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uninstall: aiohttp\n", + " Found existing installation: aiohttp 3.11.12\n", + " Uninstalling aiohttp-3.11.12:\n", + " Successfully uninstalled aiohttp-3.11.12\n", + " Attempting uninstall: nvidia-cusolver-cu12\n", + " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", + " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", + " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", + "Successfully installed aiohttp-3.10.11 dataclasses-json-0.6.7 httpx-sse-0.4.0 langchain-community-0.3.18 langchain-pinecone-0.2.3 langchain-tests-0.3.12 langchain_huggingface-0.1.2 marshmallow-3.26.1 mypy-extensions-1.0.0 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 pinecone-5.4.2 pinecone-plugin-inference-3.1.0 pinecone-plugin-interface-0.0.7 pydantic-settings-2.8.1 pytest-asyncio-0.25.3 pytest-socket-0.7.0 python-dotenv-1.0.1 syrupy-4.8.2 typing-inspect-0.9.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install pypdf langchain_openai" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "iAL0DcX9OhDY", + "outputId": "2dc1678f-da5c-442b-b1f0-cf5e2bf5686a" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pypdf\n", + " Downloading pypdf-5.3.0-py3-none-any.whl.metadata (7.2 kB)\n", + "Collecting langchain_openai\n", + " Downloading langchain_openai-0.3.7-py3-none-any.whl.metadata (2.3 kB)\n", + "Collecting langchain-core<1.0.0,>=0.3.39 (from langchain_openai)\n", + " Downloading langchain_core-0.3.40-py3-none-any.whl.metadata (5.9 kB)\n", + "Requirement already satisfied: openai<2.0.0,>=1.58.1 in /usr/local/lib/python3.11/dist-packages (from 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langchain-core 0.3.37\n", + " Uninstalling langchain-core-0.3.37:\n", + " Successfully uninstalled langchain-core-0.3.37\n", + "Successfully installed langchain-core-0.3.40 langchain_openai-0.3.7 pypdf-5.3.0 tiktoken-0.9.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install pinecone" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "wl8ughQLMh2W", + "outputId": "847cbd90-6d41-4af8-d4f2-471c475ba676" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pinecone in /usr/local/lib/python3.11/dist-packages (5.4.2)\n", + "Requirement already satisfied: certifi>=2019.11.17 in /usr/local/lib/python3.11/dist-packages (from pinecone) (2025.1.31)\n", + "Requirement already satisfied: pinecone-plugin-inference<4.0.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from pinecone) (3.1.0)\n", + "Requirement already satisfied: pinecone-plugin-interface<0.0.8,>=0.0.7 in /usr/local/lib/python3.11/dist-packages (from pinecone) (0.0.7)\n", + "Requirement already satisfied: python-dateutil>=2.5.3 in /usr/local/lib/python3.11/dist-packages (from pinecone) (2.8.2)\n", + "Requirement already satisfied: tqdm>=4.64.1 in /usr/local/lib/python3.11/dist-packages (from pinecone) (4.67.1)\n", + "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.11/dist-packages (from pinecone) (4.12.2)\n", + "Requirement already satisfied: urllib3>=1.26.0 in /usr/local/lib/python3.11/dist-packages (from pinecone) (2.3.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.5.3->pinecone) (1.17.