Create README.md

README.md

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+---
+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},
+}
+```