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{
"cells": [
{
"cell_type": "markdown",
"id": "135ee16c-2741-4ebf-aca9-1d263083b3ce",
"metadata": {},
"source": [
"# End of week 1 exercise\n",
"\n",
"Build a tutor tool by using Ollama."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"import ollama\n",
"from IPython.display import Markdown, display, clear_output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"MODEL_LLAMA = 'llama3.2'"
]
},
{
"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",
"\"\"\"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [],
"source": [
"# Get Llama 3.2 to answer, with streaming\n",
"\n",
"\n",
"messages=[{\"role\":\"user\",\"content\":question}]\n",
"\n",
"for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n",
" print(chunk['message']['content'], end='', flush=True)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1f71014-e780-4d3f-a227-1a7c18158a4c",
"metadata": {},
"outputs": [],
"source": [
"#Alternative answer with streaming in Markdown!\n",
"\n",
"def stream_response():\n",
" messages = [{\"role\": \"user\", \"content\": question}]\n",
" \n",
" display_markdown = display(Markdown(\"\"), display_id=True)\n",
"\n",
" response_text = \"\"\n",
" for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n",
" \n",
" response_text += chunk['message']['content']\n",
" clear_output(wait=True) # Clears previous output\n",
" display_markdown.update(Markdown(response_text)) # Updates Markdown dynamically\n",
"\n",
"# Run the function\n",
"stream_response()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c38fdd2a-4b09-402c-ba46-999b22b0cb15",
"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,
"nbformat_minor": 5
}