openai_api/embedding.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "## 准备工作\n",
    "\n",
    "1.确保您按照[README](README-CN.md)中的说明在环境中设置了API密钥\n",
    "\n",
    "2.安装依赖包"
   ]
  },
  {
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     "outputs_hidden": true
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     "name": "stdout",
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     "text": [
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      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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     ]
    }
   ],
   "source": [
    "!pip install tiktoken openai pandas matplotlib plotly scikit-learn numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 生成 Embedding (基于 text-embedding-ada-002 模型)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "嵌入对于处理自然语言和代码非常有用，因为其他机器学习模型和算法（如聚类或搜索）可以轻松地使用和比较它们。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Embedding](images/embedding-vectors.svg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 亚马逊美食评论数据集(amazon-fine-food-reviews)\n",
    "\n",
    "Source:[美食评论数据集](https://www.kaggle.com/snap/amazon-fine-food-reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![dataset](images/amazon-fine-food-reviews.png)\n",
    "\n",
    "\n",
    "该数据集包含截至2012年10月用户在亚马逊上留下的共计568,454条美食评论。为了说明目的，我们将使用该数据集的一个子集，其中包括最近1,000条评论。这些评论都是用英语撰写的，并且倾向于积极或消极。每个评论都有一个产品ID、用户ID、评分、标题（摘要）和正文。\n",
    "\n",
    "我们将把评论摘要和正文合并成一个单一的组合文本。模型将对这个组合文本进行编码，并输出一个单一的向量嵌入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入 pandas 包。Pandas 是一个用于数据处理和分析的 Python 库\n",
    "# 提供了 DataFrame 数据结构，方便进行数据的读取、处理、分析等操作。\n",
    "import pandas as pd\n",
    "# 导入 tiktoken 库。Tiktoken 是 OpenAI 开发的一个库，用于从模型生成的文本中计算 token 数量。\n",
    "import tiktoken\n",
    "# 从 openai.embeddings_utils 包中导入 get_embedding 函数。\n",
    "# 这个函数可以获取 GPT-3 模型生成的嵌入向量。\n",
    "# 嵌入向量是模型内部用于表示输入数据的一种形式。\n",
    "from openai.embeddings_utils import get_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B003JK537S</td>\n",
       "      <td>A3JBPC3WFUT5ZP</td>\n",
       "      <td>1</td>\n",
       "      <td>Arrived in pieces</td>\n",
       "      <td>Not pleased at all. When I opened the box, mos...</td>\n",
       "      <td>Title: Arrived in pieces; Content: Not pleased...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time   ProductId          UserId  Score  \\\n",
       "0  1351123200  B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "1  1351123200  B003JK537S  A3JBPC3WFUT5ZP      1   \n",
       "\n",
       "                                             Summary  \\\n",
       "0  where does one  start...and stop... with a tre...   \n",
       "1                                  Arrived in pieces   \n",
       "\n",
       "                                                Text  \\\n",
       "0  Wanted to save some to bring to my Chicago fam...   \n",
       "1  Not pleased at all. When I opened the box, mos...   \n",
       "\n",
       "                                            combined  \n",
       "0  Title: where does one  start...and stop... wit...  \n",
       "1  Title: Arrived in pieces; Content: Not pleased...  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_datapath = \"data/fine_food_reviews_1k.csv\"\n",
    "df = pd.read_csv(input_datapath, index_col=0)\n",
    "df = df[[\"Time\", \"ProductId\", \"UserId\", \"Score\", \"Summary\", \"Text\"]]\n",
    "df = df.dropna()\n",
    "\n",
    "# 将 \"Summary\" 和 \"Text\" 字段组合成新的字段 \"combined\"\n",
    "df[\"combined\"] = (\n",
    "    \"Title: \" + df.Summary.str.strip() + \"; Content: \" + df.Text.str.strip()\n",
    ")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Title: where does one  start...and stop... wit...\n",
       "1      Title: Arrived in pieces; Content: Not pleased...\n",
       "2      Title: It isn't blanc mange, but isn't bad . ....\n",
       "3      Title: These also have SALT and it's not sea s...\n",
       "4      Title: Happy with the product; Content: My dog...\n",
       "                             ...                        \n",
       "995    Title: Delicious!; Content: I have ordered the...\n",
       "996    Title: Good Training Treat; Content: My dog wi...\n",
       "997    Title: Jamica Me Crazy Coffee; Content: Wolfga...\n",
       "998    Title: Party Peanuts; Content: Great product f...\n",
       "999    Title: I love Maui Coffee!; Content: My first ...\n",
       "Name: combined, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"combined\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Embedding 模型关键参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型类型\n",
    "# 建议使用官方推荐的第二代嵌入模型：text-embedding-ada-002\n",
    "embedding_model = \"text-embedding-ada-002\"\n",
    "# text-embedding-ada-002 模型对应的分词器（TOKENIZER）\n",
    "embedding_encoding = \"cl100k_base\"\n",
    "# text-embedding-ada-002 模型支持的输入最大 Token 数是8191，向量维度 1536\n",
    "# 在我们的 DEMO 中过滤 Token 超过 8000 的文本\n",
    "max_tokens = 8000  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 将样本减少到最近的1,000个评论，并删除过长的样本\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置要筛选的评论数量为1000\n",
    "top_n = 1000\n",
    "# 对DataFrame进行排序，基于\"Time\"列，然后选取最后的2000条评论。\n",
    "# 这个假设是，我们认为最近的评论可能更相关，因此我们将对它们进行初始筛选。\n",
    "df = df.sort_values(\"Time\").tail(top_n * 2) \n",
    "# 丢弃\"Time\"列，因为我们在这个分析中不再需要它。\n",
    "df.drop(\"Time\", axis=1, inplace=True)\n",
    "# 从'embedding_encoding'获取编码\n",
    "encoding = tiktoken.get_encoding(embedding_encoding)\n",
    "\n",
    "# 计算每条评论的token数量。我们通过使用encoding.encode方法获取每条评论的token数，然后把结果存储在新的'n_tokens'列中。\n",
    "df[\"n_tokens\"] = df.combined.apply(lambda x: len(encoding.encode(x)))\n",
    "\n",
    "# 如果评论的token数量超过最大允许的token数量，我们将忽略（删除）该评论。\n",
    "# 我们使用.tail方法获取token数量在允许范围内的最后top_n（1000）条评论。\n",
    "df = df[df.n_tokens <= max_tokens].tail(top_n)\n",
    "\n",
    "# 打印出剩余评论的数量。\n",
    "len(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 生成 Embeddings 并保存（非必须步骤，可直接复用项目中文件）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实际生成会耗时几分钟\n",
    "# 提醒：非必须步骤，可直接复用项目中的嵌入文件 fine_food_reviews_with_embeddings_1k\n",
    "df[\"embedding\"] = df.combined.apply(lambda x: get_embedding(x, engine=embedding_model))\n",
    "\n",
    "output_datapath = \"data/fine_food_reviews_with_embeddings_1k_2146.csv\"\n",
    "\n",
    "df.to_csv(output_datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.读取 fine_food_reviews_with_embeddings_1k 嵌入文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_datapath = \"data/fine_food_reviews_with_embeddings_1k.csv\"\n",
    "\n",
    "df_embedded = pd.read_csv(embedding_datapath, index_col=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看 Embedding 结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      [0.007018072064965963, -0.02731654793024063, 0...\n",
       "297    [-0.003140551969408989, -0.009995664469897747,...\n",
       "296    [-0.01757248118519783, -8.266511576948687e-05,...\n",
       "295    [-0.0013932279543951154, -0.011112828738987446...\n",
       "294    [-0.01757248118519783, -8.266511576948687e-05,...\n",
       "                             ...                        \n",
       "623    [0.00011091353371739388, -0.00466986745595932,...\n",
       "624    [-0.020869314670562744, -0.013138455338776112,...\n",
       "625    [-0.009749102406203747, -0.0068712360225617886...\n",
       "619    [-0.00521062919870019, 0.0009606690146028996, ...\n",
       "999    [-0.006057822611182928, -0.015015840530395508,...\n",
       "Name: embedding, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded[\"embedding\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34402"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_embedded[\"embedding\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_embedded[\"embedding\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded[\"embedding\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "\n",
    "# 将字符串转换为向量\n",
