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Tag: vector

  • Image Embeddings in Vector Databases (Multi Modal Embedded data) : From Novice to Master

    Image Embeddings in Vector DBs: From Novice to Master Let’s unlock a powerful capability: using **image embedding models** to store and find data in Vector DBs. This allows for truly groundbreaking applications like reverse image search, visual similarity recommendations, and multimodal search (searching images with text queries). This guide will detail the concepts, use cases,… Read more

  • Vector Databases vs. MongoDB: Storing & Finding Data (Multi Modal Embedded Data) – A Master’s Guide

    Vector DBs vs. MongoDB: Storing & Finding Data – A Master’s Guide In the rapidly evolving landscape of AI and data, a new type of database has emerged: the Vector Database. While MongoDB excels at storing and querying diverse, semi-structured documents, Vector DBs are purpose-built for a very specific, yet increasingly critical, type of data:… Read more

  • Agentic AI Workflow Tutorial for Beginners: Building a Smart Customer Service Assistant

    Agentic AI Workflow Tutorial for Beginners (Expanded) Welcome to the exciting world of Agentic AI! This expanded tutorial will delve deeper into the core concepts and provide more detailed explanations for each component, including illustrative (but not executable) code snippets and conceptual datasets. We’ll continue with our goal of building a basic Smart Customer Service… Read more

  • Mastering LangChain and LangGraph: From Novice to Expert

    Mastering LangChain and LangGraph: From Novice to Expert You’re about to become an expert in building powerful AI applications using LangChain and LangGraph. These two frameworks are essential tools for anyone looking to go beyond simple prompts and create sophisticated, intelligent systems powered by Large Language Models (LLMs). We’ll start with the fundamentals of LangChain,… Read more

  • Mastering Mosaic AI Vector Search: From Novice to Expert

    Mastering Mosaic AI Vector Search: From Novice to Expert You’re about to embark on a journey from understanding the basics of vector search to becoming an expert in leveraging Databricks’ powerful Mosaic AI Vector Search. This technology is at the heart of making AI truly intelligent, enabling Large Language Models (LLMs) and other AI systems… Read more

  • Mosaic AI Agent Framework vs. LangGraph: A Detailed Comparison

    Mosaic AI Agent Framework vs. LangGraph: A Detailed Comparison When building sophisticated AI agents, developers often face a choice between general-purpose frameworks and platform-specific solutions. This comparison will delve into two prominent options: Databricks‘ Mosaic AI Agent Framework and LangGraph (a module of LangChain), highlighting their strengths, weaknesses, and ideal use cases. Both frameworks aim… Read more

  • Detailed Guide to Using Databricks with Agentic AI

    Detailed Guide to Using Databricks with Agentic AI Databricks, with its unified Lakehouse Platform, offers a robust environment for developing, deploying, and managing Agentic AI systems. Agentic AI involves AI models (often Large Language Models – LLMs) that can reason, plan, use tools, and take autonomous actions. This guide will detail how to leverage Databricks… Read more

  • Microsoft AI-Powered Coding Tools

    Microsoft AI Coding Tools Microsoft offers a comprehensive ecosystem of AI-powered coding tools and services, deeply integrated across its developer platforms like Azure and GitHub, and productivity suites like Microsoft 365. These tools leverage advanced AI models, including OpenAI’s GPT series, to enhance productivity, improve code quality, and automate development workflows. 1. GitHub Copilot GitHub… Read more

  • Post-Quantum Cryptography (PQC): Securing the Future

    Post-Quantum Cryptography (PQC) Explained for Novices (More Context) In our increasingly digital world, the security of our information relies heavily on cryptography, the art of writing and solving codes. Think of it as the invisible shield protecting everything from your online banking to government secrets. Currently, this shield is strong against regular computers, but the… Read more

  • Image Object Identification Explained (Detailed)

