Automating Customer Support with LLaMA: Building a Real-World AI-Powered Ticket Manager

Author: Japan Gor | Medium for GJAM

Introduction

Building an AI-Powered Support Ticket Manager with LLaMA for Real-Life Customer Service Efficiency

Managing customer support tickets can be a daunting task for any business, especially when dealing with a high volume of customer inquiries. Imagine if you could automatically categorize, prioritize, and respond to support tickets based on customer queries using artificial intelligence. By leveraging LLaMA (Large Language Model Meta AI), you can do just that!

In this article, we'll walk you through building a fully functional AI-powered ticket manager using LLaMA. This tool will help businesses automate ticket handling, save time, and improve customer satisfaction by generating appropriate responses, categorizing tickets, and prioritizing urgent issues.

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Step 1: Set Up the Environment

First, you need to set up the environment. Install the following dependencies using pip.

pip install llama-cpp-python pinecone-client fastapi uvicorn transformers torch

This will include LLaMA for natural language processing, Pinecone for embeddings and fast retrieval, and FastAPI for building the API.

Step 2: Creating a Ticket Management System

We'll start by defining a system to manage support tickets. The system should create and store tickets with metadata such as ticket ID, customer query, and priority.

Code: Create a Support Ticket

import json

# Function to create a support ticket
def create_ticket(ticket_id, customer_query):
ticket = {
"ticket_id": ticket_id,
"customer_query": customer_query,
"category": "Uncategorized", # Default category
"priority": "Medium" # Default priority
}
# Store ticket as JSON
with open(f"tickets/ticket_{ticket_id}.json", "w") as file:
json.dump(ticket, file)
print(f"Ticket {ticket_id} created.")

# Example: Create a support ticket
create_ticket("001", "I'm unable to access my account.")

Step 3: Using LLaMA for Ticket Categorization and Prioritization

Now, we’ll use the LLaMA model to automatically categorize and assign priorities to tickets based on the customer’s query.

Code: Categorize and Prioritize Tickets using LLaMA

from llama_cpp import LLaMA

# Load the LLaMA model
model_path = "path_to_llama_model.bin" # Replace with actual model path
llama = LLaMA(model_path)

# Function to categorize and prioritize a support ticket
def categorize_and_prioritize(ticket_id):
# Load the ticket
with open(f"tickets/ticket_{ticket_id}.json", "r") as file:
ticket = json.load(file)

# Generate the categorization and prioritization using LLaMA
prompt = f"Categorize and assign priority to this support ticket: {ticket['customer_query']}"
result = llama.generate(prompt)
response = result['choices'][0]['text'].strip().split(", ")

# Update the ticket with category and priority
ticket["category"], ticket["priority"] = response[0].split(": ")[1], response[1].split(": ")[1]

# Save the updated ticket
with open(f"tickets/ticket_{ticket_id}.json", "w") as file:
json.dump(ticket, file)

print(f"Ticket {ticket_id} categorized as {ticket['category']} with priority {ticket['priority']}.")

# Example: Categorize and prioritize ticket
categorize_and_prioritize("001")

In this example, the LLaMA model will process the customer query and return a category (e.g., "Technical Support") and priority (e.g., "High").

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Step 4: Building the FastAPI Backend for Ticket Management

Next, we’ll expose the ticket system as an API using FastAPI. This will allow your support team to interact with the system, create tickets, and retrieve information through HTTP requests.

Code: FastAPI for Ticket Management

from fastapi import FastAPI
import json

app = FastAPI()

# Create a new support ticket
@app.post("/create_ticket")
async def create_support_ticket(ticket_id: str, query: str):
create_ticket(ticket_id, query)
return {"status": "Ticket created successfully", "ticket_id": ticket_id}

# Categorize and prioritize a support ticket
@app.post("/categorize_ticket")
async def categorize_ticket(ticket_id: str):
categorize_and_prioritize(ticket_id)
return {"status": "Ticket categorized successfully", "ticket_id": ticket_id}

# Run the FastAPI app
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)

Once this API is running, support staff can send requests to create, categorize, and prioritize tickets through endpoints like /create_ticket and /categorize_ticket.

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Step 5: Generate Response Suggestions for Support Tickets

Now that we’ve categorized and prioritized the tickets, we can generate response suggestions to help the support team quickly reply to customers.

Code: Generate Response Suggestions

# Function to generate a response suggestion using LLaMA
def generate_response(ticket_id):
with open(f"tickets/ticket_{ticket_id}.json", "r") as file:
ticket = json.load(file)

# Generate a response based on the ticket's category and query
prompt = f"Generate a response for a {ticket['category']} issue: {ticket['customer_query']}"
result = llama.generate(prompt)

# Extract the generated response
response = result['choices'][0]['text'].strip()

print(f"Response for ticket {ticket_id}: {response}")
return response

# Example: Generate a response for a ticket
generate_response("001")

In this example, the LLaMA model will generate an appropriate response based on the ticket’s category and customer query, helping the support team respond faster.

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Step 6: Testing and Finalizing the Support Ticket Manager

Now that we have a fully functional support ticket manager, you can test it by creating tickets, categorizing them, and generating responses. Here's an example of how this tool works in a real-world scenario:

1. Create a new ticket: The customer submits a query such as, "I can't log into my account."

2. Categorize and prioritize: The system categorizes it as "Account Issue" and sets the priority to "High."

3. Generate response: The system generates a response such as, "We apologize for the inconvenience. Please reset your password using the 'Forgot Password' option."

With this setup, you can scale customer support without increasing manpower, as the tool automates much of the initial ticket triage and response process.

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Wrapping Up

By leveraging LLaMA for categorizing and generating responses to support tickets, this AI-powered support ticket manager can significantly streamline your customer service operations. It automates the manual process of sorting, prioritizing, and responding to support tickets, helping support teams handle more tickets efficiently.

With LLaMA’s natural language capabilities, you can ensure that your customer support responses are relevant, accurate, and timely, improving customer satisfaction and operational efficiency.

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If you have any questions about implementing this tool or need assistance in building out your own AI-powered support system, feel free to reach out to me!

Japan Gor