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import userdata\n", + "# GOOGLE_APPLICATION_CREDENTIALS = userdata.get('GOOGLE_APPLICATION_CREDENTIALS')\n", + "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')\n", + "PINECONE_API_KEY = userdata.get('PINECONE_API_KEY')" + ], + "metadata": { + "id": "mkjOc3yRFt1c" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#**Document loading and Ingestion**" + ], + "metadata": { + "id": "xZywIvc8NTKv" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain_community.document_loaders import TextLoader, PyPDFLoader, DirectoryLoader\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter" + ], + "metadata": { + "id": "RA6cVNj0i7sU" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#Extract Data From the PDF File\n", + "def load_pdf_file(data):\n", + " loader= DirectoryLoader(data,\n", + " glob=\"*.pdf\",\n", + " loader_cls=PyPDFLoader)\n", + "\n", + " documents=loader.load()\n", + "\n", + " return documents\n" + ], + "metadata": { + "id": "-j5BuhPNFt43" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "extracted_data=load_pdf_file(data='/content/Data')" + ], + "metadata": { + "id": "4XmX9K-1KpIx" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "extracted_data[0].page_content" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140 + }, + "id": "er8nkiQ9OsSU", + "outputId": "afc9e08a-b90f-4c99-96f2-6a7fcb4d91a0" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'YOLOv9: Learning What You Want to Learn\\nUsing Programmable Gradient Information\\nChien-Yao Wang1,2, I-Hau Yeh2, and Hong-Yuan Mark Liao1,2,3\\n1Institute of Information Science, Academia Sinica, Taiwan\\n2National Taipei University of Technology, Taiwan\\n3Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan\\nkinyiu@iis.sinica.edu.tw, ihyeh@emc.com.tw, and liao@iis.sinica.edu.tw\\nAbstract\\nToday’s deep learning methods focus on how to design\\nthe most appropriate objective functions so that the pre-\\ndiction results of the model can be closest to the ground\\ntruth. Meanwhile, an appropriate architecture that can\\nfacilitate acquisition of enough information for prediction\\nhas to be designed. Existing methods ignore a fact that\\nwhen input data undergoes layer-by-layer feature extrac-\\ntion and spatial transformation, large amount of informa-\\ntion will be lost. This paper will delve into the important is-\\nsues of data loss when data is transmitted through deep net-\\nworks, namely information bottleneck and reversible func-\\ntions. We proposed the concept of programmable gradi-\\nent information (PGI) to cope with the various changes\\nrequired by deep networks to achieve multiple objectives.\\nPGI can provide complete input information for the tar-\\nget task to calculate objective function, so that reliable\\ngradient information can be obtained to update network\\nweights. In addition, a new lightweight network architec-\\nture – Generalized Efficient Layer Aggregation Network\\n(GELAN), based on gradient path planning is designed.\\nGELAN’s architecture confirms that PGI has gained su-\\nperior results on lightweight models. We verified the pro-\\nposed GELAN and PGI on MS COCO dataset based ob-\\nject detection. The results show that GELAN only uses\\nconventional convolution operators to achieve better pa-\\nrameter utilization than the state-of-the-art methods devel-\\noped based on depth-wise convolution. PGI can be used\\nfor variety of models from lightweight to large. It can be\\nused to obtain complete information, so that train-from-\\nscratch models can achieve better results than state-of-the-\\nart models pre-trained using large datasets, the compari-\\nson results are shown in Figure 1. The source codes are at:\\nhttps://github.com/WongKinYiu/yolov9.