docbot_Mistral.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "collapsed": true,
        "id": "bBKvs7Z65c2k",
        "outputId": "3cfc52c4-8866-474d-84de-9b4a448f3bf2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading monotonic-1.6-py2.py3-none-any.whl (8.2 kB)\n",
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            "  Downloading httptools-0.6.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (341 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m33.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting uvloop!=0.15.0,!=0.15.1,>=0.14.0 (from uvicorn)\n",
            "  Downloading uvloop-0.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
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            "\u001b[?25hCollecting watchfiles>=0.13 (from uvicorn)\n",
            "  Downloading watchfiles-0.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m52.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->bs4) (2.5)\n",
            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.0.1->kubernetes>=28.1.0->chromadb) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth>=1.0.1->kubernetes>=28.1.0->chromadb) (4.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata<=7.1,>=6.0->opentelemetry-api>=1.2.0->chromadb) (3.19.2)\n",
            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain-core<0.3,>=0.2.2->langchain_openai)\n",
            "  Downloading jsonpointer-3.0.0-py2.py3-none-any.whl (7.6 kB)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.35.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.18.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich<14,>=10.14.0->streamlit) (0.1.2)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime>=1.14.1->chromadb)\n",
            "  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime>=1.14.1->chromadb) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=1.0.1->kubernetes>=28.1.0->chromadb) (0.6.0)\n",
            "Building wheels for collected packages: src, pypika, ffmpy\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for src (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for src\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for src\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53724 sha256=3209d4f075ac5ae2e65ae6cba2736c6ed6ba4dbff6e3af2048b6baf60b7f5e8f\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=a8e199c154bd6a1f4a6416bf9f7e83bd97689017761d8ca72bf8b7b3d2f90922\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
            "Successfully built pypika ffmpy\n",
            "Failed to build src\n",
            "\u001b[31mERROR: Could not build wheels for src, which is required to install pyproject.toml-based projects\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install langchain_openai python-dotenv streamlit langchain_community langserve fastapi uvicorn sse_starlette bs4 chromadb faiss-cpu gradio pypdf src"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fix numpy in colab\n",
        "import numpy\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# fix triton in colab\n",
        "!export LC_ALL=\"en_US.UTF-8\"\n",
        "!export LD_LIBRARY_PATH=\"/usr/lib64-nvidia\"\n",
        "!export LIBRARY_PATH=\"/usr/local/cuda/lib64/stubs\"\n",
        "!ldconfig /usr/lib64-nvidia\n",
        "\n",
        "!git clone https://github.com/dvmazur/mixtral-offloading.git --quiet\n",
        "!cd mixtral-offloading && pip install -q -r requirements.txt\n",
        "!huggingface-cli download lavawolfiee/Mixtral-8x7B-Instruct-v0.1-offloading-demo --quiet --local-dir Mixtral-8x7B-Instruct-v0.1-offloading-demo\n",
        "#4.42.3\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "EUgXzNkiC1zg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "\n",
        "sys.path.append(\"mixtral-offloading\")\n",
        "import torch\n",
        "from torch.nn import functional as F\n",
        "from hqq.core.quantize import BaseQuantizeConfig\n",
        "from huggingface_hub import snapshot_download\n",
        "from IPython.display import clear_output\n",
        "from tqdm.auto import trange\n",
        "from transformers import AutoConfig, AutoTokenizer\n",
        "from transformers.utils import logging as hf_logging\n",
        "from transformers import TextStreamer\n",
        "\n",
        "from src.build_model import OffloadConfig, QuantConfig, build_model"
      ],
      "metadata": {
        "id": "rg0YKfdRC_eo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243,
