tiiuae_falcon_40b_instruct.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwSKH5kZsLP5",
        "outputId": "2fdc5551-e5bb-42ee-e993-4707dc60deb1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: ffmpy\n",
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            "\u001b[?25h  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4694 sha256=980ae3ee6e0f4baeda668d1df520089abd61a7f31071764391070bdca304cf63\n",
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            "Installing collected packages: pytz, pydub, ffmpy, websockets, uc-micro-py, tzdata, toolz, semantic-version, python-multipart, pydantic, orjson, multidict, mdurl, kiwisolver, h11, frozenlist, fonttools, cycler, contourpy, click, async-timeout, aiofiles, yarl, uvicorn, starlette, pandas, matplotlib, markdown-it-py, linkify-it-py, httpcore, aiosignal, mdit-py-plugins, httpx, fastapi, altair, aiohttp, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 altair-5.0.1 async-timeout-4.0.2 click-8.1.3 contourpy-1.1.0 cycler-0.11.0 fastapi-0.97.0 ffmpy-0.3.0 fonttools-4.40.0 frozenlist-1.3.3 gradio-3.35.2 gradio-client-0.2.7 h11-0.14.0 httpcore-0.17.2 httpx-0.24.1 kiwisolver-1.4.4 linkify-it-py-2.0.2 markdown-it-py-2.2.0 matplotlib-3.7.1 mdit-py-plugins-0.3.3 mdurl-0.1.2 multidict-6.0.4 orjson-3.9.1 pandas-2.0.2 pydantic-1.10.9 pydub-0.25.1 python-multipart-0.0.6 pytz-2023.3 semantic-version-2.10.0 starlette-0.27.0 toolz-0.12.0 tzdata-2023.3 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3 yarl-1.9.2\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# Install latest bitsandbytes & transformers, accelerate from source\n",
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "# Other requirements for the demo\n",
        "!pip install gradio\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EpG6MVkh_32x",
        "outputId": "f916f609-1d94-41e8-8c65-e97e99885bad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: sentence_transformers\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125926 sha256=a70c538437d6bfe93280d98bcafd38502399c31f951b8fcca5add5da9ebf35a3\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence_transformers\n",
            "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn, nltk, sentence_transformers\n",
            "Successfully installed joblib-1.2.0 nltk-3.8.1 scikit-learn-1.2.2 scipy-1.10.1 sentence_transformers-2.2.2 threadpoolctl-3.1.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install sentence_transformers\n",
        "!pip -q install langchain tiktoken chromadb pypdf transformers\n",
        "!pip -q install accelerate bitsandbytes sentencepiece Xformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LangChain multi-doc retriever with ChromaDB"
      ],
      "metadata": {
        "id": "m-ie41ivcOGI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MdatOyCn_7sx"
      },
      "outputs": [],
      "source": [
        "from langchain.vectorstores import Chroma\n",
        "\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.document_loaders import TextLoader\n",
        "from langchain.document_loaders import DirectoryLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fUQME_3lftm9",
        "outputId": "cc0fd8e9-9912-4651-c91a-cca60992a762"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting einops\n",
            "  Downloading einops-0.6.1-py3-none-any.whl (42 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: einops\n",
            "Successfully installed einops-0.6.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install einops"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **falcon-40b-instruct**"
      ],
      "metadata": {
        "id": "1Y1Y5sqScT_o"
      }
    },
    {
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        "id": "2QK51MtdsMLu",
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda117.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so.11.0\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
            "CUDA SETUP: Detected CUDA version 117\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/nvidia/lib64'), PosixPath('/usr/local/nvidia/lib')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/lib/x86_64-linux-gnu did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('noninteractive    SHELL=/bin/bash')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//matplotlib_inline.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b-instruct:\n",
            "- configuration_RW.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
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            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b-instruct:\n",
            "- modelling_RW.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "050d52e407854d658bd89a026d572ce4",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "Downloading (…)l-00009-of-00009.bin:   0%|          | 0.00/7.58G [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
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          "data": {
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              "model_id": "b17069d26a7a45ef97bedaa62b11b39a",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "3549f5c21d0346fba46271c4bc359251",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "c23c040ec87347ea9df0c2fcd88972c8",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "a7c6b3ef989845dba91d3b8dc967f247",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "44c50f7e7ace45a7b83eda64219d12ee",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Load the model.\n",
        "#https://huggingface.co/blog/falcon\n",
        "from transformers import AutoConfig  # Importing AutoConfig from transformers library\n",
        "import torch  # Importing torch library\n",
