exercises/quickstart.ipynb

{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart\n",
    "\n",
    "This notebook is just to make sure that everything works. Firstly, create a file called `.env` in this folder, and add the following content, obviously with your values. There are two ways to authenticate: using a token or using the Azure CLI (AAD authentication). The Azure CLI is the recommended way.\n",
    "\n",
    "```\n",
    "OPENAI_API_KEY=xxxxxx\n",
    "OPENAI_API_BASE=https://xxxxxxx.openai.azure.com/\n",
    "```\n",
    "\n",
    "When authenticating using Azure CLI + Azure AD, open a terminal and run the following command: `az login --use-device-code`. This will open a browser window where you can authenticate. After authentication, use Option 1 code to run and you are good to go.\n",
    "\n",
    "*(You could as well run the following command to get the token: `az account get-access-token --resource https://management.core.windows.net/ --query accessToken -o tsv`. Copy the token and add it to the `.env` file and then use the token to authenticate.)*\n",
    "\n",
    "> **🔔 Please note that in all following notebooks you will need to set the authentication method accordingly!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tiktoken\n",
    "import openai\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dotenv import load_dotenv\n",
    "from azure.identity import DefaultAzureCredential\n",
    "from openai.embeddings_utils import cosine_similarity\n",
    "from tenacity import retry, wait_random_exponential, stop_after_attempt\n",
    "\n",
    "# Load environment variables\n",
    "load_dotenv()\n",
    "\n",
    "# Option 1 - Use Azure AD authentication with az cli (use az login in terminal)\n",
    "default_credential = DefaultAzureCredential()\n",
    "token = default_credential.get_token(\"https://cognitiveservices.azure.com/.default\")\n",
    "openai.api_type = \"azure_ad\"\n",
    "openai.api_base = os.environ.get(\"OPENAI_API_BASE\")\n",
    "openai.api_key = token.token\n",
    "openai.api_version = \"2022-12-01\"\n",
    "\n",
    "# Option 2 - Using Access Key\n",
    "# openai.api_type = \"azure\"\n",
    "# openai.api_base = os.environ.get(\"OPENAI_API_BASE\")\n",
    "# openai.api_key = os.environ.get(\"OPENAI_API_KEY\")\n",
    "# openai.api_version = \"2022-12-01\"\n",
    "\n",
    "# Define embedding model and encoding\n",
    "EMBEDDING_MODEL = 'text-embedding-ada-002'\n",
    "COMPLETION_MODEL = 'text-davinci-003'\n",
    "encoding = tiktoken.get_encoding('cl100k_base')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if completitions work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Who's there?\n",
      "\n",
      "Boo.\n",
      "\n",
      "Boo who?\n",
      "\n"
     ]
    }
   ],
   "source": [
    "response = openai.Completion.create(engine=\"text-davinci-003\",\n",
    "                                    prompt=\"Knock knock.\",\n",
    "                                    temperature=0)\n",
    "print(response.choices[0].text)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do it in a streaming fashion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "1. Cool off & indulge: Our Ice Cream experience\n",
      "2. Get your Scoop on!\n",
      "3. Welcome to the Coolest Place in Town!\n",
      "4. Deliciously creamy...Just What You Need!\n",
      "5. The Perfect Sweet Treat!\n",
      "6. A Cold Taste of Heaven!\n",
      "7. A Las Vegas of Deliciousness!\n",
      "8. Get Cone Crazy!\n",
      "9. Refreshingly Irresistible!\n",
      "10. Make Every Day a Sundae!"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "for resp in openai.Completion.create(engine='text-davinci-003', prompt='Give me 10 taglines for an ice cream shop', max_tokens=512, stream=True):\n",
    "    sys.stdout.write(resp.choices[0].text)\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if embeddings work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "e = openai.Embedding.create(input=\"Hello World!\", engine=EMBEDDING_MODEL)[\"data\"][0][\"embedding\"]\n",
    "print(e)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if tokenizer works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9906, 1917, 0]\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "tokens = encoding.encode(\"Hello world!\")\n",
    "print(tokens)\n",
    "print(len(tokens))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also let's check if LangChain works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Q: What did the baby corn say to the mama corn?\n",
      "A: Where's my popcorn?\n",
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     ]
    }
   ],
   "source": [
    "import os\n",
    "import openai\n",
    "from dotenv import load_dotenv\n",
    "from langchain.llms import AzureOpenAI\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "\n",
    "# Load environment variables\n",
    "load_dotenv()\n",
    "\n",
    "# Configure Azure OpenAI Service API\n",
    "openai.api_type = \"azure\"\n",
    "openai.api_version = \"2022-12-01\"\n",
    "openai.api_base = os.getenv('OPENAI_API_BASE')\n",
    "openai.api_key = os.getenv(\"OPENAI_API_KEY\")\n",
    "\n",
    "# Create a completion\n",
    "llm = AzureOpenAI(deployment_name=\"text-davinci-003\")\n",
    "joke = llm(\"Tell me a dad joke\")\n",
    "print(joke)\n",
    "\n",
    "# Create embeddings\n",
    "embeddings = OpenAIEmbeddings(model=\"text-embedding-ada-002\", chunk_size=1)\n",
    "text = \"This is a test document.\"\n",
    "\n",
    "# Embed a single document\n",
    "query_result = embeddings.embed_query(text)\n",
    "print(query_result)\n",
    "\n",
    "# Embed multiple documents at once\n",
    "doc_result = embeddings.embed_documents([text, text])"
   ]
  }
 ],
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