    "df_embedded[\"embedding_vec\"] = df_embedded[\"embedding\"].apply(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1536"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_embedded[\"embedding_vec\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
       "      <th>embedding_vec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "      <td>52</td>\n",
       "      <td>[0.007018072064965963, -0.02731654793024063, 0...</td>\n",
       "      <td>[0.007018072064965963, -0.02731654793024063, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>B003VXHGPK</td>\n",
       "      <td>A21VWSCGW7UUAR</td>\n",
       "      <td>4</td>\n",
       "      <td>Good, but not Wolfgang Puck good</td>\n",
       "      <td>Honestly, I have to admit that I expected a li...</td>\n",
       "      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>\n",
       "      <td>178</td>\n",
       "      <td>[-0.003140551969408989, -0.009995664469897747,...</td>\n",
       "      <td>[-0.003140551969408989, -0.009995664469897747,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ProductId          UserId  Score  \\\n",
       "0    B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "297  B003VXHGPK  A21VWSCGW7UUAR      4   \n",
       "\n",
       "                                               Summary  \\\n",
       "0    where does one  start...and stop... with a tre...   \n",
       "297                   Good, but not Wolfgang Puck good   \n",
       "\n",
       "                                                  Text  \\\n",
       "0    Wanted to save some to bring to my Chicago fam...   \n",
       "297  Honestly, I have to admit that I expected a li...   \n",
       "\n",
       "                                              combined  n_tokens  \\\n",
       "0    Title: where does one  start...and stop... wit...        52   \n",
       "297  Title: Good, but not Wolfgang Puck good; Conte...       178   \n",
       "\n",
       "                                             embedding  \\\n",
       "0    [0.007018072064965963, -0.02731654793024063, 0...   \n",
       "297  [-0.003140551969408989, -0.009995664469897747,...   \n",
       "\n",
       "                                         embedding_vec  \n",
       "0    [0.007018072064965963, -0.02731654793024063, 0...  \n",
       "297  [-0.003140551969408989, -0.009995664469897747,...  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 使用 t-SNE 可视化 1536 维 Embedding 美食评论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入 NumPy 包，NumPy 是 Python 的一个开源数值计算扩展。这种工具可用来存储和处理大型矩阵，\n",
    "# 比 Python 自身的嵌套列表（nested list structure)结构要高效的多。\n",
    "import numpy as np\n",
    "# 从 matplotlib 包中导入 pyplot 子库，并将其别名设置为 plt。\n",
    "# matplotlib 是一个 Python 的 2D 绘图库，pyplot 是其子库，提供了一种类似 MATLAB 的绘图框架。\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# 从 sklearn.manifold 模块中导入 TSNE 类。\n",
    "# TSNE (t-Distributed Stochastic Neighbor Embedding) 是一种用于数据可视化的降维方法，尤其擅长处理高维数据的可视化。\n",
    "# 它可以将高维度的数据映射到 2D 或 3D 的空间中，以便我们可以直观地观察和理解数据的结构。\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_embedded[\"embedding_vec\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先，确保你的嵌入向量都是等长的\n",
    "assert df_embedded['embedding_vec'].apply(len).nunique() == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将嵌入向量列表转换为二维 numpy 数组\n",
    "matrix = np.vstack(df_embedded['embedding_vec'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个 t-SNE 模型，t-SNE 是一种非线性降维方法，常用于高维数据的可视化。\n",
    "# n_components 表示降维后的维度（在这里是2D）\n",
    "# perplexity 可以被理解为近邻的数量\n",
    "# random_state 是随机数生成器的种子\n",
    "# init 设置初始化方式\n",
    "# learning_rate 是学习率。\n",
    "tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用 t-SNE 对数据进行降维，得到每个数据点在新的2D空间中的坐标\n",
    "vis_dims = tsne.fit_transform(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义了五种不同的颜色，用于在可视化中表示不同的等级\n",
    "colors = [\"red\", \"darkorange\", \"gold\", \"turquoise\", \"darkgreen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从降维后的坐标中分别获取所有数据点的横坐标和纵坐标\n",
