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Tag: embeddings

  • Powering Intelligence: Understanding the Electricity and Cost of 1 Million RAG Queries

    Powering Intelligence: Understanding the Electricity and Cost of 1 Million RAG Queries for Solution Architects As solution architects, you’re tasked with designing robust, scalable, and economically viable AI systems. Retrieval-Augmented Generation (RAG) has emerged as a transformative pattern for deploying large language models (LLMs), offering a compelling alternative to continuous fine-tuning by grounding responses in… Read more

  • 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

  • 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

  • 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

  • Most Used Data Science Algorithms for Retail Checkout Video Analysis

    Detailed Data Science Algorithms for Retail Checkout Video Analysis Detailed Data Science Algorithms for Retail Checkout Video Analysis This article provides an in-depth look at the data science algorithms employed for analyzing video data from retail checkouts, covering both the computer vision techniques for processing the visual information and the machine learning/statistical methods for extracting… 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

  • Understanding Loss Functions in Machine Learning

    Understanding Loss Functions in Machine Learning Understanding Loss Functions in Machine Learning In machine learning, a loss function, also known as a cost function or error function, is a mathematical function that quantifies the difference between the predicted output of a model and the actual (ground truth) value. The primary goal during the training of… 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

  • Comparing DynamoDB vs MongoDB for Vector Embedding

    Comparing DynamoDB vs MongoDB for Vector Embedding Both Amazon DynamoDB and MongoDB offer capabilities for working with vector embeddings, but they approach it with different underlying architectures and strengths. Choosing the right database depends on your specific use case, scalability requirements, query patterns, and existing infrastructure. DynamoDB for Vector Embedding DynamoDB, a fully managed NoSQL… Read more

  • Comparing Vector DB Embedding Use Cases: Neo4j vs MongoDB

    Comparing Vector DB Embedding Use Cases: Neo4j vs MongoDB Both Neo4j and MongoDB have integrated vector embedding capabilities, but their strengths and ideal use cases differ significantly due to their fundamental data models. Neo4j: The Graph-Centric Approach Focus: Excels at managing and querying highly connected data and relationships. Vector embeddings enhance its ability to perform… Read more

  • Detailed Guide to MongoDB Vector Embedding Similarity Search

    Detailed Guide to MongoDB Vector Embedding Similarity Search Performing similarity searches using vector embeddings in MongoDB allows you to find documents that are semantically or conceptually similar based on the numerical representations of their content. This technique is powerful for applications like recommendation systems, semantic search, and anomaly detection. For a general introduction to MongoDB,… Read more

  • Detailed Explanation: Vector Embedding vs Feature Store

    Detailed Explanation: Vector Embedding vs Feature Store Vector Embeddings: Deep Dive Detailed Explanation: At its core, a vector embedding is a way to represent complex data as a point in a multi-dimensional space. The magic lies in how these representations are learned or constructed. The goal is to capture the underlying semantic meaning, relationships, and… Read more

  • Tensor Multiplication (Element-wise) with PyTorch and CUDA

    Tensor Multiplication (Element-wise) with PyTorch and CUDA Element-wise Tensor Multiplication, also known as Hadamard product, involves multiplying corresponding elements of two tensors that have the same shape. Utilizing CUDA on a GPU significantly accelerates this operation through parallel processing. Code Example with PyTorch and CUDA import torch # Check if CUDA is available and set… Read more

  • Vector Embeddings in LLMs: A Detailed Explanation

    Vector Embeddings in LLMs: A Detailed Explanation What are Vector Embeddings? Vector embeddings are numerical representations of data points, such as words, phrases, sentences, or even entire documents. These representations exist as vectors in a high-dimensional space. The key idea behind vector embeddings is to capture the semantic meaning and relationships between these data points,… Read more

  • Understanding Transformer Models in LLMs

    Transformer Models in LLMs 1. Core Innovation: Self-Attention The Transformer model’s revolutionary aspect for Large Language Models (LLMs) and Natural Language Processing (NLP) lies in its ability to process sequential data efficiently and understand context effectively. Unlike sequential models like Recurrent Neural Networks (RNNs), Transformers can process entire sequences in parallel. The key to this… Read more

  • AI Agent with Long-Term Memory on Google Cloud

    AI Agent with Long-Term Memory on Google Cloud Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Google Cloud Platform (GCP) offers… Read more

  • AI Agent with Long-Term Memory on Azure

    AI Agent with Long-Term Memory on Azure Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Microsoft Azure offers a comprehensive suite… Read more

  • AI Agent with Long-Term Memory on AWS

    AI Agent with Long-Term Memory on AWS Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Amazon Web Services (AWS) offers a… Read more

  • Diffusion Transformers (DiTs)

    Diffusion Transformers (DiTs) Diffusion Transformers (DiTs): A Detailed Discussion Diffusion Transformers (DiTs) represent a novel and increasingly impactful class of image generation models that combine the strengths of diffusion models and the transformer architecture. This hybrid approach aims to leverage the high-quality image synthesis capabilities of diffusion models with the scalability and global context understanding… Read more

  • Implementing Graph-Based Retrieval Augmented Generation

    Implementing Graph-Based Retrieval Augmented Generation Implementing Graph-Based Retrieval Augmented Generation This document outlines the implementation of a system that combines the power of Large Language Models (LLMs) with structured knowledge from a graph database to perform advanced question answering. This approach, known as Graph-Based Retrieval Augmented Generation (RAG), allows us to answer complex queries that… Read more

  • Detailed Implementation of Backend-Only Advanced RAG with Multi-Hop Retrieval

    Detailed Implementation of Backend-Only Advanced RAG with Multi-Hop Retrieval This article provides a comprehensive guide to implementing a backend-only Retrieval-Augmented Generation (RAG) system enhanced with Multi-Hop Retrieval capabilities. This advanced technique, leveraging LangChain’s SelfQueryRetriever, OpenAI’s language models and embeddings, and ChromaDB for vector storage, enables more sophisticated question answering over a knowledge base. Understanding Multi-Hop… Read more

  • Backend-Only Advanced RAG with Multi-Step Self-Correction

    Backend-Only Advanced RAG with Multi-Step Self-Correction Backend-Only Advanced RAG with Multi-Step Self-Correction This HTML document describes a backend-only implementation of a Retrieval-Augmented Generation (RAG) system featuring an advanced Multi-Step Self-Correction mechanism using Python, LangChain, OpenAI, and ChromaDB. Overview The goal of this project is to demonstrate how to build a RAG pipeline where the language… Read more