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# to you under the Apache License, Version 2.0 (the
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# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
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# KIND, either express or implied.  See the License for the
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# under the License.

import asyncio
import logging
from typing import Dict, Any, List, Optional
import json
from datetime import datetime

class ResilientDBKnowledgeBase:
    """
    Comprehensive knowledge base for ResilientDB ecosystem, applications, and distributed systems
    """
    
    def __init__(self):
        self.applications_catalog = self._initialize_applications_catalog()
        self.research_papers = self._initialize_research_database()
        self.architecture_knowledge = self._initialize_architecture_knowledge()
        self.performance_benchmarks = self._initialize_performance_data()
        self.use_case_database = self._initialize_use_cases()
        
    def _initialize_applications_catalog(self) -> Dict[str, Any]:
        """Comprehensive catalog of all ResilientDB applications and their capabilities"""
        return {
            "debitable": {
                "name": "Debitable",
                "category": "Social Media & Content",
                "description": "Decentralized social media platform built on ResilientDB",
                "key_features": [
                    "Byzantine fault-tolerant social networking",
                    "Censorship-resistant content sharing",
                    "Decentralized identity management",
                    "Cryptographic content verification",
                    "Democratic content moderation through consensus"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with social consensus overlay",
                    "data_structure": "Immutable post blockchain with reputation system",
                    "scalability": "Handles thousands of concurrent users",
                    "storage": "Distributed content storage with redundancy"
                },
                "use_cases": [
                    "Uncensored journalism and whistleblowing",
                    "Democratic decision-making platforms",
                    "Academic discourse without institutional bias",
                    "Community-driven content curation"
                ],
                "research_significance": "Demonstrates how BFT consensus can enable truly decentralized social networks resistant to both technical failures and malicious actors",
                "implementation_highlights": [
                    "Novel reputation consensus algorithm",
                    "Integration of social graphs with blockchain consensus",
                    "Advanced Sybil attack resistance mechanisms"
                ]
            },
            
            "draftres": {
                "name": "DraftRes",
                "category": "Gaming & Entertainment",
                "description": "Fantasy sports platform with transparent, tamper-proof draft and scoring systems",
                "key_features": [
                    "Provably fair draft algorithms",
                    "Immutable player statistics recording",
                    "Transparent scoring calculations",
                    "Multi-party computation for privacy",
                    "Automated smart contract payouts"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with game-state validation",
                    "data_structure": "Event-sourced game state with cryptographic proofs",
                    "real_time_processing": "Sub-second transaction confirmation for live games",
                    "privacy_features": "Zero-knowledge proofs for private league data"
                },
                "use_cases": [
                    "Professional fantasy sports leagues",
                    "Esports tournament management",
                    "Prediction markets with game outcomes",
                    "Skill-based gaming platforms"
                ],
                "research_significance": "Showcases real-time consensus applications and demonstrates how BFT systems can handle high-frequency, low-latency gaming scenarios",
                "economic_model": "Demonstrates cryptocurrency integration with traditional gaming economics"
            },
            
            "arrayan": {
                "name": "Arrayán",
                "category": "Supply Chain & Logistics",
                "description": "Comprehensive supply chain transparency and traceability platform",
                "key_features": [
                    "End-to-end product traceability",
                    "Multi-stakeholder verification system",
                    "Automated compliance checking",
                    "Real-time quality monitoring",
                    "Counterfeit prevention mechanisms"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with multi-party validation",
                    "data_structure": "Merkle DAG for complex supply relationships",
                    "iot_integration": "IoT sensor data validation through consensus",
                    "compliance_engine": "Automated regulatory compliance verification"
                },
                "use_cases": [
                    "Food safety and origin verification",
                    "Pharmaceutical supply chain integrity",
                    "Luxury goods authentication",
                    "Carbon footprint tracking",
                    "Ethical sourcing verification"
                ],
                "research_significance": "Demonstrates practical BFT applications in global supply chains with multiple untrusted parties",
                "industry_impact": "Addresses billion-dollar problems in supply chain fraud and safety"
            },
            
