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+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "8f74ea4e7b59436ab27676c276efc792": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ }
+ }
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6_fTHEr-GwqE"
+ },
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install -q aiohappyeyeballs==2.4.3 aiohttp==3.10.10 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.6.2.post1 attrs==24.2.0 backoff==2.2.1 beautifulsoup4==4.12.3 certifi==2024.8.30 cffi==1.17.1 chardet==5.2.0 charset-normalizer==3.4.0 click==8.1.7 cryptography==43.0.1 dataclasses-json==0.6.7 datasets==3.0.1 dill==0.3.8 distro==1.9.0 emoji==2.14.0 eval-type-backport==0.2.0 faiss-cpu==1.9.0 filelock==3.16.1 filetype==1.2.0 frozenlist==1.4.1 fsspec[http]==2024.6.1 greenlet==3.1.1 h11==0.14.0 httpcore==1.0.6 httpx==0.27.2 huggingface-hub==0.26.0 idna==3.10 jinja2==3.1.4 jiter==0.6.1 joblib==1.4.2 jsonpatch==1.33 jsonpath-python==1.0.6 jsonpointer==3.0.0 langchain==0.2.16 langchain-community==0.2.16 langchain-core==0.2.38 langchain-huggingface==0.0.3 langchain-openai==0.1.23 langchain-text-splitters==0.2.4 langdetect==1.0.9 langsmith==0.1.136 lxml==5.3.0 markupsafe==3.0.2 marshmallow==3.23.0 mpmath==1.3.0 multidict==6.1.0 multiprocess==0.70.16 mypy-extensions==1.0.0 nest-asyncio==1.6.0 networkx==3.4.1 nltk==3.9.1 numpy==1.26.4 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-nccl-cu12==2.21.5 nvidia-nvjitlink-cu12==12.4.127 nvidia-nvtx-cu12==12.4.127 openai==1.52.0 orjson==3.10.7 packaging==24.1 pandas==2.2.3 pillow==11.0.0 propcache==0.2.0 psutil==6.1.0 pyarrow==17.0.0 pycparser==2.22 pydantic==2.9.2 pydantic-core==2.23.4 pypdf==5.0.1 python-dateutil==2.8.2 python-iso639==2024.4.27 python-magic==0.4.27 python-pptx==1.0.2 pytz==2024.2 pyyaml==6.0.2 rapidfuzz==3.10.0 regex==2024.9.11 requests==2.32.3 requests-toolbelt==1.0.0 safetensors==0.4.5 scikit-learn==1.5.2 scipy==1.14.1 sentence-transformers==3.2.0 six==1.16.0 sniffio==1.3.1 soupsieve==2.6 sqlalchemy==2.0.36 sympy==1.13.1 tabulate==0.9.0 tenacity==8.5.0 threadpoolctl==3.5.0 tiktoken==0.8.0 tokenizers==0.20.1 torch==2.5.0 tqdm==4.66.5 transformers==4.45.2 triton==3.1.0 typing-extensions==4.12.2 typing-inspect==0.9.0 tzdata==2024.2 unstructured==0.15.7 unstructured-client==0.26.1 urllib3==2.2.3 wrapt==1.16.0 xlsxwriter==3.2.0 xxhash==3.5.0 yarl==1.15.4\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JRkQn1rGTr1h",
+ "outputId": "941c05f0-d7a6-4572-b0e2-7aebd591ced1"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━��━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m447.4/447.4 kB\u001b[0m \u001b[31m32.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.0/5.0 MB\u001b[0m \u001b[31m96.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m77.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m107.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m82.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m54.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.7/188.7 MB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m86.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m114.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.4/4.4 MB\u001b[0m \u001b[31m95.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m287.3/287.3 kB\u001b[0m \u001b[31m24.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.2/41.2 MB\u001b[0m \u001b[31m50.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m93.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.2/6.2 MB\u001b[0m \u001b[31m59.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m95.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m906.4/906.4 MB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.9/9.9 MB\u001b[0m \u001b[31m116.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.5/209.5 MB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.0/314.0 kB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h\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",
+ "cudf-cu12 24.6.1 requires pandas<2.2.3dev0,>=2.0, but you have pandas 2.2.3 which is incompatible.\n",
+ "cudf-cu12 24.6.1 requires pyarrow<16.2.0a0,>=16.1.0, but you have pyarrow 17.0.0 which is incompatible.\n",
+ "fastai 2.7.17 requires torch<2.5,>=1.10, but you have torch 2.5.0 which is incompatible.\n",
+ "gensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.14.1 which is incompatible.\n",
+ "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.2.3 which is incompatible.\n",
+ "jupyter-server 1.24.0 requires anyio<4,>=3.1.0, but you have anyio 4.6.2.post1 which is incompatible.\n",
