formultiiple.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rHfhuOcFIM6E"
   },
   "source": [
    "#**Step 1: Install All the Required Packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qsA6xR7oGmoZ",
    "outputId": "c017ea64-d414-4c5a-ce2b-6aa49d13dd9c"
   },
   "outputs": [],
   "source": [
    "#!pip -q install langchain\n",
    "#!pip -q install bitsandbytes accelerate xformers einops\n",
    "#!pip -q install datasets loralib sentencepiece\n",
    "#!pip -q install pypdf\n",
    "\n",
    "#!pip -q install sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LHQbIhq3OMB-",
    "outputId": "bd87bf5b-81f4-4f0a-bf0b-29472eaa46d4"
   },
   "outputs": [],
   "source": [
    "#!pip install chromadb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KS3BVnVbyAYr",
    "outputId": "10819e48-a259-44a1-ad0e-b0cc20cd9fc6"
   },
   "outputs": [],
   "source": [
    "#!pip install openai\n",
    "#!pip install tiktoken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wdMi5csDI57_"
   },
   "source": [
    "#**Step 2: Import All the Required Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "fnCwE83cIpaW"
   },
   "outputs": [],
   "source": [
    "from langchain.document_loaders import PyPDFLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "NsPrXLUBJGs6"
   },
   "outputs": [],
   "source": [
    "from langchain.document_loaders import TextLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Rf0I9tVUKEk6"
   },
   "outputs": [],
   "source": [
    "from langchain.document_loaders import Docx2txtLoader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "UnQ1wQ3sLDCI"
   },
   "outputs": [],
   "source": [
    "from langchain.text_splitter import CharacterTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "CwL28Bl6MtBR"
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings import HuggingFaceEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "nW6LntbfNZYu"
   },
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Chroma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "G2y6OYAkObHA"
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import notebook_login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "X9DiVACTPfWx"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "8d4O7Jz8PfbC"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForCausalLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "7dW0NL5gQ2Ph"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\transformers\\utils\\generic.py:260: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  torch.utils._pytree._register_pytree_node(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\transformers\\utils\\generic.py:260: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  torch.utils._pytree._register_pytree_node(\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "5d5fB9MXSGV2"
   },
   "outputs": [],
   "source": [
    "from langchain import HuggingFacePipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Ht8TUPXsRr0_"
   },
   "outputs": [],
   "source": [
    "from langchain.chains import ConversationalRetrievalChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "PvePppDWSxJM"
   },
   "outputs": [],
   "source": [
    "from langchain.memory import ConversationBufferMemory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "4m8LiUl4xIO2"
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "0XXyzEW1xIVb"
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "eh3j9VS5JLQY"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "8eRHhti8B-Tw"
   },
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4micSehLLxUa"
   },
   "source": [
    "#**Step 3: Load the Documents and Extract Text From Them**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "arhUc1aPJNSg"
   },
   "outputs": [],
   "source": [
    "!mkdir denish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "itRfVkSfJhaL"
   },
   "outputs": [],
   "source": [
    "document=[]\n",
    "for file in os.listdir(\"docs\"):\n",
    "  if file.endswith(\".pdf\"):\n",
    "    pdf_path=\"./docs/\"+file\n",
    "    loader=PyPDFLoader(pdf_path)\n",
    "    document.extend(loader.load())\n",
    "  elif file.endswith('.docx') or file.endswith('.doc'):\n",
    "    doc_path=\"./docs/\"+file\n",
    "    loader=Docx2txtLoader(doc_path)\n",
    "    document.extend(loader.load())\n",
    "  elif file.endswith('.txt'):\n",
    "    text_path=\"./docs/\"+file\n",
    "    loader=TextLoader(text_path)\n",
    "    document.extend(loader.load())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xl9p848iKzK8",
    "outputId": "5546d38b-33e2-457d-f576-e3a4ffd9fca7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content=' \\n     Denish Lamichhane  \\nTarkeshwor -5 Kathmandu  \\n9866998742  \\ndenishlamichhane1@gmail.com  \\n \\n2024 -03-13 \\nKathmandu, Nepal  \\nSubject : Application for Data Science Internship Position  \\n \\nDear Sir/Madam,  \\nI am writing to express my deep interest in the Data Science Internship  position at your \\ncompany, as seen on LinkedIn. With my proficiency in Python  and a passion for data \\nanalysis , I am eager to contribute to your team and enhance my skills in the field of data \\nscience.  \\nHaving worked on real -life projects, I am keen to further develop my abilities in a fast -\\npaced environment. I am confident that my analytical skills and enthusiasm for data science \\nmake me a valuable addition to your team.  \\nThank you for considering my application. I look forward to the opportunity to discuss how \\nI can contribute to your projects as a Data Science Intern.  \\nSincerely,  \\nDenish Lamichhane  ', metadata={'source': './docs/cover Letter.pdf', 'page': 0})]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cc9grhjvrJ1O",
