Pgvector is a popular Postgres extension used for vector similarity searches, particularly in AI-powered applications. It performs sequential scans by default, which provide exact searches with 100% recall but can become inefficient with large datasets. To enhance performance, especially for larger datasets, the Inverted File Index (ivfflat) can be used for approximate nearest neighbor (ANN) searches, which involves creating k-means centroids to partition data into clusters, thus reducing the number of vectors analyzed. By adjusting parameters such as the number of lists and probes in ivfflat, users can optimize the balance between search speed and accuracy; however, experimentation is crucial to find optimal settings tailored to specific datasets. The article suggests starting with certain baselines for these parameters based on dataset size and emphasizes the importance of tuning these settings to maximize pgvector's efficiency in vector similarity searches.