+ "torchaudio 2.4.1+cu121 requires torch==2.4.1, but you have torch 2.5.0 which is incompatible.\n",
+ "torchvision 0.19.1+cu121 requires torch==2.4.1, but you have torch 2.5.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0m"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "links = ['https://www.gallup.com/workplace/390776/percent-feel-employer-cares-wellbeing-plummets.aspx', 'https://newsroom.cigna.com/business-case-addressing-loneliness-workforce', 'https://www.gallup.com/workplace/608675/new-workplace-employee-engagement-stagnates.aspx', 'https://www.wsj.com/lifestyle/workplace/american-workers-loneliness-research-35793dc4', 'https://hbr.org/resources/pdfs/comm/achievers/hbr_achievers_report_sep13.pdf', 'https://unmind.com/blog/wellbeing-washing?locale=en', 'https://www.mckinsey.com/industries/healthcare/our-insights/how-prioritizing-health-is-a-prescription-for-us-prosperity', 'https://www.workhuman.com/resources/reports-guides/amplifying-wellbeing-at-work-and-beyond/', 'https://d341ezm4iqaae0.cloudfront.net/ews/20220817145319/Work-Wellbeing-2022-Insights-Report-.pdf', 'https://www.gallup.com/394373/indicator-employee-engagement.aspx', 'https://thrivemyway.com/burnout-stats/', 'https://www.mckinsey.com/featured-insights/diversity-and-inclusion/the-state-of-burnout-for-women-in-the-workplace', 'https://www.thelancet.com/journals/lanam/article/PIIS2667-193X(21)00087-9/fulltext', 'https://www.apa.org/pubs/reports/work-well-being/2022-mental-health-support', 'https://www.gallup.com/workplace/393497/world-trillion-workplace-problem.aspx', 'https://www.apa.org/pubs/reports/work-in-america/2023-workplace-health-well-being', 'https://www.forbes.com/sites/tracybrower/2024/06/04/how-the-modern-workplace-affects-loneliness/', 'https://www.aihr.com/blog/employee-experience-statistics/', 'https://www.wellable.co/resources/employee-wellness-industry-trends-reports/2024/', 'https://www.mindsharepartners.org/mentalhealthatworkreport-2021', 'https://www.ringover.com/blog/worst-industries-for-burnout', 'https://www.usnews.com/news/health-news/articles/2024-02-01/loneliness-is-plaguing-americans-in-2024-poll', 'https://www.gallup.com/workplace/397058/increasing-importance-best-friend-work.aspx', 'https://www.psychiatry.org/News-room/News-Releases/New-APA-Poll-One-in-Three-Americans-Feels-Lonely-E', 'https://www.workhuman.com/blog/how-recognition-can-help-reduce-stress-and-burnout/', 'https://news.harvard.edu/gazette/story/2023/10/how-social-isolation-loneliness-can-shorten-your-life/', 'https://www.youtube.com/watch?v=sgthz0xu_Gw', 'https://www.quantumworkplace.com/future-of-work/remote-work-statistics', 'https://www.zippia.com/advice/employee-wellness-statistics/', 'https://www.zippia.com/advice/employee-engagement-statistics/', 'https://www.nytimes.com/2024/05/06/well/loneliness-mental-health-age.html', 'https://www.nami.org/about-mental-illness/mental-health-by-the-numbers/', 'https://www.aarp.org/pri/topics/health/coverage-access/medicare-spends-more-on-socially-isolated-older-adults/', 'https://www.forbes.com/sites/williamhaseltine/2024/05/29/battling-loneliness-the-new-public-health-crisis/', 'https://apnews.com/article/loneliness-workplace-job-wellness-c2dee7b29121a470afb0a6792387e28b',\n",
+ "'https://www.mckinsey.com/industries/healthcare/our-insights/national-surveys-reveal-disconnect-between-employees-and-employers-around-mental-health-need', 'https://www.fastcompany.com/91140052/workplace-loneliness-2024-increase-virtual-employees-gallup', 'https://www.gallup.com/workplace/645566/employees-worldwide-feel-lonely.aspx', 'https://resumelab.com/career-advice/mental-health-at-work', 'https://www.hhs.gov/sites/default/files/sg-social-connection-fact-cards.pdf', 'https://www.gallup.com/workplace/349484/state-of-the-global-workplace.aspx', 'https://www.weforum.org/agenda/2023/07/business-benefits-of-boosting-employee-health-and-well-being/', 'https://www.cbsnews.com/video/how-workplace-loneliness-can-impact-job-personal-life/', 'https://theharrispoll.com/briefs/workplace-mental-health-awareness-week/', 'https://www.workhuman.com/why-workhuman/case-studies/eaton-driving-retention-with-recognition/']"
+ ],
+ "metadata": {
+ "id": "vNbUItlBHf22"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from langchain_community.document_loaders import PyPDFLoader, UnstructuredURLLoader\n",
+ "import requests\n",
+ "\n",
+ "def load_documents_from_url(url):\n",
+ " try:\n",
+ " # Check if it's a PDF\n",
+ " if url.endswith(\".pdf\"):\n",
+ " try:\n",
+ " loader = PyPDFLoader(url)\n",
+ " return loader.load()\n",
+ " except Exception as e:\n",
+ " print(f\"Error loading PDF from {url}: {e}\")\n",
