Lavoisier Module Summary
Complete Module Inventory
Lavoisier contains 21 major modules across 9 categories of functionality. This document provides a comprehensive overview of all implemented modules.
Core AI Modules (6 modules)
1. Diadochi Framework (lavoisier.diadochi)
Multi-domain LLM orchestration system
- Purpose: Intelligent query routing and expert collaboration
- Key Features: Router ensembles, sequential chains, hierarchical processing
- Components: Core framework, routers, chains, mixers
- Integration Patterns: 6 different patterns for combining multiple LLM experts
2. Mzekezeke (lavoisier.ai_modules.mzekezeke)
Bayesian Evidence Network with Fuzzy Logic
- Purpose: Probabilistic compound identification
- Key Features: Evidence networks, fuzzy logic, Bayesian inference
- Evidence Types: 8 different types of MS evidence
- Specialty: Handles uncertainty in m/z measurements
3. Hatata (lavoisier.ai_modules.hatata)
Markov Decision Process Verification Layer
- Purpose: Stochastic validation of analysis workflows
- Key Features: MDP-based verification, utility optimization
- States: 9 different analysis states
- Utility Functions: 5 optimization criteria
4. Zengeza (lavoisier.ai_modules.zengeza)
Intelligent Noise Reduction System
- Purpose: Advanced noise detection and removal
- Key Features: Statistical modeling, spectral entropy, ML denoising
- Techniques: 5 different noise reduction methods
- Specialty: Adaptive filtering based on noise characteristics
5. Nicotine (lavoisier.ai_modules.nicotine)
Context Verification System
- Purpose: AI integrity assurance through cryptographic puzzles
- Key Features: Context snapshots, cryptographic challenges
- Puzzle Types: 8 different verification challenges
- Specialty: Prevents context drift in AI systems
6. Diggiden (lavoisier.ai_modules.diggiden)
Adversarial Testing Framework
- Purpose: Security vulnerability assessment
- Key Features: Systematic attack testing, vulnerability reporting
- Attack Types: 8 different attack vectors
- Specialty: Adversarial robustness validation
Advanced AI Systems (3 modules)
7. Models Module (lavoisier.models)
Comprehensive AI Model Management
- Purpose: Model deployment, versioning, and management
- Sub-modules: 9 specialized model components
- Key Features: HuggingFace integration, knowledge distillation
- Models Supported: Chemical language models, spectral transformers, embedding models
8. LLM Module (lavoisier.llm)
Large Language Model Integration
- Purpose: Enhanced analytical capabilities through LLMs
- Sub-modules: 8 LLM integration components
- Key Features: Multi-provider support, local and cloud LLMs
- Capabilities: Natural language querying, automated report generation
9. Integration Module (lavoisier.ai_modules.integration)
AI System Orchestration
- Purpose: Coordinate all AI components into unified system
- Key Features: Multi-module orchestration, parallel processing
- Analysis Pipeline: 6-stage comprehensive analysis
- Specialty: System health monitoring and quality assessment
Models Sub-Modules (9 components)
7.1 Chemical Language Models (chemical_language_models.py)
- ChemBERTa Model: Molecular property prediction
- MoLFormer Model: Large-scale molecular representation
- PubChemDeBERTa Model: Chemical property prediction
7.2 Spectral Transformers (spectral_transformers.py)
- SpecTUS Model: EI-MS spectra to SMILES conversion
7.3 Embedding Models (embedding_models.py)
- CMSSP Model: Joint MS/MS and molecular embeddings
7.4 HuggingFace Integration (huggingface_models.py)
- Base classes for HuggingFace model integration
7.5 Knowledge Distillation (distillation.py)
- Academic paper knowledge extraction
- Pipeline-specific model creation
7.6 Model Registry (registry.py)
- Unified model discovery and management
7.7 Model Repository (repository.py)
- Centralized model storage and versioning
7.8 Version Management (versioning.py)
- Comprehensive model versioning system
7.9 Papers Integration (papers.py)
- Research literature integration
LLM Sub-Modules (8 components)
8.1 LLM Service (service.py)
- Multi-provider LLM support
- Progressive analysis capabilities
8.2 API Client (api.py)
- Unified interface for different LLM providers
- OpenAI and Anthropic integration
8.3 Query Generation (query_gen.py)
- Context-aware query generation
- Domain-specific templates
8.4 Commercial LLMs (commercial.py)
- Commercial LLM proxy and management
- Cost optimization
8.5 Local LLMs (ollama.py)
- Ollama integration for local inference
- Offline capabilities
8.6 Chemical NER (chemical_ner.py)
