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Merge pull request #299 from ken-027/main

Week 1 Contribution to the LLM Engineering Course
pull/312/merge
Ed Donner 4 weeks ago committed by GitHub
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  1. 274
      week1/community-contributions/day2_exercise.ipynb
  2. 3876
      week1/community-contributions/day5_ollama_version.ipynb
  3. 290
      week2/community-contributions/tool_integration_gradio_using_anthropic_api.ipynb

274
week1/community-contributions/day2_exercise.ipynb

@ -0,0 +1,274 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "41136d6f-07bc-4f6f-acba-784b8e5707b1",
"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": "8612b4f7-5c31-48f3-8423-261914509617",
"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": "508bd442-7860-4215-b0f2-57f7adefd807",
"metadata": {},
"outputs": [],
"source": [
"# Create a messages list using the same format that we used for OpenAI\n",
"\n",
"messages = [\n",
" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc7e8ada-4f8d-4090-be64-4aa72e03ac58",
"metadata": {},
"outputs": [],
"source": [
"# Let's just make sure the model is loaded\n",
"\n",
"!ollama pull llama3.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4afd2e56-191a-4e31-949e-9b9376a39b5a",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# There's actually an alternative approach that some people might prefer\n",
"# You can use the OpenAI client python library to call Ollama:\n",
"\n",
"from openai import OpenAI\n",
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
"\n",
"response = ollama_via_openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=messages\n",
")\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "365f3d83-2601-42fb-89cc-98a4e1f79e0d",
"metadata": {},
"outputs": [],
"source": [
"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
"response = ollama_via_openai.chat.completions.create(model=MODEL, messages=[{\"role\":\"user\", \"content\":message}])\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "29c383ae-bf5b-41bc-b5af-a22f851745dc",
"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": "dc61e30f-653f-4554-b1cd-6e61a0e2430a",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"ed = Website(\"https://edwarddonner.com\")\n",
"print(ed.title)\n",
"print(ed.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db2066fb-3079-4775-832a-dcc0f19beb6e",
"metadata": {},
"outputs": [],
"source": [
"\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": "af81b070-b6fe-4b18-aa0b-c03cd76a0adf",
"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": "4e66291b-23b1-4915-b6a3-11a4b6a4db66",
"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": "67c92f47-4a3b-491f-af00-07fda470087e",
"metadata": {},
"outputs": [],
"source": [
"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": "db1b9085-e5e7-4ec9-a264-acc389085ada",
"metadata": {},
"outputs": [],
"source": [
"messages_for(ed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "677bfc2f-19ac-46a0-b67e-a2b2ddf9cf6b",
"metadata": {},
"outputs": [],
"source": [
"def summarize(url):\n",
" website = Website(url)\n",
" response = ollama_via_openai.chat.completions.create(\n",
" model = MODEL,\n",
" messages = messages_for(website)\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee3242ba-b695-4b1e-8a91-2fdeb536c2e7",
"metadata": {},
"outputs": [],
"source": [
"summarize(\"https://edwarddonner.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85142cb8-ce0c-4c31-8b26-bb1744cf99ec",
"metadata": {},
"outputs": [],
"source": [
"def display_summary(url):\n",
" summary = summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63db51a7-dd03-4514-8954-57156967f82c",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"display_summary(\"https://app.daily.dev/posts/bregman-arie-devops-exercises-linux-jenkins-aws-sre-prometheus-docker-python-ansible-git-k-yli9wthnf\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "conda-base-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

