reordered README

Author: raosukrit67

nemo/NeMo-Data-Designer/README.md

@@ -2,34 +2,23 @@
 
 This directory contains the tutorial notebooks for getting started with NeMo Data Designer.
 
-## 📚 Table of Contents
+## 📦 Set Up the Environment
 
-### 🚀 Intro Tutorials
+We will use the `uv` package manager to set up our environment and install the necessary dependencies. If you don't have `uv` installed, you can follow the installation instructions from the [uv documentation](https://docs.astral.sh/uv/getting-started/installation/).
 
-| Notebook                                          | Description                                                                      |
-|---------------------------------------------------|----------------------------------------------------------------------------------|
-| [1-the-basics.ipynb](./intro-tutorials/1-the-basics.ipynb) | Learn the basics of Data Designer by generating a simple product review dataset |
-| [2-structured-outputs-and-jinja-expressions.ipynb](./intro-tutorials/2-structured-outputs-and-jinja-expressions.ipynb) | Explore advanced data generation using structured outputs and Jinja expressions |
-| [3-seeding-with-a-dataset.ipynb](./intro-tutorials/3-seeding-with-a-dataset.ipynb) | Discover how to seed synthetic data generation with an external dataset         |
-| [4-custom-model-configs.ipynb](./intro-tutorials/4-custom-model-configs.ipynb) | Master creating and using custom model configurations                           |
+Once you have `uv` installed, be sure you are in the `Nemo-Data-Designer` directory and run the following command:
 
-### 🎯 Advanced Tutorials
+```bash
+uv sync
+```
 
-| Notebook                                          | Domain              | Description                                                     |
-|---------------------------------------------------|---------------------|-----------------------------------------------------------------|
-| [person-sampler-tutorial.ipynb](./advanced/person-samplers/person-sampler-tutorial.ipynb) | Persona Samplers    | Generate realistic personas using the person sampler |
-| [clinical-trials.ipynb](./advanced/healthcare-datasets/clinical-trials.ipynb) | Healthcare          | Build synthetic clinical trial datasets with realistic PII for testing data protection |
-| [insurance-claims.ipynb](./advanced/healthcare-datasets/insurance-claims.ipynb) | Healthcare          | Create synthetic insurance claims datasets with realistic claim data and processing information |
-| [physician-notes-with-realistic-personal-details.ipynb](./advanced/healthcare-datasets/physician-notes-with-realistic-personal-details.ipynb) | Healthcare          | Generate realistic patient data and physician notes with embedded personal information |
-| [w2-dataset.ipynb](./advanced/forms/w2-dataset.ipynb) | Forms & Documents   | Generate synthetic W-2 tax form datasets with realistic employee and employer information |
-| [multi-turn-conversation.ipynb](./advanced/multi-turn-chat/multi-turn-conversation.ipynb) | Conversational AI   | Build synthetic conversational data with realistic person details and multi-turn dialogues |
-| [visual-question-answering-using-vlm.ipynb](./advanced/multimodal/visual-question-answering-using-vlm.ipynb) | Multimodal          | Create visual question answering datasets using Vision Language Models |
-| [product-question-answer-generator.ipynb](./advanced/qa-generation/product-question-answer-generator.ipynb) | Q&A Generation      | Build product information datasets with corresponding questions and answers |
-| [generate-rag-evaluation-dataset.ipynb](./advanced/rag-examples/generate-rag-evaluation-dataset.ipynb) | RAG & Retrieval     | Generate diverse RAG evaluation datasets for testing retrieval-augmented generation systems |
-| [reasoning-traces.ipynb](./advanced/reasoning/reasoning-traces.ipynb) | Reasoning           | Build synthetic reasoning traces to demonstrate step-by-step problem-solving processes |
-| [text-to-python.ipynb](./advanced/text-to-code/text-to-python.ipynb) | Text-to-Code        | Generate Python code from natural language instructions with validation and evaluation |
-| [text-to-python-evol.ipynb](./advanced/text-to-code/text-to-python-evol.ipynb) | Text-to-Code        | Build advanced Python code generation with evolutionary improvements and iterative refinement |
-| [text-to-sql.ipynb](./advanced/text-to-code/text-to-sql.ipynb) | Text-to-Code        | Create SQL queries from natural language descriptions with validation and testing |
+This will create a virtual environment and install the necessary dependencies. Activate the virtual environment by running the following command:
+
+```bash
+source .venv/bin/activate
+```
+
+Be sure to select this virtual environment as your kernel when running the notebooks.
 
