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[ "%%capture\n", "!pip install -q transformers einops accelerate langchain bitsandbytes" ] }, { "cell_type": "code", "source": [ "!huggingface-cli login" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A5DHAJ8h0-ZI", "outputId": "2dc19291-27b4-41bc-b8f0-f6b88fc6335c" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", "\n", " To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n", "Enter your token (input will not be visible): \n", "Add token as git credential? (Y/n) y\n", "Token is valid (permission: fineGrained).\n", "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub.\n", "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n", "\n", "git config --global credential.helper store\n", "\n", "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n", "Token has not been saved to git credential helper.\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "code", "source": [ "!mkdir pdfs" ], "metadata": { "id": "3KGGYpVD1CTL" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "%%capture\n", "pip install pypdf" ], "metadata": { "id": "ZHu8cmLu8WpR" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "%%capture\n", "pip install langchain-community" ], "metadata": { "id": "7hyYqv9I8h5E" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain.document_loaders import PyPDFDirectoryLoader" ], "metadata": { "id": "M8FrfyH08rvb" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "loader=PyPDFDirectoryLoader(\"pdfs\")" ], "metadata": { "id": "M8GMH3Bn98ox" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "data=loader.load()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "474U6ChiBgy1", "outputId": "f8581339-2b3b-48fe-a87e-c4d709130544" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:pypdf.generic._base:could not convert string to float: '0.00-10' : FloatObject (b'0.00-10') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-10' : FloatObject (b'0.00-10') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-10' : FloatObject (b'0.00-10') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-10' : FloatObject (b'0.00-10') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-30' : FloatObject (b'0.00-30') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-30' : FloatObject (b'0.00-30') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-30' : FloatObject (b'0.00-30') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-30' : FloatObject (b'0.00-30') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-20' : FloatObject (b'0.00-20') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-20' : FloatObject (b'0.00-20') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-20' : FloatObject (b'0.00-20') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-20' : FloatObject (b'0.00-20') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-20' : FloatObject (b'0.00-20') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-20' : FloatObject (b'0.00-20') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-20' : FloatObject (b'0.00-20') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-20' : FloatObject (b'0.00-20') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-50' : FloatObject (b'0.00-50') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-50' : FloatObject (b'0.00-50') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-50' : FloatObject (b'0.00-50') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-50' : FloatObject (b'0.00-50') invalid; use 0.0 instead\n", "WARNING:pypdf.generic._base:could not convert string to float: '0.00-50' : FloatObject (b'0.00-50') invalid; use 0.0 instead\n" ] } ] }, { "cell_type": "code", "source": [ "# we are going to create the chunks\n", "from langchain.text_splitter import