Browse Source

Package updates, more Ollama, fixes

pull/33/head
Edward Donner 5 months ago
parent
commit
7f8697654d
  1. 2
      README.md
  2. 38
      environment.yml
  3. 2
      week1/Guide to Jupyter.ipynb
  4. 2
      week1/Intermediate Python.ipynb
  5. 2
      week1/community-contributions/Week1-Challenge-Brochure-Translation.ipynb
  6. 2
      week1/community-contributions/day1-selenium-for-javascript-sites.ipynb
  7. 2
      week1/community-contributions/day5-improved-comments-spanish.ipynb
  8. 625
      week1/community-contributions/day5-stream.ipynb
  9. 68
      week1/day1.ipynb
  10. 41
      week1/day2 EXERCISE.ipynb
  11. 20
      week1/day5.ipynb
  12. 18
      week1/troubleshooting.ipynb
  13. 2
      week1/week1 EXERCISE.ipynb
  14. 2
      week2/community-contributions/Week2_Day2_AddGeminModel.ipynb
  15. 12
      week2/community-contributions/day1-azure-aws-ollama.ipynb
  16. 2
      week2/community-contributions/day1-with-3way.ipynb
  17. 2
      week2/community-contributions/day2.ipynb
  18. 2
      week2/community-contributions/day4.ipynb
  19. 2
      week2/community-contributions/task1.ipynb
  20. 52
      week2/day1.ipynb
  21. 3
      week2/day2.ipynb
  22. 2
      week2/day3.ipynb
  23. 9
      week2/day4.ipynb
  24. 8
      week2/day5.ipynb
  25. 2
      week2/week2 EXERCISE.ipynb
  26. 2
      week3/day1.ipynb
  27. 2
      week3/day2.ipynb
  28. 2
      week3/day3.ipynb
  29. 2
      week3/day4.ipynb
  30. 2
      week3/day5.ipynb
  31. 24
      week4/day3.ipynb
  32. 2
      week4/day4.ipynb
  33. BIN
      week4/optimized
  34. 85
      week4/optimized.cpp
  35. 2
      week5/community-contributions/day3 - extended for Obsidian files and separate ingestion.ipynb
  36. 2
      week5/community-contributions/day4 - taking advantage of separate ingestion.ipynb
  37. 2
      week5/day1.ipynb
  38. 10
      week5/day2.ipynb
  39. 2
      week5/day3.ipynb
  40. 17
      week5/day4.5.ipynb
  41. 2
      week5/day4.ipynb
  42. 14
      week5/day5.ipynb
  43. 2
      week6/day1.ipynb
  44. 5
      week8/agents/messaging_agent.py
  45. 2
      week8/day1.ipynb
  46. 2
      week8/day2.0.ipynb
  47. 2
      week8/day2.1.ipynb
  48. 2
      week8/day2.2.ipynb
  49. 2
      week8/day2.3.ipynb
  50. 2
      week8/day2.4.ipynb
  51. 2
      week8/day3.ipynb
  52. 2
      week8/day4.ipynb
  53. 22
      week8/day5.ipynb
  54. 18
      week8/memory.json

2
README.md

@ -17,7 +17,7 @@ https://edwarddonner.com/2024/11/13/llm-engineering-resources/
## Instant Gratification instructions for Week 1, Day 1
We will start the course by installing Ollama so you can see results immediately!
1. Download and install Ollama from https://ollama.com
1. Download and install Ollama from https://ollama.com noting that on a PC you might need to have administrator permissions for the install to work properly
2. On a PC, start a Command prompt / Powershell (Press Win + R, type `cmd`, and press Enter). On a Mac, start a Terminal (Applications > Utilities > Terminal).
3. Run `ollama run llama3.2` or for smaller machines try `ollama run llama3.2:1b`
4. If this doesn't work, you may need to run `ollama serve` in another Powershell (Windows) or Terminal (Mac), and try step 3 again

