Ted/move examples to openai cookbook (#112)

* removes examples (which can now be found in the OpenAI Cookbook repo)

* Update numpy requirements due to vuln in 1.21.6

* adds api_type to ErrorObject

* formats by Black

Author: ted-at-openai

README.md

@@ -54,7 +54,7 @@ print(completion.choices[0].text)
 
 ### Microsoft Azure Endpoints
 
-In order to use the library with Microsoft Azure endpoints, you need to set the api_type, api_base and api_version in addition to the api_key. The api_type must be set to 'azure' and the others correspond to the properites of your endpoint.
+In order to use the library with Microsoft Azure endpoints, you need to set the api_type, api_base and api_version in addition to the api_key. The api_type must be set to 'azure' and the others correspond to the properties of your endpoint.
 In addition, the deployment name must be passed as the engine parameter.
 
 ```python
@@ -78,8 +78,9 @@ print(search)
 ```
 
 Please note that for the moment, the Microsoft Azure endpoints can only be used for completion, search and fine-tuning operations.
-For a detailed example on how to use fine-tuning and other operations using Azure endpoints, please check out the following Jupyter notebook:
-[Using Azure fine-tuning](https://github.com/openai/openai-python/blob/main/examples/azure/finetuning.ipynb)
+For a detailed example on how to use fine-tuning and other operations using Azure endpoints, please check out the following Jupyter notebooks:
+* [Using Azure fine-tuning](https://github.com/openai/openai-cookbook/tree/main/examples/azure/finetuning.ipynb)
+* [Using Azure embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/azure/embeddings.ipynb)
 
 ### Microsoft Azure Active Directory Authentication
 
@@ -118,7 +119,18 @@ openai api completions.create -e ada -p "Hello world"
 
 ## Example code
 
-Examples of how to use [embeddings](https://github.com/openai/openai-python/tree/main/examples/embeddings), [fine tuning](https://github.com/openai/openai-python/tree/main/examples/finetuning), [semantic search](https://github.com/openai/openai-python/tree/main/examples/semanticsearch), and [codex](https://github.com/openai/openai-python/tree/main/examples/codex) can be found in the [examples folder](https://github.com/openai/openai-python/tree/main/examples).
+Examples of how to use this Python library to accomplish various tasks can be found in the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). It contains code examples for:
+
+* Classification using fine-tuning
+* Clustering
+* Code search
+* Customizing embeddings
+* Question answering from a corpus of documents
+* Recommendations
+* Visualization of embeddings
+* And more
+
+Prior to July 2022, this OpenAI Python library hosted code examples in its examples folder, but since then all examples have been migrated to the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/).
 
