How Enterprise h2oGPTe Grounds LLMs with RAG
- Generative AI
- Enterprise h2oGPTe
This lesson walks through how Enterprise h2oGPTe grounds large language models in enterprise knowledge through retrieval-augmented generation. It covers ingestion of more than 50 file formats, including PDFs, audio, and video, using multi-engine OCR, speaker identification, and native connectors to sources like SharePoint and S3. It explains the built-in vector database and how hybrid search combines semantic similarity with BM25 keyword matching, reranked with cross encoders. It closes by noting that retrieval can also trigger agent workflows that iteratively retrieve, reason, and execute tools.
Key takeaways
- How h2oGPTe ingests over 50 file formats, including transcribing audio and video and extracting key frames
- How hybrid search combines semantic similarity and BM25 keyword matching, then reranks results with cross encoders
- Why access control is enforced at the collection level so users only retrieve documents they are authorized to see
- How retrieval can trigger agent workflows that iteratively retrieve, reason, and execute tools
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