RAG Archives - Page 3 of 4 - ITFROMZERO

Artificial Intelligence tutorial - IT technology blog

Docling: Breaking the PDF ‘Curse’ for RAG Systems – From Messy Data to Clean Markdown

By IT From Zero May 10, 2026

Complex PDF processing is often the biggest bottleneck in RAG. Discover how IBM's Docling uses AI to understand layouts and convert PDFs into accurate Markdown, helping LLMs understand data deeply and provide smarter answers.

Crawl4AI: Transform Websites into Clean Markdown for RAG with Just a Few Lines of Python Code

By IT From Zero May 7, 2026

Eliminate messy web data worries when building RAG systems. A detailed guide on using Crawl4AI to convert websites into standardized Markdown, boosting your AI system's efficiency by 70%.

Database

pgvector Guide: Storing and Searching Vector Embeddings in PostgreSQL for AI and RAG Applications

By IT From Zero April 22, 2026

pgvector is a PostgreSQL extension that lets you store and search vector embeddings directly in your existing database — no need to deploy a separate system. This guide covers installation, HNSW index creation, semantic search, and integrating pgvector into a Python RAG pipeline, with practical monitoring tips from real-world experience.

Evaluating RAG with RAGAS: From ‘Gut Feeling’ to Faithfulness, Answer Relevance, and Context Precision Metrics

By IT From Zero April 20, 2026

Stop evaluating AI chatbots based on gut feeling. Use RAGAS to accurately measure Faithfulness, Answer Relevance, and Context Precision.

Cleaning PDF/Office Data for RAG with Unstructured.io: Turning ‘Trash’ into Gold

By IT From Zero April 6, 2026

Discover how Unstructured.io solves data extraction from PDF and Office files for RAG. Boost accuracy by 35% with smart table processing and text cleaning.

Guide to Using Dify to Create No-Code AI Workflows: Optimizing Your Workflow

By IT From Zero March 17, 2026

Dify helps you build powerful AI applications, from chatbots to summarization tools, without writing code. This guide compares methods, analyzes pros and cons, and provides detailed steps to get you started immediately.

Building a RAG System with RAGFlow: From Setup to Efficient Production Deployment

By IT From Zero March 10, 2026

Retrieval-Augmented Generation (RAG) systems help LLMs provide more accurate answers by retrieving information from external data sources. RAGFlow is a platform for building, managing, and efficiently deploying RAG. This article guides you through RAGFlow installation, creating RAG applications, configuring data and LLMs, deploying APIs, and optimizing for production.

Qdrant Installation and Usage Guide: A Powerful Vector Database for AI and RAG Applications

By IT From Zero March 10, 2026

This article provides a detailed guide on how to install and use Qdrant, a powerful vector database, to build effective AI and RAG applications. I share practical experience after more than 6 months of deploying Qdrant in a production environment, analyzing its pros and cons, and providing sample Python code to help you get started immediately.

Building a RAG Application with LangChain and Python: From Failure to Production

By IT From Zero February 28, 2026

Six months of hands-on experience building a RAG (Retrieval-Augmented Generation) system with LangChain and Python for an internal document lookup chatbot. From hallucination issues and context window limitations to a solution that actually works in production, with complete code examples.

Building a RAG Application with LangChain and Python — A Step-by-Step Guide for Beginners

By IT From Zero February 28, 2026

A step-by-step guide to building a RAG (Retrieval-Augmented Generation) application with LangChain and Python: from loading documents and creating a vector store with Chroma, to building a complete chain that lets your LLM answer questions based on your own data.