Notes For BCA Students

 In this blog, we’ll walk through a step-by-step guide to building an AI application that can intelligently answer questions based on your website’s content using LangChain , Ollama , and ChromaDB . This approach leverages Retrieval-Augmented Generation (RAG) , enabling you to use a pre-trained language model while grounding its responses in your own data — without needing to fine-tune the model. What We'll Use LangChain – for chaining together data loading, embedding, retrieval, and prompt logic Ollama – to run open-source LLMs (like LLaMA or Mistral) locally ChromaDB – as an efficient vector store WebsiteLoader – to extract data directly from your website RecursiveTextSplitter – for clean and structured chunking of long web content Step 1: Install Dependencies pip install langchain chromadb beautifulsoup4 unstructured requests tiktoken Step 2: Load Website Content from langchain_community.document_loaders import WebBaseLoader urls = [     "https:/...

  Introduction The evolution of Artificial Intelligence (AI) has witnessed remarkable advancements, from simple rule-based systems to deep learning models that power today's intelligent applications. However, a new paradigm is emerging that promises to redefine how AI systems interact with their environments: Agentic AI. This concept introduces AI models capable of autonomy, decision-making, and goal-driven behavior, pushing the boundaries of what machines can achieve independently. What is Agentic AI? Agentic AI refers to artificial intelligence systems designed to operate autonomously by setting objectives, making decisions, and executing tasks without direct human intervention. Unlike traditional AI, which requires predefined inputs and outputs, agentic AI can: Set and pursue goals based on high-level instructions. Adapt to dynamic environments by learning and evolving from interactions. Self-correct and optimize through feedback loops and reinforcement learning. Coordinate w...