docs

.github/workflows/release.yaml

@@ -32,7 +32,7 @@ jobs:
         run: ./configure && make
 
       # TODO how cache this?
-      - run: sudo apt-get install -y cmake libgomp1
+      - run: sudo apt-get update -y && sudo apt-get install -y cmake libgomp1
       - run: make loadable-release static-release
       - uses: actions/upload-artifact@v3
         with:
@@ -361,4 +361,4 @@ jobs:
       - run: cargo publish --no-verify
         working-directory: ./bindings/rust
         env:
-          CARGO_REGISTRY_TOKEN: ${{ secrets.CARGO_REGISTRY_TOKEN }}
+          CARGO_REGISTRY_TOKEN: ${{ secrets.CARGO_REGISTRY_TOKEN }}

site/getting-started.md

@@ -82,7 +82,7 @@ create virtual table vss_demo using vss0(
 
 Notice the `2` declaration in the `a` column definition. This is a required argument that tells `sqlite-vss` how many dimensions our vectors have. Also note that prefixing the virtual table name with `vss_` is a convention but not required.
 
-Now we have an empty `vss_demo`. Let's insert our vectors!
+Let's insert our vectors!
 
 ```sqlite
 insert into vss_demo(rowid, a)
@@ -99,7 +99,7 @@ insert into vss_demo(rowid, a)
   ');
 ```
 
-Here we are using [SQLite's builting JSON support](https://www.sqlite.org/json1.html) to define our vector data in a JSON string. `sqlite-vss` supports reading vectors and JSON in a few different formats, which you [can learn more about here](api-reference#inserting-data).
+Here we are using [SQLite's builting JSON support](https://www.sqlite.org/json1.html) to define our vector data in a JSON string. `sqlite-vss` supports vectors in a few different formats, which you [can learn more about here](api-reference#inserting-data).
 
 <p align="center"> <img src="./demo_q1.png" width="75%"> </p>
 
@@ -111,7 +111,6 @@ select
 from vss_lookup
 where vss_search(a, json('[2.0, 2.0]'))
 limit 3;
-
 ```
 
 ```

site/using/datasette.md

@@ -2,15 +2,15 @@
 
 [![Datasette](https://img.shields.io/pypi/v/datasette-sqlite-vss.svg?color=B6B6D9&label=Datasette+plugin&logoColor=white&logo=python)](https://datasette.io/plugins/datasette-sqlite-vss)
 
-`sqlite-vss` is also distributed as a [Datasette plugin](https://docs.datasette.io/en/stable/plugins.html) in the form of [`datasette-sqlite-vss`](https://pypi.org/project/datasette-sqlite-vss/), which can be installed like so:
+`sqlite-vss` is also distributed as a [Datasette plugin](https://docs.datasette.io/en/stable/plugins.html) with [`datasette-sqlite-vss`](https://pypi.org/project/datasette-sqlite-vss/), which can be installed like so:
 
 ```bash
 datasette install datasette-sqlite-vss
 ```
 
-`datasette-sqlite-vss` is just a PyPi package and can also be installed with `pip`, but `datasette install` will ensure it's downloaded in the same Python environment as your Datasette installation, and will automatically include `sqlite-vss` in future Datasette instances.
+`datasette-sqlite-vss` is just a PyPi package and can also be installed with `pip`. However, `datasette install` ensures that the plugin will be downloaded in the same Python environment as your Datasette installation, and will automatically include `sqlite-vss` in future Datasette instances.
 
-If you're using the `datasette publish` command, you can [use the `--install` flag](https://docs.datasette.io/en/stable/plugins.html#deploying-plugins-using-datasette-publish) to include `datasette-sqlite-vss` in your Datasette projects.
+If you're using the [`datasette publish`](https://docs.datasette.io/en/stable/publish.html) command, you can [use the `--install` flag](https://docs.datasette.io/en/stable/plugins.html#deploying-plugins-using-datasette-publish) to include `datasette-sqlite-vss` in your Datasette projects.
 
 ```bash
 datasette publish cloudrun data.db \

site/using/deno.md

@@ -51,7 +51,7 @@ stmt.run(JSON.stringify(embedding));
 
 ### Vectors as Bytes
 
-Alternatively, if your vectors in Node.js are represented as a [Float32Array](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array), you can access the [`.buffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray/buffer) accessor to insert the underlying ArrayBuffer.
+Alternatively, if your vectors in Node.js are represented as a [Float32Array](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array), use the [`.buffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray/buffer) accessor to insert the underlying ArrayBuffer.
 
 ```js
 const embedding = new Float32Array([0.1, 0.2, 0.3]);

site/using/go.md

@@ -6,10 +6,18 @@
 The Go bindings for `sqlite-vss` are still in beta and are subject to change. If you come across problems, [please comment on the Go tracking issue](https://github.com/asg017/sqlite-vss/issues/49).
 :::
 
-1. binary struct pack
-
 ## Installing `sqlite-vss` into Go Projects
 
+The official `sqlite-vss` Go bindings can be installed like so:
+
+```
+go get -u github.com/asg017/sqlite-vss/bindings/go
+```
+
+You are required to provide pre-compiled static library files of `sqlite-vss`. You can do this by compiling `sqlite-vss` yourself, or [from a Github Release](https://github.com/asg017/sqlite-vss/releases).
+
+Once you have a directory with the pre-compiled static library files, you
+
 ## Working with Vectors in Go
 
 If your vectors in Go are represented as a slice of floats, you can insert them into a `vss0` table as a JSON string with [`json.Marshal`](https://pkg.go.dev/encoding/json#Marshal):

site/using/nodejs.md

@@ -39,7 +39,7 @@ stmt.run(JSON.stringify(embedding));
 
 ### Vectors as Bytes
 
-Alternatively, if your vectors in Node.js are represented as a [Float32Array](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array), you can access the [`.buffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray/buffer) accessor to insert the underlying ArrayBuffer.
+Alternatively, if your vectors in Node.js are represented as a [Float32Array](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array), use the [`.buffer`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray/buffer) accessor to insert the underlying ArrayBuffer.
 
 ```js
 const embedding = new Float32Array([0.1, 0.2, 0.3]);