+ " return None\n",
+ "\n",
+ " # Fetch the content and check for video pages\n",
+ " try:\n",
+ " response = requests.head(url, timeout=10) # Timeout for fetching headers\n",
+ " content_type = response.headers.get('Content-Type', '')\n",
+ " except Exception as e:\n",
+ " print(f\"Error fetching headers from {url}: {e}\")\n",
+ " return None\n",
+ "\n",
+ " # Ignore video content (flagged for now)\n",
+ " if 'video' in content_type:\n",
+ " return None\n",
+ " if 'youtube' in url:\n",
+ " return None\n",
+ "\n",
+ " # Otherwise, treat it as an HTML page\n",
+ " try:\n",
+ " loader = UnstructuredURLLoader([url])\n",
+ " return loader.load()\n",
+ " except Exception as e:\n",
+ " print(f\"Error loading HTML from {url}: {e}\")\n",
+ " return None\n",
+ " except Exception as e:\n",
+ " print(f\"General error loading from {url}: {e}\")\n",
+ " return None"
+ ],
+ "metadata": {
+ "id": "6qGbwTMFHiPb"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "documents = []\n",
+ "for link in links:\n",
+ " doc = load_documents_from_url(link)\n",
+ " #print(f\"loaded doc from {link}\")\n",
+ " if doc:\n",
+ " documents.extend(doc)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "sekHJutbHwTb",
+ "outputId": "19dd5e52-1fdc-431b-ae09-b7591253aa54"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "WARNING:pypdf.generic._base:could not convert string to float: b'0.00-5031024' : FloatObject (b'0.00-5031024') invalid; use 0.0 instead\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Error fetching headers from https://www.usnews.com/news/health-news/articles/2024-02-01/loneliness-is-plaguing-americans-in-2024-poll: HTTPSConnectionPool(host='www.usnews.com', port=443): Read timed out. (read timeout=10)\n",
+ "Error loading PDF from https://www.hhs.gov/sites/default/files/sg-social-connection-fact-cards.pdf: Check the url of your file; returned status code 403\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+ "\n",
+ "text_splitter = RecursiveCharacterTextSplitter(\n",
+ " chunk_size = 750,\n",
+ " chunk_overlap = 20,\n",
+ " length_function = len\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "NSzhEHcPIhwz"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "training_documents = text_splitter.split_documents(documents)"
+ ],
+ "metadata": {
+ "id": "PJQZy1n2Imbh"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "len(training_documents)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "er-5Ut_uIp2s",
+ "outputId": "a816bbec-2cba-4bab-a39d-ad7a0f64d273"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "674"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import uuid\n",
+ "\n",
+ "id_set = set()\n",
+ "\n",
+ "for document in training_documents:\n",
+ " id = str(uuid.uuid4())\n",
+ " while id in id_set:\n",
+ " id = uuid.uuid4()\n",
+ " id_set.add(id)\n",
+ " document.metadata[\"id\"] = id"
+ ],
+ "metadata": {
+ "id": "wjUTJOMhIq40"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "training_split_documents = training_documents[:300]\n",
+ "val_split_documents = training_documents[300:350]\n",
+ "test_split_documents = training_documents[350:400]"
+ ],
+ "metadata": {
+ "id": "LA823RrcIvFP"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "import getpass\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter Your OpenAI API Key: \")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hJ-vyjLoJ7PJ",
+ "outputId": "50558fd8-e657-47f1-a497-5a40d2b2ff19"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Enter Your OpenAI API Key: ··········\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from langchain_openai import ChatOpenAI\n",
+ "\n",
+ "qa_chat_model = ChatOpenAI(\n",
+ " model=\"gpt-4o-mini\",\n",
+ " temperature=0\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "BDecxbNyKFkt"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from langchain_core.prompts import ChatPromptTemplate\n",
+ "\n",
+ "qa_prompt = \"\"\"\\\n",
+ "Given the following context, you must generate questions based on only the provided context.\n",
+ "\n",
+ "You are to generate {n_questions} questions which should be provided in the following format:\n",
+ "\n",
+ "1. QUESTION #1\n",
+ "2. QUESTION #2\n",
+ "...\n",
+ "\n",
+ "Context:\n",
+ "{context}\n",
+ "\"\"\"\n",
+ "\n",
+ "qa_prompt_template = ChatPromptTemplate.from_template(qa_prompt)"
+ ],
+ "metadata": {
+ "id": "U0hNkr5VLZ9R"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "question_generation_chain = qa_prompt_template | qa_chat_model"
+ ],
+ "metadata": {
+ "id": "M3M3RG-wLfRb"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import tqdm\n",
+ "\n",
+ "def create_questions(documents, n_questions):\n",
