Buhera-Lavoisier Integration Guide
This guide explains how Buhera integrates with Lavoisier’s AI modules to provide goal-directed mass spectrometry analysis.
Table of Contents
- Architecture Overview
- AI Module Integration
- Python-Rust Bridge
- Execution Flow
- Error Handling
- Performance Considerations
- Extending the Integration
Architecture Overview
Buhera acts as the orchestration layer that coordinates Lavoisier’s AI modules for objective-focused analysis:
┌─────────────────────────────────────────────────────────────────┐
│ Buhera Integration Stack │
├─────────────────────────────────────────────────────────────────┤
│ Buhera Script (.bh) │
│ ├─ Objective declaration │
│ ├─ Validation rules │
│ └─ Analysis phases │
├─────────────────────────────────────────────────────────────────┤
│ Buhera Language Core (Rust) │
│ ├─ Parser: Converts .bh files to AST │
│ ├─ Validator: Pre-flight validation system │
│ └─ Executor: Orchestrates analysis execution │
├─────────────────────────────────────────────────────────────────┤
│ Python Bridge (PyO3) │
│ ├─ Script execution in Python context │
│ ├─ Data marshaling between Rust and Python │
│ └─ Error handling and result collection │
├─────────────────────────────────────────────────────────────────┤
│ Lavoisier AI Modules (Python) │
│ ├─ BuheraIntegration: Main coordination class │
│ ├─ Enhanced modules with objective awareness │
│ └─ Goal-directed evidence network building │
├─────────────────────────────────────────────────────────────────┤
│ Core Lavoisier Framework │
│ ├─ Numerical processing pipeline │
│ ├─ Visual processing pipeline │
│ └─ Data I/O and storage systems │
└─────────────────────────────────────────────────────────────────┘
AI Module Integration
Mzekezeke: Objective-Aware Bayesian Networks
Traditional Mzekezeke Usage
# Generic evidence network without objectives
network = mzekezeke.build_evidence_network(data)
Buhera-Enhanced Mzekezeke
# Objective-focused evidence network
network = mzekezeke.build_evidence_network(
data=data,
objective="diabetes_biomarker_discovery",
evidence_priorities=["pathway_membership", "ms2_fragmentation"],
biological_constraints=["glycolysis_upregulated", "insulin_resistance"],
success_criteria={"sensitivity": 0.85, "specificity": 0.85}
)
How Objectives Enhance Mzekezeke
- Evidence Weighting: Automatically adjusts evidence weights based on objective
- Pathway Focus: Prioritizes evidence from relevant biological pathways
- Success Monitoring: Continuously evaluates progress toward objective criteria
- Constraint Enforcement: Validates results against biological constraints
Implementation Details
class ObjectiveAwareBayesianNetwork:
def __init__(self, objective: BuheraObjective):
self.objective = objective
self.evidence_weights = self._calculate_objective_weights()
self.constraints = self._parse_biological_constraints()
def _calculate_objective_weights(self):
"""Calculate evidence weights based on objective type"""
if "biomarker" in self.objective.target.lower():
return {
"pathway_membership": 1.3,
"clinical_correlation": 1.2,
"ms2_fragmentation": 1.1,
"mass_match": 1.0
}
elif "metabolism" in self.objective.target.lower():