\\n1. Introduction\\nDeep learning-based models have demonstrated far bet-\\nter performance than past artificial intelligence systems in\\nvarious fields, such as computer vision, language process-\\ning, and speech recognition. In recent years, researchers\\nFigure 1. Comparisons of the real-time object detecors on MS\\nCOCO dataset. The GELAN and PGI-based object detection\\nmethod surpassed all previous train-from-scratch methods in terms\\nof object detection performance. In terms of accuracy, the new\\nmethod outperforms RT DETR [43] pre-trained with a large\\ndataset, and it also outperforms depth-wise convolution-based de-\\nsign YOLO MS [7] in terms of parameters utilization.\\nin the field of deep learning have mainly focused on how\\nto develop more powerful system architectures and learn-\\ning methods, such as CNNs [21–23, 42, 55, 71, 72], Trans-\\nformers [8, 9, 40, 41, 60, 69, 70], Perceivers [26, 26, 32, 52,\\n56, 81, 81], and Mambas [17, 38, 80]. In addition, some\\nresearchers have tried to develop more general objective\\nfunctions, such as loss function [5, 45, 46, 50, 77, 78], la-\\nbel assignment [10, 12, 33, 67, 79] and auxiliary supervi-\\nsion [18, 20, 24, 28, 29, 51, 54, 68, 76]. The above studies\\nall try to precisely find the mapping between input and tar-\\nget tasks. However, most past approaches have ignored that\\ninput data may have a non-negligible amount of informa-\\ntion loss during the feedforward process. This loss of in-\\nformation can lead to biased gradient flows, which are sub-\\nsequently used to update the model. The above problems\\ncan result in deep networks to establish incorrect associa-\\ntions between targets and inputs, causing the trained model\\nto produce incorrect predictions.\\n1\\narXiv:2402.13616v2 [cs.CV] 29 Feb 2024'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Split the Data into Text Chunks\n", + "def text_split(extracted_data):\n", + " text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n", + " text_chunks=text_splitter.split_documents(extracted_data)\n", + " return text_chunks" + ], + "metadata": { + "id": "GztOY7zpFtyI" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "text_chunks=text_split(extracted_data)\n", + "print(\"Length of Text Chunks\", len(text_chunks))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lsNUiXHaFtt2", + "outputId": "0828967e-e0b3-4711-a5e3-8c9362ae6588" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Length of Text Chunks 91\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "\n", + "#Download the Embeddings from Hugging Face\n", + "def download_hugging_face_embeddings():\n", + " embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')\n", + " return embeddings" + ], + "metadata": { + "id": "Ja3Th0wPO3bQ" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "embeddings = download_hugging_face_embeddings()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 493, + "referenced_widgets": [ + "60c03e09fea745bda2707dec1059f2bd", + "9d83cd978660451abd94907b187e0d1a", + "180b940f01cf47488f3097cf43f62b8b", + "c09df67d201b49f69dd6100fa519e9b8", + "68abbda6d5b842d5aac0a2185d682074", + "8e6099b56c6444aea0fbca0a0b9a5c19", + "595a5ed1b42b459fb3ab1d36b8b852ca", + "05298c9f56bd4f9ca9f365bbeab4be51", + "3ce4209c54a043b3ad46b67e180ef92d", + "bde2e11c37d342fca04e125897119d29", + "01391ed5fa3c42ff9b15a8e2b21eef2f", + "aa888136a11b4f5db62621d4d7866978", + "2daa0cf63e044b78913ee7b2e8ad649f", + "5ad1bbadf52745b68fd70968d32be794", + "6ae10e60466c44338681073a2f36737f", + "f2f177ad9f26485e911f9a8f42b70c3b", + "4fe26331c52841f89d12ad37dd4168ca", + "2c9f273230ac40538d3ab71ab7a94e44", + "b2da8cc0e77443998eabeaed61abd1c8", + "75d3d66afa7b4641a2963a26fd6ef70