          "referenced_widgets": [
            "0410dba563a44b28bfa445fba1581003",
            "0518186d60fe44648cc0f6f810ce3513",
            "e896b1793975447c957c8bc12f888885",
            "e75cac1a672a4b57bfcd1f29631b0b80",
            "6dc8a99d7a88484598d6a9e443aa4d4a",
            "e04eaa11b1be492985a8fdc733341a4f",
            "0036094b40fe4784a9e299211eff0f50",
            "5d7feba2c0ee4b939e43716c5e30eadf",
            "403251f7a67a4cebbbc8a7e361b23c8e",
            "f11630ee10d8420c8b8ba27b0f462a87",
            "06c60d16e95d4321a7cc258bcfa5e43b"
          ]
        },
        "outputId": "aeff0c60-716d-46ae-e9ad-47fa36229900"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hqq_aten package not installed. HQQBackend.ATEN backend will not work unless you install the hqq_aten lib in hqq/kernels.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0410dba563a44b28bfa445fba1581003"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import dotenv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "j8JA4MDkyUXH",
        "outputId": "a39196f1-b21f-4f07-8066-5448d36f42c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'dotenv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-7022b5b29d69>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdotenv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dotenv'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0oonjfSb4spz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "16eb066a-ca38-4172-fb0b-5251914050ff"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'dotenv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-e3c7279f3879>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdotenv\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dotenv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain_community\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdocument_loaders\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTextLoader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain_community\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdocument_loaders\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mWebBaseLoader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbs4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dotenv'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "import os\n",
        "# from dotenv import load_dotenv\n",
        "from langchain_community.document_loaders import TextLoader\n",
        "from langchain_community.document_loaders import WebBaseLoader\n",
        "import bs4\n",
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_community.embeddings import OpenAIEmbeddings\n",
        "from langchain_community.vectorstores import Chroma\n",
        "\n",
        "from langchain.chains.question_answering import load_qa_chain\n",
        "from langchain.llms import OpenAI\n",
        "from openai import OpenAI\n",
        "\n",
        "import gradio as gr\n",
        "import random\n",
        "import time\n",
        "import re\n",
        "\n",
        "from google.colab import userdata"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "8024752159794a5aa2e594007a66d15d",
            "bfdd6e008896435e902538bc1806127a",
            "6ff8c886a3414c05a5f3b2c66d837634",
            "685885a188854a9cb1f6e82b220165db",
            "7c8a381ebe004b92b4bd2fca4ea20f88",
            "2cd1e299a0654939a83cf0361f4c5f35",
            "c37f9c762f4f4bab9bad999296b35c42",
            "bad82fba4a034d5689bd3f3bfff753c1",
            "61cf992b86a547e99c71cbea48b96a08",
            "69214fd92a1a4b0fb62089716978d002",
            "2f8e78c8ed094081bf97a93b321a3f3f",
            "0fca73f0e0bf47fa98e85da7b85813b0",
            "14cf7e164b8443f08e4f587f3e160901",
            "54d4843d89da490c9dea90c2044497db",
            "8fd3f459044a47318c29b3ea4f7b5cab",
            "86cf2da283d54d3eaa7f977bc8143b75",
            "e10d49b5a30d4925af952aa0d80bfd7d",
            "e5a1747041234e719713521f0c59e93a",
            "ece9b86ad0514133884aaf21f5371923",
            "702f29820bea49a8af7476319ebff356",
            "7ec0bcb964a94bf6bd5191cd15cbd00d",
            "7b1c5d3de2da47099cb400dd96a64a69",
            "24e3b2c4327b49ab8f45ade322b6503c",
            "0ec83e14f3e3416f99a6a0c7ba30e69d",
            "431ed094a88548b9ba96bae8e11e80a0",
            "f0875e75e33641a599c4319a479fa784",
            "827a447c04494546a8f8664be411a350",
            "3fd73855fde242238d7402697a54e809",
