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{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"metadata": {},
"source": [
"# **End of week 1 exercise**\n",
"\n",
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
]
},
{
"cell_type": "markdown",
"id": "c70e5ab1",
"metadata": {},
"source": [
"## **1. Get a response from your favorite AI Tutor** "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from openai import OpenAI\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from IPython.display import Markdown, display, update_display"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "65dace69",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv()\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"if api_key and api_key.startswith('sk-proj-') and len(api_key) > 10:\n",
" print(\"API key looks good so far\")\n",
"else:\n",
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"MODEL_LLAMA = 'llama3.2'\n",
"\n",
"openai = OpenAI()\n",
"\n",
"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "3673d863",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"\"\"You are the software engnieer, phd in mathematics, machine learning engnieer, and other topics\"\"\"\n",
"system_prompt += \"\"\"\n",
"When responding, always use Markdown for formatting. For any code, use well-structured code blocks with syntax highlighting,\n",
"For instance:\n",
"```python\n",
"\n",
"sample_list = [for i in range(10)]\n",
"```\n",
"Another example\n",
"```javascript\n",
" function displayMessage() {\n",
" alert(\"Hello, welcome to JavaScript!\");\n",
" }\n",
"\n",
"```\n",
"\n",
"Break down explanations into clear, numbered steps for better understanding. \n",
"Highlight important terms using inline code formatting (e.g., `function_name`, `variable`).\n",
"Provide examples for any concepts and ensure all examples are concise, clear, and relevant.\n",
"Your goal is to create visually appealing, easy-to-read, and informative responses.\n",
"\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "1df78d41",
"metadata": {},
"outputs": [],
"source": [
"def tutor_user_prompt(question):\n",
" # Ensure the question is properly appended to the user prompt.\n",
" user_prompt = (\n",
" \"Please carefully explain the following question in a step-by-step manner for clarity:\\n\\n\"\n",
" )\n",
" user_prompt += question\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "6dccbccb",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"def askTutor(question, MODEL):\n",
" # Generate the user prompt dynamically.\n",
" user_prompt = tutor_user_prompt(question)\n",
" \n",
" # OpenAI API call to generate response.\n",
" if MODEL == 'gpt-4o-mini':\n",
" print(f'You are getting response from {MODEL}')\n",
" stream = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" stream=True\n",
" )\n",
" else:\n",
" MODEL == 'llama3.2'\n",
" print(f'You are getting response from {MODEL}')\n",
" stream = ollama_via_openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" stream=True\n",
" )\n",
"\n",
" # Initialize variables for response processing.\n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" \n",
" # Process the response stream and update display dynamically.\n",
" for chunk in stream:\n",
" # Safely access the content attribute.\n",
" response_chunk = getattr(chunk.choices[0].delta, \"content\", \"\")\n",
" if response_chunk: # Check if response_chunk is not None or empty\n",
" response += response_chunk\n",
" # No replacement of Markdown formatting here!\n",
" update_display(Markdown(response), display_id=display_handle.display_id)\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
"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",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
"metadata": {},
"outputs": [],
"source": [
"askTutor(question=question, MODEL=MODEL_GPT)"
]
},
{
"cell_type": "markdown",
"id": "b79f9479",
"metadata": {},
"source": [
"## **2. Using both LLMs collaboratively approach**"
]
},
{
"cell_type": "markdown",
"id": "80e3c8f5",
"metadata": {},
"source": [
"- I thought about like similar the idea of a RAG (Retrieval-Augmented Generation) approach, is an excellent idea to improve responses by refining the user query and producing a polished, detailed final answer. Two LLM talking each other its cool!!! Here's how we can implement this:\n",
"\n",
"**Updated Concept:**\n",
"1. Refine Query with Ollama:\n",
" - Use Ollama to refine the raw user query into a well-structured prompt.\n",
" - This is especially helpful when users input vague or poorly structured queries.\n",
"2. Generate Final Response with GPT:\n",
" - Pass the refined prompt from Ollama to GPT to generate the final, detailed, and polished response.\n",
"3. Return the Combined Output:\n",
" - Combine the input, refined query, and the final response into a single display to ensure clarity."
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "60f5ac2d",
"metadata": {},
"outputs": [],
"source": [
"def refine_with_ollama(raw_question):\n",
" \"\"\"\n",
" Use Ollama to refine the user's raw question into a well-structured prompt.\n",
" \"\"\"\n",
" print(\"Refining the query using Ollama...\")\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant. Refine and structure the following user input.\"},\n",
"\n",
" {\"role\": \"user\", \"content\": raw_question},\n",
" ]\n",
" response = ollama_via_openai.chat.completions.create(\n",
" model=MODEL_LLAMA,\n",
" messages=messages,\n",
" stream=False # Non-streamed refinement\n",
" )\n",
" refined_query = response.choices[0].message.content\n",
" return refined_query"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "2aa4c9f6",
"metadata": {},
"outputs": [],
"source": [
"def ask_with_ollama_and_gpt(raw_question):\n",
" \"\"\"\n",
" Use Ollama to refine the user query and GPT to generate the final response.\n",
" \"\"\"\n",
" # Step 1: Refine the query using Ollama\n",
" refined_query = refine_with_ollama(raw_question)\n",
" \n",
" # Step 2: Generate final response with GPT\n",
" print(\"Generating the final response using GPT...\")\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": refined_query},\n",
" ]\n",
" stream = openai.chat.completions.create(\n",
" model=MODEL_GPT,\n",
" messages=messages,\n",
" stream=True # Stream response for dynamic display\n",
" )\n",
"\n",
" # Step 3: Combine responses\n",
" response = \"\"\n",
" display_handle = display(Markdown(f\"### Refined Query:\\n\\n{refined_query}\\n\\n---\\n\\n### Final Response:\"), display_id=True)\n",
" for chunk in stream:\n",
" response_chunk = getattr(chunk.choices[0].delta, \"content\", \"\")\n",
" if response_chunk:\n",
" response += response_chunk\n",
" update_display(Markdown(f\"### Refined Query:\\n\\n{refined_query}\\n\\n---\\n\\n### Final Response:\\n\\n{response}\"), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "4150e857",
"metadata": {},
"outputs": [],
"source": [
"# Example Usage\n",
"question = \"\"\"\n",
"Please explain what this code does:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2b8935f",
"metadata": {},
"outputs": [],
"source": [
"ask_with_ollama_and_gpt(raw_question=question)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "086a5294",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
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}