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import streamlit as st
from openai import OpenAI
from mem0 import Memory
import os
import json
from datetime import datetime, timedelta
# Set up the Streamlit App
st.title("AI Customer Support Agent with Memory 🛒")
st.caption("Chat with a customer support assistant who remembers your past interactions.")
# Set the OpenAI API key
openai_api_key = st.text_input("Enter OpenAI API Key", type="password")
if openai_api_key:
os.environ['OPENAI_API_KEY'] = openai_api_key
class CustomerSupportAIAgent:
def __init__(self):
# Initialize Mem0 with Qdrant as the vector store
config = {
"vector_store": {
"provider": "qdrant",
"config": {
"host": "localhost",
"port": 6333,
}
},
}
try:
self.memory = Memory.from_config(config)
except Exception as e:
st.error(f"Failed to initialize memory: {e}")
st.stop() # Stop execution if memory initialization fails
self.client = OpenAI()
self.app_id = "customer-support"
def handle_query(self, query, user_id=None):
try:
# Search for relevant memories
relevant_memories = self.memory.search(query=query, user_id=user_id)
# Build context from relevant memories
context = "Relevant past information:\n"
if relevant_memories and "results" in relevant_memories:
for memory in relevant_memories["results"]:
if "memory" in memory:
context += f"- {memory['memory']}\n"
# Generate a response using OpenAI
full_prompt = f"{context}\nCustomer: {query}\nSupport Agent:"
response = self.client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a customer support AI agent for TechGadgets.com, an online electronics store."},
{"role": "user", "content": full_prompt}
]
)
answer = response.choices[0].message.content
# Add the query and answer to memory
self.memory.add(query, user_id=user_id, metadata={"app_id": self.app_id, "role": "user"})
self.memory.add(answer, user_id=user_id, metadata={"app_id": self.app_id, "role": "assistant"})
return answer
except Exception as e:
st.error(f"An error occurred while handling the query: {e}")
return "Sorry, I encountered an error. Please try again later."
def get_memories(self, user_id=None):
try:
# Retrieve all memories for a user
return self.memory.get_all(user_id=user_id)
except Exception as e:
st.error(f"Failed to retrieve memories: {e}")
return None
def generate_synthetic_data(self, user_id: str) -> dict | None:
try:
today = datetime.now()
order_date = (today - timedelta(days=10)).strftime("%B %d, %Y")
expected_delivery = (today + timedelta(days=2)).strftime("%B %d, %Y")
prompt = f"""Generate a detailed customer profile and order history for a TechGadgets.com customer with ID {user_id}. Include:
1. Customer name and basic info
2. A recent order of a high-end electronic device (placed on {order_date}, to be delivered by {expected_delivery})
3. Order details (product, price, order number)
4. Customer's shipping address
5. 2-3 previous orders from the past year
6. 2-3 customer service interactions related to these orders
7. Any preferences or patterns in their shopping behavior
Format the output as a JSON object."""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a data generation AI that creates realistic customer profiles and order histories. Always respond with valid JSON."},
{"role": "user", "content": prompt}
]
)
customer_data = json.loads(response.choices[0].message.content)
# Add generated data to memory
for key, value in customer_data.items():
if isinstance(value, list):
for item in value:
self.memory.add(
json.dumps(item),
user_id=user_id,
metadata={"app_id": self.app_id, "role": "system"}
)
else:
self.memory.add(
f"{key}: {json.dumps(value)}",
user_id=user_id,
metadata={"app_id": self.app_id, "role": "system"}
)
return customer_data
except Exception as e:
st.error(f"Failed to generate synthetic data: {e}")
return None
# Initialize the CustomerSupportAIAgent
support_agent = CustomerSupportAIAgent()
# Sidebar for customer ID and memory view
st.sidebar.title("Enter your Customer ID:")
previous_customer_id = st.session_state.get("previous_customer_id", None)
customer_id = st.sidebar.text_input("Enter your Customer ID")
if customer_id != previous_customer_id:
st.session_state.messages = []
st.session_state.previous_customer_id = customer_id
st.session_state.customer_data = None
# Add button to generate synthetic data
if st.sidebar.button("Generate Synthetic Data"):
if customer_id:
with st.spinner("Generating customer data..."):
st.session_state.customer_data = support_agent.generate_synthetic_data(customer_id)
if st.session_state.customer_data:
st.sidebar.success("Synthetic data generated successfully!")
else:
st.sidebar.error("Failed to generate synthetic data.")
else:
st.sidebar.error("Please enter a customer ID first.")
if st.sidebar.button("View Customer Profile"):
if st.session_state.customer_data:
st.sidebar.json(st.session_state.customer_data)
else:
st.sidebar.info("No customer data generated yet. Click 'Generate Synthetic Data' first.")
if st.sidebar.button("View Memory Info"):
if customer_id:
memories = support_agent.get_memories(user_id=customer_id)
if memories:
st.sidebar.write(f"Memory for customer **{customer_id}**:")
if memories and "results" in memories:
for memory in memories["results"]:
if "memory" in memory:
st.write(f"- {memory['memory']}")
else:
st.sidebar.info("No memory found for this customer ID.")
else:
st.sidebar.error("Please enter a customer ID to view memory info.")
# Initialize the chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display the chat history
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input
query = st.chat_input("How can I assist you today?")
if query and customer_id:
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": query})
with st.chat_message("user"):
st.markdown(query)
# Generate and display response
with st.spinner("Generating response..."):
answer = support_agent.handle_query(query, user_id=customer_id)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": answer})
with st.chat_message("assistant"):
st.markdown(answer)
elif not customer_id:
st.error("Please enter a customer ID to start the chat.")
else:
st.warning("Please enter your OpenAI API key to use the customer support agent.") |