+ " questions = {}\n",
+ " relevant_docs = {}\n",
+ " for doc in tqdm.tqdm(documents, desc=\"Processing documents\"):\n",
+ " context = doc.page_content\n",
+ " doc_id = doc.metadata[\"id\"]\n",
+ "\n",
+ " # Generate questions for this document\n",
+ " result = question_generation_chain.invoke({\"context\": context, \"n_questions\": n_questions})\n",
+ " generated_questions = result.content.strip().split('\\n')\n",
+ "\n",
+ " # Process each generated question\n",
+ " for q in generated_questions:\n",
+ " # Remove the numbering from the question\n",
+ " q = q.split('. ', 1)[-1].strip()\n",
+ "\n",
+ " # Generate a unique ID for this question\n",
+ " question_id = str(uuid.uuid4())\n",
+ "\n",
+ " # Add the question to our questions dict\n",
+ " questions[question_id] = q\n",
+ "\n",
+ " # Add the document ID to our relevant_docs dict\n",
+ " relevant_docs[question_id] = [doc_id]\n",
+ "\n",
+ " return questions, relevant_docs"
+ ],
+ "metadata": {
+ "id": "v-JFty4-Lkb5"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "training_questions, training_relevant_contexts = create_questions(training_split_documents,2)\n",
+ "val_questions, val_relevant_contexts = create_questions(val_split_documents,2)\n",
+ "test_questions, test_relevant_contexts = create_questions(test_split_documents,2)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "NFFKli-fNPn8",
+ "outputId": "1b894311-6ddd-45dd-94f3-8e2ea3f94083"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Processing documents: 100%|██████████| 300/300 [05:59<00:00, 1.20s/it]\n",
+ "Processing documents: 100%|██████████| 50/50 [00:55<00:00, 1.12s/it]\n",
+ "Processing documents: 100%|██████████| 50/50 [01:11<00:00, 1.44s/it]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import json\n",
+ "\n",
+ "training_corpus = {train_item.metadata[\"id\"] : train_item.page_content for train_item in training_split_documents}\n",
+ "\n",
+ "train_dataset = {\n",
+ " \"questions\" : training_questions,\n",
+ " \"relevant_contexts\" : training_relevant_contexts,\n",
+ " \"corpus\" : training_corpus\n",
+ "}\n",
+ "\n",
+ "with open(\"training_dataset.jsonl\", \"w\") as f:\n",
+ " json.dump(train_dataset, f)"
+ ],
+ "metadata": {
+ "id": "BH6TFaP2NVjO"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "val_corpus = {val_item.metadata[\"id\"] : val_item.page_content for val_item in val_split_documents}\n",
+ "\n",
+ "val_dataset = {\n",
+ " \"questions\" : val_questions,\n",
+ " \"relevant_contexts\" : val_relevant_contexts,\n",
+ " \"corpus\" : val_corpus\n",
+ "}\n",
+ "\n",
+ "with open(\"val_dataset.jsonl\", \"w\") as f:\n",
+ " json.dump(val_dataset, f)"
+ ],
+ "metadata": {
+ "id": "SWCkYR8lNXIM"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_corpus = {test_item.metadata[\"id\"] : test_item.page_content for test_item in test_split_documents}\n",
+ "\n",
+ "test_dataset = {\n",
+ " \"questions\" : test_questions,\n",
+ " \"relevant_contexts\" : test_relevant_contexts,\n",
+ " \"corpus\" : train_corpus\n",
+ "}\n",
+ "\n",
+ "with open(\"test_dataset.jsonl\", \"w\") as f:\n",
+ " json.dump(test_dataset, f)"
+ ],
+ "metadata": {
+ "id": "UEXTp4OcNYzD"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from sentence_transformers import SentenceTransformer\n",
+ "\n",
+ "model = SentenceTransformer(\"Snowflake/snowflake-arctic-embed-l\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 532,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "cbAGn_KGPY7f",
+ "outputId": "e76d9b85-677a-4c91-a25c-b08d8c7876cd"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+ " from tqdm.autonotebook import tqdm, trange\n",
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_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<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "config_sentence_transformers.json: 0%| | 0.00/252 [00:00<?, ?B/s]"
+ ],
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+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "README.md: 0%| | 0.00/84.5k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
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+ ],
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
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+ },
+ {
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+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
