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| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/compustar/c217ea659660e1988dde66b39b7e4653/employee_handbook_qna_gpt3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "id": "LLjqAc2shLiW" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "!pip install -U transformers sentence-transformers openai gradio --quiet\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!wget https://gist.githubusercontent.com/compustar/250b8aaef7f490037b7414fac94f1c50/raw/586193049a425c44418526a69b0daee03ecb81c7/HKIHRMEmployeeHandbook.txt" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "CrVX1zN3jNEM", | |
| "outputId": "f04c2ed1-f3f9-44f3-a1db-661e744ff6da" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--2023-01-25 06:25:13-- https://gist.githubusercontent.com/compustar/250b8aaef7f490037b7414fac94f1c50/raw/586193049a425c44418526a69b0daee03ecb81c7/HKIHRMEmployeeHandbook.txt\n", | |
| "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", | |
| "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.108.133|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 53190 (52K) [text/plain]\n", | |
| "Saving to: ‘HKIHRMEmployeeHandbook.txt.1’\n", | |
| "\n", | |
| "\r HKIHRMEmp 0%[ ] 0 --.-KB/s \rHKIHRMEmployeeHandb 100%[===================>] 51.94K --.-KB/s in 0.004s \n", | |
| "\n", | |
| "2023-01-25 06:25:13 (14.0 MB/s) - ‘HKIHRMEmployeeHandbook.txt.1’ saved [53190/53190]\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "with open('HKIHRMEmployeeHandbook.txt') as f:\n", | |
| " full_text = f.read()" | |
| ], | |
| "metadata": { | |
| "id": "b650X5jfjLeC" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "passages = [line.strip() for line in full_text.split('\\n \\n') if len(line.strip()) > 0]" | |
| ], | |
| "metadata": { | |
| "id": "hVGMmXPAjqxw" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import openai\n", | |
| "\n", | |
| "openai.api_key = input(\"OpenAI API Key: \")" | |
| ], | |
| "metadata": { | |
| "id": "6CNtFeHxu2x1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sentence_transformers import SentenceTransformer, util\n", | |
| "from transformers import GPT2TokenizerFast\n", | |
| "\n", | |
| "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n", | |
| "model_name = 'multi-qa-mpnet-base-cos-v1'\n", | |
| "bi_encoder = SentenceTransformer(model_name)\n", | |
| "top_k = 10 # Number of passages we want to retrieve with the bi-encoder\n", | |
| "corpus_embeddings = bi_encoder.encode(passages, convert_to_tensor=True, show_progress_bar=True)\n" | |
| ], | |
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| { | |
| "cell_type": "code", | |
| "source": [ | |
| "query = 'what should I do if I worked overtime?'\n", | |
| "\n", | |
| "# Encode the query using the bi-encoder and find potentially relevant passages\n", | |
| "question_embedding = bi_encoder.encode(query, convert_to_tensor=True)\n", | |
| "hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)\n", | |
| "hits = [hit for hit in hits[0]] # Get the hits for the first query\n", | |
| "\n", | |
| "hits = sorted([hit['corpus_id'] for hit in hits])\n", | |
| "context = \"\\n\".join([passages[hit] for hit in hits])\n", | |
| "\n", | |
| "print(context)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "enCZYByMqz7k", | |
| "outputId": "06bcc45f-6c95-4c5d-b6f5-0e7c34ab30dd" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "2.2 Working Hours\n", | |
| "3.2 Overtime Compensation\n", | |
| "Prior approval must be obtained from respective supervisor for working overtime. Overtime for employees at grade 7-10 will be compensated by pay at the following rates:\n", | |
| "Category 1 \n", | |
| "a)\tFor non-shift employees, Monday to Friday starting from an hour after normal working hour to 12:00 midnight and Saturday after 2:00 p.m. \n", | |
| "b)\tFor shift employees, overtime worked beyond his \n", | |
| "shift \n", | |
| " \t \n", | |
| "1.5 times hourly rate\n", | |
| "Overtime for employees at grade 4-6 will be compensated by way of time off. Such compensation leave will only be granted when their department’ s workload permits. Any overtime not compensated in the form of leave by the end of the calendar year will be paid in the following February at the rate of 1.5 times the employee's hourly rate. \n", | |
| " \n", | |
| "3.3 Annual Bonus\n", | |
| "Employees who work overtime in the following conditions are entitled to claim for meal allowance:\n", | |
| "-\tovertime for 3 consecutive hours from Monday to Saturday; \n", | |
| "-\tovertime for 6 consecutive hours on Sunday or public holidays\n", | |
| "Employees who are required to report for duty outside office hours in case of emergency will be entitled to an emergency allowance and compensation leave\n", | |
| "4.5 Compensation Leave or Time-off for Overtime Work\n", | |
| "Employees at grade 4-6 who have worked overtime will be compensated by way of time off. Such compensation leave will only be granted when their department’ s workload permits. Please also refer to “Overtime Compensation” under Section 3.2 of this handbook for details.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# instruction_suffix = '. If the answer cannot be found in the context, rewrite the question that is more related to the context by starting with \"Do you want to ask\" and then elaborate the answer'\n", | |
