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Category: LLMs

  • Building a Weather Chatbot with Langchain

    Building a Weather Chatbot with Langchain This article demonstrates how to create a simple chatbot using Langchain that can fetch and provide current weather information based on city names or zip codes. We’ll utilize the power of Large Language Models (LLMs) and a simple custom tool to achieve this. Prerequisites Python 3.6+ Langchain Library: Install… Read more

  • Building a Stock Price Chatbot with Langchain

    Building a Stock Price Chatbot with Langchain This article demonstrates how to create a simple chatbot using Langchain that can fetch and provide current and historical stock prices. We’ll leverage the power of Large Language Models (LLMs) and the yfinance library to build this interactive tool. Prerequisites Python 3.6+ Langchain Library: Install using pip install… Read more

  • What is Langchain? (For Beginners)

    Understanding Langchain Imagine you’re building a really smart assistant, like a chatbot that can answer complex questions or write creative stories. Now, this assistant needs to do more than just look up facts; it needs to understand language, connect different pieces of information, and even use other tools. That’s where Langchain comes in. Think of… Read more

  • Current Buzzwords in Tech (May, 2025)

    Current Buzzwords in Tech (May, 2025) A look at the trending terms in the technology landscape as of May 10, 2025. 1. Artificial Intelligence (AI) and its Subfields Generative AI (GenAI) AI’s ability to create new content like text, images, audio, and code, increasingly integrated into various applications. Details: Advancements in models, multimodal capabilities, ethical… Read more

  • Agentic AI: The Critical Role of Explainable AI (XAI)

    Agentic AI: The Critical Role of Explainable AI (XAI) Agentic AI promises a significant evolution in how artificial intelligence systems operate, enabling autonomous, intelligent, and adaptive behavior. However, the full potential and responsible deployment of these powerful systems hinge on our ability to understand their decision-making processes. This is where Explainable AI (XAI) becomes not… Read more

  • Agentic AI for Business Process Management (BPM): A Detailed Exploration

    Agentic AI for Business Process Management (BPM): A Detailed Exploration Agentic AI represents a significant evolution in Business Process Management (BPM), promising a new level of autonomy, intelligence, and adaptability to how organizations manage their workflows. Understanding Agentic AI Agentic AI refers to artificial intelligence entities capable of perceiving, reasoning, acting, and learning autonomously to… 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

  • Agentic AI Increase Power Consumption Bills? – A Detailed Look

    Energy Costs of LLMs in Agentic AI – Detailed Analysis The integration of Large Language Models (LLMs) into Agentic AI architectures is indeed expected to significantly contribute to higher power consumption bills for enterprises. This stems from the inherent energy demands of LLMs coupled with the continuous and often complex operations required by autonomous agents.… Read more

  • Energy Costs of Using LLMs within Enterprise

    Energy Costs of Using LLMs within Enterprise The energy costs of using Large Language Models (LLMs) within an enterprise are a multifaceted issue with implications for both operational expenses and environmental sustainability. These costs arise primarily from two key stages in the LLM lifecycle: training and inference. Factors Influencing Energy Consumption Model Size: The number… Read more

  • AMD vs. NVIDIA LLM Performance

    AMD vs. NVIDIA LLM Performance (May 2025) This article compares the performance of AMD and NVIDIA hardware when running Large Language Models (LLMs) as of May 2025, based on recent reports and trends. Key Factors Influencing LLM Performance VRAM (Video RAM) The size of the GPU‘s memory is crucial for handling large LLMs. Larger models… Read more

  • A2A (Agent-to-Agent) vs. MCP (Model Context Protocol)

    A2A (Agent-to-Agent) vs. MCP (Model Context Protocol) A2A (Agent-to-Agent) vs. MCP (Model Context Protocol) Here’s a comparison between A2A (Agent-to-Agent Protocol) and MCP (Model Context Protocol) in the context of AI agents: A2A (Agent-to-Agent Protocol): Primary Focus: Standardizing communication and interoperability between different AI agents, regardless of their origin or framework. Aims to give AI… Read more

