feat: add support for add to async python API (#1037)

In order to add support for `add` we needed to migrate the rust `Table`
trait to a `Table` struct and `TableInternal` trait (similar to the way
the connection is designed).

While doing this we also cleaned up some inconsistencies between the
SDKs:

* Python and Node are garbage collected languages and it can be
difficult to trigger something to be freed. The convention for these
languages is to have some kind of close method. I added a close method
to both the table and connection which will drop the underlying rust
object.
* We made significant improvements to table creation in
https://github.com/lancedb/lancedb/commit/cc5f2136a61234a9cdbe20e19c56459db8ca1051
for the `node` SDK. I copied these changes to the `nodejs` SDK.
* The nodejs tables were using fs to create tmp directories and these
were not getting cleaned up. This is mostly harmless but annoying and so
I changed it up a bit to ensure we cleanup tmp directories.
* ~~countRows in the node SDK was returning `bigint`. I changed it to
return `number`~~ (this actually happened in a previous PR)
* Tables and connections now implement `std::fmt::Display` which is
hooked into python's `__repr__`. Node has no concept of a regular "to
string" function and so I added a `display` method.
* Python method signatures are changing so that optional parameters are
always `Optional[foo] = None` instead of something like `foo = False`.
This is because we want those defaults to be in rust whenever possible
(though we still need to mention the default in documentation).
* I changed the python `AsyncConnection/AsyncTable` classes from
abstract classes with a single implementation to just classes because we
no longer have the remote implementation in python.

Note: this does NOT add the `add` function to the remote table. This PR
was already large enough, and the remote implementation is unique
enough, that I am going to do all the remote stuff at a later date (we
should have the structure in place and correct so there shouldn't be any
refactor concerns)

---------

Co-authored-by: Will Jones <willjones127@gmail.com>

nodejs/lancedb/embedding/embedding_function.ts

@@ -0,0 +1,68 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { type Float } from 'apache-arrow'
+
+/**
+ * An embedding function that automatically creates vector representation for a given column.
+ */
+export interface EmbeddingFunction<T> {
+  /**
+   * The name of the column that will be used as input for the Embedding Function.
+   */
+  sourceColumn: string
+
+  /**
+   * The data type of the embedding
+   *
+   * The embedding function should return `number`.  This will be converted into
+   * an Arrow float array.  By default this will be Float32 but this property can
+   * be used to control the conversion.
+   */
+  embeddingDataType?: Float
+
+  /**
+   * The dimension of the embedding
+   *
+   * This is optional, normally this can be determined by looking at the results of
+   * `embed`.  If this is not specified, and there is an attempt to apply the embedding
+   * to an empty table, then that process will fail.
+   */
+  embeddingDimension?: number
+
+  /**
+   * The name of the column that will contain the embedding
+   *
+   * By default this is "vector"
+   */
+  destColumn?: string
+
+  /**
+   * Should the source column be excluded from the resulting table
+   *
+   * By default the source column is included.  Set this to true and
+   * only the embedding will be stored.
+   */
+  excludeSource?: boolean
+
+  /**
+   * Creates a vector representation for the given values.
+   */
+  embed: (data: T[]) => Promise<number[][]>
+}
+
+export function isEmbeddingFunction<T> (value: any): value is EmbeddingFunction<T> {
+  return typeof value.sourceColumn === 'string' &&
+      typeof value.embed === 'function'
+}

nodejs/lancedb/embedding/openai.ts

@@ -0,0 +1,57 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { type EmbeddingFunction } from './embedding_function'
+import type OpenAI from 'openai'
+
+export class OpenAIEmbeddingFunction implements EmbeddingFunction<string> {
+  private readonly _openai: OpenAI
+  private readonly _modelName: string
+
+  constructor (sourceColumn: string, openAIKey: string, modelName: string = 'text-embedding-ada-002') {
+    /**
+     * @type {import("openai").default}
+     */
+    let Openai
+    try {
+      // eslint-disable-next-line @typescript-eslint/no-var-requires
+      Openai = require('openai')
+    } catch {
+      throw new Error('please install openai@^4.24.1 using npm install openai')
+    }
+
+    this.sourceColumn = sourceColumn
+    const configuration = {
+      apiKey: openAIKey
+    }
+
+    this._openai = new Openai(configuration)
+    this._modelName = modelName
+  }
+
+  async embed (data: string[]): Promise<number[][]> {
+    const response = await this._openai.embeddings.create({
+      model: this._modelName,
+      input: data
+    })
+
+    const embeddings: number[][] = []
+    for (let i = 0; i < response.data.length; i++) {
+      embeddings.push(response.data[i].embedding)
+    }
+    return embeddings
+  }
+
+  sourceColumn: string
+}