38
environment.yml

@ -7,44 +7,44 @@ dependencies:
- pip
- python-dotenv
- requests
- beautifulsoup4
- pydub
- numpy
- pandas
- scipy
- pytorch
- jupyterlab
- ipywidgets
- pyarrow
- anthropic
- google-generativeai
- matplotlib
- scikit-learn
- chromadb
- langchain
- langchain-text-splitters
- langchain-openai
- langchain-experimental
- langchain-chroma
- faiss-cpu
- tiktoken
- jupyter-dash
- plotly
- twilio
- duckdb
- feedparser
- sentencepiece
- pyarrow
- faiss-cpu
- pip:
- beautifulsoup4
- plotly
- bitsandbytes
- transformers
- sentence-transformers
- datasets
- accelerate
- sentencepiece
- bitsandbytes
- openai
- anthropic
- google-generativeai
- gradio
- gensim
- modal
- ollama
- psutil
- setuptools
- speedtest-cli
- speedtest-cli
- langchain
- langchain-core
- langchain-text-splitters
- langchain-openai
- langchain-chroma
- langchain-community
- faiss-cpu
- feedparser
- twilio
- pydub

2
week1/Guide to Jupyter.ipynb

@ -372,7 +372,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week1/Intermediate Python.ipynb

@ -462,7 +462,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week1/community-contributions/Week1-Challenge-Brochure-Translation.ipynb

@ -338,7 +338,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week1/community-contributions/day1-selenium-for-javascript-sites.ipynb

@ -376,7 +376,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week1/community-contributions/day5-improved-comments-spanish.ipynb

