Simple example just for completions with davinci
Christian Muertz
2 years ago
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83f359aa95
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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 completions example\n",
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"In this example we'll try to go over all operations needed to get completions working using the Azure endpoints. \\\n",
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"This example focuses on completions but also touches on the some other 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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"For the following sections to work properly we first have to setup some things. Let's start with the `api_type`, `api_base` and `api_version`. To find your `api_base` go to https://portal.azure.com, find your resource and then under \"Resource Management\" -> \"Keys and Endpoints\" look for the \"Endpoint\" value."
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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_type = 'azure'\n",
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"openai.api_version = '2022-12-01'\n",
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"\n",
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"openai.api_base = '' # Please add your endpoint here"
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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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"We next have to setup the `api_key`. We can either get the key from the portal or we can get it through Microsoft Active Directory Authentication."
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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: Portal\n",
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"Let's first look at getting the key from the portal. Go to https://portal.azure.com, find your resource and then under \"Resource Management\" -> \"Keys and Endpoints\" look for one of the \"Keys\" values."
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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"
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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) Setup: Microsoft Active Directory Authentication\n",
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"Let's now see how we can get a key via Microsoft Active Directory Authentication."
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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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"\"\"\"\n",
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"from azure.identity import DefaultAzureCredential\n",
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"\n",
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"default_credential = DefaultAzureCredential()\n",
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"token = default_credential.get_token(\"https://cognitiveservices.azure.com\")\n",
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"\n",
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"openai.api_key = token.token\n",
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"\"\"\""
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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 `text-davinci-002` model that we can use to create completions."
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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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"Create a new deployment by going to your Resource in your portal under \"Resource Management\" -> \"Model deployments\". Select `text-davinci-002` as the model."
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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-davinci-002\"\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\":\"standard\"})\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: Wait for deployment to succeed\n",
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"Now let's check the status of the newly created deployment and wait till it is 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(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} has status: {status}')\n",
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"while status not in [\"succeeded\", \"failed\"]:\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} has 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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"### Completions\n",
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"Now let's send a sample completion 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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"prompt = \"The food was delicious and the waiter\"\n",
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"completion = openai.Completion.create(deployment_id=deployment_id,\n",
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" prompt=prompt, stop=\".\", temperature=0)\n",
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" \n",
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"print(f\"{prompt}{completion['choices'][0]['text']}.\")"
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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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"kernelspec": {
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"display_name": "Python 3",
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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": "3.10.8"
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},
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"vscode": {
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"interpreter": {
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"hash": "3a5103089ab7e7c666b279eeded403fcec76de49a40685dbdfe9f9c78ad97c17"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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