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Articles by Kanchan Bawane
Sending Real-Time Logs to Splunk Cloud Using Universal Forwarder
This comprehensive guide walks you through the process of setting up Splunk Universal Forwarder to send real-time logs to Splunk Cloud. Learn how to enhance your organization’s log management capabilities, from installation to troubleshooting. Introduction to Splunk Cloud and Universal Forwarder What is Splunk Cloud? Splunk Cloud is a powerful cloud-based platform for collecting, analyzing, and visualizing machine-generated data from various sources.
Sending Data to Splunk Cloud Using HTTP Event Collector (HEC)
In our previous blog, we explored how to set up Splunk Cloud and index dummy data using the upload option. Now that you have your Splunk Cloud environment up and running, let’s take it a step further. In this blog, we’ll dive into the HTTP Event Collector (HEC), a powerful feature in Splunk that allows you to send data to Splunk over HTTP or HTTPS. This is particularly useful for real-time data ingestion from various sources, such as applications or cloud services.
Understanding Splunk and Setting Up Splunk Cloud
Splunk is a powerful platform designed for searching, monitoring, and analyzing machine-generated data via a web-style interface. It captures, indexes, and correlates real-time data in a searchable repository, from which it can generate graphs, reports, alerts, dashboards, and visualizations. It’s designed for anyone who wants to gain insights from their data without the need to manage the underlying infrastructure.
Building a Conversational Search Application with Azure Cognitive Search and OpenAI Embedding
Introduction In this blog, we will show you how to build a conversational search application that can interact with Azure Cognitive Search (ACS) and retrieve relevant content from a web-scraped index by asking natural language questions, requesting for summary information, and using vector search.
How to Scrape a Website and Extract its Content
Introduction The web is a vast source of information, but it is not always easy to access and use for natural language applications. In this blog post, we will show you how to crawl and scrape the target URL, extract and clean the content, and store it in Azure Blob Storage. We will use Python as the programming language, and some popular libraries such as requests, asyncio, BeautifulSoup, and lxml.
How to use Azure Blob Data and Store it in Azure Cognitive Search along with Vectors
Introduction In the previous blog post, we showed you how to scrap a website, extract its content using Python, and store it in Azure Blob Storage. In this blog post, we will show you how to use the Azure Blob data and store it in Azure Cognitive Search (ACS) along with vectors. We will use some popular libraries such as OpenAI Embeddings and Azure Search to create and upload the vectors to ACS. We will also show you how to use the vectors for semantic search and natural language applications.
Building a Private ChatBot with Langchain, Azure OpenAI & Faiss Vector DB for Local Document Query
In this blog, we will explore how we can effectively utilize Langchain, Azure OpenAI Text embedding ADA model and Faiss Vector Store to build a private chatbot that can query a document uploaded from local storage. A private chatbot is a chatbot that can interact with you using natural language and provide you with information or services that are relevant to your needs and preferences.
Coveo Headless Library Integration with SAPUI5 Framework: Development Environment Setup - Phase I
In this blog, we will explore how to integrate Coveo Headless, a powerful search and relevance platform, with OpenUI5, a popular UI framework for building web applications. As search functionality becomes increasingly crucial for modern applications, this integration will allow us to create an advanced search experience within OpenUI5 projects. Introduction Coveo Headless is a search and relevance platform that offers a set of APIs to build tailored search experiences.
Build a Search Interface Using SAPUI5 Framework with Coveo Headless Library- Phase II
In our previous blog post, “Coveo Headless Library Integration with OPENUI5 Framework: Development Environment Setup – Phase I,” we started the integration between Coveo Headless and OpenUi5. Coveo Headless is a search and relevance platform, and OpenUI5 is a dynamic UI framework for web application development. Phase I establishes the foundation for what we’re about to explore in this Phase II edition, so if you haven’t had a chance to read it, we highly recommend doing so.
Generate Embeddings using OpenAI Service
Embeddings are essential in the fields of natural language processing (NLP) and machine learning because they convert words and phrases into numerical vectors. By successfully capturing semantic linkages and contextual meanings, these vectors help machines comprehend and process human language. We will examine the idea of embeddings in this blog, learn about their uses, and investigate how to create and incorporate them using Azure Cognitive Search.
Translating Different Content Types Using Helsinki-NLP ML Model from Hugging Face
Introduction In Hugging Face, a translation model is a pre-trained deep learning model that can be used for machine translation tasks, These models are pre-trained on large amounts of multilingual data and fine-tuned on translation-specific datasets. To use a translation model in Hugging Face, we typically load the model using the from_pretrained() function, which fetches the pre-trained weights and configuration.
Getting Started with Coveo: A Comprehensive Overview for Tech Enthusiasts
Introduction: Welcome, to this comprehensive blog post about COVEO – a cutting-edge technology that is revolutionizing the way we search and access information. In this blog, we will delve into what COVEO is, its key features, benefits, and how it enhances the overall user experience. Whether you’re an IT professional, a developer, or simply curious about the latest tech innovations, this blog will offer valuable insights into the world of COVEO. What is COVEO?
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