@ -274,7 +274,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

625
week1/community-contributions/day5-stream.ipynb

@ -0,0 +1,625 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"# A full business solution\n",
"\n",
"## Now we will take our project from Day 1 to the next level\n",
"\n",
"### BUSINESS CHALLENGE:\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.\n",
"\n",
"See the end of this notebook for examples of real-world business applications.\n",
"\n",
"And remember: I'm always available if you have problems or ideas! Please do reach out."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5b08506-dc8b-4443-9201-5f1848161363",
"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\n",
"\n",
"# from Kamran; to use Llama instead of chatgpt;\n",
"# imports\n",
"\n",
"import ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"# Initialize and constants\n",
"\n",
"# Commented out belwo lines;\n",
"# load_dotenv()\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()\n",
"\n",
"# Added by Kamran.\n",
"MODEL_LLAMA = 'llama3.2'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "106dd65e-90af-4ca8-86b6-23a41840645b",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\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)\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": "code",
"execution_count": null,
"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a",
"metadata": {},
"outputs": [],
"source": [
"ed = Website(\"https://edwarddonner.com\")\n",
"ed.links"
]
},
{
"cell_type": "markdown",
"id": "1771af9c-717a-4fca-bbbe-8a95893312c3",
"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. \n",
"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n",
"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n",
"\n",
"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n",
"\n",
"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6957b079-0d96-45f7-a26a-3487510e9b35",
"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": "b97e4068-97ed-4120-beae-c42105e4d59a",
"metadata": {},
"outputs": [],
"source": [
"print(link_system_prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3",
"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": "6bcbfa78-6395-4685-b92c-22d592050fd7",
"metadata": {},
"outputs": [],
"source": [
"print(get_links_user_prompt(ed))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a29aca19-ca13-471c-a4b4-5abbfa813f69",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"# Get Llama 3.2 to answer\n",
"\n",
"# def get_links(url):\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)\n",
"\n",
"def get_links(url):\n",
" website = Website(url)\n",
" response = ollama.chat(\n",
" model=MODEL_LLAMA,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
" ]\n",
" )\n",
" result = response['message']['content']\n",
" print(f\"About to parse this into json: {result}\")\n",
" return json.loads(result)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2",
"metadata": {},
"outputs": [],
"source": [
"anthropic = Website(\"https://anthropic.com\")\n",
"anthropic.links"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924",
"metadata": {},
"outputs": [],
"source": [
"get_links(\"https://anthropic.com\")"
]
},
{
"cell_type": "markdown",
"id": "0d74128e-dfb6-47ec-9549-288b621c838c",
"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": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5",
"metadata": {},
"outputs": [],
"source": [
"def get_all_details(url):\n",
" result = \"Landing page:\\n\"\n",
" result += Website(url).get_contents()\n",
" links = get_links(url)\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": "5099bd14-076d-4745-baf3-dac08d8e5ab2",
"metadata": {},
"outputs": [],
"source": [
"print(get_all_details(\"https://anthropic.com\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2",
"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 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.\"\n",
"\n",
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n",
"\n",
"# 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.\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23",
"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)\n",
" user_prompt = user_prompt[:20_000] # Truncate if more than 20,000 characters\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd909e0b-1312-4ce2-a553-821e795d7572",
"metadata": {},
"outputs": [],
"source": [
"get_brochure_user_prompt(\"Anthropic\", \"https://anthropic.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46",
"metadata": {},
"outputs": [],
"source": [
"# def create_brochure(company_name, url):\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",
"# display(Markdown(result))\n",
"\n",
"def create_brochure(company_name, url):\n",
" response = ollama.chat(\n",
" model=MODEL_LLAMA,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ]\n",
" )\n",
" result = response['message']['content']\n",
" display(Markdown(result))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e093444a-9407-42ae-924a-145730591a39",
"metadata": {},
"outputs": [],
"source": [
"create_brochure(\"Anthropic\", \"https://anthropic.com\")"
]
},
{
"cell_type": "markdown",
"id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18",
"metadata": {},
"source": [
"## Finally - a minor improvement\n",
"\n",
"With a small adjustment, we can change this so that the results stream back from OpenAI,\n",
"with the familiar typewriter animation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "51db0e49-f261-4137-aabe-92dd601f7725",
"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",
"# # For just a simple output you can do the following two lines;\n",
"# # for chunk in stream:\n",
"# # print(chunk.choices[0].delta.content or '',end='')\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)\n",
"\n",
"def stream_brochure(company_name, url):\n",
" stream = ollama.chat(\n",
" model=MODEL_LLAMA,\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",
" # For just a simple output you can do the following two lines;\n",
" # for chunk in stream:\n",
" # print(chunk['message']['content'] or '', end='')\n",
"\n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk['message']['content'] or ''\n",
" response = response.replace(\"```\", \"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d",
"metadata": {},
"outputs": [],
"source": [
"stream_brochure(\"Anthropic\", \"https://anthropic.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b",
"metadata": {},
"outputs": [],
"source": [
"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n",
"\n",
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5567d103-74ee-4a7a-997c-eaf2c3baf7f4",
"metadata": {},
"outputs": [],
"source": [
"def test_llama_response_basic(company_name, url):\n",
" try:\n",
" response = ollama.chat(\n",
" model=MODEL_LLAMA,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ]\n",
" )\n",
"\n",
" # Print the entire raw response for debugging purposes\n",
" print(\"Raw response received:\", response)\n",
"\n",
" # Check if the response contains 'message' and 'content'\n",
" if 'message' in response and 'content' in response['message']:\n",
" response_content = response['message']['content']\n",
" print(\"Content from response:\", response_content)\n",
" return response_content\n",
" else:\n",
" print(\"Response does not contain expected 'message' or 'content'\")\n",
" return response\n",
"\n",
" except Exception as e:\n",
" print(f\"An error occurred: {e}\")\n",
" return {}\n",
"\n",
"# Example usage\n",
"test_llama_response_basic(\"HuggingFace\", \"https://huggingface.co\")\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "a27bf9e0-665f-4645-b66b-9725e2a959b5",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business applications</h2>\n",
" <span style=\"color:#181;\">In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n",
"\n",
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n",
"\n",
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "14b2454b-8ef8-4b5c-b928-053a15e0d553",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you move to Week 2 (which is tons of fun)</h2>\n",
" <span style=\"color:#900;\">Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "17b64f0f-7d33-4493-985a-033d06e8db08",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">A reminder on 2 useful resources</h2>\n",
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n",
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8fbce9d-51e5-4e8c-a7a9-c88ad02fffdf",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv()\n",
"hf_token=os.getenv(\"HF_TOKEN\")\n",
"print(f\"Using this HF Token: {hf_token}\")\n",
"\n",
"API_URL = \"https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-1B\"\n",
"headers = {\"Authorization\": f\"Bearer {hf_token}\"}\n",
"\n",
"def query(payload):\n",
"\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
"\treturn response.json()\n",
"\t\n",
"output = query({\n",
"\t\"inputs\": \"2 + 2 is \",\n",
"})\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec2b37af-566e-4b0b-ad4a-8b46cc346e46",
"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
}