    Image Object Identification Explained for Novices (Detailed) Imagine equipping a computer with the ability to “see” and understand the content of images, specifically identifying the different objects present within them. This capability, known as image object identification, is a cornerstone of computer vision, enabling machines to interpret and interact with the visual world. It involves… Read more

  • Transformer vs. RNN: A Detailed Explanation

    Transformer vs. RNN: A Detailed Explanation This document provides a comprehensive explanation of the differences between Recurrent Neural Networks (RNNs) and Transformers, two pivotal architectures in deep learning for processing sequential data like text, audio, and time series. Recurrent Neural Networks (RNNs): Remembering the Past, Step-by-Step RNNs are neural networks designed to process sequential data… Read more

  • Understanding Weaviate: A Library of Meaning

    Weaviate Internal Concepts Explained for Novices Imagine a special library where books aren’t just organized by title or author, but by the very essence of their content. That’s the core idea behind Weaviate, a powerful vector database that helps computers understand and search through information based on its meaning. 1. The Building Blocks: Objects and… Read more

  • Exploring Graph Databases vs Vector Databases: A Detailed Comparison

    Exploring Graph Databases vs Vector Databases: A Detailed Comparison This document provides an in-depth exploration of graph databases and vector databases, highlighting their core concepts, functionalities, and architectural considerations to help you choose the right tool for your data needs. Graph Databases: Unraveling the Fabric of Connected Data Core Concepts Nodes (Vertices): Represent entities with… Read more

  • Vector DB Weaviate Advanced Internal Concepts and Code Snippets

    Weaviate Internal Concepts and Code Snippets This document explores the core internal concepts of Weaviate, an open-source vector database, and provides illustrative code snippets using the Python client library to demonstrate its usage. Internal Concepts of Weaviate Schema and Collections Schema: Defines the structure of your data, including classes (now called Collections in newer versions),… Read more

  • Vector DB Pinecone Advanced Internal Concepts and Architecture

    Advanced Pinecone Internal Concepts and Architecture Advanced Pinecone Internal Concepts and Architecture This document builds upon the foundational understanding of Pinecone’s internals and delves into more advanced concepts, complemented by illustrative code snippets and a high-level architectural overview. As Pinecone’s exact architecture is proprietary, these are informed inferences based on advanced vector database techniques and… Read more

  • Vector DB Pinecone Internal Concepts and Code Snippets

    Pinecone Internal Concepts and Code Snippets This document explores the inferred internal concepts of Pinecone, a vector database, and provides illustrative code snippets using the Python client library to demonstrate its usage. Internal Concepts of Pinecone (Inferred) Index Structure Sharding: Data is likely distributed across multiple servers for scalability. Replication: Redundancy is probably implemented for… Read more

  • Neural Network Nodes and Activation Functions

    Neural Network Nodes and Activation Functions In artificial neural networks, the fundamental building blocks are nodes (also called neurons or units). These nodes perform computations on incoming data and pass the result to other nodes in the network. A crucial component of each node is its activation function, which introduces non-linearity and determines the node’s… Read more

  • Data Structure of Trained ML Models

    Data Structure of Trained ML Models Once a machine learning model is trained, its “knowledge” is stored in a specific data structure that allows it to make predictions on new, unseen data. The exact structure varies depending on the type of model and the library used for training. However, the core idea is to save… Read more

  • Neural Network Data Structure Details

    Neural Network Data Structure Neural Network Data Structure A neural network’s data structure is fundamentally organized in layers of interconnected nodes (also called neurons or units). These layers process and transform data as it flows through the network, inspired by the structure of the human brain (AWS Definition). 1. Nodes (Neurons/Units): Basic Building Block: Each… Read more

  • Detailed Exploration of LangChain Chains and Use Cases

    Detailed Exploration of LangChain Chains and Use Cases LangChain’s “Chains” are composable sequences of components, allowing you to build sophisticated applications by linking together Language Models (LLMs), prompts, utilities, and other chains. Let’s explore each of the core chain types with more detail and practical use cases. 1. LLMChain: Structuring Language Model Interactions Detail: The… Read more

