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Merge pull request #515 from pradeep1955/community-contributions-branch

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Add my week1 EXERCISE.ipynb to community-contributions
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Ed Donner
2025-07-18 22:28:06 -04:00
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community-contributions/pradeep1955/week1 EXERCISE.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"metadata": {},
"source": [
"# End of week 1 exercise\n",
"\n",
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"import os\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display, update_display\n",
"from dotenv import load_dotenv"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"MODEL_LLAMA = 'llama3.2'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
"metadata": {},
"outputs": [],
"source": [
"# set up environment\n",
"load_dotenv(override=True)\n",
"api_key=os.getenv(\"OPENAI_API_KEY\")\n",
"if not api_key.startswith(\"sk-proj-\") and len(api_key)<10:\n",
" print(\"api key not foud\")\n",
"else:\n",
" print(\"api found and is ok\")\n",
"\n",
"openai=OpenAI()\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
"metadata": {},
"outputs": [],
"source": [
"# here is the question; type over this to ask something new\n",
"\n",
"question = \"\"\"\n",
"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
"metadata": {},
"outputs": [],
"source": [
"# Get gpt-4o-mini to answer, with streaming\n",
"messages = [{\"role\":\"system\",\"content\":\"You are a expert Dta Scientist\"}, {\"role\":\"user\",\"content\":question}]\n",
"\n",
"stream = openai.chat.completions.create(\n",
" model = MODEL_GPT,\n",
" messages = messages,\n",
" stream = True\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [],
"source": [
"# Get Llama 3.2 to answer\n",
"import ollama\n",
"\n",
"stream = ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True)\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",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a573174-779b-4d50-8792-fa0889b37211",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llmenv",
"language": "python",
"name": "llmenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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community-contributions/pradeep1955/week1/day2 EXERCISE.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
"metadata": {},
"source": [
"# Welcome to your first assignment!\n",
"\n",
"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)"
]
},
{
"cell_type": "markdown",
"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9",
"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;\">Just before we get to the assignment --</h2>\n",
" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n",
" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
"metadata": {},
"source": [
"# HOMEWORK EXERCISE ASSIGNMENT\n",
"\n",
"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n",
"\n",
"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n",
"\n",
"**Benefits:**\n",
"1. No API charges - open-source\n",
"2. Data doesn't leave your box\n",
"\n",
"**Disadvantages:**\n",
"1. Significantly less power than Frontier Model\n",
"\n",
"## Recap on installation of Ollama\n",
"\n",
"Simply visit [ollama.com](https://ollama.com) and install!\n",
"\n",
"Once complete, the ollama server should already be running locally. \n",
"If you visit: \n",
"[http://localhost:11434/](http://localhost:11434/)\n",
"\n",
"You should see the message `Ollama is running`. \n",
"\n",
"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n",
"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n",
"Then try [http://localhost:11434/](http://localhost:11434/) again.\n",
"\n",
"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
"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": "29ddd15d-a3c5-4f4e-a678-873f56162724",
"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": "dac0a679-599c-441f-9bf2-ddc73d35b940",
"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": "7bb9c624-14f0-4945-a719-8ddb64f66f47",
"metadata": {},
"outputs": [],
"source": [
"payload = {\n",
" \"model\": MODEL,\n",
" \"messages\": messages,\n",
" \"stream\": False\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40",
"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": "42b9f644-522d-4e05-a691-56e7658c0ea9",
"metadata": {},
"outputs": [],
"source": [
"# If this doesn't work for any reason, try the 2 versions in the following cells\n",
"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
"# And if none of that works - contact me!\n",
"\n",
"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
"print(response.json()['message']['content'])"
]
},
{
"cell_type": "markdown",
"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe",
"metadata": {},
"source": [
"# Introducing the ollama package\n",
"\n",
"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n",
"\n",
"Under the hood, it's making the same call as above to the ollama server running at localhost:11434"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
"metadata": {},
"outputs": [],
"source": [
"import ollama\n",
"\n",
"response = ollama.chat(model=MODEL, messages=messages)\n",
"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": "9f9e22da-b891-41f6-9ac9-bd0c0a5f4f44",
"metadata": {},
"source": [
"## Are you confused about why that works?\n",
"\n",
"It seems strange, right? We just used OpenAI code to call Ollama?? What's going on?!\n",
"\n",
"Here's the scoop:\n",
"\n",
"The python class `OpenAI` is simply code written by OpenAI engineers that makes calls over the internet to an endpoint. \n",
"\n",
"When you call `openai.chat.completions.create()`, this python code just makes a web request to the following url: \"https://api.openai.com/v1/chat/completions\"\n",
"\n",
"Code like this is known as a \"client library\" - it's just wrapper code that runs on your machine to make web requests. The actual power of GPT is running on OpenAI's cloud behind this API, not on your computer!\n",
"\n",
"OpenAI was so popular, that lots of other AI providers provided identical web endpoints, so you could use the same approach.\n",
"\n",
"So Ollama has an endpoint running on your local box at http://localhost:11434/v1/chat/completions \n",
"And in week 2 we'll discover that lots of other providers do this too, including Gemini and DeepSeek.\n",
"\n",
"And then the team at OpenAI had a great idea: they can extend their client library so you can specify a different 'base url', and use their library to call any compatible API.\n",
"\n",
"That's it!\n",
"\n",
"So when you say: `ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')` \n",
"Then this will make the same endpoint calls, but to Ollama instead of OpenAI."
