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Version: v1.6.1 🚧

Tutorials: UI

Learn how to find information 10x faster

Learning path​

  • Tutorial 1: A quick introduction to Enterprise h2oGPTe

    This tutorial explores the general Enterprise h2oGPTe flow and UI to ask questions or obtain insights about a Document (or Documents).

  • Tutorial 2: Build an AI-powered chatbot (model) to enhance a website's search capabilities

    This tutorial with Enterprise h2oGPTe and Python builds an AI-powered chatbot to replace the function of a website's search bar, which, in turn, builds something better to enable users to obtain better answers to their questions about the website. In this tutorial, we will create an AI-powered chatbot to enhance the search capabilities of the H2O Model Validation documentation website.

  • Tutorial 3: Importing and interacting with audio

    This tutorial explores the workflow for importing audio to a Collection so that you can ask questions about it later. To understand the workflow, we will use the audio recording of a lecture given on April 8, 2010, at the Peabody Museum of Archaeology and Ethnology at Harvard University.

  • Tutorial 4: Importing and interacting with images

    This tutorial explores the workflow for importing images to a Collection so that you can ask questions about it later. To understand the workflow, we will explore a sample image of a medical invoice.

  • Tutorial 5: Dataset analysis with Enterprise h2oGPTe-agents

    This tutorial explores dataset analysis with Enterprise h2pGPTe-agents. In this tutorial, we will ask an Enterprise h2oGPTe-agent the following:

    Using the Boston Housing Dataset, calculate the correlation between the RM (average number of rooms per dwelling) and MEDV (median value of owner-occupied homes in $1000s) columns. Next, create a scatter plot with RM on the x-axis and MEDV on the y-axis to visualize their relationship, and add a trend line to illustrate the positive correlation.

    Enterprise h2pGPTe-agents enhance the functionality and versatility of Enterprise h2oGPTe to execute a broader range of tasks autonomously. In other words, this setting allows the large language model (LLM) to perform actions such as running code, generating plots, searching the web, conducting research, and developing and preparing models.

  • Tutorial 6: Model development and Regex preparation with Enterprise h2oGPTe-agents

    This tutorial explores model development and preparation with Enterprise h2oGPTe-agents. In this tutorial, we will ask an Enterprise h2oGPTe-agent the following:

    Create a regression model to predict housing prices. Use the renowned "Boston Housing Dataset." The target column should be the "MEDV" column. Generate the model and make it accessible in the "Downloadable files" section.

    Enterprise h2pGPTe-agents enhance the functionality and versatility of Enterprise h2oGPTe to execute a broader range of tasks autonomously. In other words, this setting allows the LLM to perform actions such as running code, generating plots, searching the web, conducting research, and developing and preparing Machine Learning (ML) models.

  • Tutorial 7: Guardrails and personally identifiable information (PII) in Enterprise h2oGPTe

    This tutorial explores the guard mechanisms available in Enterprise h2oGPTe to safeguard against the generation of harmful content and protect Personally Identifiable Information (PII). Large Language Models (LLMs) can generate content that could be dangerous or expose sensitive data. Enterprise h2oGPTe offers out-of-the-box guard models, such as Prompt Guard and Llama Guard 3, and tools like Presidio, a DeBERTa-based classifier, and regex patterns for PII detection, redaction, and input filtering to mitigate these risks.

    In this tutorial, you will learn how to access, enable, and customize these guardrails within a Collection, ensuring better security, ethical content generation, and protection of sensitive information.

  • Tutorial 8: With an Extractor, transform unstructured document content into structured JSON data

    This tutorial demonstrates how to utilize Extractors in Enterprise h2oGPTe to convert unstructured document content into structured JSON data. While documents can contain valuable information, their unstructured nature often makes it challenging to analyze efficiently. Extractors address this challenge by transforming the content of these documents into structured formats that can be readily utilized by individuals and applications requiring organized data.

    In this tutorial, we will illustrate how Extractors function by extracting specific pieces of information from Alphabet's Form 10-K.

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