126 lines
3.0 KiB
Plaintext
126 lines
3.0 KiB
Plaintext
{
|
|
"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
|
|
}
|