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Category: Chatbot

  • 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

  • BPM AI Agents Explained

    BPM AI Agents Explained for Novices (Detailed) Imagine the inner workings of a company as a network of interconnected pathways – these pathways represent the various business processes that drive operations, from fulfilling customer orders to managing supply chains and handling internal approvals. Business Process Management (BPM) is the discipline of understanding, designing, executing, documenting,… 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

  • 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

  • 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

  • AI Agent with Short-Term Memory on AWS

    AI Agent with Short-Term Memory on AWS In the realm of Artificial Intelligence, creating agents that can effectively interact with their environment and solve complex tasks often requires equipping them with a form of short-term memory, also known as “scratchpad” or working memory. This allows the agent to temporarily store and process information relevant to… 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

  • Intelligent Chatbot with RAG using React and Python

    Intelligent Chatbot with RAG using React and Python This guide will walk you through building an intelligent chatbot using React.js for the frontend and Python with Flask for the backend, enhanced with Retrieval-Augmented Generation (RAG). RAG allows the chatbot to ground its responses in external knowledge sources, leading to more accurate and contextually relevant answers.… Read more

  • Building an Intelligent Chatbot with React and Python and Generative AI

    Building an Intelligent Chatbot with React and Python Building an Intelligent Chatbot with React and Python This comprehensive guide will walk you through the process of building an intelligent chatbot using React.js for the frontend and Python with Flask for the backend, leveraging the power of Generative AI for natural and engaging conversations. We’ll cover… Read more

  • Building a Simple Chatbot with React with Python Backend

    Building a Simple Chatbot with React with Python Backend This guide will walk you through the fundamental steps of creating a basic chatbot using React.js for the user interface and a conceptual backend. We’ll break down the process into manageable parts, explaining each stage with code examples. What is a Chatbot? At its core, a… Read more

  • Building a Simple Chatbot with React and NodeJS

    Building a Simple Chatbot with React and NodeJS This guide will walk you through the fundamental steps of creating a basic chatbot using React.js for the user interface and a conceptual backend. We’ll break down the process into manageable parts, explaining each stage with code examples. What is a Chatbot? At its core, a chatbot… Read more

  • Detailed Workflow for Claims Adjudication with AI Integration

    Detailed Workflow for Claims Adjudication with AI Integration The claims adjudication process is being significantly enhanced by the integration of Artificial Intelligence (AI) at various stages. The following workflow highlights where AI tools and techniques can be applied to improve efficiency, accuracy, and speed. Phase 1: Claim Submission and Initial Review – AI Assistance Step… Read more

  • Comparing Top LLMs

    Comparing Top LLMs (April 2025) The landscape of Large Language Models (LLMs) is constantly evolving. Here’s a comparison of some of the top contenders as of late April 2025, keeping in mind that rankings & capabilities can shift rapidly: Top 8 LLMs (Based on Current Trends & Capabilities): GPT-4o (OpenAI): Known for its strong general… Read more

  • SoundHound AI and Its Competitors

    SoundHound AI and Its Competitors (2025) SoundHound AI (SOUN) Overview: SoundHound AI is a company that specializes in voice and conversational artificial intelligence (AI) solutions. Their technology is used in various industries, including automotive, TV, IoT (smart devices), restaurants, customer service, healthcare, finance, and retail. They offer products like Smart Answering, Smart Ordering, Dynamic Drive… Read more

  • Building a Personalized Banking Chat Agent with React.js, RAG, LLM, and Redis with sample code

    Here we outline a more detailed structure with conceptual sample code snippets for each layer of a conceptual personalized bank FAQ chat agent. Keep in mind that this is a simplified illustration, and a production-ready system would involve more robust error handling, security measures, and integration logic. I. Knowledge Base Preparation: Step 1: Data Collection… Read more

  • Intelligent Chat Agent UI with Retrieval-Augmented Generation (RAG) and a Large Language Model (LLM) using Amazon OpenSearch

    In today’s digital age, providing efficient and accurate customer support is paramount. Intelligent chat agents, powered by the latest advancements in Natural Language Processing (NLP), offer a promising avenue for addressing user queries effectively. This comprehensive article will guide you through the process of building a sophisticated Chat Agent UI application that leverages the power… Read more

  • k-NN (k-Nearest Neighbors) search in OpenSearch

    To perform a k-NN (k-Nearest Neighbors) search in OpenSearch after loading your manuals (or any documents) as vector embeddings, you’ll use the knn query within the OpenSearch search API. Here’s how you can do it: Understanding the knn Query The knn query in OpenSearch allows you to find the k most similar vectors to a… Read more

  • Retrieval Augmented Generation (RAG) with LLMs

    Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate information from external sources during the response generation process. This approach addresses some of the inherent limitations of LLMs, such as their inability to access up-to-date information or domain-specific knowledge. How RAG… Read more