| "# instruction_suffix = '. If the answer cannot be found in the context, write \"I don\\'t know\"'\n", | |
| "template = \"\"\"Context:\n", | |
| "<<context>>\n", | |
| "\n", | |
| "Answer the following question:\n", | |
| "Q: <<query>>\n", | |
| "A: \n", | |
| "\"\"\"\n", | |
| "prompt = template.replace('<<context>>', context).replace('<<query>>', query)\n", | |
| "prompt_length = len(tokenizer(prompt)['input_ids'])\n", | |
| "print(prompt_length)\n", | |
| "print(prompt)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "RargfxuAu6gb", | |
| "outputId": "5bb63146-2340-46e0-b57e-b23e1b226030" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "383\n", | |
| "Context:\n", | |
| "2.2 Working Hours\n", | |
| "3.2 Overtime Compensation\n", | |
| "Prior approval must be obtained from respective supervisor for working overtime. Overtime for employees at grade 7-10 will be compensated by pay at the following rates:\n", | |
| "Category 1 \n", | |
| "a)\tFor non-shift employees, Monday to Friday starting from an hour after normal working hour to 12:00 midnight and Saturday after 2:00 p.m. \n", | |
| "b)\tFor shift employees, overtime worked beyond his \n", | |
| "shift \n", | |
| " \t \n", | |
| "1.5 times hourly rate\n", | |
| "Overtime for employees at grade 4-6 will be compensated by way of time off. Such compensation leave will only be granted when their department’ s workload permits. Any overtime not compensated in the form of leave by the end of the calendar year will be paid in the following February at the rate of 1.5 times the employee's hourly rate. \n", | |
| " \n", | |
| "3.3 Annual Bonus\n", | |
| "Employees who work overtime in the following conditions are entitled to claim for meal allowance:\n", | |
| "-\tovertime for 3 consecutive hours from Monday to Saturday; \n", | |
| "-\tovertime for 6 consecutive hours on Sunday or public holidays\n", | |
| "Employees who are required to report for duty outside office hours in case of emergency will be entitled to an emergency allowance and compensation leave\n", | |
| "4.5 Compensation Leave or Time-off for Overtime Work\n", | |
| "Employees at grade 4-6 who have worked overtime will be compensated by way of time off. Such compensation leave will only be granted when their department’ s workload permits. Please also refer to “Overtime Compensation” under Section 3.2 of this handbook for details.\n", | |
| "\n", | |
| "Answer the following question:\n", | |
| "Q: what should I do if I worked overtime?\n", | |
| "A: \n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = openai.Completion.create(engine=\"text-davinci-003\", prompt=prompt, max_tokens=4096-prompt_length, temperature=0.2)\n", | |
| "print(response)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "gpBDc6dWrR5c", | |
| "outputId": "4a887a4e-9ced-4168-eb66-a4eae141e80c" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "{\n", | |
| " \"choices\": [\n", | |
| " {\n", | |
| " \"finish_reason\": \"stop\",\n", | |
| " \"index\": 0,\n", | |
| " \"logprobs\": null,\n", | |
| " \"text\": \"If you are an employee at grade 7-10, you should obtain prior approval from your supervisor and you will be compensated by pay at the rate of 1.5 times your hourly rate. If you are an employee at grade 4-6, you will be compensated by way of time off. Such compensation leave will only be granted when your department\\u2019s workload permits. Any overtime not compensated in the form of leave by the end of the calendar year will be paid in the following February at the rate of 1.5 times your hourly rate.\"\n", | |
| " }\n", | |
| " ],\n", | |
| " \"created\": 1674628000,\n", | |
| " \"id\": \"cmpl-6cTWyVHTDfzbpUDRzlQj29i1lBSBp\",\n", | |
| " \"model\": \"text-davinci-003\",\n", | |
| " \"object\": \"text_completion\",\n", | |
| " \"usage\": {\n", | |
| " \"completion_tokens\": 110,\n", | |
| " \"prompt_tokens\": 378,\n", | |
| " \"total_tokens\": 488\n", | |
| " }\n", | |
| "}\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import textwrap\n", | |
| "print(\"\\n\".join(textwrap.wrap(response['choices'][0]['text'])))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Y0_MTwt78vfT", | |
| "outputId": "3cd6a78c-e26f-45ec-861a-75a976e66582" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "If you are an employee at grade 7-10, you should obtain prior approval\n", | |
| "from your supervisor and you will be compensated by pay at the rate of\n", | |
| "1.5 times your hourly rate. If you are an employee at grade 4-6, you\n", | |
| "will be compensated by way of time off. Such compensation leave will\n", | |
| "only be granted when your department’s workload permits. Any overtime\n", | |
| "not compensated in the form of leave by the end of the calendar year\n", | |
| "will be paid in the following February at the rate of 1.5 times your\n", | |
| "hourly rate.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def answer(query):\n", | |
| " # Encode the query using the bi-encoder and find potentially relevant passages\n", | |
| " question_embedding = bi_encoder.encode(query, convert_to_tensor=True)\n", | |
| " hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)\n", | |
| " hits = [hit for hit in hits[0] if hit['score']] # Get the hits for the first query\n", | |
| "\n", | |
| " hits = sorted([hit['corpus_id'] for hit in hits])\n", | |
| " context = \"\\n\".join([passages[hit] for hit in hits])\n", | |
| "\n", | |
| " template = \"\"\"Context:\n", | |
| "<<context>>\n", | |
| "\n", | |
| "Answer the following question by paraphrasing it and then elaborate the answer:\n", | |
| "Q: <<query>>\n", | |
| "A: \n", | |
| "\"\"\"\n", | |
| " prompt = template.replace('<<context>>', context).replace('<<query>>', query)\n", | |
| " prompt_length = len(tokenizer(prompt)['input_ids'])\n", | |
| " response = openai.Completion.create(engine=\"text-davinci-003\", prompt=prompt, max_tokens=4096-prompt_length, temperature=0.2)\n", | |
| " return response['choices'][0]['text']\n", | |
| "\n", | |
| "query = \"\"\n", | |
| "while True:\n", | |
| " query = input(\"Q: \")\n", | |
| " if query == \"xxx\": break\n", | |
| " ans = \"\\n\".join(textwrap.wrap(answer(query)))\n", | |
| " print(f\"A: {ans}\")\n", | |
| " print(\"=\"*70)\n", | |
| " print()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "0zwRVSlD9DNc", | |
| "outputId": "09b3ac2a-6229-4f0a-a6e2-8377d3e92c68" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Q: do I get paid if I got sick?\n", | |
| "A: Yes, employees who are not able to report for duty due to illness are\n", | |
| "entitled to full pay sick leave, subject to a maximum of the lesser of\n", | |
| "120 days or the number of sickness days that the employee has\n", | |
| "accumulated according to provisions in the Employment Ordinance.\n", | |
| "Additionally, employees who suffer from injury arising out of and in\n", | |
| "the course of employment are entitled to compensation in accordance\n", | |
| "with the Employee Compensation Ordinance.\n", | |
| "======================================================================\n", | |
| "\n", | |
| "Q: what should I do if I got sick?\n", | |
| "A: If I become ill, I should notify my immediate supervisor as soon as\n", | |
| "possible and provide a medical certificate if necessary. I am entitled\n", | |
| "to full pay sick leave, subject to a maximum of 120 days or the number\n", | |
| "of sickness days I have accumulated according to the Employment\n", | |
| "Ordinance. If I have to leave during office hours due to sickness or\n", | |
| "personal reasons, I must obtain prior approval from either my\n", | |
| "supervisor, department manager or the Human Resources Department.\n", | |
| "======================================================================\n", | |
| "\n", | |
| "Q: xxx\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#@title GUI\n", | |
| "import gradio as gr\n", | |
| "examples = [\n", | |
| " [\"can I carry forward annual leave?\"],\n", | |
| " [\"do I entitle to compensation leave?\"],\n", | |
| " [\"how many days of annual leave do I have?\"],\n", | |
| " [\"how to reimburse the company expenses?\"],\n", | |
| " [\"do I get paid if I got sick?\"],\n", | |
| " [\"what should I do if I got sick?\"],\n", | |
| "]\n", | |
| "\n", | |
| "title = \"Q&A Demo\"\n", | |
| "\n", | |
| "def inference(text):\n", | |
| " return answer(text)\n", | |
| "\n", | |
| "io = gr.Interface(\n", | |
| " inference,\n", | |
| " gr.Textbox(lines=3),\n", | |
| " outputs=[\n", | |
| " gr.Textbox(lines=3, label=\"GPT 3.5\")\n", | |
| " ],\n", | |
| " title=title,\n", | |
| " examples=examples\n", | |
| ")\n", | |
| "io.launch()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 619 | |
| }, | |
| "id": "u2mJxMdgbLP8", | |
| "outputId": "ac721342-609b-4f2a-f426-c59358639d96" | |
| }, | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", | |
| "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n", | |
| "\n", | |
| "To create a public link, set `share=True` in `launch()`.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.Javascript object>" | |
| ], | |
| "application/javascript": [ | |
| "(async (port, path, width, height, cache, element) => {\n", | |
| " if (!google.colab.kernel.accessAllowed && !cache) {\n", | |
| " return;\n", | |
| " }\n", | |
| " element.appendChild(document.createTextNode(''));\n", | |
| " const url = await google.colab.kernel.proxyPort(port, {cache});\n", | |
| "\n", | |
| " const external_link = document.createElement('div');\n", | |
| " external_link.innerHTML = `\n", | |
| " <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n", | |
| " Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n", | |
| " https://localhost:${port}${path}\n", | |
| " </a>\n", | |
| " </div>\n", | |
| " `;\n", | |
| " element.appendChild(external_link);\n", | |
| "\n", | |
| " const iframe = document.createElement('iframe');\n", | |
| " iframe.src = new URL(path, url).toString();\n", | |
| " iframe.height = height;\n", | |
| " iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n", | |
| " iframe.width = width;\n", | |
| " iframe.style.border = 0;\n", | |
| " element.appendChild(iframe);\n", | |
| " })(7860, \"/\", \"100%\", 500, false, window.element)" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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