Vector Index

A vector index is a specialized data structure that accelerates similarity search over large collections of vector embeddings.

Without an index, finding the most similar items to a query requires comparing it against every single vector in the database — a process known as brute-force or flat search.

Brute-force search is:

• ✅ Accurate: Returns the exact most similar results — no approximation.
• ⚠️ Painfully slow at scale: With millions or billions of vectors, queries can take seconds or minutes, making them impractical for real-time applications.

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Approximate vs. Exact Search

Most vector indexes use Approximate Nearest Neighbor (ANN) algorithms.

Instead of finding the exact closest matches, ANN finds very close approximations — often indistinguishable in quality for practical purposes — while delivering massive gains in speed and efficiency.

For real-world uses, such as semantic search or recommendations, this small accuracy trade-off delivers huge gains in speed and scalability. In short: good enough to be correct, but lightning fast ✨.

Recall: Measuring Approximation Quality

Recall is the standard metric for evaluating how well an ANN algorithm preserves result quality.

It quantifies the fraction of true nearest neighbors — identified by an exact (brute-force) search — that appear in the top‑k results returned by the approximate method:

$$\textcolor{#2563eb}{ \text{Recall@}k = \frac{\text{Number of true nearest neighbors in top-}k}{k} }$$

Example:

• If you request the top 10 results and 9 of them match the true top 10 from a brute-force search, your recall@10 is 90%.
• High recall (e.g., ≥ 96%) typically means the approximation is practically indistinguishable from exact search for most applications.

Different index types (e.g., HNSW) and their parameters (e.g., ef_search) let you fine-tune the balance between recall, query speed, and resource usage — so you can optimize for your specific accuracy and performance requirements.

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Vector Index Types

Zvec supports three vector index types, each suited to different use cases, dataset sizes, and performance requirements:

1. Flat (Brute-Force) index
2. HNSW (Hierarchical Navigable Small World)
3. IVF (Inverted File Index)