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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b9471aa1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display\n",
"\n",
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ff4eb891",
"metadata": {},
"outputs": [],
"source": [
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n",
"groq_api_key = os.getenv('GROQ_API_KEY') \n",
"\n",
"challenge_question_prompt = \"\"\"Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence.\n",
"Answer only with the question, no explanation.\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "94877c65",
"metadata": {},
"outputs": [],
"source": [
"def challenge_question(challenge_question_prompt):\n",
" messages = [\n",
" {\"role\": \"user\", \"content\": challenge_question_prompt}\n",
" ]\n",
"\n",
" challenge_question = OpenAI(api_key=openai_api_key).chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=messages\n",
" ).choices[0].message.content\n",
"\n",
"\n",
" display(Markdown(challenge_question))\n",
" return challenge_question"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8631a755",
"metadata": {},
"outputs": [],
"source": [
"models = [\"gpt-4o-mini\", \"deepseek-chat\", \"gemini-2.0-flash\", \"llama-3.3-70b-versatile\"]\n",
"api_urls = [\"https://api.openai.com/v1/\", \"https://api.deepseek.com/v1\", \"https://generativelanguage.googleapis.com/v1beta/openai/\", \"https://api.groq.com/openai/v1\"]\n",
"api_keys = [openai_api_key, deepseek_api_key, google_api_key, groq_api_key]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ddcdbfb1",
"metadata": {},
"outputs": [],
"source": [
"answers = []\n",
"\n",
"def answer_challenge_question(model, url, api_key, challenge_question):\n",
" messages = [{\"role\":\"user\", \"content\": challenge_question}]\n",
" answer = OpenAI(api_key=api_key, base_url=url).chat.completions.create(\n",
" model=model, \n",
" messages=messages\n",
" ).choices[0].message.content\n",
" answers.append(answer)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "97807e26",
"metadata": {},
"outputs": [],
"source": [
"import threading\n",
"\n",
"def ask_question_to_llm(challenge_question):\n",
" for index in range(len(models)):\n",
" thread = threading.Thread(target=answer_challenge_question, args=[models[index], api_urls[index], api_keys[index], challenge_question])\n",
" thread.start()\n",
" thread.join()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "aebed0c9",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import dis\n",
"\n",
"\n",
"def judge_llms(challenge_question_prompt, answers):\n",
" results = ''\n",
" for index, answer in enumerate(answers):\n",
" results += f\"Response from competitor model: {models[index]}\\n\\n\"\n",
" results += answer + \"\\n\\n\"\n",
"\n",
"\n",
" judge_prompt = f\"\"\"You are judging a competition between {len(models)} competitors.\n",
" Each model has been given this question:\n",
"\n",
" {challenge_question_prompt}\n",
"\n",
" Your job is to evaluate each response for clarity and strength of argument, and rank them in order of best to worst.\n",
" Respond with JSON, and only JSON, with the following format:\n",
" {{\"results\": [\"best competitor model\", \"second best competitor model\", \"third best competitor model\", ...]}}\n",
"\n",
" Here are the responses from each competitor:\n",
"\n",
" {results}\n",
"\n",
" Now respond with the JSON with the ranked order of the competitors, nothing else. Do not include markdown formatting or code blocks.\"\"\"\n",
"\n",
" display(Markdown(judge_prompt))\n",
"\n",
" messages = [{\"role\": \"user\", \"content\": judge_prompt}]\n",
" judge = OpenAI(api_key=openai_api_key).chat.completions.create(\n",
" model=\"o3-mini\", \n",
" messages=messages\n",
" ).choices[0].message.content\n",
" display(Markdown(judge))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d73b6507",
"metadata": {},
"outputs": [],
"source": [
"challenge_question = challenge_question(challenge_question_prompt)\n",
"ask_question_to_llm(challenge_question)\n",
"judge_llms(challenge_question_prompt=challenge_question_prompt, answers=answers)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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