68
week1/day1.ipynb

@ -144,6 +144,36 @@
"# openai = OpenAI(api_key=\"your-key-here-starting-sk-proj-\")"
]
},
{
"cell_type": "markdown",
"id": "442fc84b-0815-4f40-99ab-d9a5da6bda91",
"metadata": {},
"source": [
"# Let's make a quick call to a Frontier model to get started, as a preview!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a58394bf-1e45-46af-9bfd-01e24da6f49a",
"metadata": {},
"outputs": [],
"source": [
"# To give you a preview -- calling OpenAI with these messages is this easy:\n",
"\n",
"message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=[{\"role\":\"user\", \"content\":message}])\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "2aa190e5-cb31-456a-96cc-db109919cd78",
"metadata": {},
"source": [
"## OK onwards with our first project"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -257,9 +287,45 @@
"[\n",
" {\"role\": \"system\", \"content\": \"system message goes here\"},\n",
" {\"role\": \"user\", \"content\": \"user message goes here\"}\n",
"]\n",
"\n",
"To give you a preview, the next 2 cells make a rather simple call - we won't stretch the might GPT (yet!)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5",
"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": "21ed95c5-7001-47de-a36d-1d6673b403ce",
"metadata": {},
"outputs": [],
"source": [
"# To give you a preview -- calling OpenAI with system and user messages:\n",
"\n",
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47",
"metadata": {},
"source": [
"## And now let's build useful messages for GPT-4o-mini, using a function"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -493,7 +559,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

41
week1/day2 EXERCISE.ipynb

@ -158,6 +158,35 @@
"print(response['message']['content'])"
]
},
{
"cell_type": "markdown",
"id": "a4704e10-f5fb-4c15-a935-f046c06fb13d",
"metadata": {},
"source": [
"## Alternative approach - using OpenAI python library to connect to Ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23057e00-b6fc-4678-93a9-6b31cb704bff",
"metadata": {},
"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": "markdown",
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
@ -165,8 +194,16 @@
"source": [
"# NOW the exercise for you\n",
"\n",
"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI"
"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": "code",
"execution_count": null,
"id": "402d5686-4e76-4110-b65a-b3906c35c0a4",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
@ -185,7 +222,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

20
week1/day5.ipynb

@ -206,8 +206,10 @@
"metadata": {},
"outputs": [],
"source": [
"anthropic = Website(\"https://anthropic.com\")\n",
"anthropic.links"
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n",
"\n",
"huggingface = Website(\"https://huggingface.co\")\n",
"huggingface.links"
]
},
{
@ -217,7 +219,7 @@
"metadata": {},
"outputs": [],
"source": [
"get_links(\"https://anthropic.com\")"
"get_links(\"https://huggingface.co\")"
]
},
{
@ -255,7 +257,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(get_all_details(\"https://anthropic.com\"))"
"print(get_all_details(\"https://huggingface.co\"))"
]
},
{
@ -287,7 +289,7 @@
" 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)\n",
" user_prompt = user_prompt[:20_000] # Truncate if more than 20,000 characters\n",
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
" return user_prompt"
]
},
@ -298,7 +300,7 @@
"metadata": {},
"outputs": [],
"source": [
"get_brochure_user_prompt(\"Anthropic\", \"https://anthropic.com\")"
"get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
@ -327,7 +329,7 @@
"metadata": {},
"outputs": [],
"source": [
"create_brochure(\"Anthropic\", \"https://anthropic.com\")"
"create_brochure(\"HuggingFace\", \"https://huggingface.com\")"
]
},
{
@ -373,7 +375,7 @@
"metadata": {},
"outputs": [],
"source": [
"stream_brochure(\"Anthropic\", \"https://anthropic.com\")"
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
@ -473,7 +475,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

18
week1/troubleshooting.ipynb

@ -331,6 +331,22 @@
"from diagnostics import Diagnostics\n",
"Diagnostics().run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7cd162d4-ebde-4a39-a9b0-bee3633907cb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b053b313-7ccc-4aff-a562-2479eb890918",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
@ -349,7 +365,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week1/week1 EXERCISE.ipynb

@ -96,7 +96,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week2/community-contributions/Week2_Day2_AddGeminModel.ipynb

@ -572,7 +572,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.11.11"
}
},
"nbformat": 4,

