rag-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Load the dependencies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pyasn1-modules>=0.2.1->google-auth>=1.0.1->kubernetes>=28.1.0->chromadb->-r requirements.txt (line 21)) (0.6.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Load the PDF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import PyPDFLoader\n",
    "loader = PyPDFLoader(\"document.pdf\")\n",
    "pages = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(page_content, Attention Is All You Need\\nAshi...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 0})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(page_content, Recurrent models typically fact...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 1})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(page_content, Figure 1: The Transformer - mod...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 2})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(page_content, Scaled Dot-Product Attention\\n ...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 3})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(page_content, MultiHead( Q,K,V ) = Concat(hea...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 4})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(page_content, Table 1: Maximum path lengths, ...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 5})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(page_content, the input sequence centered aro...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 6})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(page_content, Table 2: The Transformer achiev...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 7})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(page_content, Table 3: Variations on the Tran...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 8})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(page_content, References\\n[1]Jimmy Lei Ba, Ja...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': 9})</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(page_content, [21] Minh-Thang Luong, Hieu Pha...</td>\n",
       "      <td>(metadata, {'source': 'document.pdf', 'page': ...</td>\n",
       "      <td>(type, Document)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    0  \\\n",
       "0   (page_content, Attention Is All You Need\\nAshi...   \n",
       "1   (page_content, Recurrent models typically fact...   \n",
       "2   (page_content, Figure 1: The Transformer - mod...   \n",
       "3   (page_content, Scaled Dot-Product Attention\\n ...   \n",
       "4   (page_content, MultiHead( Q,K,V ) = Concat(hea...   \n",
       "5   (page_content, Table 1: Maximum path lengths, ...   \n",
       "6   (page_content, the input sequence centered aro...   \n",
       "7   (page_content, Table 2: The Transformer achiev...   \n",
       "8   (page_content, Table 3: Variations on the Tran...   \n",
       "9   (page_content, References\\n[1]Jimmy Lei Ba, Ja...   \n",
       "10  (page_content, [21] Minh-Thang Luong, Hieu Pha...   \n",
       "\n",
       "                                                    1                 2  \n",
       "0   (metadata, {'source': 'document.pdf', 'page': 0})  (type, Document)  \n",
       "1   (metadata, {'source': 'document.pdf', 'page': 1})  (type, Document)  \n",
       "2   (metadata, {'source': 'document.pdf', 'page': 2})  (type, Document)  \n",
       "3   (metadata, {'source': 'document.pdf', 'page': 3})  (type, Document)  \n",
       "4   (metadata, {'source': 'document.pdf', 'page': 4})  (type, Document)  \n",
       "5   (metadata, {'source': 'document.pdf', 'page': 5})  (type, Document)  \n",
       "6   (metadata, {'source': 'document.pdf', 'page': 6})  (type, Document)  \n",
       "7   (metadata, {'source': 'document.pdf', 'page': 7})  (type, Document)  \n",
       "8   (metadata, {'source': 'document.pdf', 'page': 8})  (type, Document)  \n",
       "9   (metadata, {'source': 'document.pdf', 'page': 9})  (type, Document)  \n",
