docs: better formatting for Node API docs (#1892)

* Sets `"useCodeBlocks": true` * Adds a post-processing script `nodejs/typedoc_post_process.js` that puts the parameter description on the same line as the parameter name, like it is in our Python docs. This makes the text hierarchy clearer in those sections and also makes the sections shorter.
2026-01-14 07:42:58 +00:00 · 2024-12-09 17:04:09 -08:00
parent a43193c99b
commit db125013fc
45 changed files with 1538 additions and 488 deletions
--- a/docs/src/js/namespaces/embedding/classes/TextEmbeddingFunction.md
+++ b/docs/src/js/namespaces/embedding/classes/TextEmbeddingFunction.md
@@ -0,0 +1,236 @@
+[**@lancedb/lancedb**](../../../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / TextEmbeddingFunction
+
+# Class: `abstract` TextEmbeddingFunction&lt;M&gt;
+
+an abstract class for implementing embedding functions that take text as input
+
+## Extends
+
+- [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`string`, `M`&gt;
+
+## Type Parameters
+
+• **M** *extends* `FunctionOptions` = `FunctionOptions`
+
+## Constructors
+
+### new TextEmbeddingFunction()
+
+```ts
+new TextEmbeddingFunction<M>(): TextEmbeddingFunction<M>
+```
+
+#### Returns
+
+[`TextEmbeddingFunction`](TextEmbeddingFunction.md)&lt;`M`&gt;
+
+#### Inherited from
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`constructor`](EmbeddingFunction.md#constructors)
+
+## Methods
+
+### computeQueryEmbeddings()
+
+```ts
+computeQueryEmbeddings(data): Promise<number[] | Float32Array | Float64Array>
+```
+
+Compute the embeddings for a single query
+
+#### Parameters
+
+* **data**: `string`
+
+#### Returns
+
+`Promise`&lt;`number`[] \| `Float32Array` \| `Float64Array`&gt;
+
+#### Overrides
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`computeQueryEmbeddings`](EmbeddingFunction.md#computequeryembeddings)
+
+***
+
+### computeSourceEmbeddings()
+
+```ts
+computeSourceEmbeddings(data): Promise<number[][] | Float32Array[] | Float64Array[]>
+```
+
+Creates a vector representation for the given values.
+
+#### Parameters
+
+* **data**: `string`[]
+
+#### Returns
+
+`Promise`&lt;`number`[][] \| `Float32Array`[] \| `Float64Array`[]&gt;
+
+#### Overrides
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`computeSourceEmbeddings`](EmbeddingFunction.md#computesourceembeddings)
+
+***
+
+### embeddingDataType()
+
+```ts
+embeddingDataType(): Float<Floats>
+```
+
+The datatype of the embeddings
+
+#### Returns
+
+`Float`&lt;`Floats`&gt;
+
+#### Overrides
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`embeddingDataType`](EmbeddingFunction.md#embeddingdatatype)
+
+***
+
+### generateEmbeddings()
+
+```ts
+abstract generateEmbeddings(texts, ...args): Promise<number[][] | Float32Array[] | Float64Array[]>
+```
+
+#### Parameters
+
+* **texts**: `string`[]
+
+* ...**args**: `any`[]
+
+#### Returns
+
+`Promise`&lt;`number`[][] \| `Float32Array`[] \| `Float64Array`[]&gt;
+
+***
+
+### init()?
+
+```ts
+optional init(): Promise<void>
+```
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+#### Inherited from
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`init`](EmbeddingFunction.md#init)
+
+***
+
+### ndims()
+
+```ts
+ndims(): undefined | number
+```
+
+The number of dimensions of the embeddings
+
+#### Returns
+
+`undefined` \| `number`
+
+#### Inherited from
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`ndims`](EmbeddingFunction.md#ndims)
+
+***
+
+### sourceField()
+
+```ts
+sourceField(): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]
+```
+
+sourceField is used in combination with `LanceSchema` to provide a declarative data model
+
+#### Returns
+
+[`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
+
+#### See
+
+lancedb.LanceSchema
+
+#### Overrides
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`sourceField`](EmbeddingFunction.md#sourcefield)
+
+***
+
+### toJSON()
+
+```ts
+abstract toJSON(): Partial<M>
+```
+
+Convert the embedding function to a JSON object
+It is used to serialize the embedding function to the schema
+It's important that any object returned by this method contains all the necessary
+information to recreate the embedding function
+
+It should return the same object that was passed to the constructor
+If it does not, the embedding function will not be able to be recreated, or could be recreated incorrectly
+
+#### Returns
+
+`Partial`&lt;`M`&gt;
+
+#### Example
+
+```ts
+class MyEmbeddingFunction extends EmbeddingFunction {
+  constructor(options: {model: string, timeout: number}) {
+    super();
+    this.model = options.model;
+    this.timeout = options.timeout;
+  }
+  toJSON() {
+    return {
+      model: this.model,
+      timeout: this.timeout,
+    };
+}
+```
+
+#### Inherited from
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`toJSON`](EmbeddingFunction.md#tojson)
+
+***
+
+### vectorField()
+
+```ts
+vectorField(optionsOrDatatype?): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]
+```
+
+vectorField is used in combination with `LanceSchema` to provide a declarative data model
+
+#### Parameters
+
+* **optionsOrDatatype?**: `DataType`&lt;`Type`, `any`&gt; \| `Partial`&lt;`FieldOptions`&lt;`DataType`&lt;`Type`, `any`&gt;&gt;&gt;
+
+#### Returns
+
+[`DataType`&lt;`Type`, `any`&gt;, `Map`&lt;`string`, [`EmbeddingFunction`](EmbeddingFunction.md)&lt;`any`, `FunctionOptions`&gt;&gt;]
+
+#### See
+
+lancedb.LanceSchema
+
+#### Inherited from
+
+[`EmbeddingFunction`](EmbeddingFunction.md).[`vectorField`](EmbeddingFunction.md#vectorfield)