python/notebooks/11-streaming-completions.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "68e1c158",
   "metadata": {},
   "source": [
    "# Streaming Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a77bdf89",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m pip install semantic-kernel==0.3.10.dev0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508ad44f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import semantic_kernel as sk\n",
    "from semantic_kernel.connectors.ai import ChatRequestSettings, CompleteRequestSettings\n",
    "from semantic_kernel.connectors.ai.open_ai import AzureTextCompletion, AzureChatCompletion, OpenAITextCompletion, OpenAIChatCompletion\n",
    "from semantic_kernel.connectors.ai.hugging_face import HuggingFaceTextCompletion"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d8ddffc1",
   "metadata": {},
   "source": [
    "First, we will set up the text and chat services we will be submitting prompts to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f8dcbc6",
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   "source": [
    "kernel = sk.Kernel()\n",
    "\n",
    "# Configure Azure LLM service\n",
    "deployment, api_key, endpoint = sk.azure_openai_settings_from_dot_env()\n",
    "azure_text_service = AzureTextCompletion(\"text-davinci-003\", endpoint, api_key)\n",
    "azure_chat_service = AzureChatCompletion(\"gpt-35-turbo\", endpoint, api_key)\n",
    "\n",
    "# Configure OpenAI service\n",
    "api_key, org_id = sk.openai_settings_from_dot_env()\n",
    "oai_text_service = OpenAITextCompletion(\"text-davinci-003\", api_key, org_id)\n",
    "oai_chat_service = OpenAIChatCompletion(\"gpt-3.5-turbo\", api_key, org_id)\n",
    "\n",
    "# Configure Hugging Face service\n",
    "hf_text_service = HuggingFaceTextCompletion(\"distilgpt2\", task=\"text-generation\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "50561d82",
   "metadata": {},
   "source": [
    "Next, we'll set up the completion request settings for text completion services."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628c843e",
   "metadata": {},
   "outputs": [],
   "source": [
    "request_settings = CompleteRequestSettings(\n",
    "    max_tokens=150,\n",
    "    temperature=0.7,\n",
    "    top_p=1,\n",
    "    frequency_penalty=0.5,\n",
    "    presence_penalty=0.5\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "857a9c89",
   "metadata": {},
   "source": [
    "## Streaming Open AI Text Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2979db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"what is the purpose of a rubber duck?\"\n",
    "stream = oai_text_service.complete_stream_async(prompt, request_settings)\n",
    "async for text in stream:\n",
    "    print(text, end = \"\") # end = \"\" to avoid newlines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4288d09f",
   "metadata": {},
   "source": [
    "## Streaming Azure Open AI Text Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5319f14d",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"provide me a list of possible meanings for the acronym 'ORLD'\"\n",
    "stream = azure_text_service.complete_stream_async(prompt, request_settings)\n",
    "async for text in stream:\n",
    "    print(text, end = \"\") # end = \"\" to avoid newlines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eb548f9c",
   "metadata": {},
   "source": [
    "## Streaming Hugging Face Text Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9525e4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"The purpose of a rubber duck is\"\n",
    "stream = hf_text_service.complete_stream_async(prompt, request_settings)\n",
    "async for text in stream:\n",
    "    print(text, end = \"\") # end = \"\" to avoid newlines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "da632e12",
   "metadata": {},
   "source": [
    "Here, we're setting up the settings for Chat completions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5f11e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "chat_request_settings = ChatRequestSettings(\n",
    "    max_tokens=150,\n",
    "    temperature=0.7,\n",
    "    top_p=1,\n",
    "    frequency_penalty=0.5,\n",
    "    presence_penalty=0.5,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d6bf238e",
   "metadata": {},
   "source": [
    "## Streaming OpenAI Chat Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dabc6a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"It's a beautiful day outside, birds are singing, flowers are blooming. On days like these, kids like you...\"\n",
    "stream = oai_chat_service.complete_chat_stream_async([(\"user\", prompt)], chat_request_settings)\n",
    "async for text in stream:\n",
    "    print(text, end = \"\") # end = \"\" to avoid newlines"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cdb8f740",
   "metadata": {},
   "source": [
    "## Streaming Azure OpenAI Chat Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b74a64a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"Tomorow is going to be a great day, I can feel it. I'm going to wake up early, go for a run, and then...\"\n",
    "stream = azure_chat_service.complete_chat_stream_async([(\"user\", prompt)], chat_request_settings)\n",
    "async for text in stream:\n",
    "    print(text, end = \"\") # end = \"\" to avoid newlines"
   ]
  }
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