            "echo": {
                "name": "Echo",
                "category": "Communication & Messaging",
                "description": "Secure, decentralized messaging platform with Byzantine fault tolerance",
                "key_features": [
                    "End-to-end encrypted messaging",
                    "Censorship-resistant communication",
                    "Decentralized message routing",
                    "Group consensus messaging",
                    "Message integrity guarantees"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT for message ordering and delivery",
                    "encryption": "Double-ratchet protocol with BFT key exchange",
                    "routing": "Decentralized message routing with fault tolerance",
                    "storage": "Distributed message storage with redundancy"
                },
                "use_cases": [
                    "Secure military and government communications",
                    "Journalist and activist secure communications",
                    "Corporate communications with integrity requirements",
                    "Emergency response coordination"
                ],
                "research_significance": "Explores consensus mechanisms for real-time communication systems",
                "privacy_innovations": "Novel approaches to combining consensus with privacy-preserving protocols"
            },
            
            "utxo_lenses": {
                "name": "UTXO Lenses",
                "category": "Blockchain Analytics & Visualization",
                "description": "Advanced blockchain transaction analysis and visualization platform",
                "key_features": [
                    "Real-time transaction flow visualization",
                    "UTXO set analysis and optimization",
                    "Transaction pattern recognition",
                    "Blockchain forensics capabilities",
                    "Performance bottleneck identification"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with analytical workload distribution",
                    "data_processing": "Stream processing for real-time blockchain analysis",
                    "visualization_engine": "Interactive graph-based transaction visualization",
                    "machine_learning": "AI-powered transaction pattern analysis"
                },
                "use_cases": [
                    "Cryptocurrency compliance and AML",
                    "Blockchain performance optimization",
                    "Academic blockchain research",
                    "Forensic investigation of blockchain crimes"
                ],
                "research_significance": "Demonstrates how BFT systems can support complex analytical workloads while maintaining consensus",
                "innovation": "First BFT-based blockchain analytics platform with real-time capabilities"
            },
            
            "rescounty": {
                "name": "ResCounty",
                "category": "Government & Public Services",
                "description": "Transparent, tamper-proof digital governance platform for local governments",
                "key_features": [
                    "Transparent voting and decision-making",
                    "Immutable public record keeping",
                    "Citizen participation platforms",
                    "Automated policy execution",
                    "Multi-jurisdiction coordination"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with civic validation nodes",
                    "governance_layer": "Smart contracts for automated policy execution",
                    "identity_system": "Decentralized digital identity for citizens",
                    "audit_system": "Cryptographic audit trails for all government actions"
                },
                "use_cases": [
                    "Municipal budget transparency",
                    "Land registry and property records",
                    "Permit and licensing systems",
                    "Public procurement transparency",
                    "Inter-governmental coordination"
                ],
                "research_significance": "Explores how BFT consensus can enable transparent, corruption-resistant governance",
                "social_impact": "Potential to reduce corruption and increase citizen trust in government"
            },
            
            "crypogo": {
                "name": "CrypoGo",
                "category": "Financial Services & DeFi",
                "description": "High-performance decentralized finance platform with advanced trading capabilities",
                "key_features": [
                    "High-frequency decentralized trading",
                    "Automated market making with BFT guarantees",
                    "Cross-chain asset management",
                    "Yield farming with provable returns",
                    "Decentralized derivatives trading"
                ],
                "technical_details": {
                    "consensus_mechanism": "Optimized PBFT for financial transactions",
                    "trading_engine": "Sub-millisecond order matching with consensus",
                    "liquidity_protocol": "Novel AMM design with BFT price oracles",
                    "cross_chain": "BFT-secured cross-chain bridges"
                },
                "use_cases": [
                    "High-frequency algorithmic trading",
                    "Institutional DeFi services",
                    "Cross-border remittances",
                    "Decentralized hedge funds",
                    "Synthetic asset creation and trading"
                ],
                "research_significance": "Pushes the boundaries of BFT performance for financial applications",
                "performance_metrics": "Achieves 100,000+ TPS with sub-second finality for financial transactions"
            },
            
            "explorer": {
                "name": "Explorer",
                "category": "Blockchain Infrastructure & Tools",
                "description": "Advanced blockchain explorer and network monitoring platform",
                "key_features": [
                    "Real-time blockchain state visualization",
                    "Transaction tracing and analysis",
                    "Network health monitoring",
                    "Consensus mechanism visualization",
                    "Historical data analysis"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with monitoring overlay",
                    "data_indexing": "Real-time blockchain indexing and search",
                    "api_layer": "RESTful and GraphQL APIs for blockchain data",
                    "caching_layer": "Distributed caching for high-performance queries"
                },
                "research_significance": "Provides insights into BFT network behavior and performance characteristics",
                "developer_tools": "Essential infrastructure for ResilientDB application development"
            },
            
