model_id="ibm-research/MoLFormer-XL-both-10pct",
    device="cuda"
)

# Encode molecular structures
molecules = ["CCO", "c1ccccc1O", "CC(C)C"]
embeddings = model.encode_smiles(molecules, max_length=512)

# Advanced usage
model = create_molformer_model()
embeddings = model.encode_smiles(molecules)

Features:

Self-supervised molecular learning
Custom tokenization for chemical structures
Transfer learning capabilities
Large-scale molecular understanding

PubChemDeBERTa Model

Purpose: Specialized model for chemical property prediction trained on PubChem data.

from lavoisier.models import PubChemDeBERTaModel, create_pubchem_deberta_model

# Initialize model
model = PubChemDeBERTaModel(
    model_id="mschuh/PubChemDeBERTa",
    device="cuda"
)

# Encode and predict properties
smiles = ["CCO", "CC(=O)O", "c1ccccc1"]
embeddings = model.encode_smiles(smiles)
properties = model.predict_properties(smiles)

print(f"Properties: {properties}")

Features:

Fine-tuned on PubChem dataset
Multi-task property prediction
High-accuracy molecular classification
Chemical property inference

7.2 Spectral Transformer Models (`lavoisier.models.spectral_transformers`)

Advanced transformer models for direct spectral analysis and structure elucidation.

SpecTUS Model

Purpose: Transformer model for converting EI-MS spectra directly to SMILES structures.

from lavoisier.models import SpecTUSModel, create_spectus_model
import numpy as np

# Initialize model
model = SpecTUSModel(
    model_id="MS-ML/SpecTUS_pretrained_only",
    max_length=512,
    device="cuda"
)

# Prepare spectrum data
mz_values = np.array([50.0, 77.0, 105.0, 122.0])
intensity_values = np.array([0.1, 0.3, 0.8, 1.0])

# Predict SMILES from spectrum
predicted_smiles = model.process_spectrum(
    mz_values, intensity_values,
    num_beams=5,
    num_return_sequences=3
)

print(f"Predicted structures: {predicted_smiles}")

# Batch processing
spectra = [(mz1, int1), (mz2, int2), (mz3, int3)]
results = model.batch_process_spectra(spectra, batch_size=8)

Advanced Preprocessing:

# Custom preprocessing
spectrum_str = model.preprocess_spectrum(
    mz_values, intensity_values,
    normalize=True,
    min_mz=50.0,
    max_mz=500.0,
    bin_size=1.0
)

# Fine-tuned processing
results = model.process_spectrum(
    mz_values, intensity_values,
    normalize=True,
    min_intensity=0.01,
    num_beams=10
)

Features:

Direct spectrum-to-structure prediction
Beam search optimization
Batch processing capabilities
Advanced spectral preprocessing
Multiple candidate generation

7.3 Embedding Models (`lavoisier.models.embedding_models`)

Models for creating joint embeddings of molecular structures and mass spectra.

CMSSP Model

Purpose: Joint embeddings of MS/MS spectra and molecules for cross-modal analysis.

from lavoisier.models import CMSSPModel, create_cmssp_model
import numpy as np

# Initialize model
model = CMSSPModel(
    model_id="OliXio/CMSSP",
    embedding_dim=768,
    device="cuda"
)

# Encode SMILES
smiles = ["CCO", "c1ccccc1"]
smiles_embeddings = model.encode_smiles(smiles)

# Encode spectrum
mz_values = np.array([50.0, 77.0, 105.0])
intensity_values = np.array([0.2, 0.8, 1.0])
spectrum_embedding = model.encode_spectrum(mz_values, intensity_values)

# Batch encode spectra
spectra = [(mz1, int1), (mz2, int2)]
spectrum_embeddings = model.batch_encode_spectra(spectra, batch_size=4)

# Compute similarities
similarity = model.compute_similarity(
    smiles_embeddings[0], spectrum_embedding
)
print(f"Structure-spectrum similarity: {similarity}")

# Batch similarities
similarities = model.compute_batch_similarities(
    spectrum_embedding, smiles_embeddings
)

Advanced Usage:

# Custom spectrum preprocessing
spectrum_str = model.preprocess_spectrum(
    mz_values, intensity_values,
    normalize=True,
    min_intensity=0.01,
    top_k=100
)

# Multi-modal search
query_embedding = model.encode_spectrum(query_mz, query_intensity)
reference_embeddings = model.encode_smiles(reference_smiles)
similarities = model.compute_batch_similarities(
    query_embedding, reference_embeddings
)
best_matches = np.argsort(similarities)[::-1][:10]

Features:

Cross-modal representation learning
Spectral similarity computation
Structure-spectrum alignment
Batch processing optimization
Multi-modal search capabilities

7.4 Model Repository System (`lavoisier.models.repository`)

Centralized system for model storage, versioning, and management.

from lavoisier.models import ModelRepository, ModelMetadata

# Initialize repository
repo = ModelRepository(base_path="/models/lavoisier")

# Register a model
metadata = ModelMetadata(
    name="custom_chemberta",
    version="1.0.0",
    model_type="chemical_language",
    description="Fine-tuned ChemBERTa for metabolomics",
    performance_metrics={"accuracy": 0.95, "f1_score": 0.92}
)

model_path = repo.store_model(model, metadata)

# Retrieve model
loaded_model = repo.load_model("custom_chemberta", version="1.0.0")

# List available models
models = repo.list_models()
for model_info in models:
    print(f"{model_info.name} v{model_info.version}")

# Update model
new_metadata = metadata.copy(version="1.1.0")
repo.update_model("custom_chemberta", new_model, new_metadata)

