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Add azure embeddings example notebook (#104)
* Remove invalid inheritances from Embedding and Completion - Remove ListableAPIResource inheritance - Remove DeletableAPIResource * Add embeddings example notebook * bump version
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‎examples/azure/embeddings.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Azure embeddings example\n",
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"In this example we'll try to go over all operations for embeddings that can be done using the Azure endpoints. \\\n",
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"This example focuses on finetuning but touches on the majority of operations that are also available using the API. This example is meant to be a quick way of showing simple operations and is not meant as a tutorial."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import openai\n",
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"from openai import cli"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup\n",
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"In the following section the endpoint and key need to be set up of the next sections to work. \\\n",
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"Please go to https://portal.azure.com, find your resource and then under \"Resource Management\" -> \"Keys and Endpoints\" look for the \"Endpoint\" value and one of the Keys. They will act as api_base and api_key in the code below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"openai.api_key = '' # Please add your api key here\n",
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"openai.api_base = '' # Please add your endpoint here\n",
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"\n",
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"openai.api_type = 'azure'\n",
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"openai.api_version = '2022-03-01-preview' # this may change in the future"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Deployments\n",
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"In this section we are going to create a deployment using the finetune model that we just adapted and then used the deployment to create a simple completion operation."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Deployments: Create Manually\n",
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"Let's create a deployment using the text-similarity-curie-001 engine. You can create a new deployment by going to your Resource in your portal under \"Resource Management\" -> \"Deployments\"."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### (Optional) Deployments: Create Programatically\n",
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"We can also create a deployment using code:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = \"text-similarity-curie-001\"\n",
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"\n",
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"# Now let's create the deployment\n",
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"print(f'Creating a new deployment with model: {model}')\n",
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"result = openai.Deployment.create(model=model, scale_settings={\"scale_type\":\"manual\", \"capacity\": 1})\n",
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"deployment_id = result[\"id\"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### (Optional) Deployments: Retrieving\n",
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"Now let's check the status of the newly created deployment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(f'Checking for deployment status.')\n",
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"resp = openai.Deployment.retrieve(id=deployment_id)\n",
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"status = resp[\"status\"]\n",
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"print(f'Deployment {deployment_id} is with status: {status}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Deployments: Listing\n",
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"Now because creating a new deployment takes a long time, let's look in the subscription for an already finished deployment that succeeded."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print('While deployment running, selecting a completed one.')\n",
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"deployment_id = None\n",
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"result = openai.Deployment.list()\n",
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"for deployment in result.data:\n",
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" if deployment[\"status\"] == \"succeeded\":\n",
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" deployment_id = deployment[\"id\"]\n",
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" break\n",
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"\n",
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"if not deployment_id:\n",
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" print('No deployment with status: succeeded found.')\n",
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"else:\n",
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" print(f'Found a successful deployment with id: {deployment_id}.')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Embeddings\n",
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"Now let's send a sample embedding to the deployment."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = openai.Embedding.create(engine=deployment_id,\n",
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" input=\"The food was delicious and the waiter...\")\n",
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" \n",
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"print(embeddings)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### (Optional) Deployments: Delete\n",
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"Finally let's delete the deployment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(f'Deleting deployment: {deployment_id}')\n",
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"openai.Deployment.delete(sid=deployment_id)"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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},
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎openai/api_resources/completion.py

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from openai.error import InvalidRequestError, TryAgain
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class Completion(EngineAPIResource, ListableAPIResource, DeletableAPIResource):
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class Completion(EngineAPIResource):
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engine_required = False
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OBJECT_NAME = "completions"
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‎openai/api_resources/embedding.py

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from openai.error import InvalidRequestError, TryAgain
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class Embedding(EngineAPIResource, ListableAPIResource, DeletableAPIResource):
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class Embedding(EngineAPIResource):
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engine_required = False
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OBJECT_NAME = "embeddings"
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