            "monitoring": {
                "name": "Monitoring",
                "category": "Infrastructure & DevOps",
                "description": "Comprehensive monitoring and alerting system for ResilientDB networks",
                "key_features": [
                    "Real-time consensus health monitoring",
                    "Performance metrics collection and analysis",
                    "Automated anomaly detection",
                    "Predictive failure analysis",
                    "Multi-network monitoring dashboard"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with embedded monitoring agents",
                    "metrics_collection": "Low-overhead performance data collection",
                    "alerting_system": "Multi-channel alerting with smart routing",
                    "machine_learning": "AI-powered anomaly detection and prediction"
                },
                "research_significance": "Enables deep understanding of BFT system behavior under various conditions",
                "operational_impact": "Critical for maintaining high-availability BFT networks"
            },
            
            "resilientdb_cli": {
                "name": "ResilientDB CLI",
                "category": "Developer Tools & APIs",
                "description": "Comprehensive command-line interface for ResilientDB development and management",
                "key_features": [
                    "Full blockchain management from command line",
                    "Transaction creation and submission tools",
                    "Network configuration and deployment",
                    "Performance testing and benchmarking",
                    "Development workflow automation"
                ],
                "technical_details": {
                    "consensus_integration": "Direct integration with PBFT consensus layer",
                    "scripting_support": "Advanced scripting and automation capabilities",
                    "plugin_architecture": "Extensible plugin system for custom tools",
                    "multi_network": "Support for multiple network configurations"
                },
                "research_significance": "Enables researchers to easily experiment with BFT configurations and parameters",
                "developer_productivity": "Significantly reduces development time for BFT applications"
            },
            
            "resview": {
                "name": "ResView",
                "category": "Data Visualization & Analytics",
                "description": "Advanced data visualization platform for blockchain and consensus data",
                "key_features": [
                    "Interactive consensus visualization",
                    "Real-time network topology mapping",
                    "Transaction flow analysis",
                    "Performance metrics dashboards",
                    "Custom visualization builder"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with visualization data streams",
                    "rendering_engine": "High-performance WebGL-based visualization",
                    "data_processing": "Real-time stream processing for live visualizations",
                    "export_capabilities": "Publication-quality visualization exports"
                },
                "research_significance": "Enables intuitive understanding of complex BFT behaviors and network dynamics",
                "educational_impact": "Powerful tool for teaching distributed systems concepts"
            },
            
            "reslens": {
                "name": "ResLens",
                "category": "Security & Compliance",
                "description": "Advanced security analysis and compliance monitoring for ResilientDB networks",
                "key_features": [
                    "Real-time security threat detection",
                    "Compliance monitoring and reporting",
                    "Byzantine behavior analysis",
                    "Network vulnerability assessment",
                    "Automated incident response"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with security monitoring overlay",
                    "threat_detection": "ML-powered anomaly detection for security threats",
                    "compliance_engine": "Automated compliance checking for various regulations",
                    "forensics": "Advanced forensic capabilities for incident investigation"
                },
                "research_significance": "Advances the field of BFT security monitoring and threat detection",
                "enterprise_value": "Critical for enterprise adoption of BFT systems"
            },
            
            "coinsensus": {
                "name": "Coinsensus",
                "category": "Consensus Research & Development",
                "description": "Experimental consensus algorithm testing and development platform",
                "key_features": [
                    "Multi-consensus algorithm support",
                    "Consensus algorithm performance comparison",
                    "Byzantine fault injection testing",
                    "Consensus parameter optimization",
                    "Novel consensus algorithm development"
                ],
                "technical_details": {
                    "consensus_abstraction": "Pluggable consensus algorithm framework",
                    "simulation_engine": "Large-scale consensus simulation capabilities",
                    "parameter_tuning": "Automated consensus parameter optimization",
                    "fault_injection": "Sophisticated Byzantine fault injection framework"
                },
                "research_significance": "Primary platform for advancing consensus algorithm research",
                "academic_impact": "Used by researchers worldwide for consensus algorithm development"
            },
            
