From Scratch AI

Welcome to your first AI-powered assistant tutorial! In this guide, you'll build a simple, interactive AI assistant using Streamlit and the Claude API. By the end, you'll have a fully functioning web app capable of answering questions using artificial intelligence—no fancy coding skills required. Let's dive in! What You'll Need / Dependencies: Here's what you'll need to follow along: Python (version 3.8 or higher): Download Python . Claude API Key: Sign up for a free API key from Anthropic . Streamlit: An easy-to-use framework to create web apps quickly. Anthropic Python library: To communicate with the Claude API. Installing Dependencies: Create a new folder, open your terminal (or command prompt), and navigate into it. Run these commands: python -m venv venv source venv/bin/activate # (macOS/Linux) .\venv\Scripts\activate # (Windows) pip install streamlit anthropic Step-by-Step Instructions: Step 1: Setting Up Your Project S...

Ready to Call Your First AI API? Ever heard about Claude—the friendly, helpful AI model created by Anthropic? Today, you'll learn how to write your very first Python script to interact with Claude's API. By the end, you'll have a simple script that sends your question to Claude and receives a clear, human-like response. No fancy skills required—let's dive right in! What You'll Need / Dependencies: You'll need a few things set up on your computer before we start: Python 3.x: Download from python.org. pip (Python package manager): Usually comes with Python by default. Anthropic Python library: Install easily using pip. Claude API Key: Sign up at Anthropic.com to get your key. Here's how to install the required library using pip: pip install anthropic After installing, save your Claude API key in a safe place—we'll use it shortly! Step-by-Step Instructions: Step 1: Import the Anthropic library Open your favorite code editor and start a new Python ...

So you've played with basic RAG (Retrieval-Augmented Generation), but now you're ready to level up. In this tutorial, you’ll learn how to build a smarter RAG system that can: 💾 Ingest multiple documents (PDF or TXT) 🔍 Use embeddings for fast, relevant search 🤖 Generate responses using Claude based on the most relevant content This is perfect for anyone creating a smart knowledge assistant, internal wiki search, or custom Q&A bot. 🎯 What We’ll Use Tool Purpose Python Core logic Streamlit User interface Claude API Answer generation OpenAI / TensorFlow Embeddings FAISS Efficient vector search PyMuPDF PDF text extraction --- 📦 Step 1: Install the Tools pip install streamlit faiss-cpu openai python-dotenv PyMuPDF anthropic tiktoken 🔐 Create a .env file with: OPENAI_...

Ever wished ChatGPT or Claude could answer questions using your own documents or website content? That’s exactly what Retrieval-Augmented Generation (RAG) does. It’s like giving your AI assistant access to a custom knowledge base — so it can generate smarter, more relevant answers. 🔍 What is RAG in Simple Terms? RAG combines two powerful steps: Retrieval: It searches a set of documents (like PDFs, websites, or notes) to find relevant chunks. Generation: It feeds those chunks into an LLM (like Claude or GPT) to generate an accurate, helpful answer. Think of it like this: User → “What are the steps for applying for a mortgage?” RAG → Searches your finance PDFs → Finds a page with the steps → Sends it to Claude → Claude writes a helpful answer based on that info. 📦 What You'll Build In this tutorial, we’ll create a simple RAG system that: 📝 Lets you upload a PDF or TXT file 🔍 Searches that file for relevant content 🤖 Sends it to Claude to answer use...