shakespeare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "drama = 'othello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "paragraphs = pd.read_csv(f'{drama}.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speech</th>\n",
       "      <th>created_at</th>\n",
       "      <th>character</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tush, never tell me! I take it much unkindly T...</td>\n",
       "      <td>946684800000</td>\n",
       "      <td>#Roderigo_Oth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>’Sblood, but you’ll not hear me! If ever I did...</td>\n",
       "      <td>946684800001</td>\n",
       "      <td>#Iago_Oth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thou toldst me thou didst hold him in thy hate.</td>\n",
       "      <td>946684800002</td>\n",
       "      <td>#Roderigo_Oth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Despise me If I do not. Three great ones of th...</td>\n",
       "      <td>946684800003</td>\n",
       "      <td>#Iago_Oth</td>\n",
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       "      <th>4</th>\n",
       "      <td>By heaven, I rather would have been his hangman.</td>\n",
       "      <td>946684800004</td>\n",
       "      <td>#Roderigo_Oth</td>\n",
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      "text/plain": [
       "                                              speech    created_at  \\\n",
       "0  Tush, never tell me! I take it much unkindly T...  946684800000   \n",
       "1  ’Sblood, but you’ll not hear me! If ever I did...  946684800001   \n",
       "2    Thou toldst me thou didst hold him in thy hate.  946684800002   \n",
       "3  Despise me If I do not. Three great ones of th...  946684800003   \n",
       "4   By heaven, I rather would have been his hangman.  946684800004   \n",
       "\n",
       "       character  \n",
       "0  #Roderigo_Oth  \n",
       "1      #Iago_Oth  \n",
       "2  #Roderigo_Oth  \n",
       "3      #Iago_Oth  \n",
       "4  #Roderigo_Oth  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paragraphs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = paragraphs.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\zsomk\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')  # Download the Punkt tokenizer models if you haven't already\n",
    "\n",
    "from nltk.tokenize import sent_tokenize\n",
    "\n",
    "sentences['speech_sent'] = sentences['speech'].apply(nltk.sent_tokenize)\n",
    "\n",
    "sentences = sentences.explode('speech_sent')\n",
    "# Drop the 'Paragraphs' column if you no longer need it\n",
    "sentences.drop(columns=['speech'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>946684800001</td>\n",
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       "      <td>’Sblood, but you’ll not hear me!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>946684800001</td>\n",
       "      <td>#Iago_Oth</td>\n",
       "      <td>If ever I did dream of such a matter, Abhor me.</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>Thou toldst me thou didst hold him in thy hate.</td>\n",
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       "      <td>Let it be hid.—Gratiano, keep the house, And s...</td>\n",
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       "      <th>1181</th>\n",
       "      <td>946684801181</td>\n",
       "      <td>#Lodovico_Oth</td>\n",
       "      <td>To Cassio.</td>\n",
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       "      <th>1181</th>\n",
       "      <td>946684801181</td>\n",
       "      <td>#Lodovico_Oth</td>\n",
       "      <td>To you, lord governor, Remains the censure of ...</td>\n",
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       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>946684801181</td>\n",
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       "      <td>The time, the place, the torture, O, enforce it.</td>\n",
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       "      <th>1181</th>\n",
       "      <td>946684801181</td>\n",
       "      <td>#Lodovico_Oth</td>\n",
       "      <td>Myself will straight aboard, and to the state ...</td>\n",
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       "<p>2634 rows × 3 columns</p>\n",
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      "text/plain": [
       "        created_at      character  \\\n",
       "0     946684800000  #Roderigo_Oth   \n",
       "0     946684800000  #Roderigo_Oth   \n",
       "1     946684800001      #Iago_Oth   \n",
       "1     946684800001      #Iago_Oth   \n",
       "2     946684800002  #Roderigo_Oth   \n",
       "...            ...            ...   \n",
       "1181  946684801181  #Lodovico_Oth   \n",
       "1181  946684801181  #Lodovico_Oth   \n",
       "1181  946684801181  #Lodovico_Oth   \n",
       "1181  946684801181  #Lodovico_Oth   \n",
       "1181  946684801181  #Lodovico_Oth   \n",
       "\n",
       "                                            speech_sent  \n",
       "0                                  Tush, never tell me!  \n",
       "0     I take it much unkindly That thou, Iago, who h...  \n",
       "1                      ’Sblood, but you’ll not hear me!  \n",
       "1       If ever I did dream of such a matter, Abhor me.  \n",
       "2       Thou toldst me thou didst hold him in thy hate.  \n",
       "...                                                 ...  \n",
       "1181  Let it be hid.—Gratiano, keep the house, And s...  \n",
       "1181                                         To Cassio.  \n",
       "1181  To you, lord governor, Remains the censure of ...  \n",
       "1181   The time, the place, the torture, O, enforce it.  \n",
       "1181  Myself will straight aboard, and to the state ...  \n",
       "\n",
       "[2634 rows x 3 columns]"
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     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders.dataframe import DataFrameLoader\n",
    "from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores import FAISS\n",
    "\n",
    "embedder = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\") # or all-mpnet-base-v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence_embeddings = embedder.embed_documents(sentences['speech_sent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences['embeddings'] = sentence_embeddings\n",
    "sentences.to_csv(f'{drama}_embeddings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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       " -0.02002420462667942]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(sentences['speech_sent'].values, sentence_embeddings))[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "text_embedding_pairs = list(zip(sentences['speech_sent'].values, sentence_embeddings))\n",
    "db = FAISS.from_embeddings(text_embedding_pairs, embedder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the document, split it into chunks, embed each chunk and load it into the vector store.\n",
    "raw_documents = DataFrameLoader(sentences, 'speech_sent').load()\n",
    "#text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "#documents = text_splitter.split_documents(raw_documents)\n",
    "db = FAISS.from_documents(raw_documents, embedder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.save_local(f\"{drama}.faiss_index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = FAISS.load_local(f\"{drama}.faiss_index\", embedder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(Document(page_content='If ever I did dream of such a matter, Abhor me.'),\n",
       "  3.0471697e-13),\n",
       " (Document(page_content='’Tis a shrewd doubt, though it be but a dream.'),\n",
       "  1.0715976),\n",
       " (Document(page_content='This accident is not unlike my dream.'), 1.0934726),\n",
       " (Document(page_content='Pray heaven it be State matters, as you think, and no conception Nor no jealous toy concerning you.'),\n",
       "  1.132489)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.similarity_search_with_score('If ever I did dream of such a matter, Abhor me.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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