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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Welcome to the Second Lab - Week 1, Day 3\n",
"\n",
"Today we will work with lots of models! This is a way to get comfortable with APIs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left; width:100%\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../assets/stop.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#ff7800;\">Important point - please read</h2>\n",
" <span style=\"color:#ff7800;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations.<br/><br/>If you have time, I'd love it if you submit a PR for changes in the community_contributions folder - instructions in the resources. Also, if you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Start with imports - ask ChatGPT to explain any package that you don't know\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from anthropic import Anthropic\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Always remember to do this!\n",
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAi API Key exists and begins sk-proj-\n",
"Google API Key exists and begins AIzaSyAc\n"
]
}
],
"source": [
"# Print the key prefixes to help with any debugging\n",
"\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 does not exist\")\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 does not exist\")\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"request = \"Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence. \"\n",
"request += \"Answer only with the question, no explanation.\"\n",
"messages = [{\"role\": \"user\", \"content\": request}]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'role': 'user',\n",
" 'content': 'Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence. Answer only with the question, no explanation.'}]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"How might the application of ethical principles in artificial intelligence differ when addressing global issues such as climate change versus localized societal issues like housing inequality, and what are the potential implications of these differences?\n"
]
}
],
"source": [
"openai = OpenAI()\n",
"response = openai.chat.completions.create(\n",
" model=\"gpt-4o\",\n",
" messages=messages,\n",
")\n",
"question = response.choices[0].message.content\n",
"print(question)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"competitors = []\n",
"answers = []\n",
"messages = [{\"role\": \"user\", \"content\": question}]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"The application of ethical principles in artificial intelligence (AI) can differ significantly when addressing global issues like climate change compared to localized societal issues such as housing inequality. These differences arise from the scope, stakeholders involved, data availability, and the nature of the solutions required. Here are a few key distinctions and their implications:\n",
"\n",
"1. **Scope and Scale:**\n",
" - **Global Issues (Climate Change):** AI applications for global issues like climate change require a broad, systemic approach. These issues often involve large datasets spanning multiple countries and domains (e.g., meteorological data, energy consumption patterns). The ethical principle of beneficence (promoting good) is emphasized, along with considerations of fairness and equity at a global scale.\n",
" - **Localized Issues (Housing Inequality):** AI applications here focus on specific communities or regions. The solutions need to be tailored to local contexts, respecting cultural norms and priorities. Localized issues might prioritize procedural fairness and addressing biases in data that could exacerbate existing inequalities.\n",
"\n",
"2. **Stakeholders and Inclusivity:**\n",
" - **Global Issues:** Addressing climate change with AI involves diverse stakeholders, including international organizations, governments, NGOs, and multinational corporations. Achieving inclusivity and representation is challenging but essential, as solutions must consider varied global impacts and interests, following the ethical principle of justice.\n",
" - **Localized Issues:** Stakeholders include local governments, community organizations, and residents. The ethical focus might be more on participatory designs and ensuring that marginalized communities have a voice in the AI solutions affecting them.\n",
"\n",
"3. **Data and Privacy:**\n",
" - **Global Issues:** Data for global issues often involves international collaborations and can include sensitive or proprietary information from multiple sources. Ensuring privacy and security while promoting transparency is crucial but complex, often requiring new frameworks for data governance that respect cross-border legal and ethical standards.\n",
" - **Localized Issues:** These might involve personal data, raising concerns about privacy and consent. Ethical AI applications need to incorporate strong data privacy and protection measures, ensuring individuals' rights are not compromised.\n",
"\n",
"4. **Technological Solutions:**\n",
" - **Global Issues:** AI solutions could include predictive modeling for climate impacts, optimization of renewable energy systems, or global policy simulations. Such applications must consider long-term impacts and unintended consequences on a worldwide scale.\n",
" - **Localized Issues:** AI can help identify patterns in housing inequality, optimize resource allocation, or enhance urban planning. The implications here involve ensuring that technology does not perpetuate systemic biases and that it accounts for context-specific nuances.\n",
"\n",
"**Implications:**\n",
"- **Effectiveness and Fairness:** The effectiveness of AI solutions and their potential for bias can vary greatly. Ethical AI must ensure fairness by considering how solutions affect different groups, particularly those disadvantaged, whether on a global or local level.\n",
"- **Accountability and Governance:** Global issues may require international frameworks for accountability, while localized issues need robust local governance mechanisms. Both demand transparent decision-making processes to maintain public trust.\n",
