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.
In this blog post I explore PostgreSQL range types and how you can use them from Entity Framework Core.
Entity Framework Core is great for performing crud operations, but did you know that you can go beyond basic relational queries with Entity Framework Core? Do you need to store and query non-structured data in JSON documents? Do you have hierarchical data that you wish to query with Entity Framework Core? Are you required to keep historical information about data that has been modified or maybe even deleted?
If you answered Yes to any of the above questions, this is the talk you do not want to miss.
Entity Framework Core is great for performing crud operations, but did you know that you can go beyond basic relational queries with Entity Framework Core? Do you need to store and query non-structured data in JSON documents? Do you have hierarchical data that you wish to query with Entity Framework Core? Are you required to keep historical information about data that has been modified or maybe even deleted? If you answered Yes to any of the above questions, this is the talk you do not want to miss.
Relational databases are great for storing relational data and performing crud operations, but did you know that you can go beyond basic relational queries with RDBMS? Do you need to store and query non-structured data in JSON documents? Or perhaps you want to store spatial data and find the nearest points of interest to a given location? What if you have a lot of records and need to sift through them and run full-text search queries? Are you required to keep historical information about data that has been modified or maybe even deleted? If you answered Yes to any of the above questions, this is the talk you do not want to miss.
LINQPad.QueryPlanVisualizer is a custom visualizer for LINQPad that shows a database query plan inside LINQPad. It also shows missing indexes for the query that you can create directly from LINQPad.
Since then, a new major version, LINQPad 6, was released that targets .NET Core 3 and .NET 5 and uses Entity Framework Core (as well as LINQ-to-SQL) for running Linq queries.
To support these changes, I have just released a new version of LINQPad.
In the previous article I introduced EntityFramework.Exceptions, a library which simplifies handling exceptions in Entity Framework Core but the library had one important limitation. In order to use it you had to inherit your custom DbContext from ExceptionProcessorContextBase class. This means that if you wanted to use some other base class for your DbContext you were out of luck. The latest version of the library solves this issue by
Handle database errors easily when working with Entity Framework Core. Supports SQLServer, PostgreSQL, Oracle, SQLite and MySql
When using Entity Framework Core for data access all database exceptions are wrapped in DbUpdateException. If you need to know whether the exception was caused by a unique constraint, value being too long or value missing for a required column you need to dig into the concrete DbException subclass instance and check the error number to determine the exact cause.
EntityFramework.Exceptions simplifies this by handling all the database specific details and throwing different exceptions for different cases. All you have to do is