LLAMA2(LLAMA_index).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ms88mMuuMQ9s"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqKZFj2XMKo5",
        "outputId": "5a52560f-689e-412b-c6bc-7aa46b164ecd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting pypdf\n",
            "  Downloading pypdf-3.14.0-py3-none-any.whl (269 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m269.8/269.8 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pypdf\n",
            "Successfully installed pypdf-3.14.0\n",
            "Collecting python-dotenv\n",
            "  Downloading python_dotenv-1.0.0-py3-none-any.whl (19 kB)\n",
            "Installing collected packages: python-dotenv\n",
            "Successfully installed python-dotenv-1.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install pypdf\n",
        "!pip install python-dotenv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdtzrpW6MYhm",
        "outputId": "a8a8db46-fccc-4683-f063-b8b2ad0dc20f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.4/77.4 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IdCZDUpJMiBQ",
        "outputId": "c22f4464-c4ad-482a-85c2-a3a134129179"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets bitsandbytes einops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0Rs_DJOMkA2",
        "outputId": "dda2ea48-88fc-49f2-a191-e1e4237e47d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting sentence_transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.65.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (2.0.1+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.15.2+cu118)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.10.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (3.8.1)\n",
            "Collecting sentencepiece (from sentence_transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.16.4)\n",
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            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence_transformers) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence_transformers) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence_transformers) (2.0.0)\n",
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            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence_transformers) (16.0.6)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (2022.10.31)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.3.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->sentence_transformers) (8.1.6)\n",
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            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence_transformers) (9.4.0)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2023.7.22)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (3.4)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence_transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence_transformers\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125925 sha256=7ce4c4f0faa14f77307e3bebf67e391a9e39a2f8b7574786e7c858653140a7f4\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence_transformers\n",
            "Installing collected packages: sentencepiece, sentence_transformers\n",
            "Successfully installed sentence_transformers-2.2.2 sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "!pip install sentence_transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDwEkCkZMl3a",
        "outputId": "ea2a743d-35bd-477c-b23f-af5ea6992e12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting llama-index\n",
            "  Downloading llama_index-0.7.16-py3-none-any.whl (626 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m626.2/626.2 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tiktoken (from llama-index)\n",
            "  Downloading tiktoken-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
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            "\u001b[?25hCollecting dataclasses-json (from llama-index)\n",
            "  Downloading dataclasses_json-0.5.14-py3-none-any.whl (26 kB)\n",
            "Collecting langchain>=0.0.218 (from llama-index)\n",
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            "\u001b[?25hRequirement already satisfied: sqlalchemy>=2.0.15 in /usr/local/lib/python3.10/dist-packages (from llama-index) (2.0.19)\n",
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            "Collecting openai>=0.26.4 (from llama-index)\n",
            "  Downloading openai-0.27.8-py3-none-any.whl (73 kB)\n",
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            "Collecting typing-inspect>=0.8.0 (from llama-index)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
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            "Collecting langsmith<0.1.0,>=0.0.11 (from langchain>=0.0.218->llama-index)\n",
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            "Installing collected packages: mypy-extensions, marshmallow, typing-inspect, tiktoken, openapi-schema-pydantic, langsmith, openai, dataclasses-json, langchain, llama-index\n",
            "Successfully installed dataclasses-json-0.5.14 langchain-0.0.249 langsmith-0.0.16 llama-index-0.7.16 marshmallow-3.20.1 mypy-extensions-1.0.0 openai-0.27.8 openapi-schema-pydantic-1.2.4 tiktoken-0.4.0 typing-inspect-0.9.0\n"
          ]
        }
      ],
      "source": [
        "!pip install llama-index\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iWYE3_QdMoKa"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "import logging\n",
        "from pathlib import Path\n",
        "import sys\n",
        "\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        ")\n",
        "\n",
        "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
        "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