    "x = [x for x,y in vis_dims]\n",
    "y = [y for x,y in vis_dims]\n",
    "\n",
    "# 根据数据点的评分（减1是因为评分是从1开始的，而颜色索引是从0开始的）获取对应的颜色索引\n",
    "color_indices = df_embedded.Score.values - 1\n",
    "\n",
    "# 确保你的数据点和颜色索引的数量匹配\n",
    "assert len(vis_dims) == len(df_embedded.Score.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Amazon ratings visualized in language using t-SNE')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个基于预定义颜色的颜色映射对象\n",
    "colormap = matplotlib.colors.ListedColormap(colors)\n",
    "# 使用 matplotlib 创建散点图，其中颜色由颜色映射对象和颜色索引共同决定，alpha 是点的透明度\n",
    "plt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3)\n",
    "\n",
    "# 为图形添加标题\n",
    "plt.title(\"Amazon ratings visualized in language using t-SNE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**t-SNE降维后，产生了大约3个大类，其中1个大类的评论大多是负面的。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 使用 K-Means 聚类，然后使用 t-SNE 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/envs/langchain/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# 从 scikit-learn中导入 KMeans 类。KMeans 是一个实现 K-Means 聚类算法的类。\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# np.vstack 是一个将输入数据堆叠到一个数组的函数（在垂直方向）。\n",
    "# 这里它用于将所有的 ada_embedding 值堆叠成一个矩阵。\n",
    "# matrix = np.vstack(df.ada_embedding.values)\n",
    "\n",
    "# 定义要生成的聚类数。\n",
    "n_clusters = 4\n",
    "\n",
    "# 创建一个 KMeans 对象，用于进行 K-Means 聚类。\n",
    "# n_clusters 参数指定了要创建的聚类的数量；\n",
    "# init 参数指定了初始化方法（在这种情况下是 'k-means++'）；\n",
    "# random_state 参数为随机数生成器设定了种子值，用于生成初始聚类中心。\n",
    "# n_init=10 消除警告 'FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4'\n",
    "kmeans = KMeans(n_clusters = n_clusters, init='k-means++', random_state=42, n_init=10)\n",
    "\n",
    "# 使用 matrix（我们之前创建的矩阵）来训练 KMeans 模型。这将执行 K-Means 聚类算法。\n",
    "kmeans.fit(matrix)\n",
    "\n",
    "# kmeans.labels_ 属性包含每个输入数据点所属的聚类的索引。\n",
    "# 这里，我们创建一个新的 'Cluster' 列，在这个列中，每个数据点都被赋予其所属的聚类的标签。\n",
    "df_embedded['Cluster'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3\n",
       "297    2\n",
       "296    3\n",
       "295    0\n",
       "294    3\n",
       "      ..\n",
       "623    2\n",
       "624    0\n",
       "625    1\n",
       "619    2\n",
       "999    2\n",
       "Name: Cluster, Length: 1000, dtype: int32"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded['Cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
       "      <th>embedding_vec</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "      <td>52</td>\n",
       "      <td>[0.007018072064965963, -0.02731654793024063, 0...</td>\n",
       "      <td>[0.007018072064965963, -0.02731654793024063, 0...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>B003VXHGPK</td>\n",
       "      <td>A21VWSCGW7UUAR</td>\n",
       "      <td>4</td>\n",
       "      <td>Good, but not Wolfgang Puck good</td>\n",
       "      <td>Honestly, I have to admit that I expected a li...</td>\n",
       "      <td>Title: Good, but not Wolfgang Puck good; Conte...</td>\n",
       "      <td>178</td>\n",
       "      <td>[-0.003140551969408989, -0.009995664469897747,...</td>\n",
       "      <td>[-0.003140551969408989, -0.009995664469897747,...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ProductId          UserId  Score  \\\n",
       "0    B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "297  B003VXHGPK  A21VWSCGW7UUAR      4   \n",
       "\n",
       "                                               Summary  \\\n",
       "0    where does one  start...and stop... with a tre...   \n",
       "297                   Good, but not Wolfgang Puck good   \n",
       "\n",
       "                                                  Text  \\\n",
       "0    Wanted to save some to bring to my Chicago fam...   \n",
       "297  Honestly, I have to admit that I expected a li...   \n",
       "\n",
       "                                              combined  n_tokens  \\\n",