12
week2/community-contributions/day1-azure-aws-ollama.ipynb

@ -22,7 +22,7 @@
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Important Note - Please read me</h2>\n",
@ -41,7 +41,7 @@
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" <img src=\"../../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">Reminder about the resources page</h2>\n",
@ -610,7 +610,7 @@
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you continue</h2>\n",
@ -646,7 +646,7 @@
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" <img src=\"../../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business relevance</h2>\n",
@ -667,7 +667,7 @@
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@ -681,7 +681,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week2/community-contributions/day1-with-3way.ipynb

@ -641,7 +641,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week2/community-contributions/day2.ipynb

@ -466,7 +466,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week2/community-contributions/day4.ipynb

@ -292,7 +292,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week2/community-contributions/task1.ipynb

@ -315,7 +315,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

52
week2/day1.ipynb

@ -104,8 +104,8 @@
"outputs": [],
"source": [
"# import for google\n",
"# in rare cases, this seems to give an error on some systems. Please reach out to me if this happens,\n",
"# or you can feel free to skip Gemini - it's the lowest priority of the frontier models that we use\n",
"# in rare cases, this seems to give an error on some systems, or even crashes the kernel\n",
"# If this happens to you, simply ignore this cell - I give an alternative approach for using Gemini later\n",
"\n",
"import google.generativeai"
]
@ -148,14 +148,22 @@
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI, Anthropic and Google\n",
"# All 3 APIs are similar\n",
"# Having problems with API files? You can use openai = OpenAI(api_key=\"your-key-here\") and same for claude\n",
"# Having problems with Google Gemini setup? Then just skip Gemini; you'll get all the experience you need from GPT and Claude.\n",
"# Connect to OpenAI, Anthropic\n",
"\n",
"openai = OpenAI()\n",
"\n",
"claude = anthropic.Anthropic()\n",
"claude = anthropic.Anthropic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "425ed580-808d-429b-85b0-6cba50ca1d0c",
"metadata": {},
"outputs": [],
"source": [
"# This is the set up code for Gemini\n",
"# Having problems with Google Gemini setup? Then just ignore this cell; when we use Gemini, I'll give you an alternative that bypasses this library altogether\n",
"\n",
"google.generativeai.configure()"
]
@ -308,7 +316,9 @@
"metadata": {},
"outputs": [],
"source": [
"# The API for Gemini has a slightly different structure\n",
"# The API for Gemini has a slightly different structure.\n",
"# I've heard that on some PCs, this Gemini code causes the Kernel to crash.\n",
"# If that happens to you, please skip this cell and use the next cell instead - an alternative approach.\n",
"\n",
"gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-1.5-flash',\n",
@ -318,6 +328,28 @@
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49009a30-037d-41c8-b874-127f61c4aa3a",
"metadata": {},
"outputs": [],
"source": [
"# As an alternative way to use Gemini that bypasses Google's python API library,\n",
"# Google has recently released new endpoints that means you can use Gemini via the client libraries for OpenAI!\n",
"\n",
"gemini_via_openai_client = OpenAI(\n",
" api_key=google_api_key, \n",
" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
")\n",
"\n",
"response = gemini_via_openai_client.chat.completions.create(\n",
" model=\"gemini-1.5-flash\",\n",
" messages=prompts\n",
")\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -534,7 +566,7 @@
"\n",
"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n",
"\n",
"Try doing this yourself before you look at the solutions.\n",
"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n",
"\n",
"## Additional exercise\n",
"\n",
@ -584,7 +616,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

3
week2/day2.ipynb

@ -186,6 +186,7 @@
"source": [
"# Adding share=True means that it can be accessed publically\n",
"# A more permanent hosting is available using a platform called Spaces from HuggingFace, which we will touch on next week\n",
"# NOTE: Some Anti-virus software and Corporate Firewalls might not like you using share=True. If you're at work on on a work network, I suggest skip this test.\n",
"\n",
"gr.Interface(fn=shout, inputs=\"textbox\", outputs=\"textbox\", flagging_mode=\"never\").launch(share=True)"
]
@ -565,7 +566,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week2/day3.ipynb

@ -296,7 +296,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

9
week2/day4.ipynb

@ -44,7 +44,12 @@
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()"
"openai = OpenAI()\n",
"\n",
"# As an alternative, if you'd like to use Ollama instead of OpenAI\n",
"# Check that Ollama is running for you locally (see week1/day2 exercise) then uncomment these next 2 lines\n",
"# MODEL = \"llama3.2\"\n",
"# openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n"
]
},
{
@ -249,7 +254,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