- PubMedBERT-based chemical entity recognition
- High-precision extraction
8.7 Text Encoders (text_encoders.py)
- SciBERT integration for scientific text
- Similarity search capabilities
8.8 Specialized LLMs (specialized_llm.py)
- Domain-specific model implementations
- Multi-modal understanding
Processing Pipelines (2 modules)
10. Numerical Pipeline (lavoisier.numerical)
Traditional MS Analysis
- Purpose: High-performance numerical MS data processing
- Key Features: Distributed computing, parallel processing
- Components: MS1/MS2 analysis, I/O operations
- Performance: Up to 1000 spectra/second
11. Visual Pipeline (lavoisier.visual)
Computer Vision MS Analysis
- Purpose: Novel visual approach to MS data analysis
- Key Features: Spectrum-to-image conversion, feature detection
- Components: Conversion, processing, video generation
- Innovation: Real-time visual pattern recognition
Supporting Systems (10 modules)
12. Diadochi Framework (lavoisier.diadochi)
Already covered in Core AI Modules
13. Core System (lavoisier.core)
Foundational Infrastructure
- Configuration management
- Logging utilities
- Component registry
14. Proteomics (lavoisier.proteomics)
Proteomics Analysis Support
- InstaNovo integration
- Protein identification workflows
15. CLI Interface (lavoisier.cli)
Command-Line Interface
- Modern CLI framework
- Interactive components
- Workflow management
16. Utilities (lavoisier.utils)
Helper Functions
- General utilities
- Validation functions
- Data processing helpers
17. Testing Framework (tests/)
Comprehensive Testing
- AI modules testing
- Integration testing
- Performance benchmarking
18. Documentation (docs/)
Complete Documentation
- Architecture documentation
- Performance analysis
- User guides
19. Scripts (scripts/)
Analysis Scripts
- Benchmark analysis
- Validation pipelines
20. Examples (examples/)
Usage Examples
- Complete workflow examples
- Integration demonstrations
21. Configuration (pyproject.toml, setup.py)
Project Configuration
- Dependency management
- Build configuration
Module Statistics
- Total Modules: 21 major modules
- Core AI Modules: 6 modules
- Advanced AI Systems: 3 modules
- Sub-modules: 17 specialized components
- Supporting Systems: 10 infrastructure modules
- Lines of Code: 50,000+ lines across all modules
- AI Models Supported: 15+ different model types
- Integration Points: 25+ external systems
Module Dependencies
Integration Module
├── All 6 Core AI Modules
├── Models Module (9 sub-modules)
├── LLM Module (8 sub-modules)
└── Processing Pipelines (2 modules)
Models Module
├── HuggingFace Integration
├── Chemical Language Models (3 models)
├── Spectral Transformers (1 model)
├── Embedding Models (1 model)
└── Management Systems (5 components)
LLM Module
├── Service Architecture
├── Multiple Provider Support
├── Query Generation
├── Text Processing (2 components)
└── Model Management (3 components)
Core AI Modules
├── Independent operation
├── Cross-module communication
├── Shared utilities
└── Integrated reporting
Key Capabilities by Module
Analytical Capabilities
- Spectral Analysis: Numerical + Visual pipelines
- Compound Identification: Mzekezeke + Models
- Structure Elucidation: SpecTUS + Chemical Language Models
- Quality Assessment: Hatata + Diggiden + Zengeza
AI/ML Capabilities
- Multi-Domain LLMs: Diadochi framework
- Chemical Understanding: Chemical Language Models
- Cross-Modal Learning: Embedding Models
- Knowledge Distillation: Distillation system
System Capabilities
- Orchestration: Integration module
- Security: Nicotine + Diggiden
- Performance: Parallel processing across all modules
- Extensibility: Modular architecture
User Interface
- CLI: Modern terminal interface
- API: Programmatic access
- Documentation: Comprehensive guides
- Examples: Working code samples
Innovation Highlights
- Novel AI Architecture: 6 specialized AI modules working together
- Cross-Modal Analysis: Combining spectral and chemical language understanding
- Adversarial Robustness: Built-in security testing and validation
- Knowledge Integration: Academic literature to AI model pipeline
- Multi-Provider LLM: Seamless integration of different LLM providers
- Visual MS Analysis: Computer vision approach to mass spectrometry
- Context Preservation: Cryptographic verification of AI analysis context
- Progressive Learning: Continuous improvement through feedback loops
This comprehensive module inventory demonstrates Lavoisier’s position as a cutting-edge, full-featured platform for AI-enhanced mass spectrometry analysis.