Core Components

Data Agent Architecture

Component Overview

The Data Agent serves as the foundational layer of Kaizen AI's analytical capabilities, responsible for real-time blockchain data collection, normalization, and initial processing across multiple networks. This component operates as a high-throughput, low-latency data pipeline that transforms raw blockchain events into structured, analyzable information.

Primary Responsibilities:

• Multi-chain blockchain monitoring and event collection

• Smart contract interaction analysis and state tracking

• Market data aggregation from decentralized exchanges

• Data validation, normalization, and enrichment

• Real-time event streaming to downstream analytical components

Technical Architecture

Multi-Chain Data Collection Framework

┌─────────────────────────────────────────────────────────────────┐
│                      Data Agent Core                            │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │ Ethereum    │  │   Solana    │  │  Future     │             │
│  │ Collector   │  │ Collector   │  │ Chains      │             │
│  │             │  │             │  │             │             │
│  │• Geth RPC   │  │• RPC Nodes  │  │• Base       │             │
│  │• Alchemy    │  │• BigTable   │  │• Polygon    │             │
│  │• Infura     │  │• Quicknode  │  │• Arbitrum   │             │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
├─────────────────────────────────────────────────────────────────┤
│                    Data Processing Engine                       │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │ Validation  │  │Normalization│  │ Enrichment  │             │
│  │ Layer       │  │ Layer       │  │ Layer       │             │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
├─────────────────────────────────────────────────────────────────┤
│                    Event Distribution                           │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │ Message     │  │ WebSocket   │  │ Database    │             │
│  │ Queue       │  │ Streams     │  │ Storage     │             │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
└─────────────────────────────────────────────────────────────────┘

Ethereum Data Collection

RPC Node Management

interface EthereumCollector {
  nodeEndpoints: RPCEndpoint[];
  blockProcessing: BlockProcessor;
  eventFilters: EventFilter[];
  contractWatchers: ContractWatcher[];
}

class EthereumDataCollector {
  private rpcPool: ConnectionPool;
  private eventProcessor: EventProcessor;
  private stateTracker: StateTracker;
  
  constructor(config: EthereumConfig) {
    this.rpcPool = new ConnectionPool({
      endpoints: [
        { provider: "alchemy", priority: 1, maxConnections: 10 },
        { provider: "infura", priority: 2, maxConnections: 8 },
        { provider: "local_geth", priority: 3, maxConnections: 5 }
      ],
      failoverStrategy: "round_robin",
      healthCheck: "30s"
    });
  }
  
  async collectBlockData(blockNumber: number): Promise<ProcessedBlock> {
    const rawBlock = await this.rpcPool.getBlock(blockNumber);
    const transactions = await this.processTransactions(rawBlock.transactions);
    const events = await this.extractEvents(transactions);
    
    return {
      blockNumber,
      timestamp: rawBlock.timestamp,
      transactionCount: transactions.length,
      processedTransactions: transactions,
      extractedEvents: events,
      gasMetrics: this.calculateGasMetrics(transactions)
    };
  }
}

Smart Contract Event Processing

// Event Filter Configuration
const eventFilters = {
  erc20_transfers: {
    topics: ["0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef"],
    decoder: "Transfer(address,address,uint256)",
    priority: "high"
  },
  uniswap_swaps: {
    address: "0x5C69bEe701ef814a2B6a3EDD4B1652CB9cc5aA6f",
    topics: ["0xd78ad95fa46c994b6551d0da85fc275fe613ce37657fb8d5e3d130840159d822"],
    decoder: "Swap(address,uint256,uint256,uint256,uint256,address)",
    priority: "high"
  },
  liquidity_events: {
    topics: ["0x4c209b5fc8ad50758f13e2e1088ba56a560dff690a1c6fef26394f4c03821c4f"],
    decoder: "Mint(address,uint256,uint256)",
    priority: "medium"
  }
};

class EventProcessor {
  async processEvent(rawEvent: RawEvent): Promise<ProcessedEvent> {
    const eventSignature = rawEvent.topics[0];
    const filter = this.getFilterBySignature(eventSignature);
    
    if (!filter) return null;
    
    const decodedEvent = this.decodeEvent(rawEvent, filter.decoder);
    const enrichedEvent = await this.enrichEvent(decodedEvent);
    
    return {
      eventType: filter.type,
      contractAddress: rawEvent.address,
      blockNumber: rawEvent.blockNumber,
      transactionHash: rawEvent.transactionHash,
      decodedData: decodedEvent,
      enrichmentData: enrichedEvent,
      timestamp: Date.now(),
      network: "ethereum"
    };
  }
}

Solana Data Collection

Program Monitoring Architecture

// Solana Data Collector Implementation
pub struct SolanaCollector {
    rpc_client: RpcClient,
    program_watchers: HashMap<Pubkey, ProgramWatcher>,
    account_watchers: Vec<AccountWatcher>,
    signature_subscribers: Vec<SignatureSubscriber>,
}

impl SolanaCollector {
    pub async fn monitor_token_program(&self) -> Result<(), CollectorError> {
        let token_program_id = spl_token::id();
        
        // Subscribe to all token program interactions
        let subscription = self.rpc_client
            .program_subscribe(&token_program_id, None)
            .await?;
            
        while let Some(update) = subscription.next().await {
            match update {
                Ok(account_update) => {
                    self.process_account_update(account_update).await?;
                },
                Err(e) => {
                    log::error!("Subscription error: {}", e);
                }
            }
        }
        
        Ok(())
    }
    
    async fn process_transaction(&self, signature: &Signature) -> ProcessedTransaction {
        let transaction = self.rpc_client
            .get_transaction(signature, UiTransactionEncoding::JsonParsed)
            .await
            .unwrap();
            
        let parsed_instructions = self.parse_instructions(&transaction);
        let account_changes = self.analyze_account_changes(&transaction);
        
        ProcessedTransaction {
            signature: signature.to_string(),
            block_time: transaction.block_time,
            slot: transaction.slot,
            instructions: parsed_instructions,
            account_changes,
            fee: transaction.meta.unwrap().fee,
        }
    }
}

Data Processing Pipeline

Validation Layer

class DataValidator:
    def __init__(self):
        self.schema_validators = {
            "ethereum_transaction": EthereumTransactionSchema(),
            "solana_transaction": SolanaTransactionSchema(),
            "token_transfer": TokenTransferSchema(),
            "swap_event": SwapEventSchema()
        }
        
    def validate_data(self, data: Dict, data_type: str) -> ValidationResult:
        validator = self.schema_validators.get(data_type)
        if not validator:
            return ValidationResult(valid=False, error="Unknown data type")
            
        try:
            validated_data = validator.validate(data)
            return ValidationResult(valid=True, data=validated_data)
        except ValidationError as e:
            return ValidationResult(valid=False, error=str(e))
            
    def check_data_integrity(self, data: Dict) -> bool:
        # Verify data consistency and completeness
        required_fields = self.get_required_fields(data.get("type"))
        
        for field in required_fields:
            if field not in data or data[field] is None:
                return False
                