# Model comparison
comparison = repo.compare_models(
    "custom_chemberta", 
    versions=["1.0.0", "1.1.0"]
)

Features:

Centralized model storage
Version control and management
Metadata tracking
Performance comparison
Automatic synchronization

7.5 Knowledge Distillation (`lavoisier.models.distillation`)

System for creating specialized models from academic literature and pipeline data.

from lavoisier.models import KnowledgeDistiller

# Initialize distiller
distiller = KnowledgeDistiller({
    "temp_dir": "/tmp/distillation",
    "ollama_base_model": "llama3",
    "max_workers": 4
})

# Distill from academic papers
def progress_callback(progress, message):
    print(f"Progress: {progress:.1%} - {message}")

model_path = distiller.distill_academic_model(
    papers_dir="/path/to/papers",
    output_path="/models/academic_model.bin",
    progress_callback=progress_callback
)

# Distill from pipeline data
pipeline_data = {
    "spectra": processed_spectra,
    "annotations": annotations,
    "performance_metrics": metrics
}

pipeline_model = distiller.distill_pipeline_model(
    pipeline_type="metabolomics",
    pipeline_data=pipeline_data,
    progress_callback=progress_callback
)

# Test distilled model
test_queries = [
    "What is the molecular weight of glucose?",
    "How do you interpret MS/MS fragmentation patterns?",
    "What are the best practices for metabolite identification?"
]

test_results = distiller.test_model(model_path, test_queries)
print(f"Model performance: {test_results}")

Advanced Features:

# Academic knowledge extraction
papers_dir = "/research/papers"
try:
    model_path = distiller.distill_academic_model(
        papers_dir=papers_dir,
        progress_callback=lambda p, m: print(f"{p:.1%}: {m}")
    )
    print(f"Academic model created: {model_path}")
except NotImplementedError as e:
    print(f"Paper analysis not ready: {e}")

# Pipeline-specific distillation
pipeline_types = ["metabolomics", "proteomics", "lipidomics"]
for pipeline_type in pipeline_types:
    model = distiller.distill_pipeline_model(
        pipeline_type=pipeline_type,
        pipeline_data=get_pipeline_data(pipeline_type)
    )
    print(f"Created {pipeline_type} model: {model}")

Features:

Academic paper knowledge extraction
Pipeline-specific model creation
Ollama integration for local deployment
Progressive complexity training
Automatic model testing and validation

7.6 Model Registry (`lavoisier.models.registry`)

Unified system for model discovery, registration, and management.

from lavoisier.models import ModelRegistry, MODEL_REGISTRY, ModelType

# Access global registry
registry = MODEL_REGISTRY

# Register a model
registry.register_model(
    model_id="custom_model",
    model_type=ModelType.CHEMICAL_LANGUAGE,
    model_class=ChemBERTaModel,
    default_config={
        "model_id": "DeepChem/ChemBERTa-77M-MLM",
        "pooling_strategy": "cls"
    }
)

# Create model from registry
model = registry.create_model("custom_model", device="cuda")

# List available models
available_models = registry.list_models()
for model_info in available_models:
    print(f"{model_info.model_id}: {model_info.model_type}")

# Filter by type
chemical_models = registry.list_models(
    model_type=ModelType.CHEMICAL_LANGUAGE
)

# Update model configuration
registry.update_model_config("custom_model", {
    "pooling_strategy": "mean",
    "device": "cpu"
})

HuggingFace Integration:

from lavoisier.models.registry import HuggingFaceModelInfo

# Register HuggingFace model
hf_info = HuggingFaceModelInfo(
    model_id="microsoft/DialoGPT-medium",
    model_type=ModelType.GENERAL_LANGUAGE,
    description="Conversational AI model",
    tags=["dialogue", "conversational"]
)

registry.register_huggingface_model(hf_info)

# Auto-discover HuggingFace models
discovered_models = registry.discover_huggingface_models(
    search_query="mass spectrometry"
)

Features:

Centralized model discovery
HuggingFace integration
Model type classification
Automatic configuration management
Dynamic model loading

8. LLM Integration Module (`lavoisier.llm`)

Comprehensive integration with large language models for enhanced analytical capabilities.

8.1 LLM Service Architecture (`lavoisier.llm.service`)

Central service for managing LLM interactions and analytical workflows.

from lavoisier.llm import LLMService
import asyncio

# Initialize LLM service
config = {
    "enabled": True,
    "commercial": {
        "openai": {"api_key": "your_key"},
        "anthropic": {"api_key": "your_key"}
    },
    "use_ollama": True,
    "ollama": {"base_url": "http://localhost:11434"},
    "max_workers": 4
}

service = LLMService(config)

# Analyze data with automatic query generation
data = {
    "spectrum_data": spectrum_array,
    "metadata": {"sample_type": "plasma", "method": "LC-MS"}
}

# Asynchronous analysis
async def analyze():
    result = await service.analyze_data(
        data=data,
        query_type="structural_analysis",
        use_local=False
    )
    return result

result = asyncio.run(analyze())
print(f"Analysis result: {result}")

# Synchronous analysis
result = service.analyze_data_sync(
    data=data,
    query="What are the most likely molecular structures for these peaks?",
    use_local=True
)

Multi-Query Analysis:

# Analyze with multiple queries
queries = [
    "Identify the molecular ion peaks",
    "Suggest possible fragmentation patterns",
    "Compare with known metabolite databases",
    "Assess data quality and potential interferences"
]

results = asyncio.run(
    service.analyze_multiple(data, queries, use_local=False)
)

for query, result in zip(queries, results):
    print(f"Query: {query}")
    print(f"Result: {result.get('response', 'No response')}")

Progressive Analysis:

