Comprehensive comparison for RAG Framework technology in AI applications

import os
import autogen
from autogen import AssistantAgent, UserProxyAgent
from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent
from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent
import chromadb
from typing import Optional, List, Dict

# Configuration for AutoGen RAG-based customer support system
class CustomerSupportRAG:
    def __init__(self, docs_path: str, collection_name: str = "customer_docs"):
        self.docs_path = docs_path
        self.collection_name = collection_name
        
        # LLM configuration with error handling
        self.llm_config = {
            "timeout": 600,
            "cache_seed": 42,
            "config_list": [
                {
                    "model": "gpt-4",
                    "api_key": os.getenv("OPENAI_API_KEY"),
                    "temperature": 0.3
                }
            ],
        }
        
        if not os.getenv("OPENAI_API_KEY"):
            raise ValueError("OPENAI_API_KEY environment variable not set")
    
    def initialize_agents(self) -> tuple:
        """Initialize RAG agents for document retrieval and response generation"""
        try:
            # Create retrieval-augmented assistant
            assistant = RetrieveAssistantAgent(
                name="CustomerSupportAssistant",
                system_message="You are a helpful customer support agent. Answer questions based on the provided documentation. If information is not in the docs, clearly state that.",
                llm_config=self.llm_config,
            )
            
            # Create user proxy with RAG capabilities
            ragproxyagent = RetrieveUserProxyAgent(
                name="RAGProxy",
                human_input_mode="NEVER",
                max_consecutive_auto_reply=3,
                retrieve_config={
                    "task": "qa",
                    "docs_path": self.docs_path,
                    "collection_name": self.collection_name,
                    "chunk_token_size": 2000,
                    "model": self.llm_config["config_list"][0]["model"],
                    "client": chromadb.PersistentClient(path="/tmp/chromadb"),
                    "embedding_model": "all-MiniLM-L6-v2",
                    "get_or_create": True,
                },
                code_execution_config=False,
            )
            
            return assistant, ragproxyagent
        
        except Exception as e:
            raise RuntimeError(f"Failed to initialize agents: {str(e)}")
    
    def query(self, question: str, context: Optional[Dict] = None) -> str:
        """Process customer query using RAG"""
        try:
            assistant, ragproxyagent = self.initialize_agents()
            
            # Add context if provided
            enhanced_question = question
            if context:
                context_str = "\n".join([f"{k}: {v}" for k, v in context.items()])
                enhanced_question = f"Context:\n{context_str}\n\nQuestion: {question}"
            
            # Initiate RAG-based chat
            ragproxyagent.initiate_chat(
                assistant,
                problem=enhanced_question,
                n_results=5,
            )
            
            # Extract response from chat history
            response = ragproxyagent.chat_messages[assistant][-1]["content"]
            return response
        
        except Exception as e:
            return f"Error processing query: {str(e)}"

# Example usage
if __name__ == "__main__":
    # Initialize RAG system with product documentation
    support_rag = CustomerSupportRAG(
        docs_path="./product_docs",
        collection_name="product_kb"
    )
    
    # Query with customer context
    customer_context = {
        "customer_id": "CUST-12345",
        "product": "Enterprise Plan",
        "issue_type": "billing"
    }
    
    response = support_rag.query(
        "How do I upgrade my subscription and what are the payment options?",
        context=customer_context
    )
    
    print(f"Support Response: {response}")