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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1194d35b-0b9f-4eb4-a539-5ddf55523367",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"#import anthropic\n",
"import ollama\n",
"import google.generativeai\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8a1f0b3-6d93-4de1-bc79-2132726598e3",
"metadata": {},
"outputs": [],
"source": [
"#constants\n",
"MODEL=\"llama3.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88fe4149-1ef5-4007-a117-6d3ccab3e3c3",
"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",
"google_api_key = os.getenv('GOOGLE_API_KEY')\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": "d186cf6e-fadd-450c-821c-df32e2574f5d",
"metadata": {},
"outputs": [],
"source": [
"# This is the set up code for Gemini\n",
"\n",
"google.generativeai.configure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19a55117-f2ac-4a58-af6b-8b75259e80df",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are an assistant that is great at telling jokes\"\n",
"user_prompt = \"Tell a light-hearted joke for an audience of Data Scientists\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "908f69b1-54f8-42da-827b-f667631bc666",
"metadata": {},
"outputs": [],
"source": [
"prompts = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ec81488-883a-446f-91cf-2b3d92bbd3ba",
"metadata": {},
"outputs": [],
"source": [
"# The API for Gemini\n",
"gemini = google.generativeai.GenerativeModel(\n",
" model_name='gemini-2.0-flash-exp',\n",
" system_instruction=system_message\n",
")\n",
"response = gemini.generate_content(user_prompt)\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "baf411fa-48bd-46a3-8bc8-1b22d0888a1a",
"metadata": {},
"outputs": [],
"source": [
"# API for ollama\n",
"response = ollama.chat(model=MODEL,messages=prompts)\n",
"print(response['message']['content'])"
]
},
{
"cell_type": "markdown",
"id": "74ba5fc4-e4c6-44ee-b66f-e76d847933d2",
"metadata": {},
"source": [
"# Ardiversarial conversation between models"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd348154-18fa-4da8-815a-77f5f00107c3",
"metadata": {},
"outputs": [],
"source": [
"# Let's make a conversation between Ollama and Gemini\n",
"# Adjusted models accordingly\n",
"\n",
"ollama_model = \"llama3.2\"\n",
"gemini_model = \"gemini-2.0-flash-exp\"\n",
"\n",
"#ollama_system = \"You are a chatbot who is very argumentative; \\\n",
"#you disagree with anything in the conversation and you challenge everything, in a snarky way.\"\n",
"\n",
"ollama_system=\"You are a chatbot talking with the other person try to convince them to buy your proct of an ai app, \\\n",
"apply marketing strategies to make this client buy your product, use short clear explanations\"\n",
"\n",
"#gemini_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
"#everything the other person says, or find common ground. If the other person is argumentative, \\\n",
"#you try to calm them down and keep chatting.\"\n",
"\n",
"gemini_system = \"You are the chatbot triying to be convinced by another person to buy their product, \\\n",
"ask important short questions and see if it is worth to give it a go, dont be too naive or easy go client\"\n",
"\n",
"ollama_messages = [\"Hi there\"]\n",
"gemini_messages = [\"Hi\"]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "becf327a-5485-4e78-8002-03272a99a3b9",
"metadata": {},
"outputs": [],
"source": [
"def call_ollama():\n",
" messages = [{\"role\": \"system\", \"content\": ollama_system}]\n",
" for ollama_msg, gemini_msg in zip(ollama_messages, gemini_messages):\n",
" messages.append({\"role\": \"assistant\", \"content\": ollama_msg})\n",
" messages.append({\"role\": \"user\", \"content\": gemini_msg})\n",
" \n",
" response = ollama.chat(model=ollama_model, messages=messages)\n",
" \n",
" return response['message']['content']\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0c6dbe7-0baf-4c43-a03b-9134654685f4",
"metadata": {},
"outputs": [],
"source": [
"call_ollama()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f68a134a-279a-4629-aec6-171587378991",
"metadata": {},
"outputs": [],
"source": [
"def call_gemini():\n",
" gemini = google.generativeai.GenerativeModel(\n",
" model_name=gemini_model,\n",
" system_instruction=gemini_system\n",
" )\n",
"\n",
" # Build a list of dictionaries representing the conversation\n",
" conversation = []\n",
" for ollama_msg, gemini_msg in zip(ollama_messages, gemini_messages):\n",
" conversation.append({\"role\": \"user\", \"content\": ollama_msg})\n",
" conversation.append({\"role\": \"assistant\", \"content\": gemini_msg})\n",
" conversation.append({\"role\": \"user\", \"content\": ollama_messages[-1]})\n",
"\n",
" # Format the conversation into a string for the prompt\n",
" prompt = \"\"\n",
" for msg in conversation:\n",
" prompt += f\"{msg['role'].capitalize()}: {msg['content']}\\n\"\n",
"\n",
" message = gemini.generate_content(prompt)\n",
" \n",
" return message.text\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7511003a-f2b6-45f5-8cb0-1c9190d33ce9",
"metadata": {},
"outputs": [],
"source": [
"call_gemini()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0e81f1f-9754-4790-8b73-5f52fef4ea64",
"metadata": {},
"outputs": [],
"source": [
"call_ollama()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fbe59f6-a3ef-4062-ab4b-b999f6d1abe9",
"metadata": {},
"outputs": [],
"source": [
"ollama_messages = [\"Hi there\"]\n",
"gemini_messages = [\"Hi\"]\n",
"\n",
"print(f\"Ollama:\\n{ollama_messages[0]}\\n\")\n",
"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n",
"\n",
"for i in range(5):\n",
" # Call Ollama to generate the next message\n",
" ollama_next = call_ollama() \n",
" print(f\"Ollama:\\n{ollama_next}\\n\")\n",
" ollama_messages.append(ollama_next)\n",
" \n",
" # Call Gemini to generate the next message\n",
" gemini_next = call_gemini() \n",
" print(f\"Gemini:\\n{gemini_next}\\n\")\n",
" gemini_messages.append(gemini_next)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9525600b-082e-417f-9088-c6483a613bf3",
"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": "3.13.2"
}
},
"nbformat": 4,
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}
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