c", + "4e98c73776ab4eae8c0b40da1b12efeb", + "6d4675d1c525425dbcafcd5aa8fde220", + "513fb4b166784713bdcab34fc265f399", + "5ba5ec9e8b484ebf8e6b1adb6b475ad8", + "6e31ed27be0845bd802d8045d130cfe7", + "e5f89971327f4b5bb61098850676a3ba", + "0b3624bd6860400497a694c6e75b37b9", + "35be7a497484405a94fb25aa7127ea18", + "a4f4e66c0e584d28838acbc34c6b083f", + "3161cd2535584e999b9cb873fbfa219d", + "6d71401ddb744c59a7916c1ff77132dc", + "b608f7781db94ff190ee00a7031c0f38", + "47ac3a72f1d04e03915b2f32964a1ea4", + "6767007dc32045e8bcac249652c6ad65", + "4846afcdd9674173bdf679ec9b142df9", + "03d9a2138c6141749a8a3c73d86eb79c", + "22b0b8b2553b4fd0890539bb931024a8", + "5355c4bf00504c2683d800b80882c9a5", + "7bf2748803d148dd868b6f6eb906f4d1", + "3ac0708f601e40cda5705da7367ba97d", + "9309f01208534b4890de0f61be523f84", + "d0b4a8ffda3d4c7186c14080b2da6411", + "ee3419bdde4a41f392c7e42eff629f7a", + "d07394fc86284bbeb14f68814a972e44", + "faec5a0975034495aa0ad01b74c7e641", + "122d4451bd2d4abcb4ff3ab3096fa10d", + "d0fed480d74d4e5db1752394299bc0da", + "afae1e40c71645a9b0581e52d692e229", + "a1a3d48aea9a423487fc52f3d84102aa", + "a0cd01ae39f349b198ad8ffd728a9ab4", + "1526310390e34a3ea70f96795fb56d36", + "a2333bd4668d49bbaceb78e4c2e467a8", + "f66df70fb8a0405f9d40a6a225cef4fd", + "da8680e4219e492b8261becd4c828400", + "cda22d3231c34781ae3b787cf3521cfe", + "b14884d35c3b48e5b59d17eddaa81076", + "9096a8046c5d47e78d766944c863c565", + "cb785ed0c32a48b48d9c8f891d6e988a", + "1934ea70b777413ca2d4e3b4f48c5f4a", + "4c00ed477ec346368a6b3d94aa848a1a", + "ead76d76d7d54abaab835daf980c4f37", + "175f5fb483d24ae08093f987c32cf2fd", + "f8a4df9c01c04b4c9f482d84501bcf11", + "27f2cc72785542569f817db8684a679d", + "4695c544ac2c4ef39aa225a2d0fcddd8", + "9703375a2ac847aabe140b98093452d3", + "7b03a82d0ff144769dd41156deb1e24e", + "b8087a7fc96d45279885fe5a098ef231", + "91ed1eb52f8e4cde9ceca535db946479", + "3046a8694a5c43888365660f3996e1f9", + "334d315f02db409e8f413fcfb88aa408", + "39c30dbdc5764fb5a8292e77e884f976", + "6c95d0cf169b4e8a8298ce76b3d3e36c", + "2858ba925dde488a9d0d1a661beeca4c", + "27377aa48d86495193fdede2c23b8f69", + "71c3f58ccff44cc1835c8f88342431a8", + "54721da8624e4178a95c4ad04c23d37e", + "09fdf6d34f1b46dfb372bfe55a8fb661", + "0714edd6cfc74036899ca85679a2e142", + "7035923cc58d46999d962f4c41c8fda5", + "6d4ab8b740c046619bfea8b323399c43", + "df9c914a265c4fd88659e9f2c94190fc", + "f1209f458582484aac44ca478c2a0a99", + "7565b6bc884a4e91bfdd982ea81456d2", + "a5ad0355fecb456789a3612d1259e8e6", + "ecb908d361ef4777bbea0aa8ee611abf", + "b5b44b4483c54461a9376214bf79b2a8", + "82e2b0a6bb72431e9e9e9ee91b3f992b", + "19ed05a94d124ac3ac6a67a717d9f767", + "8b4000739cbc446fb818ae5d5c121f2e", + "29f6e967ba5340149f68ce31764070b0", + "7915676f1e7141a88295efd07368cbfa", + "9f2f3a0415c940649be0d0e2d198ecb6", + "a09bd70f238147cfbf9ddff323d1f940", + "75f90e99c6014c6080807d91ca4b84c0", + "1965890d23bb409b8f1d5bb98e3bfa07", + "f95472f4349e48a59a1817b852a8302c", + "9de25caa968345e2a39acd790bd81c4f", + "18d9e47a71634957bab86a6540bdc383", + "6f4dce62a7454b2981ee9aea0ce12645", + "b179d0c8776e41e6975a8ebb3c157c43", + "99904e6aa3ab47e0a84828aa63bac09f", + "5f0080304ed14bfbb0aa4a2d738ba83a", + "d5ebc7f16e2a4c9d9e900b4875c3bcc8", + "eeb1060ccab246a3b573f3ed1fdd3284", + "ec79dd79488e4fc7b0aa76da6895b69e", + "9cada4cb978c4ba8a15bc8893a08a087", + "8286e67473434de3bdea10a1c95d5452", + "4a55c50a2b6e4059909f54ed9e00f462", + "bb8beecd5be14c7da47c64e029a1dd29", + "796c1691a3d94f4abcd89af7c17867e3", + "3852e441efb44c6f9dd04565c6394211", + "dd46562fc0c84cff886c77fc32ffa79f", + "795321f0451b4b799f7224fe1a798432", + "9c72cf9c80844ea1a77a28509c901a67", + "8193b0034dd04774acbc29f15222981b", + "c1483741201d45399dd097b45dd470c4", + "b2bbe13af3c442ae96051f0214e97872", + "2f5cd7c5e1ef4d3ab1f486bc17d25b32", + "d317641a469f441b9132da9b2b1c9342", + "948b81d031754a24ae9b1d49cff46dfa" + ] + }, + "id": "JADR73qSO3YY", + "outputId": "389625c9-460e-43af-e2a2-00e5e92124c5" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: 