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How to import ticket data from Zendesk API to Weaviate

Zendesk is a cloud-based customer service and support platform. Zendesk Support API, which is also known as the Ticketing API lets’s you access support tickets data. By analyzing this data, businesses can gain insights into customer needs, behavior, trends, and make data-driven decisions. The newest type of databases, vector databases, can help in advanced analysis of tickets data such as identifying common issues and sentiment analysis.

In this guide, we’ll show you how to import Zendesk ticket data to one of the vector databases, Weaviate. We’ll use dlt to connect to the Zendesk API, extract the ticket data, and load it into Weaviate for querying.

For our example we will use "subject" and "description" fields from a ticket as a text content to perform vector search on.


We're going to use some ready-made components from the dlt ecosystem to make this process easier:

  1. A Zendesk verified source to extract the tickets from the API.
  2. A Weaviate destination to load the data into a Weaviate instance.


  1. Create a new folder for your project, navigate to it, and create a virtual environment:

    mkdir zendesk-weaviate
    cd zendesk-weaviate
    python -m venv venv
    source venv/bin/activate
  2. Install dlt with Weaviate support

    pip install "dlt[weaviate]"
  3. Install dlt Zendesk verified source

    dlt init zendesk weaviate

The last command dlt init initializes dlt project: it downloads the verified source and installs it in your project folder.



Before we configure the source and destination, you need to make sure you have access to API in both Zendesk and Weaviate.

Head to the Zendesk docs to see how to fetch credentials for Zendesk API. In this guide, we're using the email address and password authentication method. Once you have fetched the credentials, you can configure the source. Add the following lines to the dlt secrets file ~/.dlt/secrets.toml:

password = "..."
subdomain = "..."
email = "..."


For the destination we're using Weaviate Cloud Services. You would need to create an account and get a URL and an API key for your Weaviate instance. We're also be using OpenAI API to generate embeddings for the text data needed to perform vector search. If you haven't already, you would need to create an account and get an API key for OpenAI API.

When you have the credentials, add more lines to the dlt secrets file ~/.dlt/secrets.toml:

url = "https://weaviate_url"
api_key = "api_key"

X-OpenAI-Api-Key = "sk-..."

Customizing the pipeline

When you run dlt init zendesk weaviate, dlt creates a file called in the current directory. This file contains an example pipeline that you can use to load data from Zendesk source. Let's edit this file to make it work for our use case:

import dlt
from dlt.destinations.adapters import weaviate_adapter

from zendesk import zendesk_support

def main():
# 1. Create a pipeline
pipeline = dlt.pipeline(

# 2. Initialize Zendesk source to get the ticket data
zendesk_source = zendesk_support(load_all=False)
tickets =

info =
# 3. Here we use a special function to tell Weaviate
# which fields to vectorize
vectorize=["subject", "description"],

return info

if __name__ == "__main__":
load_info = main()

Let's go through the code above step by step:

  1. We create a pipeline with the name weaviate_zendesk_pipeline and the destination weaviate.
  2. Then, we initialize the Zendesk verified source. We only need to load the tickets data, so we get tickets resource from the source by getting the tickets attribute.
  3. Weaviate is a special kind of destination that requires vectorizing (or embedding) the data before loading it. Here, we use the weaviate_adapter() function to tell dlt which fields Weaviate should vectorize. In our case, we vectorize the subject and description fields from each ticket. That means that Weaviate will be able to perform vector search (or similarity search) on content of these fields.
  4. runs the pipeline and returns information about the load process.

Running the pipeline

Now that we have the pipeline configured, we can run the Python script:


We have successfully loaded the data from Zendesk to Weaviate. Let's check it out.

Query the data

We can now run a vector search query on the data we loaded into Weaviate. Create a new Python file called and add the following code:

import weaviate
client = weaviate.Client(

response = (
.get("ZendeskData_Tickets", ["subject", "description"])
"concepts": ["problems with password"],


The above code instantiates a Weaviate client and does a similarity search on the data we loaded. The query searches for tickets that are similar to the text “problems with password”. The output should be similar to:

"data": {
"Get": {
"ZendeskData_Tickets": [
"subject": "How do I change the password for my account?",
"description": "I forgot my password and I can't log in.",
"_additional": {
"distance": 0.235
"subject": "I can't log in to my account.",
"description": "The credentials I use to log in don't work.",
"_additional": {
"distance": 0.247

Incremental loading

During our first load, we loaded all tickets from Zendesk API. But what if users create new tickets? Or update existing ones? dlt solves this case by supporting incremental loading. And you don't need to change anything in your pipeline to enable it: Zendesk source supports incremental loading out of the box based on the updated_at field. That means that dlt will only load tickets that were created or updated after the last load.

What's next?

If you interested in learning more about Weaviate support in dlt, check out the Weaviate destination docs. We have also have demos of different sources of data for Weaviate in our Jyputer notebooks:

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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