        # Additional integrity checks
        return self.validate_timestamps(data) and self.validate_addresses(data)

Normalization Layer

interface NormalizedEvent {
  id: string;
  type: EventType;
  network: Network;
  contractAddress: string;
  blockNumber: number;
  timestamp: number;
  data: Record<string, any>;
  metadata: EventMetadata;
}

class DataNormalizer {
  normalize(rawData: RawBlockchainData): NormalizedEvent[] {
    const normalizedEvents: NormalizedEvent[] = [];
    
    for (const event of rawData.events) {
      const normalized = {
        id: this.generateEventId(event),
        type: this.mapEventType(event),
        network: rawData.network,
        contractAddress: event.address.toLowerCase(),
        blockNumber: event.blockNumber,
        timestamp: this.normalizeTimestamp(event),
        data: this.normalizeEventData(event),
        metadata: this.extractMetadata(event)
      };
      
      normalizedEvents.push(normalized);
    }
    
    return normalizedEvents;
  }
  
  private normalizeEventData(event: RawEvent): Record<string, any> {
    switch (event.type) {
      case "Transfer":
        return {
          from: event.data.from.toLowerCase(),
          to: event.data.to.toLowerCase(),
          amount: this.normalizeAmount(event.data.value),
          token: event.address.toLowerCase()
        };
      case "Swap":
        return {
          tokenIn: event.data.tokenIn.toLowerCase(),
          tokenOut: event.data.tokenOut.toLowerCase(),
          amountIn: this.normalizeAmount(event.data.amountIn),
          amountOut: this.normalizeAmount(event.data.amountOut),
          recipient: event.data.to.toLowerCase()
        };
      default:
        return event.data;
    }
  }
}

Performance Characteristics

Throughput Specifications

performance_targets:
  ethereum:
    events_per_second: 1000
    block_processing_latency: "<500ms"
    transaction_processing: "50ms_avg"
    memory_usage: "<2GB_per_instance"
    
  solana:
    transactions_per_second: 2000
    account_update_latency: "<200ms"
    instruction_processing: "10ms_avg"
    memory_usage: "<1.5GB_per_instance"
    
  general:
    data_validation_time: "<50ms"
    normalization_time: "<100ms"
    database_write_time: "<200ms"
    error_rate_target: "<0.1%"

Scaling Configuration

const scalingConfig = {
  horizontal_scaling: {
    min_instances: 2,
    max_instances: 10,
    scale_up_threshold: "cpu_usage > 70% for 5min",
    scale_down_threshold: "cpu_usage < 30% for 15min",
    target_requests_per_instance: 500
  },
  
  connection_pooling: {
    ethereum_rpc: {
      pool_size: 20,
      max_retries: 3,
      timeout: "30s",
      keepalive: true
    },
    solana_rpc: {
      pool_size: 15,
      max_retries: 5,
      timeout: "10s",
      keepalive: true
    }
  },
  
  caching_strategy: {
    block_cache_ttl: "1h",
    transaction_cache_ttl: "30m",
    contract_metadata_ttl: "24h",
    max_cache_size: "5GB"
  }
};

---

Scoring Engine Design

Component Overview

The Scoring Engine represents the analytical heart of Kaizen AI, combining traditional rule-based logic with advanced machine learning models to generate comprehensive risk assessments. This component processes multi-dimensional data inputs to produce the signature Kaizen Score (0-100) along with detailed risk breakdowns and confidence intervals.

Core Capabilities:

• Multi-factor risk assessment with weighted scoring

• Ensemble machine learning models for pattern recognition

• Real-time score updates based on changing conditions

• Confidence interval calculation and uncertainty quantification

• Historical performance tracking and model validation

Machine Learning Architecture

Model Ensemble Framework

from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor
from xgboost import XGBClassifier
from tensorflow import keras
import numpy as np

class ScoringEnsemble:
    def __init__(self):
        self.models = {
            "honeypot_detector": {
                "model": XGBClassifier(
                    n_estimators=100,
                    max_depth=6,
                    learning_rate=0.1,
                    random_state=42
                ),
                "weight": 0.25,
                "confidence_threshold": 0.8
            },
            
            "rug_pull_predictor": {
                "model": GradientBoostingRegressor(
                    n_estimators=150,
                    max_depth=5,
                    learning_rate=0.05
                ),
                "weight": 0.30,
                "confidence_threshold": 0.75
            },
            
            "liquidity_analyzer": {
                "model": RandomForestClassifier(
                    n_estimators=200,
                    max_depth=8,
                    min_samples_split=5
                ),
                "weight": 0.20,
                "confidence_threshold": 0.7
            },
            
            "social_sentiment_nn": {
                "model": self.build_neural_network(),
                "weight": 0.15,
                "confidence_threshold": 0.65
            },
            
            "whale_behavior_lstm": {
                "model": self.build_lstm_model(),
                "weight": 0.10,
                "confidence_threshold": 0.6
            }
        }
        
    def build_neural_network(self):
        model = keras.Sequential([
            keras.layers.Dense(128, activation='relu', input_shape=(50,)),
            keras.layers.Dropout(0.3),
            keras.layers.Dense(64, activation='relu'),
            keras.layers.Dropout(0.2),
            keras.layers.Dense(32, activation='relu'),
            keras.layers.Dense(1, activation='sigmoid')
        ])
        
        model.compile(
            optimizer='adam',
            loss='binary_crossentropy',
            metrics=['accuracy', 'precision', 'recall']
        )
        
        return model

Feature Engineering Pipeline

class FeatureEngineer:
    def __init__(self):
        self.feature_extractors = {
            "contract_features": ContractFeatureExtractor(),
            "economic_features": EconomicFeatureExtractor(),
            "social_features": SocialFeatureExtractor(),
            "behavioral_features": BehaviorFeatureExtractor()
        }
        
    def extract_features(self, project_data: Dict) -> np.ndarray:
        feature_vectors = []
        
        # Contract-based features
        contract_features = self.extract_contract_features(project_data)
        feature_vectors.extend(contract_features)
        
        # Economic features
        economic_features = self.extract_economic_features(project_data)
        feature_vectors.extend(economic_features)
        
        # Social features
        social_features = self.extract_social_features(project_data)
        feature_vectors.extend(social_features)
        
        # Behavioral features
        behavioral_features = self.extract_behavioral_features(project_data)
        feature_vectors.extend(behavioral_features)
        
        return np.array(feature_vectors)
        
    def extract_contract_features(self, data: Dict) -> List[float]:
        return [
            float(data.get("is_verified", 0)),
            float(data.get("has_proxy", 0)),
            float(data.get("is_mintable", 0)),
            float(data.get("is_pausable", 0)),
            float(data.get("owner_renounced", 0)),
            data.get("audit_score", 0.0) / 100.0,
            min(data.get("function_count", 100) / 100.0, 1.0),
            data.get("complexity_score", 0.0) / 100.0
        ]
        
    def extract_economic_features(self, data: Dict) -> List[float]:
        return [
            min(data.get("liquidity_locked_months", 0) / 12.0, 1.0),
            data.get("initial_liquidity_ratio", 0.0),
            min(data.get("holder_count", 1000) / 1000.0, 1.0),
            data.get("top_10_holder_percentage", 100.0) / 100.0,
            data.get("trading_volume_24h", 0.0) / 1000000.0,  # Normalized to millions
            float(data.get("has_trading_fees", 0)),
            data.get("market_cap", 0.0) / 10000000.0  # Normalized to 10M
        ]

Risk Assessment Framework

Multi-Dimensional Risk Calculation

interface RiskFactors {
  technical: TechnicalRisk;
  economic: EconomicRisk;
  social: SocialRisk;
  governance: GovernanceRisk;
}

interface TechnicalRisk {
  contractVerification: number;
  auditStatus: number;
  codeComplexity: number;
  upgradeability: number;
  honeypotRisk: number;
}

class RiskCalculator {
  calculateKaizenScore(data: ProjectData): ScoringResult {
    const riskFactors = this.assessRiskFactors(data);
    const mlPredictions = this.getMachineLearningPredictions(data);
    const confidence = this.calculateConfidence(data, mlPredictions);
    
    // Weighted risk calculation
    const technicalScore = this.calculateTechnicalScore(riskFactors.technical);
    const economicScore = this.calculateEconomicScore(riskFactors.economic);
    const socialScore = this.calculateSocialScore(riskFactors.social);
    const governanceScore = this.calculateGovernanceScore(riskFactors.governance);
    
