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.")