            "respirer": {
                "name": "Respirer",
                "category": "Healthcare & Medical Records",
                "description": "Secure, privacy-preserving medical records management system",
                "key_features": [
                    "Patient-controlled medical data",
                    "HIPAA-compliant data sharing",
                    "Medical research data aggregation",
                    "Emergency medical data access",
                    "Pharmaceutical supply chain integration"
                ],
                "technical_details": {
                    "consensus_mechanism": "PBFT with privacy-preserving protocols",
                    "encryption": "Advanced homomorphic encryption for medical data",
                    "access_control": "Fine-grained access control with patient consent",
                    "interoperability": "HL7 FHIR integration for healthcare standards"
                },
                "use_cases": [
                    "Electronic health records management",
                    "Medical research data sharing",
                    "Pharmaceutical clinical trials",
                    "Emergency medical response",
                    "Healthcare provider coordination"
                ],
                "research_significance": "Demonstrates how BFT systems can handle sensitive healthcare data",
                "regulatory_compliance": "Designed to meet strict healthcare privacy regulations"
            }
        }
    
    def _initialize_research_database(self) -> Dict[str, Any]:
        """Database of ResilientDB research papers, innovations, and academic contributions"""
        return {
            "core_papers": {
                "resilientdb_fabric": {
                    "title": "ResilientDB: Global Scale Resilient Blockchain Fabric",
                    "authors": ["Mohammad Sadoghi", "et al."],
                    "venue": "VLDB 2020",
                    "key_contributions": [
                        "Novel pipeline-based PBFT implementation",
                        "Geo-scale deployment capabilities",
                        "Performance optimizations for blockchain workloads"
                    ],
                    "performance_results": "Achieved 2M+ TPS with global deployment",
                    "innovation": "First system to demonstrate PBFT at internet scale"
                },
                "nexres": {
                    "title": "NexRes: A Consensus Algorithm for Blockchain with Incentive-based Validation",
                    "key_contributions": [
                        "Economic incentives integrated with consensus",
                        "Game-theoretic analysis of validator behavior",
                        "Novel punishment mechanisms for Byzantine actors"
                    ]
                },
                "speedb": {
                    "title": "SpeedB: A Novel Blockchain Architecture for High-Performance Applications",
                    "key_contributions": [
                        "Parallel transaction processing in BFT systems",
                        "Advanced caching mechanisms for blockchain data",
                        "Optimized storage layer for high-throughput applications"
                    ]
                }
            },
            "research_areas": {
                "consensus_optimization": [
                    "Pipeline-based PBFT implementations",
                    "Parallel consensus processing",
                    "Consensus parameter optimization",
                    "Adaptive consensus algorithms"
                ],
                "scalability": [
                    "Sharding in BFT systems",
                    "Cross-shard consensus protocols",
                    "Hierarchical consensus architectures",
                    "State channel integration"
                ],
                "privacy": [
                    "Zero-knowledge proofs in BFT systems",
                    "Privacy-preserving consensus",
                    "Confidential transactions with consensus",
                    "Multi-party computation integration"
                ],
                "applications": [
                    "BFT for IoT networks",
                    "Edge computing with consensus",
                    "Financial system applications",
                    "Supply chain transparency"
                ]
            }
        }
    
    def _initialize_architecture_knowledge(self) -> Dict[str, Any]:
        """Deep architectural knowledge of ResilientDB systems"""
        return {
            "core_components": {
                "consensus_engine": {
                    "description": "High-performance PBFT implementation with pipeline optimization",
                    "key_features": [
                        "Multi-threaded consensus processing",
                        "Batch-based transaction ordering",
                        "View change optimization",
                        "Message aggregation and compression"
                    ],
                    "performance_characteristics": {
                        "latency": "Sub-millisecond consensus rounds",
                        "throughput": "100K+ TPS per consensus group",
                        "scalability": "Linear scaling with replica count",
                        "fault_tolerance": "f Byzantine failures in 3f+1 system"
                    }
                },
                "storage_layer": {
                    "description": "Optimized blockchain storage with advanced indexing",
                    "components": [
                        "Block storage with Merkle tree verification",
                        "State database with MVCC support",
                        "Transaction log with compression",
                        "Index structures for fast queries"
                    ]
                },
                "networking": {
                    "description": "High-performance networking layer for consensus messages",
                    "features": [
                        "UDP-based consensus messaging",
                        "TCP for reliable data transfer",
                        "Message batching and compression",
                        "Network partition tolerance"
                    ]
                }
            },
            "deployment_patterns": {
                "single_datacenter": "High-performance local consensus",
                "multi_datacenter": "Geo-distributed consensus with WAN optimization",
                "edge_deployment": "Lightweight consensus for IoT and edge devices",
                "hybrid_cloud": "Multi-cloud deployment with consensus coordination"
            }
        }
    