    // Apply machine learning adjustments
    const mlAdjustment = this.calculateMLAdjustment(mlPredictions);
    
    const rawScore = (
      technicalScore * 0.30 +
      economicScore * 0.25 +
      socialScore * 0.25 +
      governanceScore * 0.20
    );
    
    const finalScore = Math.max(0, Math.min(100, rawScore + mlAdjustment));
    
    return {
      score: Math.round(finalScore),
      confidence: confidence,
      breakdown: {
        technical: technicalScore,
        economic: economicScore,
        social: socialScore,
        governance: governanceScore,
        mlAdjustment: mlAdjustment
      },
      riskLevel: this.categorizeRisk(finalScore),
      explanation: this.generateExplanation(riskFactors, mlPredictions)
    };
  }
  
  private calculateTechnicalScore(technical: TechnicalRisk): number {
    const factors = [
      { value: technical.contractVerification, weight: 0.25 },
      { value: technical.auditStatus, weight: 0.30 },
      { value: technical.codeComplexity, weight: 0.15 },
      { value: technical.upgradeability, weight: 0.15 },
      { value: (100 - technical.honeypotRisk), weight: 0.15 }
    ];
    
    return factors.reduce((score, factor) => 
      score + (factor.value * factor.weight), 0
    );
  }
}

Real-Time Score Updates

Event-Driven Score Recalculation

class RealTimeScorer:
    def __init__(self):
        self.score_cache = {}
        self.update_thresholds = {
            "liquidity_change": 0.05,  # 5% change triggers update
            "holder_change": 0.02,     # 2% change triggers update
            "social_spike": 2.0,       # 2x normal activity
            "whale_movement": 0.01     # 1% of supply moved
        }
        
    async def handle_blockchain_event(self, event: BlockchainEvent):
        affected_projects = self.identify_affected_projects(event)
        
        for project in affected_projects:
            if self.should_update_score(project, event):
                new_score = await self.recalculate_score(project)
                await self.publish_score_update(project, new_score)
                
    def should_update_score(self, project: str, event: BlockchainEvent) -> bool:
        if event.type == "liquidity_change":
            change_ratio = abs(event.new_value - event.old_value) / event.old_value
            return change_ratio > self.update_thresholds["liquidity_change"]
            
        elif event.type == "large_transfer":
            supply_percentage = event.amount / project.total_supply
            return supply_percentage > self.update_thresholds["whale_movement"]
            
        elif event.type == "social_activity_spike":
            activity_ratio = event.current_activity / event.baseline_activity
            return activity_ratio > self.update_thresholds["social_spike"]
            
        return False
        
    async def recalculate_score(self, project: str) -> ScoringResult:
        # Fetch latest data for all dimensions
        latest_data = await self.data_aggregator.get_project_data(project)
        
        # Recalculate with updated data
        new_score = self.scoring_engine.calculate_score(latest_data)
        
        # Update cache
        self.score_cache[project] = new_score
        
        return new_score

Model Training and Validation

Continuous Learning Pipeline

class ModelTrainer:
    def __init__(self):
        self.training_scheduler = TrainingScheduler()
        self.validation_metrics = ValidationMetrics()
        self.model_versioning = ModelVersioning()
        
    async def retrain_models(self):
        # Collect new training data
        training_data = await self.collect_training_data()
        validation_data = await self.collect_validation_data()
        
        for model_name, model_config in self.models.items():
            # Train new model version
            new_model = await self.train_model(
                model_name, 
                training_data, 
                model_config
            )
            
            # Validate performance
            performance = await self.validate_model(
                new_model, 
                validation_data
            )
            
            # Compare with current model
            if self.is_improvement(performance, model_name):
                await self.deploy_model(new_model, model_name)
                await self.archive_old_model(model_name)
                
    def collect_training_data(self) -> TrainingDataset:
        # Collect data from past 30 days
        end_date = datetime.now()
        start_date = end_date - timedelta(days=30)
        
        # Get projects with known outcomes
        labeled_projects = self.get_labeled_projects(start_date, end_date)
        
        training_features = []
        training_labels = []
        
        for project in labeled_projects:
            features = self.feature_engineer.extract_features(project.data)
            label = project.actual_outcome  # 0 = scam, 1 = legitimate
            
            training_features.append(features)
            training_labels.append(label)
            
        return TrainingDataset(
            features=np.array(training_features),
            labels=np.array(training_labels)
        )

---

Social Intelligence Layer

Component Overview

The Social Intelligence Layer aggregates and analyzes social media data across multiple platforms to provide sentiment analysis, community health metrics, and manipulation detection. This component leverages advanced natural language processing and network analysis to identify authentic community engagement versus artificial hype and coordinated manipulation.

Key Capabilities:

• Multi-platform social media monitoring (Twitter, Telegram, Discord, Farcaster)

• Real-time sentiment analysis with context understanding

• Influencer network mapping and impact assessment

• Shill detection and coordinated manipulation identification

• Viral content analysis and authenticity verification

Multi-Platform Data Aggregation

Platform Integration Architecture

interface SocialPlatform {
  name: string;
  apiClient: PlatformAPI;
  rateLimits: RateLimit;
  dataProcessor: PlatformProcessor;
}

class SocialAggregator {
  private platforms: Map<string, SocialPlatform>;
  private dataBuffer: SocialDataBuffer;
  private processingQueue: ProcessingQueue;
  
  constructor() {
    this.platforms = new Map([
      ["twitter", new TwitterIntegration()],
      ["telegram", new TelegramIntegration()],
      ["discord", new DiscordIntegration()],
      ["farcaster", new FarcasterIntegration()]
    ]);
  }
  
  async collectSocialData(projectId: string, timeframe: number): Promise<SocialDataset> {
    const dataPromises = Array.from(this.platforms.values()).map(platform =>
      platform.collectData(projectId, timeframe)
    );
    
    const platformData = await Promise.allSettled(dataPromises);
    const aggregatedData = this.aggregateData(platformData);
    
    return this.processAggregatedData(aggregatedData);
  }
  
  private aggregateData(platformData: PromiseSettledResult<any>[]): RawSocialData {
    const aggregated: RawSocialData = {
      posts: [],
      mentions: [],
      engagement: {},
      metadata: {}
    };
    
    platformData.forEach((result, index) => {
      if (result.status === 'fulfilled') {
        const platform = Array.from(this.platforms.keys())[index];
        aggregated.posts.push(...result.value.posts);
        aggregated.mentions.push(...result.value.mentions);
        aggregated.engagement[platform] = result.value.engagement;
        aggregated.metadata[platform] = result.value.metadata;
      }
    });
    
    return aggregated;
  }
}

Twitter Integration

import tweepy
from typing import List, Dict
import asyncio

class TwitterCollector:
    def __init__(self, api_credentials: Dict[str, str]):
        self.api = tweepy.Client(
            bearer_token=api_credentials["bearer_token"],
            consumer_key=api_credentials["consumer_key"],
            consumer_secret=api_credentials["consumer_secret"],
            access_token=api_credentials["access_token"],
            access_token_secret=api_credentials["access_token_secret"]
        )
        
    async def collect_project_mentions(self, project_symbol: str, hours: int = 24) -> List[Dict]:
        query = f"${project_symbol} OR #{project_symbol} -is:retweet lang:en"
        
        tweets = tweepy.Paginator(
            self.api.search_recent_tweets,
            query=query,
            tweet_fields=["created_at", "author_id", "public_metrics", "context_annotations"],
            user_fields=["public_metrics", "verified", "created_at"],
            expansions=["author_id"],
            max_results=100
        ).flatten(limit=1000)
        
