meta-llama-3-8b-rag.ipynb

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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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      "\u001b[?25hDownloading bitsandbytes-0.42.0-py3-none-any.whl (105.0 MB)\r\n",
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      "\u001b[?25hDownloading peft-0.8.2-py3-none-any.whl (183 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m183.4/183.4 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading langchain-0.1.16-py3-none-any.whl (817 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m817.7/817.7 kB\u001b[0m \u001b[31m45.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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      "\u001b[?25hDownloading faiss_cpu-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m64.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading dill-0.3.7-py3-none-any.whl (115 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading fsspec-2023.10.0-py3-none-any.whl (166 kB)\r\n",
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      "\u001b[?25hDownloading langchain_community-0.0.33-py3-none-any.whl (1.9 MB)\r\n",
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      "\u001b[?25hDownloading langchain_text_splitters-0.0.1-py3-none-any.whl (21 kB)\r\n",
      "Downloading langsmith-0.1.49-py3-none-any.whl (115 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.2/115.2 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading packaging-23.2-py3-none-any.whl (53 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading responses-0.18.0-py3-none-any.whl (38 kB)\r\n",
      "Downloading tyro-0.8.3-py3-none-any.whl (102 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.0/102.0 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading orjson-3.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.1/141.1 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading shtab-1.7.1-py3-none-any.whl (14 kB)\r\n",
      "Installing collected packages: shtab, packaging, orjson, fsspec, faiss-cpu, dill, responses, multiprocess, bitsandbytes, tyro, langsmith, accelerate, transformers, langchain-core, datasets, trl, sentence-transformers, peft, langchain-text-splitters, langchain-community, evaluate, langchain\r\n",
      "  Attempting uninstall: packaging\r\n",
      "    Found existing installation: packaging 21.3\r\n",
      "    Uninstalling packaging-21.3:\r\n",
      "      Successfully uninstalled packaging-21.3\r\n",
      "  Attempting uninstall: orjson\r\n",
      "    Found existing installation: orjson 3.9.10\r\n",
      "    Uninstalling orjson-3.9.10:\r\n",
      "      Successfully uninstalled orjson-3.9.10\r\n",
      "  Attempting uninstall: fsspec\r\n",
      "    Found existing installation: fsspec 2024.2.0\r\n",
      "    Uninstalling fsspec-2024.2.0:\r\n",
      "      Successfully uninstalled fsspec-2024.2.0\r\n",
      "  Attempting uninstall: dill\r\n",
      "    Found existing installation: dill 0.3.8\r\n",
      "    Uninstalling dill-0.3.8:\r\n",
      "      Successfully uninstalled dill-0.3.8\r\n",
      "  Attempting uninstall: multiprocess\r\n",
      "    Found existing installation: multiprocess 0.70.16\r\n",
      "    Uninstalling multiprocess-0.70.16:\r\n",
      "      Successfully uninstalled multiprocess-0.70.16\r\n",
      "  Attempting uninstall: accelerate\r\n",
      "    Found existing installation: accelerate 0.29.3\r\n",
      "    Uninstalling accelerate-0.29.3:\r\n",
      "      Successfully uninstalled accelerate-0.29.3\r\n",
      "  Attempting uninstall: transformers\r\n",
      "    Found existing installation: transformers 4.39.3\r\n",
      "    Uninstalling transformers-4.39.3:\r\n",
      "      Successfully uninstalled transformers-4.39.3\r\n",
      "  Attempting uninstall: datasets\r\n",
      "    Found existing installation: datasets 2.18.0\r\n",
      "    Uninstalling datasets-2.18.0:\r\n",
      "      Successfully uninstalled datasets-2.18.0\r\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "cudf 23.8.0 requires cubinlinker, which is not installed.\r\n",
      "cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "cudf 23.8.0 requires ptxcompiler, which is not installed.\r\n",
      "cuml 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "dask-cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "keras-cv 0.8.2 requires keras-core, which is not installed.\r\n",
      "keras-nlp 0.9.3 requires keras-core, which is not installed.\r\n",
      "tensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.\r\n",
      "apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.7 which is incompatible.\r\n",
      "apache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 1.26.4 which is incompatible.\r\n",
      "apache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 15.0.2 which is incompatible.\r\n",
