6 min readDevelopment
HyDE and Query Expansion: Do They Still Help?
We test HyDE and query expansion against current baselines to see if these methods still provide a significant boost to retrieval accuracy.
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Vector Search
Read 6 articles on Vector Search by Ashish Srivastava — practical guides covering implementation patterns, security, and production lessons.
6 posts
6 min readBackendFeatured
pgvector at 100 Million Vectors: Lessons Learned
Scaling pgvector to 100 million vectors requires careful index tuning and partitioning. Here are the key lessons learned from production deployments.
6 min readBackend
Metadata Filtering at Scale: Vector Search
Learn how to implement high-performance metadata filtering for vector databases, covering multi-tenant isolation, payload indexing strategies, and selectivity optimization.
Part 4 · Retrieval Engineering
6 min readDevelopmentFeatured
Late Interaction Retrieval with ColBERT and ColPali
A technical overview of ColBERT and ColPali, focusing on late interaction mechanisms and multi-vector retrieval strategies for ML and search engineers.
Part 3 · Retrieval Engineering
8 min readDevelopmentFeatured
ANN Indexes: HNSW, IVF, and DiskANN Compared
Explore the trade-offs between HNSW, IVF, and DiskANN indexes. Learn how recall, latency, and quantization impact vector search performance in production systems.
Part 2 · Retrieval Engineering
6 min readDevelopmentFeatured
Choosing an Embedding Model in 2026
Navigate the 2026 landscape of embedding models. Learn how to evaluate MTEB scores, leverage Matryoshka dimensions, and handle multilingual data for production RAG.
Part 3 · RAG from Zero to Production