RAG REALITY CHECK: HNSW EVERYWHERE, FILTERS DECIDE; READ FEWER IMAGES
Most vector stores use HNSW, so your filtering and scale decide whether pgvector is enough or you need Qdrant, Pinecone, or Weaviate. A hands-on comparison sho...
Most vector stores use HNSW, so your filtering and scale decide whether pgvector is enough or you need Qdrant, Pinecone, or Weaviate.
A hands-on comparison shows pgvector, Qdrant, Pinecone, and Weaviate all lean on approximate HNSW and live on the recall vs latency vs memory tradeoff; the big practical gap is metadata filtering and when each system falls over under your workload Vector Databases Compared.
On document parsing costs, stop captioning every image in a PDF. Use a cheap filter → type check → OCR → vision cascade, and only pay for images likely to matter to retrieval Making a PDF’s Images Searchable for RAG.
If you care about public answers naming your docs, AI engines now cite differently: entity strength and answer-first pages matter, and Google’s AI Overviews pull from deeper results via sub-queries How AI engines actually decide what to cite.
Choosing the wrong vector store or index settings can lock you into months of rework and higher latency at scale.
Selective image reading cuts token and GPU spend without hurting retrieval quality.
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terminal
Run your own ANN bake-off: fix recall targets and vary metadata filter selectivity; chart latency/QPS for pgvector, Qdrant, Pinecone, and Weaviate.
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terminal
Prototype a PDF image cascade (filter → type → OCR → VLM), then measure cost and recall deltas on real queries.
Legacy codebase integration strategies...
- 01.
Start with pgvector inside existing PostgreSQL; only migrate to a dedicated vector store when filter cardinality and latency SLOs fail.
- 02.
Introduce the image-reading cascade behind a feature flag to de-risk accuracy regressions on existing RAG pipelines.
Fresh architecture paradigms...
- 01.
If you expect heavy boolean filters, multi-tenant isolation, and strict p99, plan on Qdrant or Weaviate from day one.
- 02.
Design parsing to separate locating images from captioning so you can adapt costs per document and per query.
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