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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.
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@@ -10,7 +10,7 @@ An embedding function that automatically creates vector representation for a giv
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## Extended by
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- [`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)
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- [`TextEmbeddingFunction`](TextEmbeddingFunction.md)
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## Type Parameters
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@@ -22,7 +22,9 @@ An embedding function that automatically creates vector representation for a giv
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### new EmbeddingFunction()
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> **new EmbeddingFunction**<`T`, `M`>(): [`EmbeddingFunction`](EmbeddingFunction.md)<`T`, `M`>
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```ts
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new EmbeddingFunction<T, M>(): EmbeddingFunction<T, M>
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```
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#### Returns
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@@ -32,13 +34,15 @@ An embedding function that automatically creates vector representation for a giv
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### computeQueryEmbeddings()
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> **computeQueryEmbeddings**(`data`): `Promise`<`number`[] \| `Float32Array` \| `Float64Array`>
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```ts
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computeQueryEmbeddings(data): Promise<number[] | Float32Array | Float64Array>
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```
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Compute the embeddings for a single query
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#### Parameters
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• **data**: `T`
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* **data**: `T`
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#### Returns
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@@ -48,13 +52,15 @@ Compute the embeddings for a single query
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### computeSourceEmbeddings()
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> `abstract` **computeSourceEmbeddings**(`data`): `Promise`<`number`[][] \| `Float32Array`[] \| `Float64Array`[]>
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```ts
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abstract computeSourceEmbeddings(data): Promise<number[][] | Float32Array[] | Float64Array[]>
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```
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Creates a vector representation for the given values.
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#### Parameters
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• **data**: `T`[]
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* **data**: `T`[]
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#### Returns
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@@ -64,7 +70,9 @@ Creates a vector representation for the given values.
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### embeddingDataType()
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> `abstract` **embeddingDataType**(): `Float`<`Floats`>
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```ts
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abstract embeddingDataType(): Float<Floats>
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```
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The datatype of the embeddings
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@@ -74,9 +82,23 @@ The datatype of the embeddings
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***
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### init()?
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```ts
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optional init(): Promise<void>
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```
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#### Returns
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`Promise`<`void`>
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***
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### ndims()
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> **ndims**(): `undefined` \| `number`
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```ts
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ndims(): undefined | number
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```
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The number of dimensions of the embeddings
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@@ -88,15 +110,16 @@ The number of dimensions of the embeddings
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### sourceField()
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> **sourceField**(`optionsOrDatatype`): [`DataType`<`Type`, `any`>, `Map`<`string`, [`EmbeddingFunction`](EmbeddingFunction.md)<`any`, `FunctionOptions`>>]
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```ts
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sourceField(optionsOrDatatype): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]
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```
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sourceField is used in combination with `LanceSchema` to provide a declarative data model
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#### Parameters
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• **optionsOrDatatype**: `DataType`<`Type`, `any`> \| `Partial`<`FieldOptions`<`DataType`<`Type`, `any`>>>
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The options for the field or the datatype
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* **optionsOrDatatype**: `DataType`<`Type`, `any`> \| `Partial`<`FieldOptions`<`DataType`<`Type`, `any`>>>
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The options for the field or the datatype
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#### Returns
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@@ -110,7 +133,9 @@ lancedb.LanceSchema
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### toJSON()
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> `abstract` **toJSON**(): `Partial`<`M`>
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```ts
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abstract toJSON(): Partial<M>
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```
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Convert the embedding function to a JSON object
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It is used to serialize the embedding function to the schema
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@@ -145,13 +170,15 @@ class MyEmbeddingFunction extends EmbeddingFunction {
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### vectorField()
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> **vectorField**(`optionsOrDatatype`?): [`DataType`<`Type`, `any`>, `Map`<`string`, [`EmbeddingFunction`](EmbeddingFunction.md)<`any`, `FunctionOptions`>>]
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```ts
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vectorField(optionsOrDatatype?): [DataType<Type, any>, Map<string, EmbeddingFunction<any, FunctionOptions>>]
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```
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vectorField is used in combination with `LanceSchema` to provide a declarative data model
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#### Parameters
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• **optionsOrDatatype?**: `DataType`<`Type`, `any`> \| `Partial`<`FieldOptions`<`DataType`<`Type`, `any`>>>
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* **optionsOrDatatype?**: `DataType`<`Type`, `any`> \| `Partial`<`FieldOptions`<`DataType`<`Type`, `any`>>>
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#### Returns
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