docs: update FTS docs for JS SDK (#1634)

Signed-off-by: BubbleCal <bubble-cal@outlook.com>

docs/src/js/classes/Index.md

@@ -68,3 +68,25 @@ currently is also a memory intensive operation.
 #### Returns
 
 [`Index`](Index.md)
+
+### fts()
+
+> `static` **fts**(`options`?): [`Index`](Index.md)
+
+Create a full text search index
+
+This index is used to search for text data.  The index is created by tokenizing the text
+into words and then storing occurrences of these words in a data structure called inverted index
+that allows for fast search.
+
+During a search the query is tokenized and the inverted index is used to find the rows that
+contain the query words.  The rows are then scored based on BM25 and the top scoring rows are
+sorted and returned.
+
+#### Parameters
+
+• **options?**: `Partial`<[`FtsOptions`](../interfaces/FtsOptions.md)>
+
+#### Returns
+
+[`Index`](Index.md)

docs/src/js/classes/Table.md

@@ -501,16 +501,28 @@ Get the schema of the table.
 
 #### search(query)
 
-> `abstract` **search**(`query`): [`VectorQuery`](VectorQuery.md)
+> `abstract` **search**(`query`, `queryType`, `ftsColumns`): [`VectorQuery`](VectorQuery.md)
 
 Create a search query to find the nearest neighbors
-of the given query vector
+of the given query vector, or the documents
+with the highest relevance to the query string.
 
 ##### Parameters
 
 • **query**: `string`
 
-the query. This will be converted to a vector using the table's provided embedding function
+the query. This will be converted to a vector using the table's provided embedding function,
+or the query string for full-text search if `queryType` is "fts".
+
+• **queryType**: `string` =  `"auto"` \| `"fts"`
+
+the type of query to run. If "auto", the query type will be determined based on the query.
+
+• **ftsColumns**: `string[] | str` =  undefined
+
+the columns to search in. If not provided, all indexed columns will be searched.
+
+For now, this can support to search only one column.
 
 ##### Returns