"- **Sustainability and Adaptability:** Ethical principles should guide the sustainability of solutions. Global challenges require adaptable and scalable AI models, while localized issues benefit from agile and context-specific approaches.\n",
"\n",
"In summary, implementing ethical principles in AI for global versus localized issues requires nuanced consideration of scope, stakeholder engagement, data governance, and context-specific solutions. These differences can influence the effectiveness, equity, and acceptance of AI-driven initiatives, underlining the importance of tailored ethical frameworks for each type of issue."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# The API we know well\n",
"\n",
"model_name = \"gpt-4o\"\n",
"\n",
"response = openai.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "\"Could not resolve authentication method. Expected either api_key or auth_token to be set. Or for one of the `X-Api-Key` or `Authorization` headers to be explicitly omitted\"",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mTypeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m model_name = \u001b[33m\"\u001b[39m\u001b[33mclaude-3-7-sonnet-latest\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 5\u001b[39m claude = Anthropic()\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m response = \u001b[43mclaude\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m answer = response.content[\u001b[32m0\u001b[39m].text\n\u001b[32m 9\u001b[39m display(Markdown(answer))\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/anthropic/_utils/_utils.py:283\u001b[39m, in \u001b[36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 281\u001b[39m msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[32m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 282\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m283\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/anthropic/resources/messages/messages.py:978\u001b[39m, in \u001b[36mMessages.create\u001b[39m\u001b[34m(self, max_tokens, messages, model, metadata, service_tier, stop_sequences, stream, system, temperature, thinking, tool_choice, tools, top_k, top_p, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m 971\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m DEPRECATED_MODELS:\n\u001b[32m 972\u001b[39m warnings.warn(\n\u001b[32m 973\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe model \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m is deprecated and will reach end-of-life on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDEPRECATED_MODELS[model]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 974\u001b[39m \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m,\n\u001b[32m 975\u001b[39m stacklevel=\u001b[32m3\u001b[39m,\n\u001b[32m 976\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m978\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 979\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/v1/messages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 980\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 981\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 983\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 984\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 985\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 986\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mservice_tier\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 987\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstop_sequences\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop_sequences\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 988\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 989\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msystem\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msystem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 990\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtemperature\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 991\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mthinking\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mthinking\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 992\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtool_choice\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 993\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtools\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 994\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_k\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 995\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtop_p\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 996\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 997\u001b[39m \u001b[43m \u001b[49m\u001b[43mmessage_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMessageCreateParamsStreaming\u001b[49m\n\u001b[32m 998\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\n\u001b[32m 999\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmessage_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMessageCreateParamsNonStreaming\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1000\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1001\u001b[39m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1002\u001b[39m \u001b[43m \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 1003\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1004\u001b[39m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mMessage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1005\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 1006\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mRawMessageStreamEvent\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1007\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:1293\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m 1279\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m 1280\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1281\u001b[39m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1288\u001b[39m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1289\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m 1290\u001b[39m opts = FinalRequestOptions.construct(\n\u001b[32m 1291\u001b[39m method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m 1292\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1293\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:1009\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m 