Conversational Applications with LLMs

Conversational Applications with LLMs

Introduction

Language models make it possible to build sophisticated conversational applications that can understand users and respond naturally. This tutorial explores the different ways to implement chatbots and virtual assistants with LLMs.

Types of Conversational Applications

1. Chatbots

• Customer service
• Technical support
• Sales and marketing
• Interactive FAQ
• Personal assistant

2. Virtual Assistants

• Personal assistant
• Productivity assistant
• Educational assistant
• Health assistant
• Financial assistant

3. Conversational Agents

• Research agents
• Recommendation agents
• Planning agents
• Decision-making agents
• Coordination agents

Implementation

1. Conversation Foundation

from openai import OpenAI
import json
from typing import List, Dict, Any

class ConversationManager:
    def __init__(self, api_key: str):
        self.client = OpenAI(api_key=api_key)
        self.conversation_history: List[Dict[str, str]] = []
    
    def add_message(self, role: str, content: str):
        self.conversation_history.append({
            "role": role,
            "content": content
        })
    
    def get_response(self, user_message: str) -> str:
        # Add the user's message
        self.add_message("user", user_message)
        
        # Get the model's response
        response = self.client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=self.conversation_history,
            temperature=0.7
        )
        
        # Extract and save the response
        assistant_message = response.choices[0].message.content
        self.add_message("assistant", assistant_message)
        
        return assistant_message
    
    def clear_history(self):
        self.conversation_history = []