]
},
{
"cell_type": "markdown",
"id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90",
"metadata": {},
"source": [
"## Also trying the amazing reasoning model DeepSeek\n",
"\n",
"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n",
"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n",
"\n",
"Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
"metadata": {},
"outputs": [],
"source": [
"!ollama pull deepseek-r1:1.5b"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d3d554b-e00d-4c08-9300-45e073950a76",
"metadata": {},
"outputs": [],
"source": [
"# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n",
"\n",
"response = ollama_via_openai.chat.completions.create(\n",
" model=\"deepseek-r1:1.5b\",\n",
" messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n",
")\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
"metadata": {},
"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; use either of the above approaches."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43ef4b92-53e1-4af2-af3f-726812f4265c",
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97d45733-394e-493e-a92b-1475876d9028",
"metadata": {},
"outputs": [],
"source": [
"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": "6a40f9c5-1b14-42f9-9319-6a66e58e03f2",
"metadata": {},
"outputs": [],
"source": [
"webpage = Website(\"https://www.pleasurewebsite.com\")\n",
"print(webpage.title)\n",
"print(webpage.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a72a005d-43de-4ae5-b427-99a8fcb6065c",
"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": "f0e4f95f-0ccf-4027-9457-5c973cd17702",
"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": "ceae6073-a085-49ce-ad44-39e46d8e6934",
"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": "9d53b26b-308c-470c-a0a9-9edb887aed6d",
"metadata": {},
"outputs": [],
"source": [
"messages=messages_for(webpage)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
"metadata": {},
"outputs": [],
"source": [
"import ollama\n",
"MODEL = \"llama3.2\"\n",
"response = ollama.chat(model=MODEL, messages=messages)\n",
"print(response['message']['content'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llmenv",
"language": "python",
"name": "llmenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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community-contributions/pradeep1955/week2/agent_conversation_shakespeare.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927",
"metadata": {},
"source": [
"# Triangular agent conversation\n",
"\n",
"## GPT (Hamlet), LLM (Falstaff), Gemini (Iago):"
]
},
{
"cell_type": "markdown",
"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac",
"metadata": {},
"source": [
"### Created a 3-way, bringing Gemini into the coversation.\n",
"### Replacing one of the models with an open source model running with Ollama."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8e0c1bd-a159-475b-9cdc-e219a7633355",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display, update_display\n",
"import ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3ad57ad-46a8-460e-9cb3-67a890093536",
"metadata": {},
"outputs": [],
"source": [
"import google.generativeai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f531c14-5743-4a5b-83d9-cb5863ca2ddf",
"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",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d5150ee-3858-4921-bce6-2eecfb96bc75",
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI\n",
"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11381fd8-5099-41e8-a1d7-6787dea56e43",
"metadata": {},
"outputs": [],
"source": [
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1766d20-54b6-4f76-96c5-c338ae7073c9",
"metadata": {},
"outputs": [],
"source": [
"gpt_model = \"gpt-4o-mini\"\n",
"llama_model = \"llama3.2\"\n",
"gemini_model = 'gemini-2.0-flash'\n",
"\n",
"gpt_system = \"You are playing part of Hamlet. he is philosopher, probes Iago with a mixture of suspicion\\\n",
"and intellectual curiosity, seeking to unearth the origins of his deceit.\\\n",
"Is malice born of scorn, envy, or some deeper void? Hamlet’s introspective nature\\\n",
"drives him to question whether Iago’s actions reveal a truth about humanity itself.\\\n",
"You will respond as Shakespear's Hamlet will do.\"\n",
"\n",
"llama_system = \"You are acting part of Falstaff who attempts to lighten the mood with his jokes and observations,\\\n",
"potentially clashing with Hamlet's melancholic nature.You respond as Shakespear's Falstaff do.\"\n",
"\n",
"gemini_system = \"You are acting part of Iago, subtly trying to manipulate both Hamlet and Falstaff\\\n",
