h2oGPT is a self-hosted platform designed for running large language models and executing retrieval-augmented generation workflows locally. It provides a comprehensive web interface that allows users to index private document collections into searchable databases, enabling context-aware question answering and summarization without exposing sensitive data to external services.
The platform distinguishes itself by offering a modular architecture that supports both local model execution and connections to external inference servers. It facilitates the development of autonomous agents capable of performing multi-step tasks by delegating actions to various tools and models. Beyond simple chat, the system includes capabilities for fine-tuning models on local hardware and managing the full lifecycle of predictive assets, from data ingestion and feature engineering to model deployment and performance monitoring.
The software covers a broad range of enterprise-grade requirements, including document intelligence for extracting structured data from unstructured files, multi-GPU training support, and robust access control mechanisms. It provides tools for model explainability, compliance tracking, and collaborative experiment management to ensure transparency and reproducibility in machine learning workflows.
The project is designed for containerized deployment, utilizing standard configuration files to ensure consistent execution across local and cloud environments.
