Unleashing the Power of Qlik Sense with OpenAI's REST Connection

Unleashing the Power of Qlik Sense with OpenAI's REST Connection

Table of Contents:

1. Introduction
2. Accessing OpenAI Connector for Qlik Sense Users 2.1. On-Premise Users 2.2. Cloud Users
3. Usage of REST API for Private Language Models 3.1. Large Language Models 3.2. Small Language Models
4. Creating the REST Connection 4.1. Selecting the Data Source 4.2. Configuring the Parameters 4.3. Generating Query Headers
5. Retrieving Data from OpenAI 5.1. Making API Requests 5.2. Modifying the Code for Custom Queries
6. Analyzing the Response 6.1. Usage Statistics 6.2. Token Consumption 6.3. Returning the Answer
7. Enhancing the Query
8. Conclusion

Accessing OpenAI Connector for Qlik Sense Users

Qlik Sense users now have the opportunity to access the open AI connector for both on-premise and cloud-Based environments. This new feature allows users to tap into a wealth of information and stay up-to-date with the latest developments. In this article, we will explore how Qlik Sense users can utilize the open AI connector, its benefits, and the steps required to set it up.

Introduction With the introduction of the open AI connector, Qlik Sense users can now easily connect to the open AI platform and access a vast array of language models. Whether you are an on-premise user or use Qlik Sense on the cloud, this feature is available to enhance your data analysis experience.

Accessing the OpenAI Connector for On-Premise Users For on-premise users of Qlik Sense, the open AI connector offers a seamless way to connect to language models and retrieve valuable insights. By utilizing a REST connection, users can access large language models provided by open AI.

Accessing the OpenAI Connector for Cloud Users Cloud users of Qlik Sense can also take advantage of the open AI connector. Whether you want to reach a private language model or a small language model that you have trained yourself, the open AI connector can facilitate this connection. Since all large language models use REST connections, Qlik Sense's REST API can be utilized to retrieve data from the open AI platform.

Creating the REST Connection To make use of the open AI connector, Qlik Sense users need to create a REST connection. This involves selecting the data source and configuring the parameters for the connection. The REST API URL for open AI's complete method should be specified along with the appropriate body, which contains the query.

Generating Query Headers In order to authorize the REST connection, query headers need to be created. These headers specify the authorization type, which is typically a bearer token obtained from the open AI platform. Additionally, a content type header must be added to specify that the data will be passed as a JSON structure.

Retrieving Data from OpenAI Once the REST connection is established, users can retrieve data from the open AI platform by making API requests. By specifying the appropriate selections and modifying the code, users can parse the response into tables and access the information they need.

Analyzing the Response After making a request, users can analyze the response from the open AI platform. Usage statistics provide insights into token consumption and the cost of calling the API. The answer to the query can be retrieved and analyzed to gather valuable insights.

Enhancing the Query To make the most out of the open AI connector, users can enhance their queries by customizing the request body. This allows users to ask any questions they want in the format they prefer, generating more meaningful and actionable responses.

Conclusion The open AI connector for Qlik Sense offers users the ability to tap into the power of language models and access valuable insights. Whether on-premise or in the cloud, users can easily set up the open AI connector and retrieve data using the REST API. By leveraging this feature, users can enhance their data analysis capabilities and make informed decisions based on the insights derived from the open AI platform.