8
week2/day5.ipynb

@ -296,7 +296,7 @@
"id": "f4975b87-19e9-4ade-a232-9b809ec75c9a",
"metadata": {},
"source": [
"## Audio\n",
"## Audio (NOTE - Audio is optional for this course - feel free to skip Audio if it causes trouble!)\n",
"\n",
"And let's make a function talker that uses OpenAI's speech model to generate Audio\n",
"\n",
@ -410,12 +410,14 @@
"source": [
"# For Windows users\n",
"\n",
"## if you get a permissions error writing to a temp file, then this code should work instead.\n",
"## First try the Mac version above, but if you get a permissions error writing to a temp file, then this code should work instead.\n",
"\n",
"A collaboration between students Mark M. and Patrick H. and Claude got this resolved!\n",
"\n",
"Below are 3 variations - hopefully one of them will work on your PC. If not, message me please!\n",
"\n",
"And for Mac people - all 3 of the below work on my Mac too - please try these if the Mac version gave you problems.\n",
"\n",
"## PC Variation 1"
]
},
@ -695,7 +697,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week2/week2 EXERCISE.ipynb

@ -43,7 +43,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week3/day1.ipynb

@ -41,7 +41,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week3/day2.ipynb

@ -41,7 +41,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week3/day3.ipynb

@ -37,7 +37,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week3/day4.ipynb

@ -31,7 +31,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week3/day5.ipynb

@ -43,7 +43,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

24
week4/day3.ipynb

@ -505,13 +505,13 @@
"outputs": [],
"source": [
"def execute_python(code):\n",
" try:\n",
" output = io.StringIO()\n",
" sys.stdout = output\n",
" exec(code)\n",
" finally:\n",
" sys.stdout = sys.__stdout__\n",
" return output.getvalue()"
" try:\n",
" output = io.StringIO()\n",
" sys.stdout = output\n",
" exec(code)\n",
" finally:\n",
" sys.stdout = sys.__stdout__\n",
" return output.getvalue()"
]
},
{
@ -581,14 +581,6 @@
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77a80857-4632-4de8-a28f-b614bcbe2f40",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
@ -607,7 +599,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week4/day4.ipynb

@ -696,7 +696,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

BIN
week4/optimized

Binary file not shown.

85
week4/optimized.cpp

@ -1,51 +1,72 @@
#include <iostream>
#include <random>
#include <vector>
#include <chrono>
#include <limits>
#include <iomanip>
// Function to generate random numbers using Mersenne Twister
std::mt19937 gen(42);
using namespace std;
using namespace chrono;
class LCG {
private:
uint64_t value;
static const uint64_t a = 1664525;
static const uint64_t c = 1013904223;
static const uint64_t m = 1ULL << 32;
public:
LCG(uint64_t seed) : value(seed) {}
uint64_t next() {
value = (a * value + c) % m;
return value;
}
};
int64_t max_subarray_sum(int n, uint64_t seed, int min_val, int max_val) {
LCG lcg(seed);
vector<int64_t> random_numbers(n);
for (int i = 0; i < n; ++i) {
random_numbers[i] = lcg.next() % (max_val - min_val + 1) + min_val;
}
int64_t max_sum = numeric_limits<int64_t>::min();
int64_t current_sum = 0;
int64_t min_sum = 0;
// Function to calculate maximum subarray sum
int max_subarray_sum(int n, int min_val, int max_val) {
std::uniform_int_distribution<> dis(min_val, max_val);
int max_sum = std::numeric_limits<int>::min();
int current_sum = 0;
for (int i = 0; i < n; ++i) {
current_sum += dis(gen);
if (current_sum > max_sum) {
max_sum = current_sum;
}
if (current_sum < 0) {
current_sum = 0;
}
current_sum += random_numbers[i];
max_sum = max(max_sum, current_sum - min_sum);
min_sum = min(min_sum, current_sum);
}
return max_sum;
}
// Function to calculate total maximum subarray sum
int total_max_subarray_sum(int n, int initial_seed, int min_val, int max_val) {
gen.seed(initial_seed);
int total_sum = 0;
int64_t total_max_subarray_sum(int n, uint64_t initial_seed, int min_val, int max_val) {
int64_t total_sum = 0;
LCG lcg(initial_seed);
for (int i = 0; i < 20; ++i) {
total_sum += max_subarray_sum(n, min_val, max_val);
uint64_t seed = lcg.next();
total_sum += max_subarray_sum(n, seed, min_val, max_val);
}
return total_sum;
}
int main() {
int n = 10000; // Number of random numbers
int initial_seed = 42; // Initial seed for the Mersenne Twister
int min_val = -10; // Minimum value of random numbers
int max_val = 10; // Maximum value of random numbers
// Timing the function
auto start_time = std::chrono::high_resolution_clock::now();
int result = total_max_subarray_sum(n, initial_seed, min_val, max_val);
auto end_time = std::chrono::high_resolution_clock::now();
std::cout << "Total Maximum Subarray Sum (20 runs): " << result << std::endl;
std::cout << "Execution Time: " << std::setprecision(6) << std::fixed << std::chrono::duration<double>(end_time - start_time).count() << " seconds" << std::endl;
const int n = 10000;
const uint64_t initial_seed = 42;
const int min_val = -10;
const int max_val = 10;
auto start_time = high_resolution_clock::now();
int64_t result = total_max_subarray_sum(n, initial_seed, min_val, max_val);
auto end_time = high_resolution_clock::now();
auto duration = duration_cast<microseconds>(end_time - start_time);
cout << "Total Maximum Subarray Sum (20 runs): " << result << endl;
cout << "Execution Time: " << fixed << setprecision(6) << duration.count() / 1e6 << " seconds" << endl;
return 0;
}

