Pinecone Medium Article.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pinecone-client\n",
    "# !pip install sentence_transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connecting to Pinecone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manfye\\Anaconda3\\lib\\site-packages\\pinecone\\index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import itertools\n",
    "import pinecone\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "api_key = \"<YOUR API KEY>\"\n",
    "pinecone.init(api_key=api_key, environment='asia-northeast1-gcp')\n",
    "\n",
    "index_name = \"manfye-test\"\n",
    "\n",
    "index = pinecone.Index(index_name=index_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IndexDescription(name='manfye-test', metric='cosine', replicas=1, dimension=300.0, shards=1, pods=1, pod_type='s1.x1', status={'ready': True, 'state': 'Ready'}, metadata_config=None, source_collection='')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinecone.describe_index(index_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WhoAmIResponse(username='07f62cb', user_label='default', projectname='c716a21')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pinecone.whoami()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dimension': 300,\n",
       " 'index_fullness': 0.0,\n",
       " 'namespaces': {'': {'vector_count': 10}},\n",
       " 'total_vector_count': 10}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.describe_index_stats()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    'ticketno': [1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010],\n",
    "    'complains': [\n",
    "        'Broken navigation button on the website',\n",
    "        'Incorrect pricing displayed for a product',\n",
    "        'Unable to reset password',\n",
    "        'App crashes on the latest iOS update',\n",
    "        'Payment processing error during checkout',\n",
    "        'Wrong product delivered',\n",
    "        'Delayed response from customer support',\n",
    "        'Excessive delivery time for an order',\n",
    "        'Difficulty in finding a specific product',\n",
    "        'Error in applying a discount coupon'\n",
    "    ]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticketno</th>\n",
       "      <th>complains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Broken navigation button on the website</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>Incorrect pricing displayed for a product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>Unable to reset password</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>App crashes on the latest iOS update</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>Payment processing error during checkout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1006</td>\n",
       "      <td>Wrong product delivered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1007</td>\n",
       "      <td>Delayed response from customer support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1008</td>\n",
       "      <td>Excessive delivery time for an order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1009</td>\n",
       "      <td>Difficulty in finding a specific product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1010</td>\n",
       "      <td>Error in applying a discount coupon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticketno                                  complains\n",
       "0      1001    Broken navigation button on the website\n",
       "1      1002  Incorrect pricing displayed for a product\n",
       "2      1003                   Unable to reset password\n",
       "3      1004       App crashes on the latest iOS update\n",
       "4      1005   Payment processing error during checkout\n",
       "5      1006                    Wrong product delivered\n",
       "6      1007     Delayed response from customer support\n",
       "7      1008       Excessive delivery time for an order\n",
       "8      1009   Difficulty in finding a specific product\n",
       "9      1010        Error in applying a discount coupon"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dimension': 300,\n",
       " 'index_fullness': 0.0,\n",
       " 'namespaces': {},\n",
       " 'total_vector_count': 0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.describe_index_stats()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SentenceTransformer(\"average_word_embeddings_glove.6B.300d\")\n",
    "\n",
    "df[\"question_vector\"] = df.complains.apply(lambda x: model.encode(str(x)).tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticketno</th>\n",
       "      <th>complains</th>\n",
       "      <th>question_vector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>Broken navigation button on the website</td>\n",
       "      <td>[-0.2649574875831604, -0.17953598499298096, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>Incorrect pricing displayed for a product</td>\n",
       "      <td>[0.10973000526428223, 0.3845505118370056, 0.12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>Unable to reset password</td>\n",
       "      <td>[-0.21170000731945038, 0.2875896692276001, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>App crashes on the latest iOS update</td>\n",
       "      <td>[-0.008352995850145817, -0.13370580971240997, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>Payment processing error during checkout</td>\n",
       "      <td>[-0.17191250622272491, 0.37106096744537354, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1006</td>\n",
       "      <td>Wrong product delivered</td>\n",
       "      <td>[0.04049666225910187, 0.22198998928070068, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1007</td>\n",
       "      <td>Delayed response from customer support</td>\n",
       "      <td>[-0.16759249567985535, -0.19862000644207, 0.04...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1008</td>\n",
       "      <td>Excessive delivery time for an order</td>\n",
       "      <td>[-0.012894751504063606, -0.08605450391769409, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1009</td>\n",
       "      <td>Difficulty in finding a specific product</td>\n",
       "      <td>[-0.20433326065540314, 0.3896175026893616, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1010</td>\n",
       "      <td>Error in applying a discount coupon</td>\n",
       "      <td>[-0.17083999514579773, 0.1372150033712387, 0.1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticketno                                  complains  \\\n",