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+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "21fd1f3af7b04518b8f49daeb363fbcd"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "special_tokens_map.json: 0%| | 0.00/695 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9fcd1e8468ac42c6a8ce932a93521d86"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "1_Pooling/config.json: 0%| | 0.00/297 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b6f84ef812bd48498db848f10fe25034"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from torch.utils.data import DataLoader\n",
+ "from torch.utils.data import Dataset\n",
+ "from sentence_transformers import InputExample"
+ ],
+ "metadata": {
+ "id": "4L0IhYOhPjih"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "BATCH_SIZE = 20"
+ ],
+ "metadata": {
+ "id": "PGIibYQbYTsG"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "corpus = train_dataset['corpus']\n",
+ "queries = train_dataset['questions']\n",
+ "relevant_docs = train_dataset['relevant_contexts']\n",
+ "\n",
+ "examples = []\n",
+ "for query_id, query in queries.items():\n",
+ " doc_id = relevant_docs[query_id][0]\n",
+ " text = corpus[doc_id]\n",
+ " example = InputExample(texts=[query, text])\n",
+ " examples.append(example)"
+ ],
+ "metadata": {
+ "id": "gwCWE6P5YUAr"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "loader = DataLoader(\n",
+ " examples, batch_size=BATCH_SIZE\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "7lfqA0zuYWSj"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from sentence_transformers.losses import MatryoshkaLoss, MultipleNegativesRankingLoss\n",
+ "\n",
+ "matryoshka_dimensions = [1024, 768, 512, 256, 128, 64]\n",
+ "inner_train_loss = MultipleNegativesRankingLoss(model)\n",
+ "train_loss = MatryoshkaLoss(\n",
+ " model, inner_train_loss, matryoshka_dims=matryoshka_dimensions\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "iCSmAoC8YZxA"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from sentence_transformers.evaluation import InformationRetrievalEvaluator\n",
+ "\n",
+ "corpus = val_dataset['corpus']\n",
+ "queries = val_dataset['questions']\n",
+ "relevant_docs = val_dataset['relevant_contexts']\n",
+ "\n",
+ "evaluator = InformationRetrievalEvaluator(queries, corpus, relevant_docs)"
+ ],
+ "metadata": {
+ "id": "0Vq0ATplYyDF"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "EPOCHS = 5"
+ ],
+ "metadata": {
+ "id": "L9fo5lYEYzAP"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "warmup_steps = int(len(loader) * EPOCHS * 0.1)\n",
+ "\n",
+ "model.fit(\n",
+ " train_objectives=[(loader, train_loss)],\n",
+ " epochs=EPOCHS,\n",
+ " warmup_steps=warmup_steps,\n",
+ " output_path='finetuned_arctic',\n",
+ " show_progress_bar=True,\n",
+ " evaluator=evaluator,\n",
+ " evaluation_steps=50,\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 332,
+ "referenced_widgets": [
+ "4d17ad5463f1438e9b20c59fe894d557",
+ "dc62eab61fc0476caf9426b070626a10",
+ "cea48efd810444b0a2a7e6ff3e7cf7a8",
+ "658f14d4a1b24059835ca7000c654b50",
+ "c50314ec5cfd4bb3a977d3d48c1c9866",
+ "cbe5c074dabc42adb911b32fa1568627",
+ "d21974605053434e9bdd24955112e485",
+ "d5466d6d71634070a726e05d006136c2",
+ "33624ccae0bd4418812d1125081a66bd",
+ "0994c576d05e4cfe926897a68de60274",
+ "64980dad05ac431cbab34ab4afc3cfb0"
+ ]
+ },
+ "id": "JAafxVE8Y054",
+ "outputId": "29203ea8-2595-48fc-d174-bd0d079b4438"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='150' max='150' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [150/150 02:47, Epoch 5/5]\n",
+ " </div>\n",
+ " <table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: left;\">\n",
+ " <th>Step</th>\n",
+ " <th>Training Loss</th>\n",
+ " <th>Validation Loss</th>\n",
+ " <th>Cosine Accuracy@1</th>\n",
+ " <th>Cosine Accuracy@3</th>\n",
+ " <th>Cosine Accuracy@5</th>\n",
+ " <th>Cosine Accuracy@10</th>\n",
+ " <th>Cosine Precision@1</th>\n",
+ " <th>Cosine Precision@3</th>\n",
+ " <th>Cosine Precision@5</th>\n",
+ " <th>Cosine Precision@10</th>\n",
+ " <th>Cosine Recall@1</th>\n",
+ " <th>Cosine Recall@3</th>\n",
+ " <th>Cosine Recall@5</th>\n",
+ " <th>Cosine Recall@10</th>\n",
+ " <th>Cosine Ndcg@10</th>\n",
+ " <th>Cosine Mrr@10</th>\n",
+ " <th>Cosine Map@100</th>\n",
+ " <th>Dot Accuracy@1</th>\n",
+ " <th>Dot Accuracy@3</th>\n",
+ " <th>Dot Accuracy@5</th>\n",
+ " <th>Dot Accuracy@10</th>\n",
+ " <th>Dot Precision@1</th>\n",
+ " <th>Dot Precision@3</th>\n",
+ " <th>Dot Precision@5</th>\n",
+ " <th>Dot Precision@10</th>\n",
+ " <th>Dot Recall@1</th>\n",
+ " <th>Dot Recall@3</th>\n",
+ " <th>Dot Recall@5</th>\n",
+ " <th>Dot Recall@10</th>\n",
+ " <th>Dot Ndcg@10</th>\n",
+ " <th>Dot Mrr@10</th>\n",
+ " <th>Dot Map@100</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>30</td>\n",
+ " <td>No log</td>\n",
+ " <td>No log</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.940000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.313333</td>\n",
