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@@ -0,0 +1,15 @@
+
diff --git a/.github/logos/mozilla.svg b/.github/logos/mozilla.svg
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+++ b/.github/logos/mozilla.svg
@@ -0,0 +1,24 @@
+
+
+
diff --git a/.github/logos/sqlitecloud.small.svg b/.github/logos/sqlitecloud.small.svg
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+
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+++ b/.github/logos/sqlitecloud.svg
@@ -0,0 +1,18 @@
+
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+
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diff --git a/.gitignore b/.gitignore
index ddfdce6..404b43b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,7 +12,6 @@ dist/
*.pyc
*.db-journal
-*.svg
alexandria/
openai/
diff --git a/README.md b/README.md
index 75003aa..65b3fd2 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,11 @@
# `sqlite-vec`
+[](https://discord.gg/VCtQ8cGhUs)
+
An extremely small, "fast enough" vector search SQLite extension that runs
anywhere! A successor to [sqlite-vss](https://github.com/asg017/sqlite-vss)
-> [!IMPORTANT]
-> *`sqlite-vec` is a work-in-progress and not ready for general usage! I plan to launch a "beta" version in the next month or so. Watch this repo for updates, and read [this blog post](https://alexgarcia.xyz/blog/2024/building-new-vector-search-sqlite/index.html) for more info.*
+> [!IMPORTANT] > _`sqlite-vec` is a work-in-progress and not ready for general usage! I plan to launch a "beta" version in the next month or so. Watch this repo for updates, and read [this blog post](https://alexgarcia.xyz/blog/2024/building-new-vector-search-sqlite/index.html) for more info._
- Store and query float, int8, and binary vectors in `vec0` virtual tables
- Pre-filter vectors with `rowid IN (...)` subqueries
@@ -12,6 +13,22 @@ anywhere! A successor to [sqlite-vss](https://github.com/asg017/sqlite-vss)
runs anywhere SQLite runs (Linux/MacOS/Windows, in the browser with WASM,
Raspberry Pis, etc.)
+
+
## Sample usage
```sql
@@ -50,9 +67,9 @@ limit 2;
## Roadmap
-Not currently implemented, but planned in the future (after initial beta version):
+Not currently implemented, but planned in the future (after initial `v0.1.0` version):
-- Approximate nearest neighbors search (IVF and HNSW)
+- Approximate nearest neighbors search (DiskANN, IVF, maybe HNSW?)
- Metadata filtering + custom internal partitioning
- More vector types (float16, int16, sparse, etc.) and distance functions
@@ -67,7 +84,24 @@ Additionally, there will be pre-compiled and pre-packaged packages of `sqlite-ve
- Datasette and sqlite-utils plugins
- Pre-compiled loadable extensions on Github releases
+## Sponors
-## Support
+Development of `sqlite-vec` is supported by multiple generous sponsors! Mozilla is the main sponsor through the new Builders project.
-Is your company interested in sponsoring `sqlite-vec` development? Send me an email to get more info: https://alexgarcia.xyz
+
+
+
+
+`sqlite-vec` is also sponsored by the following companies:
+
+
+
+As well as multiple individual supporters on [Github sponsors](https://github.com/sponsors/asg017/)!
+
+If your company interested in sponsoring `sqlite-vec` development, send me an email to get more info: https://alexgarcia.xyz
+
+## See Also
+
+- [**`sqlite-ecosystem`**](https://github.com/asg017/sqlite-ecosystem), Maybe more 3rd party SQLite extensions I've developed
+- [**`sqlite-rembed`**](https://github.com/asg017/sqlite-rembed), Generate text embeddings from remote APIs like OpenAI/Nomic/Ollama, meant for testing and SQL scripts
+- [**`sqlite-lembed`**](https://github.com/asg017/sqlite-lembed), Generate text embeddings locally from embedding models in the `.gguf` format
+
SQLite Cloud.
+See the Sponsors section for more details.
+
+
+
+As well as multiple individual supporters on [Github sponsors](https://github.com/sponsors/asg017/)!
+
+If your company interested in sponsoring `sqlite-vec` development, send me an email to get more info: https://alexgarcia.xyz
+
+## See Also
+
+- [**`sqlite-ecosystem`**](https://github.com/asg017/sqlite-ecosystem), Maybe more 3rd party SQLite extensions I've developed
+- [**`sqlite-rembed`**](https://github.com/asg017/sqlite-rembed), Generate text embeddings from remote APIs like OpenAI/Nomic/Ollama, meant for testing and SQL scripts
+- [**`sqlite-lembed`**](https://github.com/asg017/sqlite-lembed), Generate text embeddings locally from embedding models in the `.gguf` format