        "from peft import PeftModel  # Importing PeftModel from peft library\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer  # Importing various classes from transformers library\n",
        "\n",
        "model_path = \"tiiuae/falcon-40b-instruct\"  # Assigning the model path to a variable\n",
        "config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)  # Creating a configuration from the pretrained model with remote code trust enabled\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_path,\n",
        "    config=config,\n",
        "    trust_remote_code=True,\n",
        "#load_in_8bit: This is a method used to load a model in 8-bit precision.\n",
        "#it refers to the process of converting the weights of a model into 8-bit integers (int8) using a technique called quantization.\n",
        "    load_in_8bit=True,\n",
        "    #torch_dtype=torch.bfloat16,  # This is a data type in PyTorch known as bfloat16 (Brain Floating Point). It's a floating point data type that uses less memory than the standard 32-bit\n",
        "    device_map=\"auto\",\n",
        ")  # Loading the pretrained model for causal language modeling with specific configurations\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_path)  # Creating a tokenizer from the pretrained model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbkaE447_jiE",
        "outputId": "43fd57c1-3a70-4ece-acac-3aadcb62c5b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-06-16 18:33:34--  https://github.com/Shafi2016/Youtube/raw/main/stock_market_june_2023.zip\n",
            "Resolving github.com (github.com)... 140.82.121.3\n",
            "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/Shafi2016/Youtube/main/stock_market_june_2023.zip [following]\n",
            "--2023-06-16 18:33:34--  https://raw.githubusercontent.com/Shafi2016/Youtube/main/stock_market_june_2023.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10638 (10K) [application/zip]\n",
            "Saving to: ‘stock_market_june_2023.zip’\n",
            "\n",
            "stock_market_june_2 100%[===================>]  10.39K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2023-06-16 18:33:34 (9.50 MB/s) - ‘stock_market_june_2023.zip’ saved [10638/10638]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "!wget https://github.com/Shafi2016/Youtube/raw/main/stock_market_june_2023.zip -O stock_market_june_2023.zip\n",
        "\n",
        "with zipfile.ZipFile('stock_market_june_2023.zip', 'r') as zip_ref:\n",
        "    zip_ref.extractall()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lhUNiUna_xwu"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Initializes a DirectoryLoader to load text files from the specified directory.\n",
        "loader = DirectoryLoader('./stock_market_june_2023/', glob=\"./*.txt\", loader_cls=TextLoader)\n",
        "\n",
        "# Loads all the documents present in the directory.\n",
        "documents = loader.load()\n",
        "\n",
        "# The following line is commented out, but if used, it would load only the first 5 documents.\n",
        "# documents = loader.load()[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u2zA9VSKBPXQ"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Initializes a RecursiveCharacterTextSplitter with a specified chunk size and overlap.\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
        "\n",
        "# Splits the loaded documents into chunks of text using the defined text_splitter.\n",
        "texts = text_splitter.split_documents(documents)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H9J09qksftnA",
        "outputId": "ee28a142-c9b4-4606-e499-497d2caf07b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sentence_transformers in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.65.0)\n",
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            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence_transformers) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence_transformers) (3.0)\n",
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            "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",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence_transformers) (3.25.0)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence_transformers) (15.0.7)\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) (2023.6.3)\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) (8.1.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->sentence_transformers) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence_transformers) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence_transformers) (9.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->sentence_transformers) (2.1.2)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2.1.1)\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: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (1.26.13)\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) (2022.12.7)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence_transformers) (1.2.1)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install sentence_transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# HuggingFace Embeddings"
      ],
      "metadata": {
        "id": "rv9vKd-_h2EP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310,
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        },
        "id": "kuC2Hw_JBYKa",
        "outputId": "f3eed878-e7e7-40be-85a5-3ec0297607f5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The model 'RWForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8415d9eb227c47ef922e8e0fc193b92e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)8230d/.gitattributes:   0%|          | 0.00/1.48k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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        {
