guanaco_33b_merged.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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            "Collecting uc-micro-py (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio)\n",
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4694 sha256=29faf67a32bbf00e97dcf9af4151c442389ec1441c19a1bafd631762999bc17f\n",
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            "Successfully built ffmpy\n",
            "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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            "Collecting scikit-learn (from sentence_transformers)\n",
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            "\u001b[?25hCollecting threadpoolctl>=2.0.0 (from scikit-learn->sentence_transformers)\n",
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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=aa14781e2e9ad5d356504a94dc81931cb9cc36957516a8a13c4017fa59079adb\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": "code",
      "execution_count": null,
      "metadata": {
        "id": "MdatOyCn_7sx"
      },
      "outputs": [],
      "source": [
        "from langchain.vectorstores import Chroma\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\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[31m2.0 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": [
        "# timdettmers/guanaco-33b-merged"
      ],
      "metadata": {
        "id": "ZPcl_iwiIM99"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 878,
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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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          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3c318f7b153b405997bab0db4efefd12",
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          "metadata": {},
          "output_type": "display_data"
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              "model_id": "f6704507d80e43f3bc58622425fa34fc",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
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              "model_id": "9ea1d4916dae4a1b8c5c7e927d9d7422",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "265c485028ea4ff1bbb16555c35f73be",
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              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "57fde7e2a48b453c8952f77ff596f26a",
              "version_major": 2,
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            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "9fba0aa24423474f970365017883c4f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "b9ed74beacf74b45b0e845117b35687b",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "7489e46917654f1faead13ae10cb2574",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Load the model.\n",
        "from transformers import AutoConfig\n",
        "import torch\n",
        "from peft import PeftModel\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer\n",
        "# Note: It can take a while to download LLaMA and add the adapter modules.\n",
        "# You can also use the 13B model by loading in 4bits\n",
        "\n",
        "\n",
        "model_name = \"timdettmers/guanaco-33b-merged\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    device_map= \"auto\"\n",
        ")\n",
        "# m = PeftModel.from_pretrained(m, adapters_name)\n",
        "# m = m.merge_and_unload()\n",
        "tokenizer = LlamaTokenizer.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LangChain multi-doc retriever with ChromaDB"
      ],
      "metadata": {
        "id": "rxsimoE9Idc7"
      }
    },
    {
      "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 22:50:30--  https://github.com/Shafi2016/Youtube/raw/main/stock_market_june_2023.zip\n",
            "Resolving github.com (github.com)... 140.82.113.3\n",
            "Connecting to github.com (github.com)|140.82.113.3|: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 22:50:30--  https://raw.githubusercontent.com/Shafi2016/Youtube/main/stock_market_june_2023.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|: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 0s      \n",
            "\n",
            "2023-06-16 22:50:30 (74.0 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",
        "loader = DirectoryLoader('./stock_market_june_2023/', glob=\"./*.txt\", loader_cls=TextLoader)\n",
        "#documents = loader.load()#[:5]  # Only use the first 5 files\n",
        "documents = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u2zA9VSKBPXQ"
      },
      "outputs": [],
      "source": [
        "#splitting the text into\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
        "texts = text_splitter.split_documents(documents)"
      ]
    },
    {
      "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",
            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.31.0.dev0)\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+cu117)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.15.2+cu117)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.24.1)\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",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.1.99)\n",
            "Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.15.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (3.9.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (2.28.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (4.6.3)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (23.1)\n",
            "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",
            "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",
            "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": "IfANokiyIn7d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310,
          "referenced_widgets": [
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        },
        "id": "kuC2Hw_JBYKa",
        "outputId": "f3eed878-e7e7-40be-85a5-3ec0297607f5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3bb1def7999a4fbbae17c0db9b391677",
              "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"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fa86cf1dc7c34e8b8c40d500ae45284f",
              "version_major": 2,
              "version_minor": 0
            },
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        {
          "name": "stderr",
          "output_type": "stream",
          "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",
        "from transformers import pipeline\n",
        "from sentence_transformers import SentenceTransformer\n",
        "# Moving model to GPU if available\n",