      "cudf 23.8.0 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.4.0 which is incompatible.\r\n",
      "cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.1.4 which is incompatible.\r\n",
      "cudf 23.8.0 requires protobuf<5,>=4.21, but you have protobuf 3.20.3 which is incompatible.\r\n",
      "cudf 23.8.0 requires pyarrow==11.*, but you have pyarrow 15.0.2 which is incompatible.\r\n",
      "cuml 23.8.0 requires dask==2023.7.1, but you have dask 2024.4.1 which is incompatible.\r\n",
      "dask-cuda 23.8.0 requires dask==2023.7.1, but you have dask 2024.4.1 which is incompatible.\r\n",
      "dask-cuda 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.1.4 which is incompatible.\r\n",
      "dask-cudf 23.8.0 requires dask==2023.7.1, but you have dask 2024.4.1 which is incompatible.\r\n",
      "dask-cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.1.4 which is incompatible.\r\n",
      "distributed 2023.7.1 requires dask==2023.7.1, but you have dask 2024.4.1 which is incompatible.\r\n",
      "gcsfs 2024.2.0 requires fsspec==2024.2.0, but you have fsspec 2023.10.0 which is incompatible.\r\n",
      "google-cloud-bigquery 2.34.4 requires packaging<22.0dev,>=14.3, but you have packaging 23.2 which is incompatible.\r\n",
      "jupyterlab 4.1.6 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\r\n",
      "jupyterlab-lsp 5.1.0 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\r\n",
      "libpysal 4.9.2 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\r\n",
      "momepy 0.7.0 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.\r\n",
      "osmnx 1.9.2 requires shapely>=2.0, but you have shapely 1.8.5.post1 which is incompatible.\r\n",
      "pathos 0.3.2 requires dill>=0.3.8, but you have dill 0.3.7 which is incompatible.\r\n",
      "pathos 0.3.2 requires multiprocess>=0.70.16, but you have multiprocess 0.70.15 which is incompatible.\r\n",
      "raft-dask 23.8.0 requires dask==2023.7.1, but you have dask 2024.4.1 which is incompatible.\r\n",
      "s3fs 2024.2.0 requires fsspec==2024.2.0, but you have fsspec 2023.10.0 which is incompatible.\r\n",
      "spopt 0.6.0 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\r\n",
      "tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.2.1 which is incompatible.\r\n",
      "ydata-profiling 4.6.4 requires numpy<1.26,>=1.16.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mSuccessfully installed accelerate-0.26.1 bitsandbytes-0.42.0 datasets-2.16.1 dill-0.3.7 evaluate-0.4.1 faiss-cpu-1.8.0 fsspec-2023.10.0 langchain-0.1.16 langchain-community-0.0.33 langchain-core-0.1.44 langchain-text-splitters-0.0.1 langsmith-0.1.49 multiprocess-0.70.15 orjson-3.10.1 packaging-23.2 peft-0.8.2 responses-0.18.0 sentence-transformers-2.7.0 shtab-1.7.1 transformers-4.38.2 trl-0.7.11 tyro-0.8.3\r\n",
      "Collecting unstructured\r\n",
      "  Downloading unstructured-0.13.2-py3-none-any.whl.metadata (30 kB)\r\n",
      "Collecting chardet (from unstructured)\r\n",
      "  Downloading chardet-5.2.0-py3-none-any.whl.metadata (3.4 kB)\r\n",
      "Collecting filetype (from unstructured)\r\n",
      "  Downloading filetype-1.2.0-py2.py3-none-any.whl.metadata (6.5 kB)\r\n",
      "Collecting python-magic (from unstructured)\r\n",
      "  Downloading python_magic-0.4.27-py2.py3-none-any.whl.metadata (5.8 kB)\r\n",
      "Requirement already satisfied: lxml in /opt/conda/lib/python3.10/site-packages (from unstructured) (5.2.1)\r\n",
      "Requirement already satisfied: nltk in /opt/conda/lib/python3.10/site-packages (from unstructured) (3.2.4)\r\n",
      "Requirement already satisfied: tabulate in /opt/conda/lib/python3.10/site-packages (from unstructured) (0.9.0)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from unstructured) (2.31.0)\r\n",
      "Requirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.10/site-packages (from unstructured) (4.12.2)\r\n",
      "Requirement already satisfied: emoji in /opt/conda/lib/python3.10/site-packages (from unstructured) (2.11.0)\r\n",
      "Requirement already satisfied: dataclasses-json in /opt/conda/lib/python3.10/site-packages (from unstructured) (0.6.4)\r\n",
      "Collecting python-iso639 (from unstructured)\r\n",
      "  Downloading python_iso639-2024.2.7-py3-none-any.whl.metadata (13 kB)\r\n",
      "Collecting langdetect (from unstructured)\r\n",
      "  Downloading langdetect-1.0.9.tar.gz (981 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m34.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from unstructured) (1.26.4)\r\n",
      "Collecting rapidfuzz (from unstructured)\r\n",
      "  Downloading rapidfuzz-3.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\r\n",
      "Requirement already satisfied: backoff in /opt/conda/lib/python3.10/site-packages (from unstructured) (2.2.1)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from unstructured) (4.9.0)\r\n",