       "10  (metadata, {'source': 'document.pdf', 'page': ...  (type, Document)  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(pages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "page_content='Attention Is All You Need\\nAshish Vaswani∗\\nGoogle Brain\\navaswani@google.comNoam Shazeer∗\\nGoogle Brain\\nnoam@google.comNiki Parmar∗\\nGoogle Research\\nnikip@google.comJakob Uszkoreit∗\\nGoogle Research\\nusz@google.com\\nLlion Jones∗\\nGoogle Research\\nllion@google.comAidan N. Gomez∗†\\nUniversity of Toronto\\naidan@cs.toronto.eduŁukasz Kaiser∗\\nGoogle Brain\\nlukaszkaiser@google.com\\nIllia Polosukhin∗‡\\nillia.polosukhin@gmail.com\\nAbstract\\nThe dominant sequence transduction models are based on complex recurrent or\\nconvolutional neural networks that include an encoder and a decoder. The best\\nperforming models also connect the encoder and decoder through an attention\\nmechanism. We propose a new simple network architecture, the Transformer,\\nbased solely on attention mechanisms, dispensing with recurrence and convolutions\\nentirely. Experiments on two machine translation tasks show these models to\\nbe superior in quality while being more parallelizable and requiring signiﬁcantly\\nless time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-\\nto-German translation task, improving over the existing best results, including\\nensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task,\\nour model establishes a new single-model state-of-the-art BLEU score of 41.0 after\\ntraining for 3.5 days on eight GPUs, a small fraction of the training costs of the\\nbest models from the literature.\\n1 Introduction\\nRecurrent neural networks, long short-term memory [ 12] and gated recurrent [ 7] neural networks\\nin particular, have been ﬁrmly established as state of the art approaches in sequence modeling and\\ntransduction problems such as language modeling and machine translation [ 29,2,5]. Numerous\\nefforts have since continued to push the boundaries of recurrent language models and encoder-decoder\\narchitectures [31, 21, 13].\\n∗Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started\\nthe effort to evaluate this idea. Ashish, with Illia, designed and implemented the ﬁrst Transformer models and\\nhas been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head\\nattention and the parameter-free position representation and became the other person involved in nearly every\\ndetail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and\\ntensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and\\nefﬁcient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and\\nimplementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating\\nour research.\\n†Work performed while at Google Brain.\\n‡Work performed while at Google Research.\\n31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.' metadata={'source': 'document.pdf', 'page': 0}\n"
     ]
    }
   ],
   "source": [
    "page = pages[0]\n",
    "print(page)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attention Is All You Need\n",
      "Ashish Vaswani∗\n",
      "Google Brain\n",
      "avaswani@google.comNoam Shazeer∗\n",
      "Google Brain\n",
      "noam@google.comNiki Parmar∗\n",
      "Google Research\n",
      "nikip@google.comJakob Uszkoreit∗\n",
      "Google Research\n",
      "usz@google.com\n",
      "Llion Jones∗\n",
      "Google Research\n",
      "llion@google.comAidan N. Gomez∗†\n",
      "University of Toronto\n",
      "aidan@cs.toronto.eduŁukasz Kaiser∗\n",
      "Google Brain\n",
      "lukaszkaiser@google.com\n",
      "Illia Polosukhin∗‡\n",
      "illia.polosukhin@gmail.com\n",
      "Abstract\n",
      "The dominant sequence transduction models are based on complex recurrent or\n",
      "conv\n"
     ]
    }
   ],
   "source": [
    "print(page.page_content[0:500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'source': 'document.pdf', 'page': 0}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'document.pdf'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page.metadata['source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Document'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page.type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Load environment variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "load_dotenv(find_dotenv(), override=True)\n",
    "\n",
    "\n",
    "token = os.environ['HUGGINGFACEHUB_API_TOKEN']\n",
    "openai.api_key = os.environ['OPENAI_API_KEY']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. Document Splitting to Chunks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_splitter = CharacterTextSplitter(\n",
    "    separator=\"\\n\",\n",
    "    chunk_size=2000,\n",
    "    chunk_overlap=150,\n",
    "    length_function=len\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = text_splitter.split_documents(pages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chunks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Create Embeddings for the chunks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import HuggingFaceEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 0.3.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
