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