What's Changed

• Added BsonRefId to allow explicit DbRefs in LINQ expressions by @rklfss in #2741
• Fix transaction finalizer causing LiteDB.LiteException/System.ObjectDisposedException by @ejdre-vestas in #2721

New Contributors

• @rklfss made their first contribution in #2741
• @ejdre-vestas made their first contribution in #2721

Full Changelog: v6.0.0-prerelease.0075...v6.0.0-prerelease.77

What's Changed

• Repo Runner by @JKamsker in #2690
• Improve build pipeline by @JKamsker in #2692
• Add support for GroupBy by @JKamsker in #2694
• Use fixed stack buffers in string serializers by @JKamsker in #2695
• Add support for custom dictionary key types (#546) by @JKamsker in #2701
• Expand CI coverage across Windows and multi-target tests by @JKamsker in #2702
• Fix/dotnet 10 expression breaking change by @JKamsker in #2703
• Fix publish-prerelease CI by @JKamsker in #2704
• Fix/2670 memoryextensions net10 followup by @LPEdwin in #2710
• Feat/fix mutexes by @JKamsker in #2711
• Fix/url encoded mutex by @JKamsker in #2709
• Add windows ci matrix by @JKamsker in #2718
• Fix ReadFull must read PAGE_SIZE bytes [{0}] by @isbdnt1 in #2717
• Add macOS coverage to CI workflow by @JKamsker in #2719
• Adding EndsWith method for Query and QueryAny by @rube200 in #2727
• Fix index corruption #2724 by @JKamsker in #2737

New Contributors

• @LPEdwin made their first contribution in #2710
• @isbdnt1 made their first contribution in #2717
• @rube200 made their first contribution in #2727

Full Changelog: v6.0.0-prerelease.0052...v6.0.0-prerelease.0075

🚀 LiteDB v6.0 Prerelease#52: Introducing Vector Search!

This release marks a significant milestone for LiteDB with the introduction of Vector Search! This powerful new feature enables you to build modern AI-powered applications, such as semantic search, recommendation engines, and Retrieval-Augmented Generation (RAG) systems, directly within LiteDB.

✨ Major New Feature: Vector Search

• Native Vector Storage & Indexing: We've added a native BsonVector type (for float[]) and a high-performance vector index based on the HNSW algorithm. You can now perform fast Approximate Nearest Neighbor (ANN) searches using standard distance metrics: Cosine (default), Euclidean, and DotProduct.

• New Fluent Query API: The query API has been extended with intuitive methods for vector operations.

  using LiteDB.Vector; // New namespace for vector extensions!
  
  // Create a vector index on your embedding property
  var docs = db.GetCollection<