      "Collecting unstructured-client<=0.18.0 (from unstructured)\r\n",
      "  Downloading unstructured_client-0.18.0-py3-none-any.whl.metadata (4.9 kB)\r\n",
      "Requirement already satisfied: wrapt in /opt/conda/lib/python3.10/site-packages (from unstructured) (1.14.1)\r\n",
      "Requirement already satisfied: certifi>=2023.7.22 in /opt/conda/lib/python3.10/site-packages (from unstructured-client<=0.18.0->unstructured) (2024.2.2)\r\n",
      "Requirement already satisfied: charset-normalizer>=3.2.0 in /opt/conda/lib/python3.10/site-packages (from unstructured-client<=0.18.0->unstructured) (3.3.2)\r\n",
      "Collecting dataclasses-json-speakeasy>=0.5.11 (from unstructured-client<=0.18.0->unstructured)\r\n",
      "  Downloading dataclasses_json_speakeasy-0.5.11-py3-none-any.whl.metadata (25 kB)\r\n",
      "Requirement already satisfied: idna>=3.4 in /opt/conda/lib/python3.10/site-packages (from unstructured-client<=0.18.0->unstructured) (3.6)\r\n",
      "Collecting jsonpath-python>=1.0.6 (from unstructured-client<=0.18.0->unstructured)\r\n",
      "  Downloading jsonpath_python-1.0.6-py3-none-any.whl.metadata (12 kB)\r\n",
      "Requirement already satisfied: marshmallow>=3.19.0 in /opt/conda/lib/python3.10/site-packages (from unstructured-client<=0.18.0->unstructured) (3.21.1)\r\n",
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      "Building wheels for collected packages: langdetect\r\n",
      "  Building wheel for langdetect (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993225 sha256=9af965ed13afb337c91cb822b941b33e35fcb9d73278e62d4f672eeeb4b46517\r\n",
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      "\u001b[?25h  Created wheel for pdfminer: filename=pdfminer-20191125-py3-none-any.whl size=6140077 sha256=086dc1acc47c2f67d818527ff0a13c3ef89a692711ec94cff381dcdcdc788d22\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/4e/c1/68/f7bd0a8f514661f76b5cbe3b5f76e0033d79f1296012cbbf72\r\n",
      "Successfully built pdfminer\r\n",
      "Installing collected packages: pdfminer\r\n",
      "Successfully installed pdfminer-20191125\r\n",
      "Collecting pdfminer.six\r\n",
      "  Downloading pdfminer.six-20231228-py3-none-any.whl.metadata (4.2 kB)\r\n",
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      "\u001b[?25hInstalling collected packages: pdfminer.six\r\n",
      "Successfully installed pdfminer.six-20231228\r\n"
     ]
    }
   ],
   "source": [
    "# Install Pytorch & other libraries\n",
    "!pip install \"torch==2.1.2\" tensorboard\n",
    "\n",
    "# Install Hugging Face libraries\n",
    "!pip install  --upgrade \\\n",
    "  \"transformers==4.38.2\" \\\n",
    "  \"datasets==2.16.1\" \\\n",
    "  \"accelerate==0.26.1\" \\\n",
    "  \"evaluate==0.4.1\" \\\n",
    "  \"bitsandbytes==0.42.0\" \\\n",
    "  \"trl==0.7.11\" \\\n",
    "  \"peft==0.8.2\" \\\n",
    "    \"langchain\" \\\n",
    "\"sentence-transformers\" \\\n",
    "\"faiss-cpu\"\n",
    "!pip install unstructured\n",
    "!pip install pdfminer\n",
    "!pip install pdfminer.six"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81a39dcf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T06:44:31.030362Z",
     "iopub.status.busy": "2024-04-19T06:44:31.029699Z",
     "iopub.status.idle": "2024-04-19T06:44:31.251383Z",
     "shell.execute_reply": "2024-04-19T06:44:31.250558Z"
    },
    "papermill": {
     "duration": 0.247887,
     "end_time": "2024-04-19T06:44:31.253564",
     "exception": false,
     "start_time": "2024-04-19T06:44:31.005677",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "hf_token = user_secrets.get_secret(\"hf_token\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47573e23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T06:44:31.301892Z",
     "iopub.status.busy": "2024-04-19T06:44:31.301629Z",
     "iopub.status.idle": "2024-04-19T06:46:35.880194Z",
     "shell.execute_reply": "2024-04-19T06:46:35.879283Z"
    },
    "papermill": {
     "duration": 124.604752,
     "end_time": "2024-04-19T06:46:35.882370",
     "exception": false,
     "start_time": "2024-04-19T06:44:31.277618",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-19 06:44:38.634319: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-19 06:44:38.634423: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-19 06:44:38.762394: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd493a71b7704a2eb48ecc8bcd954ad2",
       "version_major": 2,
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "f0b766f582594134b8a77312a1b375f9",
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       "model_id": "b5a49b165e3146e3822edfc9f6ad1cf5",
       "version_major": 2,
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      "text/plain": [
       "Downloading shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "dfd07630bd7a49429d464a9e6a6fae7a",
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     "metadata": {},
