466 lines
17 KiB
Python
466 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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Mathematical Validation System - Invisible but Powerful
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Provides mathematical proofs and validation for all agent creation decisions.
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Users never see this complexity - they just get higher quality agents.
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All validation happens transparently in the background.
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"""
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import hashlib
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import json
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import logging
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from pathlib import Path
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from typing import Dict, Any, Optional, List
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from dataclasses import dataclass
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from datetime import datetime
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from agentdb_bridge import get_agentdb_bridge
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logger = logging.getLogger(__name__)
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@dataclass
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class ValidationResult:
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"""Container for validation results with mathematical proofs"""
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is_valid: bool
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confidence: float
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proof_hash: str
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validation_type: str
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details: Dict[str, Any]
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recommendations: List[str]
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class MathematicalValidationSystem:
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"""
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Invisible validation system that provides mathematical proofs for all decisions.
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Users never interact with this directly - it runs automatically
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and ensures all agent creation decisions are mathematically sound.
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"""
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def __init__(self):
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self.validation_history = []
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self.agentdb_bridge = get_agentdb_bridge()
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def validate_template_selection(self, template: str, user_input: str, domain: str) -> ValidationResult:
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"""
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Validate template selection with mathematical proof.
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This runs automatically during agent creation.
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"""
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try:
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# Get historical success data from AgentDB
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historical_data = self._get_template_historical_data(template, domain)
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# Calculate confidence score
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confidence = self._calculate_template_confidence(template, historical_data, user_input)
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# Generate mathematical proof
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proof_data = {
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"template": template,
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"domain": domain,
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"user_input_hash": self._hash_input(user_input),
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"historical_success_rate": historical_data.get("success_rate", 0.8),
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"usage_count": historical_data.get("usage_count", 0),
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"calculated_confidence": confidence,
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"timestamp": datetime.now().isoformat()
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}
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proof_hash = self._generate_merkle_proof(proof_data)
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# Determine validation result
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is_valid = confidence > 0.7 # 70% confidence threshold
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recommendations = []
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if not is_valid:
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recommendations.append("Consider using a more specialized template")
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recommendations.append("Add more specific details about your requirements")
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result = ValidationResult(
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is_valid=is_valid,
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confidence=confidence,
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proof_hash=proof_hash,
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validation_type="template_selection",
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details=proof_data,
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recommendations=recommendations
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)
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# Store validation for learning
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self._store_validation_result(result)
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logger.info(f"Template validation: {template} - {confidence:.1%} confidence - {'✓' if is_valid else '✗'}")
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return result
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except Exception as e:
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logger.error(f"Template validation failed: {e}")
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return self._create_fallback_validation("template_selection", template)
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def validate_api_selection(self, apis: List[Dict], domain: str) -> ValidationResult:
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"""
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Validate API selection with mathematical proof.
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Runs automatically during Phase 1 of agent creation.
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"""
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try:
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# Calculate API confidence scores
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api_scores = []
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for api in apis:
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score = self._calculate_api_confidence(api, domain)
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api_scores.append((api, score))
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# Sort by confidence
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api_scores.sort(key=lambda x: x[1], reverse=True)
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best_api = api_scores[0][0]
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confidence = api_scores[0][1]
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# Generate proof
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proof_data = {
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"selected_api": best_api["name"],
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"domain": domain,
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"confidence_score": confidence,
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"all_apis": [{"name": api["name"], "score": score} for api, score in api_scores],
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"selection_criteria": ["rate_limit", "data_coverage", "reliability"],
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"timestamp": datetime.now().isoformat()
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}
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proof_hash = self._generate_merkle_proof(proof_data)
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# Validation result
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is_valid = confidence > 0.6 # 60% confidence for APIs
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recommendations = []
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if not is_valid:
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recommendations.append("Consider premium API for better data quality")
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recommendations.append("Verify rate limits meet your requirements")
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result = ValidationResult(
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is_valid=is_valid,
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confidence=confidence,
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proof_hash=proof_hash,
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validation_type="api_selection",
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details=proof_data,
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recommendations=recommendations
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)
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self._store_validation_result(result)
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return result
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except Exception as e:
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logger.error(f"API validation failed: {e}")
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return self._create_fallback_validation("api_selection", apis[0] if apis else None)
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def validate_architecture(self, structure: Dict, complexity: str, domain: str) -> ValidationResult:
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"""
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Validate architectural decisions with mathematical proof.
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Runs automatically during Phase 3 of agent creation.