      "  warn_deprecated(\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from tqdm.autonotebook import tqdm, trange\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "embedding = HuggingFaceEmbeddings()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. VectorStores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Chroma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "persist_dir = 'docs/chroma_document_pdf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -rf 'docs/chroma_document_pdf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectordb = Chroma.from_documents(\n",
    "    documents=chunks,\n",
    "    embedding=embedding,\n",
    "    persist_directory=persist_dir\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: Since Chroma 0.4.x the manual persistence method is no longer supported as docs are automatically persisted.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "vectordb.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n"
     ]
    }
   ],
   "source": [
    "print(vectordb._collection.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Similarity Search on the documents\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"What does this document explain about?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='sequence (y1,...,y m)of symbols one element at a time. At each step the model is auto-regressive\\n[9], consuming the previously generated symbols as additional input when generating the next.\\nThe Transformer follows this overall architecture using stacked self-attention and point-wise, fully\\nconnected layers for both the encoder and decoder, shown in the left and right halves of Figure 1,\\nrespectively.\\n3.1 Encoder and Decoder Stacks\\nEncoder: The encoder is composed of a stack of N= 6 identical layers. Each layer has two\\nsub-layers. The ﬁrst is a multi-head self-attention mechanism, and the second is a simple, position-\\n2', metadata={'page': 1, 'source': 'document.pdf'}),\n",
       " Document(page_content='[31] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang\\nMacherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine\\ntranslation system: Bridging the gap between human and machine translation. arXiv preprint\\narXiv:1609.08144 , 2016.\\n[32] Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with\\nfast-forward connections for neural machine translation. CoRR , abs/1606.04199, 2016.\\n11', metadata={'page': 10, 'source': 'document.pdf'}),\n",
       " Document(page_content='Table 3: Variations on the Transformer architecture. Unlisted values are identical to those of the base\\nmodel. All metrics are on the English-to-German translation development set, newstest2013. Listed\\nperplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to\\nper-word perplexities.\\nN d modeldffh d kdvPdropϵlstrain PPL BLEU params\\nsteps (dev) (dev)×106\\nbase 6 512 2048 8 64 64 0.1 0.1 100K 4.92 25.8 65\\n(A)1 512 512 5.29 24.9\\n4 128 128 5.00 25.5\\n16 32 32 4.91 25.8\\n32 16 16 5.01 25.4\\n(B)16 5.16 25.1 58\\n32 5.01 25.4 60\\n(C)2 6.11 23.7 36\\n4 5.19 25.3 50\\n8 4.88 25.5 80\\n256 32 32 5.75 24.5 28\\n1024 128 128 4.66 26.0 168\\n1024 5.12 25.4 53\\n4096 4.75 26.2 90\\n(D)0.0 5.77 24.6\\n0.2 4.95 25.5\\n0.0 4.67 25.3\\n0.2 5.47 25.7\\n(E) positional embedding instead of sinusoids 4.92 25.7\\nbig 6 1024 4096 16 0.3 300K 4.33 26.4 213\\nIn Table 3 rows (B), we observe that reducing the attention key size dkhurts model quality. This\\nsuggests that determining compatibility is not easy and that a more sophisticated compatibility\\nfunction than dot product may be beneﬁcial. We further observe in rows (C) and (D) that, as expected,\\nbigger models are better, and dropout is very helpful in avoiding over-ﬁtting. In row (E) we replace our\\nsinusoidal positional encoding with learned positional embeddings [ 8], and observe nearly identical\\nresults to the base model.\\n7 Conclusion\\nIn this work, we presented the Transformer, the ﬁrst sequence transduction model based entirely on\\nattention, replacing the recurrent layers most commonly used in encoder-decoder architectures with\\nmulti-headed self-attention.\\nFor translation tasks, the Transformer can be trained signiﬁcantly faster than architectures based\\non recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014\\nEnglish-to-French translation tasks, we achieve a new state of the art. In the former task our best\\nmodel outperforms even all previously reported ensembles.', metadata={'page': 8, 'source': 'document.pdf'})]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectordb.similarity_search(question, k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieval\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Document(page_content='[31] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang\\nMacherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine\\ntranslation system: Bridging the gap between human and machine translation. arXiv preprint\\narXiv:1609.08144 , 2016.