Tutorial | Build a conversational interface with Dataiku Answers#

Get started#

Dataiku includes two fully-featured chatbot user interfaces: Answers and Agent Connect.

In this tutorial, you’ll use Dataiku Answers to build one such chat interface, without code, to generate answers from a knowledge bank created from either an Embed documents or Embed dataset recipe.

Objectives#

In this tutorial, you will:

  • Create an Answers web application from a knowledge bank.

  • Configure the application to provide relevant answers from a sample of the Dataiku documentation sources.

  • Upload an additional document into the application for an on-the-fly analysis.

Prerequisites#

This tutorial assumes your Flow includes a knowledge bank of a sample of Dataiku documentation sources. Accordingly, you’ll first need to create this knowledge bank by following:

In addition to those prerequisites, you’ll also need to meet any requirements noted in the reference documentation on Answers, including an SQL connection.

Tip

You don’t need previous experience with Large Language Models (LLMs), though it would be useful to read the article Concept | Embed recipes and Retrieval Augmented Generation (RAG) before completing this tutorial.

Create the conversational interface#

The first step to building a Dataiku Answers chat application is to initiate it from a knowledge bank found in the Flow.

  1. From the Flow, open the knowledge_bank object.

  2. In the Usage tab, click Create Your Dataiku Answers Chat Application.

  3. In the dialog box, name the webapp answers_app_dataiku_help.

  4. Click Create.

Dataiku screenshot of the dialog to create an Answers webapp.

Tip

Before creating a conversational interface with Dataiku Answers, you may wish to test that the knowledge bank has correctly extracted the information from its input sources. One quick testing option is to create a retrieval augmented LLM from the knowledge bank. Then explore the responses of the augmented LLM using the chat mode of a Prompt Studio. You can find an example of this approach in Quick Start | Dataiku for Generative AI.

Configure the conversational interface#

You can configure a Dataiku Answers webapp in many ways.

See also

This tutorial examines only a few of the most relevant settings. See Answers in the reference documentation for complete details.

Set the mandatory options#

Even though you’ll configure this application to generate responses using the knowledge bank, you’ll need to select an LLM. You’ll also need to provide details for two SQL datasets:

  • A conversation history dataset for logging queries, responses and associated metadata

  • A user profile dataset for storing all user feedback.

  1. In the Main LLM field, select an available LLM.

  2. For the Conversation history dataset field:

    • Click New Dataset.

    • Name it prompt_history.

    • Store it in a SQL connection.

    • Click Create Dataset.

  3. For the User profile dataset field:

    • Click New Dataset.

    • Name it user_profile.

    • Store it in a SQL connection.

    • Click Create Dataset.

Dataiku screenshot of mandatory settings of an Answers webapp.

Configure the user feedback options#

The Answers webapp provides the option to gather user feedback by offering positive and negative feedback choices. You can configure what these choices should be and how to store them. If supported by the underlying LLM, you can also let users upload documents.

  1. For Positive feedback choices, offer the following three choices:

    • Click + Add an Element three times.

    • Enter the following strings in each field: Complete, Correct, Easy to understand.

  2. For Negative feedback choices, offering the following four choices:

    • Click + Add an Element four times.

    • Enter the following strings in each field: Incorrect, Inaccurate, Does not respect the instructions, Shocking/Risky.

  3. If applicable to you, for Upload Documents Folder, select dataiku_doc.

  4. Enable the Allow user feedback option, and then configure a dataset to store it.

  5. For the General feedback dataset field:

    • Click New Dataset.

    • Name it user_feedback.

    • Store it using a SQL connection.

    • Click Create Dataset.

Dataiku screenshot of the conversations store config settings of an Answers webapp.

Set the knowledge retrieval parameters#

The Knowledge retrieval parameters allows you to configure which knowledge bank to use and customize its use.

  1. In the Knowledge bank field, ensure that knowledge_bank is selected.

  2. In the Customize knowledge bank’s name field, enter Dataiku Knowledge Bank. This name will be displayed on the user interface.

  3. Uncheck the Let ‘Answers’ decide when to use the Knowledge Bank based option to require the LLM to use the knowledge bank to answer any questions.

  4. In the Configure your LLM in the context of the knowledge bank field, enter the following text:

    Please act as a support engineer who would help any users of Dataiku DSS.
Use the knowledge bank whenever the topic from the prompt is covered by the documents in the knowledge bank.

  5. Ensure that the Display source extracts is enabled to provide the users with the sources used to generate the answers.

Dataiku screenshot of the knowledge retrieval parameters section of an Answers webapp.

Customize the conversational interface#

The End user configuration section allows you to configure the conversational interface: title, subtitle, placeholder text in the prompt field, etc.

Customize it a bit.

  1. In the Displayed title field, enter Ask the Dataiku Documentation.

  2. Next to Example questions, replace the following examples with the following strings:

    • What's the LLM mesh?

    • How to do Prompt Engineering in Dataiku?

    • What's the difference between the Group recipe and the Window recipe in Dataiku?

Set up the backend#

You’re nearly finished!

  1. In the Backend section, enable the Auto-start backend option.

  2. You should be able to inherit the project’s default container, but if you run into a problem below, set the Container option to None - use backend to execute.

  3. Click Save and View Webapp at the bottom of the Edit tab.

Once you’ve defined all these settings, you should see the following interface:

Screenshot of the Answers conversational interface.

Use the conversational interface#

Once your webapp is up and running, you can allow users to start engaging with it. You can do this knowing that datasets like prompt_history, user_profile, and user_feedback are collecting the information necessary to evaluate the app’s performance.

Ask a question#

Ask a question to check how the interface works.

  1. Click on the first example provided: What’s the LLM Mesh? It enters the question in the input field.

  2. Click the Send button.

The LLM returns the response and allows you to:

  1. View the sources used to answer the question.

  2. Copy the response.

  3. Give your feedback, using the strings set up previously or entering your own comment.

Screenshot of the a response in the conversational interface.

Upload a document to the conversational interface#

If your LLM supports it, upload a PDF to ask a question on some new content.

  1. Download the PDF here and store it on your computer.

  2. In the conversational interface, click the Upload a file or image (Attach a file icon.) icon in the input field and select the new PDF.

  3. Enter the following prompt:

    How to create a multimodal knowledge bank?

  4. Click Send.

Tip

If you open the dataiku_doc folder in the Flow, you’ll find the new PDF stored inside it. Recall in Configure the user feedback options, you set dataiku_doc as the destination for uploaded documents.

Next steps#

Congratulations on building your first Dataiku Answers chat application! Feel free to continue refining it and exploring other possible settings to meet your use cases.

See also

For more information on the Answers application, see Dataiku Answers in the reference documentation.