Technical Breakthroughs in Orama Hybrid Search
Orama's Hybrid Search feature represents an important advancement in search technology, combining the advantages of full-text and vector search. In practice, this means that users can perform both keyword-based text searches and semantic-based vector searches for more accurate and comprehensive results.
In terms of implementation details, Orama allows storing textual data and vector data (e.g., 1536-dimensional embedding vectors) in the same index. This design allows the system to perform complex queries, such as matching both keywords and semantic similarities of documents. Documents can contain a rich set of field types, including strings, numbers, nested objects, and vector arrays.
Hybrid search is particularly suitable for modern AI application scenarios such as RAG (Retrieval Augmented Generation) systems. In these scenarios, the system needs to understand both the user's textual expression and the deep semantic intent.Orama's performance in this area has been validated in several large-scale production environments.
This answer comes from the articleOrama: a high-performance full-text book and vector search engineThe
