{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Ss2EukDFYJ1y" }, "source": [ "#**Goal:**" ] }, { "cell_type": "markdown", "metadata": { "id": "9uwcLaHfYIQK" }, "source": [ "**The primary goal of this project is to create a chatbot capable of answering user queries based on content extracted from a set of provided PDF documents. The chatbot is trained using a Language Model (LLM), and the information is stored and retrieved from a vector database. The project aims to showcase the ability to leverage both pre-trained language models and vector databases to build an intelligent and informative chatbot.**" ] }, { "cell_type": "markdown", "metadata": { "id": "D4yTn8e3WBaX" }, "source": [ "#**Installing Required Packages**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zyYbg3taax-1", "outputId": "75a43249-72a7-4531-fc16-b9ae3f05c737" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://download.pytorch.org/whl/cu117\n", "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n", "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu121)\n", "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n", "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n", "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n", "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.6)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.11.17)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n" ] } ], "source": [ "!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 --upgrade\n", "!pip install langchain einops accelerate transformers bitsandbytes scipy\n", "!pip install xformers sentencepiece\n", "!pip install llama-index==0.7.21 llama_hub==0.0.19\n", "!pip install sentence-transformers\n", "!pip install gradio==3.48.0" ] }, { "cell_type": "markdown", "metadata": { "id": "Upillmi5WOP6" }, "source": [ "#**Importing Transformer Classes and Setting up Model**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "a8AHjb2Jax-2" }, "outputs": [], "source": [ "# Import transformer classes for generaiton\n", "from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer\n", "# Import torch for datatype attributes\n", "import torch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "x8xCtdElax-3" }, "outputs": [], "source": [ "# Define variable to hold llama2 weights naming\n", "name = \"mistralai/Mistral-7B-Instruct-v0.2\"\n", "# Set auth token variable from hugging face\n", "auth_token = \"AUTH_TOKEN\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 200, "referenced_widgets": [ "d40a77ce63884e7f83f37a911a126575", "987773915e6f4f3395bb8814056bb423", "ac61be9c4fda4da8b8e9043807082cd4", "4470b5cda3d34e1982419705dafcddbb", "6185b1b79531476c90676976d595dde8", "0b6804c0ef224219995d35739e6190a1", "1613848c5e3c402a9937a2c10cc5d0d4", "6f59638777ea43d59bcca1489348c7d4", "f39c96ae31c646b592a57c6deeaf04ad", "e7a596c1164d4338a69e28ecb3a4c9b2", "b3717b30cd524ea89791feb6645c91b6", "702d1ae80bb64d7eae4e5b9a4fda1fa9", "8a75e7e771e746cda637832b01bda586", "2023c90daa6640bd9a06a60708da39c1", "98d65dc46e0143dfb8664aa83586be08", "4404a9bfd936431a81aa3aa31baa9660", "68346c734382453ab406df7f9f69a259", "1f2c1ed38f00459c87b46226f7eaf54e", "49f40a6a957d492bb78f399c67bfed53", "4fee0abbddd741a3bad7d13157da6d2d", "e375582cb2db46f890846078e21fcdfc", "dff250f90167456780e7cf552fb25451", "226a6de8d6f8459085be50f462bd5b6e", "b65a338463b146eda7b7af2b8774b6e2", "0f18982d9776498ba8ff5509564f4835", "576e4f7e4a584bf183d42c46aa5a07a8", "bddbaee666054c19a504cabf71f567bc", "090bb74117564398a94c231a46774696", "be8f243563424355a059088110aaab31", "ee58584ca57a45e0b76d795acb4a204a", "722c47a0877e441cbf3bb14d42538eb3", "21fb6061640549b7a1e8b82d7e0fd81b", "cbf403f203384c29bb51ddfc1e222d98", "8d1c5ac6b64f41ed8711e23af30c8267", "f965528e41b54211824391f301d9efc8", "454454b3326149e69edd9ff5beb7a890", "40ca202492574369ad2c589fd72173f5", "555ad9f70aff442e836db7840480976e", "d54ef5035f1043a09a38a09e4b081059", "890da379d9924addbf82f1e9ec3e5874", "67e4f0b01f254e0fa1b2903bbb978c2e", "470b125dc0e34302b7b21e4e950978d9", "885108bec37d40edb91948ae1d4bd7f4", "c01679d10a6e4b06954b0196f0feaa2d" ] }, "id": "Fepv_akgax-3", "outputId": "1a346859-ad64-41f9-ad8c-b73c11384ec0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py:671: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d40a77ce63884e7f83f37a911a126575", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/1.46k [00:00[INST] <>\n", "You are a helpful, respectful and honest assistant. Always answer as\n", "helpfully as possible, while being safe. Your answers should not include\n", "any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.