Add api reference

README.md

@@ -61,6 +61,18 @@ vector_from_json(json_string) -- converts a json array of type TEXT into BLOB(a
 vector_to_json(vector_blob) -- converts a vector of type BLOB(c-style float32 array) into a json array of type TEXT
 vector_distance(vector_blob1, vector_blob2, distance_type_str) -- calculate vector distance between two vectors, distance_type_str could be 'l2', 'cosine', 'ip' 
 ```
+In fact, one can easily implement brute force searching using `vector_distance`:
+```sql
+-- use a normal sqlite table
+create table my_table(rowid integer primary key, embedding blob);
+
+-- insert 
+insert into my_table(rowid, embedding) values (0, {your_embedding});
+-- search for 10 nearest neighbors using l2 squared distance
+select rowid from my_table order by vector_distance({query_vector}, embedding, 'l2') asc limit 10
+
+```
+
 ### Virtual Table
 The core of vectorlite is the [virtual table](https://www.sqlite.org/vtab.html) module, which is used to hold vector index.
 A vectorlite table can be created using:

doc/conf.py

@@ -6,10 +6,10 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'Example'
-copyright = 'workshop participant'
-author = 'workshop participant'
-release = '0.1'
+project = 'vectorlite'
+copyright = 'vectorlite contributors'
+author = '[email protected]'
+release = '0.2.0'
 
 
 # -- General configuration ---------------------------------------------------

doc/index.rst

@@ -3,12 +3,12 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to Example's documentation!
+Welcome to vectorlite's documentation!
 ===================================
 
 .. toctree::
    :maxdepth: 2
    :caption: Contents:
 
    markdown/overview.md
-   markdown/another-feature.md
+   markdown/api.md