Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
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Overview
In this guide, we’ll explore an example of creating a conversational AI system with memory:
- A customer service bot that can recall previous interactions and provide personalized responses.
Setup and Configuration
Install necessary libraries:
pip install pyautogen mem0ai openai
First, we’ll import the necessary libraries and set up our configurations.
import os
from autogen import ConversableAgent
from mem0 import MemoryClient
from openai import OpenAI
# Configuration
OPENAI_API_KEY = 'sk-xxx' # Replace with your actual OpenAI API key
MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key from https://app.mem0.ai
USER_ID = "customer_service_bot"
# Set up OpenAI API key
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
os.environ['MEM0_API_KEY'] = MEM0_API_KEY
# Initialize Mem0 and AutoGen agents
memory_client = MemoryClient()
agent = ConversableAgent(
"chatbot",
llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
code_execution_config=False,
human_input_mode="NEVER",
)
Storing Conversations in Memory
Add conversation history to Mem0 for future reference:
conversation = [
{"role": "assistant", "content": "Hi, I'm Best Buy's chatbot! How can I help you?"},
{"role": "user", "content": "I'm seeing horizontal lines on my TV."},
{"role": "assistant", "content": "I'm sorry to hear that. Can you provide your TV model?"},
{"role": "user", "content": "It's a Sony - 77\" Class BRAVIA XR A80K OLED 4K UHD Smart Google TV"},
{"role": "assistant", "content": "Thank you for the information. Let's troubleshoot this issue..."}
]
memory_client.add(messages=conversation, user_id=USER_ID)
print("Conversation added to memory.")
Retrieving and Using Memory
Create a function to get context-aware responses based on user’s question and previous interactions:
def get_context_aware_response(question):
relevant_memories = memory_client.search(question, user_id=USER_ID)
context = "\n".join([m["memory"] for m in relevant_memories])
prompt = f"""Answer the user question considering the previous interactions:
Previous interactions:
{context}
Question: {question}
"""
reply = agent.generate_reply(messages=[{"content": prompt, "role": "user"}])
return reply
# Example usage
question = "What was the issue with my TV?"
answer = get_context_aware_response(question)
print("Context-aware answer:", answer)
Multi-Agent Conversation
For more complex scenarios, you can create multiple agents:
manager = ConversableAgent(
"manager",
system_message="You are a manager who helps in resolving complex customer issues.",
llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
human_input_mode="NEVER"
)
def escalate_to_manager(question):
relevant_memories = memory_client.search(question, user_id=USER_ID)
context = "\n".join([m["memory"] for m in relevant_memories])
prompt = f"""
Context from previous interactions:
{context}
Customer question: {question}
As a manager, how would you address this issue?
"""
manager_response = manager.generate_reply(messages=[{"content": prompt, "role": "user"}])
return manager_response
# Example usage
complex_question = "I'm not satisfied with the troubleshooting steps. What else can be done?"
manager_answer = escalate_to_manager(complex_question)
print("Manager's response:", manager_answer)
Conclusion
By integrating AutoGen with Mem0, you’ve created a conversational AI system with memory capabilities. This example demonstrates a customer service bot that can recall previous interactions and provide context-aware responses, with the ability to escalate complex issues to a manager agent.
This integration enables the creation of more intelligent and personalized AI agents for various applications, such as customer support, virtual assistants, and interactive chatbots.
Help
In case of any questions, please feel free to reach out to us using one of the following methods:
