ChatPDF_h2ogpt.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/R3gm/Colab-resources/blob/main/ChatPDF_h2ogpt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajTjpWJ9-8qg"
      },
      "source": [
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "# H2OGPT INFERENCE\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vpiBaR_9-Fiw"
      },
      "source": [
        "| Code Credits | Link |\n",
        "| ----------- | ---- |\n",
        "| 🎉 Repository | [![GitHub Repository](https://img.shields.io/github/stars/h2oai/h2ogpt?style=social)](https://github.com/h2oai/h2ogpt) |\n",
        "| 🚀 Online inference | [[Online h2o]](https://gpt.h2o.ai) |\n",
        "| 🔥 Discover More Colab Notebooks | [![GitHub Repository](https://img.shields.io/badge/GitHub-Repository-black?style=flat-square&logo=github)](https://github.com/R3gm/InsightSolver-Colab/) |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2EJxbcss8rtz"
      },
      "source": [
        "# Submit a file in the red square"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QC1kaJzI7uan"
      },
      "source": [
        "![submit.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4CZSdHO7rUy"
      },
      "source": [
        "# Prompt mode\n",
        "`Content or file:` This Agreement is governed by English law and the parties submit to the exclusive jurisdiction of the English courts in relation to any dispute (contractual or non-contractual) concerning this Agreement save that either party may apply to any court for an injunction or other relief to protect its Intellectual Property Rights. Source: 28-pl Content: No Waiver. Failure or delay in exercising any right or remedy under this Agreement shall not constitute a waiver of such (or any other) right or remedy.\n",
        "\n",
        "11.7 Severability. The invalidity, illegality or unenforceability of any term (or part of a term) of this Agreement shall not affect the continuation in force of the remainder of the term (if any) and this Agreement.\n",
        "\n",
        "11.8 No Agency. Except as expressly stated otherwise, nothing in this Agreement shall create an agency, partnership or joint venture of any kind between the parties.\n",
        "\n",
        "11.9 No Third-Party Beneficiaries. Source: 30-pl Content: (b) if Google believes, in good faith, that the Distributor has violated or caused Google to violate any Anti-Bribery Laws (as defined in Clause 8.5) or that such a violation is reasonably likely to occur, Source: 4-pl  \n",
        "\n",
        "\n",
        "`QUESTION:` Which state/country’s law governs the interpretation of the contract?\n",
        "\n",
        "\n",
        "`FINAL ANSWER:` This Agreement is governed by English law. SOURCES: 28-pl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yi9F4Gb09aFp"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWIkHXJnuYX2",
        "outputId": "706f1675-a4f7-4523-f52d-e537355e40a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fatal: destination path 'h2ogpt' already exists and is not an empty directory.\n",
            "/content/h2ogpt\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\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.\n",
            "argilla 1.7.0 requires numpy<1.24.0, but you have numpy 1.24.2 which is incompatible.\n",
            "argilla 1.7.0 requires pandas<2.0.0,>=1.0.0, but you have pandas 2.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.27.1, but you have requests 2.31.0 which is incompatible.\n",
            "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.24.2 which is incompatible.\n",
            "tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.24.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m\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.\n",
            "google-colab 1.0.0 requires requests==2.27.1, but you have requests 2.31.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip -q install psutil\n",
        "!git clone https://github.com/h2oai/h2ogpt.git\n",
        "%cd h2ogpt\n",
        "!pip install -q -r requirements.txt\n",
        "user_path = '/content/docs'\n",
        "!pip install -q -r requirements_optional_langchain.txt\n",
        "\n",
        "import psutil\n",
        "import os\n",
        "pid = os.getpid()\n",
        "py = psutil.Process(pid)\n",
        "py.kill()\n",
        "print('Ignore the warnings')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_u6Eke0sAURo"
      },
      "source": [
        "# Run gradio UI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUlU9fpjAR8-",
        "outputId": "12a88992-11c2-4787-face-230f2022e58f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/h2ogpt\n",
            "2023-05-23 00:35:35.094648: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('//172.28.0.1'), PosixPath('http')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-2dlzv0dqlweqe --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n",
            "Using Model h2oai/h2ogpt-oig-oasst1-512-6.9b\n",
            "Generating model with params:\n",
            "load_8bit: True\n",
            "load_half: True\n",
            "infer_devices: True\n",
            "base_model: h2oai/h2ogpt-oig-oasst1-512-6.9b\n",
            "tokenizer_base_model: \n",
            "lora_weights: \n",
            "gpu_id: 0\n",
            "prompt_type: plain\n",
            "temperature: 0.1\n",
            "top_p: 0.75\n",
            "top_k: 40\n",
            "num_beams: 1\n",
            "repetition_penalty: 1.07\n",
            "num_return_sequences: 1\n",
            "do_sample: False\n",
            "max_new_tokens: 256\n",
            "min_new_tokens: 0\n",
            "early_stopping: False\n",
            "max_time: 180\n",
            "debug: False\n",
            "save_dir: None\n",
            "share: True\n",
            "local_files_only: False\n",
            "resume_download: True\n",
            "use_auth_token: False\n",
            "trust_remote_code: True\n",
            "src_lang: English\n",
            "tgt_lang: Russian\n",
            "gradio: True\n",
            "gradio_avoid_processing_markdown: False\n",
            "chat: True\n",
            "chat_context: False\n",
            "stream_output: True\n",
            "show_examples: False\n",
            "verbose: False\n",
            "h2ocolors: True\n",
            "height: 400\n",
            "show_lora: True\n",
            "login_mode_if_model0: False\n",
            "block_gradio_exit: True\n",
            "concurrency_count: 1\n",
            "api_open: False\n",
            "allow_api: True\n",
            "input_lines: 1\n",
            "auth: None\n",
            "sanitize_user_prompt: True\n",
            "sanitize_bot_response: True\n",
            "extra_model_options: []\n",
            "extra_lora_options: []\n",
            "score_model: OpenAssistant/reward-model-deberta-v3-large-v2\n",
            "auto_score: True\n",
            "eval_sharegpt_prompts_only: 0\n",
            "eval_sharegpt_prompts_only_seed: 1234\n",
            "eval_sharegpt_as_output: False\n",
            "langchain_mode: UserData\n",
            "visible_langchain_modes: ['UserData', 'MyData']\n",
            "user_path: user_path\n",
            "load_db_if_exists: True\n",
            "keep_sources_in_context: False\n",
            "db_type: chroma\n",
            "use_openai_embedding: False\n",
            "use_openai_model: False\n",
            "hf_embedding_model: sentence-transformers/all-MiniLM-L6-v2\n",
            "allow_upload_to_user_data: True\n",
            "allow_upload_to_my_data: True\n",
            "enable_url_upload: True\n",
            "enable_text_upload: True\n",
            "enable_sources_list: True\n",
            "chunk: True\n",
            "chunk_size: 512\n",
            "k: 4\n",
            "n_jobs: -1\n",
            "enable_captions: True\n",
            "captions_model: Salesforce/blip-image-captioning-base\n",
            "pre_load_caption_model: False\n",
            "caption_gpu: True\n",
            "enable_ocr: False\n",
            "is_hf: False\n",
            "is_gpth2oai: False\n",
            "is_public: False\n",
            "is_low_mem: False\n",
            "admin_pass: None\n",
            "raise_generate_gpu_exceptions: True\n",
            "n_gpus: 1\n",
            "model_lower: h2oai/h2ogpt-oig-oasst1-512-6.9b\n",
            "first_para: False\n",
            "text_limit: None\n",
            "placeholder_instruction: Enter a question or imperative.\n",
            "placeholder_input: \n",
            "examples: [['Jeff: Can I train a ? Transformers model on Amazon SageMaker? \\nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \\nJeff: ok.\\nJeff: and how can I get started? \\nJeff: where can I find documentation? \\nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face', '', '', True, 'plain', 0.1, 0.75, 40, 4, 256, 0, False, 180, 1.0, 1, False, True, '', '', 'Disabled'], ['Translate English to French', 'Good morning', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Give detailed answer for whether Einstein or Newton is smarter.', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Explain in detailed list, all the best practices for coding in python.', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Create a markdown table with 3 rows for the primary colors, and 2 columns, with color name and hex codes.', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Translate to German:  My name is Arthur', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], [\"Please answer to the following question. Who is going to be the next Ballon d'or?\", '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Please answer the following question. What is the boiling point of Nitrogen?', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Answer the following yes/no question. Can you write a whole Haiku in a single tweet?', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Simplify the following expression: (False or False and True). Explain your answer.', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], [\"Premise: At my age you will probably have learnt one lesson. Hypothesis:  It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?\", '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['The square root of x is the cube root of y. What is y to the power of 2, if x = 4?', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['Answer the following question by reasoning step by step.  The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['def area_of_rectangle(a: float, b: float):\\n    \"\"\"Return the area of the rectangle.\"\"\"', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['# a function in native python:\\ndef mean(a):\\n    return sum(a)/len(a)\\n\\n# the same function using numpy:\\nimport numpy as np\\ndef mean(a):', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled'], ['X = np.random.randn(100, 100)\\ny = np.random.randint(0, 1, 100)\\n\\n# fit random forest classifier with 20 estimators', '', '', True, 'plain', 0.1, 0.75, 40, 1, 256, 0, False, 180, 1.07, 1, False, True, '', '', 'Disabled']]\n",
            "task_info: Auto-complete phrase, code, etc.\n",
            "Command: generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b --langchain_mode=UserData --user_path=user_path --load_8bit=True --share=True\n",
            "Hash: 585d7667a6d0849470feb9cfb755b4560743b130\n",
            "Prep: persist_directory=db_dir_UserData exists, using\n",
            "DO Loading db: UserData\n",
            "Using embedded DuckDB with persistence: data will be stored in: db_dir_UserData\n",
            "DONE Loading db: UserData\n",
            "Get h2oai/h2ogpt-oig-oasst1-512-6.9b model\n",
            "device_map: {'': 0}\n",
            "Loading checkpoint shards: 100% 3/3 [01:03<00:00, 21.15s/it]\n",
            "Get OpenAssistant/reward-model-deberta-v3-large-v2 model\n",
            "device_map: {'': 0}\n",
            "Running on local URL:  http://0.0.0.0:7860\n",
            "Running on public URL: https://53289ab485b17b9a8b.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n",
            "Started GUI\n"
          ]
        }
      ],
      "source": [
        "%cd h2ogpt\n",
        "!python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b --langchain_mode=UserData --user_path=user_path  --load_8bit=True --share=True"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "authorship_tag": "ABX9TyNZY1Ja36x30mgKd1WdEZsr",
      "gpuType": "T4",
      "include_colab_link": true,
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}