{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AliCloud PAI EAS\n",
    "Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Eas Service\n",
    "\n",
    "One who want to use eas llms must set up eas service first. When the eas service is launched, eas_service_rul and eas_service token can be got. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information. Try to set environment variables to init eas service url and token:\n",
    "\n",
    "```base\n",
    "export EAS_SERVICE_URL=XXX\n",
    "export EAS_SERVICE_TOKEN=XXX\n",
    "```\n",
    "or run as follow codes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from langchain.chat_models import PaiEasChatEndpoint\n",
    "from langchain.chat_models.base import HumanMessage\n",
    "\n",
    "os.environ[\"EAS_SERVICE_URL\"] = \"Your_EAS_Service_URL\"\n",
    "os.environ[\"EAS_SERVICE_TOKEN\"] = \"Your_EAS_Service_Token\"\n",
    "chat = PaiEasChatEndpoint(\n",
    "    eas_service_url=os.environ[\"EAS_SERVICE_URL\"],\n",
    "    eas_service_token=os.environ[\"EAS_SERVICE_TOKEN\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Chat Model\n",
    "You can use the default settings to call eas service as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = chat([HumanMessage(content=\"write a funny joke\")])\n",
    "print(\"output:\", output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, call eas service with new inference params:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = {\"temperature\": 0.8, \"top_p\": 0.8, \"top_k\": 5}\n",
    "output = chat([HumanMessage(content=\"write a funny joke\")], **kwargs)\n",
    "print(\"output:\", output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, run a stream call to get a stream response:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = chat.stream([HumanMessage(content=\"hi\")], streaming=True)\n",
    "for output in outputs:\n",
    "    print(\"stream output:\", output)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
