docs_to_vectordb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add all the dataframe to database\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv(\".env\")\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "sys.path.append(\"\")\n",
    "\n",
    "##\n",
    "# import openai\n",
    "text_embedding_model = \"text-embedding-ada-002\"\n",
    "# from openai.embeddings_utils import get_embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Langchain Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import langchain\n",
    "## For generating and persisting Embeddings\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.vectorstores.pgvector import PGVector\n",
    "\n",
    "embeddings = OpenAIEmbeddings(model=text_embedding_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Supabase Database Connection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "DB_PASSWORD = os.environ[\"SUPABASE_PASSWORD\"]\n",
    "DB_DBUSER = os.environ[\"SUPABASE_DBUSER\"]\n",
    "DB_DATABASE = os.environ[\"SUPABASE_DATABASE\"]\n",
    "DB_HOST = os.environ[\"SUPABASE_HOST\"]\n",
    "DB_PORT = os.environ[\"SUPABASE_PORT\"]\n",
    "DB_CONN_STRING = (\n",
    "    f\"postgresql://{DB_DBUSER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_DATABASE}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mahabharata Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TT Dataframe size (200, 8)\n",
      "KT Dataframe size (2376, 10)\n",
      "KMGT Dataframe size (4641, 8)\n",
      "Combined Dataframe size (7217, 14)\n"
     ]
    },
    {
     "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>book_number</th>\n",
       "      <th>section</th>\n",
       "      <th>section_name</th>\n",
       "      <th>text</th>\n",
       "      <th>para_number</th>\n",
       "      <th>book_name</th>\n",
       "      <th>num_tokens</th>\n",
       "      <th>chunk_id</th>\n",
       "      <th>chapter_name</th>\n",
       "      <th>title</th>\n",
       "      <th>commentary</th>\n",
       "      <th>section_number</th>\n",
       "      <th>source</th>\n",
       "      <th>chapter_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>The Mahabharata\\n\\nof\\n\\nKrishna-Dwaipayana Vy...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bhishma Parva</td>\n",
       "      <td>93</td>\n",
       "      <td>cid_13a1a2c891a146f8a7744f4da9ceb5d5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SECTION I</td>\n",
       "      <td>Jamvu-khanda Nirmana Parva</td>\n",
       "      <td>\\n\\nOM! HAVING BOWED down to Narayana, and Nar...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bhishma Parva</td>\n",
       "      <td>1121</td>\n",
       "      <td>cid_49e9381b5aec4f428ef43b2f5bfff46c</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SECTION I</td>\n",
       "      <td>Jamvu-khanda Nirmana Parva</td>\n",
       "      <td>should strike another, giving notice. No one s...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bhishma Parva</td>\n",
       "      <td>173</td>\n",
       "      <td>cid_4788eb7749de4bc8acd622c987006dc8</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SECTION II</td>\n",
       "      <td></td>\n",
       "      <td>\\nVaisampayana said,--\"Seeing then the two arm...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bhishma Parva</td>\n",
       "      <td>1115</td>\n",
       "      <td>cid_fcd1ab24b779460fa62532ea21a82485</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SECTION II</td>\n",
       "      <td></td>\n",
       "      <td>applauded by the righteous, even that (constel...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bhishma Parva</td>\n",
       "      <td>107</td>\n",
       "      <td>cid_1eecac1f06e6445eb74afbada0266d33</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  book_number     section                section_name  \\\n",
       "0         6.0                                           \n",
       "1         6.0   SECTION I  Jamvu-khanda Nirmana Parva   \n",
       "2         6.0   SECTION I  Jamvu-khanda Nirmana Parva   \n",
       "3         6.0  SECTION II                               \n",
       "4         6.0  SECTION II                               \n",
       "\n",
       "                                                text para_number  \\\n",
       "0  The Mahabharata\\n\\nof\\n\\nKrishna-Dwaipayana Vy...         1.0   \n",
       "1  \\n\\nOM! HAVING BOWED down to Narayana, and Nar...         1.0   \n",
       "2  should strike another, giving notice. No one s...         2.0   \n",
       "3  \\nVaisampayana said,--\"Seeing then the two arm...         1.0   \n",
       "4  applauded by the righteous, even that (constel...         2.0   \n",
       "\n",
       "       book_name  num_tokens                              chunk_id  \\\n",
       "0  Bhishma Parva          93  cid_13a1a2c891a146f8a7744f4da9ceb5d5   \n",