        processed_tweets = []
        for tweet in tweets:
            processed_tweet = {
                "id": tweet.id,
                "text": tweet.text,
                "created_at": tweet.created_at.isoformat(),
                "author_id": tweet.author_id,
                "metrics": {
                    "retweet_count": tweet.public_metrics["retweet_count"],
                    "like_count": tweet.public_metrics["like_count"],
                    "reply_count": tweet.public_metrics["reply_count"],
                    "quote_count": tweet.public_metrics["quote_count"]
                },
                "platform": "twitter"
            }
            processed_tweets.append(processed_tweet)
            
        return processed_tweets
        
    def analyze_user_authenticity(self, user_data: Dict) -> float:
        """Calculate user authenticity score (0-1)"""
        factors = {
            "account_age": min(user_data.get("account_age_days", 0) / 365, 1.0),
            "follower_ratio": min(user_data.get("followers_count", 0) / max(user_data.get("following_count", 1), 1), 1.0),
            "engagement_rate": user_data.get("avg_engagement_rate", 0.0),
            "verified_status": 1.0 if user_data.get("verified", False) else 0.0,
            "bio_completeness": 1.0 if user_data.get("description") else 0.0
        }
        
        weights = {
            "account_age": 0.3,
            "follower_ratio": 0.25,
            "engagement_rate": 0.2,
            "verified_status": 0.15,
            "bio_completeness": 0.1
        }
        
        authenticity_score = sum(factors[factor] * weights[factor] for factor in factors)
        return min(authenticity_score, 1.0)

Natural Language Processing Pipeline

Sentiment Analysis Engine

from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import torch
from textblob import TextBlob
import re

class SentimentAnalyzer:
    def __init__(self):
        # Initialize multiple models for ensemble analysis
        self.models = {
            "crypto_bert": pipeline(
                "sentiment-analysis",
                model="ElKulako/cryptobert",
                tokenizer="ElKulako/cryptobert"
            ),
            "finbert": pipeline(
                "sentiment-analysis",
                model="ProsusAI/finbert",
                tokenizer="ProsusAI/finbert"
            ),
            "general_sentiment": pipeline(
                "sentiment-analysis",
                model="cardiffnlp/twitter-roberta-base-sentiment"
            )
        }
        
        self.crypto_lexicon = self.load_crypto_lexicon()
        
    def analyze_sentiment(self, text: str) -> Dict[str, float]:
        # Preprocess text
        cleaned_text = self.preprocess_text(text)
        
        # Get predictions from each model
        sentiments = {}
        for model_name, model in self.models.items():
            try:
                result = model(cleaned_text)[0]
                sentiments[model_name] = {
                    "label": result["label"],
                    "score": result["score"]
                }
            except Exception as e:
                print(f"Error with {model_name}: {e}")
                sentiments[model_name] = {"label": "NEUTRAL", "score": 0.5}
        
        # Apply crypto-specific adjustments
        crypto_sentiment = self.analyze_crypto_sentiment(cleaned_text)
        
        # Ensemble prediction
        final_sentiment = self.ensemble_sentiment(sentiments, crypto_sentiment)
        
        return final_sentiment
        
    def preprocess_text(self, text: str) -> str:
        # Remove URLs
        text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE)
        
        # Remove user mentions and hashtags for sentiment (keep for other analysis)
        text = re.sub(r'@\w+|#\w+', '', text)
        
        # Remove excessive whitespace
        text = re.sub(r'\s+', ' ', text).strip()
        
        return text
        
    def analyze_crypto_sentiment(self, text: str) -> Dict[str, float]:
        positive_indicators = [
            "moon", "bullish", "pump", "gem", "diamond hands", "hodl",
            "100x", "to the moon", "buy the dip", "strong community"
        ]
        
        negative_indicators = [
            "rug pull", "scam", "dump", "exit scam", "honeypot",
            "paper hands", "dead project", "abandoned", "suspicious"
        ]
        
        text_lower = text.lower()
        
        positive_count = sum(1 for indicator in positive_indicators if indicator in text_lower)
        negative_count = sum(1 for indicator in negative_indicators if indicator in text_lower)
        
        if positive_count > negative_count:
            return {"label": "POSITIVE", "score": min(0.7 + (positive_count * 0.1), 1.0)}
        elif negative_count > positive_count:
            return {"label": "NEGATIVE", "score": min(0.7 + (negative_count * 0.1), 1.0)}
        else:
            return {"label": "NEUTRAL", "score": 0.5}

Entity Recognition and Context Analysis

import spacy
from spacy import displacy
import networkx as nx

class EntityAnalyzer:
    def __init__(self):
        self.nlp = spacy.load("en_core_web_lg")
        
        # Add crypto-specific entity patterns
        ruler = self.nlp.add_pipe("entity_ruler", before="ner")
        patterns = [
            {"label": "CRYPTO_TOKEN", "pattern": [{"TEXT": {"REGEX": r"^\$[A-Z]{2,10}$"}}]},
            {"label": "WALLET_ADDRESS", "pattern": [{"TEXT": {"REGEX": r"^0x[a-fA-F0-9]{40}$"}}]},
            {"label": "CONTRACT_ADDRESS", "pattern": [{"TEXT": {"REGEX": r"^0x[a-fA-F0-9]{40}$"}}]}
        ]
        ruler.add_patterns(patterns)
        
    def extract_entities(self, text: str) -> Dict[str, List[str]]:
        doc = self.nlp(text)
        
        entities = {
            "tokens": [],
            "wallets": [],
            "contracts": [],
            "people": [],
            "organizations": [],
            "locations": []
        }
        
        for ent in doc.ents:
            if ent.label_ == "CRYPTO_TOKEN":
                entities["tokens"].append(ent.text)
            elif ent.label_ == "WALLET_ADDRESS":
                entities["wallets"].append(ent.text)
            elif ent.label_ == "CONTRACT_ADDRESS":
                entities["contracts"].append(ent.text)
            elif ent.label_ == "PERSON":
                entities["people"].append(ent.text)
            elif ent.label_ == "ORG":
                entities["organizations"].append(ent.text)
            elif ent.label_ in ["GPE", "LOC"]:
                entities["locations"].append(ent.text)
                
        return entities
        
    def build_entity_network(self, social_data: List[Dict]) -> nx.Graph:
        G = nx.Graph()
        
        for post in social_data:
            entities = self.extract_entities(post["text"])
            author = post["author_id"]
            
            # Add author node
            G.add_node(author, type="user")
            
            # Add entity nodes and relationships
            for entity_type, entity_list in entities.items():
                for entity in entity_list:
                    G.add_node(entity, type=entity_type)
                    G.add_edge(author, entity, weight=1, post_id=post["id"])
                    
        return G

Manipulation Detection

Coordinated Behavior Analysis

import pandas as pd
from sklearn.cluster import DBSCAN
from datetime import datetime, timedelta
import numpy as np

class ManipulationDetector:
    def __init__(self):
        self.suspicious_patterns = {
            "temporal_clustering": {"window": 300, "min_posts": 10},  # 5 minutes
            "content_similarity": {"threshold": 0.8},
            "account_similarity": {"threshold": 0.7},
            "engagement_anomaly": {"multiplier": 3.0}
        }
        
    def detect_shill_campaigns(self, social_data: List[Dict]) -> Dict[str, any]:
        df = pd.DataFrame(social_data)
        
        # Convert timestamps
        df['timestamp'] = pd.to_datetime(df['created_at'])
        df = df.sort_values('timestamp')
        
        # Detect temporal clustering
        temporal_clusters = self.detect_temporal_clustering(df)
        
        # Detect content similarity
        content_clusters = self.detect_content_similarity(df)
        
        # Detect account coordination
        account_clusters = self.detect_account_coordination(df)
        
        # Combine detections
        manipulation_score = self.calculate_manipulation_score(
            temporal_clusters, content_clusters, account_clusters
        )
        
        return {
            "manipulation_score": manipulation_score,
            "temporal_clusters": temporal_clusters,
            "content_clusters": content_clusters,
            "account_clusters": account_clusters,
            "suspicious_activity": manipulation_score > 0.6
        }
        
    def detect_temporal_clustering(self, df: pd.DataFrame) -> List[Dict]:
        clusters = []
        window = timedelta(seconds=self.suspicious_patterns["temporal_clustering"]["window"])
        min_posts = self.suspicious_patterns["temporal_clustering"]["min_posts"]
        
        for i in range(len(df)):
            current_time = df.iloc[i]['timestamp']
            window_end = current_time + window
            