\\n[32] Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with\\nfast-forward connections for neural machine translation. CoRR , abs/1606.04199, 2016.\\n11', metadata={'page': 10, 'source': 'document.pdf'}), Document(page_content='sequence (y1,...,y m)of symbols one element at a time. At each step the model is auto-regressive\\n[9], consuming the previously generated symbols as additional input when generating the next.\\nThe Transformer follows this overall architecture using stacked self-attention and point-wise, fully\\nconnected layers for both the encoder and decoder, shown in the left and right halves of Figure 1,\\nrespectively.\\n3.1 Encoder and Decoder Stacks\\nEncoder: The encoder is composed of a stack of N= 6 identical layers. Each layer has two\\nsub-layers. The ﬁrst is a multi-head self-attention mechanism, and the second is a simple, position-\\n2', metadata={'page': 1, 'source': 'document.pdf'}), Document(page_content='References\\n[1]Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint\\narXiv:1607.06450 , 2016.\\n[2]Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly\\nlearning to align and translate. CoRR , abs/1409.0473, 2014.\\n[3]Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc V . Le. Massive exploration of neural\\nmachine translation architectures. CoRR , abs/1703.03906, 2017.\\n[4]Jianpeng Cheng, Li Dong, and Mirella Lapata. Long short-term memory-networks for machine\\nreading. arXiv preprint arXiv:1601.06733 , 2016.\\n[5]Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk,\\nand Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical\\nmachine translation. CoRR , abs/1406.1078, 2014.\\n[6]Francois Chollet. Xception: Deep learning with depthwise separable convolutions. arXiv\\npreprint arXiv:1610.02357 , 2016.\\n[7]Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation\\nof gated recurrent neural networks on sequence modeling. CoRR , abs/1412.3555, 2014.\\n[8]Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Convolu-\\ntional sequence to sequence learning. arXiv preprint arXiv:1705.03122v2 , 2017.\\n[9]Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint\\narXiv:1308.0850 , 2013.\\n[10] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for im-\\nage recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern\\nRecognition , pages 770–778, 2016.\\n[11] Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, and Jürgen Schmidhuber. Gradient ﬂow in\\nrecurrent nets: the difﬁculty of learning long-term dependencies, 2001.\\n[12] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation ,\\n9(8):1735–1780, 1997.\\n[13] Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. Exploring', metadata={'page': 9, 'source': 'document.pdf'})]\n"
     ]
    }
   ],
   "source": [
    "from langchain.llms import Ollama\n",
    "\n",
    "question = \"Who are the primary authors of this document?\"\n",
    "docs_ss = vectordb.similarity_search(question, k=3)\n",
    "print(docs_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Document(page_content='[31] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang\\nMacherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine\\ntranslation system: Bridging the gap between human and machine translation. arXiv preprint\\narXiv:1609.08144 , 2016.\\n[32] Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with\\nfast-forward connections for neural machine translation. CoRR , abs/1606.04199, 2016.\\n11', metadata={'page': 10, 'source': 'document.pdf'}), Document(page_content='sequence (y1,...,y m)of symbols one element at a time. At each step the model is auto-regressive\\n[9], consuming the previously generated symbols as additional input when generating the next.\\nThe Transformer follows this overall architecture using stacked self-attention and point-wise, fully\\nconnected layers for both the encoder and decoder, shown in the left and right halves of Figure 1,\\nrespectively.\\n3.1 Encoder and Decoder Stacks\\nEncoder: The encoder is composed of a stack of N= 6 identical layers. Each layer has two\\nsub-layers. The ﬁrst is a multi-head self-attention mechanism, and the second is a simple, position-\\n2', metadata={'page': 1, 'source': 'document.pdf'}), Document(page_content='output values. These are concatenated and once again projected, resulting in the ﬁnal values, as\\ndepicted in Figure 2.\\nMulti-head attention allows the model to jointly attend to information from different representation\\nsubspaces at different positions. With a single attention head, averaging inhibits this.\\n4To illustrate why the dot products get large, assume that the components of qandkare independent random\\nvariables with mean 0and variance 1. Then their dot product, q·k=∑dk\\ni=1qiki, has mean 0and variance dk.\\n4', metadata={'page': 3, 'source': 'document.pdf'})]\n"