       "0    Title: where does one  start...and stop... wit...        52   \n",
       "297  Title: Good, but not Wolfgang Puck good; Conte...       178   \n",
       "\n",
       "                                             embedding  \\\n",
       "0    [0.007018072064965963, -0.02731654793024063, 0...   \n",
       "297  [-0.003140551969408989, -0.009995664469897747,...   \n",
       "\n",
       "                                         embedding_vec  Cluster  \n",
       "0    [0.007018072064965963, -0.02731654793024063, 0...        3  \n",
       "297  [-0.003140551969408989, -0.009995664469897747,...        2  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 首先为每个聚类定义一个颜色。\n",
    "colors = [\"red\", \"green\", \"blue\", \"purple\"]\n",
    "\n",
    "# 然后，你可以使用 t-SNE 来降维数据。这里，我们只考虑 'embedding_vec' 列。\n",
    "tsne_model = TSNE(n_components=2, random_state=42)\n",
    "vis_data = tsne_model.fit_transform(matrix)\n",
    "\n",
    "# 现在，你可以从降维后的数据中获取 x 和 y 坐标。\n",
    "x = vis_data[:, 0]\n",
    "y = vis_data[:, 1]\n",
    "\n",
    "# 'Cluster' 列中的值将被用作颜色索引。\n",
    "color_indices = df_embedded['Cluster'].values\n",
    "\n",
    "# 创建一个基于预定义颜色的颜色映射对象\n",
    "colormap = matplotlib.colors.ListedColormap(colors)\n",
    "\n",
    "# 使用 matplotlib 创建散点图，其中颜色由颜色映射对象和颜色索引共同决定\n",
    "plt.scatter(x, y, c=color_indices, cmap=colormap)\n",
    "\n",
    "# 为图形添加标题\n",
    "plt.title(\"Clustering visualized in 2D using t-SNE\")\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**K-MEANS 聚类可视化效果，4类：一个专注于狗粮，一个专注于负面评论，两个专注于正面评论。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 使用 Embedding 进行文本搜索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![cosine](images/cosine.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cosine_similarity 函数计算两个嵌入向量之间的余弦相似度。\n",
    "from openai.embeddings_utils import get_embedding, cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_embedded[\"embedding_vec\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个名为 search_reviews 的函数，\n",
    "# Pandas DataFrame 产品描述，数量，以及一个 pprint 标志（默认值为 True）。\n",
    "def search_reviews(df, product_description, n=3, pprint=True):\n",
    "    product_embedding = get_embedding(\n",
    "        product_description,\n",
    "        engine=\"text-embedding-ada-002\"\n",
    "    )\n",
    "    df[\"similarity\"] = df.embedding_vec.apply(lambda x: cosine_similarity(x, product_embedding))\n",
    "\n",
    "    results = (\n",
    "        df.sort_values(\"similarity\", ascending=False)\n",
    "        .head(n)\n",
    "        .combined.str.replace(\"Title: \", \"\")\n",
    "        .str.replace(\"; Content:\", \": \")\n",
    "    )\n",
    "    if pprint:\n",
    "        for r in results:\n",
    "            print(r[:200])\n",
    "            print()\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good Buy:  I liked the beans. They were vacuum sealed, plump and moist. Would recommend them for any use. I personally split and stuck them in some vodka to make vanilla extract. Yum!\n",
      "\n",
      "Jamaican Blue beans:  Excellent coffee bean for roasting. Our family just purchased another 5 pounds for more roasting. Plenty of flavor and mild on acidity when roasted to a dark brown bean and befor\n",
      "\n",
      "Delicious!:  I enjoy this white beans seasoning, it gives a rich flavor to the beans I just love it, my mother in law didn't know about this Zatarain's brand and now she is traying different seasoning\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 使用 'delicious beans' 作为产品描述和 3 作为数量，\n",
    "# 调用 search_reviews 函数来查找与给定产品描述最相似的前3条评论。\n",
    "# 其结果被存储在 res 变量中。\n",
    "res = search_reviews(df_embedded, 'delicious beans', n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3\n",
       "297    2\n",
       "296    3\n",
       "295    0\n",
       "294    3\n",
       "      ..\n",
       "623    2\n",
       "624    0\n",
       "625    1\n",
       "619    2\n",
       "999    2\n",