       "1  Bhishma Parva        1121  cid_49e9381b5aec4f428ef43b2f5bfff46c   \n",
       "2  Bhishma Parva         173  cid_4788eb7749de4bc8acd622c987006dc8   \n",
       "3  Bhishma Parva        1115  cid_fcd1ab24b779460fa62532ea21a82485   \n",
       "4  Bhishma Parva         107  cid_1eecac1f06e6445eb74afbada0266d33   \n",
       "\n",
       "  chapter_name title commentary section_number source chapter_number  \n",
       "0                                                                     \n",
       "1                                                                     \n",
       "2                                                                     \n",
       "3                                                                     \n",
       "4                                                                     "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_directory = Path(os.environ.get(\"DATA_DIRECTORY\", \"./\"))\n",
    "\n",
    "## Tiny Tales\n",
    "df_tinytales = pd.read_csv(f\"{data_directory}/tiny_tales_summaries.csv\", sep=\"|\")\n",
    "print(\"TT Dataframe size\", df_tinytales.shape)\n",
    "\n",
    "## Kaggle Tilak dataframe\n",
    "df_kaggletilak = pd.read_csv(f\"{data_directory}/kaggle_tilak_summaries.csv\", sep=\"|\")\n",
    "df_kaggletilak = df_kaggletilak.replace(np.nan, \"\")\n",
    "print(\"KT Dataframe size\", df_kaggletilak.shape)\n",
    "\n",
    "## KM Ganguli\n",
    "file_list = glob.glob(f\"{data_directory}/km_ganguli_*.csv\")\n",
    "dataframes = []\n",
    "for file in file_list:\n",
    "    # print(\"File: \", file)\n",
    "    df = pd.read_csv(file, sep=\"|\")\n",
    "    dataframes = dataframes + [df]\n",
    "\n",
    "kmgt_dataframe = pd.concat(dataframes)\n",
    "kmgt_dataframe = kmgt_dataframe.replace(np.nan, \"\")\n",
    "print(\"KMGT Dataframe size\", kmgt_dataframe.shape)\n",
    "\n",
    "## Combine Dataframes\n",
    "dataframes = [kmgt_dataframe, df_kaggletilak, df_tinytales]\n",
    "combined_dataframe = pd.concat(dataframes)\n",
    "combined_dataframe = combined_dataframe.replace(np.nan, \"\")\n",
    "print(\"Combined Dataframe size\", combined_dataframe.shape)\n",
    "combined_dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save to Vectorstore\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining a new function to save dataframe to vector store.\n",
    "The reason I am not using the default langchain `add_document` function is because it does not accept custom_ids\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addDataframeToVectorStore(\n",
    "    dataframe, vector_store, text_col=\"text\", id_col=\"chunk_id\"\n",
    "):\n",
    "    metadatas = dataframe.drop([text_col], axis=1).to_dict(\"records\")\n",
    "    result = vector_store.add_texts(\n",
    "        texts=dataframe[text_col],\n",
    "        ids=dataframe[id_col],\n",
    "        metadatas=metadatas,\n",
    "    )\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Assert this flag if you want to recreate the table.\n",
    "recreate_combined_collection = True\n",
    "\n",
    "## which dataframe to save.\n",
    "dataframe = kmgt_dataframe\n",
    "\n",
    "if recreate_combined_collection:\n",
    "    mahabharata_store = PGVector(\n",
    "        collection_name=\"mahabharata\",\n",
    "        connection_string=DB_CONN_STRING,\n",
    "        embedding_function=embeddings,\n",
    "    )\n",
    "\n",
    "    addDataframeToVectorStore(dataframe, mahabharata_store)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Companies Act 2013 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of chunks =  1154\n"
     ]
    }
   ],
   "source": [
    "from langchain.document_loaders import PyPDFLoader, UnstructuredPDFLoader, PyPDFium2Loader\n",
    "from langchain.document_loaders import PyPDFDirectoryLoader, DirectoryLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "\n",
    "filepath = './data/CompaniesAct2013.pdf'\n",
    "loader = PyPDFLoader(filepath)\n",
    "documents = loader.load()\n",
    "\n",
    "splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=1100,\n",
    "    chunk_overlap=150,\n",
    "    length_function=len,\n",
    "    is_separator_regex=False,\n",
    ")\n",
    "\n",
    "pages = splitter.split_documents(documents)\n",
    "print(\"Number of chunks = \", len(pages))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       " '43d337e0-9113-11ee-9e9e-bedec676b352',\n",
       " '43d337f4-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33808-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33812-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33826-9113-11ee-9e9e-bedec676b352',\n",
       " '43d3383a-9113-11ee-9e9e-bedec676b352',\n",
       " '43d3384e-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33862-9113-11ee-9e9e-bedec676b352',\n",
       " '43d3386c-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33880-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33894-9113-11ee-9e9e-bedec676b352',\n",