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" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "##**Create Embedding for text Chunks**" + ], + "metadata": { + "id": "KTagl7ihPlEV" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain_pinecone import PineconeVectorStore\n", + "\n", + "vector_store = PineconeVectorStore(index=index, embedding=embeddings)" + ], + "metadata": { + "id": "kBGFjaf3QZr0" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "vector_store.add_documents(documents=text_chunks)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "vlcFOl6kQdAF", + "outputId": "79450b21-5e3c-427d-9967-63844a4eaabd" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['51001868-e8dd-46b9-907c-6bd112b2bbb4',\n", + " 'a90da03d-dbb5-4d38-b2f1-9050391fc117',\n", + " '3b354502-9727-4f42-9604-5ee2ab1753a1',\n", + " '443089a0-517e-4cc0-9853-9b83035819e2',\n", + " '1486472e-b70c-429f-b6ac-0230a13fe756',\n", + " '9bc1e844-db60-4db4-bc09-df14315a5ca7',\n", + " '3f17706b-104c-4a65-882d-a3fa44f04a0d',\n", + " 'aba96d67-0838-49eb-9029-937a34cd3af6',\n", + " 'a94c54e5-3e61-4b23-93b8-ae5ccb9cec8b',\n", + " '2b8ccced-0645-47d9-9179-7881954276f0',\n", + " '8d172631-df27-4e90-8a6d-53fa06c5a712',\n", + " '3fa66654-52e6-4b6d-aad8-ad34a59e94a2',\n", + " '544e8d71-a799-4d84-876e-9f8dedc91db9',\n", + " 'fc69b8a4-bb38-456a-88ec-482a0f2e3013',\n", + " 'f5ee1ebe-0645-47cf-b483-d60e5d3ddb0a',\n", + " '2b53f71a-b170-49c9-be12-c2576a4cdaec',\n", + " '016e4aef-a401-4a30-a364-6183bd99c0a6',\n", + " 'b7adbce2-b122-484a-8d5d-50cfb99b2776',\n", + " 'd98dc780-833f-47eb-a133-42b085761f0a',\n", + " 'd9a7779b-abb6-41fc-8568-d4c4a93d7d97',\n", + " '4c0e516d-7806-49c4-9c14-7eaff2e23689',\n", + " '8f7c11e5-e58b-4e0f-aea8-42b49fd2c186',\n", + " '447fdf30-3ee9-4f66-aecd-509f0e311e0c',\n", + " '71cf7c36-7c4a-4500-a4a3-6ad31c307309',\n", + " 'f7a6e0e1-fc05-4980-901c-9844c1967c9b',\n", + " 'cbd6ad8d-8496-444b-8c53-349b3cbe8cd3',\n", + " 'bcdad0b2-d32d-4a4e-86ce-0ac5a9539059',\n", + " '8fd30734-94f9-4b9d-8da9-9a2f565dddcd',\n", + " '70dc325f-a313-40c4-bb7d-a277e8db7c58',\n", + " '5748ed6a-ac2e-41bc-b69a-b4dcb8fcbc02',\n", + " 'd4e08b89-0a00-4b79-b65a-c7c35585c3fc',\n", + " '1678bf65-d800-4160-b2f2-dfb0fa8f0c60',\n", + " '078716bf-da5e-492c-8fb7-a9cff92a86b4',\n", + " 'a6fe332e-a340-49c0-98c2-2a133b55a915',\n", + " '8db3a003-99cb-431b-8881-64681aa00f3c',\n", + " '26f65897-0a09-4bd1-8925-c4b3447280c6',\n", + " '9019a760-52aa-4cf8-89ee-78384e034ddc',\n", + " 'dbe3e1b3-08e5-4907-b849-95e06e685dda',\n", + " 'f12ee927-7963-4895-b570-04fa2f6ac987',\n", + " '1d4c9b34-c7e5-40ac-8ec7-ded7f7031b2a',\n", + " '2d8a1daa-edf1-43e1-a133-4966cdbbfe11',\n", + " 'd433d8f9-597b-4504-8bad-488e3f235696',\n", + " '87ca4605-ff84-4384-870a-2fc198d7e45c',\n", + " '2b84c329-c654-4e6c-963f-9940e94f0719',\n", + " '2903140d-a7b3-4e9b-b9c1-5c041896ab70',\n", + " '676f7237-abf2-416c-8007-cbed7807c7e7',\n", + " 'ff618d18-317d-4cb2-a71a-3278632affb2',\n", + " '4f06a055-ade6-442b-932c-7dcbaccb46c3',\n", + " '838fe06e-1002-4034-9884-6e87c6079bcf',\n", + " '8cdd1494-65a5-4364-b4b7-d01ab6f4be50',\n", + " '25940e14-d3a0-4546-9959-38e7f58f68bf',\n", + " 