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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "603cd418-504a-4b4d-b1c3-be04febf3e79", |
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"metadata": {}, |
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"source": [ |
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"# Article Title Generator\n", |
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"\n", |
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"Summarization use-case in which the user provides an article, which the LLM will analyze to suggest an SEO-optimized title.\n", |
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"\n", |
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"NOTES:\n", |
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"\n", |
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"1. This version does NOT support website scrapping. You must copy and paste the required article.\n", |
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"2. The following models were configured:\n", |
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" a. OpenAI gpt-4o-mini\n", |
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" b. Llama llama3.2\n", |
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" c. Deepseek deepseek-r1:1.5b\n", |
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" It is possible to configure additional models by adding the new model to the MODELS dictionary and its\n", |
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" initialization to the CLIENTS dictionary." |
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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": "279b0c00-9bb0-4c7f-9c6d-aa0b108274b9", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"import os\n", |
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"from dotenv import load_dotenv\n", |
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"from IPython.display import Markdown, display\n", |
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"from openai import OpenAI" |
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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": "d4730d8d-3e20-4f3c-a4ff-ed2ac0a8aa27", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# set environment variables for OpenAi\n", |
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"load_dotenv(override=True)\n", |
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"api_key = os.getenv('OPENAI_API_KEY')\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": null, |
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"id": "e773daa6-d05e-49bf-ad8e-a8ed4882b77e", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Confirming Llama is loaded\n", |
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"!ollama pull llama3.2" |
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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": "1abbb826-de66-498c-94d8-33369ad01885", |
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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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"MODELS = { 'GPT': 'gpt-4o-mini', \n", |
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" 'LLAMA': 'llama3.2', \n", |
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" 'DEEPSEEK': 'deepseek-r1:1.5b'\n", |
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" }\n", |
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"\n", |
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"CLIENTS = { 'GPT': OpenAI(), \n", |
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" 'LLAMA': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama'),\n", |
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" 'DEEPSEEK': OpenAI(base_url='http://localhost:11434/v1', api_key='ollama') \n", |
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" }" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "6f490fe4-32d5-41f3-890d-ecf4e5e01dd4", |
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"metadata": {}, |
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"source": [ |
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"### Copy & paste your article (without a title)" |
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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": "ddd76319-13ce-480b-baa7-cab6a5c88168", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# article - copy & paste your article\n", |
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"article = \"\"\"\n", |
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" REPLACE WITH YOUR ARTICLE CONTENT\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": "1914afad-dbd8-4c1f-8e68-80b0e5d743a9", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# system prompt\n", |
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"system_prompt = \"\"\"\n", |
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" You are an experienced SEO-focused copywriter. The user will provide an article, and your task is to analyze its content and generate the most effective, keyword-optimized title to maximize SEO performance.Respond in Markdown format.\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": "176cfac7-5e6d-4d4a-a1c4-1b63b60de1f7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# user prompt\n", |
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"user_prompt = f\"Following the article to be analyzed. Respond in Markdown format./n/n{article}\"\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": "c45fc7d7-08c9-4e34-b427-b928a219bb94", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# message list\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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" ]" |
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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": "f67b881f-1040-4cf7-82c5-e85f4c0bd252", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# call model and get answer\n", |
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"def get_answer(model):\n", |
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" # set required client\n", |
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" client = CLIENTS[model]\n", |
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"\n", |
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" # call model\n", |
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" response = client.chat.completions.create(\n", |
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" model=MODELS[model],\n", |
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" messages=messages\n", |
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" )\n", |
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"\n", |
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" # closing LLM client connection\n", |
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" client.close()\n", |
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" \n", |
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" # return answer\n", |
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" return response.choices[0].message.content\n", |
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" " |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "947b42ed-5b43-486d-8af3-e5b671c1fd0e", |
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"metadata": {}, |
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"source": [ |
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"### Get OpenAI Suggested Title" |
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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": "eb6f66e3-ab99-4f76-9358-896cb43c1fa1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# get openAi answer\n", |
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"answer = get_answer('GPT')\n", |
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"\n", |
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"# display openAi answer\n", |
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"display(Markdown(f\"### {MODELS['GPT']} Answer\\n\\n{answer}\" ))" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "70073ebf-a00a-416b-854d-642d450cd99b", |
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"metadata": {}, |
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"source": [ |
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"### Get Llama Suggested Title" |
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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": "caa190bb-de5f-45cc-b671-5d62688f7b25", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# get openAi answer\n", |
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"answer = get_answer('LLAMA')\n", |
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"\n", |
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"# display openAi answer\n", |
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"display(Markdown(f\"### {MODELS['LLAMA']} Answer\\n\\n{answer}\" ))" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "811edc4f-20e2-482d-ac89-fae9d1b70bed", |
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"metadata": {}, |
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"source": [ |
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"### Get Deepseek Suggested Title" |
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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": "082628e4-ff4c-46dd-ae5f-76578eb017ad", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# get openAi answer\n", |
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"answer = get_answer('DEEPSEEK')\n", |
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"\n", |
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"# display openAi answer\n", |
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"display(Markdown(f\"### {MODELS['DEEPSEEK']} Answer\\n\\n{answer}\" ))" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "7fc404a6-3a91-4c09-89de-867d3d69b4b2", |
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"metadata": {}, |
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"source": [ |
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"### Suggested future improvements\n", |
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"\n", |
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"1. Add support for website scrapping to replace copy/pasting of articles.\n", |
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"2. Improve the system_prompt to provide specific SEO best practices to adopt during the title generation.\n", |
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"3. Rephrase the system_prompt to ensure the model provides a single Title (not a list of suggestions). \n", |
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"4. Add the logic that would allow each model to assess the recommendations from the different models and \n", |
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" select the best among these. " |
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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": "1af8260b-5ba1-4eeb-acd0-02de537b1bf4", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"S" |
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] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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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": "3.11.11" |
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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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} |
Loading…
Reference in new issue