---
license: mit
task_categories:
- feature-extraction
tags:
- vector-database
- semantic-search
- ivf-index
- embeddings
- information-retrieval
size_categories:
- 10M<n<100M
---

# 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

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