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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 langchain_openai) (1.61.1)\n", "Collecting tiktoken<1,>=0.7 (from langchain_openai)\n", " Downloading tiktoken-0.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n", "Requirement already satisfied: langsmith<0.4,>=0.1.125 in /usr/local/lib/python3.11/dist-packages (from 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"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": [] } ] }