Inverted File Product Quantization (IVF_PQ): Accelerate Vector Search by Creating Indices

Vector similarity search is a crucial technique for retrieving relevant data from large datasets by identifying similar vectors in an embedding space, but it requires significant memory resources when dealing with dense datasets. Product Quantization (PQ) offers a solution by compressing high-dimensional vectors into smaller, more manageable sizes without losing essential information. PQ achieves this by dividing vectors into subvectors, assigning unique centroid IDs to each, and reconstructing them as needed. Inverted File Product Quantization (IVFPQ) enhances this process by integrating an Inverted File (IVF) index with PQ, enabling efficient approximate nearest neighbor searches by narrowing the search scope to relevant vector partitions. LanceDB is a tool that facilitates the implementation of IVFPQ, allowing for rapid and effective searches, thanks to its ability to partition vector spaces and refine search results. This combination of PQ and IVFPQ results in substantial memory savings and improved search performance, making it a powerful approach for handling high-dimensional data efficiently.