guanaco_13b.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=f11b2a84bc1419cdb338ce209c56b6974cc19516fe84d1447ee050b468ceef8f\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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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
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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=a9aad76fef1fdf704d111c6e5b5feb1078453179fdcced1a09405b72b4a4476c\n",
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            "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": "VWRR51GJFI0Z"
      }
    },
    {
      "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[31m465.7 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\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": [
        "# guanaco-13b"
      ],
      "metadata": {
        "id": "vBnVi3vpFT9B"
      }
    },
    {
      "cell_type": "code",
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        {
          "name": "stderr",
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          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. \n",
            "The class this function is called from is 'LlamaTokenizer'.\n"
          ]
        }
      ],
      "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",
        "model_name = \"decapoda-research/llama-13b-hf\"\n",
        "adapters_name = 'timdettmers/guanaco-13b'\n",
        "\n",
        "\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    #load_in_4bit=True,\n",
        "    torch_dtype=torch.bfloat16,\n",
        "\n",
        "    device_map= \"auto\"\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, adapters_name)\n",
        "model = model.merge_and_unload()\n",
        "tokenizer = LlamaTokenizer.from_pretrained(model_name)\n",
        "tokenizer .bos_token_id = 1\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 21:54:21--  https://github.com/Shafi2016/Youtube/raw/main/stock_market_june_2023.zip\n",
            "Resolving github.com (github.com)... 140.82.121.4\n",
            "Connecting to github.com (github.com)|140.82.121.4|: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 21:54:24--  https://raw.githubusercontent.com/Shafi2016/Youtube/main/stock_market_june_2023.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|: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 21:54:28 (142 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": [
        "# 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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u2zA9VSKBPXQ"
      },
      "outputs": [],
      "source": [
        "# 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)"
      ]
    },
    {
      "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",
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            "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",
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            "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",
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            "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": "l6aCq2G0G5hi"
      }
    },
    {
      "cell_type": "code",
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      "metadata": {
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          "height": 310,
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        },
        "id": "kuC2Hw_JBYKa",
        "outputId": "f3eed878-e7e7-40be-85a5-3ec0297607f5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35a1f098ae7c4906a59e8e39e83bcb55",
              "version_major": 2,
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          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "a550af08da764f838e67ea8237097c69",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "7b4c5f42265743c3a67fff3fd8192553",
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              "model_id": "c491226e3df9456c925ed721e788b3c7",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "cea324996a204770803aac53d722d9d0",
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        {
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              "model_id": "d5ea40bf0d824b06aa7f6794fa98a82a",
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          },
          "metadata": {},
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        },
        {
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              "model_id": "706f626460ac4ee5a302603caa4e0212",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "341362eb06044a98a26856a06027f39f",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "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": "ptHUW5PyG8xs"
      }
    },
    {
      "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": [
        "# 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": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1259: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " I do not have this information. \n",
            "Unhelpful Answers:\n",
            "\"$10 million.\"\n",
            "\"Bill Gates personally invested $500 million in OpenAI.\"\n",
            "\"It was a one time payment of $200,000.\"\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 companies that have been highlighted for their potential stock growth include Arbor Metals Corp., Tesla Inc., Kiplin Metals Inc., Microsoft, NVIDIA, Shopify, Intuitive Surgical, MercadoLibre, and Growth stocks.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/Microsoft Corporation.txt\n",
            "stock_market_june_2023/stock_market.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 were 10 companies mentioned in the above passage.\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)"
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            " Layoffs are never easy, but sometimes they can be necessary for a company to thrive. In this case, Shopify made the difficult decision to lay off 20% of their workforce because they wanted to become more efficient and cost effective. They also sold their logistics business to Flexport so that they could focus on their core competencies and better serve their customers. These changes may have short term consequences, but they will help Shopify in the long run by allowing them to save money and become more nimble.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/Shopify.txt\n"
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        "query = \"What were the reasons behind Shopify's decision to lay off a portion of its workforce\"\n",
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            " NVIDIA's dominance in the GPU market has allowed it to establish itself as a key player in the AI industry. By controlling such a large portion of the GPU market, NVIDIA is able to leverage its resources and expertise to develop cutting-edge AI products and services.\n",
            "\n",
            "Additionally, NVIDIA's partnerships with leading AI companies have further cemented its position as a leader in the field. For example, NVIDIA's collaboration with Google on the development of their AI platform, TensorFlow, has helped both companies to innovate and stay ahead of the curve.\n",
            "\n",
            "Finally, NVIDIA's success in the GPU market has also attracted investment from major players in the tech space, including Alphabet Inc., who recently invested $500 million in NVIDIA. This investment not only provided NVIDIA with much-needed capital, but also validated its position as one of the top AI companies in the world.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/NVIDIA Corporation.txt\n"
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            " The top five companies with the highest percentage increase in stock value were:\n",
            "\n",
            "1. Palantir - 74%\n",
            "2. Nvidia - 34%\n",
            "3. Crowdstrike Holdings - 30%\n",
            "4. Shopify - 19%\n",
            "5. Patriot One Technologies - 480%\n",
            "\n",
            "These numbers are based on the information provided in the above-mentioned text. It is important to note that stock prices can fluctuate daily and past performance does not guarantee future returns.\n",
            "\n",
            "\n",
            "Sources:\n",
            "stock_market_june_2023/NVIDIA Corporation.txt\n",
            "stock_market_june_2023/Arbor Metals Corp.txt\n",
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