Integrating Ollama with Zendesk: A Comprehensive Guide

What is Ollama?

What is Zendesk?

Why Integrate Ollama with Zendesk?

• Instant Responses: Provide immediate answers to customer queries, reducing wait times.
• Data Privacy: As Ollama runs locally, your customer interactions remain secure.
• Enhanced Analytics: Using Ollama’s insights helps fine-tune responses based on historical interactions.
• Full Customization: Tailor the chatbot responses, style, & functionality to match your brand effortlessly.
• No Coding Required: The user-friendly interface allows anyone to create workflows without the technical hassle.

Step 1: Set Up Your Ollama Instance

1. Download & Install Ollama: Follow the installation guide available on the Ollama GitHub repository to get started.
2. Fetch Your Models: Pull down the models you intend to use with the command:
   

   1 2 bash ollama pull llama3.1
3. Run Your Ollama Instance:
   Use the following command to start the Ollama service locally:

   1 2 bash ollama serve

Step 2: Configure Zendesk for Integration

1. Log In to Zendesk: Access your Zendesk dashboard with administrative privileges.
2. Navigate to Integrations: Head over to Settings > Integrations > Servers & Services.
3. Add Ollama as an Integration Instance:
   • Enter the Server Hostname (this will be the IP where Ollama is running, typically localhost).
   • Specify the Port – use the default port 11434 unless you have configured it differently.
   • Set the Path to Ollama’s API path, generally
     1 /api
     .
   • Choose Trust Certificate settings based on your SSL configuration.
   • Finally, click Add Instance & Test to ensure a proper connection.

Step 3: Create Workflows with Ollama

1. Designing the Chatbot:
   Using the Zendesk platform, start building your chatbot. You can integrate various commands like:
   • 1 ollama-list-models
     : List all available models.
   • 1 ollama-conversation
     : Create a conversation thread based on user input.
   • 1 ollama-generate
     : Generate responses as per defined prompts.
2. Track Conversations:
   Ensure that relevant data & history are tracked with the command

   1 ollama-conversation

   . This will aid in continuous learning & improving responses based on user interactions.
3. Utilizing Embedding Models: If you're interested in embedding models for more complex interactions, utilize the code snippet below to handle embeddings:

   1 2 3 4 5 python from llama_index.embeddings.ollama import OllamaEmbedding ollama_embedding = OllamaEmbedding(model_name="llama2") response = ollama_embedding.get_query_embedding("What can you do?") print(response)

Step 4: Measure & Analyze Performance

• Time taken to resolve customer queries
• Customer satisfaction rates
• Frequency of specific queries

Step 5: Continuous Improvement

Best Practices for Using Ollama and Zendesk Together

• Regular Updates: Keep your Ollama models updated to leverage the latest best practices & improvements.
• Engage Users Proactively: Utilize the AI's ability to reach out and engage customers based on their previous interactions to create a personalized experience.
• Gather Feedback: Encourage your users to provide feedback on their interactions with the chatbot, which can guide future improvements.
• Train on Historical Data: Use Zendesk's rich historical data to train your Ollama model, ensuring it is well-versed in past customer inquiries.

Conclusion