       "Name: Cluster, Length: 1000, dtype: int32"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded['Cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Title: where does one  start...and stop... with a treat like this; Content: Wanted to save some to bring to my Chicago family but my North Carolina family ate all 4 boxes before I could pack. These are excellent...could serve to anyone'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选一条类别3的评论\n",
    "df_embedded['combined'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "where does one  start...and stop... with a treat like this:  Wanted to save some to bring to my Chicago family but my North Carolina family ate all 4 boxes before I could pack. These are excellent...c\n",
      "\n",
      "Perfect Gift:  I got these to give out in a goodie bag for the holidays, with a couple extras of course.  They arrived quickly, the price was right, and they were delicious!  I can't wait to give them\n",
      "\n",
      "Delicious!:  Love these packs. I have made pretzel dogs, bites and sticks. I  have even used the pretzel mix to make calzones and pizza. Both came out great.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df_embedded, df_embedded['combined'][0], n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healthy Dog Food:  This is a very healthy dog food. Good for their digestion. Also good for small puppies. My dog eats her required amount at every feeding.\n",
      "\n",
      "Doggy snacks:  My dog loves these snacks. However they are made in China and as far as I am concerned, suspect!!!! I found an abundance of American made ,human grade chicken dog snacks. Just Google fo\n",
      "\n",
      "Dogs Love Them!:  My Maltese and Cavalier King Charles love these treats!  I feel good about feeding them a healthier treat.<br />Not made in China!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df_embedded, 'dog food', n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "God Awful:  As a dabbler who enjoys spanning the entire spectrum of taste, I am more than willing to try anything once.  Both as a food aficionado and a lover of bacon, I just had to pick this up.  On\n",
      "\n",
      "Disappointed:  The metal cover has severely disformed. And most of the cookies inside have been crushed into small pieces. Shopping experience is awful. I'll never buy it online again.\n",
      "\n",
      "Just Bad:  Watery and unpleasant.  Like Yoohoo mixed with dirty dish water.  I find it quite odd that Keurig would release a product like this.  I'm sure they can come up with a decent hot chocolate a\n",
      "\n",
      "Arrived in pieces:  Not pleased at all. When I opened the box, most of the rings were broken in pieces. A total waste of money.\n",
      "\n",
      "Awesome:  They arrived before the expected time and were of fantastic quality. Would recommend to any one looking for a awesome treat\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df_embedded, 'awful', n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai.embeddings_utils import get_embedding, cosine_similarity\n",
    "\n",
    "def search_reviews(df, product_description, n=3, pprint=True):\n",
    "    product_embedding = get_embedding(\n",
    "        product_description,\n",
    "        engine=\"text-embedding-ada-002\"\n",
    "    )\n",
    "    df[\"similarity\"] = df.embedding_vec.apply(lambda x: cosine_similarity(x, product_embedding))\n",
    "\n",
    "    results = (\n",
    "        df.sort_values(\"similarity\", ascending=False)\n",
    "        .head(n)\n",
    "        .combined.str.replace(\"Title: \", \"\")\n",
    "        .str.replace(\"; Content:\", \": \")\n",
    "    )\n",
    "    if pprint:\n",
    "        for r in results:\n",
    "            print(r[:200])\n",
    "            print()\n",
    "    return results\n",
    "\n",
    "res = search_reviews(df_embedded, 'dog food', n=3)"
   ]
  }
 ],
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