|
--- |
|
license: apache-2.0 |
|
--- |
|
|
|
|
|
# embedfile |
|
|
|
|
|
Experimental CLI tool for generating and searching text embeddings, built on |
|
[llamafile](https://github.com/Mozilla-Ocho/llamafile), |
|
[`sqlite-vec`](https://github.com/asg017/sqlite-vec), |
|
[`sqlite-lembed`](https://github.com/asg017/sqlite-lembed), |
|
[the SQLite CLI](https://www.sqlite.org/cli.html), |
|
and a few other SQLite extensions. |
|
|
|
|
|
| Model | embedfile | Size (f16 quant) | |
|
| ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- | |
|
| [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | [`all-MiniLM-L6-v2.f16.embedfile`](https://huggingface.co/asg017/embedfile/resolve/main/all-MiniLM-L6-v2.f16.embedfile) | `56MB` | |
|
| [mixedbread-ai/mxbai-embed-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1) | [`mxbai-embed-xsmall-v1-f16.embedfile`](https://huggingface.co/asg017/embedfile/resolve/main/mxbai-embed-xsmall-v1-f16.embedfile) | `61MB` | |
|
| [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) | [`nomic-embed-text-v1.5.f16.embedfile`](https://huggingface.co/asg017/embedfile/resolve/main/nomic-embed-text-v1.5.f16.embedfile) | `273MB` | |
|
| [snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) | [`snowflake-arctic-embed-m-v1.5-f16.embedfile`](https://huggingface.co/asg017/embedfile/resolve/main/snowflake-arctic-embed-m-v1.5-f16.embedfile) | `221MB` | |
|
| - | [`embedfile`](https://huggingface.co/asg017/embedfile/resolve/main/embedfile) (no embedded model) | `12MB` | |
|
|
|
|
|
embedfiles run on Linux, Mac, and Windows computers in the same file, thanks to [cosmopolitan](https://github.com/jart/cosmopolitan). |
|
You can embed data from CSVs, JSON, NDJSON, and txt files from the CLI, or "eject" to the `sqlite3` CLI at any time. |
|
|
|
|
|
Here's an example, using MixedBread's xsmall model: |
|
|
|
``` |
|
$ wget https://huggingface.co/asg017/embedfile/resolve/main/mxbai-embed-xsmall-v1-f16.embedfile |
|
$ chmod u+x mxbai-embed-xsmall-v1-f16.embedfile |
|
$ ./mxbai-embed-xsmall-v1-f16.embedfile --version |
|
embedfile 0.0.1-alpha.1, llamafile 0.8.16, SQLite 3.47.0, sqlite-vec=v0.1.6, sqlite-lembed=v0.0.1-alpha.8 |
|
|
|
``` |
|
|
|
This executable file already has `sqlite-vec`, `sqlite-lembed`, and the embeddings model pre-configured. Test that embeddings work with: |
|
|
|
|
|
``` |
|
./mxbai-embed-xsmall-v1-f16.embedfile embed 'hello!' |
|
[-0.058174,0.043776,0.030660,...] |
|
``` |
|
|
|
You can embed data from CSV, JSON, NDJSON, and .txt files and save the results to a SQLite database. Here we are embedding the `text` column in the `dbpedia.min.csv` file, outputting to a `dbpedia.db` database. |
|
|
|
``` |
|
$ ./mxbai-embed-xsmall-v1-f16.embedfile import --embed text dbpedia.min.csv dbpedia.db |
|
INSERT INTO vec_items SELECT rowid, lembed("text") FROM temp.source; |
|
100%|████████████████████| 10000/10000 [02:00<00:00, 83/s] |
|
✔ dbpedia.min.csv imported into dbpedia.db, 10000 items |
|
``` |
|
|
|
That was 10,000 rows with 820,604 tokens. I got 83 embeddings per second on my older 2019 Intel Macbook. On my M1 Mac Mini I get 173 embbedings/second, and I'm sure it's faster on newer macs. |
|
|
|
Once indexed, you can search with the `search` command: |
|
|
|
``` |
|
$ ./mxbai-embed-xsmall-v1-f16.embedfile search dbpedia.db 'global warming' |
|
3240 0.852299 Attribution of recent climate change is the effort to scientifically ascertain mechanisms ... |
|
6697 0.904844 The global warming controversy concerns the public debate over whether global warming is occurring, how ... |
|
... |
|
``` |
|
|
|
|
|
At any point, if you want to "eject" and run SQL scripts yourself, the `sh` command will fire up the `sqlite3` CLI with all extensions and embeddings models pre-configured. |
|
|
|
``` |
|
$ ./mxbai-embed-xsmall-v1-f16.embedfile sh |
|
SQLite version 3.47.0 2024-10-21 16:30:22 |
|
Enter ".help" for usage hints. |
|
Connected to a transient in-memory database. |
|
Use ".open FILENAME" to reopen on a persistent database. |
|
sqlite> .mode qbox |
|
sqlite> select sqlite_version(), vec_version(), lembed_version(); |
|
┌──────────────────┬───────────────┬──────────────────┐ |
|
│ sqlite_version() │ vec_version() │ lembed_version() │ |
|
├──────────────────┼───────────────┼──────────────────┤ |
|
│ '3.47.0' │ 'v0.1.6' │ 'v0.0.1-alpha.8' │ |
|
└──────────────────┴───────────────┴──────────────────┘ |
|
sqlite> select vec_to_json(vec_slice(lembed('hello!'), 0, 8)) as sample; |
|
┌──────────────────────────────────────────────────────────────┐ |
|
│ sample │ |
|
├──────────────────────────────────────────────────────────────┤ |
|
│ '[-0.058174,0.043776,0.030660,0.047412,-0.059377,-0.036267,0 │ |
|
│ .038117,0.005184]' │ |
|
└──────────────────────────────────────────────────────────────┘ |
|
``` |
|
|
|
|