       " '43d338a8-9113-11ee-9e9e-bedec676b352',\n",
       " '43d338bc-9113-11ee-9e9e-bedec676b352',\n",
       " '43d338c6-9113-11ee-9e9e-bedec676b352',\n",
       " '43d338da-9113-11ee-9e9e-bedec676b352',\n",
       " '43d338ee-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33902-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33916-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33920-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33934-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33948-9113-11ee-9e9e-bedec676b352',\n",
       " '43d3395c-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33970-9113-11ee-9e9e-bedec676b352',\n",
       " '43d3397a-9113-11ee-9e9e-bedec676b352',\n",
       " '43d3398e-9113-11ee-9e9e-bedec676b352',\n",
       " '43d339a2-9113-11ee-9e9e-bedec676b352',\n",
       " '43d339b6-9113-11ee-9e9e-bedec676b352',\n",
       " '43d339c0-9113-11ee-9e9e-bedec676b352',\n",
       " '43d339d4-9113-11ee-9e9e-bedec676b352',\n",
       " '43d339e8-9113-11ee-9e9e-bedec676b352',\n",
       " '43d339fc-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33a10-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33a24-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33a38-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33a4c-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33a60-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33a6a-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33a7e-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33a92-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33aa6-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33ab0-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33ac4-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33ad8-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33aec-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b00-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b14-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b1e-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b32-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b46-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b5a-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b6e-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b78-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33b8c-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33ba0-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33bb4-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33bbe-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33bd2-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33be6-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33bfa-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33c18-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33c22-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33c36-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33c4a-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33c5e-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33c72-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33c7c-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33c90-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33ca4-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33cb8-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33ccc-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33ce0-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33cea-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33cfe-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d12-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d26-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d3a-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d44-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d58-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d6c-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d80-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d8a-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33d9e-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33db2-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33dc6-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33dda-9113-11ee-9e9e-bedec676b352',\n",
       " '43d33de4-9113-11ee-9e9e-bedec676b352',\n",
       " ...]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ca_store = PGVector(\n",
    "    collection_name=\"legal_docs\",\n",
    "    connection_string=DB_CONN_STRING,\n",
    "    embedding_function=embeddings,\n",
    ")\n",
    "ca_store.add_documents(pages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Done\n",
    "\n",
    "<div class=\"alert alert-success\"><b>Success! </b> \n",
    "    All the documents are saved in the vector store</div>\n",
    "</div>\n"
   ]
  }
 ],
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