fix: align the col name for embedding (#76)

* fix: align the col name for embedding

Signed-off-by: Keming <[email protected]>

* fix typos lint

Signed-off-by: Keming <[email protected]>

---------

Signed-off-by: Keming <[email protected]>
Co-authored-by: Keming <[email protected]>

Author: kemingy

src/usage/indexing.md

@@ -5,8 +5,8 @@ Indexing is the process of building a data structure that allows for efficient s
 To construct an index for vectors, first create a table named `items` with a column named `embedding` of type `vector(n)`. Then, populate the table with generated data.
 
 ```sql
-CREATE TABLE items (val vector(3));
-INSERT INTO items (val) SELECT ARRAY[random(), random(), random()]::real[] FROM generate_series(1, 1000);
+CREATE TABLE items (embedding vector(3));
+INSERT INTO items (embedding) SELECT ARRAY[random(), random(), random()]::real[] FROM generate_series(1, 1000);
 ```
 
 ::: tip

src/use-case/sparse-vector.md

@@ -200,7 +200,7 @@ D_{L2} = \Sigma (x_i - y_i) ^ 2
 $$
 For sparse vectors, `vector_dot_ops` calculates the dot product, which is a more efficient method.
 $$
-D_{dot} = - \Sigma x_iy_i
+D_{dot} = - \Sigma x_i y_i
 $$
 
 
@@ -266,4 +266,4 @@ scores = reranker.compute_score([["The text you want to search...", candidate] f
 reranked_text = [t for _, t in sorted(zip(scores, mix_text))]
 ```
 
-Congratulations! We have now completed our first step into the world of sparse vector search.
+Congratulations! We have now completed our first step into the world of sparse vector search.

typos.toml

@@ -0,0 +1,3 @@
+[default]
+extend-ignore-re = ['\$.+\$']
+extend-ignore-identifiers-re = ['\$.+\$']