'cea70f34-03f5-417d-acff-b150f87fab55',\n", + " '5e8af3a8-81ce-4362-a3b7-05de55b0dc1c',\n", + " '40114c94-cb57-428e-9981-a3d9024fd409',\n", + " '084485a6-2508-4db2-8fb7-0c8867cc82ef',\n", + " 'e561a959-6061-46ac-a381-cbe1e231edcd',\n", + " 'c753d4b2-850a-4987-b6b5-e4b630757f2d',\n", + " '848481d7-bb1a-4f8b-adab-0525c9fb00c4',\n", + " 'cfa0ce2d-4c9c-47c7-bbba-aef6f392432b',\n", + " '4fa548ff-b3e2-4dd6-ae6b-d68b25c4f6bb',\n", + " '7892db2e-39fa-4f32-8fb8-198bd90f4f19',\n", + " '1463f794-ac03-43d4-a35d-486cab755319',\n", + " 'b61e11d9-5138-4aed-aacc-2f46c9948de2',\n", + " '21fa31dc-01cf-4766-a5b6-76372b7bc96d',\n", + " '3de07487-e37a-432d-af3b-41ee27f69052',\n", + " '48cb102c-2f22-4fb0-8fd9-586c798646b2',\n", + " '398e4744-3cf5-45e0-9090-5f397be5e61d',\n", + " '8e5249f4-e81f-48eb-b39b-d087e3c34805',\n", + " 'c92a057b-ffef-4215-96a0-81a2b2c87ebf',\n", + " '9d464f7d-e7af-4f13-b13a-6a12b72a20bd',\n", + " '1f74283b-8834-4f13-9acb-5d66943c6e61',\n", + " '23d1cf77-5248-4ce2-8cab-513c58681f8a',\n", + " '054ae75d-ef21-4d0a-807b-86f55e23621a',\n", + " 'cef98bde-b176-49dd-9be0-12cd80b3426a',\n", + " '36038c71-4fe9-40e4-bc10-519a86249b77',\n", + " 'da67e5eb-7654-426e-a99e-c82087ed03d7',\n", + " '8c097d98-778c-4106-8a7c-87c6f302a341',\n", + " '89c850ba-0941-44bc-907d-4b8267154448',\n", + " 'bcf0d4f2-5c95-4e31-9818-a211836408a4',\n", + " '212bddc0-18a5-405a-bfaf-6bd54c13378d',\n", + " 'dd4d5c73-a6f3-4f9a-a1b4-ceecf720775e',\n", + " '709b1cda-5120-4891-b72c-7978adbcc5c1',\n", + " 'db13e613-66e3-4d2d-a869-2dd1ecd62674',\n", + " '4e58bb53-6aaf-4385-a8c4-15727e5cc000',\n", + " 'c08138f7-4cd6-451d-ba87-3f5a92df1bc5',\n", + " 'dd7b6c2b-2ca3-4d6c-a63e-457d47211411',\n", + " '733a845d-ed09-4240-bf9d-894196ea10de',\n", + " 'd1c54eda-7e26-4efa-93e0-b85a7f3005f6',\n", + " '378cb74b-398c-4d1e-8ad6-bcf3c53ba40b',\n", + " '25c6fc39-2891-4eb4-b77a-f7666edb7a64',\n", + " '624ca3be-980c-4d21-abb5-ee578b7835ea']" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "##**Create Retriever**" + ], + "metadata": { + "id": "kTSHW7QTkmWe" + } + }, + { + "cell_type": "code", + "source": [ + "retriever = vector_store.as_retriever(search_type=\"similarity\",\n", + " search_kwargs={\"k\":5}\n", + " )" + ], + "metadata": { + "id": "Ry3PvJlAQPux" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "retrieved_docs = retriever.invoke(\"what is Programmable Gradient Information?\")\n", + "res = [doc.page_content for doc in retrieved_docs]\n", + "res[0]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140 + }, + "id": "4b5AVD2CRfse", + "outputId": "9c750fc4-cb53-401c-99cd-f466b244d912" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Figure 3. PGI and related network architectures and methods. (a) Path Aggregation Network (PAN)) [37], (b) Reversible Columns\\n(RevCol) [3], (c) conventional deep supervision, and (d) our proposed Programmable Gradient Information (PGI). PGI is mainly composed\\nof three components: (1) main branch: architecture used for inference, (2) auxiliary reversible branch: generate reliable gradients to supply\\nmain branch for backward transmission, and (3) multi-level auxiliary information: control main branch learning plannable multi-level of\\nsemantic information.