@ -220,7 +220,12 @@
"cell_type": "code",
"cell_type": "code",
"execution_count": null,
"execution_count": null,
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
"metadata": {},
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"outputs": [],
"source": [
"source": [
"!ollama pull deepseek-r1:1.5b"
"!ollama pull deepseek-r1:1.5b"
@ -253,10 +258,230 @@
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
]
]
},
},
{
"cell_type": "markdown",
"id": "ffaa3470-884c-467e-b4ce-c1b8d39294da",
"metadata": {},
"source": [
"This is the code from day 1 notebook. Here we create the class to extract the text from the website, using BeautifulSoup library, and the we execute it to see the the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d8c9f01-ca12-4018-b7fa-698c9fa1aa93",
"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": "6fd198df-bac5-42c5-83a0-06c5f71fb76a",
"metadata": {},
"outputs": [],
"source": [
"# Let's try one out. Change the website and add print statements to follow along.\n",
"\n",
"ed = Website(\"https://edwarddonner.com\")\n",
"print(ed.title)\n",
"print(ed.text)"
]
},
{
"cell_type": "markdown",
"id": "995b637d-a5db-4ad9-ac78-5980fd7ef112",
"metadata": {},
"source": [
"#### Define the system prompt, to instruct the model how we want to respond to our query. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee810d49-e88a-4137-a4be-98812e0d0748",
"metadata": {},
"outputs": [],
"source": [
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
"\n",
"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": "482b5d4c-69ed-4332-abb5-8b0986dcf368",
"metadata": {},
"outputs": [],
"source": [
"# A function that writes a User Prompt that asks for summaries of websites:\n",
"\n",
"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": "d966cb09-3ca2-49f7-8462-f6ef26c01159",
"metadata": {},
"outputs": [],
"source": [
"print(user_prompt_for(ed))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f9be84f-4cd7-4ce7-8f33-e60d16f02852",
"metadata": {},
"outputs": [],
"source": [
"# For test purpose\n",
"\n",
"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": "f5cb0e9f-eb56-4633-ba4c-76817be98856",
"metadata": {},
"outputs": [],
"source": [
"# To give you a preview -- calling ollama with system and user messages:\n",
"\n",
"import ollama\n",
"\n",
"response = ollama.chat(model=MODEL, messages=messages)\n",
"print(response['message']['content'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c554903f-eb04-4a16-87fc-f1d9ff58f6d9",
"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": "6b64b814-123f-436d-9366-4c762ac4b89a",
"metadata": {},
"outputs": [],
"source": [
"# Try this out, and then try for a few more websites\n",
"\n",
"messages_for(ed)"
]
},
{
"cell_type": "markdown",
"id": "d1ef4be2-ef3a-4b5d-8d18-f2eafa9d6a93",
"metadata": {},
"source": [
"### So, here let's run the summarize by using ollama and see how appears."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c46edc5-c85d-4ad0-89fd-39c4fdc44a5d",
"metadata": {},
"outputs": [],
"source": [
"# And now: call the ollama API. \n",
"\n",
"def summarize(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model = MODEL,\n",
" messages = messages_for(website)\n",
" )\n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "466c2f78-91ca-4ed2-b60b-40661d0b6f68",
"metadata": {},
"outputs": [],
"source": [
"summarize(\"https://edwarddonner.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ab7c9a1-70fd-421c-be06-c36eb6c9aedf",
"metadata": {},
"outputs": [],
"source": [
"# A function to display this nicely in the Jupyter output, using markdown\n",
"\n",
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1cedc9d9-6a76-4225-82c1-82240da16260",
"metadata": {},
"outputs": [],
"source": [
"display_summary(\"https://edwarddonner.com\")"
]
},
{
{
"cell_type": "code",
"cell_type": "code",
"execution_count": null,
"execution_count": null,
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
"id": "82c48586-33c8-4797-a24f-41602c1297b3 ",
"metadata": {},
"metadata": {},
"outputs": [],
"outputs": [],
"source": []
"source": []
@ -264,7 +489,7 @@
],
],
"metadata": {
"metadata": {
"kernelspec": {
"kernelspec": {
"display_name": "Python 3 (ipykernel) ",
"display_name": "llms ",
"language": "python",
"language": "python",
"name": "python3"
"name": "python3"
},
},