Author: admin

In today’s fast-paced digital world, customer support is evolving beyond scripted responses and long wait times. Enter the era of AI-powered agents that can handle queries in real-time, pulling data from multiple sources while maintaining context across conversations. In this blog post, we’ll dive into how to create an advanced AI customer support agent using…

To integrate with Zendesk and Slack APIs for development and testing (e.g., populating demo data), you’ll need developer or sandbox accounts. These allow you to experiment without affecting production environments. Below are step-by-step instructions for setting up developer accounts for each. Zendesk Developer Account Zendesk provides a free trial or sandbox environment for developers to…

In the era of large language models (LLMs), Retrieval-Augmented Generation (RAG) has become a cornerstone for building intelligent applications that ground responses in custom knowledge bases. But what if your data isn’t just a few documents—it’s a massive corpus of unstructured text, like PDFs, emails, or scraped web content? Scaling RAG while maintaining accuracy, efficiency,…

Imagine a team of specialized AI “writers” collaborating in real-time: one brainstorming wild plot twists, another fleshing out unforgettable characters, a third weaving vivid scenes, and a fourth polishing it all to perfection. Sounds like a dream writer’s room? It’s not—it’s the reality you can build today with CrewAI, an open-source framework that turns AI…

This blog post explores a HR agent built with FlaskAPI and LangChain, designed to handle Paid Time Off (PTO) requests and policy queries intelligently. By combining a Retrieval-Augmented Generation (RAG) system with agentic workflows, this solution automates PTO management and provides context-aware policy answers, all while maintaining a lightweight and scalable architecture. To understand the…

A ReAct Agent (Reasoning + Acting) is an AI framework that combines the reasoning capabilities of large language models (LLMs) with the ability to take actions in an iterative loop to solve complex tasks. It integrates verbal reasoning (thinking through a problem step-by-step) with actionable steps (using tools or APIs to interact with external environments)…

While writing blogs on AI, I encountered numerous compatibility issues within the Python ecosystem on Windows. Creating functional examples is often more challenging compared to other operating systems like macOS and Linux distributions. Recently, I discovered that setting up Linux distributions via the Windows Subsystem for Linux (WSL) can resolve many of these issues, streamlining…

What is Agentic RAG? Agentic RAG (Agent-Driven Retrieval-Augmented Generation) is an innovative advancement of the Retrieval-Augmented Generation (RAG) model, infused with autonomous decision-making capabilities. Unlike traditional RAG, which enhances large language models (LLMs) with real-time data retrieval to generate informed responses, Agentic RAG empowers the AI to act as an independent agent, making proactive choices…

In the previous blog we have used basic implementation of FAISS, in this blog lets deep dive into FAISS and its fine tune techniques. Most importantly how faiss can make our search faster. FAISS provides 3 index types IndexFlatL2 IndexFlatL2 measures the L2 (or Euclidean) distance between all given points between our query vector, and…

FAISS (Facebook AI Similarity Search) is an open-source, vector database library that enables efficient storage, retrieval, and similarity search of dense vector embeddings. It overcomes traditional query search engine limitations, allowing for: Ideal for applications requiring fast and accurate matching, FAISS is a powerful tool for searching and retrieving similar multimedia documents, such as images, videos,…