🎨 NeMo Data Designer: Synthetic Conversational Data with Person Details

📚 What you'll learn

• This notebook demonstrates how to use the NeMo Data Designer to build a synthetic data generation pipeline step-by-step.

• We will create multi-turn user-assistant dialogues tailored for fine-tuning language models, enhanced with realistic person details.

• These datasets could be used for developing and enhancing conversational AI applications, including customer
  support chatbots, virtual assistants, and interactive learning systems.

👋 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.

Define Pydantic Models for Structured Outputs

You can use Pydantic to define a structure for the messages that are produced by Data Designer

🎲 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.

🦜 Adding LLM Generated columns

Now define the columns that the model will generate. These prompts instruct the LLM to produce the actual conversation:

• a system prompt to guide how the AI assistant engages in the conversation with the user,
• the conversation, and
• finally, we generate a toxicity_label to assess user toxicity over the entire conversation.
  

💬🤖 AI Assistant system prompt and conversation

We generate a system prompt to base the AI assistant and then generate the entire conversation.

🔍 LLM-as-a-Judge: Toxicity Assessment

When generating our synthetic dataset, we need to determine the quality of the generated dialogs.
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.