Approximate nearest neighbor (ANN) search methods like those in Faiss accelerate similarity searches over Sentence Transformer embeddings by reducing the number of comparisons needed to find matches, while maintaining acceptable accuracy through smart indexing strategies. Instead of exhaustively comparing a query embedding to every vector in the dataset (as in exact search), ANN methods use preprocessed structures like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) to limit the search to a subset of candidate vectors. This trade-off between precision and speed is controlled by configurable parameters, allowing developers to balance performance based on their use case.
Faiss’s HNSW index organizes embeddings into a hierarchical graph structure where each node connects to a small set of neighbors. During a search, the algorithm navigates this graph, starting at a high level (coarse relationships) and progressively moving to lower levels (finer details). This approach skips irrelevant regions of the vector space, drastically reducing the number of distance computations. For example, with HNSW, a query might only evaluate 1% of the total vectors while still finding 95% of the true top matches. Similarly, IVF divides the dataset into clusters (using techniques like k-means) and only searches the clusters closest to the query. If a dataset has 1M embeddings split into 1,000 clusters, a query might check 10-50 clusters instead of all 1M vectors. Both methods exploit the inherent structure of Sentence Transformer embeddings, which map semantically similar text to nearby regions in the vector space, ensuring that shortcuts in the search process don’t skip meaningful candidates.
Accuracy is preserved by tuning index parameters. For HNSW, increasing the efSearch parameter (the size of the candidate queue during traversal) or the number of graph connections (m) improves recall at the cost of higher memory and compute. For IVF, raising the nprobe value (number of clusters to search) increases the likelihood of including relevant vectors. For example, in a retrieval task with 10M embeddings, using IVF with 4,096 clusters and nprobe=32 might achieve 90% recall at 50x faster speeds than exact search. Developers can validate these trade-offs using metrics like recall@k or by testing on a subset of labeled data. By combining these techniques with efficient distance calculations (e.g., inner product for cosine similarity) and hardware optimizations (like GPU acceleration in Faiss), ANN methods enable scalable real-world applications like semantic search or recommendation systems without significant accuracy loss.