8/27/2024

Using Ollama for Customer Support Ticket Classification

In the fast-paced world of customer service, efficiency is key. Imagine if there was a way to streamline responses to customer inquiries while ensuring that urgent tickets are prioritized. Well, using Ollama as a tool for customer support ticket classification offers just that! In this post, we’ll explore how Ollama can be utilized effectively for efficiently categorizing customer support tickets.

What is Ollama?

Ollama is a powerful platform that allows you to RUN large language models (LLMs) directly on your local system. This means you can leverage advanced artificial intelligence without dependency on external APIs, making it perfect for privacy-sensitive applications like customer support. With Ollama, you can seamlessly create, train, and deploy AI chatbots & assistants tailored to your business needs.

Why Ticket Classification?

Every day, countless customer support tickets flood into businesses. These tickets can range from urgent issues that require immediate attention to routine inquiries that can wait. Manually sorting through these tickets isn’t just time-consuming; it leads to inefficiencies & potential customer dissatisfaction. By automating the classification of support tickets, businesses can:

Prioritize critical issues without delay.
Reduce the workload on support teams.
Enhance overall customer satisfaction due to faster response times.

How Ollama Works for Ticket Classification

To successfully utilize Ollama for ticket classification, we’ll break it down into manageable steps:

Step 1: Setting Up Ollama

First, you need to install Ollama on your local system. You can find the installation instructions on the Ollama website. Ensure you have the necessary hardware requirements to run the models effectively.

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# Example command to run a model in Ollama
ollama run llama3

After installing, you can run various models based on your needs. Ollama supports multiple large language models for different tasks.

Step 2: Defining Your Ticket Classification Use Case

For this customer support ticket classification project, let’s create a simple structure where:

Urgent Issues: Require immediate attention.
Routine Inquiries: Can be handled within standard response time.
Non-Support-related Messages: Gathers feedback or junk that can be filtered out.

Step 3: Creating AI Agents

Utilizing the Crew.ai framework, you can define a crew of AI agents responsible for ticket classification.

Here’s how you can set this up using Ollama:

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from crewai import Agent, Task, Crew, Process
from langchain.llms import ollama

# Load the model
model = ollama(model="llama3")

ticket = "Customer support ticket: Unable access account"

# Define Ticket Classifier Agent
def classify_ticket_agent():
    return Agent(
        role="Ticket Classifier",
        goal="Classify tickets into urgent, routine, and non-support related.",
        backstory="You are an AI assistant helping streamline support processes.",
        verbose=True,
        allow_delegation=False,
        llm=model
    )

classifier = classify_ticket_agent()

Step 4: Creating Tasks for Ticket Classification

Similar to creating agents, you’ll create tasks for the agents.

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# Task to classify the support ticket
classify_task = Task(
    description=f"Classify following support ticket: '{ticket}'",
    agent=classifier,
    expected_output="One of the three options: 'urgent', 'routine', 'non-support-related'"
)

Step 5: Creating Crew for Ticket Processing

Put everything together by creating a crew consisting of your classifier agents and tasks.

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crew = Crew(
    agents=[classifier],
    tasks=[classify_task],
    verbose=2,
    process=Process.sequential
)

Step 6: Executing the Crew to Classify Tickets

Execute the crew to process your tickets and classify them appropriately.

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output = crew.kickoff()
print(output)

Step 7: Handling Outcome Actions

Based on the classification results, take appropriate actions. You could set it up so that:

Urgent Issues trigger immediate notifications to support staff.
Routine Inquiries get directed to the appropriate support teams.
Non-Support-related Messages are filtered out.

Here’s a sample snippet of how you might handle these outcomes:

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if output == 'urgent':
    notify_support_team(ticket)
elif output == 'routine':
    assign_ticket_to_team(ticket)
else:
    filter_ticket(ticket)

Benefits of Using Ollama for Ticket Classification

Privacy-Focused: By running models locally, sensitive customer data remains secure.
Customizable: Tailor your models exactly to your business needs & improve accuracy over time as you train the models.
Cost-Effective: Minimize reliance on expensive external services while maximizing quality.
Real-Time Processing: Respond to customers quickly, enhancing user satisfaction & retention rates.

Conclusion: Join the Future of Customer Support with Arsturn

In conclusion, using Ollama for customer support ticket classification not only streamlines operations but significantly enhances user experience. If you're interested in maximizing customer engagement through advanced AI technologies, consider integrating Arsturn into your workflow. Arsturn.com offers an effortless, no-code AI chatbot solution designed to engage your audience and drive conversions. With powerful AI capabilities, you can create customized chatbots that seamlessly interact with customers before they even reach your support team.

Take your customer service strategies to the next level; unlock the true potential of AI with Arsturn today!

Interested in doing more with AI? Explore how Arsturn can transform your audience engagement with truly personalized chatbot experiences designed just for YOU!