    "outputId": "73e59453-ed88-4555-e277-a45244b236d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(document)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uITod3JGL59W"
   },
   "source": [
    "#**Step 4: Split the Document into Chunks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "CkOAlCQaK7xJ"
   },
   "outputs": [],
   "source": [
    "document_splitter=CharacterTextSplitter(separator='\\n', chunk_size=500, chunk_overlap=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "Rx-Kg9NEMF3i"
   },
   "outputs": [],
   "source": [
    "\n",
    "document_chunks=document_splitter.split_documents(document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-SbVQuZ0MRTd",
    "outputId": "7fd6f786-a1ca-4ea6-e3d4-bfa63bd58ff1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(document_chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "knyzL2cDMNnF",
    "outputId": "a6b128f2-f8d6-470b-8a59-1c7d1a4ff008"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content='Denish Lamichhane  \\nTarkeshwor -5 Kathmandu  \\n9866998742  \\ndenishlamichhane1@gmail.com  \\n \\n2024 -03-13 \\nKathmandu, Nepal  \\nSubject : Application for Data Science Internship Position  \\n \\nDear Sir/Madam,  \\nI am writing to express my deep interest in the Data Science Internship  position at your \\ncompany, as seen on LinkedIn. With my proficiency in Python  and a passion for data \\nanalysis , I am eager to contribute to your team and enhance my skills in the field of data \\nscience.', metadata={'source': './docs/cover Letter.pdf', 'page': 0})"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "document_chunks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p02zc4-KfQZf",
    "outputId": "14ee10b4-063c-41a4-d882-973ccd8e98f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content='science.  \\nHaving worked on real -life projects, I am keen to further develop my abilities in a fast -\\npaced environment. I am confident that my analytical skills and enthusiasm for data science \\nmake me a valuable addition to your team.  \\nThank you for considering my application. I look forward to the opportunity to discuss how \\nI can contribute to your projects as a Data Science Intern.  \\nSincerely,  \\nDenish Lamichhane', metadata={'source': './docs/cover Letter.pdf', 'page': 0})"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "document_chunks[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A3DdRaiAMeK4"
   },
   "source": [
    "#**Step 5: Download the Embeddings from Hugging Face, Download the Sentence Transformer Embeddings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sentence-transformers in c:\\users\\user\\anaconda3\\lib\\site-packages (2.5.1)\n",
      "Requirement already satisfied: transformers<5.0.0,>=4.32.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sentence-transformers) (4.32.1)\n",
      "Requirement already satisfied: tqdm in c:\\users\\user\\anaconda3\\lib\\site-packages (from sentence-transformers) (4.66.2)\n",
      "Requirement already satisfied: torch>=1.11.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sentence-transformers) (2.2.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\user\\anaconda3\\lib\\site-packages (from sentence-transformers) (1.26.4)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\user\\anaconda3\\lib\\site-packages (from sentence-transformers) (1.3.0)\n",
      "Requirement already satisfied: scipy in c:\\users\\user\\anaconda3\\lib\\site-packages (from sentence-transformers) (1.11.1)\n",
      "Requirement already satisfied: huggingface-hub>=0.15.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sentence-transformers) (0.15.1)\n",
      "Requirement already satisfied: Pillow in c:\\users\\user\\anaconda3\\lib\\site-packages (from sentence-transformers) (10.0.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\user\\anaconda3\\lib\\site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (3.9.0)\n",
      "Requirement already satisfied: fsspec in c:\\users\\user\\anaconda3\\lib\\site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2024.3.1)\n",
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      "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (6.0.1)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (4.9.0)\n",
      "Requirement already satisfied: packaging>=20.9 in c:\\users\\user\\anaconda3\\lib\\site-packages (from huggingface-hub>=0.15.1->sentence-transformers) (23.2)\n",
      "Requirement already satisfied: sympy in c:\\users\\user\\anaconda3\\lib\\site-packages (from torch>=1.11.0->sentence-transformers) (1.11.1)\n",
