diff --git a/week1/community-contributions/brochure-builder-with-multishot-prompting.ipynb b/week1/community-contributions/brochure-builder-with-multishot-prompting.ipynb new file mode 100644 index 0000000..3427a82 --- /dev/null +++ b/week1/community-contributions/brochure-builder-with-multishot-prompting.ipynb @@ -0,0 +1,402 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9905f163-759f-474b-8f7a-7d14da0df44d", + "metadata": {}, + "source": [ + "### BUSINESS CHALLENGE: Using Multi-shot Prompting\n", + "#### Day 5\n", + "\n", + "Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n", + "\n", + "We will be provided a company name and their primary website." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0895f24-65ff-4624-8ae0-15d2d400d8f0", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n", + "\n", + "import os\n", + "import requests\n", + "import json\n", + "from typing import List\n", + "from dotenv import load_dotenv\n", + "from bs4 import BeautifulSoup\n", + "from IPython.display import Markdown, display, update_display\n", + "from openai import OpenAI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7794aa70-5962-4669-b86f-b53639f4f9ea", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize and constants\n", + "\n", + "load_dotenv(override=True)\n", + "api_key = os.getenv('OPENAI_API_KEY')\n", + "\n", + "if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", + " print(\"API key looks good so far\")\n", + "else:\n", + " print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", + " \n", + "MODEL = 'gpt-4o-mini'\n", + "openai = OpenAI()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63bf8631-2746-4255-bec1-522855d3e812", + "metadata": {}, + "outputs": [], + "source": [ + "# A class to represent a Webpage\n", + "\n", + "# Some websites need you to use proper headers when fetching them:\n", + "headers = {\n", + " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", + "}\n", + "\n", + "class Website:\n", + " \"\"\"\n", + " A utility class to represent a Website that we have scraped, now with links\n", + " \"\"\"\n", + "\n", + " def __init__(self, url):\n", + " self.url = url\n", + " response = requests.get(url, headers=headers)\n", + " self.body = response.content\n", + " soup = BeautifulSoup(self.body, 'html.parser')\n", + " self.title = soup.title.string if soup.title else \"No title found\"\n", + " if soup.body:\n", + " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", + " irrelevant.decompose()\n", + " self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n", + " else:\n", + " self.text = \"\"\n", + " links = [link.get('href') for link in soup.find_all('a')]\n", + " self.links = [link for link in links if link]\n", + "\n", + " def get_contents(self):\n", + " return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\"" + ] + }, + { + "cell_type": "markdown", + "id": "1e7bb527-e769-4245-bb91-ae65e64593ff", + "metadata": {}, + "source": [ + "## First step: Have GPT-4o-mini figure out which links are relevant\n", + "\n", + "### Use a call to gpt-4o-mini to read the links on a webpage, and respond in structured JSON. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ce303ae-b967-4261-aadc-02dafa54db4a", + "metadata": {}, + "outputs": [], + "source": [ + "link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n", + "You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n", + "such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n", + "link_system_prompt += \"You should respond in JSON as in this example:\"\n", + "link_system_prompt += \"\"\"\n", + "{\n", + " \"links\": [\n", + " {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n", + " {\"type\": \"careers page\", \"url\": \"https://another.full.url/careers\"}\n", + " ]\n", + "}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d24a4c0c-a1d1-4897-b2a7-4128d25c2e08", + "metadata": {}, + "outputs": [], + "source": [ + "def get_links_user_prompt(website):\n", + " user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n", + " user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n", + "Do not include Terms of Service, Privacy, email links.\\n\"\n", + " user_prompt += \"Links (some might be relative links):\\n\"\n", + " user_prompt += \"\\n\".join(website.links)\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8103fc11-5bc0-41c4-8c97-502c9e96429c", + "metadata": {}, + "outputs": [], + "source": [ + "def get_links(url): # 1st inference\n", + " website = Website(url)\n", + " response = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": link_system_prompt},\n", + " {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n", + " ],\n", + " response_format={\"type\": \"json_object\"}\n", + " )\n", + " result = response.choices[0].message.content\n", + " return json.loads(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc84a695-515d-4292-9a95-818f4fe3d20e", + "metadata": {}, + "outputs": [], + "source": [ + "huggingface = Website(\"https://huggingface.co\")" + ] + }, + { + "cell_type": "markdown", + "id": "91896908-1632-41fc-9b8b-39a7638d8dd1", + "metadata": {}, + "source": [ + "## Second step: make the brochure!