Querying

Learn how to implement spatial search in RavenDB with Python. Covers radius queries, custom polygon shapes, WKT syntax, and reverse point-in-polygon lookup using the Flat Finder demo.

Run semantic searches over your documents with RavenDB 7.0 vector search: generate embeddings on the server with no external tools, and query them through static indexes or auto-indexes.

Build a minimal ASP.NET Core 9 API that queries RavenDB documents, including optional LINQ-based filtering and Swagger-based testing.

How RavenDB projections return only the fields you need, why projected data comes from documents by default, and how to store fields in a static index and tune projection behavior for faster reads.

Match accented terms like Kraków against both Kraków and Krakow. Compares manual diacritics normalization with a custom ASCIIFoldingFilter analyzer for Lucene and Corax indexes.

Connect an AI provider like OpenAI, create an embeddings generation task, and query your documents by meaning with RavenDB vector search.

How to use dynamic fields in RavenDB static indexes to query on properties that aren't defined at index-creation time, plus the pitfalls to watch for with duplicate field names and stored projections.