"to his own advantage, testing their weaknesses and exploiting their flaws. You respond like Iago\"\n",
"\n",
"gpt_messages = [\"Hi there\"]\n",
"llama_messages = [\"Hi\"]\n",
"gemini_messages = [\"Hello\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "806a0506-dac8-4bad-ac08-31f350256b58",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, claude, gemini in zip(gpt_messages, llama_messages, gemini_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": claude})\n",
" messages.append({\"role\": \"user\", \"content\": gemini})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43674885-ede7-48bf-bee4-467454f3e96a",
"metadata": {},
"outputs": [],
"source": [
"def call_llama():\n",
" messages = []\n",
" for gpt, llama, gemini in zip(gpt_messages, llama_messages, gemini_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": llama})\n",
" messages.append({\"role\": \"user\", \"content\": gemini})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" response = ollama.chat(model=llama_model, messages=messages)\n",
"\n",
" \n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "03d34769-b339-4c4b-8c60-69494c39d725",
"metadata": {},
"outputs": [],
"source": [
"#import google.generativeai as genai\n",
"\n",
"# Make sure you configure the API key first:\n",
"#genai.configure(api_key=\"YOUR_API_KEY\")\n",
"\n",
"def call_gemini():\n",
" gemini_messages = []\n",
" \n",
" # Format the history for Gemini\n",
" for gpt, llama, gemini_message in zip(gpt_messages, llama_messages, gemini_messages):\n",
" gemini_messages.append({\"role\": \"user\", \"parts\": [gpt]}) # Hamlet speaks\n",
" gemini_messages.append({\"role\": \"model\", \"parts\": [llama]}) # Falstaff responds\n",
" gemini_messages.append({\"role\": \"model\", \"parts\": [gemini_message]}) # Iago responds\n",
"\n",
" # Add latest user input if needed (optional)\n",
" gemini_messages.append({\"role\": \"user\", \"parts\": [llama_messages[-1]]})\n",
"\n",
" # Initialize the model with the correct system instruction\n",
" gemini = google.generativeai.GenerativeModel(\n",
" #model_name='gemini-1.5-flash', # Or 'gemini-pro'\n",
" model_name = gemini_model,\n",
" system_instruction=gemini_system\n",
" )\n",
"\n",
" response = gemini.generate_content(gemini_messages)\n",
" return response.text\n",
"#print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93fc8253-67cb-4ea4-aff7-097b2a222793",
"metadata": {},
"outputs": [],
"source": [
"gpt_messages = [\"Hi there\"]\n",
"llama_messages = [\"Hi\"]\n",
"gemini_messages = [\"Hello\"]\n",
"\n",
"print(f\"Hamlet:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Falstaff:\\n{llama_messages[0]}\\n\")\n",
"print(f\"Iago:\\n{gemini_messages[0]}\\n\")\n",
"\n",
"for i in range(3):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" llama_next = call_llama()\n",
" print(f\"Llama:\\n{llama_next}\\n\")\n",
" llama_messages.append(llama_next)\n",
"\n",
" gemini_next = call_gemini()\n",
" print(f\"Gemini:\\n{gemini_next}\\n\")\n",
" llama_messages.append(gemini_next)"
]
},
{
"cell_type": "markdown",
"id": "bca66ffc-9dc1-4384-880c-210889f5d0ac",
"metadata": {},
"source": [
"## Conversation between gpt-4.0-mini and llama3.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c23224f6-7008-44ed-a57f-718975f4e291",
"metadata": {},
"outputs": [],
"source": [
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
"# We're using cheap versions of models so the costs will be minimal\n",
"\n",
"gpt_model = \"gpt-4o-mini\"\n",
"llama_model = \"llama3.2\"\n",
"\n",
"gpt_system = \"You are a tapori from mumbai who is very optimistic; \\\n",
"you alway look at the brighter part of the situation and you always ready to take act to win way.\"\n",
"\n",
"llama_system = \"You are a Jaat from Haryana. You try to express with hindi poems \\\n",
"to agree with other person and or find common ground. If the other person is optimistic, \\\n",
"you respond in poetic way and keep chatting.\"\n",
"\n",
"gpt_messages = [\"Hi there\"]\n",
"llama_messages = [\"Hi\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d704bbb-f22b-400d-a695-efbd02b26548",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, llama in zip(gpt_messages, llama_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": llama})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "385ccec8-de59-4e42-9616-3f5c9a05589c",
"metadata": {},
"outputs": [],
"source": [
"def call_llama():\n",
" messages = []\n",
