demo_3_code_generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
    "os.environ[\"OPENAI_API_BASE\"] = \"https://yourterminal.openai.azure.com/\" #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!此处设置自己的terminal\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"your api key\" #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!此处设置自己的API Key\n",
    "os.environ[\"OPENAI_API_VERSION\"] = \"2023-05-15\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import PyPDFLoader\n",
    "loader = PyPDFLoader(\"./data/CloudPSS SDK.pdf\")\n",
    "docs = loader.load_and_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=500, chunk_overlap=0, add_start_index=True\n",
    ")\n",
    "all_splits = text_splitter.split_documents(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='SDK 简介\\nCloudPSS SDK 是基于 CloudPSS-A PI封装的模型及软件开发套件。用户可通过编写 Python、Matlab 等脚本构建自定义模型，或是\\n调用 CloudPSS 平台中的模型修改、仿真计算功能，实现诸如自动修改模 型、批量仿真计算、自动化生成报告等复杂且繁琐的功能。\\n用户也可在其自己的应用程序中调用 CloudPSS 仿真引擎，实现仿真驱动的高级分析应用。\\nCloudPSS SDK 包含模型层、算法层和应用层三种开发套件，其中：\\n1. 模 型 层 开 发 套 件帮助用户在 CloudPSS SimS tudio 官方潮流计算、电磁暂态仿真、移频电磁暂态仿真、综合能源能量流计算等内\\n核中开发第三方模型或用户自定义模型。目前，模型层 SDK 已开放基于 Matlab 函数的自定义控制元件接入，后续将进一步开放\\nPython、C/C++ 的标准元件开发套件。\\n2. 算 法 层 开 发 套 件帮助用户在 CloudPSS FuncS tudio 中集成自己的算法内核，从而借助 CloudPSS XS tudio 平台快速开发并部署\\n自己的计算应用。\\n3. 应 用 层 开 发 套 件帮助用户在利用脚本的形式快速调用 CloudPSS 官方计算内核和第三方接入的计算内核，从而方便用户开发高级\\n计算分析应用。其中，SimS tudio-SDK 现已支持 SimS tudio 中的模型修改和 潮 流 计 算、 电 磁 暂 态 仿 真、 综 合 能 源 能 量 流 计 算三\\n种计算内核。\\nCloudPSS-SDK\\n模型层 算法层 应用层\\nMatlab 函数 C/C++ Python 函数 FuncStudio-SDK SimStudio-SDK AppStudio-SDK\\n665', metadata={'page': 1, 'source': './data/CloudPSS SDK.pdf'}),\n",
       " Document(page_content='CloudPSS 接口文档\\nToken 申请 与设置\\nModel 类\\nRunner 类\\nFunction 类\\nFunctionJob 类\\nProject 类\\n681', metadata={'page': 17, 'source': './data/CloudPSS SDK.pdf'}),\n",
       " Document(page_content='CloudPSS 框架介绍\\n框架图\\n仿真项目架构\\n667', metadata={'page': 3, 'source': './data/CloudPSS SDK.pdf'}),\n",
       " Document(page_content='Token 申请与设置\\n首先点击 主 页标签页进入 CloudPSS 主页，然后点击左下角的 设 置按钮进入设置页面。\\n设置页面\\n点击 SDK Token 申 请标签进入 Token 申请界面，并选择 时 效。\\n基本设置界面申请t oken\\n682', metadata={'page': 18, 'source': './data/CloudPSS SDK.pdf'})]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load documents\n",
    "# 运行前，请确保已经完成向量数据库生成，./data/chroma_db未被破坏\n",
    "from langchain.vectorstores.chroma import Chroma\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "db3 = Chroma(persist_directory=\"./data/chroma_db/\", embedding_function=OpenAIEmbeddings(deployment=\"your embedding name\", #!!!!!!!!!!!此处设置自己的text embedding name\n",
    "                                                                                        disallowed_special=()))\n",
    "db3.similarity_search(\"CloudPSS SDK的功能\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = db3.as_retriever(\n",
    "    search_type=\"similarity\", \n",
    "    search_kwargs={\"k\": 8},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chat_models import AzureChatOpenAI\n",
    "llm = AzureChatOpenAI(deployment_name=\"your deployment name\", #!!!!!!!!!!!此处设置自己的deployment name\n",
    "                      model_name=\"gpt-35-turbo\",\n",
