A real-world example of hybrid fusion search using the SurrealDB docs search

The blog post explores the implementation of a hybrid fusion search model for SurrealDB documentation, combining full-text and vector search techniques to enhance the relevance of search results. Full-text search involves splitting and modifying text to match query terms, while vector search utilizes OpenAI's embeddings to capture semantic meanings. The search functionality integrates both methods using Reciprocal Rank Fusion (RRF) to provide a comprehensive search experience. The implementation is detailed in the SurrealDB documentation repository, and it allows users to locally deploy and test the search feature. Additionally, the post provides a simplified example to demonstrate the hybrid search logic, encouraging readers to explore the approach for their own applications.