Semantic search in Rust with SurrealDB and Mistral AI

The article provides a comprehensive guide on using SurrealDB and Mistral AI for semantic search in Rust, demonstrating how to store AI-generated embeddings as vectors in SurrealDB and query them using SurrealQL. By leveraging the mistralai-client crate, users can generate embeddings with Mistral AI, store them in an embedded SurrealDB database, and perform a semantic search to find the closest matches to a given query. The guide covers setting up the environment, generating embeddings, creating a vector table and index, and implementing a semantic search function. It highlights the use of the HNSW index for optimizing search performance and provides examples of semantic queries for different topics. The process involves transforming textual data into embeddings and querying them to retrieve contextually similar documents, showcasing the potential of combining AI with database technology for advanced search capabilities.