+ " <td>0.192000</td>\n",
+ " <td>0.098000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.940000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.902293</td>\n",
+ " <td>0.876429</td>\n",
+ " <td>0.878171</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.940000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.313333</td>\n",
+ " <td>0.192000</td>\n",
+ " <td>0.098000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.940000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.902293</td>\n",
+ " <td>0.876429</td>\n",
+ " <td>0.878171</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50</td>\n",
+ " <td>No log</td>\n",
+ " <td>No log</td>\n",
+ " <td>0.830000</td>\n",
+ " <td>0.950000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.830000</td>\n",
+ " <td>0.316667</td>\n",
+ " <td>0.192000</td>\n",
+ " <td>0.099000</td>\n",
+ " <td>0.830000</td>\n",
+ " <td>0.950000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.912213</td>\n",
+ " <td>0.886845</td>\n",
+ " <td>0.887754</td>\n",
+ " <td>0.830000</td>\n",
+ " <td>0.950000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.830000</td>\n",
+ " <td>0.316667</td>\n",
+ " <td>0.192000</td>\n",
+ " <td>0.099000</td>\n",
+ " <td>0.830000</td>\n",
+ " <td>0.950000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.912213</td>\n",
+ " <td>0.886845</td>\n",
+ " <td>0.887754</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>60</td>\n",
+ " <td>No log</td>\n",
+ " <td>No log</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.950000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.316667</td>\n",
+ " <td>0.192000</td>\n",
+ " <td>0.099000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.950000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.910558</td>\n",
+ " <td>0.884540</td>\n",
+ " <td>0.885449</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.950000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.316667</td>\n",
+ " <td>0.192000</td>\n",
+ " <td>0.099000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.950000</td>\n",
+ " <td>0.960000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.910558</td>\n",
+ " <td>0.884540</td>\n",
+ " <td>0.885449</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>90</td>\n",
+ " <td>No log</td>\n",
+ " <td>No log</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.920000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.306667</td>\n",
+ " <td>0.194000</td>\n",
+ " <td>0.099000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.920000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.910413</td>\n",
+ " <td>0.884429</td>\n",
+ " <td>0.885338</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.920000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.306667</td>\n",
+ " <td>0.194000</td>\n",
+ " <td>0.099000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.920000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.910413</td>\n",
+ " <td>0.884429</td>\n",
+ " <td>0.885338</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>No log</td>\n",
+ " <td>No log</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.310000</td>\n",
+ " <td>0.194000</td>\n",
+ " <td>0.099000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.909796</td>\n",
+ " <td>0.883595</td>\n",
+ " <td>0.884504</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.310000</td>\n",
+ " <td>0.194000</td>\n",
+ " <td>0.099000</td>\n",
+ " <td>0.820000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.990000</td>\n",
+ " <td>0.909796</td>\n",
+ " <td>0.883595</td>\n",
+ " <td>0.884504</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " <td>No log</td>\n",
+ " <td>No log</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.310000</td>\n",
+ " <td>0.194000</td>\n",
+ " <td>0.098000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.903215</td>\n",
+ " <td>0.877595</td>\n",
+ " <td>0.879338</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.310000</td>\n",
+ " <td>0.194000</td>\n",
+ " <td>0.098000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.903215</td>\n",
+ " <td>0.877595</td>\n",
+ " <td>0.879338</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>150</td>\n",
+ " <td>No log</td>\n",
+ " <td>No log</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.310000</td>\n",
+ " <td>0.194000</td>\n",
+ " <td>0.098000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.903653</td>\n",
+ " <td>0.878095</td>\n",
+ " <td>0.879838</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.310000</td>\n",
+ " <td>0.194000</td>\n",
+ " <td>0.098000</td>\n",
+ " <td>0.810000</td>\n",