RecursiveCharacterTextSplitter" ], "metadata": { "id": "mX6l79NBEpQ7" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "text_chunks=RecursiveCharacterTextSplitter(chunk_size=300,chunk_overlap=20)" ], "metadata": { "id": "oH_NnKsFEyfb" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "chunks=text_chunks.split_documents(data)" ], "metadata": { "id": "JE7gq3cBE1Kc" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "len(chunks)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6FclieVRE47s", "outputId": "e10de323-ceed-4e75-8c64-e23d88ddb454" }, "execution_count": 12, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "39469" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [ "# creating embeddings using huggingface embedding model\n", "from langchain.embeddings import HuggingFaceEmbeddings" ], "metadata": { "id": "L1J7Kph6E_Va" }, "execution_count": 13, "outputs": [] }, { "cell_type": "code", "source": [ "%%capture\n", "pip install sentence-transformers" ], "metadata": { "id": "hu5gYvN6FFzc" }, "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "embeddings=HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 573, "referenced_widgets": [ "6021316125fc4f8b99ae3fb328c14307", "837c6f8cd7414eff85542f5abe070f81", "848c3feecc894412a8d4a3bf93497ecc", "fd74858a908c400d932c17a1854a7fd9", "88e0cbaf49294f678cd02b5ab83d158c", "9bfc8f94914d40588fa43444a9f30baa", "c58689d5860543a6b58e036e55de5dcf", "44c692730c8546e68368ef87ac5585af", "2eb6489c13664fa781bc9a2381e8af25", "83fdb727d1cc4c949e071fed686b030f", "ae670c2ebc9e4aa78670e9d57d8f2052", "34c0037fa4454172b8d6d8c000f7d7fe", "1ae2ce3b68e14467af319f93b0f1bb02", "3beadcd4287c4fc6b1399f8003c884ac", "588e234130014c938051c404394df951", "8c3411db0c044831b368c6a1c1e03b9c", "3c029fad0c2d43cb82edc0b87e48dae0", "5c3d8e819b4b4659b37d21e9df5b6075", "5b115f7f8301452fbf3d89872d675279", "271900aeedbd4dad832b48fe6396522e", "15e917e4cad34a8ab5827c326b3b31a7", "4482e4a6ed4147189a19f06d6b5756a1", "cc4c7159733446aabaa97f7255ae6392", "96f0205537734e7cbdf9e369d8b12aac", "ebe0e53fc86a4ddf90ef55d90f8c2e2a", "0e3cc271ffb647ee8688d7455c33f7ff", "150af5587c404347a83da88192f324ab", "b5c4644ef8334128b2bb16c4dae77c8e", "e2e080dc2b1747ab8010ca3e072a4780", "b842cde898cd4c3caf6b90aca9b8a9ea", "1d8c555d9077429589581258210f2c8e", "3f499e2d8b97421fa168f3c60c5a5721", "d67cd7276e9a4ad6a68729dd53c33580", "425cc1a27d034c60969b4774f39311d2", "c7b6071510064ba4802e44c0a2ba7af1", "9f394b02b4674d779c6e986222049bc0", "5dd1b8800fb74aeebdb7aea56e5097ce", "6948cedca15043ceac2274f4d1e07bfb", "c6c5fb9e46f747f4bc6901ef5923b3f7", "2158a489562d4db3a8a67442ee419d53", "7969e138fcde449c8eb805a6ac71d304", "38f0968105f34023951d312276f87ed8", "ebb7a7a3b45849c1872ac7f5cb39540b", "3bc6b398dfe24e35bd3a1433d9e07745", "6137caf2f0b14d9baf1a64887db585ad", "3008945cf0234694a155d3e0b54084ea", "13e4fdd06c614dbb92b6993cc04b835d", "a6eb20c16a444f8598489673d531b710", "267661146ede4583ae0e44eca12f61de", "1d7f887678574087abda783247543cb6", "497f9b9eb47c4bf2a41289b9a34aeaa9", "d98f16838eb04673bff6302103cbe7af", "112d25b2fdd64a8889a1b8c674751bd1", "722b114dc70e411a815e83075b938bbd", "5b1a5fb649b24ae2b750e320edeeac1c", "d2086a37606d48cbb104cc49f0f76933", "47febb493c4b41bbbd5e9e703908ca41", "566a788f44b54a5fa4f8d90524aa0604", "3fc8cda7baf74d9c85a75d732bea7fc2", "14b32c0f17514b9a865fa71cb21e9d6d", "32f8b602f9214ad58910ce5c0603cb3e", "84def99a199a464f8dc191f263dc2f61", "60f42be0571a49a495f30a38df1304ba", "4f4acbdfde724521bd44e5412a152362", "199072de2340404cbf5ca17448e8e583", "d640ea4215ca43049567c5d591095d5f", "bffd361e948d48e899bf14c42cd87cfc", "3def35cdb39f4ad6a29a739a8520ec64", "8d224158086549b48463ca16a4cd9ed2", "22bfc46f40434781b0a2d151e793e029", "cc0b7c4d19674e02bc6ccb7557f66a64", "e6c53741a38344ed8954dcfa7247a7d7", "f092df14a3aa4bce8d90ef030872e504", "28369231c7f04ffe8c1e98354c1da3b2", "cc0bbc64b35949f6b5fe5fd36e4e4da0", "f50cbc596c274809bab01017f3e7e9fd", "6a031244eda543a79b2247a43454ede8", "0f4da7a3995e4b01b916bb02d0d71857", "4d86cf21eb624f72a2334138f17a7402", "7cf8152b4d55479eb33ae9e0fb129c6b", "82f21134627d4c8cb59e8ff6ce89a9c1", "4c64f8a01d4a