Weaviate 1.40 adds PathSeer, a third HNSW filter strategy from a SIGMOD 2026 paper, contributed by the paper's first author. How it works, and how it compares to sweeping and ACORN.
Learn about the challenges of filtered vector search and how Weaviate tackles them with ACORN.
In-depth technical breakdown of how binary quantization works and how to use it in Weaviate.
Boosting Weaviate using SIMD-AVX512, Loop Unrolling and Compiler Optimizations
The details behind how you can compress vectors using PQ with little loss of recall!
Fine-tuning LlaMA 7B to use the Weaviate GraphQL APIs
Using the Weaviate Tile Encoder to compress vectors with Product Quantization.
Implementing HNSW + Product Quantization (PQ) vector compression in Weaviate.
Vector search on disks: How does Vamana compare to HNSW?
Self-Supervised Retrieval can surpass BM25 and Supervised techniques. This technique also pairs very well alongside BM25 in Hybrid Retrieval. Learn more about it.