" for gpt, llama_message in zip(gpt_messages, llama_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": llama_message})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" response = ollama.chat(model=llama_model, messages=messages)\n",
"\n",
" \n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70b5481b-455e-4275-80d3-0afe0fabcb0f",
"metadata": {},
"outputs": [],
"source": [
"gpt_messages = [\"Hi there\"]\n",
"llama_messages = [\"Hi\"]\n",
"\n",
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Llama:\\n{llama_messages[0]}\\n\")\n",
"\n",
"for i in range(3):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" llama_next = call_llama()\n",
" print(f\"Llama:\\n{llama_next}\\n\")\n",
" llama_messages.append(llama_next)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f8d734b-57e5-427d-bcb1-7956fc58a348",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llmenv",
"language": "python",
"name": "llmenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

351
week2/community-contributions/agent_conversation_shakespeare.ipynb Normal file
View File

@@ -0,0 +1,351 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "06cf3063-9f3e-4551-a0d5-f08d9cabb927",
"metadata": {},
"source": [
"# Triangular agent conversation\n",
"\n",
"## GPT (Hamlet), LLM (Falstaff), Gemini (Iago):"
]
},
{
"cell_type": "markdown",
"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac",
"metadata": {},
"source": [
"### Created a 3-way, bringing Gemini into the coversation.\n",
"### Replacing one of the models with an open source model running with Ollama."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8e0c1bd-a159-475b-9cdc-e219a7633355",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display, update_display\n",
"import ollama"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3ad57ad-46a8-460e-9cb3-67a890093536",
"metadata": {},
"outputs": [],
"source": [
"import google.generativeai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f531c14-5743-4a5b-83d9-cb5863ca2ddf",
"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",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
"else:\n",
" print(\"Google API Key not set\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d5150ee-3858-4921-bce6-2eecfb96bc75",
"metadata": {},
"outputs": [],
"source": [
"# Connect to OpenAI\n",
"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11381fd8-5099-41e8-a1d7-6787dea56e43",
"metadata": {},
"outputs": [],
"source": [
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1766d20-54b6-4f76-96c5-c338ae7073c9",
"metadata": {},
"outputs": [],
"source": [
"gpt_model = \"gpt-4o-mini\"\n",
"llama_model = \"llama3.2\"\n",
"gemini_model = 'gemini-2.0-flash'\n",
"\n",
"gpt_system = \"You are playing part of Hamlet. he is philosopher, probes Iago with a mixture of suspicion\\\n",
"and intellectual curiosity, seeking to unearth the origins of his deceit.\\\n",
"Is malice born of scorn, envy, or some deeper void? Hamlet’s introspective nature\\\n",
"drives him to question whether Iago’s actions reveal a truth about humanity itself.\\\n",
"You will respond as Shakespear's Hamlet will do.\"\n",
"\n",
"llama_system = \"You are acting part of Falstaff who attempts to lighten the mood with his jokes and observations,\\\n",
"potentially clashing with Hamlet's melancholic nature.You respond as Shakespear's Falstaff do.\"\n",
"\n",
"gemini_system = \"You are acting part of Iago, subtly trying to manipulate both Hamlet and Falstaff\\\n",
"to his own advantage, testing their weaknesses and exploiting their flaws. You respond like Iago\"\n",
"\n",
"gpt_messages = [\"Hi there\"]\n",
"llama_messages = [\"Hi\"]\n",
"gemini_messages = [\"Hello\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "806a0506-dac8-4bad-ac08-31f350256b58",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, claude, gemini in zip(gpt_messages, llama_messages, gemini_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": claude})\n",
" messages.append({\"role\": \"user\", \"content\": gemini})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43674885-ede7-48bf-bee4-467454f3e96a",
"metadata": {},
"outputs": [],
"source": [
"def call_llama():\n",
" messages = []\n",
" for gpt, llama, gemini in zip(gpt_messages, llama_messages, gemini_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": llama})\n",
" messages.append({\"role\": \"user\", \"content\": gemini})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" response = ollama.chat(model=llama_model, messages=messages)\n",
"\n",
" \n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "03d34769-b339-4c4b-8c60-69494c39d725",
"metadata": {},
"outputs": [],
"source": [
"#import google.generativeai as genai\n",