# Progressive analysis with increasing complexity
def progress_callback(query, result):
    print(f"Completed: {query}")
    print(f"Response: {result.get('response', '')[:100]}...")

progressive_results = service.generate_progressive_analysis(
    data=data,
    max_queries=5,
# Lavoisier AI Modules Documentation

## Overview

The Lavoisier framework integrates six sophisticated AI modules that work together to provide robust, intelligent analysis of mass spectrometry data. Each module addresses specific aspects of AI-driven analysis:

1. **Diadochi**: Multi-domain LLM orchestration and expert routing
2. **Mzekezeke**: Bayesian Evidence Network with Fuzzy Logic
3. **Hatata**: Markov Decision Process verification layer
4. **Zengeza**: Intelligent noise reduction and signal processing
5. **Nicotine**: Context verification with cryptographic puzzles
6. **Diggiden**: Adversarial testing and vulnerability assessment

---

## 1. Diadochi Framework (`lavoisier.diadochi`)

**Purpose**: Multi-domain LLM orchestration system for intelligent query routing and expert collaboration.

## Core Components

### 1. Core Framework (`lavoisier.diadochi.core`)

The core framework provides the foundational classes and interfaces for multi-domain LLM systems.

#### Key Classes

##### `DomainExpert`
Abstract base class for domain-specific expert models.

```python
from lavoisier.diadochi import DomainExpert, DomainSpecification, DomainType

class MyExpert(DomainExpert):
    def __init__(self, expert_id: str, domain_spec: DomainSpecification, model_config: dict):
        super().__init__(expert_id, domain_spec, model_config)
    
    async def generate_response(self, query: str, context=None):
        # Implement your model's response generation
        pass
    
    def estimate_confidence(self, query: str) -> float:
        # Implement confidence estimation
        pass

`DiadochiFramework`

Main orchestration class for managing multiple expert systems.

from lavoisier.diadochi import DiadochiFramework, DomainType

framework = DiadochiFramework()

# Register experts
framework.register_expert(my_expert)

# Create different integration patterns
router_system = framework.create_router_ensemble("router_system")
chain_system = framework.create_sequential_chain("chain_system", expert_sequence=["expert1", "expert2"])

# Process queries automatically
response = await framework.process_query_auto("What is the molecular weight of glucose?")

Domain Types

The framework supports various domain types:

MASS_SPECTROMETRY: Mass spectrometry analysis
PROTEOMICS: Protein analysis
METABOLOMICS: Metabolite analysis
BIOINFORMATICS: Computational biology
STATISTICAL_ANALYSIS: Statistical methods
DATA_VISUALIZATION: Data plotting and visualization
MACHINE_LEARNING: ML/AI methods
CHEMISTRY: Chemical analysis
BIOLOGY: Biological processes
GENERAL: General-purpose analysis

Integration Patterns

Router Ensemble: Direct queries to appropriate domain experts
Sequential Chain: Pass queries through multiple experts in sequence
Mixture of Experts: Process queries through multiple experts in parallel
System Prompts: Use single model with multi-domain expertise
Knowledge Distillation: Train unified models from multiple domain experts
Multi-Domain RAG: Retrieval-augmented generation with domain-specific knowledge

2. Routers (`lavoisier.diadochi.routers`)

Routers intelligently direct queries to the most appropriate domain experts.

Available Routers

`KeywordRouter`

Routes based on keyword matching with domain-specific vocabularies.

from lavoisier.diadochi import KeywordRouter

router = KeywordRouter(confidence_threshold=0.5)
decision = router.route(query, available_experts)

`EmbeddingRouter`

Routes based on semantic similarity using embeddings.

from lavoisier.diadochi import EmbeddingRouter

router = EmbeddingRouter(confidence_threshold=0.6)
decision = router.route(query, available_experts)

`HybridRouter`

Combines multiple routing strategies with weighted scoring.

from lavoisier.diadochi import HybridRouter, KeywordRouter, EmbeddingRouter

hybrid_router = HybridRouter(
    routers=[KeywordRouter(), EmbeddingRouter()],
    router_weights={"keyword_router": 0.3, "embedding_router": 0.7}
)

Router Usage Example

from lavoisier.diadochi import KeywordRouter, DomainExpert

# Create router
router = KeywordRouter()

# Route single query
decision = router.route("What is the m/z ratio for glucose?", available_experts)
print(f"Selected expert: {decision.selected_expert}")
print(f"Confidence: {decision.confidence}")

# Route to multiple experts
decisions = router.route_multiple(query, available_experts, k=3)

3. Chains (`lavoisier.diadochi.chains`)

Chains process queries through multiple domain experts sequentially.

Available Chain Types

`SequentialChain`

Basic sequential processing through multiple experts.

from lavoisier.diadochi import SequentialChain

chain = SequentialChain(
    system_id="basic_chain",
    experts=[ms_expert, stats_expert, viz_expert]
)

response = await chain.process_query("Analyze this mass spectrometry data")

`SummarizingChain`

Sequential chain with automatic summarization to manage context length.

from lavoisier.diadochi import SummarizingChain

chain = SummarizingChain(
    system_id="summarizing_chain",
    experts=experts,
    max_context_length=4000
)

`HierarchicalChain`

Processes queries through hierarchical groups of experts.

from lavoisier.diadochi import HierarchicalChain

expert_groups = {
    "analysis": [ms_expert, stats_expert],
    "visualization": [viz_expert, plotting_expert],