        "\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "from llama_index import LangchainEmbedding, ServiceContext\n",
        "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
        "from llama_index.llms import HuggingFaceLLM\n",
        "from llama_index import download_loader\n",
        "from llama_index.prompts.prompts import SimpleInputPrompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dywFD6q2NWyO",
        "outputId": "b2ba753a-74c6-4bf7-a161-3c9915f64513"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import login\n",
        "login(token=\"\",add_to_git_credential =True,write_permission=True)#put your huggingface token here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yaiy-0SONkco"
      },
      "source": [
        "# Adding a custom prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sxpNcH1aMtRS"
      },
      "outputs": [],
      "source": [
        "system_prompt = \"You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided from the given document.\"\n",
        "\n",
        "# This will wrap the default prompts that are internal to llama-index\n",
        "query_wrapper_prompt = SimpleInputPrompt(\"<|USER|>{query_str}<|ASSISTANT|>\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JLa1nI41NxUV"
      },
      "source": [
        "# Loading the LLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZHtzw7_OIeE"
      },
      "outputs": [],
      "source": [
        "def create_llamav2_small(load_in_4bit=False):\n",
        "  model_name=\"meta-llama/Llama-2-7b-chat-hf\"\n",
        "\n",
        "\n",
        "  #loading the model using the configs\n",
        "  use_4bit = True\n",
        "  bnb_4bit_compute_dtype = \"float16\"\n",
        "  bnb_4bit_quant_type = \"nf4\"\n",
        "  use_nested_quant = False\n",
        "  compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "  bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant,\n",
        "  )\n",
        "\n",
        "  llm = HuggingFaceLLM(\n",
        "    context_window=4096,\n",
        "    max_new_tokens=256,\n",
        "    generate_kwargs={\"temperature\": 0.0, \"do_sample\": False},\n",
        "    system_prompt=system_prompt,\n",
        "    query_wrapper_prompt=query_wrapper_prompt,\n",
        "    tokenizer_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    model_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    device_map=\"auto\",\n",
        "    # uncomment this if using CUDA to reduce memory usage\n",
        "    model_kwargs={\"quantization_config\":bnb_config, \"max_length\": 5000, \"temperature\": 0}\n",
        "    )\n",
        "  return llm"
      ]
    },
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d01a4234b5554d7fbe1cbb1562fc14c9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "llamav2_small=create_llamav2_small()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ultHxTSrM0ul"
      },
      "source": [
        "# Loading the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PfHCTjkVQYxM"
      },
      "outputs": [],
      "source": [
        "#loading the PDF loader\n",
        "PDFReader = download_loader(\"PDFReader\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nu9s0fEAQqGj"
      },
      "outputs": [],
      "source": [
        "def read_pdf(file_path):\n",
        "  loader = PDFReader()\n",
        "  documents = loader.load_data(file=Path(file_path))\n",
        "  return documents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YTbw2rejQ1tP"
      },
      "outputs": [],
      "source": [
        "document=read_pdf('/content/drive/MyDrive/NIPS-2017-attention-is-all-you-need-Paper.pdf')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOjwa9yLPbqo"
      },
      "source": [
        "# Creating the Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "BPZEkJ97OwJO",
        "outputId": "c2758aa0-a7f5-4eea-d9ee-31d4f44e112a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c5b698abeabc407ab46a0ccd3c475b1a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)a8e1d/.gitattributes:   0%|          | 0.00/1.18k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "96842b9ed01c4000ad7b360b726a2f47",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c24933b855ee471c83762332bca55856",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)b20bca8e1d/README.md:   0%|          | 0.00/10.6k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbf21144ee434743b08a120b917a4583",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)0bca8e1d/config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "0b766d53a96f43538ac26516977471a8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)ce_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "f5625c1b82944ef381682c1f3955ec2b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "f63ee5e600da4f25afc888b82d55b2e8",
              "version_major": 2,
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            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "ab761ae5eb39494cabde995bda7c50bb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)nce_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "febcfc981dec46b2b7337e7147415d93",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "07e6f1fcba94405da65acdf65f8f7863",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)a8e1d/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "84ea9526313a4aaeaba4c612a8f0f61a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5587e9cd7a6249df88092bc1f9b79ad9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)8e1d/train_script.py:   0%|          | 0.00/13.1k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "04d0a5bef0764f708e34e974cc83e287",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)b20bca8e1d/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0826c9b0ab424148aa42829a9f542401",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)bca8e1d/modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "embed_model = LangchainEmbedding(\n",
