more docs work

site/features/knn.md

@@ -2,14 +2,120 @@
 
 ## `vec0` virtual tables
 
-## Manually with `vec_distance_l2()`
+<!-- TODO match on vector column, k vs limit, distance_metric configurable, etc.-->
 
+## Manually with SQL scalar functions
+
+You don't need a `vec0` virtual table to perform KNN searches with `sqlite-vec`.
+You could store vectors in regular columns in a regular tables, like so:
 
 ```sql
-create table items(
+create table documents(
+  id integer primary key,
   contents text,
-  contents_embedding float[768] (check vec_f32(contents_embedding))
+  -- a 4-dimensional floating-point vector
+  contents_embedding blob
+);
+
+insert into documents values
+  (1, 'alex', vec_f32('[1.1, 1.1, 1.1, 1.1]')),
+  (2, 'brian', vec_f32('[2.2, 2.2, 2.2, 2.2]')),
+  (3, 'craig', vec_f32('[3.3, 3.3, 3.3, 3.3]'));
+```
+
+When you want to find similar vectors, you can manually use
+[`vec_distance_L2()`](../api-reference.md#vec_distance_l2),
+[`vec_distance_L1()`](../api-reference.md#vec_distance_l1),
+or [`vec_distance_cosine()`](../api-reference.md#vec_distance_cosine),
+and an `ORDER BY` clause to perform a brute-force KNN query.
+
+```sql
+select
+  id,
+  contents,
+  vec_distance_L2(contents_embedding, '[2.2, 2.2, 2.2, 2.2]') as distance
+from documents
+order by distance;
+
+/*
+┌────┬──────────┬──────────────────┐
+│ id │ contents │     distance     │
+├────┼──────────┼──────────────────┤
+│ 2  │ 'brian'  │ 0.0              │
+│ 3  │ 'craig'  │ 2.19999980926514 │
+│ 1  │ 'alex'   │ 2.20000004768372 │
+└────┴──────────┴──────────────────┘
+*/
+```
+
+
+
+
+If you choose this approach, it is recommended to define the "vector column" with its element type (`float`, `bit`, etc.) and dimension, for better documentation.
+It's also recommended to include a
+[`CHECK` constraint](https://www.sqlite.org/lang_createtable.html#check_constraints),
+to ensure only vectors of the correct element type and dimension exist in the table.
+
+```sql
+create table documents(
+  id integer primary key,
+  contents text,
+  contents_embedding float[4]
+    check(
+      typeof(contents_embedding) == 'blob'
+      and vec_length(contents_embedding) == 4
+    )
+);
+
+-- ❌ Fails, needs to be a BLOB input
+insert into documents values (1, 'alex', '[1.1, 1.1, 1.1, 1.1]');
+
+-- ❌ Fails, 3 dimensions, needs 4
+insert into documents values (1, 'alex', vec_f32('[1.1, 1.1, 1.1]'));
+
+-- ❌ Fails, needs to be a float32 vector
+insert into documents values (1, 'alex', vec_bit('[1.1, 1.1, 1.1, 1.1]'));
+
+-- ✅ Success! 
+insert into documents values (1, 'alex', vec_f32('[1.1, 1.1, 1.1, 1.1]'));
+```
+
+Keep in mind: **SQLite does not support custom types.**
+The example above may look like that the `contents_embedding` column has a "custom type"
+of `float[4]`, but SQLite allows for *anything* to appear as a "column type".
+
+```sql
+-- these "column types" are totally legal in SQLite
+create table students(
+  name ham_sandwich,
+  age minions[42]
 );
 ```
 
-## Static Blobs
+See [Datatypes in SQLite](https://www.sqlite.org/datatype3.html) for more info.
+
+So by itself, `float[4]` as a "column type" is not enforced by SQLite at all.
+This is why we recommend including `CHECK` constraints, to enforce that values in your vector column
+are of the correct type and length.
+
+For [strict tables](https://www.sqlite.org/stricttables.html), use the `BLOB` type and include the same `CHECK` constraints.
+
+```sql
+
+create table documents(
+  id integer primary key,
+  contents text,
+  contents_embedding blob check(vec_length(contents_embedding) == 4)
+) strict;
+```
+
+<!--
+TODO:
+
+- performance (brute force, vec0 is faster bc chunking, larger rows, move to separate table, etc.)
+- configurable "distance metrics"
+- note on `bit[]` and `int[8]` columns, require the constructor functions
+
+-->
+
+<!--## Static Blobs-->

site/features/vec0.md

@@ -0,0 +1,12 @@
+# `vec0` Virtual Table
+
+- primary keys
+- vector column definitions
+  - float/bit/int, dimension required
+  - distnace_metric
+  - chunk_size?
+- insert, updates, delete
+- fullscan, point, knn
+- distance and k hidden columns
+- `rowid in (...)`
+- joins/metadata example?