    "                      temperature=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当然可以！以下是一个简单的示例代码，展示如何使用CloudPSS SDK实现仿真和绘图：\n",
      "\n",
      "```python\n",
      "import cloudpss_sdk as cpss\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# 创建仿真模型\n",
      "model = cpss.Model()\n",
      "\n",
      "# 添加组件到仿真模型\n",
      "comp1 = model.create_component(\"Comp1\")\n",
      "comp2 = model.create_component(\"Comp2\")\n",
      "\n",
      "# 设置组件之间的连线关系\n",
      "model.connect(comp1, comp2)\n",
      "\n",
      "# 设置仿真参数\n",
      "model.set_parameter(\"time_step\", 0.1)\n",
      "model.set_parameter(\"simulation_time\", 10.0)\n",
      "\n",
      "# 运行仿真\n",
      "model.run_simulation()\n",
      "\n",
      "# 获取仿真结果\n",
      "result = model.get_result()\n",
      "\n",
      "# 绘制仿真结果图表\n",
      "time = result[\"Time\"]\n",
      "output1 = result[\"Comp1.Output\"]\n",
      "output2 = result[\"Comp2.Output\"]\n",
      "\n",
      "plt.plot(time, output1, label=\"Comp1 Output\")\n",
      "plt.plot(time, output2, label=\"Comp2 Output\")\n",
      "plt.xlabel(\"Time\")\n",
      "plt.ylabel(\"Output\")\n",
      "plt.legend()\n",
      "plt.show()\n",
      "```\n",
      "\n",
      "在这个示例中，首先创建了一个`Model`对象，然后使用`create_component`方法创建了两个组件`Comp1`和`Comp2`，然后使用`connect`方法将两个组件连接起来。接下来，使用`set_parameter`方法设置了仿真参数，包括时间步长和仿真时间。然后，调用`run_simulation`方法运行仿真，最后使用`get_result`方法获取仿真结果。最后，使用Matplotlib库绘制了两个组件的输出随时间变化的曲线图。\n",
      "\n",
      "请注意，上述代码仅为示例，具体的仿真模型和参数设置需要根据实际需求进行调整。另外，还需要安装`cloudpss_sdk`和`matplotlib`库才能运行此代码。\n"
     ]
    }
   ],
   "source": [
    "print(llm.invoke(\"请帮我编写一段代码，利用cloudpss sdk完成仿真，读取仿真结果并画图。\").content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cloudpss_sdk'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcloudpss_sdk\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mcpss\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# 创建仿真模型\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cloudpss_sdk'"
     ]
    }
   ],
   "source": [
    "import cloudpss_sdk as cpss\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建仿真模型\n",
    "model = cpss.Model()\n",
    "\n",
    "# 添加组件到仿真模型\n",
    "comp1 = model.create_component(\"Comp1\")\n",
    "comp2 = model.create_component(\"Comp2\")\n",
    "\n",
    "# 设置组件之间的连线关系\n",
    "model.connect(comp1, comp2)\n",
    "\n",
    "# 设置仿真参数\n",
    "model.set_parameter(\"time_step\", 0.1)\n",
    "model.set_parameter(\"simulation_time\", 10.0)\n",
    "\n",
    "# 运行仿真\n",
    "model.run_simulation()\n",
    "\n",
    "# 获取仿真结果\n",
    "result = model.get_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.schema.runnable import RunnablePassthrough\n",
    "from langchain.schema import StrOutputParser\n",
    "\n",
    "# Use three sentences maximum and keep the answer as concise as possible. \n",
    "# Always say \"thanks for asking!\" at the end of the answer. \n",
    "\n",
    "template = \"\"\"Use the following pieces of context to answer the question or generate code at the end. \n",
    "When you generate code, do not use \"try\" and \"exception\". Do not use \"try\" and \"except\"\n",