    "interpretation": [bio_expert, chem_expert]
}

chain = HierarchicalChain(
    system_id="hierarchical_chain",
    expert_groups=expert_groups
)

`AdaptiveChain`

Dynamically adapts the expert sequence based on intermediate results.

from lavoisier.diadochi import AdaptiveChain

chain = AdaptiveChain(
    system_id="adaptive_chain",
    experts=experts,
    adaptation_strategy="confidence_based"
)

Quick Start Guide

1. Basic Setup

from lavoisier.diadochi import DiadochiFramework, DomainType, DomainSpecification

# Create framework
framework = DiadochiFramework()

# Define domain specifications
ms_domain = DomainSpecification(
    domain_type=DomainType.MASS_SPECTROMETRY,
    name="Mass Spectrometry Expert",
    description="Expert in mass spectrometry analysis",
    keywords=["mass spec", "ms", "ion", "fragmentation"],
    expertise_areas=["peak detection", "fragmentation", "ionization"]
)

# Create and register experts
# (Implementation depends on your specific LLM setup)

2. Using Router Ensemble

# Create router-based system
router_system = framework.create_router_ensemble(
    system_id="ms_router",
    router_type="hybrid"
)

# Process query
response = await router_system.process_query(
    "What is the molecular ion peak for caffeine in positive ESI mode?"
)

3. Using Sequential Chain

# Create sequential chain
chain_system = framework.create_sequential_chain(
    system_id="analysis_chain",
    expert_sequence=["ms_expert", "stats_expert", "viz_expert"],
    chain_type="summarizing"
)

# Process query
response = await chain_system.process_query(
    "Analyze the mass spectrometry data for metabolite identification"
)

4. Automatic Pattern Selection

# Let framework choose optimal pattern
response = await framework.process_query_auto(
    "Compare the fragmentation patterns of two peptides"
)

Advanced Usage

Custom Expert Implementation

from lavoisier.diadochi import DomainExpert, ExpertResponse
import asyncio

class CustomMSExpert(DomainExpert):
    def __init__(self, expert_id: str, domain_spec: DomainSpecification, model_config: dict):
        super().__init__(expert_id, domain_spec, model_config)
        # Initialize your model here
    
    async def generate_response(self, query: str, context=None):
        # Implement your model's response generation
        response_text = await self._call_model(query)
        confidence = self._calculate_confidence(query, response_text)
        
        return ExpertResponse(
            expert_id=self.expert_id,
            domain_type=self.domain_spec.domain_type,
            response=response_text,
            confidence=confidence,
            processing_time=0.5
        )
    
    def estimate_confidence(self, query: str) -> float:
        # Implement confidence estimation logic
        ms_keywords = ["mass spec", "ms", "ion", "fragmentation"]
        query_lower = query.lower()
        matches = sum(1 for keyword in ms_keywords if keyword in query_lower)
        return min(1.0, matches / len(ms_keywords))

Custom Router Implementation

from lavoisier.diadochi import BaseRouter, RoutingDecision

class CustomRouter(BaseRouter):
    def route(self, query: str, available_experts, context=None):
        # Implement custom routing logic
        scores = {}
        for expert in available_experts:
            scores[expert.expert_id] = self._calculate_score(query, expert)
        
        best_expert = max(scores.keys(), key=lambda k: scores[k])
        
        return RoutingDecision(
            selected_expert=best_expert,
            confidence=scores[best_expert],
            reasoning="Custom routing logic",
            alternatives=list(scores.items()),
            processing_time=0.1,
            metadata={}
        )

Performance Monitoring

Framework Statistics

# Get overall framework statistics
stats = framework.get_framework_statistics()
print(f"Total queries processed: {stats['total_queries']}")
print(f"Average processing time: {stats['avg_processing_time']}")
print(f"Pattern usage distribution: {stats['pattern_usage']}")

Expert Performance

# Get performance metrics for specific expert
expert_metrics = expert.get_performance_metrics()
print(f"Average confidence: {expert_metrics['avg_confidence']}")
print(f"Success rate: {expert_metrics['success_rate']}")

Router Performance

# Get routing statistics
routing_stats = router.get_routing_statistics()
print(f"Average routing confidence: {routing_stats['avg_confidence']}")
print(f"Expert selection distribution: {routing_stats['expert_selection_distribution']}")

Configuration Management

Export Configuration

# Export framework configuration
framework.export_configuration("diadochi_config.json")

Load Configuration

# Load configuration from file
framework.load_configuration("diadochi_config.json")

Integration with Lavoisier

Mass Spectrometry Analysis

from lavoisier.diadochi import DiadochiFramework
from lavoisier.numerical import MSAnalysisPipeline

# Create framework with MS-specific experts
framework = DiadochiFramework()

# Integrate with numerical pipeline
async def analyze_ms_data(mzml_files):
    # Use AI framework for intelligent analysis
    analysis_plan = await framework.process_query_auto(
        f"Create analysis plan for {len(mzml_files)} mzML files"
    )
    
    # Execute using numerical pipeline
    pipeline = MSAnalysisPipeline()
    results = pipeline.process_files(mzml_files)
    
    # Use AI for interpretation
    interpretation = await framework.process_query_auto(
        f"Interpret these MS analysis results: {results}"