            "181cab05531445f2a4d5857ce6bbfba8",
            "b143287ba4694752aa6142625659a9ff",
            "508a404ad99840a38d31da36bb4e4439",
            "969e2bb9723f4ecc8ed736589a0d483e"
          ]
        },
        "id": "PQvUUOmf9vq3",
        "outputId": "4c392ee6-e006-4654-8304-53b805da5d45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8024752159794a5aa2e594007a66d15d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lsZblW5BwbnQ"
      },
      "outputs": [],
      "source": [
        "# client = OpenAI(api_key=userdata.get('OPENAPIKEY'))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initialize LLM"
      ],
      "metadata": {
        "id": "0KiGuygyxfPW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n",
        "quantized_model_name = \"lavawolfiee/Mixtral-8x7B-Instruct-v0.1-offloading-demo\"\n",
        "state_path = \"Mixtral-8x7B-Instruct-v0.1-offloading-demo\"\n",
        "\n",
        "config = AutoConfig.from_pretrained(quantized_model_name)\n",
        "\n",
        "device = torch.device(\"cuda:0\")\n",
        "\n",
        "##### Change this to 5 if you have only 12 GB of GPU VRAM #####\n",
        "offload_per_layer = 4\n",
        "# offload_per_layer = 5\n",
        "###############################################################\n",
        "\n",
        "num_experts = config.num_local_experts\n",
        "\n",
        "offload_config = OffloadConfig(\n",
        "    main_size=config.num_hidden_layers * (num_experts - offload_per_layer),\n",
        "    offload_size=config.num_hidden_layers * offload_per_layer,\n",
        "    buffer_size=4,\n",
        "    offload_per_layer=offload_per_layer,\n",
        ")\n",
        "\n",
        "\n",
        "attn_config = BaseQuantizeConfig(\n",
        "    nbits=4,\n",
        "    group_size=64,\n",
        "    quant_zero=True,\n",
        "    quant_scale=True,\n",
        ")\n",
        "attn_config[\"scale_quant_params\"][\"group_size\"] = 256\n",
        "\n",
        "\n",
        "ffn_config = BaseQuantizeConfig(\n",
        "    nbits=2,\n",
        "    group_size=16,\n",
        "    quant_zero=True,\n",
        "    quant_scale=True,\n",
        ")\n",
        "quant_config = QuantConfig(ffn_config=ffn_config, attn_config=attn_config)\n",
        "\n",
        "\n",
        "model = build_model(\n",
        "    device=device,\n",
        "    quant_config=quant_config,\n",
        "    offload_config=offload_config,\n",
        "    state_path=state_path,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155,
          "referenced_widgets": [
            "152ba0767cbd45d8949911f2b9ac5f03",
            "4c5d2a7883d74517a33e9447395f2460",
            "51287a72295743ee975dd00836f53736",
            "ed15ea5d20b74ca582ba0b437aa51627",
            "e4c2734185334facbfb211d538dd6b82",
            "54a7fc9ade244baa8e7a9ae486b2d9d4",
            "2d198e9a8f1d4ddca860746dcce01aab",
            "a0de571d90fb4354b6eb785bc199d090",
            "64a5cc0927cb4327bfc016dc00cb5bae",
            "7168ea5cb9c34dec9b11a0fd0d2ef2ef",
            "7fcda1cdb279430b8374a7e40b6e19a2",
            "64f46303219445b2831034db0d182bcc",
            "606623f108494bebb1f95b8e08410b4b",
            "b4a7adba28504b93bd8d086d967f7578",
            "f4e782d208db4eb0b9e182476b370616",
            "b0e3a6ac63eb44f6a2e55de9cdc8a13d",
            "db7a539848f545ff8f8ad9f844731296",
            "7616d63007214a4baa9205c462dc3b09",
            "1c178544e0c748cfaab0ad73eaeec9f0",
            "73e76a05bdd14d278b70eeeaf0cd54a2",
            "f08b5a4aa1ff43f89770d2011945c37b",
            "c825eaf738354951bd5ded1557aaa838"
          ]
        },
        "id": "XnDYwpFwDKko",
        "outputId": "7187d5fa-52f3-4daf-a363-d8dcab96fe9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/720 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "152ba0767cbd45d8949911f2b9ac5f03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/init.py:452: UserWarning: Initializing zero-element tensors is a no-op\n",
            "  warnings.warn(\"Initializing zero-element tensors is a no-op\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading experts:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64f46303219445b2831034db0d182bcc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calling LLM"
      ],
      "metadata": {
        "id": "BLoKPTx4xq8_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate (user_input):\n",