            # Count posts in time window
            window_posts = df[
                (df['timestamp'] >= current_time) & 
                (df['timestamp'] <= window_end)
            ]
            
            if len(window_posts) >= min_posts:
                clusters.append({
                    "start_time": current_time.isoformat(),
                    "end_time": window_end.isoformat(),
                    "post_count": len(window_posts),
                    "unique_authors": window_posts['author_id'].nunique(),
                    "posts": window_posts['id'].tolist()
                })
                
        return clusters
        
    def detect_content_similarity(self, df: pd.DataFrame) -> List[Dict]:
        from sklearn.feature_extraction.text import TfidfVectorizer
        from sklearn.metrics.pairwise import cosine_similarity
        
        # Vectorize text content
        vectorizer = TfidfVectorizer(max_features=1000, stop_words='english')
        text_vectors = vectorizer.fit_transform(df['text'])
        
        # Calculate similarity matrix
        similarity_matrix = cosine_similarity(text_vectors)
        
        # Find highly similar content
        threshold = self.suspicious_patterns["content_similarity"]["threshold"]
        similar_pairs = []
        
        for i in range(len(similarity_matrix)):
            for j in range(i + 1, len(similarity_matrix)):
                if similarity_matrix[i][j] > threshold:
                    similar_pairs.append({
                        "post1_id": df.iloc[i]['id'],
                        "post2_id": df.iloc[j]['id'],
                        "similarity": similarity_matrix[i][j],
                        "author1": df.iloc[i]['author_id'],
                        "author2": df.iloc[j]['author_id']
                    })
                    
        return similar_pairs

Performance Monitoring

Real-Time Analytics Dashboard

class SocialAnalyticsDashboard:
    def __init__(self):
        self.metrics = {
            "processing_latency": [],
            "sentiment_accuracy": [],
            "manipulation_detection_rate": [],
            "platform_availability": {}
        }
        
    async def update_metrics(self):
        while True:
            # Collect performance metrics
            latency = await self.measure_processing_latency()
            self.metrics["processing_latency"].append(latency)
            
            # Update platform availability
            for platform in self.platforms:
                availability = await self.check_platform_availability(platform)
                self.metrics["platform_availability"][platform] = availability
                
            # Calculate sentiment accuracy (when ground truth available)
            accuracy = await self.calculate_sentiment_accuracy()
            if accuracy:
                self.metrics["sentiment_accuracy"].append(accuracy)
                
            await asyncio.sleep(60)  # Update every minute
            
    def generate_performance_report(self) -> Dict[str, any]:
        return {
            "avg_processing_latency": np.mean(self.metrics["processing_latency"][-100:]),
            "platform_uptime": {
                platform: np.mean(status_list[-60:]) 
                for platform, status_list in self.metrics["platform_availability"].items()
            },
            "sentiment_accuracy": np.mean(self.metrics["sentiment_accuracy"][-50:]) if self.metrics["sentiment_accuracy"] else None,
            "total_posts_processed": len(self.metrics["processing_latency"]),
            "system_health": self.calculate_system_health()
        }

---

Intelligence Aggregation Module

Component Overview

The Intelligence Aggregation Module serves as the central hub for correlating and synthesizing intelligence data from multiple sources, including on-chain analysis, external intelligence providers, and behavioral pattern recognition. This component transforms raw intelligence into actionable insights through advanced correlation algorithms and risk pattern matching.

Core Functions:

• Multi-source intelligence correlation and validation

• Wallet attribution and entity resolution

• Fund flow tracking and suspicious activity detection

• Risk pattern recognition and threat intelligence

• Historical analysis and predictive modeling

Intelligence Fusion Architecture

Data Source Integration

from typing import Dict, List, Optional
from dataclasses import dataclass
from enum import Enum

class IntelligenceSource(Enum):
    ARKHAM = "arkham_intelligence"
    CHAINALYSIS = "chainalysis"
    ONCHAIN_ANALYSIS = "onchain_internal"
    SOCIAL_INTELLIGENCE = "social_internal"
    COMMUNITY_REPORTS = "community_reports"

@dataclass
class IntelligenceRecord:
    source: IntelligenceSource
    entity_id: str
    entity_type: str
    confidence: float
    timestamp: datetime
    data: Dict[str, any]
    correlation_ids: List[str]

class IntelligenceFusion:
    def __init__(self):
        self.sources = {
            IntelligenceSource.ARKHAM: ArkhamConnector(),
            IntelligenceSource.CHAINALYSIS: ChainanalysisConnector(),
            IntelligenceSource.ONCHAIN_ANALYSIS: OnChainAnalyzer(),
            IntelligenceSource.SOCIAL_INTELLIGENCE: SocialAnalyzer(),
            IntelligenceSource.COMMUNITY_REPORTS: CommunityReports()
        }
        
        self.correlation_engine = CorrelationEngine()
        self.entity_resolver = EntityResolver()
        
    async def aggregate_intelligence(self, entity_id: str) -> IntelligenceProfile:
        intelligence_records = []
        
        # Collect data from all sources
        for source, connector in self.sources.items():
            try:
                records = await connector.get_intelligence(entity_id)
                intelligence_records.extend(records)
            except Exception as e:
                logger.warning(f"Failed to collect from {source}: {e}")
                
        # Correlate and validate information
        correlated_intelligence = self.correlation_engine.correlate(intelligence_records)
        
        # Resolve entity relationships
        entity_profile = self.entity_resolver.resolve(correlated_intelligence)
        
        return entity_profile

Arkham Intelligence Integration

import aiohttp
import asyncio
from typing import Optional

class ArkhamConnector:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.arkhamintelligence.com/v1"
        self.session = None
        
    async def get_entity_info(self, address: str) -> Optional[Dict]:
        async with aiohttp.ClientSession() as session:
            headers = {
                "Authorization": f"Bearer {self.api_key}",
                "Content-Type": "application/json"
            }
            
            url = f"{self.base_url}/entity/{address}"
            
            async with session.get(url, headers=headers) as response:
                if response.status == 200:
                    data = await response.json()
                    return self.process_arkham_data(data)
                else:
                    logger.warning(f"Arkham API error: {response.status}")
                    return None
                    
    def process_arkham_data(self, raw_data: Dict) -> Dict:
        return {
            "entity_name": raw_data.get("entity", {}).get("name"),
            "entity_type": raw_data.get("entity", {}).get("type"),
            "risk_score": raw_data.get("risk_score", 0),
            "labels": raw_data.get("labels", []),
            "transactions": {
                "total_volume": raw_data.get("total_volume"),
                "transaction_count": raw_data.get("transaction_count"),
                "first_seen": raw_data.get("first_transaction_date"),
                "last_seen": raw_data.get("last_transaction_date")
            },
            "connections": raw_data.get("connected_entities", []),
            "confidence": raw_data.get("confidence", 0.5)
        }
        
    async def track_fund_flows(self, address: str, depth: int = 2) -> Dict:
        """Track fund flows from a given address"""
        flows = {"inbound": [], "outbound": [], "patterns": []}
        