3876
week1/community-contributions/day5_ollama_version.ipynb

File diff suppressed because one or more lines are too long

290
week2/community-contributions/tool_integration_gradio_using_anthropic_api.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "3f9b483c-f410-4ad3-8f3a-e33527f30f8a",
"metadata": {
"panel-layout": {
"height": 68.2639,
"visible": true,
"width": 100
}
},
"source": [
"# Project - Laptops Assistant\n",
"\n",
"A simple inventory tool integrated with Anthropic API"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cfaff08d-f6e5-4d2d-bfb8-76c154836f3d",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"import anthropic\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a04047ea-d01b-469b-93ce-ab4f4e36ca1e",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"# Print the key prefixes to help with any debugging\n",
"\n",
"load_dotenv(override=True)\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"\n",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5e00ced-f47b-4713-8174-7901e1a69881",
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI, Anthropic and Google; comment out the Claude or Google lines if you're not using them\n",
"\n",
"claude = anthropic.Anthropic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c715efd-cebf-4dc2-8c99-798f3179dd21",
"metadata": {},
"outputs": [],
"source": [
"MODEL = \"claude-3-haiku-20240307\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b029d1d-9199-483a-94b7-893680af8ad1",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a helpful assistant for an Inventory Sales called InvAI. \"\n",
"system_message += \"Give short, courteous answers, no more than 1 sentence. \"\n",
"system_message += \"Always be accurate. If you don't know the answer, say so.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8ca1197c-e6a1-4579-96c6-24e8e305cc72",
"metadata": {},
"outputs": [],
"source": [
"laptop_items = [\n",
" {\n",
" \"model\": \"Aspire 3 A315-59-570Z OPI Pure Silver\", \n",
" \"brand\": \"Acer\",\n",
" \"price\": \"$595.96\"\n",
" },\n",
" {\n",
" \"model\": \"Aspire Lite 14 AL14-31P-36BE Pure Silver\", \n",
" \"brand\": \"Acer\",\n",
" \"price\": \"$463.52\"\n",
" },\n",
" {\n",
" \"model\": \"Raider 18 HX\",\n",
" \"brand\": \"MSI\",\n",
" \"price\": \"$235.25\"\n",
" }\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d2bc76b-c1d0-4b3d-a299-9972f7687e4c",
"metadata": {},
"outputs": [],
"source": [
"def get_laptop_price(model):\n",
" print(f\"Tool get_laptop_price called for laptop model {model}\")\n",
" laptop_model = model.lower()\n",
" for item in laptop_items:\n",
" if laptop_model in item.get(\"model\").lower():\n",
" return item\n",
" return \"Unknown\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afc9b4a3-3a6f-4839-bebc-89bd598394fd",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# get_laptop_price(\"Lite 14 AL14-31P-36BE Pure SilveR\")\n",
"\n",
"get_laptop_price(\"Aspire Lite 14\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12190074-fad8-43f6-8be1-f96a08c16b59",
"metadata": {},
"outputs": [],
"source": [
"# There's a particular dictionary structure that's required to describe our function:\n",
"\n",
"price_function = {\n",
" \"name\": \"get_laptop_price\",\n",
" \"description\": (\n",
" \"Returns the laptop's price, brand, and exact model from a given query.\"\n",
" \"Use when the user asks about a laptop's price, e.g.,\"\n",
" \"'How much is this laptop?' → 'The Acer Aspire Lite 14 AL14-31P-36BE Pure Silver is priced at $463.52.'\"\n",
" ),\n",
" \"input_schema\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"model\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The model name of the laptop the customer is asking about.\"\n",
" }\n",
" },\n",
" \"required\": [\"model\"]\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "475195e1-dd78-45ba-af6d-16d7cf5c85ae",
"metadata": {},
"outputs": [],
"source": [
"# And this is included in a list of tools:\n",
"\n",
"tools = [price_function]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3834314d-fd37-4e27-9511-bd519389b31b",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history):\n",
" print(history)\n",
" messages = [{\"role\": \"user\", \"content\": message}]\n",
"\n",
" for history_message in history:\n",
" if history_message[\"role\"] == \"user\":\n",
" messages.append({\"role\": \"user\", \"content\": history_message[\"content\"]})\n",
" \n",
" response = claude.messages.create(model=MODEL, messages=messages, tools=tools, max_tokens=500)\n",
"\n",
" if len(response.content) > 1:\n",
" assistant, user, laptop_model = handle_tool_call(response)\n",
" messages.append(assistant)\n",
" messages.append(user)\n",
" response = claude.messages.create(model=MODEL, messages=messages, tools=tools, max_tokens=500)\n",
"\n",
"\n",
" return response.content[0].text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "745a9bf8-6ceb-4c1c-bfbf-b0d1f3d5d6fc",
"metadata": {},
"outputs": [],
"source": [
"# We have to write that function handle_tool_call:\n",
"\n",
"def handle_tool_call(message):\n",
" # laptop_model = message\n",
" laptop_model = message.content[1].input.get(\"model\")\n",
" laptop_item = get_laptop_price(laptop_model)\n",
" assistant = {\n",
" \"role\": \"assistant\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": message.content[0].text\n",
" },\n",
" {\n",
" \"type\": \"tool_use\",\n",
" \"id\": message.content[1].id,\n",
" \"name\": message.content[1].name,\n",
" \"input\": message.content[1].input\n",
" }\n",
" ]\n",
" }\n",
" user = {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"tool_result\",\n",
" \"tool_use_id\": message.content[1].id,\n",
" # \"content\": laptop_item.get(\"price\")\n",
" \"content\": json.dumps(laptop_item)\n",
" }\n",
" ]\n",
" }\n",
" \n",
"\n",
" return assistant, user, laptop_model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9408eeb4-d07b-4193-92cd-197610ed942e",
"metadata": {},
"outputs": [],
"source": [
"gr.ChatInterface(fn=chat, type=\"messages\").launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "conda-base-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
},
"panel-cell-order": [
"3f9b483c-f410-4ad3-8f3a-e33527f30f8a"
]
},
"nbformat": 4,
"nbformat_minor": 5
}
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