\\n4. Methodology\\n4.1. Programmable Gradient Information\\nIn order to solve the aforementioned problems, we pro-\\npose a new auxiliary supervision framework called Pro-\\ngrammable Gradient Information (PGI), as shown in Fig-\\nure 3 (d). PGI mainly includes three components, namely\\n(1) main branch, (2) auxiliary reversible branch, and (3)\\nmulti-level auxiliary information. From Figure 3 (d) we'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 25 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#**LLM OpenAI**" + ], + "metadata": { + "id": "Oxfqq0GTR1ms" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain_openai import OpenAI\n", + "llm = OpenAI(api_key=OPENAI_API_KEY, temperature=0, max_tokens=500)" + ], + "metadata": { + "id": "ggjVLpYbR4i4" + }, + "execution_count": 26, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "res = llm.invoke(\"generate 3 question about yolo 9 \")\n", + "res" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "NL4tTa9QTXTd", + "outputId": "a8828e2c-5806-48e2-a863-3364acaf637d" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\n\\n1. What are the key features of YOLO 9 that differentiate it from previous versions?\\n2. How does YOLO 9 improve upon object detection accuracy compared to previous versions?\\n3. Can YOLO 9 be used for real-time object detection in complex environments?'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 27 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#**Retrieval Chain**" + ], + "metadata": { + "id": "C1ONIbOmVV4N" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.chains import create_retrieval_chain\n", + "from langchain.chains.combine_documents import create_stuff_documents_chain\n", + "from langchain_core.prompts import ChatPromptTemplate" + ], + "metadata": { + "id": "f1oOh4dCRfjN" + }, + "execution_count": 33, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "system_prompt = (\n", + " \"You are a AI expert who can generate questions from retrieved docs. \"\n", + " \"You will be given a topic to generate question about\"\n", + " \"Use the following pieces of retrieved context to generate \"\n", + " \"new questions based on the topic given.\"\n", + " \"say that you don't know if the input given is beyond the scope of the retrieved context \"\n", + " \" dont respond with anything accept for generated question\"\n", + " \"Use three sentences maximum and keep the generated questions concise.\"\n", + " \"\\n\\n\"\n", + " \"{context}\"\n", + ")\n", + "\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\"system\", system_prompt),\n", + " (\"human\", \"{input}\"),\n", + " ]\n", + ")" + ], + "metadata": { + "id": "z_KLSDBNSL9d" + }, + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "question_answer_chain = create_stuff_documents_chain(llm, prompt)\n", + "rag_chain = create_retrieval_chain(retriever, question_answer_chain)" + ], + "metadata": { + "id": "vZn52wk2TmFF" + }, + "execution_count": 35, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "response = rag_chain.invoke({\"input\": \"heartattacks\"})\n", + "print(response[\"answer\"])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gh2IDzK5T3hv", + "outputId": "361d9ad6-502c-4ca8-a313-2539260ff047" + }, + "execution_count": 42, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "What is the average precision for YOLOv8-N [15]?\n", + "\n", + "What is the highest average precision for YOLOv8-X [15]?\n", + "\n", + "What is the average precision for DAMO YOLO-T [75]?\n", + "\n", + "What is the highest average precision for Gold YOLO-N [61]?\n", + "\n", + "What is the average precision for YOLO MS-N [7]?\n", + "\n", + "What is the highest average precision for GELAN-S (Ours)?\n", + "\n", + "What is the average precision for YOLOv9-E (S)?\n", + "\n", + "What is the highest average precision for PPYOLOE+-L [74] (C)?\n", + "\n", + "What is the average precision for PPYOLOE-L [74] (I)?