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🧠 OpenAI Quickstart Project (Flask + API)

This project is a simple web application built using Flask and the OpenAI API. It allows users to submit a prompt and receive an AI-generated response.

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📋 Table of Contents

• About
• Initial Setup
• Current Features
• Contributing
• Author

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About

This project is a simple web app built with Flask and OpenAI API. It lets users send prompts and get AI responses.

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create your feature branch (git checkout -b feature-name)
3. Commit your changes (git commit -m 'Add feature')
4. Push to the branch (git push origin feature-name)
5. Open a Pull Request

Please follow the existing code style and write clear commit messages.

🛠️ Initial Setup

Below are all the steps I followed manually to configure the environment. This demonstrates mastery of essential web development tools and AI integration.

1. Clone the repository

git clone https://github.com/your-username/openai-quickstart-01.git cd openai-quickstart-01

2. Create and activate the virtual environment
   Windows:
   python -m venv venv
   venv\Scripts\activate

Linux/Mac:
python3 -m venv venv
source venv/bin/activate

3. Install dependencies
   pip install -r requirements.txt

If you don't have a requirements.txt, create it with:
pip freeze > requirements.txt

Make sure it includes at least:
flask
openai
python-dotenv

4. Configure the OpenAI API key
   Create a .env file with the following content:
   OPENAI_API_KEY=your-api-key-here

Important: Use .gitignore to never upload your key to GitHub.

5. Expected basic structure
   openai-quickstart-01/
   ├── app.py
   ├── .env
   ├── requirements.txt
   ├── README.md
   ├── templates/
   │ └── index.html
   ├── static/
   │ └── css/
   │ └── style.css

6. Run the app locally
   flask run

Open your browser at: http://127.0.0.1:5000

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📌 Current Features

• Web interface with a prompt submission form
• Integration with OpenAI API
• Basic error handling
• User input length limit
• Simple CSS styling for frontend

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👨‍💻 Author

Lucas Alencar
ADS student, mobile-first beginner developer, passionate about Python, AI, and transformative technologies.
GitHub: https://github.com/lucasalencarxisto-stack

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-Training-ChatGPT-to-Recognize-Custom-Keywords-

Train ChatGPT to recognize custom keywords and simulate memory using prompt engineering. A personal semantic tagging system for your conversations.

🧠 Training ChatGPT to Recognize Custom Keywords

Empowering conversations through memory and semantic tags.

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📌 Project Description

This project demonstrates how to train ChatGPT to recognize and respond to custom keywords (a.k.a. "semantic bookmarks") in natural conversation. Using simple prompts and context memory, you can simulate an intelligent tagging system — similar to how developers use variables, or how your brain retrieves associated ideas when hearing a trigger word.

✨ Why this matters:
As AI assistants evolve, building systems that understand your unique way of thinking becomes crucial. This project showcases how to build a personal AI-enhanced memory by teaching ChatGPT to associate specific concepts, notes, or documents with keywords you define — creating an illusion of persistent memory.

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🧪 How It Works

• You define a custom keyword (e.g., openai-quickstart-01, Ideia_portifólio.01)
• ChatGPT is instructed to remember and recognize that keyword when mentioned
• The keyword links to a piece of content or context from a past interaction
• You can later retrieve that information just by typing the keyword

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🛠️ Use Cases

• 🗂️ Creating personal knowledge bases
• 🧭 Building navigation systems within long conversations
• 🧠 Externalizing memory for brainstorming and study
• 🧩 Managing multiple projects, notes, or documents

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📚 Example Prompt

Salvar palavra-chave: openai-quickstart-01

🌍 Multilingual Awareness
This project also includes translations of the full concept and documentation in:

🇧🇷 Portuguese

🇪🇸 Spanish

🇨🇳 Mandarin Chinese

(See /translations folder or the bottom of this README)

🤖 Future Goals
 Build a plugin or browser extension for ChatGPT to automate keyword tagging

 Create a web interface to manage stored tags and values

 Integrate with GitHub for saving and syncing memory contexts

👨‍💻 Author
Lucas Alencar
Student of Systems Analysis and Development | Aspiring OpenAI Dev | Passionate about AI and Memory-Augmented Interfaces
🔗 GitHub Profile: https://github.com/lucasalencarxisto-stack

📝 License
MIT License — use freely, learn openly, evolve collaboratively.

