333 lines
10 KiB
Plaintext
333 lines
10 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": "markdown",
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"id": "c70e5ab1",
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"metadata": {},
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"source": [
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"## **1. Get a response from your favorite AI Tutor** "
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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": 1,
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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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"import os\n",
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"from openai import OpenAI\n",
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"import json\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import Markdown, display, update_display"
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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": "65dace69",
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"metadata": {},
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"outputs": [],
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"source": [
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"load_dotenv()\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"if api_key and api_key.startswith('sk-proj-') and len(api_key) > 10:\n",
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" print(\"API key looks good so far\")\n",
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"else:\n",
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" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")"
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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": 3,
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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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"\n",
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"MODEL_GPT = 'gpt-4o-mini'\n",
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"MODEL_LLAMA = 'llama3.2'\n",
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"\n",
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"openai = OpenAI()\n",
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"\n",
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"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')"
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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": 38,
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"id": "3673d863",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_prompt = \"\"\"You are the software engnieer, phd in mathematics, machine learning engnieer, and other topics\"\"\"\n",
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"system_prompt += \"\"\"\n",
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"When responding, always use Markdown for formatting. For any code, use well-structured code blocks with syntax highlighting,\n",
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"For instance:\n",
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"```python\n",
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"\n",
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"sample_list = [for i in range(10)]\n",
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"```\n",
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"Another example\n",
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"```javascript\n",
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" function displayMessage() {\n",
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" alert(\"Hello, welcome to JavaScript!\");\n",
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" }\n",
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"\n",
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"```\n",
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"\n",
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"Break down explanations into clear, numbered steps for better understanding. \n",
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"Highlight important terms using inline code formatting (e.g., `function_name`, `variable`).\n",
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"Provide examples for any concepts and ensure all examples are concise, clear, and relevant.\n",
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"Your goal is to create visually appealing, easy-to-read, and informative responses.\n",
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"\n",
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"\"\"\"\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": 39,
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"id": "1df78d41",
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"metadata": {},
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"outputs": [],
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"source": [
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"def tutor_user_prompt(question):\n",
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" # Ensure the question is properly appended to the user prompt.\n",
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" user_prompt = (\n",
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" \"Please carefully explain the following question in a step-by-step manner for clarity:\\n\\n\"\n",
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" )\n",
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" user_prompt += question\n",
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" return user_prompt"
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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": 43,
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"id": "6dccbccb",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"def askTutor(question, MODEL):\n",
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" # Generate the user prompt dynamically.\n",
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" user_prompt = tutor_user_prompt(question)\n",
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" \n",
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" # OpenAI API call to generate response.\n",
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" if MODEL == 'gpt-4o-mini':\n",
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" print(f'You are getting response from {MODEL}')\n",
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" stream = openai.chat.completions.create(\n",
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" model=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ],\n",
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" stream=True\n",
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" )\n",
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" else:\n",
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" MODEL == 'llama3.2'\n",
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" print(f'You are getting response from {MODEL}')\n",
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" stream = ollama_via_openai.chat.completions.create(\n",
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" model=MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ],\n",
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" stream=True\n",
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" )\n",
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"\n",
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" # Initialize variables for response processing.\n",
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" response = \"\"\n",
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" display_handle = display(Markdown(\"\"), display_id=True)\n",
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" \n",
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" # Process the response stream and update display dynamically.\n",
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" for chunk in stream:\n",
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" # Safely access the content attribute.\n",
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" response_chunk = getattr(chunk.choices[0].delta, \"content\", \"\")\n",
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" if response_chunk: # Check if response_chunk is not None or empty\n",
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" response += response_chunk\n",
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" # No replacement of Markdown formatting here!\n",
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" update_display(Markdown(response), display_id=display_handle.display_id)\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": 44,
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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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"# 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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"Please explain what this code does and why:\n",
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"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\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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"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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"askTutor(question=question, MODEL=MODEL_GPT)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b79f9479",
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"metadata": {},
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"source": [
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"## **2. Using both LLMs collaboratively approach**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "80e3c8f5",
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"metadata": {},
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"source": [
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"- 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",
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"\n",
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"**Updated Concept:**\n",
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"1. Refine Query with Ollama:\n",
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" - Use Ollama to refine the raw user query into a well-structured prompt.\n",
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" - This is especially helpful when users input vague or poorly structured queries.\n",
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"2. Generate Final Response with GPT:\n",
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" - Pass the refined prompt from Ollama to GPT to generate the final, detailed, and polished response.\n",
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"3. Return the Combined Output:\n",
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" - Combine the input, refined query, and the final response into a single display to ensure clarity."
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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": 59,
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"id": "60f5ac2d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def refine_with_ollama(raw_question):\n",
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" \"\"\"\n",
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" Use Ollama to refine the user's raw question into a well-structured prompt.\n",
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" \"\"\"\n",
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" print(\"Refining the query using Ollama...\")\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are a helpful assistant. Refine and structure the following user input.\"},\n",
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"\n",
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" {\"role\": \"user\", \"content\": raw_question},\n",
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" ]\n",
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" response = ollama_via_openai.chat.completions.create(\n",
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" model=MODEL_LLAMA,\n",
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" messages=messages,\n",
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" stream=False # Non-streamed refinement\n",
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" )\n",
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" refined_query = response.choices[0].message.content\n",
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" return refined_query"
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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": 60,
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"id": "2aa4c9f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"def ask_with_ollama_and_gpt(raw_question):\n",
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" \"\"\"\n",
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" Use Ollama to refine the user query and GPT to generate the final response.\n",
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" \"\"\"\n",
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" # Step 1: Refine the query using Ollama\n",
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" refined_query = refine_with_ollama(raw_question)\n",
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" \n",
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" # Step 2: Generate final response with GPT\n",
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" print(\"Generating the final response using GPT...\")\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": refined_query},\n",
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" ]\n",
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" stream = openai.chat.completions.create(\n",
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" model=MODEL_GPT,\n",
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" messages=messages,\n",
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" stream=True # Stream response for dynamic display\n",
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" )\n",
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"\n",
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" # Step 3: Combine responses\n",
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" response = \"\"\n",
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" display_handle = display(Markdown(f\"### Refined Query:\\n\\n{refined_query}\\n\\n---\\n\\n### Final Response:\"), display_id=True)\n",
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" for chunk in stream:\n",
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" response_chunk = getattr(chunk.choices[0].delta, \"content\", \"\")\n",
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" if response_chunk:\n",
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" response += response_chunk\n",
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" 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)"
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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": 61,
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"id": "4150e857",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Example Usage\n",
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"question = \"\"\"\n",
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"Please explain what this code does:\n",
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"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\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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"id": "f2b8935f",
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"metadata": {},
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"outputs": [],
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"source": [
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"ask_with_ollama_and_gpt(raw_question=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": "086a5294",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llm_env",
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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": "3.11.9"
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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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