+ " <td>0.930000</td>\n",
+ " <td>0.970000</td>\n",
+ " <td>0.980000</td>\n",
+ " <td>0.903653</td>\n",
+ " <td>0.878095</td>\n",
+ " <td>0.879838</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Computing widget examples: 0%| | 0/1 [00:00<?, ?example/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4d17ad5463f1438e9b20c59fe894d557"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "from langchain_community.vectorstores import FAISS\n",
+ "from langchain_openai.embeddings import OpenAIEmbeddings\n",
+ "from langchain_core.documents import Document"
+ ],
+ "metadata": {
+ "id": "h5E-MWDpaQdf"
+ },
+ "execution_count": 31,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from tqdm.auto import tqdm\n",
+ "\n",
+ "def evaluate_openai(\n",
+ " dataset,\n",
+ " embed_model,\n",
+ " top_k=5,\n",
+ " verbose=False,\n",
+ "):\n",
+ " corpus = dataset['corpus']\n",
+ " questions = dataset['questions']\n",
+ " relevant_docs = dataset['relevant_contexts']\n",
+ " documents = [Document(page_content=content, metadata={\"id\": doc_id}) for doc_id, content in corpus.items()]\n",
+ " vectorstore = FAISS.from_documents(documents, embed_model)\n",
+ "\n",
+ " retriever = vectorstore.as_retriever(search_kwargs={\"k\": top_k})\n",
+ "\n",
+ " eval_results = []\n",
+ " for id, question in tqdm(questions.items()):\n",
+ " retrieved_nodes = retriever.invoke(question)\n",
+ " retrieved_ids = [node.metadata[\"id\"] for node in retrieved_nodes]\n",
+ " expected_id = relevant_docs[id][0]\n",
+ " is_hit = expected_id in retrieved_ids\n",
+ " eval_results.append({\"id\": id, \"question\": question, \"expected_id\": expected_id, \"is_hit\": is_hit})\n",
+ "\n",
+ " return eval_results"
+ ],
+ "metadata": {
+ "id": "ggptAYuGZyQu"
+ },
+ "execution_count": 30,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "te3_openai = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
+ "te3_results = evaluate_openai(test_dataset, te3_openai)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "52444a0b6cc549099592373e965722b3",
+ "0f790898fe714f0a989a2baf3c0e6d84",
+ "599bcfd810884081953e60b54f44c4e5",
+ "1ddb804c39254e18884dfa1918fc64e7",
+ "afc935b51bc844a1bd797ab88649a5e5",
+ "4f53ea34b83742d99df25bc807c2fe62",
+ "bcd4bd6015a4450fbfe5c34bf9126baa",
+ "58d02c136c3849939e6933858291a508",
+ "c88baf41057e4c3385bf8aa259a10cf6",
+ "8f18eb6447954e7589411ef80fafefa8",
+ "621c79e9a16043068edc23ccb8206034"
+ ]
+ },
+ "id": "E8VZseA7aJGo",
+ "outputId": "17c592f2-1be6-4c5e-ca1d-a01e5bbb41b4"
+ },
+ "execution_count": 32,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/100 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "52444a0b6cc549099592373e965722b3"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "te3_results_df = pd.DataFrame(te3_results)"
+ ],
+ "metadata": {
+ "id": "b5n1GZsoaMYI"
+ },
+ "execution_count": 33,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "te3_hit_rate = te3_results_df[\"is_hit\"].mean()\n",
+ "te3_hit_rate"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "AWGrM_mgaVgX",
+ "outputId": "77c562cb-3cca-4bfc-c7d6-01b209bbcb28"
+ },
+ "execution_count": 34,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0.99"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 34
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from langchain_huggingface import HuggingFaceEmbeddings\n",
+ "\n",
+ "huggingface_embeddings = HuggingFaceEmbeddings(model_name=\"Snowflake/snowflake-arctic-embed-l\")\n",
+ "arctic_embed_m_results = evaluate_openai(test_dataset, huggingface_embeddings)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "5cb3d8db517849fa9e293d97b199a655",
+ "cd98d007d5fa4703a3b3be3548015f84",
+ "57b7cd2e9c8449579a20f38d671367ba",
+ "320f679e56ce42ef8df02f00442c28ff",
+ "27b3558af5d3431fb4fe4b34e8c1a9ef",
+ "ac78963b4c854d3b8937ffcd9e7cb9c2",
+ "1ee0dc71df634b94a8b5b0782bc50633",
+ "bbc79f7bee7c4682828f5cd9f6c5398a",
+ "49f04e7d1f94471c80f8f241b68c8fa2",
+ "f32735cc9bfe4778a4136684d6eb6c28",
+ "bd503534c2e740baae59a3d08cf6ddd0"
+ ]
+ },
+ "id": "8EWulySQajpv",
+ "outputId": "61152d75-bc35-4302-f251-57fa4664ee2b"
+ },
+ "execution_count": 35,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/100 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "5cb3d8db517849fa9e293d97b199a655"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "arctic_embed_m_results_df = pd.DataFrame(arctic_embed_m_results)"
+ ],
+ "metadata": {
+ "id": "Oc3BR2XxamOC"
+ },
+ "execution_count": 36,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "arctic_embed_m_hit_rate = arctic_embed_m_results_df[\"is_hit\"].mean()\n",
