README.md

---
license: other
language:
- en
pretty_name: Danbooru Tag Wiki Vector DB
size_categories:
- 1K<n<10K
task_categories:
- sentence-similarity
- feature-extraction
tags:
- danbooru
- anime
- tags
- embeddings
- vector-database
- sqlite
- sqlite-vec
- semantic-search
- harrier-oss
- gemma
---

# Danbooru Tag Wiki Vector DB

A single-file SQLite database of [Danbooru](https://danbooru.donmai.us)
general-category tag wiki pages, with a `sqlite-vec` virtual table holding
640-dim embeddings of each cleaned wiki body. Built to enable natural-language
search over Danbooru's tag vocabulary — give it a phrase like
*"a girl wearing a sailor uniform"* and get back the tags whose wiki
descriptions match.

Source code (fetcher, embedder, query CLI) lives at
[github.com/JackBinary/danbooru-db](https://github.com/JackBinary/danbooru-db).

## At a glance

| | |
|---|---|
| File | `danbooru.db` (single SQLite file, ~36 MB) |
| Tags | 9,322 general-category tags with `post_count >= 1000` and a valid wiki page |
| Embedded | 9,287 tags (a few wiki bodies are empty/stub) |
| Embedding dim | 640 |
| Embedding model | [`mykor/harrier-oss-v1-270m-GGUF`](https://huggingface.co/mykor/harrier-oss-v1-270m-GGUF) (BF16 at index time) — a GGUF of `microsoft/harrier-oss-v1-270m`, a 270M-param Gemma-embedding model with last-token pooling |
| Vector storage | [`sqlite-vec`](https://github.com/asg017/sqlite-vec) `vec0` virtual table |
| Pooling | last-token, L2-normalized |
| Max input | 248 tokens per wiki body (≈1000 chars) — see *Caveats* |

## Schema

Two tables in one SQLite file:

### `tags`
One row per general-category tag.

| column | type | notes |
|---|---|---|
| `rowid` | INTEGER PK | joins to `vec_tags.rowid` |
| `name` | TEXT UNIQUE | e.g. `cat_ears`, `long_hair` |
| `post_count` | INTEGER | Danbooru post count at fetch time |
| `tag_id` | INTEGER | Danbooru tag id |
| `wiki_id` | INTEGER | Danbooru wiki page id |
| `body_raw` | TEXT | Original dtext source from the wiki |
| `body_clean` | TEXT | dtext stripped; `See Also` section extracted; everything from the first `Posts` header onward dropped. **This is what was embedded.** |
| `see_also` | TEXT | JSON array of tag names from the wiki's `See Also` section |
| `other_names` | TEXT | JSON array of alternate names |
| `wiki_updated_at` | TEXT | ISO 8601 |
| `fetched_at` | TEXT | ISO 8601 |
| `embedded_at` | TEXT | ISO 8601, NULL if not embedded |

### `vec_tags`
A `sqlite-vec` virtual table:

```sql
CREATE VIRTUAL TABLE vec_tags USING vec0(embedding float[640]);
```

Keyed by `rowid` matching `tags.rowid`. Vectors are stored as L2-normalized
float32, so cosine similarity equals `1 - distance/2` for the L2 distance
that `sqlite-vec` returns by default.

## Usage

You need the `sqlite-vec` extension loaded into your SQLite connection
(plain SQLite will error on `vec_tags`). In Python:

```python
import sqlite3, sqlite_vec
conn = sqlite3.connect("danbooru.db")
conn.enable_load_extension(True)
sqlite_vec.load(conn)
conn.enable_load_extension(False)

# Plain metadata query — no extension needed for this one:
for name, pc in conn.execute(
    "SELECT name, post_count FROM tags ORDER BY post_count DESC LIMIT 5"
):
    print(name, pc)
```

Top 5 tags by post count (sanity check):
```
1girl              7884730
solo               6603611
long_hair          5804917
breasts            4638498
looking_at_viewer  4565846
```

### Semantic search

To do retrieval you need to embed a query with the **same model family** as
the index. Harrier expects an instruction prefix for queries (not docs):

```
Instruct: <task>
Query: <text>
```

The companion CLI uses Q8_0 at query time against the BF16 index (cosine
≈ 0.9997 between BF16 and Q8_0 query vectors, so target ranks against the
BF16 corpus are unchanged but Q8_0 is ~5× faster to load and run):

```sh
uv run danbooru-db-query --db danbooru.db "a girl wearing a sailor uniform"
```

The query is L2-normalized and matched with:

```sql
SELECT t.name, t.post_count, v.distance, t.body_clean
FROM vec_tags v
JOIN tags t ON t.rowid = v.rowid
WHERE v.embedding MATCH :query_blob AND k = 10
ORDER BY v.distance;
```

## How it was built

1. **Fetch tags** (`danbooru-db-fetch --phase tags`) — paginated tag list
   from Danbooru's API filtered to general category with `post_count >= 1000`.
2. **Fetch wikis** (`danbooru-db-fetch --phase wikis`) — wiki page for each
   tag, rate-limited to 1 request/second to be polite. dtext is parsed to
   produce `body_clean` (markup stripped, `See Also` extracted to its own
   column, content from the first `Posts` header onward dropped).
3. **Embed** (`danbooru-db-embed`) — `body_clean` truncated to 248 tokens
   and embedded with the BF16 Harrier-OSS GGUF, L2-normalized, written to
   `vec_tags`.

## Caveats

- **248-token truncation.** `llama-cpp-python` hard-caps per-sequence context
  at 256 tokens. Wiki bodies are truncated to 248 tokens (≈1000 chars) before
  embedding. Tag definitions at the top of each wiki survive; trailing related-tag
  lists do not. If you want full-document embeddings, re-embed `body_clean` with
  a different runtime.
- **General-category only.** Character/copyright/artist/meta tags are
  excluded — this is a vocabulary of *visual content* tags.
- **`post_count >= 1000` floor.** The long tail of rare tags isn't here.
- **Wiki content is a snapshot.** Fetched May 2026. `post_count` and wiki
  bodies drift over time; rebuild from the source repo to refresh.
- **Some bodies are empty.** 35 of 9,322 tags have a wiki page but an empty
  `body_clean` after cleanup and are not embedded.

## License

The embeddings, schema, and cleaned bodies in this database are derived from
Danbooru's tag wikis, which are user-contributed content on
[danbooru.donmai.us](https://danbooru.donmai.us). Original wiki text remains
the property of its contributors and is subject to Danbooru's terms of use.
The build pipeline (the GitHub repo) is published under its repository
license; this dataset card and the SQLite container are released for research
and personal use. If you redistribute, credit Danbooru and the wiki authors.

## Citation

If this dataset is useful in published work, please cite the embedding model
and the source:

```bibtex
@misc{harrier-oss-v1-270m,
  title  = {Harrier-OSS-v1-270M},
  author = {Microsoft},
  url    = {https://huggingface.co/microsoft/harrier-oss-v1-270m},
}

@misc{danbooru-tag-wiki-vector-db,
  title  = {Danbooru Tag Wiki Vector DB},
  author = {JackBinary},
  url    = {https://github.com/JackBinary/danbooru-db},
  year   = {2026},
}
```