    def _initialize_performance_data(self) -> Dict[str, Any]:
        """Real-world performance benchmarks and optimization insights"""
        return {
            "benchmark_results": {
                "local_network": {
                    "setup": "4 replicas, 1Gbps network, NVMe SSD",
                    "throughput": "250,000 TPS",
                    "latency": "0.5ms average",
                    "cpu_usage": "60% per replica",
                    "network_usage": "400 Mbps peak"
                },
                "wide_area_network": {
                    "setup": "4 replicas, cross-continental, 100ms RTT",
                    "throughput": "10,000 TPS", 
                    "latency": "150ms average",
                    "optimization": "Pipeline depth adjustment for WAN"
                },
                "large_scale": {
                    "setup": "100 replicas, hierarchical consensus",
                    "throughput": "1,000,000+ TPS aggregate",
                    "latency": "2ms average",
                    "scalability": "Linear scaling demonstrated"
                }
            },
            "optimization_techniques": [
                "Batch size tuning for throughput/latency trade-off",
                "Pipeline depth optimization for network conditions",
                "Memory pool management for high-throughput scenarios",
                "Network message aggregation strategies"
            ]
        }
    
    def _initialize_use_cases(self) -> Dict[str, Any]:
        """Comprehensive database of real-world ResilientDB use cases and implementations"""
        return {
            "financial_services": {
                "central_bank_digital_currencies": {
                    "description": "CBDC implementations with ResilientDB",
                    "requirements": ["High throughput", "Regulatory compliance", "Privacy controls"],
                    "benefits": ["Transparent monetary policy", "Reduced settlement times", "Enhanced fraud detection"]
                },
                "trade_finance": {
                    "description": "Letter of credit and trade document management",
                    "participants": ["Banks", "Importers", "Exporters", "Customs"],
                    "benefits": ["Reduced processing time", "Enhanced transparency", "Fraud prevention"]
                }
            },
            "healthcare": {
                "medical_records": {
                    "description": "Patient-controlled electronic health records",
                    "privacy_features": ["Selective disclosure", "Audit trails", "Emergency access"],
                    "interoperability": "HL7 FHIR compliance"
                },
                "drug_traceability": {
                    "description": "End-to-end pharmaceutical supply chain tracking",
                    "stakeholders": ["Manufacturers", "Distributors", "Pharmacies", "Regulators"],
                    "benefits": ["Counterfeit prevention", "Recall management", "Regulatory compliance"]
                }
            }
        }

    async def query_knowledge(self, query: str, domain: str = "general") -> Dict[str, Any]:
        """Advanced knowledge query processing with deep domain expertise"""
        query_lower = query.lower()
        
        # Application-specific queries
        for app_name, app_data in self.applications_catalog.items():
            if app_name.lower() in query_lower or app_data["name"].lower() in query_lower:
                return await self._generate_application_response(app_name, app_data, query)
        
        # Research and academic queries
        if any(word in query_lower for word in ["research", "paper", "academic", "study"]):
            return await self._generate_research_response(query)
        
        # Architecture and technical queries
        if any(word in query_lower for word in ["architecture", "implementation", "technical", "design"]):
            return await self._generate_architecture_response(query)
        
        # Performance and benchmarking queries
        if any(word in query_lower for word in ["performance", "benchmark", "speed", "throughput", "latency"]):
            return await self._generate_performance_response(query)
        
        # Use case and application queries
        if any(word in query_lower for word in ["use case", "application", "real world", "industry"]):
            return await self._generate_use_case_response(query)
        
        # Consensus and distributed systems queries
        if any(word in query_lower for word in ["consensus", "pbft", "byzantine", "distributed"]):
            return await self._generate_consensus_response(query)
        
        return await self._generate_general_response(query)
    
    async def _generate_application_response(self, app_name: str, app_data: Dict, query: str) -> Dict[str, Any]:
        """Generate comprehensive response about specific ResilientDB applications"""
        
        return {
            "type": "application_deep_dive",
            "application": app_data["name"],
            "category": app_data["category"],
            "comprehensive_overview": f"""
🚀 **{app_data["name"]} - Deep Technical Analysis**

**🎯 Core Purpose:**
{app_data["description"]}

**🔧 Key Technical Features:**
{chr(10).join(f"• {feature}" for feature in app_data["key_features"])}

**⚙️ Technical Implementation:**
• **Consensus Mechanism:** {app_data["technical_details"].get("consensus_mechanism", "PBFT-based")}
• **Data Architecture:** {app_data["technical_details"].get("data_structure", "Blockchain-based")}
• **Performance Profile:** {app_data["technical_details"].get("scalability", "High-performance")}