1006\u001b[39m options = \u001b[38;5;28mself\u001b[39m._prepare_options(options)\n\u001b[32m 1008\u001b[39m remaining_retries = max_retries - retries_taken\n\u001b[32m-> \u001b[39m\u001b[32m1009\u001b[39m request = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_build_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1010\u001b[39m \u001b[38;5;28mself\u001b[39m._prepare_request(request)\n\u001b[32m 1012\u001b[39m kwargs: HttpxSendArgs = {}\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:505\u001b[39m, in \u001b[36mBaseClient._build_request\u001b[39m\u001b[34m(self, options, retries_taken)\u001b[39m\n\u001b[32m 502\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 503\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnexpected JSON data type, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(json_data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, cannot merge with `extra_body`\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m505\u001b[39m headers = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_build_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mretries_taken\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 506\u001b[39m params = _merge_mappings(\u001b[38;5;28mself\u001b[39m.default_query, options.params)\n\u001b[32m 507\u001b[39m content_type = headers.get(\u001b[33m\"\u001b[39m\u001b[33mContent-Type\u001b[39m\u001b[33m\"\u001b[39m)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/anthropic/_base_client.py:446\u001b[39m, in \u001b[36mBaseClient._build_headers\u001b[39m\u001b[34m(self, options, retries_taken)\u001b[39m\n\u001b[32m 436\u001b[39m custom_headers = options.headers \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[32m 437\u001b[39m headers_dict = _merge_mappings(\n\u001b[32m 438\u001b[39m {\n\u001b[32m 439\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mx-stainless-timeout\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mstr\u001b[39m(options.timeout.read)\n\u001b[32m (...)\u001b[39m\u001b[32m 444\u001b[39m custom_headers,\n\u001b[32m 445\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m446\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_validate_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43mheaders_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_headers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 448\u001b[39m \u001b[38;5;66;03m# headers are case-insensitive while dictionaries are not.\u001b[39;00m\n\u001b[32m 449\u001b[39m headers = httpx.Headers(headers_dict)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/anthropic/_client.py:196\u001b[39m, in \u001b[36mAnthropic._validate_headers\u001b[39m\u001b[34m(self, headers, custom_headers)\u001b[39m\n\u001b[32m 193\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(custom_headers.get(\u001b[33m\"\u001b[39m\u001b[33mAuthorization\u001b[39m\u001b[33m\"\u001b[39m), Omit):\n\u001b[32m 194\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m196\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 197\u001b[39m \u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCould not resolve authentication method. Expected either api_key or auth_token to be set. Or for one of the `X-Api-Key` or `Authorization` headers to be explicitly omitted\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 198\u001b[39m )\n",
"\u001b[31mTypeError\u001b[39m: \"Could not resolve authentication method. Expected either api_key or auth_token to be set. Or for one of the `X-Api-Key` or `Authorization` headers to be explicitly omitted\""
]
}
],
"source": [
"# Anthropic has a slightly different API, and Max Tokens is required\n",
"\n",
"model_name = \"claude-3-7-sonnet-latest\"\n",
"\n",
"claude = Anthropic()\n",
"response = claude.messages.create(model=model_name, messages=messages, max_tokens=1000)\n",
"answer = response.content[0].text\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"Given a hypothetical scenario where quantum entanglement is demonstrably used for faster-than-light communication, outline the potential societal, philosophical, and ethical ramifications, focusing on the challenges it poses to existing frameworks of causality, free will, and the limitations it might impose on individual privacy and security.\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gemini = OpenAI(api_key=google_api_key, base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\")\n",
"model_name = \"gemini-2.0-flash\"\n",
"\n",
"response = gemini.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'deepseek_api_key' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[23]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m deepseek = OpenAI(api_key=\u001b[43mdeepseek_api_key\u001b[49m, base_url=\u001b[33m\"\u001b[39m\u001b[33mhttps://api.deepseek.com/v1\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2\u001b[39m model_name = \u001b[33m\"\u001b[39m\u001b[33mdeepseek-chat\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 4\u001b[39m response = deepseek.chat.completions.create(model=model_name, messages=messages)\n",
"\u001b[31mNameError\u001b[39m: name 'deepseek_api_key' is not defined"
]
}
],
"source": [
"deepseek = OpenAI(api_key=deepseek_api_key, base_url=\"https://api.deepseek.com/v1\")\n",
"model_name = \"deepseek-chat\"\n",
"\n",
"response = deepseek.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"groq = OpenAI(api_key=groq_api_key, base_url=\"https://api.groq.com/openai/v1\")\n",
"model_name = \"llama-3.3-70b-versatile\"\n",
"\n",
"response = groq.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## For the next cell, we will use Ollama\n",
"\n",
"Ollama runs a local web service that gives an OpenAI compatible endpoint, \n",
"and runs models locally using high performance C++ code.\n",
"\n",
"If you don't have Ollama, install it here by visiting https://ollama.com then pressing Download and following the instructions.\n",
"\n",
"After it's installed, you should be able to visit here: http://localhost:11434 and see the message \"Ollama is running\"\n",
"\n",
"You might need to restart Cursor (and maybe reboot). Then open a Terminal (control+\\`) and run `ollama serve`\n",
"\n",
"Useful Ollama commands (run these in the terminal, or with an exclamation mark in this notebook):\n",
"\n",