return {
"ms2_fragmentation": 1.4,
"retention_time": 1.2,
"mass_match": 1.0,
"pathway_membership": 0.9
}
# Add more objective types...
def build_network(self, data):
"""Build evidence network with objective focus"""
# Standard evidence collection
evidence = self._collect_evidence(data)
# Apply objective-specific weighting
weighted_evidence = self._apply_objective_weights(evidence)
# Validate against constraints
validated_evidence = self._validate_constraints(weighted_evidence)
return validated_evidence
Hatata: Objective-Aligned Validation
Enhanced Validation with Objectives
class ObjectiveAlignedValidator:
def __init__(self, objective: BuheraObjective):
self.objective = objective
self.success_criteria = self._parse_success_criteria()
def validate_with_objective(self, evidence_network):
"""Validate results against objective criteria"""
results = {
"success": False,
"confidence": 0.0,
"criteria_met": {},
"recommendations": []
}
# Check each success criterion
for criterion, threshold in self.success_criteria.items():
current_value = self._evaluate_criterion(evidence_network, criterion)
results["criteria_met"][criterion] = {
"current": current_value,
"threshold": threshold,
"met": current_value >= threshold
}
# Overall success assessment
results["success"] = all(
result["met"] for result in results["criteria_met"].values()
)
# Generate recommendations for improvement
if not results["success"]:
results["recommendations"] = self._generate_recommendations(results)
return results
Zengeza: Context-Preserving Noise Reduction
Objective-Aware Noise Reduction
class ContextPreservingNoiseReducer:
def __init__(self, objective_context: str):
self.objective_context = objective_context
self.preserve_patterns = self._get_preservation_patterns()
def _get_preservation_patterns(self):
"""Get patterns to preserve based on objective"""
patterns = {
"diabetes_biomarker_discovery": [
"glucose_metabolism",
"lipid_metabolism",
"amino_acid_metabolism"
],
"drug_metabolism_characterization": [
"cyp450_products",
"phase2_conjugates",
"parent_compound_fragments"
],
"environmental_analysis": [
"pesticide_patterns",
"industrial_contaminants",
"degradation_products"
]
}
return patterns.get(self.objective_context, [])
def reduce_noise(self, data):
"""Reduce noise while preserving objective-relevant signals"""
# Standard noise reduction
cleaned_data = self._standard_noise_reduction(data)
# Identify and preserve objective-relevant patterns
preserved_signals = self._identify_preserve_patterns(data)
# Merge preserved signals back
final_data = self._merge_preserved_signals(
cleaned_data, preserved_signals
)
return final_data
Nicotine: Objective-Context Verification
class ObjectiveContextVerifier:
def verify_analysis_context(self, analysis_results, objective):
"""Verify that analysis maintains objective context"""
context_checks = {
"objective_alignment": self._check_objective_alignment(
analysis_results, objective
),
"biological_coherence": self._check_biological_coherence(
analysis_results, objective.biological_constraints
),
"statistical_validity": self._check_statistical_validity(
analysis_results, objective.statistical_requirements
)
}
return context_checks
Diggiden: Objective-Specific Adversarial Testing
class ObjectiveAdversarialTester:
def test_objective_robustness(self, analysis_results, objective):
"""Test robustness of results against objective-specific perturbations"""
if "biomarker" in objective.target.lower():
return self._test_biomarker_robustness(analysis_results)
elif "metabolism" in objective.target.lower():
return self._test_metabolism_robustness(analysis_results)
# Add more objective-specific tests...
def _test_biomarker_robustness(self, results):
"""Test biomarker-specific vulnerabilities"""
perturbations = [
"batch_effect_simulation",
"population_drift",
"instrument_variation",
"storage_degradation"
]
robustness_scores = {}
for perturbation in perturbations:
score = self._apply_perturbation_and_test(results, perturbation)
robustness_scores[perturbation] = score
return robustness_scores
Python-Rust Bridge
Data Marshaling
The Python-Rust bridge handles data conversion between Buhera’s Rust core and Lavoisier’s Python modules:
// Rust side - lavoisier-buhera/src/python_bridge.rs
use pyo3::prelude::*;
#[pyclass]
pub struct BuheraScript {
#[pyo3(get)]
