Building AI Chat Bots

// AI Chat Bot with MCPCodex
// chatbot.ts

import { MCPCodex, Agent, Context } from '@mcpcodex/sdk';

export class IntelligentChatBot {
  private mcp: MCPCodex;
  private agent: Agent;
  private context: Context;

  constructor() {
    this.mcp = new MCPCodex({
      apiKey: process.env.MCPCODEX_API_KEY,
      model: 'claude-3-opus'
    });

    this.agent = this.mcp.createAgent({
      name: 'CustomerSupportBot',
      personality: 'helpful, professional, empathetic',
      capabilities: ['conversation', 'knowledge_retrieval', 'task_execution']
    });

    this.context = this.mcp.createContext({
      maxTokens: 8192,
      temperature: 0.7,
      systemPrompt: `You are a helpful customer support agent for MCPCodex.
        You can help users with:
        - Technical documentation
        - Account management
        - Troubleshooting
        - Feature explanations
        
        Always be polite, accurate, and offer to escalate complex issues.`
    });
  }

  async processMessage(userMessage: string, userId: string): Promise<ChatResponse> {
    try {
      // Add user context and history
      const userContext = await this.getUserContext(userId);
      
      // Enhance message with context
      const enhancedMessage = await this.enhanceMessage(userMessage, userContext);
      
      // Generate response with AI
      const response = await this.agent.generate({
        message: enhancedMessage,
        context: this.context,
        userId,
        features: {
          codeGeneration: true,
          webSearch: true,
          documentRetrieval: true
        }
      });

      // Process any actions or follow-ups
      const actions = await this.extractActions(response);
      
      return {
        message: response.text,
        actions,
        confidence: response.confidence,
        sources: response.sources
      };
      
    } catch (error) {
      return this.handleError(error, userMessage);
    }
  }

  async enhanceMessage(message: string, userContext: UserContext): Promise<string> {
    return `User Context: ${JSON.stringify(userContext)}
    
User Message: ${message}

Please provide a helpful response based on the user's context and message.`;
  }

  async extractActions(response: AgentResponse): Promise<Action[]> {
    const actions = [];
    
    // Check for code generation requests
    if (response.metadata?.requiresCode) {
      actions.push({
        type: 'generate_code',
        params: response.metadata.codeParams
      });
    }
    
    // Check for escalation needs
    if (response.confidence < 0.7) {
      actions.push({
        type: 'suggest_escalation',
        reason: 'Low confidence response'
      });
    }
    
    return actions;
  }
}