**🌍 Real-World Applications:**
{chr(10).join(f"• {use_case}" for use_case in app_data.get("use_cases", []))}

**🔬 Research Significance:**
{app_data.get("research_significance", "Demonstrates practical BFT applications")}

**💡 Innovation Highlights:**
{chr(10).join(f"• {highlight}" for highlight in app_data.get("implementation_highlights", ["Advanced BFT implementation"]))}
            """,
            "technical_deep_dive": app_data["technical_details"],
            "research_impact": app_data.get("research_significance", ""),
            "related_applications": self._find_related_applications(app_data["category"]),
            "implementation_guidance": f"""
**Want to build something similar?**

**Key Technologies Needed:**
• ResilientDB consensus layer
• {app_data["technical_details"].get("consensus_mechanism", "PBFT")} implementation
• Specialized data structures for {app_data["category"].lower()}

**Development Approach:**
1. Start with ResilientDB core platform
2. Implement domain-specific logic layer
3. Add {app_data["category"].lower()}-specific optimizations
4. Integrate with existing {app_data["category"].lower()} systems

**Performance Considerations:**
• Expect {app_data["technical_details"].get("scalability", "high performance")}
• Consider {app_data["category"].lower()}-specific optimization needs
• Plan for Byzantine fault tolerance requirements
            """,
            "further_exploration": f"""
**Deep Dive Questions You Can Ask:**
• "How does {app_data['name']} handle Byzantine failures in {app_data['category'].lower()}?"
• "What are the performance benchmarks for {app_data['name']}?"
• "How does {app_data['name']} compare to traditional {app_data['category'].lower()} solutions?"
• "What research papers discuss {app_data['name']} or similar systems?"
• "Show me the architecture details of {app_data['name']}"
            """
        }
    
    def _find_related_applications(self, category: str) -> List[str]:
        """Find applications in related categories"""
        related = []
        for app_name, app_data in self.applications_catalog.items():
            if app_data["category"] == category:
                related.append(app_data["name"])
        return related[:5]  # Return top 5 related apps
    
    async def _generate_research_response(self, query: str) -> Dict[str, Any]:
        """Generate response about ResilientDB research and academic contributions"""
        
        query_lower = query.lower()
        
        if "papers" in query_lower or "publications" in query_lower:
            return {
                "type": "research_overview",
                "content": f"""
📚 **ResilientDB Research Ecosystem**

**🏆 Core Publications:**

**1. ResilientDB: Global Scale Resilient Blockchain Fabric (VLDB 2020)**
• First demonstration of PBFT at internet scale
• Achieved 2M+ TPS with geo-distributed deployment
• Introduced pipeline-based consensus optimization

**2. NexRes: Incentive-based Validation in Blockchain**
• Economic incentives integrated with consensus mechanisms
• Game-theoretic analysis of validator behavior
• Novel punishment mechanisms for Byzantine actors

**3. SpeedB: High-Performance Blockchain Architecture** 
• Parallel transaction processing in BFT systems
• Advanced caching mechanisms for blockchain data
• Optimized storage layer design

**🔬 Active Research Areas:**

**Consensus Optimization:**
• Pipeline-based PBFT implementations
• Parallel consensus processing techniques
• Adaptive consensus parameter tuning
• Cross-shard consensus protocols

**Scalability Research:**
• Hierarchical consensus architectures
• State channel integration with BFT
• Sharding mechanisms for BFT systems
• Edge computing consensus protocols

**Privacy & Security:**
• Zero-knowledge proofs in BFT systems
• Privacy-preserving consensus mechanisms
• Confidential transactions with consensus
• Multi-party computation integration

**🎓 Academic Impact:**
• 50+ research papers citing ResilientDB
• Used in distributed systems courses worldwide
• Active collaboration with 20+ universities
• Open-source contributions from global research community

**📖 Want specific paper details?** Ask about any research area!
                """,
                "research_database": self.research_papers,
                "collaboration_opportunities": [
                    "Consensus algorithm optimization",
                    "Application-specific BFT protocols", 
                    "Performance analysis and benchmarking",
                    "Security analysis and formal verification"
                ]
            }
        
        elif "consensus" in query_lower:
            return await self._generate_consensus_research_response(query)
        
        else:
            return {
                "type": "general_research",
                "content": """
🔬 **ResilientDB: A Research Platform**

ResilientDB represents cutting-edge research in Byzantine fault-tolerant systems, with contributions across:

• **Theoretical Foundations:** Advancing consensus algorithm theory
• **Practical Systems:** Real-world BFT implementations at scale  
• **Performance Engineering:** Pushing the boundaries of BFT performance
• **Application Research:** Novel use cases for BFT technology

**Ask me about:**
• Specific research papers and their contributions
• Current research directions and open problems
• Academic collaborations and opportunities
• Theoretical foundations vs practical implementations
                """
            }
    
    async def _generate_consensus_research_response(self, query: str) -> Dict[str, Any]:
        """Deep dive into consensus algorithm research"""
        return {
            "type": "consensus_research",
            "content": f"""
🏛️ **Consensus Algorithm Research in ResilientDB**

**🔬 Theoretical Foundations:**

**PBFT Optimizations:**
• **Pipeline Processing:** Overlapping consensus phases for higher throughput
• **Batch Optimization:** Dynamic batching based on network conditions  
• **View Change Improvements:** Faster recovery from primary failures
• **Message Aggregation:** Reducing communication overhead

**Novel Consensus Variants:**
• **Geo-PBFT:** Optimized for wide-area network deployments
• **Adaptive PBFT:** Dynamic parameter adjustment based on network conditions
• **Hierarchical PBFT:** Multi-level consensus for large-scale systems
• **Privacy-Preserving PBFT:** Consensus with confidential transactions

**🧮 Mathematical Innovations:**

**Safety Proofs:** Formal verification of consensus safety properties
**Liveness Analysis:** Guaranteed progress under network asynchrony
**Byzantine Bounds:** Optimal fault tolerance with 3f+1 replicas
**Performance Models:** Theoretical throughput and latency bounds

**🚀 Performance Research:**

**Throughput Optimization:**
• Achieved 2M+ TPS in laboratory settings
• Linear scaling with replica count demonstrated
• Batch size optimization algorithms developed

**Latency Minimization:**
• Sub-millisecond consensus rounds achieved
• WAN optimizations for geo-distributed systems
• Edge computing consensus protocols

**🔮 Future Research Directions:**

• **Quantum-Resistant BFT:** Post-quantum cryptographic integration
• **AI-Enhanced Consensus:** Machine learning for parameter optimization  
• **Cross-Chain Consensus:** BFT protocols for blockchain interoperability
• **IoT-Scale BFT:** Lightweight consensus for resource-constrained devices

**📊 Want deeper technical details?** Ask about specific consensus optimizations!
            """,
            "technical_papers": self.research_papers["core_papers"],
            "open_problems": [
                "Optimal batch size determination for varying network conditions",
                "Byzantine fault detection and mitigation strategies",
                "Consensus performance under adversarial network conditions",
                "Integration of consensus with privacy-preserving protocols"
            ]
        }

    async def _generate_consensus_response(self, query: str) -> Dict[str, Any]:
        """Stub: Generate consensus research response"""
        return {
            "type": "consensus_research",
            "content": "PBFT consensus in ResilientDB is optimized for high throughput and low latency. Key innovations include pipeline processing, batch optimization, and hierarchical consensus architectures. Ask for more details!"
        }

    async def _generate_performance_response(self, query: str) -> Dict[str, Any]:
        """Stub: Generate performance research response"""
        return {
            "type": "performance_research",
            "content": "ResilientDB achieves 250,000 TPS locally and 1M+ TPS in large-scale deployments. Performance is optimized via batch size tuning, pipeline depth, and network optimizations. Ask for benchmarks or optimization techniques!"
        }

    async def _generate_architecture_response(self, query: str) -> Dict[str, Any]:
        """Generate detailed architecture response"""
        return {
            "type": "architecture_overview",
            "content": f"""
🏗️ **ResilientDB Architecture Deep Dive**

**🔧 Core Components:**
• **Consensus Layer**: Optimized PBFT implementation with pipeline processing
• **Storage Layer**: High-performance persistent storage with cryptographic integrity
• **Network Layer**: Asynchronous Byzantine fault-tolerant communication
• **Application Layer**: Modular APIs for diverse blockchain applications
• **Monitoring Layer**: Real-time performance metrics and health monitoring

**⚙️ Key Design Principles:**
• **Modularity**: Component-based architecture for easy customization
• **Scalability**: Hierarchical consensus for large-scale deployments
• **Performance**: Optimized for high throughput and low latency
• **Reliability**: Byzantine fault tolerance with formal verification
• **Extensibility**: Plugin architecture for research and development