      "Requirement already satisfied: networkx in c:\\users\\user\\anaconda3\\lib\\site-packages (from torch>=1.11.0->sentence-transformers) (3.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from torch>=1.11.0->sentence-transformers) (3.1.2)\n",
      "Requirement already satisfied: colorama in c:\\users\\user\\anaconda3\\lib\\site-packages (from tqdm->sentence-transformers) (0.4.6)\n",
      "Requirement already satisfied: regex!=2019.12.17 in c:\\users\\user\\anaconda3\\lib\\site-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers) (2023.12.25)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers) (0.13.2)\n",
      "Requirement already satisfied: safetensors>=0.3.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers) (0.3.2)\n",
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      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn->sentence-transformers) (2.2.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from jinja2->torch>=1.11.0->sentence-transformers) (2.1.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2024.2.2)\n",
      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sympy->torch>=1.11.0->sentence-transformers) (1.3.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install sentence-transformers"
   ]
  },
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     ]
    },
    "id": "A138fmmJMQtZ",
    "outputId": "1e952224-a104-41ed-da8b-e9a6b594884e"
   },
   "outputs": [],
   "source": [
    "embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "1P9Dbm_cxtq9"
   },
   "outputs": [],
   "source": [
    "os.environ[\"OPENAI_API_KEY\"]=\"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\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "vZCkHmS_xszP"
   },
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Mf6GzRYMzWt",
    "outputId": "bb14fafb-7374-4607-f10f-9850f72014a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OpenAIEmbeddings(client=<openai.resources.embeddings.Embeddings object at 0x0000019B518D8CD0>, async_client=<openai.resources.embeddings.AsyncEmbeddings object at 0x0000019B4E3BE7D0>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base=None, openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='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', openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=2, request_timeout=None, headers=None, tiktoken_enabled=True, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={}, skip_empty=False, default_headers=None, default_query=None, retry_min_seconds=4, retry_max_seconds=20, http_client=None)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mjmvoY9GNTTv"
   },
   "source": [
    "#**Step 6: Setting Up Chroma as our Vector Database**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VJ6jc4vCSS6Q"
   },
   "source": [
    "Converting the Document Chunks into Embedding and save them to the vector store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# errror will come due to the api billing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VoWH6s_xNQYG"
   },
   "outputs": [],
   "source": [
    "vectordb=Chroma.from_documents(document_chunks,embedding=embeddings, persist_directory='./data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "csmhafp41feh"
   },
   "outputs": [],
   "source": [
    "vectordb.persist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iKK7UgGLOkcm"
   },
   "source": [
    "#**Step 7: Login into Hugging Face Account to Download the Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
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      "63d3b4cd9e534dd3b99fb65ee2b6276e",
      "4edcb21607e146c5a855c21b4812503b",
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      "380d6b783f2446a9910ceb38bfd7e06b",
      "a68431f3638d488993693fac16bc8677",
      "496a802a200841a39092907d1f4638f4",
      "bfe74aa820d64b25b7eb7d490e7734cd",
      "c608e69edbbc49c286b9602aae0afffe",
      "471791d7ab054922bca940231058a40e",
      "639cb8166737423a8583b9c92529c6ed",
      "ecffdc09b18b4dc99e766248d6bf5d95",
      "e6c8756b208d4ec7a71c24377174a2a7",
      "767a2316a5d24acf8afe3a93861a818a",
      "9141c0795a504c1b99d394450f111eea",
      "24c8cef880154995aaa6034ab4420ae8",
      "7ba2f53b3bd4480983820c03296504c7",
      "5cc8633921064ef6b392b3be376b1604",
      "9f2f90a414ab4979829b314cfd6235e1"
     ]
    },
    "id": "qg9Zn9vqOjt2",
    "outputId": "eb5a0bbf-b20f-408e-c3a1-f955693937fa"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f788e448b3c4b8b85f80df1ca6c34f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Mqr0HMBOz6H"
   },
   "source": [
    "#**Step 8: Download the Llama 2 7B Chat Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\transformers\\models\\auto\\tokenization_auto.py:631: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "LocalTokenNotFoundError",