\n", "Please ensure that your responses are socially unbiased and positive in nature.\n", "\n", "If a question does not make any sense, or is not factually coherent, explain\n", "why instead of answering something not correct. If you don't know the answer\n", "to a question, please don't share false information.\n", "\n", "Your goal is to provide answers relating to\n", "the company.<>\n", "\"\"\"\n", "# Throw together the query wrapper\n", "query_wrapper_prompt = SimpleInputPrompt(\"{query_str} [/INST]\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "lt0MLoekax-4", "outputId": "218d3f96-13fc-4b3e-a169-12d786df143d" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'hello [/INST]'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Complete the query prompt\n", "query_wrapper_prompt.format(query_str='hello')" ] }, { "cell_type": "markdown", "metadata": { "id": "TIEuzRARW1S3" }, "source": [ "**import and setting up the LLM (HuggingFaceLLM) using the llama index wrapper. This includes creating a system prompt and a query wrapper prompt.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "spvMbCeXax-4" }, "outputs": [], "source": [ "# Import the llama index HF Wrapper\n", "from llama_index.llms import HuggingFaceLLM\n", "# Create a HF LLM using the llama index wrapper\n", "llm = HuggingFaceLLM(context_window=4096,\n", " max_new_tokens=256,\n", " system_prompt=system_prompt,\n", " query_wrapper_prompt=query_wrapper_prompt,\n", " model=model,\n", " tokenizer=tokenizer)" ] }, { "cell_type": "markdown", "metadata": { "id": "g4HePTQYW8XV" }, "source": [ "#**Bringing in Embeddings Wrapper**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "g-_7NvNjax-4" }, "outputs": [], "source": [ "# Bring in embeddings wrapper\n", "from llama_index.embeddings import LangchainEmbedding\n", "# Bring in HF embeddings - need these to represent document chunks\n", "from langchain.embeddings.huggingface import HuggingFaceEmbeddings" ] }, { "cell_type": "markdown", "metadata": { "id": "4SQxt71VXG_c" }, "source": [ "**bringing in the embeddings wrapper and HuggingFace embeddings to represent document chunks.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "j4knDFnge3Nt" }, "outputs": [], "source": [ "import locale\n", "locale.getpreferredencoding = lambda: \"UTF-8\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 589, "referenced_widgets": [ "e13d138ad4bf4305a5dea8d023cc3fba", "b1e7cf1ee7944c2386a4d311c8feafe2", "999ed51ddb7749269f3a0f1ec64f03b1", "1eae6967c82047af8134b268f623d2ee", "a3e9447d2934476d8cd638220a7e851f", "9560ddf361964ea3bdd99cce4f418fe4", "0cabe4bda2ca4a2a84612be04c6977c3", "77b918b7e6344f2caaa40be606b76484", "67f97b4b9fc84f2a89aa27049d4183a0", "3cd3354391f74c66849dff369c8a401d", "eabe8dad65ae4c0688d4dbc6a5ea5ca6", "6bbc396d0527461a8aba0557868a2190", 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"baeed12ea28b4f709ab393e1725b1f0c", "3bbc607f7b8f47a781496960171db319", "15114ad56e2a4ff2a281a687eaf2f5b2", "01d9adfca5df4f109939ff59ddfb64c8", "2f078156cc4243de938a2fe0c3209c79", "f477e7e575df4c4096574036812bde97", "c6c97c2c568e49ba887e35b97e3df9fa" ] }, "id": "vy5D_b4zax-4", "outputId": "82e3fe82-e737-4e90-80b7-bec219517738" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e13d138ad4bf4305a5dea8d023cc3fba", "version_major": 2, "version_minor": 0 }, "text/plain": [ ".gitattributes: 0%| | 0.00/1.18k [00:00:13: GradioDeprecationWarning: `capture_session` parameter is deprecated, and it has no effect\n", " iface = gr.Interface(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", "Running on public URL: https://1af141e8f07f63e5e8.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gradio as gr\n", "\n", "# Define the function to handle user inputs and return responses\n", "def chatbot_interface(user_input):\n", " response = query_engine.query(user_input)\n", " answer = response['answer']\n", " return answer\n", "\n", "# Create the Gradio interface\n", "iface = gr.Interface(\n", " fn=chatbot_interface,\n", " inputs=gr.Textbox(),\n", " outputs=gr.Textbox(),\n", " capture_session=True # This is to capture the CUDA sessions if you are using GPU\n", ")\n", "\n", "# Add a button to trigger the chatbot response\n", "iface.launch(share=True)\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "orig_nbformat": 4, "widgets": { 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