Datasets:

Abdelrahman610
/

Semantic-Search-Engine-with-Vectorized-DB

K int64	N int64	artifact_sizes dict	build dict	dev_analogue int64	index_dir string	list_stats dict	nprobe dict
205	1,000,000	{ "centroids": 52480, "ids": 4000000, "labels": 2000000, "meta": 3357, "offsets": 1648, "vectors": 256000000 }	{ "build_computations": { "S2_training_estimate": { "dot_products": 819527680, "label": "estimate" }, "S3_assignment": { "dot_products": 205000000, "label": "exact" }, "S6_scatter": { "distance_computations": 0, "rows_read": 1000000, "rows_written": 100000...	4,096	D:\College\Fourth Year\Advanced Databases\project\Semantic Search Engine with Vectorized DB\data\OpenSubtitles_en_dev_1M_emb_64_index	{ "empty": 0, "histogram_counts": [ 14, 28, 40, 39, 35, 29, 12, 3, 4, 1 ], "histogram_edges": [ 2302, 3008.8, 3715.6, 4422.4, 5129.2, 5836, 6542.799999999999, 7249.599999999999, 7956.4, 8663.2, 9370 ], "large": 0, "max": 9...	{ "1": { "average_candidates": 5224.428, "bytes_read": 446360.56, "centroid_bytes": 52480, "cost": 509513.235, "coverage_at_nprobe": 0.438, "dist_bytes": 161670.848, "ids_bytes": 20897.712, "offset_bytes": 1648, "pq_code_bytes_assumed": 83590.848, "pq_codebook_bytes_assumed": 2...

Semantic Search Engine with Vectorized DB — Artifacts

This repository hosts the pre-computed on-disk index artifacts for the 20,000,000 vector database (OpenSubtitles_en_20M_emb_64.dat), built for the Advanced Database Systems project (Cairo University, Faculty of Engineering).

📁 Repository Structure

semantic-search-artifacts/
│
├── README.md                          # Repository documentation & usage guide
│
├── production/
│   ├── m1_ivf_k4096/                  # Module 1: IVF coarse index & cluster-sorted vectors
│   │   ├── centroids.f32              # (4096, 64) float32 L2-normalized coarse centroids
│   │   ├── offsets.i64                # (4097,) int64 inverted list boundary offsets
│   │   ├── ids.i32                    # (20,000,000,) int32 original row IDs (cluster-sorted)
│   │   ├── vectors.f32                # (20,000,000, 64) float32 normalized vectors (cluster-sorted)
│   │   ├── labels.i16                 # (20,000,000,) int16 cluster labels for original rows
│   │   ├── meta.json                  # Complete build parameters, stage timestamps & invariants
│   │   └── SHA256SUMS.txt             # Cryptographic SHA-256 checksums
│   │
│   └── m2_pq/                         # Module 2: Product Quantization (in progress)
│       ├── pq_codes.*
│       ├── pq_codebooks.*
│       ├── pq_meta.json
│       └── SHA256SUMS.txt
│
└── reports/
    ├── full_index_report.json         # Complete validation & recall metrics for 20M index
    └── dev_index_report.json          # Validation report for 1M dev index

📊 Module 1 Production Index Details (`m1_ivf_k4096`)

Total Vectors ($N$): 20,000,000
Vector Dimension ($D$): 64 (float32)
Clusters ($K$): 4096
Clustering Method: MiniBatchKMeans on 2M L2-normalized vector sample, final centroids L2-normalized.
Empty Clusters: 0 (min size = 680, max size = 22,911, mean = 4,882.8)
Coverage@64 (Upper Bound Recall): 99.30% (exceeds team target of $\ge 99.0%$)
Build Time: 304 seconds (~5 minutes)

🔒 Verification & Checksums

File	Shape / Dtype	Size	SHA-256 Checksum
`centroids.f32`	`(4096, 64)` `float32`	1,048,576 B (1.0 MB)	`deb0e2e88c764503ddbcf5eaf68d3807497228d300560477abd3fe642c635fff`
`offsets.i64`	`(4097,)` `int64`	32,776 B (32 KB)	`7894cf57c10e8a4fa25aab62ccb6c4c4a8a08d4bc744091c5904fdefb1d237b4`
`ids.i32`	`(20000000,)` `int32`	80,000,000 B (80 MB)	`6675a2ac22afbec3f65b467d5f3663d4618e541293ddba4ee10d3940c6035cab`
`labels.i16`	`(20000000,)` `int16`	40,000,000 B (40 MB)	`d3294a967e330ad2ce757ff15abe4938274315b9fd6a9b43c08d9480f7c1d1b5`
`meta.json`	JSON metadata	3,407 B (~3.4 KB)	`b86fe06ba30cf377994f271908958de6fd618ea6b818caa24102ecf648515991`
`vectors.f32`	`(20000000, 64)` `float32`	5,120,000,000 B (5.12 GB)	`caaadbd8454542fbd7c329314134730ef79fd7b228130027b60f20b2a7476054`

💻 Download & Integration Instructions

In Python using `huggingface_hub`

from huggingface_hub import hf_hub_download

# Example: Download centroids and offsets for Module 3 (Retrieval)
centroids_file = hf_hub_download(
    repo_id="Abdelrahman610/Semantic-Search-Engine-with-Vectorized-DB",
    filename="production/m1_ivf_k4096/centroids.f32",
    repo_type="dataset",
)

offsets_file = hf_hub_download(
    repo_id="Abdelrahman610/Semantic-Search-Engine-with-Vectorized-DB",
    filename="production/m1_ivf_k4096/offsets.i64",
    repo_type="dataset",
)

Download the entire index folder via CLI

huggingface-cli download Abdelrahman610/Semantic-Search-Engine-with-Vectorized-DB     --repo-type dataset     --local-dir ./downloaded_artifacts

Downloads last month: 46

Number of rows:

Total file size:

5.24 GB