js docs, modal example, doc notebook integration, update doc styles (#131)

docs/src/basic.md

@@ -1,74 +1,142 @@
 # Basic LanceDB Functionality
 
+We'll cover the basics of using LanceDB on your local machine in this section.
+
+??? info "LanceDB runs embedded on your backend application, so there is no need to run a separate server."
+
+      <img src="../assets/lancedb_embedded_explanation.png" width="650px" />
+
+## Installation
+
+=== "Python"
+      ```shell
+      pip install lancedb
+      ```
+
+=== "Javascript"
+      ```shell
+      npm install vectordb
+      ```
+
 ## How to connect to a database
 
-In local mode, LanceDB stores data in a directory on your local machine. To connect to a local database, you can use the following code:
-```python
-import lancedb
-uri = "~/.lancedb"
-db = lancedb.connect(uri)
-```
+=== "Python"
+      ```python
+      import lancedb
+      uri = "~/.lancedb"
+      db = lancedb.connect(uri)
+      ```
 
-LanceDB will create the directory if it doesn't exist (including parent directories).
+      LanceDB will create the directory if it doesn't exist (including parent directories).
 
-If you need a reminder of the uri, use the `db.uri` property.
+      If you need a reminder of the uri, use the `db.uri` property.
+
+=== "Javascript"
+      ```javascript
+      const lancedb = require("vectordb");
+
+      const uri = "~./lancedb";
+      const db = await lancedb.connect(uri);
+      ```
+      
+      LanceDB will create the directory if it doesn't exist (including parent directories).
+
+      If you need a reminder of the uri, you can call `db.uri()`.
 
 ## How to create a table
 
-To create a table, you can use the following code:
-```python
-tbl = db.create_table("my_table",
-                      data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-                            {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
-```
+=== "Python"
+      ```python
+      tbl = db.create_table("my_table",
+                        data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                              {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      ```
 
-Under the hood, LanceDB is converting the input data into an Apache Arrow table
-and persisting it to disk in [Lance format](github.com/eto-ai/lance).
+      If the table already exists, LanceDB will raise an error by default.
+      If you want to overwrite the table, you can pass in `mode="overwrite"`
+      to the `create_table` method.
 
-If the table already exists, LanceDB will raise an error by default.
-If you want to overwrite the table, you can pass in `mode="overwrite"`
-to the `create_table` method.
+      You can also pass in a pandas DataFrame directly:
+      ```python
+      import pandas as pd
+      df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      tbl = db.create_table("table_from_df", data=df)
+      ```
 
-You can also pass in a pandas DataFrame directly:
-```python
-import pandas as pd
-df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-                   {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
-tbl = db.create_table("table_from_df", data=df)
-```
+=== "Javascript"
+      ```javascript
+      const tb = await db.createTable("my_table",
+                        data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                              {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      ```
+      
+!!! warning
+
+      If the table already exists, LanceDB will raise an error by default.
+      If you want to overwrite the table, you can pass in `mode="overwrite"`
+      to the `createTable` function.
+
+??? info "Under the hood, LanceDB is converting the input data into an Apache Arrow table and persisting it to disk in [Lance format](https://www.github.com/lancedb/lance)."
 
 ## How to open an existing table
 
 Once created, you can open a table using the following code:
-```python
-tbl = db.open_table("my_table")
-```
 
-If you forget the name of your table, you can always get a listing of all table names:
+=== "Python"
+      ```python
+      tbl = db.open_table("my_table")
+      ```
 
-```python
-db.table_names()
-```
+      If you forget the name of your table, you can always get a listing of all table names:
+
+      ```python
+      print(db.table_names())
+      ```
+
+=== "Javascript"
+      ```javascript
+      const tbl = await db.openTable("my_table");
+      ```
+
+      If you forget the name of your table, you can always get a listing of all table names:
+
+      ```javascript
+      console.log(db.tableNames());
+      ```
 
 ## How to add data to a table
 
 After a table has been created, you can always add more data to it using
 
-```python
-df = pd.DataFrame([{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
-                   {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}])
-tbl.add(df)
-```
+=== "Python"
+      ```python
+      df = pd.DataFrame([{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
+                        {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}])
+      tbl.add(df)
+      ```
+
+=== "Javascript"
+      ```javascript
+      await tbl.add([vector: [1.3, 1.4], item: "fizz", price: 100.0},
+              {vector: [9.5, 56.2], item: "buzz", price: 200.0}])
+      ```
 
 ## How to search for (approximate) nearest neighbors
 
 Once you've embedded the query, you can find its nearest neighbors using the following code:
 
-```python
-tbl.search([100, 100]).limit(2).to_df()
-```
+=== "Python"
+      ```python
+      tbl.search([100, 100]).limit(2).to_df()
+      ```
 
-This returns a pandas DataFrame with the results.
+      This returns a pandas DataFrame with the results.
+
+=== "Javascript"
+      ```javascript
+      const query = await tbl.search([100, 100]).limit(2).execute();
+      ```
 
 ## What's next