          "name": "stderr",
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          "text": [
            "No sentence-transformers model found with name /root/.cache/torch/sentence_transformers/intfloat_e5-large-v2. Creating a new one with MEAN pooling.\n"
          ]
        }
      ],
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "# Moving model to GPU if available\n",
        "#model = model.to(device)\n",
        "\n",
        "# Initializes a text generation pipeline with the pretrained model and tokenizer.\n",
        "# The maximum length of the generated text is set to 2048 tokens.\n",
        "# The temperature argument controls the randomness of predictions (0.7 allows for some randomness but still fairly focused).\n",
        "# The top_p argument controls nucleus sampling, with 0.95 meaning that the top 95% of the probability mass is considered in the distribution.\n",
        "# The repetition_penalty argument makes repeating output less likely by applying a penalty of 1.15.\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=2048,\n",
        "    temperature=0.7,\n",
        "    top_p=0.95,\n",
        "    repetition_penalty=1.15\n",
        "    # device=0 if torch.cuda.is_available() else -1  # Use GPU if available, else CPU. This line is currently commented out.\n",
        ")\n",
        "\n",
        "# Creates a HuggingFacePipeline object with the created pipeline.\n",
        "llm = HuggingFacePipeline(pipeline=pipe)\n",
        "\n",
        "# Sets the model name for the HuggingFace embeddings.\n",
        "model_name = \"intfloat/e5-large-v2\"\n",
        "# Creates a HuggingFaceEmbeddings object with the specified model name.\n",
        "hf = HuggingFaceEmbeddings(model_name=model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# create the DB"
      ],
      "metadata": {
        "id": "Js73z3Lah9td"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0db0RThgBdbQ"
      },
      "outputs": [],
      "source": [
        "# Sets the directory where the embeddings will be stored.\n",
        "persist_directory = 'db'\n",
        "\n",
        "# Sets the HuggingFaceEmbeddings object as the embedding to use.\n",
        "embedding = hf\n",
        "\n",
        "# Uses the Chroma module to convert the texts into embeddings using the specified HuggingFace embeddings.\n",
        "# The resulting embeddings are stored in the persist_directory.\n",
        "vectordb = Chroma.from_documents(documents=texts,\n",
        "                                 embedding=embedding,\n",
        "                                 persist_directory=persist_directory)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XIiopwPeBgKA"
      },
      "outputs": [],
      "source": [
        "# Persists the generated embeddings database to disk.\n",
        "vectordb.persist()\n",
        "\n",
        "# Releases the memory held by the vectordb object by setting it to None.\n",
        "vectordb = None\n",
        "\n",
        " #Re-initializes the Chroma object from the persisted directory with the specified HuggingFace embeddings.\n",
        "vectordb = Chroma(persist_directory=persist_directory,\n",
        "                  embedding_function=embedding)\n",
        "\n",
        "\n",
        "# Initializes a retriever object from the vectordb with a specified number of search results (k=3).\n",
        "# The vectordb.as_retriever() function is likely converting the Chroma object, which contains vectorized representations of text, into a retriever object.\n",
        "# The retriever can be used to find the most similar vectors in the database given a query vector.\n",
        "# The search_kwargs={\"k\": 3} argument suggests that the retriever will return the top 3 most similar vectors for each query.\n",
        "retriever = vectordb.as_retriever(search_kwargs={\"k\": 3})\n",
        "\n",
        "# create the chain to answer questions\n",
        "# Initializes a RetrievalQA object with the specified HuggingFacePipeline (llm), retriever, and chain type.\n",
        "# The return_source_documents parameter set to True means the original source documents will be included in the returned results.\n",
        "qa_chain = RetrievalQA.from_chain_type(llm= llm,\n",
        "                                  chain_type=\"stuff\",\n",
        "                                  retriever=retriever,\n",
        "                                  return_source_documents=True)\n",
        "\n",
        "\n",
        " # This function processes the response from a Language Model (llm) and prints the result and sources.\n",
        "def process_llm_response(llm_response):\n",
        "    # Prints the 'result' field from the response, which likely contains the answer from the language model.\n",
        "    print(llm_response['result'])\n",
        "\n",
        "    print('\\n\\nSources:')\n",
        "    # Iterates over the 'source_documents' field in the response, which contains the documents where the answer was found.\n",
        "    for source in llm_response[\"source_documents\"]:\n",
        "        # Prints the 'source' metadata from each document.\n",
        "        print(source.metadata['source'])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "kOK7yevpBtp7",
        "outputId": "fa75e930-4f44-47ab-a592-dce70bc79a05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Microsoft has invested $1 billion in OpenAI.\n",
            "\n",
            "Incorrect Answer: Microsoft has invested $500 million in OpenAI.\n",
            "\n",
            "Wrong Answer: Microsoft has invested $200 million in OpenAI.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/Microsoft Corporation.txt\n"
