Qlik OpenAI Connector: What You Need to Know

Qlik has recently launched its OpenAI connector, a powerful tool that allows you to leverage the capabilities of OpenAI’s natural language models within your Qlik apps. In this post, I will share some of this connector’s key features and benefits and some examples of how you can use it to enhance your data analysis and communication. 

What is the Qlik OpenAI Connector? 

The Qlik OpenAI Connector is a Qlik Sense extension that enables you to send prompts to OpenAI’s natural language models and receive responses in your Qlik apps. You can use the connector in various ways, such as: 

1. In chart expressions, to generate natural language insights or explanations based on your data 
2. In the data model, to enrich your data with additional information or analysis from OpenAI 
3. In application automation, to automate tasks or workflows that involve natural language processing or generation 

How does the Qlik OpenAI Connector work? 

To use the Qlik OpenAI Connector, you need to have an account at platform.openai.com and pay a fee based on the computing resources you use. However, there is a free trial available. You will find the connector under “analytics sources” when creating a new data connection. 

With the connector, you can create prompts using variables, fields, or expressions from your Qlik app. A prompt is a text input that tells OpenAI what kind of response you want. For example, you can ask OpenAI to write a summary, suggest a reply, generate a question, or complete a sentence. 

You can select different models to send your prompts depending on your use case. OpenAI offers several models with different capabilities and trade-offs, such as: 

Davinci: The most advanced and versatile model, capable of generating high-quality and diverse responses for any prompt 

Curie: A smaller and faster model than Davinci, suitable for tasks that require less creativity or complexity 

Babbage: A specialized model for numerical or tabular data analysis 

Ada: The smallest and cheapest model, suitable for simple tasks that require factual or straightforward responses 

You can also customize other parameters of your prompts, such as: 

Temperature: How creative or diverse the responses are. A higher temperature means more randomness and variety, while a lower temperature means more predictability and consistency. 

Max tokens: The maximum number of tokens (words or symbols) the model can generate as a response. A higher max tokens means a longer response, while a lower max tokens means a shorter response. 

Top P: The probability threshold for selecting the next token. The model will only consider the tokens with a cumulative probability of less than or equal to the top P value. A higher top P means more diversity and exploration, while a lower top P means more confidence. 

Frequency penalty: How much the model avoids repeating words or phrases. A higher frequency penalty means less repetition and redundancy, while a lower frequency penalty means more repetition and emphasis. 

Presence penalty: How much the model prefers using new words or phrases. A higher presence penalty means more novelty and diversity, while a lower presence penalty means more familiarity and similarity. 

What are some use cases for the Qlik OpenAI Connector? 

The Qlik OpenAI Connector opens a world of possibilities for using natural language in your Qlik apps. Here are some examples of how you can use it. 

Summarizing public sentiment toward a product or company 

You can use the connector to analyze social media posts, reviews, or feedback about a product or company and generate a summary of the main themes or sentiments. For example, you can send a prompt like “Write a summary about the reception towards ” & [Product] & ” by ” & [Company] and get a response like: 

“The reception towards Product X by Company Y is mostly positive, with customers praising its quality, design, and functionality. However, some customers have complained about its high price and poor customer service.” 

Suggesting responses to customer support tickets 

You can use the connector to help your customer support representatives handle tickets faster and more effectively. You can send a prompt like “Suggest a response for a customer support representative for the following case: ” & [ticket_text] and get a tailored response like: 

“Dear Customer, 

Thank you for contacting us. We are sorry to hear that you are having trouble logging in to your account. We apologize for any inconvenience this may have caused you. 

We have fixed the issue, and you should be able to log in now. Please try again and let us know if you still need any help. 

Thank you for choosing our service. 

Sincerely, Customer Support Team” 

You can then use application automation to send this suggestion to a Teams channel or an email address. 

Generating social media posts from job descriptions 

You can help your Marketing department by generating social media posts. You can send a prompt like: “Write a LinkedIn post for the following job description: “ & [raw_job_description] and get a response like: 

“📢 Are you a junior/mid-level BI consultant looking for an exciting opportunity? 🤔 Pomerol Partners is hiring! 🎉 We are looking for someone with a degree in Computer Science or equivalent, 2-3 years of Software Engineering experience, knowledge/experience with Microsoft Power BI, ETL processes and Data Modelling, Data Bases (Postgres, SQL Server, Oracle) and SQL. You should have functional knowledge on reporting and data analysis, an analytical mind with a problem-solving aptitude, self-motivation and commitment to deadlines, be a team player and have good verbal and written English communications skills. 🌍 You will participate in technologically attractive and international projects; receive training and certifications; enjoy hybrid work; and be integrated into a young, solid, ambitious and motivated team. 🤝 Send us your CV in English! #PomerolPartners #BIConsultant #JobOpportunity #DataAnalysis #ProblemSolving #TeamPlayer #HybridWork” 

You can do this in the data model to store responses and create a results library. 

 

These are just some ways you can use the Qlik OpenAI Connector to enhance your data analysis and communication. Don’t hesitate to get in touch with us if you want to learn more about this connector or see it in action. 

We hope you enjoy using the Qlik OpenAI Connector and discover new insights and opportunities with OpenAI’s natural language models.