**🔍 Technical Implementation:**
• **Language**: C++ core with Python bindings
• **Consensus**: Multi-threaded PBFT with batching and pipelining
• **Storage**: Custom blockchain storage with merkle tree verification
• **Networking**: High-performance TCP/UDP communication protocols
• **APIs**: REST and gRPC interfaces for application integration

**🎯 Research Features:**
• **Configurable Consensus**: Multiple BFT variants for research
• **Performance Profiling**: Built-in benchmarking and analysis tools
• **Educational Tools**: Comprehensive documentation and tutorials
• **Docker Integration**: Containerized deployment for easy experimentation

Ask me about specific architectural components for deeper technical details!
            """
        }
    
    async def _generate_use_case_response(self, query: str) -> Dict[str, Any]:
        """Generate comprehensive use case response"""
        
        # Identify applications that use social consensus or specific features
        social_apps = []
        supply_chain_apps = []
        gaming_apps = []
        financial_apps = []
        
        for app_key, app_data in self.applications_catalog.items():
            category = app_data.get("category", "").lower()
            features = str(app_data.get("key_features", [])).lower()
            description = app_data.get("description", "").lower()
            
            if "social" in features or "social" in description or "consensus" in features:
                social_apps.append(app_data["name"])
            if "supply" in category or "supply" in description:
                supply_chain_apps.append(app_data["name"])
            if "gaming" in category or "game" in description:
                gaming_apps.append(app_data["name"])
            if "financial" in category or "payment" in description or "defi" in description:
                financial_apps.append(app_data["name"])
        
        return {
            "type": "use_case_overview",
            "content": f"""
🌍 **ResilientDB Real-World Use Cases & Applications**

**🏛️ Social Consensus Applications:**
{chr(10).join(f"• {app}" for app in social_apps) if social_apps else "• Debitable - Decentralized social media with democratic content moderation"}

**🚚 Supply Chain & Logistics:**
{chr(10).join(f"• {app}" for app in supply_chain_apps) if supply_chain_apps else "• Arrayán - End-to-end supply chain traceability"}

**🎮 Gaming & Entertainment:**
{chr(10).join(f"• {app}" for app in gaming_apps) if gaming_apps else "• DraftRes - Provably fair fantasy sports platform"}

**💰 Financial Services:**
{chr(10).join(f"• {app}" for app in financial_apps) if financial_apps else "• Various DeFi applications with Byzantine fault tolerance"}

**🏥 Healthcare Applications:**
• Secure patient data management with consensus-based access control
• Medical research data sharing with privacy preservation
• Drug supply chain integrity and anti-counterfeiting

**🏛️ Government & Civic:**
• Transparent voting systems with verifiable results
• Public record management with tamper-proof storage
• Citizen identity management with privacy protection

**🏭 Enterprise & Industrial:**
• Multi-party business process automation
• Consortium blockchain networks for industry collaboration
• IoT device management with Byzantine fault tolerance

**💡 Key Advantages Across All Use Cases:**
• **Trust Without Central Authority**: Eliminates single points of failure
• **Transparent Operations**: All transactions are verifiable and auditable
• **High Performance**: Supports real-world transaction volumes
• **Regulatory Compliance**: Built-in audit trails and data integrity
• **Research-Backed**: Based on cutting-edge academic research

Ask me about specific industries or applications for detailed implementation guidance!
            """
        }

    async def _generate_general_response(self, query: str) -> Dict[str, Any]:
        """Generate general response for queries that don't match specific categories"""
        return {
            "type": "general_overview",
            "content": f"""
🔬 **ResilientDB: A Research Platform**

ResilientDB represents cutting-edge research in Byzantine fault-tolerant systems, with contributions across:
• **Theoretical Foundations:** Advancing consensus algorithm theory
• **Practical Systems:** Real-world BFT implementations at scale
• **Performance Engineering:** Pushing the boundaries of BFT performance
• **Application Research:** Novel use cases for BFT technology

**Ask me about:**
• Specific research papers and their contributions
• Current research challenges and open problems
• Performance benchmarks and optimization techniques
• Comparison with other distributed systems
• Technical architecture and implementation details

**💡 Example questions:**
• "How does ResilientDB's PBFT implementation differ from traditional PBFT?"
• "What are the latest research contributions in Byzantine fault tolerance?"
• "Show me performance comparisons with other blockchain systems"
• "Explain the consensus algorithm optimizations in ResilientDB"

Your query: "{query}"

**Recommendation:** For more specific information, ask about particular aspects like applications, consensus mechanisms, performance, or research contributions!
            """
        }