Skip to content

Day1_testing_in_llama.ipynb #275

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
+1,437 −14
Open

Day1_testing_in_llama.ipynb #275

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
357 changes: 357 additions & 0 deletions week1/Day1_testing_in_llama.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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 changes: 13 additions & 2 deletions week1/Guide to Jupyter.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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",
Expand Down
Loading