       "0      1001    Broken navigation button on the website   \n",
       "1      1002  Incorrect pricing displayed for a product   \n",
       "2      1003                   Unable to reset password   \n",
       "3      1004       App crashes on the latest iOS update   \n",
       "4      1005   Payment processing error during checkout   \n",
       "5      1006                    Wrong product delivered   \n",
       "6      1007     Delayed response from customer support   \n",
       "7      1008       Excessive delivery time for an order   \n",
       "8      1009   Difficulty in finding a specific product   \n",
       "9      1010        Error in applying a discount coupon   \n",
       "\n",
       "                                     question_vector  \n",
       "0  [-0.2649574875831604, -0.17953598499298096, 0....  \n",
       "1  [0.10973000526428223, 0.3845505118370056, 0.12...  \n",
       "2  [-0.21170000731945038, 0.2875896692276001, -0....  \n",
       "3  [-0.008352995850145817, -0.13370580971240997, ...  \n",
       "4  [-0.17191250622272491, 0.37106096744537354, 0....  \n",
       "5  [0.04049666225910187, 0.22198998928070068, -0....  \n",
       "6  [-0.16759249567985535, -0.19862000644207, 0.04...  \n",
       "7  [-0.012894751504063606, -0.08605450391769409, ...  \n",
       "8  [-0.20433326065540314, 0.3896175026893616, 0.1...  \n",
       "9  [-0.17083999514579773, 0.1372150033712387, 0.1...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chunks(iterable, batch_size=3):\n",
    "    it = iter(iterable)\n",
    "    chunk = tuple(itertools.islice(it, batch_size))\n",
    "    while chunk:\n",
    "        yield chunk\n",
    "        chunk = tuple(itertools.islice(it, batch_size))\n",
    "\n",
    "for batch in chunks([(str(t), v) for t, v in zip(df.ticketno, df.question_vector)]):\n",
    "    index.upsert(vectors=batch)\n",
    "    \n",
    "# it overwrite if ID is same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dimension': 300,\n",
       " 'index_fullness': 0.0,\n",
       " 'namespaces': {'': {'vector_count': 10}},\n",
       " 'total_vector_count': 10}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.describe_index_stats()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "             '1010': {'id': '1010',\n",
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       "                                 0.0179297477,\n",
       "                                 -0.240945]}}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.fetch([\"1010\",\"1009\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_questions = [\n",
    "    \"navigation button\",\n",
    "]\n",
    "\n",
    "query_vectors = [model.encode(str(question)).tolist() for question in query_questions]\n",
    "\n",
    "query_results = index.query(queries=query_vectors, top_k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'results': [{'matches': [{'id': '1001', 'score': 0.813644469, 'values': []},\n",
       "                          {'id': '1003', 'score': 0.357704908, 'values': []},\n",
       "                          {'id': '1004', 'score': 0.314247638, 'values': []},\n",
       "                          {'id': '1002', 'score': 0.298523784, 'values': []},\n",
       "                          {'id': '1005', 'score': 0.278602391, 'values': []}],\n",
       "              'namespace': ''}]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract matches and scores from the results\n",
    "matches = []\n",
    "scores = []\n",
    "for match in query_results['results'][0]['matches']:\n",
    "    matches.append(match['id'])\n",
    "    scores.append(match['score'])\n",
    "\n",
    "# Create DataFrame with only matches and scores\n",
    "matches_df = pd.DataFrame({'id': matches, 'score': scores})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>score</th>\n",
       "      <th>ticketno</th>\n",
       "      <th>complains</th>\n",
       "      <th>question_vector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>0.813644</td>\n",
       "      <td>1001</td>\n",
       "      <td>Broken navigation button on the website</td>\n",
       "      <td>[-0.2649574875831604, -0.17953598499298096, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>0.357705</td>\n",
       "      <td>1003</td>\n",
       "      <td>Unable to reset password</td>\n",
       "      <td>[-0.21170000731945038, 0.2875896692276001, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1004</td>\n",
       "      <td>0.314248</td>\n",
       "      <td>1004</td>\n",
       "      <td>App crashes on the latest iOS update</td>\n",
       "      <td>[-0.008352995850145817, -0.13370580971240997, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002</td>\n",
       "      <td>0.298524</td>\n",
       "      <td>1002</td>\n",
       "      <td>Incorrect pricing displayed for a product</td>\n",
       "      <td>[0.10973000526428223, 0.3845505118370056, 0.12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>0.278602</td>\n",
       "      <td>1005</td>\n",
       "      <td>Payment processing error during checkout</td>\n",
       "      <td>[-0.17191250622272491, 0.37106096744537354, 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id     score ticketno                                  complains  \\\n",
       "0  1001  0.813644     1001    Broken navigation button on the website   \n",
       "1  1003  0.357705     1003                   Unable to reset password   \n",
       "2  1004  0.314248     1004       App crashes on the latest iOS update   \n",
       "3  1002  0.298524     1002  Incorrect pricing displayed for a product   \n",
       "4  1005  0.278602     1005   Payment processing error during checkout   \n",
       "\n",
       "                                     question_vector  \n",
       "0  [-0.2649574875831604, -0.17953598499298096, 0....  \n",
       "1  [-0.21170000731945038, 0.2875896692276001, -0....  \n",
       "2  [-0.008352995850145817, -0.13370580971240997, ...  \n",
       "3  [0.10973000526428223, 0.3845505118370056, 0.12...  \n",
       "4  [-0.17191250622272491, 0.37106096744537354, 0....  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ticketno\"] = df[\"ticketno\"].astype(str)\n",
    "\n",
    "matches_df.merge(df,left_on=\"id\",right_on=\"ticketno\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.delete(deleteAll='true', namespace=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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