     ]
    }
   ],
   "source": [
    "docs_mmr = vectordb.max_marginal_relevance_search(question, k=3)\n",
    "print(docs_mmr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = vectordb.similarity_search(\n",
    "    question,\n",
    "    k=3,\n",
    "    filter={\"source\": \"document.pdf\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'page': 10, 'source': 'document.pdf'}\n",
      "{'page': 1, 'source': 'document.pdf'}\n",
      "{'page': 9, 'source': 'document.pdf'}\n"
     ]
    }
   ],
   "source": [
    "for d in docs:\n",
    "    print(d.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import Ollama\n",
    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
    "from langchain.chains.query_constructor.base import AttributeInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_field_info = [\n",
    "    AttributeInfo(\n",
    "        name=\"source\",\n",
    "        description=\"The document is a chunk and is from, should be one of `document.pdf`\",\n",
    "        type=\"string\",\n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"page\",\n",
    "        description=\"The page is one of the pages from a research paper.\",\n",
    "        type=\"integer\",\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "document_content_description = \"The document is a research paper.\"\n",
    "llm = Ollama(model='llama3', temperature=0)\n",
    "retriever = SelfQueryRetriever.from_llm(\n",
    "    llm,\n",
    "    vectordb,\n",
    "    document_content_description,\n",
    "    metadata_field_info,\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"Who are the authors of this document?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 0.3.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Document(page_content='[31] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang\\nMacherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine\\ntranslation system: Bridging the gap between human and machine translation. arXiv preprint\\narXiv:1609.08144 , 2016.\\n[32] Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with\\nfast-forward connections for neural machine translation. CoRR , abs/1606.04199, 2016.\\n11', metadata={'page': 10, 'source': 'document.pdf'}), Document(page_content='[21] Minh-Thang Luong, Hieu Pham, and Christopher D Manning. Effective approaches to attention-\\nbased neural machine translation. arXiv preprint arXiv:1508.04025 , 2015.\\n[22] Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. A decomposable attention\\nmodel. In Empirical Methods in Natural Language Processing , 2016.\\n[23] Romain Paulus, Caiming Xiong, and Richard Socher. A deep reinforced model for abstractive\\nsummarization. arXiv preprint arXiv:1705.04304 , 2017.\\n[24] Oﬁr Press and Lior Wolf. Using the output embedding to improve language models. arXiv\\npreprint arXiv:1608.05859 , 2016.\\n[25] Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words\\nwith subword units. arXiv preprint arXiv:1508.07909 , 2015.\\n[26] Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton,\\nand Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts\\nlayer. arXiv preprint arXiv:1701.06538 , 2017.\\n[27] Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdi-\\nnov. Dropout: a simple way to prevent neural networks from overﬁtting. Journal of Machine\\nLearning Research , 15(1):1929–1958, 2014.\\n[28] Sainbayar Sukhbaatar, arthur szlam, Jason Weston, and Rob Fergus. End-to-end memory\\nnetworks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors,\\nAdvances in Neural Information Processing Systems 28 , pages 2440–2448. Curran Associates,\\nInc., 2015.\\n[29] Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. Sequence to sequence learning with neural\\nnetworks. In Advances in Neural Information Processing Systems , pages 3104–3112, 2014.\\n[30] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna.\\nRethinking the inception architecture for computer vision. CoRR , abs/1512.00567, 2015.\\n[31] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang', metadata={'page': 10, 'source': 'document.pdf'}), Document(page_content='References\\n[1]Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint\\narXiv:1607.06450 , 2016.\\n[2]Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly\\nlearning to align and translate. CoRR , abs/1409.0473, 2014.\\n[3]Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc V . Le. Massive exploration of neural\\nmachine translation architectures. CoRR , abs/1703.03906, 2017.