feat: add data analysis agent example (#289)

Author: jayrodge

community/README.md

@@ -23,6 +23,10 @@ Community examples are sample code and deployments for RAG pipelines that are no
 
 ## Inventory
 
+* [NVIDIA Data Analysis Agent](./data-analysis-agent/)
+
+  This example demonstrates an interactive, agentic data analysis application that leverages NVIDIA Llama-3.1-Nemotron-Ultra-253B-v1 for advanced reasoning and data exploration. Users can upload CSV files, ask questions in natural language, and receive automated visualizations with clear, step-by-step reasoning. The implementation features a modular agent architecture for data insight, code generation, execution, and transparent reasoning.
+
 * [NVIDIA RAG in 5 minutes](./5_mins_rag_no_gpu/)
 
   This is a simple standalone implementation showing rag pipeline using Nvidia API Catalog models. It uses a simple Streamlit UI and one file implementation of a minimalistic RAG pipeline.

community/data-analysis-agent/README.md

@@ -0,0 +1,99 @@
+# Data Analysis Agent
+
+An interactive, agentic data analysis application that leverages advanced LLM reasoning to help users explore, visualize, and understand their data using NVIDIA Llama-3.1-Nemotron-Ultra-253B-v1.
+
+## Overview
+
+This repository contains a Streamlit application that demonstrates a complete workflow for data analysis:
+1. **Data Upload**: Upload CSV files for analysis
+2. **Natural Language Queries**: Ask questions about your data in plain English
+3. **Automated Visualization**: Generate relevant plots and charts
+4. **Transparent Reasoning**: Get detailed explanations of the analysis process
+
+The implementation leverages the powerful Llama-3.1-Nemotron-Ultra-253B-v1 model through NVIDIA's API, enabling sophisticated data analysis and reasoning.
+
+Learn more about the model [here](https://developer.nvidia.com/blog/build-enterprise-ai-agents-with-advanced-open-nvidia-llama-nemotron-reasoning-models/).
+
+## Features
+
+- **Agentic Architecture**: Modular agents for data insight, code generation, execution, and reasoning
+- **Natural Language Queries**: Ask questions about your data—no coding required
+- **Automated Visualization**: Instantly generate and display relevant plots
+- **Transparent Reasoning**: Get clear, LLM-generated explanations for every result
+- **Powered by NVIDIA Llama-3.1-Nemotron-Ultra-253B-v1**: State-of-the-art reasoning and interpretability
+
+![Workflow](./assets/workflow.png)
+
+## Requirements
+
+- Python 3.10+
+- Streamlit
+- NVIDIA API Key (see [Installation](#installation) section for setup instructions)
+- Required Python packages:
+  - pandas
+  - matplotlib
+  - streamlit
+  - requests
+
+## Installation
+
+1. Clone this repository:
+   ```bash
+   git clone https://github.com/NVIDIA/GenerativeAIExamples.git
+   cd GenerativeAIExamples/community/data-analysis-agent
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Set up your NVIDIA API key:
+   - Sign up or log in at [NVIDIA Build](https://build.nvidia.com/nvidia/llama-3_1-nemotron-ultra-253b-v1?integrate_nim=true&hosted_api=true&modal=integrate-nim)
+   - Generate an API key
+   - Set the API key in your environment:
+     ```bash
+     export NVIDIA_API_KEY=your_nvidia_api_key_here
+     ```
+   - Or add it to your `.env` file if you use one
+
+## Usage
+
+1. Run the Streamlit app:
+   ```bash
+   streamlit run data_analysis.py
+   ```
+
+2. Download example dataset (optional):
+   ```bash
+   wget https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv
+   ```
+
+3. Use the application:
+   - Upload a CSV file (e.g., the Titanic dataset)
+   - Ask questions in natural language
+   - View results, visualizations, and detailed reasoning
+
+## Example
+
+![App Demo](./assets/data_analysis_agent_demo.png)
+
+## Model Details
+
+The Llama-3.1-Nemotron-Ultra-253B-v1 model used in this project has the following specifications:
+- **Parameters**: 253B
+- **Features**: Advanced reasoning capabilities
+- **Use Cases**: Complex data analysis, multi-agent systems
+- **Enterprise Ready**: Optimized for production deployment
+
+## Acknowledgments
+
+- [NVIDIA Llama-3.1-Nemotron-Ultra-253B-v1](https://build.nvidia.com/nvidia/llama-3_1-nemotron-ultra-253b-v1)
+- [Streamlit](https://streamlit.io/)
+- [Pandas](https://pandas.pydata.org/)
+- [Matplotlib](https://matplotlib.org/)
+
+
+## Contributing
+
+Contributions are welcome! Please open an issue or submit a pull request.