How to Configure Vector Database Indexing

Last updated: March 2025 5 min read Applies to: ChromaDB, Qdrant, FAISS

Overview

Vector database indexing is critical for achieving fast similarity search at scale. This article covers the main indexing strategies and when to use each one. Choosing the right index can mean the difference between sub-millisecond queries and queries that take several seconds.

HNSW (Hierarchical Navigable Small World)

HNSW is the most popular indexing algorithm for vector databases. It works by building a multi-layer graph where each layer acts as a "highway" for navigation. The top layers have fewer nodes and allow fast, approximate navigation. The bottom layers have all nodes and allow precise search.

Key parameters:

M: The number of bi-directional connections per node. Higher M means better recall but more memory. Default: 16.
efConstruction: Controls index build quality. Higher values create a more accurate index but take longer to build. Default: 200.
efSearch: Controls query-time accuracy. Higher values return better results but are slower. Default: 100.

Tip: When M is too low, the graph becomes disconnected and recall drops sharply. When M is too high, memory usage grows linearly and insertion slows.

IVF (Inverted File Index)

IVF first clusters the vectors into groups using k-means, then only searches within the most relevant clusters at query time. This is faster than brute-force but requires a training step to build the cluster centroids.

The key parameter is nlist (number of clusters). More clusters means faster search but requires more data to train accurately. A common heuristic is nlist = sqrt(N) where N is the total number of vectors.

Flat Index (Brute Force)

The simplest approach: compare the query vector against every stored vector. Guarantees perfect recall but scales linearly with dataset size. Only practical for fewer than 50,000 vectors.

When to Use What

For most RAG applications with fewer than 10 million vectors, HNSW is the best choice. It offers excellent recall with low latency and no training step. IVF is better for very large datasets (100M+ vectors) where memory is a constraint. Flat index works for prototyping and small datasets.

Common Mistakes

The most common mistake is using default parameters without tuning. Increasing efSearch improves recall but adds latency. Always benchmark with your actual data and queries before deploying to production.