176 lines
4.6 KiB
Plaintext
176 lines
4.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
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"metadata": {},
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"source": [
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"# End of week 1 exercise\n",
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"\n",
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"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
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"and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c1070317-3ed9-4659-abe3-828943230e03",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"import os\n",
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"from openai import OpenAI\n",
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"from dotenv import load_dotenv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# constants\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set up environment\n",
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"system_prompt = \"\"\"\n",
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"You are a technical expert of AI and LLMs.\n",
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"\"\"\"\n",
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"\n",
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"user_prompt_prefix = \"\"\"\n",
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"Provide deep explanations of the provided text.\n",
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"\"\"\"\n",
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"\n",
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"user_prompt = \"\"\"\n",
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"Explain the provided text.\n",
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"\"\"\"\n",
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"client = OpenAI()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
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"metadata": {},
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"outputs": [],
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"source": [
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"# here is the question; type over this to ask something new\n",
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"\n",
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"question = \"\"\"\n",
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"Ollama does have an OpenAI compatible endpoint, but Gemini doesn't?\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get gpt-4o-mini to answer, with streaming\n",
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"def messages_for(question):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt_prefix + question}\n",
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" ]\n",
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"\n",
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"def run_model_streaming(model_name, question):\n",
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" stream = client.chat.completions.create(\n",
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" model=model_name,\n",
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" messages=messages_for(question),\n",
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" stream=True\n",
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" )\n",
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" for chunk in stream:\n",
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" content = chunk.choices[0].delta.content\n",
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" if content:\n",
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" print(content, end=\"\", flush=True)\n",
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"\n",
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"run_model_streaming(MODEL_GPT, question)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get Llama 3.2 to answer\n",
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"# imports\n",
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"import os\n",
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"from openai import OpenAI\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"# set up environment\n",
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"client = OpenAI(\n",
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" base_url=os.getenv(\"OPENAI_BASE_URL\", \"http://localhost:11434/v1\"),\n",
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" api_key=os.getenv(\"OPENAI_API_KEY\", \"ollama\")\n",
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")\n",
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"\n",
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"system_prompt = \"\"\"\n",
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"You are a technical expert of AI and LLMs.\n",
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"\"\"\"\n",
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"\n",
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"user_prompt_prefix = \"\"\"\n",
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"Provide deep explanations of the provided text.\n",
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"\"\"\"\n",
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"\n",
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"# question\n",
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"question = \"\"\"\n",
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"Ollama does have an OpenAI compatible endpoint, but Gemini doesn't?\n",
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"\"\"\"\n",
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"\n",
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"# message\n",
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"def messages_for(question):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt_prefix + question}\n",
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" ]\n",
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"\n",
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"# response\n",
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"def run_model(model_name, question):\n",
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" response = client.chat.completions.create(\n",
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" model=model_name,\n",
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" messages=messages_for(question)\n",
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" )\n",
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" return response.choices[0].message.content\n",
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"\n",
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"# run and print result\n",
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"print(run_model(MODEL_LLAMA, question))\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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