Getting Started with Microsoft’s Presidio: A Step-by-Step Guide to Detecting and Anonymizing Personally Identifiable Information PII in Text

Getting Started with Microsoft’s Presidio: A Step-by-Step Guide to Detecting and Anonymizing Personally Identifiable Information PII in Text

In this tutorial, we will explore how to use Microsoft’s Presidio, an open-source framework designed for detecting, analyzing, and anonymizing personally identifiable information (PII) in free-form text. Built on top of the efficient spaCy NLP library, Presidio is both lightweight and modular, making it easy to integrate into real-time applications and pipelines. We will cover…

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A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and Gemini

A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and Gemini

In this tutorial, we demonstrate how to build a multi-step, intelligent query-handling agent using LangGraph and Gemini 1.5 Flash. The core idea is to structure AI reasoning as a stateful workflow, where an incoming query is passed through a series of purposeful nodes: routing, analysis, research, response generation, and validation. Each node operates as a…

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Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)

Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)

Real-world data is often costly, messy, and limited by privacy rules. Synthetic data offers a solution—and it’s already widely used: LLMs train on AI-generated text Fraud systems simulate edge cases Vision models pretrain on fake images SDV (Synthetic Data Vault) is an open-source Python library that generates realistic tabular data using machine learning. It learns…

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Step-by-Step Guide to Build a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent Creation

Step-by-Step Guide to Build a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent Creation

In this comprehensive tutorial, we guide users through creating a powerful multi-tool AI agent using LangGraph and Claude, optimized for diverse tasks including mathematical computations, web searches, weather inquiries, text analysis, and real-time information retrieval. It begins by simplifying dependency installations to ensure effortless setup, even for beginners. Users are then introduced to structured implementations…

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A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization

A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization

Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as 4-bit quantization and LoRA (Low-Rank Adaptation). In this tutorial, we walk through a practical implementation…

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A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow

A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow

In this Colab‑ready tutorial, we demonstrate how to integrate Google’s Gemini 2.0 generative AI with an in‑process Model Context Protocol (MCP) server, using FastMCP. Starting with an interactive getpass prompt to capture your GEMINI_API_KEY securely, we install and configure all necessary dependencies: the google‑genai Python client for calling the Gemini API, fastmcp for defining and…

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A Step-by-Step Guide to Building a Semantic Search Engine with Sentence Transformers, FAISS, and all-MiniLM-L6-v2

A Step-by-Step Guide to Building a Semantic Search Engine with Sentence Transformers, FAISS, and all-MiniLM-L6-v2

Semantic search goes beyond traditional keyword matching by understanding the contextual meaning of search queries. Instead of simply matching exact words, semantic search systems capture the intent and contextual definition of the query and return relevant results even when they don’t contain the same keywords. In this tutorial, we’ll implement a semantic search system using…

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