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chore(nodejs): update docs on "table.ts" (#1255)
closes https://github.com/lancedb/lancedb/issues/1007
This commit is contained in:
@@ -169,17 +169,20 @@ export class Table {
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* // If the column has a vector (fixed size list) data type then
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* // an IvfPq vector index will be created.
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* const table = await conn.openTable("my_table");
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* await table.createIndex(["vector"]);
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* await table.createIndex("vector");
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* @example
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* // For advanced control over vector index creation you can specify
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* // the index type and options.
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* const table = await conn.openTable("my_table");
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* await table.createIndex(["vector"], I)
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* .ivf_pq({ num_partitions: 128, num_sub_vectors: 16 })
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* .build();
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* await table.createIndex("vector", {
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* config: lancedb.Index.ivfPq({
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* numPartitions: 128,
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* numSubVectors: 16,
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* }),
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* });
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* @example
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* // Or create a Scalar index
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* await table.createIndex("my_float_col").build();
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* await table.createIndex("my_float_col");
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*/
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async createIndex(column: string, options?: Partial<IndexOptions>) {
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// Bit of a hack to get around the fact that TS has no package-scope.
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@@ -197,8 +200,7 @@ export class Table {
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* vector similarity, sorting, and more.
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*
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* Note: By default, all columns are returned. For best performance, you should
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* only fetch the columns you need. See [`Query::select_with_projection`] for
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* more details.
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* only fetch the columns you need.
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*
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* When appropriate, various indices and statistics based pruning will be used to
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* accelerate the query.
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@@ -206,10 +208,13 @@ export class Table {
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* // SQL-style filtering
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* //
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* // This query will return up to 1000 rows whose value in the `id` column
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* // is greater than 5. LanceDb supports a broad set of filtering functions.
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* for await (const batch of table.query()
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* .filter("id > 1").select(["id"]).limit(20)) {
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* console.log(batch);
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* // is greater than 5. LanceDb supports a broad set of filtering functions.
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* for await (const batch of table
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* .query()
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* .where("id > 1")
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* .select(["id"])
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* .limit(20)) {
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* console.log(batch);
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* }
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* @example
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* // Vector Similarity Search
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@@ -218,13 +223,14 @@ export class Table {
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* // closest to the query vector [1.0, 2.0, 3.0]. If an index has been created
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* // on the "vector" column then this will perform an ANN search.
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* //
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* // The `refine_factor` and `nprobes` methods are used to control the recall /
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* // The `refineFactor` and `nprobes` methods are used to control the recall /
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* // latency tradeoff of the search.
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* for await (const batch of table.query()
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* .nearestTo([1, 2, 3])
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* .refineFactor(5).nprobe(10)
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* .limit(10)) {
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* console.log(batch);
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* for await (const batch of table
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* .query()
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* .where("id > 1")
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* .select(["id"])
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* .limit(20)) {
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* console.log(batch);
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* }
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* @example
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* // Scan the full dataset
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@@ -286,43 +292,45 @@ export class Table {
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await this.inner.dropColumns(columnNames);
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}
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/**
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* Retrieve the version of the table
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*
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* LanceDb supports versioning. Every operation that modifies the table increases
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* version. As long as a version hasn't been deleted you can `[Self::checkout]` that
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* version to view the data at that point. In addition, you can `[Self::restore]` the
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* version to replace the current table with a previous version.
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*/
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/** Retrieve the version of the table */
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async version(): Promise<number> {
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return await this.inner.version();
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}
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/**
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* Checks out a specific version of the Table
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* Checks out a specific version of the table _This is an in-place operation._
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*
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* Any read operation on the table will now access the data at the checked out version.
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* As a consequence, calling this method will disable any read consistency interval
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* that was previously set.
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* This allows viewing previous versions of the table. If you wish to
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* keep writing to the dataset starting from an old version, then use
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* the `restore` function.
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*
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* This is a read-only operation that turns the table into a sort of "view"
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* or "detached head". Other table instances will not be affected. To make the change
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* permanent you can use the `[Self::restore]` method.
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* Calling this method will set the table into time-travel mode. If you
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* wish to return to standard mode, call `checkoutLatest`.
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* @param {number} version The version to checkout
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* @example
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* ```typescript
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* import * as lancedb from "@lancedb/lancedb"
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* const db = await lancedb.connect("./.lancedb");
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* const table = await db.createTable("my_table", [
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* { vector: [1.1, 0.9], type: "vector" },
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* ]);
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*
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* Any operation that modifies the table will fail while the table is in a checked
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* out state.
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*
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* To return the table to a normal state use `[Self::checkout_latest]`
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* console.log(await table.version()); // 1
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* console.log(table.display());
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* await table.add([{ vector: [0.5, 0.2], type: "vector" }]);
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* await table.checkout(1);
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* console.log(await table.version()); // 2
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* ```
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*/
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async checkout(version: number): Promise<void> {
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await this.inner.checkout(version);
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}
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/**
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* Ensures the table is pointing at the latest version
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* Checkout the latest version of the table. _This is an in-place operation._
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*
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* This can be used to manually update a table when the read_consistency_interval is None
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* It can also be used to undo a `[Self::checkout]` operation
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* The table will be set back into standard mode, and will track the latest
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* version of the table.
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*/
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async checkoutLatest(): Promise<void> {
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await this.inner.checkoutLatest();
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@@ -344,9 +352,7 @@ export class Table {
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await this.inner.restore();
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}
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/**
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* List all indices that have been created with Self::create_index
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*/
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/** List all indices that have been created with {@link Table.createIndex} */
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async listIndices(): Promise<IndexConfig[]> {
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return await this.inner.listIndices();
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}
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