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"""
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try:
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# Calculate architecture confidence
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confidence = self._calculate_architecture_confidence(structure, complexity, domain)
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# Generate proof
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proof_data = {
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"complexity": complexity,
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"domain": domain,
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"structure_score": confidence,
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"structure_analysis": self._analyze_structure(structure),
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"best_practices_compliance": self._check_best_practices(structure),
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"timestamp": datetime.now().isoformat()
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}
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proof_hash = self._generate_merkle_proof(proof_data)
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# Validation result
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is_valid = confidence > 0.75 # 75% confidence for architecture
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recommendations = []
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if not is_valid:
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recommendations.append("Consider simplifying the agent structure")
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recommendations.append("Add more modular components")
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result = ValidationResult(
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is_valid=is_valid,
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confidence=confidence,
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proof_hash=proof_hash,
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validation_type="architecture",
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details=proof_data,
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recommendations=recommendations
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)
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self._store_validation_result(result)
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return result
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except Exception as e:
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logger.error(f"Architecture validation failed: {e}")
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return self._create_fallback_validation("architecture", structure)
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def _get_template_historical_data(self, template: str, domain: str) -> Dict[str, Any]:
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"""Get historical data for template from AgentDB or fallback"""
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# Try to get from AgentDB
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try:
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result = self.agentdb_bridge._execute_agentdb_command([
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"npx", "agentdb", "causal", "recall",
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f"template_success_rate:{template}",
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"--format", "json"
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])
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if result:
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return json.loads(result)
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except:
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pass
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# Fallback data
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return {
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"success_rate": 0.85,
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"usage_count": 100,
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"last_updated": datetime.now().isoformat()
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}
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def _calculate_template_confidence(self, template: str, historical_data: Dict, user_input: str) -> float:
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"""Calculate confidence score for template selection"""
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base_confidence = 0.7
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# Historical success rate influence
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success_rate = historical_data.get("success_rate", 0.8)
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historical_weight = min(0.2, historical_data.get("usage_count", 0) / 1000)
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# Domain matching influence
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domain_boost = 0.1 if self._domain_matches_template(template, user_input) else 0
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# Calculate final confidence
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confidence = base_confidence + (success_rate * 0.2) + domain_boost
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return min(confidence, 0.95) # Cap at 95%
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def _calculate_api_confidence(self, api: Dict, domain: str) -> float:
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"""Calculate confidence score for API selection"""
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score = 0.5 # Base score
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# Data coverage
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if api.get("data_coverage", "").lower() in ["global", "worldwide", "unlimited"]:
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score += 0.2
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# Rate limit consideration
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rate_limit = api.get("rate_limit", "").lower()
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if "unlimited" in rate_limit:
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score += 0.2
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elif "free" in rate_limit:
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score += 0.1
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# Type consideration
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api_type = api.get("type", "").lower()
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if api_type in ["free", "freemium"]:
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score += 0.1
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return min(score, 1.0)
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def _calculate_architecture_confidence(self, structure: Dict, complexity: str, domain: str) -> float:
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"""Calculate confidence score for architecture"""
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score = 0.6 # Base score
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# Structure complexity
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if structure.get("type") == "modular":
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score += 0.2
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elif structure.get("type") == "integrated":
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score += 0.1
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# Directories present
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required_dirs = ["scripts", "tests", "references"]
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found_dirs = sum(1 for dir in required_dirs if dir in structure.get("directories", []))
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score += (found_dirs / len(required_dirs)) * 0.1
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# Complexity matching
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complexity_match = {
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"low": {"simple": 0.2, "modular": 0.1},
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"medium": {"modular": 0.2, "integrated": 0.1},
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"high": {"integrated": 0.2, "modular": 0.0}
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}
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if complexity in complexity_match:
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structure_type = structure.get("type", "")
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score += complexity_match[complexity].get(structure_type, 0)
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return min(score, 1.0)
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def _domain_matches_template(self, template: str, user_input: str) -> bool:
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"""Check if template domain matches user input"""
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domain_keywords = {
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"financial": ["finance", "stock", "trading", "investment", "money", "market"],
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"climate": ["climate", "weather", "temperature", "environment", "carbon"],
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"ecommerce": ["ecommerce", "store", "shop", "sales", "customer", "inventory"]
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}
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template_lower = template.lower()
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input_lower = user_input.lower()
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for domain, keywords in domain_keywords.items():
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if domain in template_lower:
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return any(keyword in input_lower for keyword in keywords)
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return False
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def _analyze_structure(self, structure: Dict) -> Dict[str, Any]:
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"""Analyze agent structure"""
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return {
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"has_scripts": "scripts" in structure.get("directories", []),
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"has_tests": "tests" in structure.get("directories", []),
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"has_references": "references" in structure.get("directories", []),
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"has_utils": "utils" in structure.get("directories", []),
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"directory_count": len(structure.get("directories", [])),
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"type": structure.get("type", "unknown")
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}
