doc: use code snippet for typescript examples (#880)

The typescript code is in a fully function file, that will be run via the CI.

docs/src/ann_indexes.md

@@ -56,7 +56,7 @@ Lance supports `IVF_PQ` index type by default.
          data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
      }
      const table = await db.createTable('my_vectors', data)
-     await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 256, num_sub_vectors: 96 })
+     await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 16, num_sub_vectors: 48 })
      ```
 
 - **metric** (default: "L2"): The distance metric to use. By default it uses euclidean distance "`L2`".
@@ -221,4 +221,4 @@ On `SIFT-1M` dataset, our benchmark shows that keeping each partition 1K-4K rows
 `num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. Because
 PQ is a lossy compression of the original vector, a higher `num_sub_vectors` usually results in
 less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and
-more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.
+more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.

docs/src/basic.md

@@ -54,22 +54,18 @@
 
 === "Javascript"
 
-      ```javascript
-      const lancedb = require("vectordb");
+    ```typescript
+    --8<-- "src/basic_legacy.ts:import"
 
-      const uri = "data/sample-lancedb";
-      const db = await lancedb.connect(uri);
-      ```
+    --8<-- "src/basic_legacy.ts:open_db"
+    ```
 
 === "Rust"
 
     ```rust
-    use vectordb::connect;
-
     #[tokio::main]
     async fn main() -> Result<()> {
-        let uri = "data/sample-lancedb";
-        let db = connect(uri).await?;
+        --8<-- "src/basic.rs:connect"
     }
     ```
 
@@ -105,11 +101,7 @@ If you need a reminder of the uri, you can call `db.uri()`.
 === "Javascript"
 
     ```javascript
-    const tbl = await db.createTable(
-        "myTable",
-        [{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-         {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}]
-    )
+    --8<-- "src/basic_legacy.ts:create_table"
     ```
 
     If the table already exists, LanceDB will raise an error by default.
@@ -160,10 +152,7 @@ In this case, you can create an empty table and specify the schema.
 === "Javascript"
 
     ```typescript
-    import { Schema, Field, FixedSizeList, DataType } from "apache-arrow";
-
-    schema = new Schema([new new Field("vec", new FixedSizeList(2, new Field("item", new Float32())))])
-    tbl = await db.createTable({ name: "empty_table", schema: schema });
+    --8<-- "src/basic_legacy.ts:create_empty_table"
     ```
 
 === "Rust"
@@ -269,7 +258,7 @@ Once you've embedded the query, you can find its nearest neighbors using the fol
 === "Javascript"
 
     ```javascript
-    const query = await tbl.search([100, 100]).limit(2).execute();
+    --8<-- "src/basic_legacy.ts:search"
     ```
 
 === "Rust"
@@ -291,13 +280,13 @@ For tables with more than 50K vectors, creating an ANN index is recommended to s
 
 === "Python"
 
-    ```python
+    ```py
     tbl.create_index()
     ```
 
 === "Javascript"
 
-    ```javascript
+    ```{.typescript .ignore}
     await tbl.createIndex({})
     ```
 
@@ -324,7 +313,7 @@ This can delete any number of rows that match the filter.
 === "Javascript"
 
     ```javascript
-    await tbl.delete('item = "fizz"')
+    --8<-- "src/basic_legacy.ts:delete"
     ```
 
 === "Rust"
@@ -362,7 +351,7 @@ Use the `drop_table()` method on the database to remove a table.
 === "JavaScript"
 
       ```javascript
-      await db.dropTable('myTable')
+      --8<-- "src/basic_legacy.ts:drop_table"
       ```
 
       This permanently removes the table and is not recoverable, unlike deleting rows.

docs/src/basic.rs

@@ -0,0 +1 @@
+../../rust/vectordb/examples/simple.rs

docs/src/basic_legacy.ts

@@ -0,0 +1,53 @@
+// --8<-- [start:import]
+import * as lancedb from "vectordb";
+import { Schema, Field, Float32, FixedSizeList, Int32 } from "apache-arrow";
+// --8<-- [end:import]
+import * as fs from "fs";
+import { Table as ArrowTable, Utf8 } from "apache-arrow";
+
+const example = async () => {
+  fs.rmSync("data/sample-lancedb", { recursive: true, force: true });
+  // --8<-- [start:open_db]
+  const uri = "data/sample-lancedb";
+  const db = await lancedb.connect(uri);
+  // --8<-- [end:open_db]
+
+  // --8<-- [start:create_table]
+  const tbl = await db.createTable(
+    "myTable",
+    [
+      { vector: [3.1, 4.1], item: "foo", price: 10.0 },
+      { vector: [5.9, 26.5], item: "bar", price: 20.0 },
+    ],
+    { writeMode: lancedb.WriteMode.Overwrite }
+  );
+  // --8<-- [end:create_table]
+
+  // --8<-- [start:create_empty_table]
+  const schema = new Schema([
+    new Field("id", new Int32()),
+    new Field("name", new Utf8()),
+  ]);
+  const empty_tbl = await db.createTable({ name: "empty_table", schema });
+  // --8<-- [end:create_empty_table]
+
+  // --8<-- [start:search]
+  const query = await tbl.search([100, 100]).limit(2).execute();
+  // --8<-- [end:search]
+  console.log(query);
+
+  // --8<-- [start:delete]
+  await tbl.delete('item = "fizz"');
+  // --8<-- [end:delete]
+
+  // --8<-- [start:drop_table]
+  await db.dropTable("myTable");
+  // --8<-- [end:drop_table]
+};
+
+async function main() {
+  await example();
+  console.log("Basic example: done");
+}
+
+main();