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       "model_id": "b99d32a532a7474687f086ae4474468a",
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       "model_id": "fd2eda48075e45c3bc55d773da99509c",
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       "model_id": "0ced44a045ce4fde84eb149fd6f766aa",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1dbbfb3a8214954add46849bdef47aa",
       "version_major": 2,
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      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "dd2efc898f5e45038acde4a505fc63d0",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "cfebec61eddd4ffe9d2ce3ddd6cb657e",
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "283b44844de0413d9ac5a9d05751ab3f",
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      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a786aeeaaf042beaca829f89884be5e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/73.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from IPython.display import display_markdown\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from transformers import pipeline\n",
    "import transformers\n",
    "from langchain.document_loaders import UnstructuredPDFLoader,PDFMinerLoader,TextLoader\n",
    "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
    "from langchain.vectorstores import FAISS\n",
    "\n",
    "# Hugging Face model id\n",
    "model_id = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
    " \n",
    "\n",
    "pipeline = transformers.pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model_id,token=hf_token,\n",
    "    model_kwargs={\n",
    "        \"torch_dtype\": torch.float16,\n",
    "        \"quantization_config\": {\"load_in_4bit\": True},\n",
    "        \"low_cpu_mem_usage\": True,\n",
    "    },\n",
    ")\n",
    "\n",
    "terminators =  [\n",
    "    pipeline.tokenizer.eos_token_id,\n",
    "    pipeline.tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf798f5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T06:46:35.935425Z",
     "iopub.status.busy": "2024-04-19T06:46:35.934827Z",
     "iopub.status.idle": "2024-04-19T06:46:35.939009Z",
     "shell.execute_reply": "2024-04-19T06:46:35.938173Z"
    },
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     "duration": 0.03204,
     "end_time": "2024-04-19T06:46:35.940965",
     "exception": false,
     "start_time": "2024-04-19T06:46:35.908925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### Pdf file Path for RAG\n",
    "pdf_file_path = \"/kaggle/input/deep-learning-ian-goodfellow/DeepLearningBook.pdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbebf966",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T06:46:35.992075Z",
     "iopub.status.busy": "2024-04-19T06:46:35.991432Z",
     "iopub.status.idle": "2024-04-19T06:46:35.998494Z",
     "shell.execute_reply": "2024-04-19T06:46:35.997585Z"
    },
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     "end_time": "2024-04-19T06:46:36.000339",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Llama3_8B_gen:\n",
    "    def __init__(self,pipeline):\n",
    "        self.pipeline= pipeline\n",
    "        \n",
    "    @staticmethod\n",
    "    def generate_prompt(query,retrieved_text):\n",
    "        messages = [\n",
    "            {\"role\": \"system\", \"content\": \"Answer the Question for the Given below context and information and not prior knowledge, only give the output result \\n\\ncontext:\\n\\n{}\".format(retrieved_text) },\n",
    "            {\"role\": \"user\", \"content\": query},]\n",
    "        return pipeline.tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True)\n",
    "    \n",
    "    def generate(self,query,retrieved_context):\n",
    "        prompt = self.generate_prompt(query ,retrieved_context)\n",
    "        output =  pipeline(prompt,max_new_tokens=512,eos_token_id=terminators,do_sample=True,\n",
    "                            temperature=0.7,top_p=0.9,)         \n",
    "        return output[0][\"generated_text\"][len(prompt):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01c8a792",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T06:46:36.055020Z",
     "iopub.status.busy": "2024-04-19T06:46:36.054289Z",
     "iopub.status.idle": "2024-04-19T06:46:36.061899Z",
     "shell.execute_reply": "2024-04-19T06:46:36.061076Z"
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   "source": [
    "class Langchain_RAG:\n",
    "    def __init__(self,pdf_file_path):\n",
    "        self.embeddings = HuggingFaceEmbeddings(model_name=\"BAAI/bge-small-en-v1.5\")\n",
    "        self.pdf_file_path = pdf_file_path\n",
    "        print(\"loading pdf file , this may take time to process\")\n",
    "        self.loader = loader = PDFMinerLoader(self.pdf_file_path)   \n",
    "        self.data = self.loader.load()\n",