pub objective: String,
#[pyo3(get)]
pub validation_rules: Vec<String>,
#[pyo3(get)]
pub analysis_phases: Vec<String>,
}
#[pyclass]
pub struct ExecutionResult {
#[pyo3(get)]
pub success: bool,
#[pyo3(get)]
pub confidence: f64,
#[pyo3(get)]
pub evidence_scores: std::collections::HashMap<String, f64>,
#[pyo3(get)]
pub execution_time: f64,
}
#[pyfunction]
pub fn execute_buhera_script(script_path: String) -> PyResult<ExecutionResult> {
// Parse script
let script = parse_buhera_script(&script_path)?;
// Validate script
let validation_result = validate_script(&script)?;
if !validation_result.is_valid {
return Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(
validation_result.error_message
));
}
// Execute with Python integration
Python::with_gil(|py| {
let lavoisier = py.import("lavoisier")?;
let buhera_integration = lavoisier.getattr("ai_modules")?.getattr("buhera_integration")?;
let result = buhera_integration.call_method1("execute_script", (script,))?;
Ok(ExecutionResult {
success: result.getattr("success")?.extract()?,
confidence: result.getattr("confidence")?.extract()?,
evidence_scores: result.getattr("evidence_scores")?.extract()?,
execution_time: result.getattr("execution_time")?.extract()?,
})
})
}
Python Side Integration
# lavoisier/ai_modules/buhera_integration.py
from typing import Dict, Any, List
import time
from dataclasses import dataclass
@dataclass
class BuheraExecutionResult:
success: bool
confidence: float
evidence_scores: Dict[str, float]
execution_time: float
annotations: List[Dict[str, Any]]
recommendations: List[str]
class BuheraIntegration:
def __init__(self):
self.ai_modules = self._initialize_ai_modules()
def _initialize_ai_modules(self):
"""Initialize Lavoisier AI modules with Buhera integration"""
return {
"mzekezeke": self._initialize_mzekezeke(),
"hatata": self._initialize_hatata(),
"zengeza": self._initialize_zengeza(),
"nicotine": self._initialize_nicotine(),
"diggiden": self._initialize_diggiden()
}
def execute_script(self, script_dict: Dict[str, Any]) -> BuheraExecutionResult:
"""Execute Buhera script with Lavoisier integration"""
start_time = time.time()
try:
# Extract objective and create enhanced modules
objective = BuheraObjective.from_dict(script_dict["objective"])
enhanced_modules = self._create_objective_aware_modules(objective)
# Execute analysis phases
results = self._execute_analysis_phases(
script_dict["phases"],
enhanced_modules,
objective
)
# Validate results against objective
validation_results = enhanced_modules["hatata"].validate_with_objective(
results, objective
)
execution_time = time.time() - start_time
return BuheraExecutionResult(
success=validation_results["success"],
confidence=validation_results["confidence"],
evidence_scores=results.get("evidence_scores", {}),
execution_time=execution_time,
annotations=results.get("annotations", []),
recommendations=validation_results.get("recommendations", [])
)
except Exception as e:
execution_time = time.time() - start_time
return BuheraExecutionResult(
success=False,
confidence=0.0,
evidence_scores={},
execution_time=execution_time,
annotations=[],
recommendations=[f"Execution failed: {str(e)}"]
)
Execution Flow
1. Script Parsing and Validation
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Buhera Script │───▶│ Rust Parser │───▶│ AST Generation │
│ (.bh) │ │ (nom-based) │ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
▼
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Validation │◀───│ Pre-flight │◀───│ Validation │
│ Results │ │ Validation │ │ Rules │
└─────────────────┘ └──────────────────┘ └─────────────────┘
2. Python Integration
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Validated AST │───▶│ Python Bridge │───▶│ BuheraIntegra- │
│ │ │ (PyO3) │ │ tion Class │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
▼
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Enhanced AI │◀───│ Objective-Aware │◀───│ AI Module │
│ Modules │ │ Module Creation │ │ Initialization │
└─────────────────┘ └──────────────────┘ └─────────────────┘
3. Goal-Directed Analysis
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Data Loading │───▶│ Evidence │───▶│ Bayesian │
│ (Objective- │ │ Collection │ │ Inference │
│ Focused) │ │ │ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Context │ │ Objective- │ │ Success │