\\n[4]Jianpeng Cheng, Li Dong, and Mirella Lapata. Long short-term memory-networks for machine\\nreading. arXiv preprint arXiv:1601.06733 , 2016.\\n[5]Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk,\\nand Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical\\nmachine translation. CoRR , abs/1406.1078, 2014.\\n[6]Francois Chollet. Xception: Deep learning with depthwise separable convolutions. arXiv\\npreprint arXiv:1610.02357 , 2016.\\n[7]Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation\\nof gated recurrent neural networks on sequence modeling. CoRR , abs/1412.3555, 2014.\\n[8]Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Convolu-\\ntional sequence to sequence learning. arXiv preprint arXiv:1705.03122v2 , 2017.\\n[9]Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint\\narXiv:1308.0850 , 2013.\\n[10] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for im-\\nage recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern\\nRecognition , pages 770–778, 2016.\\n[11] Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, and Jürgen Schmidhuber. Gradient ﬂow in\\nrecurrent nets: the difﬁculty of learning long-term dependencies, 2001.\\n[12] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation ,\\n9(8):1735–1780, 1997.\\n[13] Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. Exploring', metadata={'page': 9, 'source': 'document.pdf'}), Document(page_content='9(8):1735–1780, 1997.\\n[13] Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. Exploring\\nthe limits of language modeling. arXiv preprint arXiv:1602.02410 , 2016.\\n[14] Łukasz Kaiser and Ilya Sutskever. Neural GPUs learn algorithms. In International Conference\\non Learning Representations (ICLR) , 2016.\\n[15] Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Ko-\\nray Kavukcuoglu. Neural machine translation in linear time. arXiv preprint arXiv:1610.10099v2 ,\\n2017.\\n[16] Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush. Structured attention networks.\\nInInternational Conference on Learning Representations , 2017.\\n[17] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR , 2015.\\n[18] Oleksii Kuchaiev and Boris Ginsburg. Factorization tricks for LSTM networks. arXiv preprint\\narXiv:1703.10722 , 2017.\\n[19] Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen\\nZhou, and Yoshua Bengio. A structured self-attentive sentence embedding. arXiv preprint\\narXiv:1703.03130 , 2017.\\n[20] Samy Bengio Łukasz Kaiser. Can active memory replace attention? In Advances in Neural\\nInformation Processing Systems, (NIPS) , 2016.\\n10', metadata={'page': 9, 'source': 'document.pdf'})]\n"
     ]
    }
   ],
   "source": [
    "docs = retriever.get_relevant_documents(question)\n",
    "print(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.retrievers import ContextualCompressionRetriever\n",
    "from langchain.retrievers.document_compressors import LLMChainExtractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pretty_print_docs(docs):\n",
    "    print(f\"\\n{'-' * 100}\\n\".join([f\"Document {i+1}:\\n\\n\" +\n",
    "          d.page_content for i, d in enumerate(docs)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrap our vectorstore\n",
    "llm = Ollama(temperature=0, model=\"llama3\")\n",
    "compressor = LLMChainExtractor.from_llm(llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "compression_retriever = ContextualCompressionRetriever(\n",
    "    base_compressor=compressor,\n",
    "    base_retriever=vectordb.as_retriever()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import RetrievalQA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "qa_chain = RetrievalQA.from_chain_type(\n",
    "    llm,\n",
    "    retriever=vectordb.as_retriever()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: The method `Chain.__call__` was deprecated in langchain 0.1.0 and will be removed in 0.3.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "result = qa_chain({\"query\": question})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"I don't know the answer to that question. The provided text appears to be a list of references with citations and does not contain information about the authors of the document.\""
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[\"result\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prompt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "# Build prompt\n",
    "template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say \"thanks for asking!\" at the end of the answer. \n",
    "{context}\n",
    "Question: {question}\n",
    "Helpful Answer:\"\"\"\n",