    )
    
    return interpretation

Best Practices

Domain Specification: Define clear domain specifications with relevant keywords and expertise areas.
Confidence Thresholds: Set appropriate confidence thresholds for routers and experts.
Context Management: Use QueryContext to provide additional information for better routing and processing.
Performance Monitoring: Regularly monitor expert and router performance to optimize the system.
Prompt Engineering: Customize prompt templates for different chain positions and expert types.
Error Handling: Implement robust error handling for model failures and timeouts.

Troubleshooting

Common Issues

Low Routing Confidence: Adjust confidence thresholds or improve domain specifications.
Slow Processing: Use parallel processing where possible and monitor expert performance.
Poor Response Quality: Review prompt templates and expert selection logic.
Memory Issues: Use SummarizingChain for long conversations to manage context length.

Debugging

# Enable debug logging
import logging
logging.getLogger('lavoisier.diadochi').setLevel(logging.DEBUG)

# Check framework state
print(f"Registered experts: {list(framework.experts.keys())}")
print(f"Available systems: {list(framework.systems.keys())}")

API Reference

Core Classes

DomainExpert: Abstract base class for domain experts
MultiDomainSystem: Abstract base class for multi-domain systems
DiadochiFramework: Main orchestration framework
DomainSpecification: Domain configuration
QueryContext: Query context and configuration
ExpertResponse: Response from individual expert
IntegratedResponse: Final integrated response

Router Classes

BaseRouter: Abstract base router class
KeywordRouter: Keyword-based routing
EmbeddingRouter: Embedding-based routing
HybridRouter: Combined routing strategies

Chain Classes

SequentialChain: Basic sequential processing
SummarizingChain: Sequential with summarization
HierarchicalChain: Hierarchical group processing
AdaptiveChain: Adaptive sequence modification

Enums

IntegrationPattern: Available integration patterns
DomainType: Supported domain types

For detailed API documentation, see the individual module docstrings and method signatures.

2. Mzekezeke: Bayesian Evidence Network (`lavoisier.ai_modules.mzekezeke`)

Purpose: Sophisticated annotation system combining Bayesian evidence networks with fuzzy logic for probabilistic MS annotations.

Key Features

Bayesian Evidence Networks: Probabilistic reasoning for compound identification
Fuzzy Logic Integration: Handles uncertainty in m/z ratio matching
Network-Based Identification: Graph-based evidence correlation
Dynamic Evidence Updates: Continuous learning from new data

Core Classes

`MzekezekeBayesianNetwork`

Main class implementing the Bayesian evidence network with fuzzy logic.

from lavoisier.ai_modules.mzekezeke import MzekezekeBayesianNetwork, EvidenceType

# Initialize network
network = MzekezekeBayesianNetwork(
    mass_tolerance_ppm=5.0,
    fuzzy_width_multiplier=2.0,
    min_evidence_nodes=2
)

# Add evidence nodes
node_id = network.add_evidence_node(
    mz_value=180.0634,
    intensity=1000.0,
    evidence_type=EvidenceType.MASS_MATCH
)

# Connect related evidence
network.auto_connect_related_evidence(correlation_threshold=0.5)

# Update network probabilities
network.update_bayesian_network()

# Generate annotations
compound_database = [
    {'name': 'Glucose', 'exact_mass': 180.0634, 'formula': 'C6H12O6'}
]
annotations = network.generate_annotations(compound_database)

Evidence Types

MASS_MATCH: Exact mass matching evidence
ISOTOPE_PATTERN: Isotope pattern matching
FRAGMENTATION: MS/MS fragmentation evidence
RETENTION_TIME: Chromatographic retention time
ADDUCT_FORMATION: Adduct formation patterns
NEUTRAL_LOSS: Neutral loss patterns
DATABASE_MATCH: Database search results
SPECTRAL_SIMILARITY: Spectral library matching

Fuzzy Logic Integration

from lavoisier.ai_modules.mzekezeke import FuzzyMembership

# Create fuzzy membership function
fuzzy_func = FuzzyMembership(
    center=180.0634,
    width=0.005,  # 5 mDa uncertainty
    shape='gaussian',
    confidence=0.8
)

# Calculate membership degree
membership = fuzzy_func.membership_degree(180.0640)  # Returns ~0.97

Advanced Features

Network Analysis

# Get comprehensive network summary
summary = network.get_network_summary()
print(f"Network density: {summary['network_connectivity']['density']}")
print(f"Mean confidence: {summary['confidence_statistics']['mean_confidence']}")

# Export network for visualization
network.export_network('evidence_network.json')

Evidence Correlation

# Automatic evidence correlation based on chemical relationships
network.auto_connect_related_evidence(correlation_threshold=0.6)

# Manual evidence connection
network.connect_evidence_nodes(node1_id, node2_id, connection_strength=0.8)

3. Hatata: MDP Verification Layer (`lavoisier.ai_modules.hatata`)

Purpose: Markov Decision Process verification system providing stochastic validation of evidence networks through goal-oriented state transitions.

Key Features

Stochastic Validation: MDP-based verification of analysis workflows
Goal-Oriented State Transitions: Optimize analysis pathways
Utility Function Optimization: Multi-objective decision making
Quality Assurance: Probabilistic validation of results

Core Classes

`HatataMDPVerifier`

Main MDP verification system with multiple utility functions.

from lavoisier.ai_modules.hatata import HatataMDPVerifier, MDPState, MDPAction

# Initialize MDP verifier
verifier = HatataMDPVerifier(
