# KNN Vector query: bring your own custom vector search

> KNN Vector query gives advanced teams full, custom control over semantic nearest-neighbor search via JSON.

**Category:** Hybrid Search
**Author:** NeuralSeek Team · **Published:** June 9, 2026
**Canonical:** https://neuralseek.ai/ai-grounded/knn-vector-query
**Section index:** https://neuralseek.ai/ai-grounded

KNN Vector query is one of NeuralSeek's Hybrid Search guardrails — part of the platform's 118 individually configurable, fully auditable controls. In regulated, high-volume AI, the difference between a system you can trust and one you merely hope works comes down to specific, tunable controls exactly like this one. Here is what KNN Vector query does, why it matters to the business, and how to set it for your own environment.

## What it actually does

This lets you supply a custom KNN vector query as JSON, giving full control over nearest-neighbor semantic search. Advanced teams can shape vector retrieval precisely.

## Why business teams care

Default vector search is good, but some use cases need bespoke tuning of how similarity is computed and filtered. A custom query unlocks that control without leaving the platform.

## How to tune it in practice

Use it when default vector retrieval doesn't fit, and validate the custom query against known-good results. Keep it documented since it's an advanced override.

## Common failure modes it prevents

Pure keyword search misses meaning and pure vector search misses exact terms; relying on either alone leaves answers weaker than they should be. KNN Vector query closes that gap directly. By making the behavior an explicit, enforced control rather than something left to chance, it converts a latent risk into a managed, observable event — one that surfaces in the audit trail instead of in a customer complaint or a compliance finding.

## Where it fits in the stack

It governs the retrieval engine itself, blending sparse, dense, and re-sort strategies before grounding ever runs. Because it lives in NeuralSeek's governance layer rather than inside any single model, the control holds identically whether a request routes to OpenAI, Anthropic, Gemini, Llama, Mistral, IBM watsonx, or an in-house model.

## Retrieval tuned to your content

Because the search strategy is configurable, the same platform can serve precise keyword lookups and fuzzy semantic questions, each matched to the shape of your knowledge base.

> Sometimes the best retrieval is the one you shape yourself.

## The takeaway

KNN Vector query gives advanced teams full, custom control over semantic nearest-neighbor search via JSON.

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From NeuralSeek's AI Grounded — practical, web-verified guidance on building governed, grounded enterprise AI. NeuralSeek is the model-agnostic, governed AI platform you own: any LLM (swap with no rebuild), your data in your own tenant (cloud or on-prem), 118 guardrails enforced before any action, one container that runs anywhere.