    "If you don't know the answer, just say that you don't know, don't try to make up an answer. \n",
    "{context}\n",
    "Question: {question}\n",
    "Helpful Answer:\"\"\"\n",
    "rag_prompt_custom = PromptTemplate.from_template(template)\n",
    "\n",
    "rag_chain = (\n",
    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
    "    | rag_prompt_custom\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import sys\n",
      "import cloudpss\n",
      "import json\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "if __name__ == '__main__':\n",
      "    cloudpss.setToken('{token}')\n",
      "    \n",
      "    # 获取指定 rid 的项目\n",
      "    model = cloudpss.Model.fetch('model/Demo/SDK_TEST1')\n",
      "    \n",
      "    # 启动计算任务\n",
      "    config = model.configs[0]  # 若未设置，则默认用 model 的第一个 config （参数方案）\n",
      "    job = model.jobs[0]  # 若未设置，则默认用 model 的第一个 job （计算方案）\n",
      "    runner = model.run(job, config)\n",
      "    \n",
      "    while not runner.status():\n",
      "        print('running')\n",
      "        # 获取通道分组下的曲线名称\n",
      "        plotKeys = runner.result.getPlotChannelNames(1)\n",
      "        if plotKeys is not None:\n",
      "            for val in plotKeys:\n",
      "                # 获取曲线数据\n",
      "                channel = runner.result.getPlotChannelData(1, val)\n",
      "                plt.plot(channel['x'], channel['y'], label=val)\n",
      "        \n",
      "        plt.pause(0.1)\n",
      "    \n",
      "    print('end')\n",
      "    plt.legend()\n",
      "    plt.show()\n"
     ]
    }
   ],
   "source": [
    "print(rag_chain.invoke(\"请帮我编写一段代码，利用cloudpss sdk完成仿真，读取仿真结果并画图。\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running\n",
      "running\n",
      "running\n",
      "running\n",
      "running\n",
      "running\n",
      "end\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time #!!!!!!!!!!!!!!用于程序休眠等待\n",
    "import cloudpss\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    cloudpss.setToken('{token}') #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!此处需要注册cloudpss.net，申请sdk token\n",
    "    \n",
    "    # 获取指定 rid 的项目\n",
    "    model = cloudpss.Model.fetch('model/your name/your project') #!!!!!!!!!!!!此处需要根据情况设置项目名称\n",
    "    \n",
    "    # 启动计算任务\n",
    "    config = model.configs[0]  # 若未设置，则默认用 model 的第一个 config （参数方案）\n",
    "    job = model.jobs[1]  # 若未设置，则默认用 model 的第一个 job （计算方案）!!!!!!!!!!!!!!!!!!!! 此处需要按需修改，设置对应仿真任务的job\n",
    "    runner = model.run(job, config)\n",
    "    \n",
    "    while not runner.status():\n",
    "        print('running')\n",
    "        time.sleep(1) #!!!!!!!!!!!此处增加休眠，避免不必要输出\n",
    "    #!!!!!!!!!!!!!此处缩进调整\n",
    "    # 获取通道分组下的曲线名称\n",
    "    plotKeys = runner.result.getPlotChannelNames(1)\n",
    "    if plotKeys is not None:\n",
    "        for val in plotKeys:\n",
    "            # 获取曲线数据\n",
    "            channel = runner.result.getPlotChannelData(1, val)\n",
    "            plt.plot(channel['x'], channel['y'], label=val)\n",
    "    \n",
    "    print('end')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}