🎨 NeMo Data Designer: Product Information Dataset Generator with Q&A

📚 What you'll learn

This notebook demonstrates how to use NeMo Data Designer to create a synthetic dataset of product information with corresponding questions and answers.

👋 IMPORTANT – Environment Setup

• If you haven't already, follow the instructions in the README to install the necessary dependencies.

• You may need to restart your notebook's kernel after setting up the environment.

• In this notebook, we assume you have a self-hosted instance of Data Designer up and running.

• For deployment instructions, see the Installation Options section of the NeMo Data Designer documentation.

📦 Import the essentials

• The data_designer module of nemo_microservices exposes Data Designer's high-level SDK.

• The essentials module provides quick access to the most commonly used objects.

⚙️ Initialize the NeMo Data Designer Client

• NeMoDataDesignerClient is responsible for submitting generation requests to the microservice.

🎛️ Define model configurations

• Each ModelConfig defines a model that can be used during the generation process.

• The "model alias" is used to reference the model in the Data Designer config (as we will see below).

• The "model provider" is the external service that hosts the model (see the model config docs for more details).

• By default, the microservice uses build.nvidia.com as the model provider.

🏗️ Initialize the Data Designer Config Builder

• The Data Designer config defines the dataset schema and generation process.

• The config builder provides an intuitive interface for building this configuration.

• The list of model configs is provided to the builder at initialization.

🏗️ Defining Data Structures

Now we'll define the data models and evaluation rubrics for our product information dataset.

🎲 Adding Sampler Columns

• Sampler columns offer non-LLM based generation of synthetic data.

• They are particularly useful for steering the diversity of the generated data, as we demonstrate below.

🦜 LLM-generated columns

• When prompting the LLM, we can use Jinja templating to reference other columns in the dataset.

• As we see below, nested json fields can be accessed using dot notation.

🔍 Quality Assessment: LLM-as-a-Judge

When generating our synthetic dataset, we need to determine the quality of the generated data
We use the LLM-as-a-Judge strategy to do this.

To do so, we need to define the rubric that the LLM should use to assess generation quality along with a prompt that provides relavant instructions.

🔁 Iteration is key – preview the dataset!

1. Use the preview method to generate a sample of records quickly.

2. Inspect the results for quality and format issues.

3. Adjust column configurations, prompts, or parameters as needed.

4. Re-run the preview until satisfied.

📊 Analyze the generated data

• Data Designer automatically generates a basic statistical analysis of the generated data.

• This analysis is available via the analysis property of generation result objects.

🆙 Scale up!

• Happy with your preview data?

• Use the create method to submit larger Data Designer generation jobs.