PostgreSQL on Azure is evolving beyond a traditional relational database—it's now a platform where advanced AI meets robust data management.
Semantic search or search based on the meaning analyzes the context and intent behind the query term to provide relevant results. Using Vector embeddings, the data structure behind semantic search, you can supercharge your search to include text, images, and other types of data. With vector databases you can store and index vector embeddings and provide similarity search over these embeddings.
Semantic search or search based on the meaning analyzes the context and intent behind the query term to provide relevant results. Using Vector embeddings, the data structure behind semantic search, you can supercharge your search to include text, images, and other types of data. With vector databases you can store and index vector embeddings and provide similarity search over these embeddings.
Semantic search or search based on the meaning analyzes the context and intent behind the query term to provide relevant results. Using Vector embeddings, the data structure behind semantic search, you can supercharge your search to include text, images, and other types of data. With vector databases you can store and index vector embeddings and provide similarity search over these embeddings.
Semantic search or search based on the meaning analyzes the context and intent behind the query term to provide relevant results. Using Vector embeddings, the data structure behind semantic search, you can supercharge your search to include text, images, and other types of data. With vector databases you can store and index vector embeddings and provide similarity search over these embeddings.
Semantic search or search based on the meaning analyzes the context and intent behind the query term to provide relevant results. Using Vector embeddings, the data structure behind semantic search, you can supercharge your search to include text, images, and other types of data. With vector databases you can store and index vector embeddings and provide similarity search over these embeddings.