JackBinary/danbooru-db

Danbooru Tag Wiki Vector DB

A single-file SQLite database of Danbooru 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.

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 (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 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:

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:

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):

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

The query is L2-normalized and matched with:

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. 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:

@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},
}