Flat Index
The Gold Standard for Exact Similarity Search
How It Works
Performs an exact (brute-force) similarity search by comparing the query vector against every vector in the dataset.
When to Use a Flat Index
- ✅ Small datasets
- ✅ Prototyping and experimentation
- ✅ Evaluation baselines
- ✅ Scenarios where 100% recall is non-negotiable
Best Practice: Start with Flat Index during development and testing — it’s your reliability anchor. Once you validate your approach, consider approximate indexes (like HNSW) for production-scale performance. Use the Flat index when working with tiny datasets where correctness outweighs speed.
Advantages
- ✨ Perfect Recall Guarantee — Finds true nearest neighbors
- ✨ Zero Configuration — Simple setup with no tuning required
- ✨ Instant Indexing — Build time is virtually immediate
Limitations
⚠️ Search latency grows linearly with dataset size — making it impractical for large-scale workloads.