Christian Santos ChrisFrontDev

baseado no meu outro tutorial https://gist.github.com/luzfcb/1a7f64adf5d12c2d357d0b4319fe9dcd que é mais atualizado que este. Eu não constumo atualizar esse tutorial, mas é bom para dar uma visão geral simplista das configurações.

Use o pyenv https://github.com/pyenv/pyenv para baixar, instalar e gerenciar múltiplas versões do INTERPRETADOR Python na sua maquina.

Primeiro instale as dependências:

Linux (Ubuntu):

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

	{
	"workbench.startupEditor": "newUntitledFile",
	"editor.fontSize": 14,
	"editor.lineHeight": 1.8,
	"javascript.suggest.autoImports": true,
	"javascript.updateImportsOnFileMove.enabled": "always",
	"editor.rulers": [
	80,
	120
	],

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Roberto Junior - Software Engineer</title>
	<style>
	* {
	margin: 0;
	padding: 0;