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Course: RAG from Scratch Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to build the retrieval and generation stages of a RAG pipeline from scratch. You will implement cosine similarity without external vector search libraries, build a similarity search function, design a grounding-focused prompt template, and assemble a complete end-to-end RAG system that retrieves context and generates accurate, grounded answer

Course: RAG from Scratch Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the foundational stages of a RAG pipeline: loading documents, extracting clean text, attaching metadata, enriching documents with LLM-generated keywords, and splitting them into retrievable chunks. By completing this assignment, you will have built a reusable knowledge base preparation pipeline that you can apply to any document colle

Introduction Modern satellite constellations generate petabytes of multispectral, hyperspectral, SAR, and LiDAR data every day, far outpacing the capacity of traditional analysis methods. Remote sensing professionals must interpret this imagery against historical baselines, evolving scientific literature, environmental benchmarks, and mission-specific requirements simultaneously. Satellite Data Analysis Systems powered by Retrieval-Augmented Generation (RAG) address this by d

Introduction Loan underwriting requires the rapid processing of vast financial documents, regulatory guidelines, and market data under tight deadlines, a challenge that rigid scoring models and manual review workflows are ill-equipped to handle. Underwriters must assess creditworthiness, collateral quality, and compliance requirements while keeping pace with constantly shifting lending regulations and economic conditions. Loan Underwriting Systems powered by Retrieval-Augment

Introduction Veterinary professionals must deliver accurate diagnoses across many species with unique biological differences, while keeping up with constantly evolving research and treatment guidelines. Retrieval Augmented Generation powered diagnostic systems provide real time access to veterinary literature, species specific protocols, diagnostic data, and patient history. By retrieving and synthesizing the most relevant and up to date evidence, these systems deliver contex

Introduction Traditional AI interactions require rigid command structures limiting natural communication. Developers struggle to extract optimal responses from language models without specialized knowledge. Manual experimentation with different prompting approaches consumes significant development time. Inconsistent model outputs complicate production deployment and user experience. Llama 3:8B Chat transforms AI interactions through instruction-tuned conversational capabiliti

Introduction The rapid rise in neurological research, especially in dementia detection, has increased the demand for automated methods to process brain activity data. With millions affected, early detection through non-invasive EEG is becoming essential. Manual EEG processing is slow and requires significant effort because researchers must clean noise, remove artifacts, and extract useful features from complex signals. Modern automated preprocessing pipelines solve this probl

Introduction The exponential growth of scientific literature, with millions of papers published annually, has made it increasingly difficult for researchers to keep pace with complex technical content. Traditional approaches based on manual reading and note taking create bottlenecks in knowledge discovery as scientists spend countless hours deciphering dense methodologies and synthesizing findings. Scientific Text Comprehension powered by Retrieval Augmented Generation (RAG)

Introduction Healthcare professionals face increasingly complex challenges in clinical decision making, as medical knowledge is expanding at an unprecedented pace and new clinical studies are published daily. Physicians must process vast amounts of literature, adapt to constantly evolving guidelines, and interpret complex patient data to make accurate diagnoses and treatment plans. Traditional support systems, based on static rules and limited knowledge bases, often fail to r

Introduction Academic research is more complex than ever, with millions of papers published annually across countless journals and...

Introduction In the fast-paced world of software engineering, development teams often waste time searching through scattered...

Prerequisite:  This is a continuation of the blog Part 2: Building a Database MCP Server: The Ultimate Code Walkthrough - Part 3 The Connection Traffic Controller - list_database_connections  @mcp.tool() async def list_database_connections() -> str: """List all active database connections""" connections = {} for key, manager in connection_pool.items(): db_type, db_path = key.split(':', 1) connections[key] = { 'database_path': db_path, 'database_type': db_type, 'is_sample_dat

Prerequisite:  This is a continuation of the blog Part 2: Building a Database MCP Server: The Ultimate Code Walkthrough - Part 2 Meet...

Prerequisite:  This is a continuation of the blog Part 2: Building a Database MCP Server: The Ultimate Code Walkthrough - Part 1 The...

Prerequisite:  This is a continuation of the blog Part 2: A Complete Guide to Creating a Multi-Agent Book Writing System 🧰 Tool Definitions Let’s now define all our MCP tools, one by one. 📅 Get Full Itinerary @mcp.tool() async def get_full_itinerary(city: str, days: int = 5) -> str: """Get a complete travel itinerary with direct Ollama AI using file-based data. Args: city: Name of the city to visit days: Number of days for the itinerary (default: 5) Returns: A formatted m

Prerequisite:  This is a continuation of the blog Part 1: A Complete Guide to Creating a Multi-Agent Book Writing System Making Sense of...

Ever wished you had a friend who's traveled everywhere, knows all the hidden gems, and could instantly create the perfect itinerary for...

Picture this: You're at your computer, juggling three different databases for three different projects. You keep switching connection...

In this blog, we’ll enhance our LangGraph chatbot by adding Human-in-the-Loop (HITL) capabilities. Learn how to pause AI workflows for expert input using interrupt(), seamlessly blend human reasoning into your LLM responses, and build more reliable, production-ready chat agents — all on top of the memory-powered chatbot we built previously.

Want your chatbot to remember what you said earlier? In this beginner-friendly guide, we build on our previous LangGraph chatbot and add memory using MemorySaver. Learn how to persist conversation history, manage sessions using threads, and inspect state — step-by-step.