2
week5/community-contributions/day3 - extended for Obsidian files and separate ingestion.ipynb

@ -388,7 +388,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week5/community-contributions/day4 - taking advantage of separate ingestion.ipynb

@ -421,7 +421,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week5/day1.ipynb

@ -256,7 +256,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

10
week5/day2.ipynb

@ -169,14 +169,6 @@
" print(chunk)\n",
" print(\"_________\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6965971c-fb97-482c-a497-4e81a0ac83df",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
@ -195,7 +187,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week5/day3.ipynb

@ -352,7 +352,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

17
week5/day4.5.ipynb

@ -214,7 +214,9 @@
"source": [
"## Visualizing the Vector Store\n",
"\n",
"Let's take a minute to look at the documents and their embedding vectors to see what's going on."
"Let's take a minute to look at the documents and their embedding vectors to see what's going on.\n",
"\n",
"(As a sidenote, what we're really looking at here is the distribution of the Vectors generated by OpenAIEmbeddings, retrieved from FAISS. So there's no surprise that they look the same whether they are \"from\" FAISS or Chroma.)"
]
},
{
@ -326,6 +328,17 @@
"print(result[\"answer\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "987dadc5-5d09-4059-8f2e-733d66ecc696",
"metadata": {},
"outputs": [],
"source": [
"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
"conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
]
},
{
"cell_type": "markdown",
"id": "bbbcb659-13ce-47ab-8a5e-01b930494964",
@ -387,7 +400,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week5/day4.ipynb

@ -404,7 +404,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

14
week5/day5.ipynb

@ -50,7 +50,8 @@
"import numpy as np\n",
"import plotly.graph_objects as go\n",
"from langchain.memory import ConversationBufferMemory\n",
"from langchain.chains import ConversationalRetrievalChain"
"from langchain.chains import ConversationalRetrievalChain\n",
"from langchain.embeddings import HuggingFaceEmbeddings"
]
},
{
@ -147,6 +148,10 @@
"\n",
"embeddings = OpenAIEmbeddings()\n",
"\n",
"# If you would rather use the free Vector Embeddings from HuggingFace sentence-transformers\n",
"# Then uncomment this line instead\n",
"# embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"\n",
"# Delete if already exists\n",
"\n",
"if os.path.exists(db_name):\n",
@ -289,6 +294,9 @@
"# create a new Chat with OpenAI\n",
"llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
"\n",
"# Alternative - if you'd like to use Ollama locally, uncomment this line instead\n",
"# llm = ChatOpenAI(temperature=0.7, model_name='llama3.2', base_url='http://localhost:11434/v1', api_key='ollama')\n",
"\n",
"# set up the conversation memory for the chat\n",
"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
"\n",
@ -427,7 +435,7 @@
"metadata": {},
"outputs": [],
"source": [
"view = gr.ChatInterface(chat).launch()"
"view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
]
},
{
@ -465,7 +473,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week6/day1.ipynb

