| # SPACEV1B: A billion-Scale vector dataset for text descriptors | |
| This is a dataset released by Microsoft from SpaceV, Bing web vector search scenario, for large scale vector search related research usage. It consists of more than one billion document vectors | |
| and 29K+ query vectors encoded by Microsoft SpaceV Superior model. This model is trained to capture generic intent representation for both documents and queries. | |
| The goal is to match the query vector to the closest document vectors in order to achieve topk relevant documents for each query. | |
| ## Introduction | |
| This dataset contains: | |
| * [vectors.bin](vectors.bin): It contains 1,402,020,720 100-dimensional int8-type document descriptors. | |
| * [query.bin](query.bin): It contains 29,316 100-dimensional int8-type query descriptors. | |
| * [truth.bin](truth.bin): It contains 100 nearest ground truth（include vector ids and distances) of 29,316 queries according to L2 distance. | |
| * [query_log.bin](query_log.bin): It contains 94,162 100-dimensional int8-type history query descriptors. | |
| ## How to read the vectors, queries, and truth | |
| ```python | |
| import struct | |
| import numpy as np | |
| import os | |
| part_count = len(os.listdir('vectors.bin')) | |
| for i in range(1, part_count + 1): | |
| fvec = open(os.path.join('vectors.bin', 'vectors_%d.bin' % i), 'rb') | |
| if i == 1: | |
| vec_count = struct.unpack('i', fvec.read(4))[0] | |
| vec_dimension = struct.unpack('i', fvec.read(4))[0] | |
| vecbuf = bytearray(vec_count * vec_dimension) | |
| vecbuf_offset = 0 | |
| while True: | |
| part = fvec.read(1048576) | |
| if len(part) == 0: break | |
| vecbuf[vecbuf_offset: vecbuf_offset + len(part)] = part | |
| vecbuf_offset += len(part) | |
| fvec.close() | |
| X = np.frombuffer(vecbuf, dtype=np.int8).reshape((vec_count, vec_dimension)) | |
| fq = open('query.bin', 'rb') | |
| q_count = struct.unpack('i', fq.read(4))[0] | |
| q_dimension = struct.unpack('i', fq.read(4))[0] | |
| queries = np.frombuffer(fq.read(q_count * q_dimension), dtype=np.int8).reshape((q_count, q_dimension)) | |
| ftruth = open('truth.bin', 'rb') | |
| t_count = struct.unpack('i', ftruth.read(4))[0] | |
| topk = struct.unpack('i', ftruth.read(4))[0] | |
| truth_vids = np.frombuffer(ftruth.read(t_count * topk * 4), dtype=np.int32).reshape((t_count, topk)) | |
| truth_distances = np.frombuffer(ftruth.read(t_count * topk * 4), dtype=np.float32).reshape((t_count, topk)) | |
| ``` | |
| ## License | |
| The entire dataset is under [O-UDA license](LICENSE) |