  • Various MCP Servers and Cloud Availability

    Companies Developing MCP Servers and Cloud Availability A growing number of companies are actively developing and deploying MCP (Model Context Protocol) servers to integrate their services with AI agents. Many of these servers are designed to run in or interact with cloud environments. Companies with Developed MCP Servers (Examples) Technology Platforms Cloudflare: Provides infrastructure for… Read more

  • Retrieval-Augmented Generation (RAG) Enhanced by Model Context Protocol (MCP)

    RAG Enhanced by MCP: Detailed Explanation The integration of Retrieval-Augmented Generation (RAG) with the Model Context Protocol (MCP) offers a powerful paradigm for building more intelligent and versatile Large Language Model (LLM) applications. MCP provides a structured way for LLMs to interact with external tools and data sources, which can significantly enhance the retrieval capabilities… Read more

  • Various flavors of Retrieval Augmented Generation (RAG)

    Various Types of RAG The field of Retrieval-Augmented Generation (RAG) is rapidly evolving, with several variations and advanced techniques emerging beyond the basic “naive” RAG. I. Based on the Core RAG Pipeline 1. Naive/Standard RAG The user’s query is directly used to retrieve relevant documents, and these are passed to the LLM for generation. Use… Read more

  • Exploring LangChain, LangGraph, and LangSmith

    Exploring LangChain, LangGraph, and LangSmith The LangChain ecosystem provides a comprehensive suite of tools for building, deploying, and managing applications powered by Large Language Models (LLMs). It consists of three key components: LangChain, LangGraph, and LangSmith. LangChain: The Building Blocks LangChain is an open-source framework designed to simplify the development of LLM-powered applications. It provides… Read more

  • Understanding Agentic Retrieval-Augmented Generation (RAG)

    Understanding Agentic RAG Agentic Retrieval-Augmented Generation (RAG) goes beyond standard RAG by incorporating more sophisticated agent-like behaviors to enhance the generation process. Think of it as a proactive and strategic assistant for information retrieval and content generation. Key Differences from Standard RAG Decision-Making in Retrieval: Agentic RAG decides *when* and *how* to retrieve information, unlike… Read more

  • Understanding Optimization algorithms in Machine Learning

    Understanding Optimization Algorithms in Machine Learning Here let’s look at optimization algorithms, which are methods used to find the best possible solution to a problem, often by minimizing a cost function or maximizing a reward function. In machine learning, these algorithms are crucial for training models by iteratively adjusting their parameters to improve performance on… Read more

  • Understanding Gradient Descent

    Understanding Gradient Descent Gradient Descent is a fundamental optimization algorithm used in machine learning to find the minimum of a function. In the context of training machine learning models, this function is typically the cost function (or loss function), which measures the error between the model’s predictions and the actual data. The goal of gradient… Read more

  • Detailed Explanation of Keras Library

    Detailed Explanation of Keras Library Keras: The User-Friendly Neural Network API Keras is a high-level API (Application Programming Interface) written in Python, designed for human beings, not machines. It serves as an interface for artificial neural networks, running on top of lower-level backends such as TensorFlow (primarily in modern usage). Key Features and Philosophy User-Friendliness:… Read more

  • Most Used Data Science Algorithms and Use Cases

    Most Used Data Science Algorithms and Use Cases Most Used Data Science Algorithms and Use Cases 1. Linear Regression Type: Supervised Learning (Regression) A fundamental algorithm for modeling the linear relationship between a dependent variable and one or more independent variables. Use Cases: Predicting house prices based on features like size and location. Forecasting sales… Read more

  • Implementing Locally running Mistral Chatbot with RAG

    Locally running Mistral Chatbot with RAG Let’s implement a local running chatbot with Mistral LLM using RAG to retrieve documents from a locally running Vector DB that also contains FAQs. Here’s a breakdown of the steps and the Python code to achieve this: Phase 1: Setting Up the Local Environment Install Dependencies: pip install transformers… Read more