Update README.md - alternative detailed instructions for headlines example

examples/headlines/README.md

@@ -1,49 +1,163 @@
+### 1. Prepare scripts
 
-1. `sqlite3 full.db '.read import_articles.sql'`
-2. `python3 add_embeddings.py full.db`
-3. `sqlite3 full.db '.read add_vector_search.sql'`
+1. Download lines0 library from [sqlite-lines extension](https://github.com/asg017/sqlite-lines/releases) to current folder
 
-```sql
-with similar_matches as (
-  select rowid
-  from vss_articles
-  where vss_search(
-    headline_embedding, 
-    vss_search_params(json(st_encode('charlie brown')), 20)
-  )
-), final as (
-  select 
-    articles.*
-  from similar_matches
-  left join articles on articles.rowid = similar_matches.rowid
-)
-select * from final;
+2. Create a Deno script to proxy SQL queries with extensions loaded query.ts
+
+```
+import { Database } from "https://deno.land/x/sqlite3/mod.ts";
+import * as sqlite_vss from "https://deno.land/x/sqlite_vss/mod.ts";
+
+const db = new Database("headlines.db");
+
+db.enableLoadExtension = true;
+sqlite_vss.load(db);
+
+db.loadExtension('./lines0');
+
+const sqlQuery = Deno.args[0];
+
+const stmt = db.prepare(sqlQuery);
+
+let result;
+if (Deno.args.length > 1) {
+  result = stmt.all(...Deno.args.slice(1));
+} else {
+   result = stmt.all(); 
+}
+
+for (const row of result) {
+  console.log(row);
+}
+```
+
+3. Copy [add_embeddings.py](https://github.com/asg017/sqlite-vss/blob/main/examples/headlines/build/add_embeddings.py)
+
+4. Create generate_embedding.py
+
+```
+import json
+import sys
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+def st_encode(x):
+    result = model.encode([x])
+    return json.dumps(result.tolist()[0])
+
+x = " ".join(sys.argv[1:])
+
+encoded_result = st_encode(x)
+
+print(encoded_result)
 ```
 
+### 2. Create database
+
+1. Create articles table
+
+```
+deno run --unstable -A query.ts "
+create table articles(
+  headline text,
+  headline_embedding fvector,
+  description text,
+  description_embedding fvector,
+  link text,
+  category text,
+  authors text,
+  date
+);
+```
+
+2. Import dataset into articles table
+
 ```
-datasette full.db --host 0.0.0.0 --load-extension=../../build/vss0.so --load-extension=./vector0.so --plugins-dir=./plugins
+deno run --unstable -A query.ts "
+insert into articles(headline, description, link, category, authors, date)
+  select
+    line ->> '$.headline'           as headline,
+    line ->> '$.short_description'  as description,
+    line ->> '$.link'               as link,
+    line ->> '$.category'           as category,
+    line ->> '$.authors'            as authors,
+    line ->> '$.date'               as date
+  from lines_read('News_Category_Dataset_v3.json');
+"
 ```
 
+3. Locally generate vector embeddings for each of articles (can take a while)
+
+```
+python3 add_embeddings.py headlines.db
 ```
-docker build -t x .
 
-docker run -p 8001:8001 -v $PWD/test.db:/mnt/test.db --rm -it  x
+4. Create vss_articles virtual table
 
-docker run -p 8001:8001 --rm -it \
-  -v $PWD/build.py:/build.py \
-  -v $PWD/test.db:/test.db \
-  -v $PWD/News_Category_Dataset_v3.json:/News_Category_Dataset_v3.json \
-  x python3 /build.py /test.db /News_Category_Dataset_v3.json 
 ```
+deno run --unstable -A query.ts "
+create virtual table vss_articles using vss0(
+  headline_embedding(384),
+  description_embedding(384),
+);
+"
+```
+
+5. Copy embeddings for all rows from articles to vss_articles
+
+```
+insert into vss_articles(rowid, headline_embedding, description_embedding)
+  select 
+    rowid,
+    headline_embedding,
+    description_embedding
+  from articles;
+```
+
+### 3. Perform similarity Search
 
+1. Given specific row id
 
+```
+row_id=123
 
-datasette test.db  -p 8001 -h 0.0.0.0 \
-  --plugins-dir=plugins \
-  --load-extension ./vss0 \
-  --load-extension ./vector0
+deno run --unstable -A /Users/gur/Documents/jan5/Jan05-2039.ts "
+select
+  rowid,
+  distance
+from vss_articles
+where vss_search(
+  headline_embedding,
+  (
+    select headline_embedding
+    from articles
+    where rowid = $row_id
+  )
+)
+limit 20;
+"
+```
 
+2. Given arbitrary search string
 
 ```
+search_term="first"
 
-```
+deno run --unstable -A query.ts "
+with similar_matches as (
+  select rowid
+  from vss_articles
+  where vss_search(
+    headline_embedding,
+    vss_search_params(?, 5)
+  )
+), final as (
+  select
+    articles.headline, articles.link
+  from similar_matches
+  left join articles on articles.rowid = similar_matches.rowid
+)
+select * from final;
+" "$(python3 generate_embedding.py $search_term)"
+```