/3 min
Zero-Result Recovery in Agentic Search: Progressive Filter Relaxation
Handle zero results in agentic RAG search by relaxing hard payload filters progressively, then falling back to a broad semantic query.
Topic
Vector Databases
Qdrant deep-dives, indexing, filtering, and scaling the store behind semantic search.
/4 min
Why Payload Filtering Is Qdrant's Killer Feature (and How Filterable HNSW Actually Works)
Qdrant applies payload filters during HNSW graph traversal, not after — so filtered queries stay fast and always return a full top-k.
/4 min/reference
Cosine vs Dot Product vs Euclidean: Which Similarity Metric Should You Use?
On normalized embeddings all three metrics rank identically, so pick dot product for speed; magnitude only matters with unnormalized vectors.
/4 min
From Cloud Qdrant RAG to On-Device RAG: What Actually Transfers and What Doesn't
Moving a production cloud RAG pipeline on-device: the shape transfers, but index scale, embedder size, and re-ranking budgets do not.
/4 min
Building Agentic Search on Qdrant: A Production Architecture Walkthrough
How to build a production agentic search system on Qdrant: single-shot vector+filter tool calls, Postgres hydration, and progressive relaxation.
/4 min/reference
BM25 vs BM42 vs miniCOIL vs SPLADE: Which Sparse Retriever for Hybrid RAG?
BM25 is lexical, SPLADE learns term expansion, and Qdrant's BM42/miniCOIL add neural weights to the BM25 formula—compared for hybrid RAG.