    "        print(\"<< pdf file loaded\")\n",
    "        print(\"<< chunking\")\n",
    "        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separators=[\" \", \",\", \"\\n\"])\n",
    "        self.texts = text_splitter.split_documents(self.data)\n",
    "        print(\"<< chunked\")\n",
    "        self.get_vec_value= FAISS.from_documents(self.texts, self.embeddings)\n",
    "        print(\"<< saved\")\n",
    "        self.retriever = self.get_vec_value.as_retriever(search_kwargs={\"k\": 4})\n",
    "        \n",
    "    def __call__(self,query):\n",
    "        rev = self.retriever.get_relevant_documents(query) \n",
    "        return \"\".join([i.page_content for i in rev])"
   ]
  },
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     "text": [
      "loading pdf file , this may take time to process\n",
      "<< pdf file loaded\n",
      "<< chunking\n",
      "<< chunked\n",
      "<< saved\n"
     ]
    }
   ],
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    "text_gen = Llama3_8B_gen(pipeline=pipeline)\n",
    "retriever = Langchain_RAG(pdf_file_path=pdf_file_path)"
   ]
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    "def Rag_qa(query):\n",
    "    retriever_context = retriever(query)\n",
    "    resut = text_gen.generate(query,retriever_context)\n",
    "    return resut"
   ]
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     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:226: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
      "  warnings.warn(f'Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.')\n"
     ]
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    {
     "data": {
      "text/markdown": [
       "Deep learning is an approach to machine learning that has drawn heavily on our knowledge of the human brain, statistics and applied math. Specifically, it is a type of machine learning, a technique that allows computer systems to improve with experience and data."
      ]
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    "display_markdown(Rag_qa(\"What is Deep Learning?\"),raw=True)"
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      "text/markdown": [
       "A Convolutional Neural Network (CNN) is a type of neural network architecture that is particularly well-suited for processing data with grid-like topology, such as images. The key idea behind CNNs is to leverage the spatial relationships between pixels in an image to improve performance on image-related tasks.\n",
       "\n",
       "A CNN typically consists of multiple layers, each of which performs a specific function:\n",
       "\n",
       "1. **Convolutional Layer**: This layer applies a set of filters to the input data, scanning the data in a sliding window fashion. Each filter learns to detect specific features, such as edges or textures, in the input data. The filters are typically small, 3x3 or 5x5, and are applied in a hierarchical manner, allowing the network to capture features at multiple scales.\n",
       "2. **Activation Function**: After the convolutional layer, an activation function is applied to introduce non-linearity into the network. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, and Tanh.\n",
       "3. **Pooling Layer**: The pooling layer reduces the spatial dimensions of the input data, allowing the network to capture larger features and reducing the number of parameters. Common pooling functions include Max Pooling and Average Pooling.\n",
       "4. **Flattening**: The output of the convolutional and pooling layers is flattened into a 1D vector, which is then fed into the fully connected layers.\n",
       "5. **Fully Connected Layers**: The flattened output is fed into one or more fully connected layers, which are similar to traditional neural networks. These layers learn to recognize more complex features and patterns.\n",
       "\n",
       "The key innovations in CNNs are:\n",
       "\n",
       "* **Parameter sharing**: CNNs share the same set of filters across the input data, which reduces the number of parameters and improves generalization.\n",
       "* **Spatial hierarchy**: CNNs build a spatial hierarchy of features, allowing them to capture features at multiple scales and resolutions.\n",
       "* **Translation equivariance**: CNNs are designed to be translation equivariant, meaning that the output is unaffected by the position of the input data.\n",
       "\n",
       "CNNs have been extremely successful in various computer vision tasks, such as image classification, object detection, and segmentation. They have also been used in natural language processing tasks, such as text classification and machine translation."
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    "display_markdown(Rag_qa(\"Explain Convotional Neural Network.\"),raw=True)"
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