@ -419,7 +419,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

5
week8/agents/messaging_agent.py

@ -1,10 +1,11 @@
import os
from twilio.rest import Client
# from twilio.rest import Client
from agents.deals import Opportunity
import http.client
import urllib
from agents.agent import Agent
# Uncomment the Twilio lines if you wish to use Twilio
DO_TEXT = False
DO_PUSH = True
@ -26,7 +27,7 @@ class MessagingAgent(Agent):
auth_token = os.getenv('TWILIO_AUTH_TOKEN', 'your-auth-if-not-using-env')
self.me_from = os.getenv('TWILIO_FROM', 'your-phone-number-if-not-using-env')
self.me_to = os.getenv('MY_PHONE_NUMBER', 'your-phone-number-if-not-using-env')
self.client = Client(account_sid, auth_token)
# self.client = Client(account_sid, auth_token)
self.log("Messaging Agent has initialized Twilio")
if DO_PUSH:
self.pushover_user = os.getenv('PUSHOVER_USER', 'your-pushover-user-if-not-using-env')

2
week8/day1.ipynb

@ -317,7 +317,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week8/day2.0.ipynb

@ -264,7 +264,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week8/day2.1.ipynb

@ -174,7 +174,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week8/day2.2.ipynb

@ -166,7 +166,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week8/day2.3.ipynb

@ -391,7 +391,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week8/day2.4.ipynb

@ -400,7 +400,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week8/day3.ipynb

@ -227,7 +227,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

2
week8/day4.ipynb

@ -133,7 +133,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

22
week8/day5.ipynb

@ -133,12 +133,32 @@
"And now we'll move to the price_is_right.py code, followed by price_is_right_final.py"
]
},
{
"cell_type": "markdown",
"id": "d783af8a-08a8-4e59-886a-7ca32f16bcf5",
"metadata": {},
"source": [
"# Running the final product\n",
"\n",
"## Just hit shift + enter in the next cell, and let the deals flow in!!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48506465-1c7a-433f-a665-b277a8b4665c",
"metadata": {},
"outputs": [],
"source": [
"!python price_is_right_final.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d468291f-abe2-4fd7-97a6-43c714292973",
"metadata": {},
"outputs": [],
"source": []
}
],
@ -158,7 +178,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
"version": "3.11.11"
}
},
"nbformat": 4,

18
week8/memory.json

@ -16,5 +16,23 @@
},
"estimate": 930.8824204895075,
"discount": 225.88242048950747
},
{
"deal": {
"product_description": "The Insignia Class F30 Series NS-55F301NA25 is a 55\" 4K HDR UHD Smart TV with a native resolution of 3840x2160. Featuring HDR support, it enhances color and contrast for a more dynamic viewing experience. The TV integrates seamlessly with Amazon Fire TV, working with both Amazon Alexa and Google Home for voice control. It offers three HDMI ports for multiple device connections, making it a perfect entertainment hub for your living space.",
"price": 200.0,
"url": "https://www.dealnews.com/products/Insignia/Insignia-Class-F30-Series-NS-55-F301-NA25-55-4-K-HDR-LED-UHD-Smart-TV/467523.html?iref=rss-f1912"
},
"estimate": 669.1921927283588,
"discount": 469.1921927283588
},
{
"deal": {
"product_description": "The Samsung 27-Cu. Ft. Mega Capacity 3-Door French Door Counter Depth Refrigerator combines style with spacious organization. This model features a dual auto ice maker, which ensures you always have ice on hand, and adjustable shelves that provide versatile storage options for your groceries. Designed with a sleek, fingerprint resistant finish, it not only looks modern but also simplifies cleaning. With its generous capacity, this refrigerator is perfect for large households or those who love to entertain.",
"price": 1299.0,
"url": "https://www.dealnews.com/products/Samsung/Samsung-27-Cu-Ft-Mega-Capacity-3-Door-French-Door-Counter-Depth-Refrigerator/454702.html?iref=rss-c196"
},
"estimate": 2081.647127763905,
"discount": 782.6471277639048
}
]
Loading…
Cancel
Save