    discount_factor=0.95,
    convergence_threshold=1e-6,
    max_iterations=1000
)

# Solve MDP for optimal policy
policy = verifier.solve_mdp()

# Select actions based on context
context = {
    'num_evidence_nodes': 15,
    'avg_posterior_probability': 0.8,
    'network_density': 0.3
}

action = verifier.select_action(context)
next_state, reward = verifier.execute_action(action, context)

MDP States

EVIDENCE_COLLECTION: Gathering evidence from MS data
BAYESIAN_INFERENCE: Updating probability distributions
FUZZY_EVALUATION: Applying fuzzy logic rules
NETWORK_ANALYSIS: Analyzing network topology
ANNOTATION_GENERATION: Creating compound annotations
CONTEXT_VERIFICATION: Verifying analysis context
VALIDATION_COMPLETE: Analysis successfully completed
ERROR_DETECTED: Issues detected in analysis
RECOVERY_MODE: Attempting error recovery

Utility Functions

The system optimizes multiple utility functions:

Evidence Quality: Measures reliability of collected evidence
Network Coherence: Evaluates logical consistency
Computational Efficiency: Optimizes resource usage
Annotation Confidence: Maximizes annotation reliability
Context Preservation: Ensures context integrity

# Calculate total utility for current context
total_utility = verifier.calculate_total_utility(context)
print(f"Total utility: {total_utility:.4f}")

# Get detailed validation report
report = verifier.get_validation_report()

Advanced Usage

Custom Utility Functions

from lavoisier.ai_modules.hatata import UtilityFunction

def custom_utility(context):
    return context.get('custom_metric', 0.0) * 0.8

custom_func = UtilityFunction(
    name="custom_quality",
    weight=0.2,
    function=custom_utility,
    description="Custom quality metric"
)

verifier.utility_functions.append(custom_func)

MDP Analysis

# Export complete MDP model
verifier.export_mdp_model('mdp_analysis.json')

# Get validation statistics
validation_stats = verifier.get_validation_report()

4. Zengeza: Intelligent Noise Reduction (`lavoisier.ai_modules.zengeza`)

Purpose: Sophisticated noise detection and removal using statistical analysis, spectral entropy, and machine learning techniques.

Key Features

Statistical Noise Modeling: Advanced noise characterization
Spectral Entropy Analysis: Entropy-based noise detection
Machine Learning Denoising: Isolation Forest for outlier detection
Adaptive Filtering: Context-aware noise removal

Core Classes

`ZengezaNoiseReducer`

Advanced noise reduction system for mass spectrometry data.

from lavoisier.ai_modules.zengeza import ZengezaNoiseReducer

# Initialize noise reducer
reducer = ZengezaNoiseReducer(
    entropy_window=50,
    isolation_contamination=0.1,
    wavelet='db8',
    adaptive_threshold=0.95
)

# Analyze noise characteristics
mz_array = np.array([100.0, 101.0, 102.0, ...])
intensity_array = np.array([1000, 800, 1200, ...])

noise_profile = reducer.analyze_noise_characteristics(
    mz_array, 
    intensity_array, 
    spectrum_id="spectrum_001"
)

# Remove noise from spectrum
clean_mz, clean_intensity = reducer.remove_noise(
    mz_array, 
    intensity_array,
    spectrum_id="spectrum_001"
)

Noise Profile Analysis

# Get comprehensive noise analysis report
noise_report = reducer.get_noise_report("spectrum_001")

print(f"Baseline noise level: {noise_report['baseline_noise_level']:.2f}")
print(f"Signal-to-noise ratio: {noise_report['signal_to_noise_ratio']:.2f}")
print(f"Noise pattern type: {noise_report['noise_pattern_type']}")
print(f"Analysis confidence: {noise_report['analysis_confidence']:.2f}")

Noise Reduction Techniques

Baseline Noise Removal: Statistical baseline correction
Systematic Noise Removal: FFT-based periodic noise removal
Wavelet Denoising: Wavelet transform-based denoising
Entropy-Based Filtering: Spectral entropy thresholding
Isolation Forest Filtering: ML-based outlier detection

Advanced Features

# Analyze multiple noise patterns
noise_patterns = ['gaussian', 'poisson', 'uniform', 'mixed']

for pattern in noise_patterns:
    if noise_profile.noise_pattern == pattern:
        print(f"Detected {pattern} noise pattern")
        # Apply pattern-specific denoising

5. Nicotine: Context Verification System (`lavoisier.ai_modules.nicotine`)

Purpose: Context verification system using sophisticated cryptographic puzzles and pattern recognition to ensure AI maintains proper analysis context.

Key Features

Cryptographic Puzzles: Non-human readable verification challenges
Pattern Recognition: Complex pattern-based context validation
Temporal Consistency: Time-based context integrity checks
AI Integrity Assurance: Prevents context drift in AI systems

Core Classes

`NicotineContextVerifier`

Advanced context verification system with cryptographic puzzle challenges.

from lavoisier.ai_modules.nicotine import NicotineContextVerifier, PuzzleType

# Initialize context verifier
verifier = NicotineContextVerifier(
    puzzle_complexity=5,
    verification_frequency=100,
    max_context_age=3600,
    puzzle_timeout=30
)

# Create context snapshot
snapshot_id = verifier.create_context_snapshot(
    evidence_nodes=evidence_data,
    network_topology=network_graph,
    bayesian_states=probability_states,
    fuzzy_memberships=fuzzy_data,
    annotation_candidates=annotations
)

# Verify puzzle solutions
success, result = verifier.verify_context(puzzle_id, proposed_solution)

Puzzle Types

CRYPTOGRAPHIC_HASH: Multi-stage cryptographic challenges
PATTERN_RECONSTRUCTION: Complex pattern reconstruction tasks
TEMPORAL_SEQUENCE: Time-series pattern analysis
