hologres-knowledge-base

What problem does it solve?

Build enterprise search and RAG knowledge bases on Hologres using vector indices, full-text inverted indexes, and embed-to-index workflows.

Core Features & Use Cases

• Create knowledge bases as a single column-store table combining text content, embeddings, and scalar metadata to support vector and BM25-style search in one query.
• Ingest documents with client-side or server-side embeddings, then build HGraph vector indexes and full-text indexes for hybrid search and Q&A workflows.
• Use cases include building internal knowledge bases, document repositories, and knowledge pipelines that leverage both semantic search and keyword search for accurate retrieval.

Quick Start

Create a knowledge base that stores document chunks, embeds them, and enables hybrid search across text and vectors.