        "  HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yNYXIMdFPgab"
      },
      "outputs": [],
      "source": [
        "# creating a service context\n",
        "service_context = ServiceContext.from_defaults(\n",
        "    chunk_size=1024,\n",
        "    llm=llamav2_small,\n",
        "    embed_model=embed_model\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFG6KdDORl_U"
      },
      "source": [
        "# Querying the Engine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fe9KDqrERilK"
      },
      "outputs": [],
      "source": [
        "query_engine = vector_db.as_query_engine()\n",
        "response = query_engine.query(\"What is self-attention?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISnuD9bSRqOf",
        "outputId": "494d9315-2325-4d25-d838-57d8d010d03d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Self-attention is a mechanism in neural networks that allows the model to focus on specific parts of the input data when processing it. It is called \"self-attention\" because the model is attending to its own internal state, rather than relying on external context or features. Self-attention is typically used in natural language processing tasks, such as language translation, question answering, and text summarization, where the model needs to understand the relationships between different parts of a sentence or document. In the context of the paper you provided, the authors use self-attention to learn a representation of a sentence that takes into account the relationships between different words and phrases.\n"
          ]
        }
      ],
      "source": [
        "print(response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fciVFQyLR6cL"
      },
      "outputs": [],
      "source": [
        "response = query_engine.query(\"What are some of the optimizers discussed in the paper?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgrRFyU_SBsi",
        "outputId": "8a7231c6-658a-49de-90db-d5a4f0350078"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The paper discusses several optimizers, including:\n",
            "1. Adam: This is a popular stochastic gradient descent (SGD) optimizer that adapts the learning rate for each parameter based on the magnitude of the gradient.\n",
            "2. RMSprop: This is another popular SGD optimizer that divides the learning rate by an exponentially decaying average of squared gradients.\n",
            "3. Adagrad: This optimizer adapts the learning rate for each parameter based on the magnitude of the gradient, but uses a different formula than Adam.\n",
            "4. Adadelta: This optimizer is similar to RMSprop, but uses a different formula to compute the learning rate.\n",
            "5. Nadam: This is a variant of Adam that uses the moving average of the gradient to adapt the learning rate.\n",
            "6. Stochastic gradient descent (SGD): This is a simple optimizer that computes the gradient of the loss function with respect to each parameter and updates the parameters based on the gradient.\n",
            "7. Momentum: This optimizer adds a \"momentum\" term to the gradient update, which helps the optimizer to move more quickly through the space of parameters.\n",
            "8. Nesterov Acceler\n"
          ]
        }
      ],
      "source": [
        "print(response)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O02Nqq6JSass"
      },
      "source": [
        "#Creating a single function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Goey4K1fRCZB"
      },
      "outputs": [],
      "source": [
        "def create_vector_db(documents,service_context):\n",
        "  index = VectorStoreIndex.from_documents(documents, service_context=service_context)\n",
        "  return index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fs3oYVNuWFKv"
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      "source": [
        "def create_vectordb(file_path):\n",
        "  document=read_pdf(file_path)\n",
        "  vector_db=create_vector_db(document,service_context)\n",
        "  return vector_db"
      ]
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      "metadata": {
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      "source": [
        "def qa_pdf(vector_db,query):\n",
        "  query_engine = vector_db.as_query_engine()\n",
        "  response = query_engine.query(query)\n",
        "  print(response)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "vector_db=create_vector_db('/content/LLMs(PDf).pdf')"
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    },
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        },
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      },
      "outputs": [
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The LLM being discussed in the paper is the Transformer.\n"
          ]
        }
      ],
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        "qa_pdf(vector_db,\"Which LLM is being discussed in the paper?\")"
      ]
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPT-3 was trained using a combination of masked language modeling and next sentence prediction tasks. The authors of the paper used a large corpus of text data, such as the internet, to train the model. They also used a technique called \"prompt engineering\" to generate new text prompts that the model had not seen before. The model was trained for a long time, around 100 days, and the authors claim that it achieved state-of-the-art results on a wide range of NLP tasks.\n"
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