diff --git a/week1/community-contributions/week1-collaborative-approach-two-llms.ipynb b/week1/community-contributions/week1-collaborative-approach-two-llms.ipynb new file mode 100644 index 0000000..87b820a --- /dev/null +++ b/week1/community-contributions/week1-collaborative-approach-two-llms.ipynb @@ -0,0 +1,332 @@ +{ + "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": "llm_env", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}