"`ollama pull <model_name>` downloads a model locally \n",
"`ollama ls` lists all the models you've downloaded \n",
"`ollama rm <model_name>` deletes the specified model from your downloads"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left; width:100%\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../assets/stop.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#ff7800;\">Super important - ignore me at your peril!</h2>\n",
" <span style=\"color:#ff7800;\">The model called <b>llama3.3</b> is FAR too large for home computers - it's not intended for personal computing and will consume all your resources! Stick with the nicely sized <b>llama3.2</b> or <b>llama3.2:1b</b> and if you want larger, try llama3.1 or smaller variants of Qwen, Gemma, Phi or DeepSeek. See the <A href=\"https://ollama.com/models\">the Ollama models page</a> for a full list of models and sizes.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!ollama pull llama3.2"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"ename": "NotFoundError",
"evalue": "Error code: 404 - {'error': {'message': 'model \"llama3.2\" not found, try pulling it first', 'type': 'api_error', 'param': None, 'code': None}}",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m ollama = OpenAI(base_url=\u001b[33m'\u001b[39m\u001b[33mhttp://localhost:11434/v1\u001b[39m\u001b[33m'\u001b[39m, api_key=\u001b[33m'\u001b[39m\u001b[33mollama\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 2\u001b[39m model_name = \u001b[33m\"\u001b[39m\u001b[33mllama3.2\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m response = \u001b[43mollama\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchat\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompletions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 5\u001b[39m answer = response.choices[\u001b[32m0\u001b[39m].message.content\n\u001b[32m 7\u001b[39m display(Markdown(answer))\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/openai/_utils/_utils.py:287\u001b[39m, in \u001b[36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 285\u001b[39m msg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[32m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 286\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[32m--> \u001b[39m\u001b[32m287\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/openai/resources/chat/completions/completions.py:925\u001b[39m, in \u001b[36mCompletions.create\u001b[39m\u001b[34m(self, messages, model, audio, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, reasoning_effort, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, web_search_options, extra_headers, extra_query, extra_body, timeout)\u001b[39m\n\u001b[32m 882\u001b[39m \u001b[38;5;129m@required_args\u001b[39m([\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m\"\u001b[39m], [\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mmodel\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstream\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 883\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcreate\u001b[39m(\n\u001b[32m 884\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 922\u001b[39m timeout: \u001b[38;5;28mfloat\u001b[39m | httpx.Timeout | \u001b[38;5;28;01mNone\u001b[39;00m | NotGiven = NOT_GIVEN,\n\u001b[32m 923\u001b[39m ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[32m 924\u001b[39m validate_response_format(response_format)\n\u001b[32m--> \u001b[39m\u001b[32m925\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 926\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/chat/completions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 927\u001b[39m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 928\u001b[39m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 929\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 930\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 931\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43maudio\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43maudio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 932\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfrequency_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 933\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunction_call\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 934\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfunctions\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 935\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogit_bias\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 936\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlogprobs\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 937\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_completion_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_completion_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 938\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_tokens\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 939\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 940\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodalities\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodalities\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 941\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mn\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 942\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mparallel_tool_calls\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 943\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mprediction\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mprediction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 944\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpresence_penalty\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 945\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreasoning_effort\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mreasoning_effort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 946\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse_format\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 947\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mseed\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 