     "evalue": "Token is required (`token=True`), but no token found. You need to provide a token or be logged in to Hugging Face with `huggingface-cli login` or `huggingface_hub.login`. See https://huggingface.co/settings/tokens.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLocalTokenNotFoundError\u001b[0m                   Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[56], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDenishLamichhane/ChatBotIntern\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      2\u001b[0m                                           use_auth_token\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,)\n\u001b[0;32m      5\u001b[0m model \u001b[38;5;241m=\u001b[39m AutoModelForCausalLM\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDenishLamichhane/ChatBotIntern\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m      6\u001b[0m                                              device_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      7\u001b[0m                                              torch_dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat16,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     10\u001b[0m                                               load_in_4bit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m     11\u001b[0m                                              )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\models\\auto\\tokenization_auto.py:677\u001b[0m, in \u001b[0;36mAutoTokenizer.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m    674\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m tokenizer_class\u001b[38;5;241m.\u001b[39mfrom_pretrained(pretrained_model_name_or_path, \u001b[38;5;241m*\u001b[39minputs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    676\u001b[0m \u001b[38;5;66;03m# Next, let's try to use the tokenizer_config file to get the tokenizer class.\u001b[39;00m\n\u001b[1;32m--> 677\u001b[0m tokenizer_config \u001b[38;5;241m=\u001b[39m get_tokenizer_config(pretrained_model_name_or_path, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m tokenizer_config:\n\u001b[0;32m    679\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m tokenizer_config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\models\\auto\\tokenization_auto.py:510\u001b[0m, in \u001b[0;36mget_tokenizer_config\u001b[1;34m(pretrained_model_name_or_path, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, **kwargs)\u001b[0m\n\u001b[0;32m    507\u001b[0m     token \u001b[38;5;241m=\u001b[39m use_auth_token\n\u001b[0;32m    509\u001b[0m commit_hash \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 510\u001b[0m resolved_config_file \u001b[38;5;241m=\u001b[39m cached_file(\n\u001b[0;32m    511\u001b[0m     pretrained_model_name_or_path,\n\u001b[0;32m    512\u001b[0m     TOKENIZER_CONFIG_FILE,\n\u001b[0;32m    513\u001b[0m     cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[0;32m    514\u001b[0m     force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[0;32m    515\u001b[0m     resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[0;32m    516\u001b[0m     proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[0;32m    517\u001b[0m     token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[0;32m    518\u001b[0m     revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[0;32m    519\u001b[0m     local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[0;32m    520\u001b[0m     subfolder\u001b[38;5;241m=\u001b[39msubfolder,\n\u001b[0;32m    521\u001b[0m     _raise_exceptions_for_missing_entries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    522\u001b[0m     _raise_exceptions_for_connection_errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    523\u001b[0m     _commit_hash\u001b[38;5;241m=\u001b[39mcommit_hash,\n\u001b[0;32m    524\u001b[0m )\n\u001b[0;32m    525\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m resolved_config_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    526\u001b[0m     logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not locate the tokenizer configuration file, will try to use the model config instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\utils\\hub.py:428\u001b[0m, in \u001b[0;36mcached_file\u001b[1;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[0;32m    425\u001b[0m user_agent \u001b[38;5;241m=\u001b[39m http_user_agent(user_agent)\n\u001b[0;32m    426\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    427\u001b[0m     \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[1;32m--> 428\u001b[0m     resolved_file \u001b[38;5;241m=\u001b[39m hf_hub_download(\n\u001b[0;32m    429\u001b[0m         path_or_repo_id,\n\u001b[0;32m    430\u001b[0m         filename,\n\u001b[0;32m    431\u001b[0m         subfolder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(subfolder) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m subfolder,\n\u001b[0;32m    432\u001b[0m         repo_type\u001b[38;5;241m=\u001b[39mrepo_type,\n\u001b[0;32m    433\u001b[0m         revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[0;32m    434\u001b[0m         cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[0;32m    435\u001b[0m         user_agent\u001b[38;5;241m=\u001b[39muser_agent,\n\u001b[0;32m    436\u001b[0m         force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[0;32m    437\u001b[0m         proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[0;32m    438\u001b[0m         resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[0;32m    439\u001b[0m         token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[0;32m    440\u001b[0m         local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[0;32m    441\u001b[0m     )\n\u001b[0;32m    442\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m GatedRepoError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    443\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[0;32m    444\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are trying to access a gated repo.