Posted inAI
Hybrid Search with Qdrant: Solving RAG’s ‘Amnesia’ with Specific Keywords
Is your RAG system missing exact keywords? Discover how to implement Hybrid Search with Qdrant to combine Keyword and Vector Search for superior accuracy.
Posted inAI
LanceDB: The ‘SQLite’ of Vector Databases – A Lightweight Storage Solution for AI Applications
LanceDB is a lightweight embedded vector database that functions like SQLite but for AI. Learn how to install and optimize Vector Search for your RAG applications.
Posted inMySQL
MySQL 9.0 and VECTOR: Embedding Storage Solution for AI Apps Without a Secondary DB
MySQL 9.0 officially supports the VECTOR data type. Learn how to store AI embeddings, calculate Cosine distance, and build efficient semantic search systems directly in MySQL.
Posted inAI
Flowise: Install and Build Chatbots and RAG Pipelines with a No-Code Drag-and-Drop Interface
Flowise is an open-source tool that lets you build chatbots, RAG pipelines, and AI workflows through a drag-and-drop interface — no coding required. This guide covers installation via Docker/npm and walks through building a chatbot with Ollama and a RAG pipeline for PDF document Q&A, step by step.
Posted inAI
Teaching AI Agents Long-Term Memory with Mem0: A Solution for Python Developers
Is your AI forgetting user preferences? Learn how to integrate Mem0 into Python to build long-term memory, making your AI Agents smarter and more personalized.
Posted inAI
LlamaIndex + Ollama: Building a Fully Offline Internal Document Q&A System
LlamaIndex combined with Ollama lets you build a Q&A system over PDFs, Word files, and internal websites without sending any data to the cloud. This article compares different approaches, breaks down when the offline solution makes sense, and walks through deployment step by step from installation to production.
Posted inAI
Haystack 2.0: Building Document Processing Pipelines with Document Indexing, Hybrid Retrieval, and Smart Q&A
Haystack 2.0 lets you build AI document processing pipelines as directed graphs: each component connected explicitly, making them easy to debug and extend. This guide walks through document indexing, hybrid retrieval combining BM25 and embeddings, and a production-ready Q&A pipeline in Python.
Posted inAI
A Guide to Automated LLM Unit Testing with DeepEval
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.
Posted inDatabase
Setting Up Milvus Vector Database with Docker: Building a Knowledge Store for RAG and AI
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.
Posted inDatabase
Getting Started with ChromaDB: The Open-Source Vector Database for Building AI Agent Knowledge Systems
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.