│ Preservation │ │ Weighted │ │ Criteria │
│ (Zengeza) │ │ Evidence │ │ Validation │
└─────────────────┘ └──────────────────┘ └─────────────────┘
4. Result Validation and Reporting
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Analysis │───▶│ Objective │───▶│ Success │
│ Results │ │ Validation │ │ Assessment │
│ │ │ (Hatata) │ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Robustness │ │ Context │ │ Final Report │
│ Testing │ │ Verification │ │ Generation │
│ (Diggiden) │ │ (Nicotine) │ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
Error Handling
Validation Errors
class BuheraValidationError(Exception):
def __init__(self, validation_type: str, message: str, suggestions: List[str]):
self.validation_type = validation_type
self.message = message
self.suggestions = suggestions
super().__init__(f"{validation_type}: {message}")
# Example usage
try:
result = execute_buhera_script("script.bh")
except BuheraValidationError as e:
print(f"Validation failed: {e.message}")
print("Suggestions:")
for suggestion in e.suggestions:
print(f" - {suggestion}")
Runtime Errors
class BuheraRuntimeError(Exception):
def __init__(self, phase: str, operation: str, error: str):
self.phase = phase
self.operation = operation
self.error = error
super().__init__(f"Runtime error in {phase}.{operation}: {error}")
# Error recovery mechanism
def execute_with_recovery(script_path: str) -> BuheraExecutionResult:
try:
return execute_buhera_script(script_path)
except BuheraRuntimeError as e:
# Attempt recovery based on error type
if "memory" in e.error.lower():
return execute_with_memory_optimization(script_path)
elif "timeout" in e.error.lower():
return execute_with_extended_timeout(script_path)
else:
raise
Performance Considerations
Memory Management
class MemoryEfficientExecution:
def __init__(self, max_memory_gb: float = 8.0):
self.max_memory_gb = max_memory_gb
self.current_memory_usage = 0.0
def execute_with_memory_monitoring(self, script_dict: Dict[str, Any]):
"""Execute script with memory monitoring and optimization"""
memory_monitor = MemoryMonitor()
try:
# Check if data can fit in memory
estimated_memory = self._estimate_memory_usage(script_dict)
if estimated_memory > self.max_memory_gb:
return self._execute_chunked_analysis(script_dict)
else:
return self._execute_standard_analysis(script_dict)
finally:
memory_monitor.cleanup()
Parallel Processing
class ParallelBuheraExecution:
def __init__(self, max_workers: int = 4):
self.max_workers = max_workers
def execute_parallel_validation(self, script_dict: Dict[str, Any]):
"""Execute validation rules in parallel"""
validation_tasks = self._create_validation_tasks(script_dict)
with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
validation_futures = {
executor.submit(self._execute_validation, task): task
for task in validation_tasks
}
results = {}
for future in as_completed(validation_futures):
task = validation_futures[future]
try:
results[task.name] = future.result()
except Exception as e:
results[task.name] = ValidationResult(
success=False,
error=str(e)
)
return results
Extending the Integration
Adding New AI Modules
class CustomAIModule:
def __init__(self, objective: BuheraObjective):
self.objective = objective
self.module_config = self._configure_for_objective()
def _configure_for_objective(self):
"""Configure module based on objective"""
# Custom configuration logic
pass
def process_with_objective(self, data):
"""Process data with objective awareness"""
# Custom processing logic
pass
# Register new module
def register_custom_module():
BuheraIntegration.register_ai_module("custom_module", CustomAIModule)
Custom Validation Rules
class CustomValidationRule:
def __init__(self, rule_name: str, validation_function):
self.rule_name = rule_name
self.validation_function = validation_function
def validate(self, script_dict: Dict[str, Any]) -> ValidationResult:
"""Execute custom validation logic"""
return self.validation_function(script_dict)
# Register custom validation
def register_custom_validation():
BuheraValidator.register_validation_rule(
"check_custom_requirement",
CustomValidationRule("custom_check", custom_validation_logic)
)
This integration provides a seamless bridge between Buhera’s goal-directed scripting and Lavoisier’s powerful AI modules, enabling unprecedented precision in mass spectrometry analysis.