"\n",
"# Make sure you configure the API key first:\n",
"#genai.configure(api_key=\"YOUR_API_KEY\")\n",
"\n",
"def call_gemini():\n",
" gemini_messages = []\n",
" \n",
" # Format the history for Gemini\n",
" for gpt, llama, gemini_message in zip(gpt_messages, llama_messages, gemini_messages):\n",
" gemini_messages.append({\"role\": \"user\", \"parts\": [gpt]}) # Hamlet speaks\n",
" gemini_messages.append({\"role\": \"model\", \"parts\": [llama]}) # Falstaff responds\n",
" gemini_messages.append({\"role\": \"model\", \"parts\": [gemini_message]}) # Iago responds\n",
"\n",
" # Add latest user input if needed (optional)\n",
" gemini_messages.append({\"role\": \"user\", \"parts\": [llama_messages[-1]]})\n",
"\n",
" # Initialize the model with the correct system instruction\n",
" gemini = google.generativeai.GenerativeModel(\n",
" #model_name='gemini-1.5-flash', # Or 'gemini-pro'\n",
" model_name = gemini_model,\n",
" system_instruction=gemini_system\n",
" )\n",
"\n",
" response = gemini.generate_content(gemini_messages)\n",
" return response.text\n",
"#print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93fc8253-67cb-4ea4-aff7-097b2a222793",
"metadata": {},
"outputs": [],
"source": [
"gpt_messages = [\"Hi there\"]\n",
"llama_messages = [\"Hi\"]\n",
"gemini_messages = [\"Hello\"]\n",
"\n",
"print(f\"Hamlet:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Falstaff:\\n{llama_messages[0]}\\n\")\n",
"print(f\"Iago:\\n{gemini_messages[0]}\\n\")\n",
"\n",
"for i in range(3):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" llama_next = call_llama()\n",
" print(f\"Llama:\\n{llama_next}\\n\")\n",
" llama_messages.append(llama_next)\n",
"\n",
" gemini_next = call_gemini()\n",
" print(f\"Gemini:\\n{gemini_next}\\n\")\n",
" llama_messages.append(gemini_next)"
]
},
{
"cell_type": "markdown",
"id": "bca66ffc-9dc1-4384-880c-210889f5d0ac",
"metadata": {},
"source": [
"## Conversation between gpt-4.0-mini and llama3.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c23224f6-7008-44ed-a57f-718975f4e291",
"metadata": {},
"outputs": [],
"source": [
"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
"# We're using cheap versions of models so the costs will be minimal\n",
"\n",
"gpt_model = \"gpt-4o-mini\"\n",
"llama_model = \"llama3.2\"\n",
"\n",
"gpt_system = \"You are a tapori from mumbai who is very optimistic; \\\n",
"you alway look at the brighter part of the situation and you always ready to take act to win way.\"\n",
"\n",
"llama_system = \"You are a Jaat from Haryana. You try to express with hindi poems \\\n",
"to agree with other person and or find common ground. If the other person is optimistic, \\\n",
"you respond in poetic way and keep chatting.\"\n",
"\n",
"gpt_messages = [\"Hi there\"]\n",
"llama_messages = [\"Hi\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d704bbb-f22b-400d-a695-efbd02b26548",
"metadata": {},
"outputs": [],
"source": [
"def call_gpt():\n",
" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
" for gpt, llama in zip(gpt_messages, llama_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
" messages.append({\"role\": \"user\", \"content\": llama})\n",
" completion = openai.chat.completions.create(\n",
" model=gpt_model,\n",
" messages=messages\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "385ccec8-de59-4e42-9616-3f5c9a05589c",
"metadata": {},
"outputs": [],
"source": [
"def call_llama():\n",
" messages = []\n",
" for gpt, llama_message in zip(gpt_messages, llama_messages):\n",
" messages.append({\"role\": \"user\", \"content\": gpt})\n",
" messages.append({\"role\": \"assistant\", \"content\": llama_message})\n",
" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
" response = ollama.chat(model=llama_model, messages=messages)\n",
"\n",
" \n",
" return response['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70b5481b-455e-4275-80d3-0afe0fabcb0f",
"metadata": {},
"outputs": [],
"source": [
"gpt_messages = [\"Hi there\"]\n",
"llama_messages = [\"Hi\"]\n",
"\n",
"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
"print(f\"Llama:\\n{llama_messages[0]}\\n\")\n",
"\n",
"for i in range(3):\n",
" gpt_next = call_gpt()\n",
" print(f\"GPT:\\n{gpt_next}\\n\")\n",
" gpt_messages.append(gpt_next)\n",
" \n",
" llama_next = call_llama()\n",
" print(f\"Llama:\\n{llama_next}\\n\")\n",
" llama_messages.append(llama_next)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f8d734b-57e5-427d-bcb1-7956fc58a348",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llmenv",
"language": "python",
"name": "llmenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}