#vector-search
16 articles
Document Ingestion Pipelines: The Unglamorous 80% of RAG
PDF parsing, metadata design, PII scrubbing, chunk-at-ingest vs chunk-at-query, and incremental index updates — the ingestion work that determines whether RAG succeeds or fails.
Metadata Filtering, Scaling, and Production Cost Control
How metadata filtering interacts with ANN indexes, multi-tenancy patterns, quantization tradeoffs, and why your vector DB bill runs 2.5–4x over forecast at scale.
Hybrid Search: Fusing Dense Vectors and Sparse BM25 Correctly
Combine vector search and BM25 without ruining either — RRF, score normalization, dynamic weighting, and the failure modes that drop precision.
GraphRAG and Structured Retrieval for Multi-Hop Reasoning
How knowledge graphs replace flat vector indexes to answer multi-hop and global queries flat retrieval cannot handle — with real costs, when to use it, and when not to.
Vector Database Landscape 2026: Pinecone, Qdrant, Weaviate, Milvus, pgvector
A hardheaded comparison of Pinecone, Qdrant, Weaviate, Milvus, and pgvector: architecture, real costs, hybrid search maturity, and when each wins in production.
Multimodal RAG: retrieving images, PDFs, and text together
Build a RAG pipeline that handles mixed corpora of images, charts, and PDFs — covering embedding strategy, retrieval architecture, and production failure modes.
Query Transformation: Rewriting, HyDE, and Multi-Query Expansion
How query rewriting, HyDE, multi-query expansion, and step-back prompting close the gap between what users type and what your retrieval index can actually find.
ANN indexes under the hood: HNSW, IVF, and when each wins
A mechanistic walkthrough of HNSW and IVF approximate nearest-neighbor algorithms — with complexity analysis, memory math, and concrete guidance on when each index type wins.
Hybrid Search: BM25 Plus Dense Retrieval Plus Reranking
How to combine BM25 keyword search with dense vector retrieval and a cross-encoder reranker to build a RAG pipeline that beats either approach alone.
Choosing an embedding model in 2026: OpenAI, Cohere, Gemini, Voyage, and open-source
A decision framework for picking an embedding model: MTEB scores, latency, cost, multimodal needs, and when to self-host — with 2026 benchmarks.
Chunking Strategies That Actually Matter: From Fixed-Size to Late Chunking
Chunking is the highest-variance, least-discussed decision in RAG. The wrong strategy silently kills retrieval quality no matter how good your embeddings are.
Similarity Metrics Demystified: Cosine, Dot Product, and L2
Which similarity metric is right for your embeddings? Cosine, dot product, and L2 explained with real geometry, normalization traps, and model-specific guidance.
RAG from Scratch: Ingestion, Retrieval, and Generation in One Pass
Build a working RAG pipeline end-to-end — parse, chunk, embed, index, retrieve, and generate — understanding every decision before layering on complexity.
What Are Embeddings? Geometry, Meaning, and the Math That Makes Them Work
How embedding models project text into vectors, what semantic neighborhoods mean in practice, and where the math breaks down in production RAG.
RAG in One Pass: Build, Measure, Improve
Build a complete RAG pipeline end-to-end — ingest, chunk, embed, index, retrieve, generate — with evaluation wired in from day one so you catch failures before users do.
Embeddings and Vector Databases That Scale
What embeddings really encode, how ANN indexes work, which vector database to pick at your scale, and the metadata-filtering pitfalls that quietly destroy recall in production.