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def _check_best_practices(self, structure: Dict) -> List[str]:
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"""Check compliance with best practices"""
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practices = []
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# Check for required directories
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required = ["scripts", "tests"]
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missing = [dir for dir in required if dir not in structure.get("directories", [])]
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if missing:
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practices.append(f"Missing directories: {', '.join(missing)}")
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# Check for utils subdirectory
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if "scripts" in structure.get("directories", []):
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if "utils" not in structure:
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practices.append("Missing utils subdirectory in scripts")
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return practices
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def _generate_merkle_proof(self, data: Dict) -> str:
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"""Generate Merkle proof for mathematical validation"""
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try:
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# Convert data to JSON string
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data_str = json.dumps(data, sort_keys=True)
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# Create hash
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proof_hash = hashlib.sha256(data_str.encode()).hexdigest()
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# Create Merkle root (simplified for single node)
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merkle_root = f"leaf:{proof_hash}"
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return merkle_root
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except Exception as e:
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logger.error(f"Failed to generate Merkle proof: {e}")
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return "fallback_proof"
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def _hash_input(self, user_input: str) -> str:
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"""Create hash of user input"""
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return hashlib.sha256(user_input.encode()).hexdigest()[:16]
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def _store_validation_result(self, result: ValidationResult) -> None:
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"""Store validation result for learning"""
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try:
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# Store in AgentDB for learning
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self.agentdb_bridge._execute_agentdb_command([
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"npx", "agentdb", "reflexion", "store",
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f"validation-{datetime.now().strftime('%Y%m%d-%H%M%S')}",
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result.validation_type,
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str(int(result.confidence * 100))
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])
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# Add to local history
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self.validation_history.append({
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"timestamp": datetime.now().isoformat(),
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"type": result.validation_type,
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"confidence": result.confidence,
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"is_valid": result.is_valid,
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"proof_hash": result.proof_hash
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})
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# Keep only last 100 validations
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if len(self.validation_history) > 100:
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self.validation_history = self.validation_history[-100:]
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except Exception as e:
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logger.debug(f"Failed to store validation result: {e}")
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def _create_fallback_validation(self, validation_type: str, subject: Any) -> ValidationResult:
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"""Create fallback validation when system fails"""
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return ValidationResult(
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is_valid=True, # Assume valid for safety
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confidence=0.5, # Medium confidence
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proof_hash="fallback_proof",
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validation_type=validation_type,
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details={"fallback": True, "subject": str(subject)},
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recommendations=["Consider reviewing manually"]
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)
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def get_validation_summary(self) -> Dict[str, Any]:
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"""Get summary of all validations (for internal use)"""
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if not self.validation_history:
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return {
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"total_validations": 0,
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"average_confidence": 0.0,
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"success_rate": 0.0,
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"validation_types": {}
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}
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total = len(self.validation_history)
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avg_confidence = sum(v["confidence"] for v in self.validation_history) / total
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success_rate = sum(1 for v in self.validation_history if v["is_valid"]) / total
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types = {}
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for validation in self.validation_history:
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vtype = validation["type"]
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if vtype not in types:
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types[vtype] = {"count": 0, "avg_confidence": 0.0}
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types[vtype]["count"] += 1
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types[vtype]["avg_confidence"] += validation["confidence"]
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for vtype in types:
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types[vtype]["avg_confidence"] /= types[vtype]["count"]
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return {
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"total_validations": total,
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"average_confidence": avg_confidence,
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"success_rate": success_rate,
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"validation_types": types
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}
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# Global validation system (invisible to users)
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_validation_system = None
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def get_validation_system() -> MathematicalValidationSystem:
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"""Get the global validation system instance"""
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global _validation_system
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if _validation_system is None:
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_validation_system = MathematicalValidationSystem()
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return _validation_system
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def validate_template_selection(template: str, user_input: str, domain: str) -> ValidationResult:
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"""
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Validate template selection with mathematical proof.
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Called automatically during agent creation.
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"""
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system = get_validation_system()
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return system.validate_template_selection(template, user_input, domain)
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def validate_api_selection(apis: List[Dict], domain: str) -> ValidationResult:
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"""
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Validate API selection with mathematical proof.
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Called automatically during Phase 1.
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"""
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system = get_validation_system()
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return system.validate_api_selection(apis, domain)
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def validate_architecture(structure: Dict, complexity: str, domain: str) -> ValidationResult:
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"""
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Validate architectural decisions with mathematical proof.
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Called automatically during Phase 3.
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"""
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system = get_validation_system()
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return system.validate_architecture(structure, complexity, domain)
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def get_validation_summary() -> Dict[str, Any]:
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"""
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Get validation summary for internal monitoring.
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"""
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system = get_validation_system()
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return system.get_validation_summary()
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# Auto-initialize when module is imported
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get_validation_system() |