4828a966da311dcbc098", "692f8667b4494dacacd54bf5ff0410f7", "34306b3e8ab740dbbca031d82d9df3c8", "93fd85f82b9a49139eb12c83272e5c25", "50c343aabae549afa83ea0103714f182", "8bc5f81c3db7446db89b9847467eba03", "6c5ed21806c74d2b84ac67d1c0c95d4d", "30542545e97641e2ba5d1d3fa97bba09", "28136fa57e3741f4946c62c7380f3780", "dc65a22b25974e57a9499758c3559da5", "9dec9074e4e74e3292a7dcbab4f86e4e", "dce78ea4a3024df0b684a0a98cc26473", "68044bdcabd24b2aa4577d349ed24378", "d905d5828c184e86a0c3ae7bb583aa67", "4f27965b7341441495b92c7e7ee71d40", "6393ffc42eb64d34be701809b53ce49e", "eb9520fc95c741548d379866d9272318", "28c40c00a8164977a475dd73a7fc6d21", "4bd34cb014e64dc2bcf581415f3da4f0", "0e1c63cf9f484c698dc8d43c517654c6", "240c04dfdc944e99a4fec681f21009b2", "f43a6935bae048e3989746309243c9fd", "a9c34a38725245f09c40fe473a3d84fc", "d1bdd6a541c143fd99fa35a1b38f54fa", "7610d2a70fbc40efabd6820c68baf06e", "99ad58b5392e4d7499db48f96d46abf5", "1c9d0f43ce174019b72e94226fe72574", "21887591d83e4d6c910129a9f31f2ab9", "c800ebb7c0234328954e58915aedcd8a", "422a304ab432433a9318d142f8761e46", "6d108ebaa33645dea263e80daa0cdc9a", "4b87b664a21d4e29a18890281453aa12", "7ae955f5970043328fc5509117b95b76", "17da35028e1f4c8b9deb8559d981893e", "755fcee44b444c59b8f0c84ff4331b83", "9e04d5e82fd04a0ca71eeb993e360010", "478883863f4549df823544410b2a9c94", "9058c51e8bee4e79ac9d22d2af6e2b6e", "aeea7f6c0ab646ab9a8c97eff6bdc7d7", "4cb7d44f1a374290a0ef6344fb7af1dd" ] }, "id": "hcAkTS39FNCU", "outputId": "2b9c5b74-330b-465a-e20e-e11ea41e8681" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 0.3.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n", " warn_deprecated(\n", "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", " from tqdm.autonotebook import tqdm, trange\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/349 [00:00 512). Running this sequence through the model will result in indexing errors\n", "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1249: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "a po w e rful d at a v isu\n" ] } ] }, { "cell_type": "code", "source": [ "def fetch(question,history):\n", " result=qa_chain({\"query\":question})\n", " return result[\"result\"]" ], "metadata": { "id": "1_u7Xi7oS5we" }, "execution_count": 32, "outputs": [] }, { "cell_type": "code", "source": [ "%%capture\n", "pip install gradio" ], "metadata": { "id": "Y8FRnLJCS-ME" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [ "import gradio as gr" ], "metadata": { "id": "2msvzm1OTFa_" }, "execution_count": 34, "outputs": [] }, { "cell_type": "code", "source": [ "gr.ChatInterface(\n", " fetch,\n", " chatbot=gr.Chatbot(height=300),\n", " textbox=gr.Textbox(placeholder=\"You can ask a question\", container=False, scale=7),\n", " title=\"A DSA chatbot\",\n", " description=\"You can ask questions related to Generative AI\",\n", " theme=\"soft\",\n", " examples=[\"what is Data Science?\", \"what is Python?\", \"what is LLM?\"],\n", " cache_examples=True,\n", " retry_btn=None,\n", " undo_btn=\"Delete Previous\",\n", " clear_btn=\"Clear\",\n", ").launch(share=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 800 }, "id": "Sj_jmTS-TKb2", "outputId": "ec2c68b3-eedc-4156-ab9c-d4c62703190e" }, "execution_count": 35, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Caching examples at: '/content/gradio_cached_examples/15'\n", "Caching example 1/3\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1249: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Caching example 2/3\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1249: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Caching example 3/3\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1249: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n", " warnings.warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", "Running on public URL: https://f9f048135eb12517e1.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 35 } ] } ] }