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First solution I design to get response from your favorite LLM and second is colloborative approach of two LLM added might be good specimen for the upcoming student to think about it even better and good approach to refine this approach I leave it from them

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codenigma1 5 months ago
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      week1/community-contributions/week1-collaborative-approach-two-llms.ipynb

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week1/community-contributions/week1-collaborative-approach-two-llms.ipynb

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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": "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
}
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