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mMake sure to request access at \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    445\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://huggingface.co/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_repo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and pass a token having permission to this repo either \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    446\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby logging in with `huggingface-cli login` or by passing `token=<your_token>`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    447\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\huggingface_hub\\utils\\_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[0;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:1181\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[1;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout)\u001b[0m\n\u001b[0;32m   1177\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m pointer_path\n\u001b[0;32m   1179\u001b[0m url \u001b[38;5;241m=\u001b[39m hf_hub_url(repo_id, filename, repo_type\u001b[38;5;241m=\u001b[39mrepo_type, revision\u001b[38;5;241m=\u001b[39mrevision)\n\u001b[1;32m-> 1181\u001b[0m headers \u001b[38;5;241m=\u001b[39m build_hf_headers(\n\u001b[0;32m   1182\u001b[0m     token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[0;32m   1183\u001b[0m     library_name\u001b[38;5;241m=\u001b[39mlibrary_name,\n\u001b[0;32m   1184\u001b[0m     library_version\u001b[38;5;241m=\u001b[39mlibrary_version,\n\u001b[0;32m   1185\u001b[0m     user_agent\u001b[38;5;241m=\u001b[39muser_agent,\n\u001b[0;32m   1186\u001b[0m )\n\u001b[0;32m   1188\u001b[0m url_to_download \u001b[38;5;241m=\u001b[39m url\n\u001b[0;32m   1189\u001b[0m etag \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\huggingface_hub\\utils\\_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[0;32m    116\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\huggingface_hub\\utils\\_headers.py:121\u001b[0m, in \u001b[0;36mbuild_hf_headers\u001b[1;34m(token, is_write_action, library_name, library_version, user_agent)\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     49\u001b[0m \u001b[38;5;124;03mBuild headers dictionary to send in a HF Hub call.\u001b[39;00m\n\u001b[0;32m     50\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;124;03m        If `token=True` but token is not saved locally.\u001b[39;00m\n\u001b[0;32m    119\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    120\u001b[0m \u001b[38;5;66;03m# Get auth token to send\u001b[39;00m\n\u001b[1;32m--> 121\u001b[0m token_to_send \u001b[38;5;241m=\u001b[39m get_token_to_send(token)\n\u001b[0;32m    122\u001b[0m _validate_token_to_send(token_to_send, is_write_action\u001b[38;5;241m=\u001b[39mis_write_action)\n\u001b[0;32m    124\u001b[0m \u001b[38;5;66;03m# Combine headers\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\huggingface_hub\\utils\\_headers.py:153\u001b[0m, in \u001b[0;36mget_token_to_send\u001b[1;34m(token)\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m token \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m    152\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m cached_token \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 153\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LocalTokenNotFoundError(\n\u001b[0;32m    154\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mToken is required (`token=True`), but no token found. You\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    155\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m need to provide a token or be logged in to Hugging Face with\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    156\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m `huggingface-cli login` or `huggingface_hub.login`. See\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    157\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m https://huggingface.co/settings/tokens.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    158\u001b[0m         )\n\u001b[0;32m    159\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_token\n\u001b[0;32m    161\u001b[0m \u001b[38;5;66;03m# Case implicit use of the token is forbidden by env variable\u001b[39;00m\n",
      "\u001b[1;31mLocalTokenNotFoundError\u001b[0m: Token is required (`token=True`), but no token found. You need to provide a token or be logged in to Hugging Face with `huggingface-cli login` or `huggingface_hub.login`. See https://huggingface.co/settings/tokens."
