ANN search returns close-enough vectors without guaranteeing exact nearest neighbors for every query.
Why approximation exists
Exact search becomes more expensive as vector collections grow, so indexes trade exactness for speed or memory efficiency.
Recall is the trade-off
Increasing search effort can improve recall but may increase latency or memory use.
FAISS connection
FAISS provides multiple index families suitable for ANN workloads.
Practical use
ANN is often a first-stage candidate generator before reranking, filtering, or business rules.
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Related work and reading
FAISS & Vector Search
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Retrieval Evaluation
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FAISS Retrieval Boundary
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AI Developer / ML Engineer building end-to-end AI systems from research to production, with a focus on multimodal AI, LLM applications, retrieval, MLOps, and systems engineering. He is based in Rawalpindi, Pakistan and is the founder of GROVE SYSTEMS.