        # Get transaction history
        transactions = await self.get_transaction_history(address)
        
        for tx in transactions:
            if tx["to"].lower() == address.lower():
                flows["inbound"].append({
                    "from": tx["from"],
                    "amount": tx["value"],
                    "timestamp": tx["timestamp"],
                    "tx_hash": tx["hash"]
                })
            elif tx["from"].lower() == address.lower():
                flows["outbound"].append({
                    "to": tx["to"],
                    "amount": tx["value"],
                    "timestamp": tx["timestamp"],
                    "tx_hash": tx["hash"]
                })
                
        # Analyze patterns
        flows["patterns"] = self.analyze_flow_patterns(flows)
        
        return flows

Entity Resolution and Attribution

Multi-Source Entity Correlation

from difflib import SequenceMatcher
import networkx as nx

class EntityResolver:
    def __init__(self):
        self.similarity_threshold = 0.85
        self.confidence_weights = {
            IntelligenceSource.ARKHAM: 0.9,
            IntelligenceSource.CHAINALYSIS: 0.85,
            IntelligenceSource.ONCHAIN_ANALYSIS: 0.7,
            IntelligenceSource.SOCIAL_INTELLIGENCE: 0.6,
            IntelligenceSource.COMMUNITY_REPORTS: 0.4
        }
        
    def resolve_entity(self, intelligence_records: List[IntelligenceRecord]) -> EntityProfile:
        # Group records by entity similarity
        entity_clusters = self.cluster_similar_entities(intelligence_records)
        
        # Resolve each cluster to a single entity
        resolved_entities = []
        for cluster in entity_clusters:
            resolved_entity = self.merge_entity_cluster(cluster)
            resolved_entities.append(resolved_entity)
            
        # Build entity relationship graph
        relationship_graph = self.build_relationship_graph(resolved_entities)
        
        return EntityProfile(
            entities=resolved_entities,
            relationships=relationship_graph,
            confidence=self.calculate_overall_confidence(resolved_entities)
        )
        
    def cluster_similar_entities(self, records: List[IntelligenceRecord]) -> List[List[IntelligenceRecord]]:
        clusters = []
        processed = set()
        
        for i, record in enumerate(records):
            if i in processed:
                continue
                
            cluster = [record]
            processed.add(i)
            
            for j, other_record in enumerate(records[i+1:], i+1):
                if j in processed:
                    continue
                    
                if self.entities_are_similar(record, other_record):
                    cluster.append(other_record)
                    processed.add(j)
                    
            clusters.append(cluster)
            
        return clusters
        
    def entities_are_similar(self, entity1: IntelligenceRecord, entity2: IntelligenceRecord) -> bool:
        # Check address similarity
        if entity1.entity_id == entity2.entity_id:
            return True
            
        # Check name similarity if available
        name1 = entity1.data.get("entity_name", "")
        name2 = entity2.data.get("entity_name", "")
        
        if name1 and name2:
            name_similarity = SequenceMatcher(None, name1.lower(), name2.lower()).ratio()
            if name_similarity > self.similarity_threshold:
                return True
                
        # Check transaction pattern similarity
        if self.transaction_patterns_similar(entity1, entity2):
            return True
            
        return False
        
    def merge_entity_cluster(self, cluster: List[IntelligenceRecord]) -> ResolvedEntity:
        # Weighted merge based on source confidence
        merged_data = {}
        total_confidence = 0
        
        for record in cluster:
            source_weight = self.confidence_weights[record.source]
            record_confidence = record.confidence * source_weight
            
            for key, value in record.data.items():
                if key not in merged_data:
                    merged_data[key] = {"value": value, "confidence": record_confidence}
                else:
                    # Weighted average for numerical values
                    if isinstance(value, (int, float)) and isinstance(merged_data[key]["value"], (int, float)):
                        current_weight = merged_data[key]["confidence"]
                        new_weight = record_confidence
                        total_weight = current_weight + new_weight
                        
                        merged_data[key]["value"] = (
                            (merged_data[key]["value"] * current_weight + value * new_weight) / total_weight
                        )
                        merged_data[key]["confidence"] = total_weight
                    else:
                        # Keep highest confidence value for non-numerical data
                        if record_confidence > merged_data[key]["confidence"]:
                            merged_data[key] = {"value": value, "confidence": record_confidence}
                            
        return ResolvedEntity(
            entity_id=cluster[0].entity_id,
            merged_data=merged_data,
            sources=[record.source for record in cluster],
            overall_confidence=sum(record.confidence for record in cluster) / len(cluster)
        )

Risk Pattern Recognition

Behavioral Pattern Analysis

import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import numpy as np

class RiskPatternRecognizer:
    def __init__(self):
        self.known_patterns = {
            "mixer_usage": self.detect_mixer_usage,
            "rapid_distribution": self.detect_rapid_distribution,
            "circular_trading": self.detect_circular_trading,
            "wash_trading": self.detect_wash_trading,
            "pump_and_dump": self.detect_pump_and_dump,
            "sybil_attack": self.detect_sybil_attack
        }
        
        self.anomaly_detector = IsolationForest(contamination=0.1, random_state=42)
        self.scaler = StandardScaler()
        
    def analyze_risk_patterns(self, entity_data: Dict) -> Dict[str, float]:
        risk_scores = {}
        
        # Run each pattern detection algorithm
        for pattern_name, detector in self.known_patterns.items():
            try:
                score = detector(entity_data)
                risk_scores[pattern_name] = score
            except Exception as e:
                logger.warning(f"Error detecting {pattern_name}: {e}")
                risk_scores[pattern_name] = 0.0
                
        # Machine learning anomaly detection
        ml_anomaly_score = self.detect_ml_anomalies(entity_data)
        risk_scores["ml_anomaly"] = ml_anomaly_score
        
        # Calculate overall risk score
        overall_risk = self.calculate_overall_risk(risk_scores)
        risk_scores["overall_risk"] = overall_risk
        
        return risk_scores
        
    def detect_mixer_usage(self, entity_data: Dict) -> float:
        """Detect usage of known mixing services"""
        known_mixers = [
            "0x5e4e65926ba27467555eb562121fac00d24e9dd2",  # Tornado Cash
            "0xd90e2f925da726b50c4ed8d0fb90ad053324f31b",  # Blender
            # Add more known mixer addresses
        ]
        
        transactions = entity_data.get("transactions", [])
        mixer_interactions = 0
        total_transactions = len(transactions)
        
        if total_transactions == 0:
            return 0.0
            
        for tx in transactions:
            if tx.get("to", "").lower() in [mixer.lower() for mixer in known_mixers]:
                mixer_interactions += 1
            if tx.get("from", "").lower() in [mixer.lower() for mixer in known_mixers]:
                mixer_interactions += 1
                
        mixer_ratio = mixer_interactions / total_transactions
        return min(mixer_ratio * 2.0, 1.0)  # Cap at 1.0
        
    def detect_rapid_distribution(self, entity_data: Dict) -> float:
        """Detect rapid token distribution patterns"""
        transactions = entity_data.get("transactions", [])
        
        if len(transactions) < 10:
            return 0.0
            
        # Analyze transaction timing
        timestamps = [tx.get("timestamp", 0) for tx in transactions]
        timestamps.sort()
        
        # Calculate distribution rate
        time_span = timestamps[-1] - timestamps[0]
        if time_span == 0:
            return 1.0  # All transactions in same block is highly suspicious
            
        avg_interval = time_span / len(transactions)
        
        # Rapid distribution if average interval < 1 minute
        if avg_interval < 60:
            return min((60 - avg_interval) / 60, 1.0)
        else:
            return 0.0
            
    def detect_circular_trading(self, entity_data: Dict) -> float:
        """Detect circular trading patterns"""
        transactions = entity_data.get("transactions", [])
        
        # Build transaction graph
        G = nx.DiGraph()
        
        for tx in transactions:
            from_addr = tx.get("from", "").lower()
            to_addr = tx.get("to", "").lower()
            
            if from_addr and to_addr:
                G.add_edge(from_addr, to_addr, **tx)
                