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"DenishLamichhane/ChatBotIntern\",\n",
    "                                          use_auth_token=True,)\n",
    "\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\"DenishLamichhane/ChatBotIntern\",\n",
    "                                             device_map='auto',\n",
    "                                             torch_dtype=torch.float16,\n",
    "                                             use_auth_token=True,\n",
    "                                              #load_in_8bit=True,\n",
    "                                              load_in_4bit=True\n",
    "                                             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_CH-j3rzQ7j5"
   },
   "source": [
    "#**Step 9: Creating a Hugging Face Pipeline**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7jvzHFEEQzHm"
   },
   "outputs": [],
   "source": [
    "pipe=pipeline(\"text-generation\",\n",
    "              model=model,\n",
    "              tokenizer=tokenizer,\n",
    "              torch_dtype=torch.bfloat16,\n",
    "              device_map='auto',\n",
    "              max_new_tokens=512,\n",
    "              min_new_tokens=-1,\n",
    "              top_k=30\n",
    "\n",
    "              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i-ZHxO1WRcxT"
   },
   "outputs": [],
   "source": [
    "llm=HuggingFacePipeline(pipeline=pipe, model_kwargs={'temperature':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G9vCMg5ryqA6"
   },
   "outputs": [],
   "source": [
    "llm=ChatOpenAI(temperature=0.7, model_name='gpt-3.5-turbo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k9GHRzLGpy8O",
    "outputId": "99aba783-cc62-4650-df3c-3d05b7fcc284"
   },
   "outputs": [],
   "source": [
    "llm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5nyLvxUCSf6o"
   },
   "source": [
    "#**Step 10: Creating a memory object which is necessary to track inputs/outputs and hold a conversation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iBUaCuuCRmST"
   },
   "outputs": [],
   "source": [
    "memory=ConversationBufferMemory(memory_key='chat_history', return_messages=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vaaKQ07wTJZw"
   },
   "source": [
    "#**Step 11: Creating a Conversation Retrieval QA Chain**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2iBRTgn0TOn6"
   },
   "source": [
    "The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9rHVF7DiTB5M"
   },
   "outputs": [],
   "source": [
    "#Create our Q/A Chain\n",
    "pdf_qa=ConversationalRetrievalChain.from_llm(llm=llm,\n",
    "                                             retriever=vectordb.as_retriever(search_kwargs={'k':6}),\n",
    "                                             verbose=False, memory=memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oiZRnvtuTlXw",
    "outputId": "5905c5a9-c64a-444b-9ddd-155eee8beeca"
   },
   "outputs": [],
   "source": [
    "result=pdf_qa({\"question\":\"this is trained on which dataset\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "h4pzVFkxLU36",
    "outputId": "4531ab95-fdb3-4be7-abd6-1e7c25d5dc8e"
   },
   "outputs": [],
   "source": [
    "result['answer']"
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