v24 - Dgraph Blog
We are thrilled to announce the general availability of Dgraph v24, which includes DQL and GraphQL support for Vector data type, HNSW vector indexes, and similarity search.
Adding embeddings, from OpenAI, Mistral, or open-source models, to your knowledge graph unlocks new AI capabilities and use cases, such as semantic search, entity resolution, recommendations, classification, or GraphRAG.
In addition to vector support, this release optimizes performance across the board. Thanks to our new focus internally and the extensive contributions from our vibrant community - 89 commits from 24 contributors - thank you!
Our new caching improvements significantly boost performance by focusing on storing large keys efficiently, reducing disk reads, and handling multiple transactions more effectively. Performance tests show a 32% improvement in writes. A separate change reducing over-fetching when filtering by types unlocks up to 1000x improvement in reads for customers with complex GraphQL schemas.
Dgraph v24 now supports native vector types, making it easier than ever to integrate advanced AI-driven search functionalities into your applications. Whether using DQL or GraphQL, you can now harness the power of semantic searches with vector embeddings directly within your queries.