        "  # https://colab.research.google.com/github/dvmazur/mixtral-offloading/blob/master/notebooks/demo.ipynb#scrollTo=Zf4GkspecSm8\n",
        "\n",
        "  tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "  streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
        "  past_key_values = None\n",
        "  sequence = None\n",
        "\n",
        "  user_entry = dict(role=\"user\", content=user_input)\n",
        "  input_ids = tokenizer.apply_chat_template([user_entry], return_tensors=\"pt\").to(device)\n",
        "\n",
        "  if past_key_values is None:\n",
        "    attention_mask = torch.ones_like(input_ids)\n",
        "  else:\n",
        "    seq_len = input_ids.size(1) + past_key_values[0][0][0].size(1)\n",
        "    attention_mask = torch.ones([1, seq_len - 1], dtype=torch.int, device=device)\n",
        "\n",
        "  result = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    attention_mask=attention_mask,\n",
        "    past_key_values=past_key_values,\n",
        "    streamer=streamer,\n",
        "    do_sample=True,\n",
        "    temperature=0.9,\n",
        "    top_p=0.9,\n",
        "    max_new_tokens=200,\n",
        "    pad_token_id=tokenizer.eos_token_id,\n",
        "    return_dict_in_generate=True,\n",
        "    output_hidden_states=True,\n",
        "  )\n",
        "  response = tokenizer.batch_decode(result)[0]\n",
        "  return response"
      ],
      "metadata": {
        "id": "OloEo1uiEgAK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Code for Web link"
      ],
      "metadata": {
        "id": "rPTXfJYNxtv8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rioyPEIY4sp0"
      },
      "outputs": [],
      "source": [
        "def web_link(link=None,query=None):\n",
        "\n",
        "  if(link):\n",
        "    try:\n",
        "      loader = WebBaseLoader(web_path=(link,), bs_kwargs=dict(parse_only = bs4.SoupStrainer(\n",
        "        # class_ = (\"post-header\", \"post-content\")\n",
        "        class_ = (\"mw-content-ltr mw-parser-output\")\n",
        "      )))\n",
        "\n",
        "      webpagedoc = loader.load()\n",
        "\n",
        "      text_splitter = RecursiveCharacterTextSplitter(chunk_size = 150, chunk_overlap = 50)\n",
        "\n",
        "      global documents\n",
        "      documents = text_splitter.split_documents(webpagedoc)\n",
        "\n",
        "      #vector embedding and storage\n",
        "      global db\n",
        "      db = \"DB\"\n",
        "\n",
        "      db = Chroma.from_documents(documents, OpenAIEmbeddings(openai_api_key = userdata.get('OPENAPIKEY')))\n",
        "\n",
        "      return \"Link Read\"\n",
        "\n",
        "    except Exception as e:\n",
        "      return str(e)\n",
        "\n",
        "  else:\n",
        "      result = \"No Data\"\n",
        "      response = \"\"\n",
        "      if(result==\"No Data\"):\n",
        "        result = db.similarity_search(query)\n",
        "        response = result[0].page_content\n",
        "        first = response\n",
        "        for i in range(len(result)):\n",
        "          if first != result[i].page_content:\n",
        "            response += \"\\n\" + result[i].page_content\n",
        "            break\n",
        "\n",
        "\n",
        "        # query = query + response\n",
        "\n",
        "        # stream = client.chat.completions.create(\n",
        "        #   model=\"gpt-3.5-turbo\",\n",
        "        #   messages=[{\"role\": \"user\", \"content\": query}],\n",
        "        #   stream=True,\n",
        "        # )\n",
        "      # for chunk in stream:\n",
        "      #   if chunk.choices[0].delta.content is not None:\n",
        "      #       response = response + chunk.choices[0].delta.content\n",
        "      # pass\n",
        "\n",
        "      return response"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Code from PDF file"
      ],
      "metadata": {
        "id": "7SVF_Nxfx5hp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HhyIWKwc4sp1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b04fbc34-50e7-4fc3-fc68-6a27d825c0c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "inferred "
          ]
        }
      ],
      "source": [
        "def pdf_file(path, message):\n",
        "  #implement pdf query logic here\n",
        "\n",
        "  loader = PyPDFLoader(path)\n",
        "  pdfdocs = loader.load()\n",