        # Find cycles in the graph
        try:
            cycles = list(nx.simple_cycles(G))
            cycle_count = len(cycles)
            
            # Calculate cycle score
            if cycle_count == 0:
                return 0.0
            else:
                # Normalize by number of nodes
                node_count = len(G.nodes())
                cycle_ratio = cycle_count / max(node_count, 1)
                return min(cycle_ratio, 1.0)
                
        except Exception:
            return 0.0
            
    def detect_ml_anomalies(self, entity_data: Dict) -> float:
        """Use machine learning to detect anomalous patterns"""
        features = self.extract_behavioral_features(entity_data)
        
        if len(features) == 0:
            return 0.0
            
        # Normalize features
        features_scaled = self.scaler.fit_transform([features])
        
        # Predict anomaly
        anomaly_score = self.anomaly_detector.decision_function(features_scaled)[0]
        
        # Convert to 0-1 scale (more negative = more anomalous)
        normalized_score = max(0, min(1, (-anomaly_score + 0.5) / 1.0))
        
        return normalized_score
        
    def extract_behavioral_features(self, entity_data: Dict) -> List[float]:
        """Extract numerical features for ML analysis"""
        transactions = entity_data.get("transactions", [])
        
        if not transactions:
            return []
            
        features = []
        
        # Transaction volume features
        amounts = [float(tx.get("amount", 0)) for tx in transactions]
        features.extend([
            np.mean(amounts),
            np.std(amounts),
            np.max(amounts),
            np.min(amounts),
            len(amounts)
        ])
        
        # Timing features
        timestamps = [tx.get("timestamp", 0) for tx in transactions]
        time_diffs = np.diff(sorted(timestamps))
        
        if len(time_diffs) > 0:
            features.extend([
                np.mean(time_diffs),
                np.std(time_diffs),
                np.max(time_diffs),
                np.min(time_diffs)
            ])
        else:
            features.extend([0, 0, 0, 0])
            
        # Network features
        unique_counterparties = set()
        for tx in transactions:
            unique_counterparties.add(tx.get("from", ""))
            unique_counterparties.add(tx.get("to", ""))
            
        features.append(len(unique_counterparties))
        
        return features

---

Chat Interface System

Component Overview

The Chat Interface System serves as the user-facing gateway to Kaizen AI's analytical capabilities, providing a conversational interface that can understand natural language queries, route requests to appropriate analytical agents, and present complex analysis results in an accessible format.

Core Capabilities:

• Natural language understanding and intent classification

• Context-aware conversation management

• Multi-agent query routing and orchestration

• Response generation with explanatory context

• Real-time analysis streaming and interactive follow-ups

Natural Language Processing Architecture

Intent Classification and Query Routing

from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import json
from typing import Dict, List, Tuple
from enum import Enum

class QueryIntent(Enum):
    PROJECT_ANALYSIS = "project_analysis"
    SECURITY_CHECK = "security_check"
    COMPARISON = "comparison"
    PORTFOLIO_REVIEW = "portfolio_review"
    MARKET_SENTIMENT = "market_sentiment"
    PRICE_PREDICTION = "price_prediction"
    GENERAL_INFO = "general_info"
    HELP = "help"

class IntentClassifier:
    def __init__(self):
        self.classifier = pipeline(
            "text-classification",
            model="microsoft/DialoGPT-medium",
            tokenizer="microsoft/DialoGPT-medium"
        )
        
        self.intent_patterns = {
            QueryIntent.PROJECT_ANALYSIS: [
                "analyze", "check", "review", "examine", "evaluate",
                "token", "project", "contract", "coin"
            ],
            QueryIntent.SECURITY_CHECK: [
                "safe", "scam", "honeypot", "rug pull", "security",
                "legitimate", "trusted", "risk"
            ],
            QueryIntent.COMPARISON: [
                "compare", "versus", "vs", "difference", "better",
                "between", "which"
            ],
            QueryIntent.PORTFOLIO_REVIEW: [
                "portfolio", "holdings", "positions", "my tokens",
                "my coins", "wallet"
            ],
            QueryIntent.MARKET_SENTIMENT: [
                "sentiment", "opinion", "community", "social",
                "feeling", "buzz", "discussion"
            ]
        }
        
    def classify_intent(self, query: str) -> Tuple[QueryIntent, float]:
        # Convert query to lowercase for pattern matching
        query_lower = query.lower()
        
        # Score each intent based on keyword presence
        intent_scores = {}
        
        for intent, keywords in self.intent_patterns.items():
            score = 0
            for keyword in keywords:
                if keyword in query_lower:
                    score += 1
            
            # Normalize score
            intent_scores[intent] = score / len(keywords)
            
        # Get highest scoring intent
        best_intent = max(intent_scores, key=intent_scores.get)
        confidence = intent_scores[best_intent]
        
        # Use ML model for additional validation
        ml_result = self.classifier(query)[0]
        
        # Combine rule-based and ML results
        if confidence < 0.3:  # Low confidence, rely more on ML
            return self.map_ml_to_intent(ml_result), ml_result["score"]
        else:
            return best_intent, confidence
            
    def extract_entities(self, query: str) -> Dict[str, List[str]]:
        """Extract relevant entities from the query"""
        import re
        
        entities = {
            "tokens": [],
            "addresses": [],
            "amounts": [],
            "timeframes": []
        }
        
        # Token symbols (e.g., $BTC, $ETH)
        token_pattern = r'\$([A-Z]{2,10})'
        entities["tokens"] = re.findall(token_pattern, query)
        
        # Contract addresses
        address_pattern = r'0x[a-fA-F0-9]{40}'
        entities["addresses"] = re.findall(address_pattern, query)
        
        # Amounts and numbers
        amount_pattern = r'\$?(\d+(?:\.\d+)?(?:[kmb])?)'
        entities["amounts"] = re.findall(amount_pattern, query.lower())
        
        # Timeframes
        timeframe_patterns = [
            r'(\d+)\s*(hour|day|week|month|year)s?',
            r'(last|past|next)\s*(hour|day|week|month|year)',
            r'(today|yesterday|tomorrow)'
        ]
        
        for pattern in timeframe_patterns:
            matches = re.findall(pattern, query.lower())
            entities["timeframes"].extend(matches)
            
        return entities

Multi-LLM Response Generation

import openai
import anthropic
from mistralai.client import MistralClient
import asyncio
from typing import Optional

class ResponseGenerator:
    def __init__(self, config: Dict[str, str]):
        self.openai_client = openai.OpenAI(api_key=config["openai_key"])
        self.anthropic_client = anthropic.Anthropic(api_key=config["anthropic_key"])
        self.mistral_client = MistralClient(api_key=config["mistral_key"])
        
        self.model_capabilities = {
            "gpt-4": {
                "strengths": ["complex_analysis", "detailed_explanations"],
                "cost": "high",
                "latency": "medium"
            },
            "claude-3": {
                "strengths": ["technical_accuracy", "safety"],
                "cost": "medium",
                "latency": "low"
            },
            "mistral-large": {
                "strengths": ["speed", "efficiency"],
                "cost": "low",
                "latency": "very_low"
            }
        }
        
    async def generate_response(
        self, 
        query: str, 
        context: Dict, 
        analysis_results: Dict
    ) -> str:
        # Select appropriate model based on query complexity and context
        selected_model = self.select_model(query, context)
        
        # Prepare prompt with context and analysis results
        prompt = self.build_prompt(query, context, analysis_results)
        
        # Generate response using selected model
        if selected_model == "gpt-4":
            response = await self.generate_openai_response(prompt)
        elif selected_model == "claude-3":
            response = await self.generate_anthropic_response(prompt)
        elif selected_model == "mistral-large":
            response = await self.generate_mistral_response(prompt)
        else:
            response = "I'm sorry, I couldn't process your request."
            
        return response
        
    def select_model(self, query: str, context: Dict) -> str:
        query_complexity = self.assess_query_complexity(query)
        user_tier = context.get("user_tier", "free")
        urgency = context.get("urgency", "normal")
        
        if user_tier == "premium" and query_complexity == "high":
            return "gpt-4"
        elif urgency == "high" or query_complexity == "low":
            return "mistral-large"
        else:
            return "claude-3"
            
    def build_prompt(self, query: str, context: Dict, analysis_results: Dict) -> str:
        system_prompt = """
        You are Kaizen AI, an expert cryptocurrency analyst and security researcher. 
        Your role is to help users understand crypto projects, assess risks, and make informed decisions.
        