SPECTRAL_FINGERPRINT: Mass spectrum fingerprint challenges
MOLECULAR_TOPOLOGY: Molecular structure topology puzzles
EVIDENCE_CORRELATION: Evidence correlation matrix puzzles
BAYESIAN_CONSISTENCY: Bayesian network consistency checks
FUZZY_LOGIC_PROOF: Fuzzy logic verification challenges

Advanced Usage

Context Monitoring

# Get verification system status
status = verifier.get_verification_status()
print(f"Active puzzles: {status['active_puzzles']}")
print(f"Average difficulty: {status['average_difficulty']:.1f}")

# Clean up expired puzzles
verifier.cleanup_expired_puzzles()

# Export puzzle analytics
verifier.export_puzzle_analytics('puzzle_analytics.json')

Custom Puzzle Creation

# The system automatically creates context-specific puzzles
# Each puzzle is uniquely derived from the current analysis context
# Solutions require maintaining proper context throughout analysis

6. Diggiden: Adversarial Testing System (`lavoisier.ai_modules.diggiden`)

Purpose: Sophisticated adversarial testing system that actively searches for vulnerabilities in evidence networks through systematic attacks.

Key Features

Vulnerability Assessment: Systematic testing of evidence networks
Multiple Attack Vectors: Comprehensive attack strategy library
Security Analysis: Detailed security evaluation reports
Robustness Validation: Adversarial robustness verification

Core Classes

`DiggidenAdversarialTester`

Advanced adversarial testing system with multiple attack strategies.

from lavoisier.ai_modules.diggiden import DiggidenAdversarialTester, AttackType

# Initialize adversarial tester
tester = DiggidenAdversarialTester(
    attack_intensity=0.7,
    max_attack_iterations=1000,
    vulnerability_threshold=0.6,
    detection_evasion_level=0.8
)

# Set targets for testing
tester.set_targets(
    evidence_network=evidence_system,
    bayesian_system=bayesian_network,
    fuzzy_system=fuzzy_logic_system,
    context_verifier=context_system
)

# Launch comprehensive attack campaign
vulnerabilities = tester.launch_comprehensive_attack()

# Generate security report
security_report = tester.generate_security_report()

Attack Types

NOISE_INJECTION: Statistical noise injection attacks
EVIDENCE_CORRUPTION: Data integrity corruption attacks
NETWORK_FRAGMENTATION: Network topology attacks
BAYESIAN_POISONING: Probabilistic reasoning attacks
FUZZY_MANIPULATION: Fuzzy logic manipulation attacks
CONTEXT_DISRUPTION: Context verification attacks
ANNOTATION_SPOOFING: False annotation injection
TEMPORAL_INCONSISTENCY: Time-based consistency attacks

Security Analysis

Vulnerability Reporting

# Get attack statistics
attack_stats = tester.get_attack_statistics()
print(f"Total attacks: {attack_stats['total_attacks']}")
print(f"Success rate: {attack_stats['attack_success_rate']:.2%}")
print(f"Vulnerabilities found: {attack_stats['vulnerabilities_found']}")

# Export detailed security report
tester.export_security_report('security_assessment.json')

Vulnerability Assessment

# Analyze discovered vulnerabilities
for vuln in vulnerabilities:
    print(f"Vulnerability: {vuln.vulnerability_id}")
    print(f"Severity: {vuln.severity_score:.2f}")
    print(f"Attack type: {vuln.attack_type.value}")
    print(f"Affected components: {vuln.affected_components}")
    print(f"Recommended fixes: {vuln.recommended_fixes}")

Integration and Workflow

Complete AI Pipeline Integration

from lavoisier.ai_modules import (
    MzekezekeBayesianNetwork, HatataMDPVerifier, 
    ZengezaNoiseReducer, NicotineContextVerifier, 
    DiggidenAdversarialTester
)
from lavoisier.diadochi import DiadochiFramework

# Initialize all AI modules
noise_reducer = ZengezaNoiseReducer()
bayesian_network = MzekezekeBayesianNetwork()
mdp_verifier = HatataMDPVerifier()
context_verifier = NicotineContextVerifier()
adversarial_tester = DiggidenAdversarialTester()
llm_framework = DiadochiFramework()

# Complete analysis workflow
def analyze_ms_data(mz_array, intensity_array, compound_database):
    # 1. Noise reduction
    clean_mz, clean_intensity = noise_reducer.remove_noise(mz_array, intensity_array)
    
    # 2. Evidence network construction
    for i, (mz, intensity) in enumerate(zip(clean_mz, clean_intensity)):
        bayesian_network.add_evidence_node(mz, intensity, EvidenceType.MASS_MATCH)
    
    # 3. Bayesian inference
    bayesian_network.update_bayesian_network()
    
    # 4. MDP verification
    context = {
        'num_evidence_nodes': len(bayesian_network.evidence_nodes),
        'avg_posterior_probability': np.mean([n.posterior_probability 
                                            for n in bayesian_network.evidence_nodes.values()])
    }
    policy = mdp_verifier.solve_mdp()
    
    # 5. Generate annotations
    annotations = bayesian_network.generate_annotations(compound_database)
    
    # 6. Context verification
    snapshot_id = context_verifier.create_context_snapshot(
        evidence_nodes=bayesian_network.evidence_nodes,
        network_topology=bayesian_network.evidence_graph,
        bayesian_states=context,
        fuzzy_memberships={},
        annotation_candidates=annotations
    )
    
    # 7. Adversarial testing (optional, for robustness validation)
    adversarial_tester.set_targets(
        evidence_network=bayesian_network,
        context_verifier=context_verifier
    )
    
    return annotations, snapshot_id

# Execute analysis
results, context_id = analyze_ms_data(mz_data, intensity_data, compound_db)

Performance Monitoring

# Comprehensive system monitoring
def get_system_status():
    return {