        "#model = model.to(device)\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_length= 1028,\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\n",
        "# # )\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline= pipe)\n",
        "\n",
        "model_name = \"intfloat/e5-large-v2\"\n",
        "hf = HuggingFaceEmbeddings(model_name=model_name)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# create the DB"
      ],
      "metadata": {
        "id": "Kft6WMYtIsFN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0db0RThgBdbQ"
      },
      "outputs": [],
      "source": [
        "# Embed and store the texts\n",
        "# Supplying a persist_directory will store the embeddings on disk\n",
        "persist_directory = 'db'\n",
        "\n",
        "embedding = hf\n",
        "\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": [
        "# persiste the db to disk\n",
        "vectordb.persist()\n",
        "vectordb = None\n",
        "\n",
        "# Now we can load the persisted database from disk, and use it as normal.\n",
        "vectordb = Chroma(persist_directory=persist_directory,\n",
        "                  embedding_function=embedding)\n",
        "\n",
        "retriever = vectordb.as_retriever(search_kwargs={\"k\": 3})\n",
        "\n",
        "# create the chain to answer questions\n",
        "qa_chain = RetrievalQA.from_chain_type(llm= llm,\n",
        "                                  chain_type=\"stuff\",\n",
        "                                  retriever=retriever,\n",
        "                                  return_source_documents=True)\n",
        "\n",
        "def process_llm_response(llm_response):\n",
        "    print(llm_response['result'])\n",
        "    print('\\n\\nSources:')\n",
        "    unique_sources = set(source.metadata['source'] for source in llm_response[\"source_documents\"])\n",
        "    for source in unique_sources:\n",
        "        print(source)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOK7yevpBtp7",
        "outputId": "fa75e930-4f44-47ab-a592-dce70bc79a05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Microsoft has invested 1 Billion dollars 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 that have been highlighted for their potential stock growth are:\n",
            "\n",
            "1. Arbor Metals Corp.\n",
            "2. Tesla Inc.\n",
            "3. Kiplin Metals Inc.\n",
            "4. Microsoft\n",
            "5. NVIDIA\n",
            "6. Shopify\n",
            "7. Intuitive Surgical\n",
            "8. MercadoLibre\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": [
            " There are eight companies mentioned in the text.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/stock_market.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": [
            " In early 2021, Shopify announced that it would be laying off approximately 20% of its workforce. This decision was made in response to several factors, including the impact of the COVID-19 pandemic on the global economy and the need to adjust to a rapidly evolving ecommerce landscape.\n",
            "\n",
            "The pandemic led to a sharp decline in consumer spending worldwide, which had a significant impact on Shopify's revenues. To maintain profitability, the company needed to take steps to reduce operating costs. Laying off a portion of the workforce was one way to achieve this goal.\n",
            "\n",
            "Additionally, Shopify recognized that the ecommerce space was becoming increasingly competitive, with new players entering the market and established companies expanding their offerings. In order to remain competitive, the company needed to become more efficient and agile. This required making some tough decisions about how to allocate resources and prioritize initiatives.\n",
            "\n",
            "By laying off a portion of its workforce, Shopify was able to reduce its overall operating costs while also freeing up resources to invest in areas where there are opportunities for growth. For example, the company used some of the savings from the layoffs to acquire Flexport, a leading logistics provider, which allowed them to offer a more comprehensive set of services to their customers.\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": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " NVIDIA's dominance in the GPU market gives it several advantages in the AI industry. Firstly, it allows them to invest heavily in research and development, ensuring that their products remain at the forefront of innovation. Secondly, it means that they can offer their customers access to cutting-edge technology, giving them a competitive advantage over rivals who are using less powerful hardware. Finally, it also enables NVIDIA to establish strong relationships with key players in the AI industry, such as major tech companies and startups, which can lead to new business opportunities and partnerships. Overall, NVIDIA's dominant position in the GPU market is a significant asset that helps to strengthen its strategic position in the AI industry.\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": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Here are the top 5 companies with the highest percentage gains in their stock prices from January 1st to February 28th, 2023 (as of March 1st):\n",
            "\n",
            "1. Palantir Technologies - Increase of 74.6%\n",
            "2. NVIDIA Corporation - Increase of 34.3%\n",
            "3. CrowdStrike Holdings Inc - Increase of 30.3%\n",
            "4. Shopify Inc - Increase of 19.1%\n",
            "5. Tesla Inc - Increase of 18.9%\n",
            "\n",
            "Explanation: These companies all experienced significant increases in their stock prices during this time period due to various factors such as strong earnings reports, positive news coverage, or overall market momentum. For example, Palantir Technologies is a data analytics company that benefited from its growing presence in the healthcare sector, while NVIDIA Corporation is a leading manufacturer of graphics processing units (GPUs) that saw demand for its products increase due to the resurgence of cryptocurrency mining activity.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/NVIDIA Corporation.txt\n",
            "stock_market_june_2023/stock_market.txt\n",
            "stock_market_june_2023/Tesla Inc.txt\n"
          ]
        }
      ],
      "source": [
        "query = \"Could you provide information on the top five companies, if available, and detail the increase in their stock values\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ]
    },
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