  • Model Context Protocol (MCP) Interfaces

    Model Context Protocol (MCP) Interfaces The acronym “MCP” in the context of interfaces most likely refers to the Model Context Protocol. This open protocol is designed to standardize how AI applications, especially Large Language Models (LLMs), can interact with external data sources and tools in a consistent and interoperable manner. What is the Model Context… Read more

  • How SAP and Oracle Can Use Agentic AI

    How SAP and Oracle Can Use Agentic AI SAP and Oracle, as leading enterprise software providers, are actively integrating Agentic AI capabilities into their platforms to enhance organizational productivity across various business functions. Here’s how they can leverage this transformative technology: SAP’s Use of Agentic AI: SAP is embedding “Business AI” across its portfolio, which… Read more

  • Security Issues in LangChain and MCP Servers

    Security Issues in LangChain and MCP Servers Security Issues in LangChain Prompt Injection: Maliciously crafted prompts can manipulate the LLM to perform unintended actions, bypass filters, or disclose sensitive information. This is a primary concern as user input directly influences the LLM’s behavior. Example: A user might craft a prompt like “Ignore previous instructions and… 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

  • 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 LangSmith Observability in Detail

    LangSmith Observability in Detail LangSmith provides comprehensive observability for your LLM applications, offering detailed insights into the execution flow, performance, and outputs of your chains, agents, and tools. It helps you understand what’s happening inside your LLM application, making it easier to debug, evaluate, and improve its reliability and quality. 1. Tracing: End-to-End Visibility Detailed… 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

  • Top 5 Code Generation Models (May 5, 2025)

    Top 5 Code Generation LLMs (May 5, 2025) The landscape of Large Language Models for code generation is dynamic. This list highlights five prominent models based on their performance, features, and recognition as of today. 1. GPT-4o Provider: OpenAI Key Details: Often cited as a leader in overall LLM benchmarks, including code generation. Known for… 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

  • Tensor Reshaping with PyTorch and CUDA

    Tensor Reshaping with PyTorch and CUDA Tensor Reshaping involves changing the shape of a tensor without altering its underlying data. This operation is frequently used to prepare tensors for different operations in neural networks and other numerical computations. While the reshaping operation itself is typically not computationally intensive, performing it on a GPU using CUDA… Read more

  • Matrix Multiplication with PyTorch and CUDA

    Matrix Multiplication with PyTorch and CUDA Matrix Multiplication is a fundamental operation in linear algebra and is crucial in many machine learning algorithms, especially in the layers of neural networks. CUDA significantly accelerates this operation by parallelizing the numerous multiply-accumulate operations involved. Code Example with PyTorch and CUDA import torch # Check if CUDA is… 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

  • Tensor Addition with PyTorch and CUDA

    Tensor Addition with PyTorch and CUDA Tensor Addition is a fundamental operation in tensor algebra. It involves adding corresponding elements of two tensors that have the same shape, resulting in a new tensor of the same shape where each element is the sum of the corresponding elements of the input tensors. When performed on a… Read more

  • CUDA vs. ROCm for LLM Training

    CUDA vs. ROCm CUDA (Compute Unified Device Architecture) and ROCm (Radeon Open Compute) are the two primary software platforms for General-Purpose computing on Graphics Processing Units (GPGPU) used in accelerating computationally intensive tasks, including the training of Large Language Models (LLMs). CUDA is developed by NVIDIA and is designed for their GPUs, while ROCm is… Read more

  • Exploring CUDA (Compute Unified Device Architecture)

    Exploring CUDA CUDA is a parallel computing platform and programming model developed by NVIDIA for use with their GPUs. It allows software developers to leverage the massive parallel processing power of NVIDIA GPUs for general-purpose computing tasks, significantly accelerating applications beyond traditional CPU-bound processing. 1. CUDA Architecture: The Hardware Foundation NVIDIA GPUs are designed with… Read more

  • Can AMD GPUs Train LLMs?

    Can AMD GPUs Train LLMs? AMD GPUs can be used to train Large Language Models (LLMs). While NVIDIA GPUs, particularly those with CUDA architecture, have historically dominated the LLM training landscape, AMD has been making significant strides in this area with its ROCm (Radeon Open Compute) platform. 1. ROCm Platform ROCm is AMD’s open-source software… 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