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Vector Databases and Embeddings: A Practical Engineering Guide
How embeddings are created, stored, and retrieved in vector databases, with real-world design choices for speed and relevance.
Embeddings Turn Meaning Into Vectors
Embeddings map text, images, or audio into dense vectors so similar items land near each other in space.
For product teams, embeddings are the bridge between user intent and relevant content. The quality of this mapping determines search accuracy.
Vector Databases Are Optimized for Similarity
Vector databases trade traditional indexing for approximate nearest neighbor search.
They are built to return the most similar vectors quickly, even at high scale, using indexes like HNSW or IVF.
Choosing an Index Strategy
HNSW is fast and accurate but can use more memory. IVF can scale to huge datasets with careful tuning.
Pick an index based on your constraints: memory, latency, and update frequency.
Metadata Filters Improve Precision
Metadata filters allow you to constrain search by language, product area, or permissions.
This reduces noisy results and makes the model feel smarter without changing the embedding model.
Operational Considerations
Vector indexes need maintenance: re-embedding, backfills, and versioning.
Plan for monitoring and rollback, especially when updating embedding models or chunking strategies.
Latency and Cost Tuning
Production search needs predictable latency. Measure P50 and P95 query times, then tune index parameters like efSearch or nprobe to hit your budget. Small changes can deliver large speedups without sacrificing too much relevance, but you must measure the impact on real queries, not synthetic benchmarks.
Cost control often comes from smart routing. Use cheaper indexes for broad recall and re-rank with a slower, higher quality model only when needed. This layered approach keeps the user experience fast while limiting expensive compute to the most valuable searches.
Maintain a small golden query set and track precision at k after every index or embedding change. If quality drops, roll back quickly before it affects user trust.
FAQ: Vector Search
Do I need a vector database? If you search by meaning rather than keywords, yes.
How often should I re-embed? When the content changes or you upgrade the model.
Is hybrid search better? Often yes. Combining vectors with keyword search improves precision.
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