Embeddings & Vector Databases · TRACK 04/04
Vectors in Production
Choosing, filtering, syncing, and scaling vector search in real systems.
// THE TRACK
01 · START HEREChoosing a Vector DatabaseStop comparing feature tables — use a five-question framework that narrows every vector database choice down to one or two real options.BEGINNERHybrid Search in ProductionUnderstand how combining BM25 keyword search with dense vector search — and fusing the results with reciprocal rank fusion — produces more accurate retrieval than either method alone.INTERMEDIATEMetadata FilteringUnderstand why bolting a WHERE clause onto similarity search breaks naive indexes, and the strategies real databases use instead.INTERMEDIATESimpler AlternativesRun the numbers on brute-force search before paying for infrastructure — most projects need far less than they think.INTERMEDIATEElasticsearch, Redis & MongoUse the database you already operate for embeddings — what each engine's vector support can and can't do.INTERMEDIATESyncing EmbeddingsLearn the production patterns for keeping a vector index fresh as source data changes, updates, and gets deleted, so search never returns stale results.INTERMEDIATECutting Vector Search CostsLearn where vector search spend really goes - embedding API calls, RAM-resident indexes, and storage - and the levers that cut it without tanking recall.INTERMEDIATESwitching Embedding ModelsUnderstand why you cannot mix vectors from two different embedding models and learn the dual-write, backfill, and cutover playbook for migrating safely.INTERMEDIATEModel2VecYou will understand what Model2Vec is, how distilling into static embeddings trades a little accuracy for huge size and speed gains, and when to use it.INTERMEDIATEFastEmbedYou will understand what FastEmbed is, why running embeddings on ONNX without PyTorch makes it lightweight and serverless-friendly, and where it fits.INTERMEDIATE