948\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mservice_tier\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mservice_tier\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 949\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstop\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 950\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstore\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 951\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstream\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 952\u001b[39m \u001b[43m 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961\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mCompletionCreateParamsStreaming\u001b[49m\n\u001b[32m 962\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\n\u001b[32m 963\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcompletion_create_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43mCompletionCreateParamsNonStreaming\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 964\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 965\u001b[39m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 966\u001b[39m \u001b[43m \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 967\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 968\u001b[39m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m=\u001b[49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 969\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 970\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 971\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/openai/_base_client.py:1242\u001b[39m, in \u001b[36mSyncAPIClient.post\u001b[39m\u001b[34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[39m\n\u001b[32m 1228\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpost\u001b[39m(\n\u001b[32m 1229\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1230\u001b[39m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1237\u001b[39m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 1238\u001b[39m ) -> ResponseT | _StreamT:\n\u001b[32m 1239\u001b[39m opts = FinalRequestOptions.construct(\n\u001b[32m 1240\u001b[39m method=\u001b[33m\"\u001b[39m\u001b[33mpost\u001b[39m\u001b[33m\"\u001b[39m, url=path, json_data=body, files=to_httpx_files(files), **options\n\u001b[32m 1241\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1242\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Projects/agents/.venv/lib/python3.12/site-packages/openai/_base_client.py:1037\u001b[39m, in \u001b[36mSyncAPIClient.request\u001b[39m\u001b[34m(self, cast_to, options, stream, stream_cls)\u001b[39m\n\u001b[32m 1034\u001b[39m err.response.read()\n\u001b[32m 1036\u001b[39m log.debug(\u001b[33m\"\u001b[39m\u001b[33mRe-raising status error\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1037\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._make_status_error_from_response(err.response) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1039\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m 1041\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mcould not resolve response (should never happen)\u001b[39m\u001b[33m\"\u001b[39m\n",
"\u001b[31mNotFoundError\u001b[39m: Error code: 404 - {'error': {'message': 'model \"llama3.2\" not found, try pulling it first', 'type': 'api_error', 'param': None, 'code': None}}"
]
}
],
"source": [
"ollama = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
"model_name = \"llama3.2\"\n",
"\n",
"response = ollama.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# So where are we?\n",
"\n",
"print(competitors)\n",
"print(answers)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# It's nice to know how to use \"zip\"\n",
"for competitor, answer in zip(competitors, answers):\n",
" print(f\"Competitor: {competitor}\\n\\n{answer}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Let's bring this together - note the use of \"enumerate\"\n",
"\n",
"together = \"\"\n",
"for index, answer in enumerate(answers):\n",
" together += f\"# Response from competitor {index+1}\\n\\n\"\n",
" together += answer + \"\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(together)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"judge = f\"\"\"You are judging a competition between {len(competitors)} competitors.\n",
"Each model has been given this question:\n",
"\n",
"{question}\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 number\", \"second best competitor number\", \"third best competitor number\", ...]}}\n",
"\n",
"Here are the responses from each competitor:\n",
"\n",
"{together}\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(judge)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"judge_messages = [{\"role\": \"user\", \"content\": judge}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Judgement time!\n",
"\n",
"openai = OpenAI()\n",
"response = openai.chat.completions.create(\n",
" model=\"o3-mini\",\n",
" messages=judge_messages,\n",
")\n",
"results = response.choices[0].message.content\n",
"print(results)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# OK let's turn this into results!\n",
"\n",
"results_dict = json.loads(results)\n",
"ranks = results_dict[\"results\"]\n",
"for index, result in enumerate(ranks):\n",
" competitor = competitors[int(result)-1]\n",
" print(f\"Rank {index+1}: {competitor}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left; width:100%\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../assets/exercise.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#ff7800;\">Exercise</h2>\n",
" <span style=\"color:#ff7800;\">Which pattern(s) did this use? Try updating this to add another Agentic design pattern.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left; width:100%\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../assets/business.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#00bfff;\">Commercial implications</h2>\n",
" <span style=\"color:#00bfff;\">These kinds of patterns - to send a task to multiple models, and evaluate results,\n",
" are common where you need to improve the quality of your LLM response. This approach can be universally applied\n",
" to business projects where accuracy is critical.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
}
],
"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": 2
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|