+ "arctic_embed_m_hit_rate"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9ZWlXJp0aoF_",
+ "outputId": "4e25204c-8e9a-4402-fbdf-7ea0c4eb11d1"
+ },
+ "execution_count": 37,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0.75"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "finetune_embeddings = HuggingFaceEmbeddings(model_name=\"finetuned_arctic\")\n",
+ "finetune_results = evaluate_openai(test_dataset, finetune_embeddings)\n",
+ "finetune_results_df = pd.DataFrame(finetune_results)\n",
+ "finetune_hit_rate = finetune_results_df[\"is_hit\"].mean()\n",
+ "finetune_hit_rate"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 103,
+ "referenced_widgets": [
+ "e759745eaf88463cac46f311dfc3cf1b",
+ "6be9d6dc3dff4da287263b3ee071fb84",
+ "186f45adaa17415eb15057975786b52b",
+ "beabb9d0cac7497d822938b81c58e1e5",
+ "840deaa0264241d48f3467d9fb13b945",
+ "5f054eb243844961a1716624aa706ba7",
+ "db4899db6192421793a79685d87f4ae3",
+ "4e3aeeaa17d847cd96f39d2dd369d8f0",
+ "526035fed0564bb9ba8237bc36acc7af",
+ "ac798ebfaa76488c8faec49625b30784",
+ "8ac20f4a43c2400f9250758f19248098"
+ ]
+ },
+ "id": "1bd_pwMqauR2",
+ "outputId": "802b09b1-b120-4558-a486-8e0fd437cecf"
+ },
+ "execution_count": 38,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of BertModel were not initialized from the model checkpoint at finetuned_arctic and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/100 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e759745eaf88463cac46f311dfc3cf1b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "1.0"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 38
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install huggingface_hub"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YQzDlKH0gL_x",
+ "outputId": "cbd58cfa-d996-46d7-921c-60cf009f2cae"
+ },
+ "execution_count": 66,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.26.0)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.16.1)\n",
+ "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2024.6.1)\n",
+ "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (24.1)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.2)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.32.3)\n",
+ "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.5)\n",
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.2)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4.0)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2024.8.30)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17,
+ "referenced_widgets": [
+ "0f92bbe647084ac7a97c9292e3c0af70",
+ "6ed689f73979402c85210759d39789c5",
+ "29c07d0c464948ea8899257c9dd1009c",
+ "287d4166802148ab9e415b92edcf0efe",
+ "4496a53b5c1e47ef8f0025991ce68242",
+ "3430c0b599f94762aa80f7472a467fd7",
+ "d986567e867a42d9939700039608d696",
+ "d65fa132972b49fdb9d2695f205cc224",
+ "16e3844463254d86bbf4ae1eb3414019",
+ "68d80f7b2d2c4e0c87904a8c5c7867ea",
+ "82f545a023584726a20efa74316138df",
+ "8b72b915d8e84d8c95b8a89d48c400e2",
+ "7725d7ff977b4c6a95df2f88f96018ef",
+ "724ae7a08dd044e4be0fe899d6669afa",
+ "72b8b2332d24489dbfbc9edb9ecadbad",
+ "377abb647c304782bb3d0e2d4aa551bd",
+ "4b5829de40334c8d80fe096319231b3b",
+ "de1d9c77afa941be846a7f82f2c121c6",
+ "cce7ca27217f4ab0ac8e4c043cfc5de5",
+ "19dd68bdb7db4e0e982883e62aac6b99"
+ ]
+ },
+ "id": "Qv5naM8UgQba",
+ "outputId": "ce6d210a-a3ec-4f70-ed37-6f430ce5b4c3"
+ },
+ "execution_count": 67,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0f92bbe647084ac7a97c9292e3c0af70"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.push_to_hub(\"CoExperiences/snowflake-l-marketing-tuned\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 67,
+ "referenced_widgets": [
+ "664a3cb4b96947768222ac30a8086d98",
+ "fcfb14670d89460eb5aafa0bd843ab24",
+ "bb27ae5f6ec84b0780343cc13258574d",
+ "b2efb830179c444f9763f929823b04da",
+ "937a1a058a03433d8b66499253ff14c3",
+ "aca6b53a48904038a9355feee145dd6b",
+ "eff07cd417134b0486aa31bcdf4b954c",
+ "776c7bec87184357bd09a3087b259c77",
+ "2d23870c721b4185902b03e8633bf1f6",
+ "f096f1a00be74f2d9482e059c2cb92c3",
+ "8f74ea4e7b59436ab27676c276efc792"
+ ]
+ },
+ "id": "P9dlCyWDe_xI",
+ "outputId": "500fe25c-a44f-4c8b-cf06-73bb9754c8e9"
+ },
+ "execution_count": 70,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model.safetensors: 0%| | 0.00/1.34G [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "664a3cb4b96947768222ac30a8086d98"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'https://huggingface.co/CoExperiences/snowflake-l-marketing-tuned/commit/b7bb4807d84ed09fb807256fdd7fa826dcba60b5'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 70
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file