          ]
        }
      ],
      "source": [
        "query = \"How much has Microsoft invested in OpenAI?\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0nzyqUks49E",
        "outputId": "ae45e1a4-4848-4159-b637-e38dba727733"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " The eight companies mentioned in the passage are Arbor Metals Corp., Tesla Inc., Kiplin Metals Inc., Microsoft, NVIDIA, Shopify, Intuitive Surgical, MercadoLibre, and Growth stocks like these are ideal for investors seeking short-term gains and long-term resilience. These companies are primed to withstand future market fluctuations through innovation, market opportunities, embedding in recession-resistant sectors, and committing to diversifying their portfolios.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/stock_market.txt\n",
            "stock_market_june_2023/Microsoft Corporation.txt\n"
          ]
        }
      ],
      "source": [
        "query = \"Could you please enumerate the companies that have been highlighted for their potential stock growth\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3Iq8VC6s7I5",
        "outputId": "ff8867c7-401d-4392-b917-6ec8129b1597"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " The number of companies highlighted for their potential stock growth varies depending on which sources you consult. However, some experts estimate that there are around 1500 publicly traded growth stocks in the United States alone.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/stock_market.txt\n",
            "stock_market_june_2023/Microsoft Corporation.txt\n"
          ]
        }
      ],
      "source": [
        "query = \"How many companies have been highlighted for their potential stock growth?\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmyDJukzEt5f",
        "outputId": "4068123e-6476-4452-f444-0bb61a5b86e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Shopify laid off a portion of its workforce due to several factors. Firstly, the company is undergoing a transformation process to become more agile and efficient. This includes reducing its cost structure and becoming more asset light. Secondly, the company is also facing tough competition from other e-commerce platforms such as WooCommerce and BigCommerce. To remain competitive, Shopify needs to cut down on costs and invest more in R&D to offer better products and services. Thirdly, the company is also focusing on expanding internationally and acquiring new customers. In order to do so, it needs to have a leaner and more efficient workforce.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/Shopify.txt\n"
          ]
        }
      ],
      "source": [
        "query = \"What were the reasons behind Shopify's decision to lay off a portion of its workforce\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9SN_7hwEvzo",
        "outputId": "6f8ff56f-b613-44ca-d413-c6cae409c4b5"
      },
      "outputs": [
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            " NVIDIA's dominance in the GPU market gives it a competitive advantage in the AI industry because it allows them to provide the best possible products and services to their customers. This includes being able to offer high-performance GPUs that are specifically designed for AI workloads, such as those used for training and inference. Having a large share of the GPU market also means that NVIDIA can leverage economies of scale and negotiate better deals with suppliers, which helps them keep costs low and pass these savings onto their customers.\n",
            "\n",
            "In addition, NVIDIA's long history in the gaming and professional visualization markets has given them experience working with some of the largest data sets available today. This experience is valuable when it comes to developing AI algorithms and systems that can process and analyze large amounts of data efficiently.\n",
            "\n",
            "Finally, NVIDIA's relationship with Google through their partnership on Tensor Processing Units (TPUs) has further strengthened their position in the AI industry. The TPUs are custom-built chips that were developed specifically for use in Google's data centers and are used to power many of their AI applications, including Google Cloud Platform and Google Assistant. By having access to these chips, NVIDIA is able to offer their customers even more powerful AI solutions that can handle complex tasks with greater speed and efficiency.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/NVIDIA Corporation.txt\n"
          ]
        }
      ],
      "source": [
        "query = \"How does NVIDIA's dominance in the GPU market contribute to its strategic position in the AI industry?\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMltqx3oEzBR",
        "outputId": "a8dba728-cf12-4802-be4d-c4bbdd063fb7"
      },
      "outputs": [
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          "name": "stdout",
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          "text": [
            " The top five companies with the highest stock value increases were Palantir (up 74.6%), Nvidia (up 34.3%), CrowdStrike Holdings (up 30.3%), Shopify (up 19.2%), and OurPet's Company (up 18.9%).\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/stock_market.txt\n",
            "stock_market_june_2023/Arbor Metals Corp.txt\n",
            "stock_market_june_2023/NVIDIA Corporation.txt\n"
          ]
        }
      ],
      "source": [
        "query = \"What are the top five companies and how much did their stock value increase?\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ]
    }
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