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Merge 86922b8468 into abb462e53c

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tamizhavelsm 4 weeks ago committed by GitHub
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  1. 357
      week1/Day1_testing_in_llama.ipynb
  2. 15
      week1/Guide to Jupyter.ipynb
  3. 1079
      week1/day2 EXERCISE.ipynb

357
week1/Day1_testing_in_llama.ipynb

@ -0,0 +1,357 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "d5ce5658-9ba9-487c-bb31-f9030039990e",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eec2b3ad-7b6f-4fe1-9e38-9ddbacf6adc6",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv(override=True)\n",
"api_key=os.getenv(\"OPENAI_API_KEY\")\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
"elif api_key.strip()!=api_key:\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them\")\n",
"else:\n",
" print(\"API key found and looks good so far!\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fbb17244-b2b6-4494-969a-e0192115ef96",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "642d7ffb-94a9-40c5-bf1f-9bc4570efb58",
"metadata": {},
"outputs": [],
"source": [
"# Constants\n",
"\n",
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
"HEADERS = {\"Content-Type\": \"application/json\"}\n",
"MODEL = \"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c90b4384-1a4b-4cad-8d3d-331c46d4c7f1",
"metadata": {},
"outputs": [],
"source": [
"# Create a messages list using the same format that we used for OpenAI\n",
"\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Describelangflow\"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c604ab2-6c25-4c2f-a6a2-60540ff74ca0",
"metadata": {},
"outputs": [],
"source": [
"payload = {\n",
" \"model\": MODEL,\n",
" \"messages\": messages,\n",
" \"stream\": False\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5629b49f-6ca9-466f-ad60-4d40a3b15321",
"metadata": {},
"outputs": [],
"source": [
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
"print(response.json()['message']['content'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a88d730-1c7b-4e1e-b143-5a630a2f52bd",
"metadata": {},
"outputs": [],
"source": [
"import ollama\n",
"\n",
"response = ollama.chat(model=MODEL, messages=messages)\n",
"print(response['message']['content'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62155b78-de4a-435b-867b-35b6c4b21f48",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\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",
" def __init__(self, url):\n",
" \"\"\"\n",
" Create this Website object from the given url using the BeautifulSoup library\n",
" \"\"\"\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd93d7b6-916a-45b8-9dfe-822a15c9a501",
"metadata": {},
"outputs": [],
"source": [
"ed = Website(\"https://edwarddonner.com\")\n",
"print(ed.title)\n",
"print(ed.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eea40735-c580-4d25-9317-c951e53faceb",
"metadata": {},
"outputs": [],
"source": [
"# And now: call the OpenAI API. You will get very familiar with this!\n",
"\n",
"def summarize(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model = \"llama3.2\",\n",
" messages = messages_for(website)\n",
" )\n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf844513-2b74-4306-9328-573d4996b772",
"metadata": {},
"outputs": [],
"source": [
"summarize(\"https://edwarddonner.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5c35dfb-d38d-4c59-85ed-e7551175268b",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
"and provides a short summary, ignoring text that might be navigation related. \\\n",
"Respond in markdown.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca1fa5e7-44ce-45ae-a2ff-c38bc239f101",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52b5a899-3f96-4e45-849b-bba4a3f848de",
"metadata": {},
"outputs": [],
"source": [
"print(user_prompt_for(ed))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f11f9d7d-8eb5-410b-b27c-a3e086d5bf59",
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56c19a9c-4c69-4b6d-b9ac-c3b884efb8b1",
"metadata": {},
"outputs": [],
"source": [
" response = ollama.chat(\n",
" model = \"llama3.2\",\n",
" messages = messages\n",
" )\n",
" print(response['message']['content'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0c05859c-4978-4b32-b971-0ea5d0147f00",
"metadata": {},
"outputs": [],
"source": [
"# See how this function creates exactly the format above\n",
"\n",
"def messages_for(website):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a33dd65c-01f9-4a51-bc44-74427eafef42",
"metadata": {},
"outputs": [],
"source": [
"messages_for(ed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5593d92-2f3c-44c8-abdd-9089f04ce171",
"metadata": {},
"outputs": [],
"source": [
"def summarize(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model = \"llama3.2\",\n",
" messages = messages_for(website)\n",
" )\n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f75cc47-c03e-42fd-acdf-e980031c9976",
"metadata": {},
"outputs": [],
"source": [
"summarize(\"https://edwarddonner.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d156ede-c20f-4569-997f-cd61e7b9c667",
"metadata": {},
"outputs": [],
"source": [
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81f3d230-ed53-400f-b215-2ef9f6a92935",
"metadata": {},
"outputs": [],
"source": [
"display_summary(\"https://edwarddonner.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e17cd862-44e5-4af9-9dcc-7ee3d489a45d",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

15
week1/Guide to Jupyter.ipynb

@ -32,10 +32,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "33d37cd8-55c9-4e03-868c-34aa9cab2c80",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Click anywhere in this cell and press Shift + Return\n",
"\n",

1079
week1/day2 EXERCISE.ipynb

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