        Always provide:
        1. Clear, actionable insights
        2. Risk assessments with confidence levels
        3. Explanations of your reasoning
        4. Specific recommendations when appropriate
        
        Use the analysis results provided to give accurate, data-driven responses.
        """
        
        context_info = f"""
        User Context:
        - Risk Tolerance: {context.get('risk_tolerance', 'unknown')}
        - Experience Level: {context.get('experience_level', 'unknown')}
        - Portfolio Value: {context.get('portfolio_value', 'unknown')}
        
        Analysis Results:
        {json.dumps(analysis_results, indent=2)}
        """
        
        return f"{system_prompt}\n\n{context_info}\n\nUser Query: {query}\n\nResponse:"
        
    async def generate_openai_response(self, prompt: str) -> str:
        try:
            response = await self.openai_client.chat.completions.acreate(
                model="gpt-4",
                messages=[{"role": "user", "content": prompt}],
                max_tokens=1000,
                temperature=0.3
            )
            return response.choices[0].message.content
        except Exception as e:
            logger.error(f"OpenAI API error: {e}")
            return "I encountered an error processing your request with advanced analysis."
            
    async def generate_anthropic_response(self, prompt: str) -> str:
        try:
            response = await self.anthropic_client.messages.acreate(
                model="claude-3-sonnet-20240229",
                max_tokens=1000,
                temperature=0.3,
                messages=[{"role": "user", "content": prompt}]
            )
            return response.content[0].text
        except Exception as e:
            logger.error(f"Anthropic API error: {e}")
            return "I encountered an error processing your request."

Context Management

Conversation State Management

from dataclasses import dataclass, asdict
from datetime import datetime, timedelta
import json
import redis

@dataclass
class ConversationContext:
    session_id: str
    user_id: str
    conversation_history: List[Dict]
    active_analysis: Optional[Dict]
    user_preferences: Dict
    last_updated: datetime
    
class ContextManager:
    def __init__(self, redis_client: redis.Redis):
        self.redis = redis_client
        self.context_ttl = 3600  # 1 hour
        
    async def get_context(self, session_id: str) -> Optional[ConversationContext]:
        context_data = self.redis.get(f"context:{session_id}")
        
        if context_data:
            data = json.loads(context_data)
            return ConversationContext(**data)
        else:
            return None
            
    async def update_context(self, context: ConversationContext):
        context.last_updated = datetime.now()
        context_data = json.dumps(asdict(context), default=str)
        
        self.redis.setex(
            f"context:{context.session_id}",
            self.context_ttl,
            context_data
        )
        
    async def add_to_history(self, session_id: str, message: Dict):
        context = await self.get_context(session_id)
        
        if context:
            context.conversation_history.append(message)
            
            # Keep only last 20 messages to manage memory
            if len(context.conversation_history) > 20:
                context.conversation_history = context.conversation_history[-20:]
                
            await self.update_context(context)
            
    async def set_active_analysis(self, session_id: str, analysis: Dict):
        context = await self.get_context(session_id)
        
        if context:
            context.active_analysis = analysis
            await self.update_context(context)

Query Processing Pipeline

End-to-End Query Processing

class ChatProcessor:
    def __init__(self):
        self.intent_classifier = IntentClassifier()
        self.context_manager = ContextManager()
        self.response_generator = ResponseGenerator()
        self.agent_orchestrator = AgentOrchestrator()
        
    async def process_query(self, query: str, session_id: str) -> Dict[str, any]:
        # Get conversation context
        context = await self.context_manager.get_context(session_id)
        
        if not context:
            context = await self.create_new_context(session_id)
            
        # Classify intent and extract entities
        intent, confidence = self.intent_classifier.classify_intent(query)
        entities = self.intent_classifier.extract_entities(query)
        
        # Add query to conversation history
        await self.context_manager.add_to_history(session_id, {
            "type": "user_query",
            "content": query,
            "timestamp": datetime.now().isoformat(),
            "intent": intent.value,
            "entities": entities
        })
        
        # Route to appropriate agents for analysis
        analysis_results = await self.route_to_agents(intent, entities, context)
        
        # Generate response
        response = await self.response_generator.generate_response(
            query, asdict(context), analysis_results
        )
        
        # Add response to conversation history
        await self.context_manager.add_to_history(session_id, {
            "type": "ai_response",
            "content": response,
            "timestamp": datetime.now().isoformat(),
            "analysis_results": analysis_results
        })
        
        return {
            "response": response,
            "intent": intent.value,
            "confidence": confidence,
            "entities": entities,
            "analysis_results": analysis_results,
            "suggestions": self.generate_suggestions(intent, entities)
        }
        
    async def route_to_agents(
        self, 
        intent: QueryIntent, 
        entities: Dict, 
        context: ConversationContext
    ) -> Dict:
        if intent == QueryIntent.PROJECT_ANALYSIS:
            return await self.handle_project_analysis(entities, context)
        elif intent == QueryIntent.SECURITY_CHECK:
            return await self.handle_security_check(entities, context)
        elif intent == QueryIntent.COMPARISON:
            return await self.handle_comparison(entities, context)
        elif intent == QueryIntent.PORTFOLIO_REVIEW:
            return await self.handle_portfolio_review(entities, context)
        elif intent == QueryIntent.MARKET_SENTIMENT:
            return await self.handle_sentiment_analysis(entities, context)
        else:
            return {"error": "Intent not supported"}
            
    async def handle_project_analysis(self, entities: Dict, context: ConversationContext) -> Dict:
        # Extract target for analysis
        target = None
        
        if entities["tokens"]:
            target = entities["tokens"][0]
        elif entities["addresses"]:
            target = entities["addresses"][0]
        else:
            return {"error": "No valid project identifier found"}
            
        # Orchestrate multi-agent analysis
        results = await self.agent_orchestrator.comprehensive_analysis(target)
        
        # Store active analysis for follow-up queries
        await self.context_manager.set_active_analysis(
            context.session_id, 
            {"target": target, "results": results}
        )
        
        return results
        
    def generate_suggestions(self, intent: QueryIntent, entities: Dict) -> List[str]:
        suggestions = []
        
        if intent == QueryIntent.PROJECT_ANALYSIS:
            suggestions = [
                "Check the security score",
                "Analyze social sentiment",
                "Compare with similar projects",
                "Set up monitoring alerts"
            ]
        elif intent == QueryIntent.SECURITY_CHECK:
            suggestions = [
                "Get detailed risk breakdown",
                "Check for honeypot indicators",
                "Analyze liquidity locks",
                "Review audit status"
            ]
        elif intent == QueryIntent.COMPARISON:
            suggestions = [
                "Add more projects to compare",
                "Focus on specific metrics",
                "Check historical performance",
                "Analyze market correlations"
            ]
            
        return suggestions

This comprehensive overview of Kaizen AI's core components provides the technical foundation necessary for understanding, implementing, and extending the platform's analytical capabilities across blockchain data collection, machine learning scoring, social intelligence, intelligence aggregation, and conversational AI interfaces.