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A Guide to Automated LLM Unit Testing with DeepEval

By IT From Zero June 25, 2026

DeepEval lets you write unit tests for LLM applications just like regular pytest, with metrics for AnswerRelevancy, Faithfulness, and hallucination detection. A guide from a 5-minute install to CI/CD integration for automatically controlling AI quality before every deployment.

Database

Setting Up Milvus Vector Database with Docker: Building a Knowledge Store for RAG and AI

By IT From Zero June 18, 2026

A guide to installing Milvus Vector Database with Docker Compose, creating collections, storing vector embeddings, and building a complete RAG pipeline. Includes tips on choosing index types, chunking strategies, and production optimization from real-world experience.

Database

Getting Started with ChromaDB: The Open-Source Vector Database for Building AI Agent Knowledge Systems

By IT From Zero June 13, 2026

ChromaDB is an open-source vector database for semantic storage and search — lightweight enough to run locally, powerful enough to serve as the foundation for RAG systems and AI Agents. This guide walks through installation, document indexing, semantic search queries, and Docker deployment with practical, step-by-step examples.

Installing AnythingLLM on Ubuntu: Build Your Own Ultra-Secure Local AI Knowledge Base

By IT From Zero May 11, 2026

Build a secure internal RAG system with AnythingLLM on Ubuntu. Learn how to install via Docker, connect Ollama, and optimize your local AI knowledge base for absolute data privacy.

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.

RAG

A Guide to Automated LLM Unit Testing with DeepEval

Setting Up Milvus Vector Database with Docker: Building a Knowledge Store for RAG and AI

Getting Started with ChromaDB: The Open-Source Vector Database for Building AI Agent Knowledge Systems

Installing AnythingLLM on Ubuntu: Build Your Own Ultra-Secure Local AI Knowledge Base

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

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

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

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

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

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