\n", + "\n", + "What is the highest average precision for RTMDet-L [44] (I)?\n", + "\n", + "What is the average precision for YOLOR-CSP [66] (C)?\n", + "\n", + "What is the highest average precision for YOLOv9-E (S)?\n", + "\n", + "What is the average precision for YOLOv6-L v3.0 [30] (D)?\n", + "\n", + "What is the highest average precision for RT DETR-X [43] (I)?\n", + "\n", + "What is the average precision for Gold YOLO-L [61] (C)?\n", + "\n", + "What is the highest average precision for Gold YOLO-L [61] (D)?\n", + "\n", + "What is the average precision for Gold YOLO-L [61] (I)?\n", + "\n", + "What is the highest average precision for RT DETR-R101 [43] (I)?\n", + "\n", + "What is the average precision for RTMDet-X [44] (I)?\n", + "\n", + "What is the highest average precision for YOLOR-CSP-X [66] (C)?\n", + "\n", + "What is the average precision for PPYOLOE+-X [74] (C)?\n", + "\n", + "What is the highest average precision for PPYOLOE-X [74] (I)?\n", + "\n", + "What is the average precision for YOLOR-CSP [66] (C)?\n", + "\n", + "What is the highest average precision for RT DETR-R50 [43] (I)?\n", + "\n", + "What is the average precision for YOLOv9-E (S)?\n", + "\n", + "What is the highest average precision for YOLOv6-L v3.0 [30] (D)?\n", + "\n", + "What is the average precision for Gold YOLO-L [61] (C)?\n", + "\n", + "What is the highest average precision for Gold YOLO-L [61] (D)?\n", + "\n", + "What is the average precision for Gold YOLO-L [61] (I)?\n", + "\n", + "What\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "response = rag_chain.invoke({\"input\": \"Gradient Information?\"})\n", + "print(response[\"answer\"])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AaUl0VtyT74_", + "outputId": "413e9dfc-987a-407f-f7c7-66569c895032" + }, + "execution_count": 41, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "What is the proposed Programmable Gradient Information (PGI) framework and what are its components?\n", + "\n", + "How does the information bottleneck principle relate to the problem of unreliable gradients in deep neural networks?\n", + "\n", + "Can you explain the concept of reversible functions and how they can be used to solve the problem of unreliable gradients in deep neural networks?\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "response = rag_chain.invoke({\"input\": \"quadratic equation\"})\n", + "print(response[\"answer\"])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UPu_H2pdUlNF", + "outputId": "64bb7a14-e176-4629-9425-410822b510ce" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "What is the goal for the lightweight model in terms of preserving information?\n", + "\n", + "Can the proposed deep neural network training method generate reliable gradients for shallow and lightweight neural networks?\n", + "\n", + "How does the proposed method address the issue of losing important information in the feedforward stage?\n", + "\n", + "What is the difficulty in fully preserving the information of X?\n", + "\n", + "What is the difference between I(Y, X) and I(X, X)?\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#**APP**" + ], + "metadata": { + "id": "Iu0wS-v_Wl7c" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "maB77qF1jcFE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "Vector_store = PineconeVectorStore.from_existing_index(\n", + " index_name=index_name,\n", + " embedding=embeddings\n", + ")\n" + ], + "metadata": { + "id": "2fWY3SsxWlY3" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file