        "\n",
        "  text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 50)\n",
        "  pdfdoc = text_splitter.split_documents(pdfdocs)\n",
        "\n",
        "  db = Chroma.from_documents(pdfdoc[:50], OpenAIEmbeddings(openai_api_key = userdata.get('OPENAPIKEY')))\n",
        "\n",
        "  query = message\n",
        "\n",
        "  result = db.similarity_search(query)\n",
        "\n",
        "  response = result[0].page_content\n",
        "\n",
        "  query = query + \"\\nContext:\" + response\n",
        "\n",
        "  #   # Retrieve the top 5 similar results instead of just one\n",
        "  # results = db.similarity_search(query, k=5)\n",
        "\n",
        "  # # Combine the content of the top 5 results\n",
        "  # combined_response = \"\\n\\n\".join([result.page_content for result in results])\n",
        "\n",
        "  # # Incorporate the combined context into the query\n",
        "  # query_with_context = query + \"\\n\\nContext:\\n\" + combined_response\n",
        "\n",
        "  # # Generate response using the query with combined context\n",
        "  # response = generate(query_with_context)\n",
        "\n",
        "  # stream = client.chat.completions.create(\n",
        "  #       model=\"gpt-3.5-turbo\",\n",
        "  #       messages=[{\"role\": \"user\", \"content\": query}],\n",
        "  #       stream=True,\n",
        "  #   )\n",
        "  # for chunk in stream:\n",
        "  #     if chunk.choices[0].delta.content is not None:\n",
        "  #         response = response + chunk.choices[0].delta.content\n",
        "  response = generate (query)\n",
        "\n",
        "  return response"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Code for Text file"
      ],
      "metadata": {
        "id": "9VWyYXrbxxln"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TYMyCACG4sp1"
      },
      "outputs": [],
      "source": [
        "def text_file(path, message):\n",
        "  #implement text query logic here\n",
        "\n",
        "  loader = TextLoader(path)\n",
        "  text_documents = loader.load()\n",
        "  text_splitter = RecursiveCharacterTextSplitter(chunk_size = 2000, chunk_overlap = 200)\n",
        "  documents = text_splitter.split_documents(text_documents)\n",
        "\n",
        "  db = Chroma.from_documents(documents, OpenAIEmbeddings(openai_api_key = userdata.get('OPENAPIKEY')))\n",
        "\n",
        "  query = message\n",
        "\n",
        "  result = db.similarity_search(query)\n",
        "\n",
        "  response = result[0].page_content\n",
        "\n",
        "  # query = query + response\n",
        "\n",
        "  # stream = client.chat.completions.create(\n",
        "  #       model=\"gpt-3.5-turbo\",\n",
        "  #       messages=[{\"role\": \"user\", \"content\": query}],\n",
        "  #       stream=True,\n",
        "  #   )\n",
        "  # for chunk in stream:\n",
        "  #     if chunk.choices[0].delta.content is not None:\n",
        "  #         response = response + chunk.choices[0].delta.content\n",
        "\n",
        "  return response"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interface"
      ],
      "metadata": {
        "id": "HkbQkvlAx78t"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "u4Pp7GX24sp1",
        "outputId": "09cf64e4-97ce-4f89-d8ac-8204a1ba05f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LLM, Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "its Running on public URL: https://aec8acc544384db240.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://aec8acc544384db240.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training process, and the knowledge database it uses for augmentation. The capabilities of the attacker include the ability to manipulate the knowledge database used by the LLM.\n",
            "\n",
            "[]\n",
            "The key threat here is that the\n",
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7860 <> https://aec8acc544384db240.gradio.live\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "########################### GLOBAL VARIABLES ####################################\n",
        "user_mode = None\n",
        "path_file = None    #path to the current file uploaded\n",
        "\n",
        "############################## FUNCTIONS ########################################\n",
        "def echo(message, history):\n",
        "    #message holds the input query from the user\n",