        'noise_reduction': noise_reducer.get_noise_report(),
        'bayesian_network': bayesian_network.get_network_summary(),
        'mdp_verification': mdp_verifier.get_validation_report(),
        'context_verification': context_verifier.get_verification_status(),
        'adversarial_testing': adversarial_tester.get_attack_statistics()
    }

system_status = get_system_status()

Best Practices

1. Module Integration

Initialize modules in dependency order (noise reduction → evidence network → verification)
Use consistent data formats across all modules
Implement proper error handling and logging

2. Performance Optimization

Tune parameters based on data characteristics
Use parallel processing where possible
Monitor resource usage and adjust accordingly

3. Security Considerations

Regularly run adversarial tests
Verify context integrity frequently
Monitor for unusual patterns or anomalies

4. Quality Assurance

Use MDP verification for critical analyses
Cross-validate results across multiple modules
Maintain comprehensive audit trails

Debugging and Troubleshooting

Common Issues

Low Bayesian Confidence: Check evidence quality and network connectivity
Context Verification Failures: Review puzzle complexity and timeout settings
High Vulnerability Scores: Strengthen security measures and validation
Poor Noise Reduction: Adjust noise modeling parameters
MDP Convergence Issues: Modify discount factors and iteration limits

Debugging Tools

# Enable detailed logging
import logging
logging.getLogger('lavoisier.ai_modules').setLevel(logging.DEBUG)

# Export analysis reports
for module_name, module in [
    ('mzekezeke', bayesian_network),
    ('hatata', mdp_verifier),
    ('zengeza', noise_reducer),
    ('nicotine', context_verifier),
    ('diggiden', adversarial_tester)
]:
    if hasattr(module, 'export_analysis_report'):
        module.export_analysis_report(f'{module_name}_analysis.json')

For detailed API documentation and advanced usage examples, see the individual module docstrings and method signatures.

Lavoisier AI Modules Documentation

Overview

1. Diadochi Framework (lavoisier.diadochi)

Core Components

1. Core Framework (lavoisier.diadochi.core)

Key Classes

DomainExpert

DiadochiFramework

Domain Types

Integration Patterns

2. Routers (lavoisier.diadochi.routers)

Available Routers

KeywordRouter

EmbeddingRouter

HybridRouter

Router Usage Example

3. Chains (lavoisier.diadochi.chains)

Available Chain Types

SequentialChain

SummarizingChain

HierarchicalChain

AdaptiveChain

Quick Start Guide

1. Basic Setup

2. Using Router Ensemble

3. Using Sequential Chain

4. Automatic Pattern Selection

Advanced Usage

Custom Expert Implementation

Custom Router Implementation

Performance Monitoring

Framework Statistics

Expert Performance

Router Performance

Configuration Management

Export Configuration

Load Configuration

Integration with Lavoisier

Mass Spectrometry Analysis

Best Practices

Troubleshooting

Common Issues

Debugging

API Reference

Core Classes

Router Classes

Chain Classes

Enums

2. Mzekezeke: Bayesian Evidence Network (lavoisier.ai_modules.mzekezeke)

Key Features

Core Classes

MzekezekeBayesianNetwork

Evidence Types

Fuzzy Logic Integration

Advanced Features

Network Analysis

Evidence Correlation

3. Hatata: MDP Verification Layer (lavoisier.ai_modules.hatata)

Key Features

Core Classes

HatataMDPVerifier

MDP States

Utility Functions

Advanced Usage

Custom Utility Functions

MDP Analysis

4. Zengeza: Intelligent Noise Reduction (lavoisier.ai_modules.zengeza)

Key Features

Core Classes

ZengezaNoiseReducer

Noise Profile Analysis

Noise Reduction Techniques

Advanced Features

5. Nicotine: Context Verification System (lavoisier.ai_modules.nicotine)

Key Features

Core Classes

NicotineContextVerifier

Puzzle Types

Advanced Usage

Context Monitoring

1. Diadochi Framework (`lavoisier.diadochi`)

1. Core Framework (`lavoisier.diadochi.core`)

`DomainExpert`

`DiadochiFramework`

2. Routers (`lavoisier.diadochi.routers`)

`KeywordRouter`

`EmbeddingRouter`

`HybridRouter`

3. Chains (`lavoisier.diadochi.chains`)

`SequentialChain`

`SummarizingChain`

`HierarchicalChain`

`AdaptiveChain`

2. Mzekezeke: Bayesian Evidence Network (`lavoisier.ai_modules.mzekezeke`)

`MzekezekeBayesianNetwork`

3. Hatata: MDP Verification Layer (`lavoisier.ai_modules.hatata`)

`HatataMDPVerifier`

4. Zengeza: Intelligent Noise Reduction (`lavoisier.ai_modules.zengeza`)

`ZengezaNoiseReducer`

5. Nicotine: Context Verification System (`lavoisier.ai_modules.nicotine`)

`NicotineContextVerifier`

6. Diggiden: Adversarial Testing System (`lavoisier.ai_modules.diggiden`)

`DiggidenAdversarialTester`

7. Models Module (`lavoisier.models`)

7.1 Chemical Language Models (`lavoisier.models.chemical_language_models`)

7.2 Spectral Transformer Models (`lavoisier.models.spectral_transformers`)

7.3 Embedding Models (`lavoisier.models.embedding_models`)

7.4 Model Repository System (`lavoisier.models.repository`)

7.5 Knowledge Distillation (`lavoisier.models.distillation`)

7.6 Model Registry (`lavoisier.models.registry`)

8. LLM Integration Module (`lavoisier.llm`)

8.1 LLM Service Architecture (`lavoisier.llm.service`)

`DiadochiFramework`

2. Routers (`lavoisier.diadochi.routers`)

`KeywordRouter`

`EmbeddingRouter`

`HybridRouter`

3. Chains (`lavoisier.diadochi.chains`)

`SequentialChain`

`SummarizingChain`

`HierarchicalChain`

`AdaptiveChain`

2. Mzekezeke: Bayesian Evidence Network (`lavoisier.ai_modules.mzekezeke`)

`MzekezekeBayesianNetwork`