 ## 🚀 Deploying the NeMo Data Designer Microservice
 
@@ -49,20 +38,32 @@ Alternatively, you can deploy the NeMo Data Designer microservice locally via Do
 
 To run the tutorial notebooks in the [advanced](./advanced/), you will need to have NeMo Data Designer deployed locally. Please see the [deployment guide](http://docs.nvidia.com/nemo/microservices/latest/set-up/deploy-as-microservices/data-designer/docker-compose.html) for more details.
 
-## 📦 Set Up the Environment
 
-We will use the `uv` package manager to set up our environment and install the necessary dependencies. If you don't have `uv` installed, you can follow the installation instructions from the [uv documentation](https://docs.astral.sh/uv/getting-started/installation/).
+## 📚 Tutorial Directory
 
-Once you have `uv` installed, be sure you are in the `Nemo-Data-Designer` directory and run the following command:
-
-```bash
-uv sync
-```
+### 🚀 Intro Tutorials
 
-This will create a virtual environment and install the necessary dependencies. Activate the virtual environment by running the following command:
+| Notebook                                          | Description                                                                      |
+|---------------------------------------------------|----------------------------------------------------------------------------------|
+| [1-the-basics.ipynb](./intro-tutorials/1-the-basics.ipynb) | Learn the basics of Data Designer by generating a simple product review dataset |
+| [2-structured-outputs-and-jinja-expressions.ipynb](./intro-tutorials/2-structured-outputs-and-jinja-expressions.ipynb) | Explore advanced data generation using structured outputs and Jinja expressions |
+| [3-seeding-with-a-dataset.ipynb](./intro-tutorials/3-seeding-with-a-dataset.ipynb) | Discover how to seed synthetic data generation with an external dataset         |
+| [4-custom-model-configs.ipynb](./intro-tutorials/4-custom-model-configs.ipynb) | Master creating and using custom model configurations                           |
 
-```bash
-source .venv/bin/activate
-```
+### 🎯 Advanced Tutorials
 
-Be sure to select this virtual environment as your kernel when running the notebooks.
+| Notebook                                          | Domain              | Description                                                     |
+|---------------------------------------------------|---------------------|-----------------------------------------------------------------|
+| [person-sampler-tutorial.ipynb](./advanced/person-samplers/person-sampler-tutorial.ipynb) | Persona Samplers    | Generate realistic personas using the person sampler |
+| [clinical-trials.ipynb](./advanced/healthcare-datasets/clinical-trials.ipynb) | Healthcare          | Build synthetic clinical trial datasets with realistic PII for testing data protection |
+| [insurance-claims.ipynb](./advanced/healthcare-datasets/insurance-claims.ipynb) | Healthcare          | Create synthetic insurance claims datasets with realistic claim data and processing information |
+| [physician-notes-with-realistic-personal-details.ipynb](./advanced/healthcare-datasets/physician-notes-with-realistic-personal-details.ipynb) | Healthcare          | Generate realistic patient data and physician notes with embedded personal information |
+| [w2-dataset.ipynb](./advanced/forms/w2-dataset.ipynb) | Forms & Documents   | Generate synthetic W-2 tax form datasets with realistic employee and employer information |
+| [multi-turn-conversation.ipynb](./advanced/multi-turn-chat/multi-turn-conversation.ipynb) | Conversational AI   | Build synthetic conversational data with realistic person details and multi-turn dialogues |
+| [visual-question-answering-using-vlm.ipynb](./advanced/multimodal/visual-question-answering-using-vlm.ipynb) | Multimodal          | Create visual question answering datasets using Vision Language Models |
+| [product-question-answer-generator.ipynb](./advanced/qa-generation/product-question-answer-generator.ipynb) | Q&A Generation      | Build product information datasets with corresponding questions and answers |
+| [generate-rag-evaluation-dataset.ipynb](./advanced/rag-examples/generate-rag-evaluation-dataset.ipynb) | RAG & Retrieval     | Generate diverse RAG evaluation datasets for testing retrieval-augmented generation systems |
+| [reasoning-traces.ipynb](./advanced/reasoning/reasoning-traces.ipynb) | Reasoning           | Build synthetic reasoning traces to demonstrate step-by-step problem-solving processes |
+| [text-to-python.ipynb](./advanced/text-to-code/text-to-python.ipynb) | Text-to-Code        | Generate Python code from natural language instructions with validation and evaluation |
+| [text-to-python-evol.ipynb](./advanced/text-to-code/text-to-python-evol.ipynb) | Text-to-Code        | Build advanced Python code generation with evolutionary improvements and iterative refinement |
+| [text-to-sql.ipynb](./advanced/text-to-code/text-to-sql.ipynb) | Text-to-Code        | Create SQL queries from natural language descriptions with validation and testing |