\n", + "\n", + "Assemble all the details into another prompt to GPT4-o" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab7c54e3-e654-4b1f-8671-09194b628aa0", + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_details(url): # 1st inference wrapper\n", + " result = \"Landing page:\\n\"\n", + " result += Website(url).get_contents()\n", + " links = get_links(url) # inference\n", + " # print(\"Found links:\", links)\n", + " for link in links[\"links\"]:\n", + " result += f\"\\n\\n{link['type']}\\n\"\n", + " result += Website(link[\"url\"]).get_contents()\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea9f54d1-a248-4c56-a1de-6633193de5bf", + "metadata": {}, + "outputs": [], + "source": [ + "system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n", + "and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n", + "Include details of company culture, customers and careers/jobs if you have the information.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13412c85-badd-4d79-a5ac-8283e4bb832f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_brochure_user_prompt(company_name, url):\n", + " user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n", + " user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n", + " user_prompt += get_all_details(url) # inference wrapper\n", + " user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "addc0047-ea73-4748-abc3-747ff343c134", + "metadata": {}, + "outputs": [], + "source": [ + "def create_brochure(company_name, url): # 2nd inference\n", + " response = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", + " ],\n", + " )\n", + " result = response.choices[0].message.content\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82a3b61a-da26-4265-840a-0a93f81cd048", + "metadata": {}, + "outputs": [], + "source": [ + "brochure_english = create_brochure(\"HuggingFace\", \"https://huggingface.co\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d165e3f-8fe2-4712-b098-d34d9fabe583", + "metadata": {}, + "outputs": [], + "source": [ + "display(Markdown(brochure_english))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "107a2100-3f7d-4f16-8ba7-b5da602393c6", + "metadata": {}, + "outputs": [], + "source": [ + "def stream_brochure(company_name, url):\n", + " stream = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n", + " ],\n", + " stream=True\n", + " )\n", + " \n", + " response = \"\"\n", + " display_handle = display(Markdown(\"\"), display_id=True)\n", + " for chunk in stream:\n", + " response += chunk.choices[0].delta.content or ''\n", + " response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", + " update_display(Markdown(response), display_id=display_handle.display_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26cbe9b5-3603-49a1-a676-75c7ddaacdb8", + "metadata": {}, + "outputs": [], + "source": [ + "stream_brochure(\"HuggingFace\", \"https://huggingface.co\")" + ] + }, + { + "cell_type": "markdown", + "id": "c10d8189-7f79-4991-abc4-0764369b7d64", + "metadata": {}, + "source": [ + "### Third step: Translate the entire brochure to Spanish" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "666817eb-1e8b-4fee-bbab-c0dbfe2ea7c0", + "metadata": {}, + "outputs": [], + "source": [ + "system_prompt = \"You are an assistant that analyzes the contents of a brochure \\\n", + "and translates to Spanish. Respond in markdown.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c48adb12-bc3c-48f9-ab38-b7ca895195f6", + "metadata": {}, + "outputs": [], + "source": [ + "def translate_user_prompt(company_name, url):\n", + " user_prompt = f\"Please translate the following brochure content to Spanish\\n\"\n", + " user_prompt += create_brochure(company_name, url) # inference wrapper\n", + " # user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n", + " return user_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b92b61ac-3be3-4e84-9000-ec8233697b81", + "metadata": {}, + "outputs": [], + "source": [ + "translate_user_prompt(\"HuggingFace\", \"https://huggingface.co\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bfd04f4-4381-4730-ac5d-c9fa02f906df", + "metadata": {}, + "outputs": [], + "source": [ + "def translate_brochure(): # 3rd inference\n", + " stream = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": translate_user_prompt(\"HuggingFace\", \"https://huggingface.co\")}\n", + " ],\n", + " stream=True\n", + " )\n", + " \n", + " response = \"\"\n", + " display_handle = display(Markdown(\"\"), display_id=True)\n", + " for chunk in stream:\n", + " response += chunk.choices[0].delta.content or ''\n", + " response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", + " update_display(Markdown(response), display_id=display_handle.display_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb78ed28-9ecd-4c08-ae96-d7473cbc97dd", + "metadata": {}, + "outputs": [], + "source": [ + "translate_brochure()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}