    "QA_CHAIN_PROMPT = PromptTemplate.from_template(template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run chain\n",
    "qa_chain = RetrievalQA.from_chain_type(\n",
    "    llm,\n",
    "    retriever=vectordb.as_retriever(),\n",
    "    return_source_documents=True,\n",
    "    chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = qa_chain({\"query\": question})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content='[31] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang\\nMacherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine\\ntranslation system: Bridging the gap between human and machine translation. arXiv preprint\\narXiv:1609.08144 , 2016.\\n[32] Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with\\nfast-forward connections for neural machine translation. CoRR , abs/1606.04199, 2016.\\n11', metadata={'page': 10, 'source': 'document.pdf'})"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[\"source_documents\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "pp = pprint.PrettyPrinter(width=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The authors of this document are listed at the '\n",
      " 'end. They include:\\n'\n",
      " '\\n'\n",
      " '* Yonghui Wu\\n'\n",
      " '* Mike Schuster\\n'\n",
      " '* Zhifeng Chen\\n'\n",
      " '* Quoc V Le\\n'\n",
      " '* Mohammad Norouzi\\n'\n",
      " '* Wolfgang Macherey\\n'\n",
      " '* Maxim Krikun\\n'\n",
      " '* Yuan Cao\\n'\n",
      " '* Qin Gao\\n'\n",
      " '* Klaus Macherey\\n'\n",
      " '\\n'\n",
      " 'And many others, as listed in the references.\\n'\n",
      " '\\n'\n",
      " 'Thanks for asking!')\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(result[\"result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "page_content='[31] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang\\nMacherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine\\ntranslation system: Bridging the gap between human and machine translation. arXiv preprint\\narXiv:1609.08144 , 2016.\\n[32] Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with\\nfast-forward connections for neural machine translation. CoRR , abs/1606.04199, 2016.\\n11' metadata={'page': 10, 'source': 'document.pdf'}\n"
     ]
    }
   ],
   "source": [
    "print(result[\"source_documents\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The authors of this document are listed at the end. They include:  * Yonghui Wu * Mike Schuster '\n",
      " '* Zhifeng Chen * Quoc V Le * Mohammad Norouzi * Wolfgang Macherey * Maxim Krikun * Yuan Cao * '\n",
      " 'Qin Gao * Klaus Macherey  And many others, as listed in the references.  Thanks for asking!')\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "# Get the output\n",
    "output = result[\"result\"]\n",
    "\n",
    "# If the output is a string, replace newline characters with spaces\n",
    "\n",
    "\n",
    "if isinstance(output, str):\n",
    "    output = output.replace('\\n', ' ')\n",
    "\n",
    "# Create a pretty printer\n",
    "pp = pprint.PrettyPrinter(width=100)\n",
    "\n",
    "# Use the pretty printer to print the output\n",
    "pp.pprint(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat_bishop(question, history):\n",
    "    from langchain.prompts import PromptTemplate\n",
    "\n",
    "    # Build prompt\n",
    "    template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say \"thanks for asking!\" at the end of the answer. \n",
    "    {context}\n",
    "    Question: {question}\n",
    "    Helpful Answer:\"\"\"\n",
    "    QA_CHAIN_PROMPT = PromptTemplate.from_template(template)\n",
    "\n",
    "    result = qa_chain({\"query\": question})\n",
    "\n",
    "    import pprint\n",
    "    pp = pprint.PrettyPrinter(width=50)\n",
    "\n",
    "    # Use the pretty printer to print the long string\n",
    "    pp.pprint(result[\"result\"])\n",
    "\n",
    "    # response = llm.invoke(message)\n",
    "    return result[\"result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "gradio_interface = gr.ChatInterface(\n",
    "    chat_bishop,\n",
    "    chatbot=gr.Chatbot(),\n",
    "    textbox=gr.Textbox(placeholder=\"Example: What is your Question?\",\n",
    "                       container=False, scale=7),\n",
    "    title=\"Hey, welcome. Please ask your Question\",\n",
    "    description=f\"Ask the chatbot a question!\",\n",
    "    theme='gradio/base',\n",
    "    retry_btn=None,\n",
    "    undo_btn=\"Delete Previous\",\n",
    "    clear_btn=\"Clear\",\n",
    "\n",
    ")\n",
    "\n",
    "gradio_interface.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "langchain-chatbot",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}