Multi-stage or hybrid indexing improves search efficiency by combining fast approximation techniques with precise refinement steps. This approach balances speed and accuracy by reducing the search space in initial stages while preserving most relevant candidates for deeper analysis. For example, a system might first use coarse quantization to quickly filter out obviously irrelevant data, then apply a more computationally expensive exact search on the remaining subset. By limiting the exact comparisons to a small fraction of the dataset, overall latency drops significantly without excluding high-quality results.
A common implementation involves two steps: an approximate nearest neighbor (ANN) search followed by a re-ranking phase. In the first stage, algorithms like IVF (Inverted File Index) or product quantization group vectors into clusters or compressed representations, enabling rapid similarity comparisons. For instance, FAISS's IVFADC method uses cluster centroids to narrow candidates to the most promising regions of the vector space. The second stage then calculates exact distances or applies higher-bitrate quantization only for these pre-filtered candidates. This reduces distance computations by orders of magnitude—from billions to thousands—while still evaluating the most likely matches.
The key to maintaining recall lies in configuring the initial stage to cast a wide enough net. If the coarse step retrieves 100x more candidates than the final required results, the refinement stage can recover nearly all relevant matches missed by the approximation. For example, a system seeking 10 final results might first select 1,000 candidates via ANN, then re-rank them using exact metrics. This preserves 95%+ recall in practice while cutting latency by 90% compared to full brute-force searches. Parameters like the number of clusters in IVF or the compression ratio in quantization can be tuned to balance speed and accuracy for specific use cases.