🌐 Translations
Click below to view full documentation in other languages:

🇧🇷 Versão em Português

🇪🇸 Versión en Español

🇨🇳 中文版本

(Coming soon...)

# 🧠 Training ChatGPT to Recognize Custom Keywords

A semantic keyword system to simulate memory and create a personal assistant experience using ChatGPT. This project demonstrates how to create a custom tagging system with keywords for easy context retrieval and project organization.

## 📌 Table of Contents
- [🌐 English](#-english)
- [🇧🇷 Português](#-português)
- [🇪🇸 Español](#-español)
- [🇨🇳 中文 (Mandarim)](#-中文-mandarim)

---

## 🌐 English

### 🧠 Project Overview

This experiment shows how to train ChatGPT to recognize and retrieve custom keywords, creating a kind of memory simulation through prompt engineering. It allows users to associate code names with detailed context (projects, goals, documents, ideas), making future access easier and faster.

### 🛠️ How It Works

- You define a **keyword** and give ChatGPT the context to remember.
- Later, simply call the keyword, and the AI will retrieve the associated info.
- It’s like building your personal assistant inside ChatGPT, without plugins.

### 🧪 Example

```text
User: Save the keyword "openai-quickstart-01"  
ChatGPT: ✅ Keyword "openai-quickstart-01" saved!

User: openai-quickstart-01  
ChatGPT: [Returns full context saved earlier]

📚 Use Cases
Project tracking

Learning logs

Brainstorm organization

Personal knowledge base

🇪🇸 Español
🧠 Descripción del Proyecto
Este experimento muestra cómo entrenar a ChatGPT para reconocer y recuperar palabras clave personalizadas, simulando una especie de memoria mediante ingeniería de prompts. El usuario puede asociar palabras clave a contextos importantes (proyectos, metas, documentos, ideas) para acceder a ellos fácilmente más tarde.

🛠️ Cómo Funciona
Defines una palabra clave y proporcionas el contexto a ChatGPT.

Luego, simplemente llamas a la palabra clave para recuperar la información.

Es como tener un asistente personal dentro de ChatGPT, sin necesidad de plugins.

🧪 Ejemplo
Usuario: Guardar palabra clave "openai-quickstart-01"  
ChatGPT: ✅ Palabra clave "openai-quickstart-01" guardada.

Más tarde...
Usuario: openai-quickstart-01  
ChatGPT: [Devuelve el contexto guardado anteriormente]

📚 Casos de Uso
Seguimiento de proyectos

Registros de aprendizaje

Organización de ideas

Base de conocimiento personal

🇨🇳 中文 (Mandarim)
🧠 项目概述
这个实验展示了如何训练 ChatGPT 识别和检索自定义关键词，通过提示工程模拟“记忆”系统。用户可以将关键字与重要的上下文（项目、目标、文档、想法）关联起来，便于日后快速访问。

🛠️ 工作原理
你定义一个关键词，并告诉 ChatGPT 相关内容。

以后只需输入关键词，AI 就会返回相关信息。

就像在 ChatGPT 内部构建了一个个人助理，无需插件。

🧪 示例
用户：保存关键词 "openai-quickstart-01"  
ChatGPT：✅ 关键词 "openai-quickstart-01" 已保存！

稍后...
用户：openai-quickstart-01  
ChatGPT：[返回之前保存的内容]

📚 应用场景
项目管理

学习日志

思维整理

个人知识库

🧠 Inspired by
My journey to becoming a junior developer and aiming to collaborate with OpenAI.
Follow me on GitHub and check my portfolio in progress!🧠 Inspired by
My journey to becoming a junior developer and aiming to collaborate with OpenAI.
Follow me on GitHub and check my portfolio in progress!



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