h2oai/h2ogpt

Features

• Generative AI Dashboards - Provides a comprehensive dashboard for interacting with models, managing documents, and executing RAG workflows.
• Retrieval Augmented Generation - Connects private document collections to language models for context-aware question answering and summarization.
• Local Model Execution - Executes large language models directly on local hardware to maintain data privacy and control.
• Self-Hosted AI Platforms - Offers a self-hosted interface for running large language models locally to perform document analysis.
• Local Language Model Hosting - Hosts generative AI models on local hardware to ensure data privacy and full control over sensitive information.
• Document Indexing - Indexes local files into searchable databases to enable context-aware retrieval-augmented generation.
• RAG Frameworks - Indexes private documents into searchable databases to enable context-aware responses without external data exposure.
• Agentic Workflow Orchestration - Coordinates multi-step tasks by delegating actions to external tools and models within a unified execution environment.
• Autonomous Agent Orchestration - Orchestrates autonomous agents that combine reasoning with external tools to execute complex multi-step workflows.
• Generative Content Tools - Generates situation-specific responses and summaries based on private document collections.
• Intelligent Document Processing - Extracts structured data and insights from unstructured files using automated processing and intelligent character recognition.
• Remote Inference Offloaders - Offload model execution to third-party engines while maintaining centralized control over application logic and request routing across your infrastructure.
• Model Fine-Tuning - Enables customization and training of open-source models on local infrastructure for domain-specific tasks.
• Multi-GPU Training Utilities - Distribute intensive deep learning workloads across multiple graphics processing units to accelerate model training times for large datasets.
• Private Cloud Hosting - Hosts end-to-end private AI platforms on-premises or in private clouds to ensure data sovereignty.
• Agentic Workflow Automation - Connect automated AI capabilities to enterprise platforms and document repositories to perform complex searches and execute tasks across existing business processes.
• Autonomous Task Execution - Perform multi-step tasks by delegating actions to external models and tools within an experimental environment to test complex logic.
• Inference Backends - Supports interchangeable model execution engines to allow flexible switching between local hardware and external API providers.
• Model Deployment Pipelines - Deploys models to production via real-time or batch endpoints with support for testing and updates.
• No-Code Training Interfaces - Build and tune models for text, image, video, and audio data using a no-code interface and expert-curated training techniques.
• General Purpose Models - Framework for building and deploying open-source large language models.
• Language Model Development - Open-source generative AI for private model ownership.
• Document and Unstructured Extraction - Extracts structured data from unstructured documents using optical character recognition and machine learning.
• Model Deployment Management - Manages the full lifecycle of document processing models including deployment, monitoring, and logging.
• LLM Performance Monitoring - Tracks accuracy, bias, and data drift in production to ensure models remain effective.
• AI Monitoring - Evaluates model outputs through automated testing and risk monitoring to ensure safety and compliance.
• Automated Machine Learning Tools - Perform machine learning tasks and create deep learning models using specialized engines that remove the need for manual coding or complex configuration.
• Automated Lineage Capturers - Captures metadata and event logs throughout the machine learning lifecycle to ensure traceability and reproducibility.
• Model Lineage Trackers - Records comprehensive event logs and versioning data throughout the machine learning lifecycle to ensure reproducibility and compliance.
• Data Preparation - Processes diverse unstructured data types like text, images, and audio to prepare them for predictive modeling.
• Dataset Integration - Pull data from various external storage systems and cloud repositories to prepare information for model training and analysis.
• External Model Connectors - Centralize machine learning models from various frameworks to simplify the deployment, tracking, and monitoring of your predictive assets.
• Feature Stores - Integrate with pipelines to reduce redundant data ingestion while providing metadata-driven recommendations for feature discovery and generation.
• Information Extraction - Identifies and isolates key entities like names and invoice numbers from documents using natural language processing.
• Model Deployment Pipelines - Exports and deploys finished models to external environments for production serving.
• Model Interpretability Tools - Provides visual and analytical tools to interpret and explain machine learning model decisions.
• Model Validation Tools - Verifies model performance through backtesting and drift monitoring to maintain reliability.
• Managed Cloud Deployments - Provides managed infrastructure for hosting and scaling machine learning environments in the cloud.
• Compliance and Governance - Automates data versioning and lineage tracking to ensure adherence to privacy and regulatory standards.
• Identity and Access Management - Controls user and group access to projects and model artifacts to ensure secure collaboration.
• Human-in-the-Loop Systems - Integrates human-in-the-loop evaluation to refine model performance based on real-world outcomes.
• Inference Latency Optimizers - Minimizes data transfer delays by placing models near core application logic.
• Predictive Machine Learning Analytics - Select, parameterize, and train optimal machine learning algorithms based on user-defined target variables and specific business requirements.
• Model Training Optimizers - Automates parameter adjustment and result analysis to improve model precision through repeated testing.
• Team Collaboration Management - Offers collaborative tools for data science teams to version, compare, and register machine learning models.
• Feature Engineering Tools - Automates the identification and calculation of data features to enhance predictive model performance.
• Tabular Predictive Models - Enables automated tabular predictions without requiring manual model training or complex infrastructure.
• RESTful APIs - Provide access to document processing, model training, and scoring capabilities through a standard interface to connect services with existing applications.
• AI Application Deployment Platforms - Share and deploy artificial intelligence applications across an organization using a centralized marketplace to accelerate internal innovation.
• Cloud Infrastructure Deployment - Supports containerized deployment across diverse cloud and on-premises environments to ensure portability.
• Container Deployment Configurations - Simplifies platform deployment and dependency management using standard Docker Compose configurations.

Star history