        "    #history holds the chat history in the form of a list containing lists.\n",
        "    #The inner list structure: [{User text},{Bot text}]\n",
        "\n",
        "    print(history)\n",
        "\n",
        "    #No mode\n",
        "    if(user_mode==None):\n",
        "      return \"Please select an option\"\n",
        "\n",
        "    #web link\n",
        "    if(user_mode==3):\n",
        "      url_pattern = re.compile(r'https?://\\S+')\n",
        "\n",
        "      # Search for a URL in the message text\n",
        "      matchCon = url_pattern.search(message)\n",
        "\n",
        "      if matchCon:\n",
        "          url = matchCon.group(0)  # Extract the matched URL\n",
        "          response = web_link(link=url)\n",
        "      else:\n",
        "          #function to give query to appropriate web link function and return response from it\n",
        "          response = web_link(query=message)\n",
        "\n",
        "      return response\n",
        "\n",
        "    #text file mode\n",
        "    if(user_mode==2):\n",
        "      return text_file(path_file, message) #path to current .txt file uploaded for this mode\n",
        "\n",
        "    #pdf file mode\n",
        "    if(user_mode==1):\n",
        "      return pdf_file(path_file, message)  #path to current .pdf file uploaded for this mode\n",
        "\n",
        "#---------------------------------------------------------------------------------------------#\n",
        "\n",
        "def process_files(argument):\n",
        "\n",
        "    global path_file\n",
        "    path_file = argument\n",
        "\n",
        "    return gr.File(label=\"Upload File\", visible=True,interactive=True)\n",
        "\n",
        "#---------------------------------------------------------------------------------------------#\n",
        "\n",
        "def change_mode(choice):\n",
        "    global user_mode\n",
        "\n",
        "    if choice == \"Web Link\":\n",
        "        user_mode = 3\n",
        "        return gr.File(visible=False), gr.Button(\"Submit\", visible=False), gr.Button(\"Cancel\", visible=False)\n",
        "    elif choice == \"Text File\":\n",
        "        user_mode = 2\n",
        "        return gr.File(label=\"Upload File\",visible=True), gr.Button(\"Submit\", visible=True), gr.Button(\"Cancel\", visible=True)\n",
        "    else:\n",
        "        user_mode = 1\n",
        "        return gr.File(label=\"Upload File\",visible=True), gr.Button(\"Submit\", visible=True), gr.Button(\"Cancel\", visible=True)\n",
        "\n",
        "#--------------------------------------------------------------------------------------------#\n",
        "\n",
        "def cancel_upload():\n",
        "    return gr.File(label=\"Upload File\", visible=True,interactive=True)\n",
        "\n",
        "############################## INTERFACE CODE ###################################\n",
        "with gr.Blocks() as demo:\n",
        "    #set up radio element and file input\n",
        "    with gr.Row():\n",
        "      radio = gr.Radio(\n",
        "          [\"PDF File\", \"Text File\", \"Web Link\"], label=\"Select Mode\"\n",
        "      )\n",
        "\n",
        "      # input = gr.Interface(process_files,inputs='files',outputs=None)\n",
        "      with gr.Column():\n",
        "        file_input = gr.File(label=\"Upload File\", visible=False,interactive=True)\n",
        "\n",
        "        #set up buttons\n",
        "        with gr.Row():\n",
        "            submit_btn = gr.Button(\"Submit\", visible=False)\n",
        "            cancel_btn = gr.Button(\"Cancel\", visible=False)\n",
        "\n",
        "\n",
        "    #add radio element event listener\n",
        "    radio.change(fn=change_mode, inputs=radio, outputs=[file_input,submit_btn,cancel_btn])\n",
        "\n",
        "    # Set up submit button to process files\n",
        "    submit_btn.click(fn=process_files, inputs=[file_input], outputs=[file_input])\n",
        "\n",
        "    # Set up cancel button to clear file input\n",
        "    cancel_btn.click(fn=cancel_upload, inputs=None, outputs=file_input)\n",
        "\n",
        "    #set up Chat Interface\n",
        "    gr.ChatInterface(\n",
        "        fn=echo,\n",
        "        title=\"Doc Bot\",\n",
        "    )\n",
        "\n",
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