Video by H2O.ai via YouTube
How h2oGPTe grounds LLMs in enterprise data using multimodal RAG, hybrid search, and autonomous agent workflows.
LLMs don’t inherently know your internal products, policies, or customer history. h2oGPTe bridges this gap by ingesting 50+ file formats—including documents, audio, and video—using multi-engine OCR and native enterprise connectors for SharePoint, S3, and Azure. Hybrid retrieval combines semantic similarity and BM25 with Reciprocal Rank Fusion and cross-encoder reranking for precise, citation-backed answers. Agentic workflows extend RAG further by enabling autonomous retrieval, reasoning, and tool execution.
Technical Capabilities & Resources
➤ Document Ingestion & Transformation: 50+ format support with multi-engine OCR, table preservation, and enterprise connectors.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/collections/supported-file-types
➤ Built-in & External Vector Storage: Includes Vex embedded vector database plus integrations with external vector providers.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/architecture/vector-database
➤ Advanced Hybrid Search: Combines semantic similarity, BM25, Reciprocal Rank Fusion, and cross-encoder reranking.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/chats/chat-settings#generation-approach
➤ Agentic Workflows: Autonomous agents that iteratively retrieve, reason, and trigger tool execution for complex queries.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/agents