 ### Embeddings
 
@@ -140,17 +152,17 @@ model_id = "text-similarity-davinci-001"
 embedding = openai.Embedding.create(input=text_string, engine=model_id)['data'][0]['embedding']
 ```
 
-An example of how to call the embeddings method is shown in the [get embeddings notebook](https://github.com/openai/openai-python/blob/main/examples/embeddings/Get_embeddings.ipynb).
+An example of how to call the embeddings method is shown in this [get embeddings notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Get_embeddings.ipynb).
 
 Examples of how to use embeddings are shared in the following Jupyter notebooks:
 
-- [Classification using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Classification.ipynb)
-- [Clustering using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Clustering.ipynb)
-- [Code search using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Code_search.ipynb)
-- [Semantic text search using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Semantic_text_search_using_embeddings.ipynb)
-- [User and product embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/User_and_product_embeddings.ipynb)
-- [Zero-shot classification using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Zero-shot_classification.ipynb)
-- [Recommendation using embeddings](https://github.com/openai/openai-python/blob/main/examples/embeddings/Recommendation.ipynb)
+- [Classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Classification_using_embeddings.ipynb)
+- [Clustering using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Clustering.ipynb)
+- [Code search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Code_search.ipynb)
+- [Semantic text search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Semantic_text_search_using_embeddings.ipynb)
+- [User and product embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb)
+- [Zero-shot classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Zero-shot_classification_with_embeddings.ipynb)
+- [Recommendation using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Recommendation_using_embeddings.ipynb)
 
 For more information on embeddings and the types of embeddings OpenAI offers, read the [embeddings guide](https://beta.openai.com/docs/guides/embeddings) in the OpenAI documentation.
 
@@ -160,11 +172,11 @@ Fine tuning a model on training data can both improve the results (by giving the
 
 Examples of fine tuning are shared in the following Jupyter notebooks:
 
-- [Classification with fine tuning](https://github.com/openai/openai-python/blob/main/examples/finetuning/finetuning-classification.ipynb) (a simple notebook that shows the steps required for fine tuning)
+- [Classification with fine tuning](https://github.com/openai/openai-cookbook/blob/main/examples/Fine-tuned_classification.ipynb) (a simple notebook that shows the steps required for fine tuning)
 - Fine tuning a model that answers questions about the 2020 Olympics
-  - [Step 1: Collecting data](https://github.com/openai/openai-python/blob/main/examples/finetuning/olympics-1-collect-data.ipynb)
-  - [Step 2: Creating a synthetic Q&A dataset](https://github.com/openai/openai-python/blob/main/examples/finetuning/olympics-2-create-qa.ipynb)
-  - [Step 3: Train a fine-tuning model specialized for Q&A](https://github.com/openai/openai-python/blob/main/examples/finetuning/olympics-3-train-qa.ipynb)
+  - [Step 1: Collecting data](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-1-collect-data.ipynb)
+  - [Step 2: Creating a synthetic Q&A dataset](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-2-create-qa.ipynb)
+  - [Step 3: Train a fine-tuning model specialized for Q&A](https://github.com/openai/openai-cookbook/blob/main/examples/fine-tuned_qa/olympics-3-train-qa.ipynb)
 
 Sync your fine-tunes to [Weights & Biases](https://wandb.me/openai-docs) to track experiments, models, and datasets in your central dashboard with:
 

examples/README.md

@@ -0,0 +1,7 @@
+# Examples have moved to the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/)
+
+Looking for code examples? Visit the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/), which shares examples of how to use the OpenAI Python library to accomplish common tasks.
+
+Prior to July 2022, code examples were hosted in this examples folder; going forward, code examples will be hosted in the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/).
+
+This separation will help keep the [OpenAI Python library](https://github.com/openai/openai-python) simple and small, without extra files or dependencies.

examples/azure/embeddings.ipynb

@@ -4,174 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Azure embeddings example\n",
-    "In this example we'll try to go over all operations for embeddings that can be done using the Azure endpoints. \\\n",
-    "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openai\n",
-    "from openai import cli"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "In the following section the endpoint and key need to be set up of the next sections to work. \\\n",
-    "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openai.api_key = ''  # Please add your api key here\n",
-    "openai.api_base = '' # Please add your endpoint here\n",
-    "\n",
-    "openai.api_type = 'azure'\n",
-    "openai.api_version = '2022-03-01-preview' # this may change in the future"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deployments\n",
-    "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."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Deployments: Create Manually\n",
-    "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\"."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### (Optional) Deployments: Create Programatically\n",
-    "We can also create a deployment using code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = \"text-similarity-curie-001\"\n",
-    "\n",
-    "# Now let's create the deployment\n",
-    "print(f'Creating a new deployment with model: {model}')\n",
-    "result = openai.Deployment.create(model=model, scale_settings={\"scale_type\":\"manual\", \"capacity\": 1})\n",
-    "deployment_id = result[\"id\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### (Optional) Deployments: Retrieving\n",
-    "Now let's check the status of the newly created deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'Checking for deployment status.')\n",
-    "resp = openai.Deployment.retrieve(id=deployment_id)\n",
-    "status = resp[\"status\"]\n",
-    "print(f'Deployment {deployment_id} is with status: {status}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Deployments: Listing\n",
-    "Now because creating a new deployment takes a long time, let's look in the subscription for an already finished deployment that succeeded."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('While deployment running, selecting a completed one.')\n",
-    "deployment_id = None\n",
-    "result = openai.Deployment.list()\n",
-    "for deployment in result.data:\n",
-    "    if deployment[\"status\"] == \"succeeded\":\n",
-    "        deployment_id = deployment[\"id\"]\n",
-    "        break\n",
-    "\n",
-    "if not deployment_id:\n",
-    "    print('No deployment with status: succeeded found.')\n",
-    "else:\n",
-    "    print(f'Found a successful deployment with id: {deployment_id}.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Embeddings\n",
-    "Now let's send a sample embedding to the deployment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings = openai.Embedding.create(deployment_id=deployment_id,\n",
-    "                                     input=\"The food was delicious and the waiter...\")\n",
-    "                                \n",
-    "print(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### (Optional) Deployments: Delete\n",
-    "Finally let's delete the deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'Deleting deployment: {deployment_id}')\n",
-    "openai.Deployment.delete(sid=deployment_id)"
+    "This code example has moved. You can now find it in the [OpenAI Cookbook](https://github.com/openai/openai-cookbook) at [examples/azure/embeddings.ipynb](https://github.com/openai/openai-cookbook/tree/main/examples/azure/embeddings.ipynb)."
    ]
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-  },
   "kernelspec": {
-   "display_name": "Python 3.8.10 64-bit",
+   "display_name": "Python 3.9.9 ('openai')",
    "language": "python",
    "name": "python3"
   },
@@ -185,9 +24,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.9"
   },
-  "orig_nbformat": 4
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
+   }
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2